add GPU for DenseBnAct, Erfc, Log1p, etc. in Supported Platforms.

mindspore/nn/layer/basic.py

@@ -1007,6 +1007,7 @@ class MatrixSetDiag(Cell):
     Assume `x` has :math:`k+1` dimensions :math:`[I, J, K, ..., M, N]` and `diagonal` has :math:`k`
     dimensions :math:`[I, J, K, ..., min(M, N)]`. Then the output is a tensor of rank :math:`k+1` with dimensions
     :math:`[I, J, K, ..., M, N]` where:
+
     :math:`output[i, j, k, ..., m, n] = diagnoal[i, j, k, ..., n]\ for\ m == n`
 
     :math:`output[i, j, k, ..., m, n] = x[i, j, k, ..., m, n]\ for\ m != n`

mindspore/nn/layer/combined.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -171,7 +171,7 @@ class DenseBnAct(Cell):
         Tensor of shape :math:`(N, out\_channels)`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> net = nn.DenseBnAct(3, 4)

mindspore/nn/layer/math.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -930,7 +930,7 @@ class Moments(Cell):
         - **variance** (Tensor) - The variance of input x, with the same date type as input x.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> net = nn.Moments(axis=3, keep_dims=True)

mindspore/nn/layer/pooling.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -165,7 +165,7 @@ class MaxPool1d(_PoolNd):
         Tensor of shape :math:`(N, C, L_{out}))`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> max_pool = nn.MaxPool1d(kernel_size=3, stride=1)
@@ -312,7 +312,7 @@ class AvgPool1d(_PoolNd):
         Tensor of shape :math:`(N, C_{out}, L_{out})`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> pool = nn.AvgPool1d(kernel_size=6, stride=1)

mindspore/nn/optim/lazyadam.py

@@ -1,4 +1,4 @@
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -185,7 +185,7 @@ class LazyAdam(Optimizer):
         Tensor[bool], the value is True.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> net = Net()

mindspore/ops/composite/base.py

@@ -1,6 +1,6 @@
 # This is the Python adaptation and derivative work of Myia (https://github.com/mila-iqia/myia/).
 #
-# Copyright 2020 Huawei Technologies Co., Ltd
+# Copyright 2020-2021 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.
@@ -198,6 +198,12 @@ class GradOperation(GradOperation_):
     Returns:
         The higher-order function which takes a function as argument and returns gradient function for it.
 
+    Raises:
+        TypeError: If `get_all`, `get_by_list` or `sens_param` is not a bool.
+
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> from mindspore.common import ParameterTuple
         >>> class Net(nn.Cell):
@@ -377,7 +383,7 @@ class MultitypeFuncGraph(MultitypeFuncGraph_):
 
     MultitypeFuncGraph is a class used to generate overloaded functions, considering different types as inputs.
     Initialize an `MultitypeFuncGraph` object with name, and use `register` with input types as the decorator
-    for the function to be registed. And the object can be called with different types of inputs,
+    for the function to be registered. And the object can be called with different types of inputs,
     and work with `HyperMap` and `Map`.
 
     Args:
@@ -388,6 +394,9 @@ class MultitypeFuncGraph(MultitypeFuncGraph_):
     Raises:
         ValueError: If failed to find find a matching function for the given arguments.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> # `add` is a metagraph object which will add two objects according to
         >>> # input type using ".register" decorator.
@@ -479,6 +488,9 @@ class HyperMap(HyperMap_):
         Sequence or nested sequence, the sequence of output after applying the function.
         e.g. `operation(args[0][i], args[1][i])`.
 
+    Supported Platforms:
+        ``Ascend`` ``GPU`` ``CPU``
+
     Examples:
         >>> from mindspore import dtype as mstype
         >>> nest_tensor_list = ((Tensor(1, mstype.float32), Tensor(2, mstype.float32)),

mindspore/ops/operations/math_ops.py

@@ -1620,13 +1620,13 @@ class Exp(PrimitiveWithInfer):
         out_i = e^{x_i}
 
     Inputs:
-        - **input_x** (Tensor) - The input tensor. The data type mast be float16 or float32.
+        - **input_x** (Tensor) - The input tensor.
 
     Outputs:
         Tensor, has the same shape and dtype as the `input_x`.
 
     Raises:
-        TypeError: If dtype of `input_x` is neither float16 nor float32.
+        TypeError: If `input_x` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -1648,7 +1648,7 @@ class Exp(PrimitiveWithInfer):
         return x_shape
 
     def infer_dtype(self, x_type):
-        validator.check_tensor_dtype_valid("x", x_type, [mstype.float16, mstype.float32], self.name)
+        validator.check_subclass("x", x_type, mstype.tensor, self.name)
         return x_type
 
     def infer_value(self, x):
@@ -1759,13 +1759,13 @@ class Log(PrimitiveWithInfer):
     Returns the natural logarithm of a tensor element-wise.
 
     Inputs:
-        - **input_x** (Tensor) - The input tensor. With float16 or float32 data type. The value must be greater than 0.
+        - **input_x** (Tensor) - The input tensor. The value must be greater than 0.
 
     Outputs:
         Tensor, has the same shape as the `input_x`.
 
     Raises:
-        TypeError: If dtype of `input_x` is neither float16 nor float32.
+        TypeError: If `input_x` is not a Tensor.
 
     Supported Platforms:
         ``Ascend`` ``GPU`` ``CPU``
@@ -1787,7 +1787,6 @@ class Log(PrimitiveWithInfer):
 
     def infer_dtype(self, x):
         validator.check_subclass("x", x, mstype.tensor, self.name)
-        validator.check_tensor_dtype_valid("x", x, [mstype.float16, mstype.float32], self.name)
         return x
 
     def infer_value(self, x):
@@ -1813,7 +1812,7 @@ class Log1p(PrimitiveWithInfer):
         TypeError: If dtype of `input_x` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> input_x = Tensor(np.array([1.0, 2.0, 4.0]), mindspore.float32)
@@ -1895,7 +1894,7 @@ class Erfc(PrimitiveWithInfer):
         TypeError: If dtype of `input_x` is neither float16 nor float32.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> input_x = Tensor(np.array([-1, 0, 1, 2, 3]), mindspore.float32)
@@ -3959,7 +3958,7 @@ class SquareSumAll(PrimitiveWithInfer):
         - **output_y2** (Tensor) - The same type as the `input_x1`.
 
     Supported Platforms:
-        ``Ascend``
+        ``Ascend`` ``GPU``
 
     Examples:
         >>> input_x1 = Tensor(np.array([0, 0, 2, 0]), mindspore.float32)

mindspore/ops/operations/nn_ops.py

@@ -6820,6 +6820,14 @@ class CTCGreedyDecoder(PrimitiveWithCheck):
         - **log_probability** (Tensor) - A tensor with shape of (`batch_size`, 1),
           containing sequence log-probability, has the same type as `inputs`.
 
+    Raises:
+        TypeError: If `merge_repeated` is not a bool.
+        ValueError: If length of shape of `inputs` is not equal to 3.
+        ValueError: If length of shape of `sequence_length` is not equal to 1.
+
+    Supported Platforms:
+        ``Ascend``
+
     Examples:
         >>> inputs = Tensor(np.random.random((2, 2, 3)), mindspore.float32)
         >>> sequence_length = Tensor(np.array([2, 2]), mindspore.int32)

tests/ut/python/ops/test_math_ops.py

@@ -111,10 +111,10 @@ def test_pow():
 
 def test_exp():
     """ test_exp """
-    input_tensor = Tensor(np.array([[2, 2], [3, 3]], np.float32))
+    input_tensor = Tensor(np.array([[2, 2], [3, 3]]))
     testexp = P.Exp()
     result = testexp(input_tensor)
-    expect = np.exp(np.array([[2, 2], [3, 3]], np.float32))
+    expect = np.exp(np.array([[2, 2], [3, 3]]))
     assert np.all(result.asnumpy() == expect)