!48206 fix_ops_any_all_master

Merge pull request !48206 from yide12/tensor_isneginf_master

docs/api/api_python/mindspore.ops.rst

@@ -297,8 +297,10 @@ Reduction函数
     :nosignatures:
     :template: classtemplate.rst
 
+    mindspore.ops.all
     mindspore.ops.amax
     mindspore.ops.amin
+    mindspore.ops.any
     mindspore.ops.argmax
     mindspore.ops.argmin
     mindspore.ops.cummax

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

@@ -0,0 +1,23 @@
+mindspore.ops.all
+=================
+
+.. py:function:: mindspore.ops.all(x, axis=(), keep_dims=False)
+
+    默认情况下，通过对维度中所有元素进行“逻辑与”来减少 `x` 的维度。也可以沿轴减少 `x` 的维度。通过控制 `keep_dim` 来确定输出和输入的维度是否相同。
+
+    参数：
+        - **x** (Tensor[bool]) - 输入Tensor，其数据类型为bool型。shape是 :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
+        - **axis** (Union[int, tuple(int), list(int)], 可选) - 要减少的维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。默认值：()，缩小所有维度。
+        - **keep_dims** (bool, 可选) - 如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
+
+    返回：
+        Tensor，数据类型是bool。
+
+        - 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个零维Tensor，表示输入Tensor中所有元素进行“逻辑与”。
+        - 如果 `axis` 为int，取值为2，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_1, x_3, ..., x_R)` 。
+        - 如果 `axis` 为tuple(int)或list(int)，取值为(2, 3)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_1, x_4, ..., x_R)` 。
+
+    异常：
+        - **TypeError** - `keep_dims` 不是bool类型。
+        - **TypeError** - `x` 不是Tensor。
+        - **TypeError** - `axis` 不是以下数据类型之一：int、tuple或list。

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

@@ -0,0 +1,23 @@
+mindspore.ops.any
+=================
+
+.. py:function:: mindspore.ops.any(x, axis=(), keep_dims=False)
+
+    默认情况下，通过对维度中所有元素进行“逻辑或”来减少 `x` 的维度。也可以沿轴减少 `x` 的维度。通过控制 `keep_dim` 来确定输出和输入的维度是否相同。
+
+    参数：
+        - **x** (Tensor[bool]) - 输入Tensor，其数据类型为bool型。shape是 :math:`(N, *)` ，其中 :math:`*` 表示任意数量的附加维度。秩应小于8。
+        - **axis** (Union[int, tuple(int), list(int)], 可选) - 要减少的维度。只允许常量值。假设 `x` 的秩为r，取值范围[-r,r)。默认值：()，缩小所有维度。
+        - **keep_dims** (bool, 可选) - 如果为True，则保留缩小的维度，大小为1。否则移除维度。默认值：False。
+
+    返回：
+        Tensor，数据类型是bool。
+
+        - 如果 `axis` 为()，且 `keep_dims` 为False，则输出一个零维Tensor，表示输入Tensor中所有元素进行“逻辑或”。
+        - 如果 `axis` 为int，取值为2，并且 `keep_dims` 为False，则输出的shape为 :math:`(x_1, x_3, ..., x_R)` 。
+        - 如果 `axis` 为tuple(int)或list(int)，取值为(2, 3)，并且 `keep_dims` 为False，则输出Tensor的shape为 :math:`(x_1, x_4, ..., x_R)` 。
+
+    异常：
+        - **TypeError** - `keep_dims` 不是bool类型。
+        - **TypeError** - `x` 不是Tensor。
+        - **TypeError** - `axis` 不是以下数据类型之一：int、tuple或list。

docs/api/api_python_en/mindspore.ops.rst

@@ -297,8 +297,10 @@ Reduction Functions
     :nosignatures:
     :template: classtemplate.rst
 
+    mindspore.ops.all
     mindspore.ops.amax
     mindspore.ops.amin
+    mindspore.ops.any
     mindspore.ops.argmax
     mindspore.ops.argmin
     mindspore.ops.cummax

mindspore/python/mindspore/ops/function/math_func.py

@@ -7674,8 +7674,7 @@ def _check_matmul_shapes(shape1, shape2, prim_name=None):
     r_shape2 = shape2[:-2]
     max_len = max(len(r_shape1), len(r_shape2))
     for i in range(max_len):
-        items = [it[i - max_len + len(it)] if i - max_len +
+        items = [it[i - max_len + len(it)] if i - max_len + len(it) >= 0 else 1 for it in (r_shape1, r_shape2)]
-                 len(it) >= 0 else 1 for it in (r_shape1, r_shape2)]
         max_size = max(items)
         shape_out.append(max_size)
     return tuple(shape_out)
@@ -8470,23 +8469,24 @@ def kron(x, y):
 
 def all(x, axis=(), keep_dims=False):
     r"""
-    Reduces a dimension of a tensor by the "logicalAND" of all elements in the dimension, by default. And also can
+    Reduces a dimension of `x` by the "logicalAND" of all elements in the dimension, by default. And also can
     reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
     controlling `keep_dims`.
 
     Args:
-        x (Tensor[bool]): The input tensor. The dtype of the tensor to be reduced is bool.
+        x (Tensor[bool]): The input Tensor. The dtype of the Tensor is bool.
             :math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
-        axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Default: (), reduce all dimensions.
+        axis (Union[int, tuple(int), list(int)], optional): The dimensions to reduce.
-            Only constant value is allowed. Must be in the range [-rank(x), rank(x)).
+            Only constant value is allowed. Supposed the rank of `x` is r,
-        keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
+            axis must be in the range [-rank(x), rank(x)). Default: (), all dimensions are reduced.
-                          If false, don't keep these dimensions. Default : False.
+        keep_dims (bool, optional): If true, keep these reduced dimensions and the length is 1.
+            If false, don't keep these dimensions. Default : False.
 
     Returns:
         Tensor, the dtype is bool.
 
         - If axis is (), and keep_dims is False,
-          the output is a 0-D tensor representing the "logical and" of all elements in the input tensor.
+          the output is a 0-D Tensor representing the "logical and" of all elements in the input Tensor.
         - If axis is int, set as 2, and keep_dims is False,
           the shape of output is :math:`(x_1, x_3, ..., x_R)`.
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,
@@ -8523,23 +8523,24 @@ def all(x, axis=(), keep_dims=False):
 
 def any(x, axis=(), keep_dims=False):
     r"""
-    Reduces a dimension of a tensor by the "logical OR" of all elements in the dimension, by default. And also can
+    Reduces a dimension of `x` by the "logical OR" of all elements in the dimension, by default. And also can
     reduce a dimension of `x` along the axis. Determine whether the dimensions of the output and input are the same by
     controlling `keep_dims`.
 
     Args:
-        x (Tensor[bool]): The input tensor. The dtype of the tensor to be reduced is bool.
+        x (Tensor[bool]): The input Tensor. The dtype of the Tensor is bool.
             :math:`(N,*)` where :math:`*` means, any number of additional dimensions, its rank should be less than 8.
-        axis (Union[int, tuple(int), list(int)]): The dimensions to reduce. Default: (), reduce all dimensions.
+        axis (Union[int, tuple(int), list(int)], optional): The dimensions to reduce.
-            Only constant value is allowed. Must be in the range [-rank(x), rank(x)).
+            Only constant value is allowed. Supposed the rank of `x` is r,
-        keep_dims (bool): If true, keep these reduced dimensions and the length is 1.
+            axis must be in the range [-rank(x), rank(x)). Default: (), all dimensions are reduced.
-                         If false, don't keep these dimensions. Default : False.
+        keep_dims (bool, optional): If true, keep these reduced dimensions and the length is 1.
+            If false, don't keep these dimensions. Default : False.
 
     Returns:
         Tensor, the dtype is bool.
 
         - If axis is (), and keep_dims is False,
-          the output is a 0-D tensor representing the "logical or" of all elements in the input tensor.
+          the output is a 0-D Tensor representing the "logical or" of all elements in the input Tensor.
         - If axis is int, set as 2, and keep_dims is False,
           the shape of output is :math:`(x_1, x_3, ..., x_R)`.
         - If axis is tuple(int), set as (2, 3), and keep_dims is False,

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

@@ -4897,12 +4897,12 @@ def pixel_shuffle(x, upscale_factor):
                          "by `upscale_factor` squared.")
     c = c // upscale_factor ** 2
     input_perm = (pre + (c, upscale_factor, upscale_factor, h, w))
-    reshape = ops.Reshape()
+    reshape = _get_cache_prim(P.Reshape)()
+    transpose = _get_cache_prim(P.Transpose)()
     x = reshape(x, input_perm)
     input_perm = [i for i in range(length - 2)]
     input_perm = input_perm + [length, length - 2, length + 1, length - 1]
     input_perm = tuple(input_perm)
-    transpose = ops.Transpose()
     x = transpose(x, input_perm)
     x = reshape(x, (pre + (c, upscale_factor * h, upscale_factor * w)))
     return x
@@ -4953,12 +4953,12 @@ def pixel_unshuffle(x, downscale_factor):
     h = h // downscale_factor
     w = w // downscale_factor
     input_perm = (pre + (c, h, downscale_factor, w, downscale_factor))
-    reshape = ops.Reshape()
+    reshape = _get_cache_prim(P.Reshape)()
+    transpose = _get_cache_prim(P.Transpose)()
     x = reshape(x, input_perm)
     input_perm = [i for i in range(length - 2)]
     input_perm = input_perm + [length - 1, length + 1, length - 2, length]
     input_perm = tuple(input_perm)
-    transpose = ops.Transpose()
     x = transpose(x, input_perm)
     x = reshape(x, (pre + (c * downscale_factor * downscale_factor, h, w)))
     return x

tests/st/ops/test_func_all.py

@@ -0,0 +1,51 @@
+# Copyright 2023 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 pytest
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import Tensor, ops
+
+
+class Net(nn.Cell):
+    def construct(self, x):
+        return ops.all(x, axis=1, keep_dims=True)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_ops_all(mode):
+    """
+    Feature: ops.all
+    Description: Verify the result of all
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    x = Tensor([[True, True],
+                [False, True],
+                [True, False],
+                [False, False]], ms.bool_)
+    net = Net()
+    output = net(x)
+    expect_output = Tensor([[True],
+                            [False],
+                            [False],
+                            [False]], ms.bool_)
+    assert all(output == expect_output)

tests/st/ops/test_func_any.py

@@ -0,0 +1,51 @@
+# Copyright 2023 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 pytest
+import mindspore as ms
+import mindspore.nn as nn
+from mindspore import Tensor, ops
+
+
+class Net(nn.Cell):
+    def construct(self, x):
+        return ops.any(x, axis=1, keep_dims=True)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.platform_arm_cpu
+@pytest.mark.platform_x86_gpu_training
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE])
+def test_ops_any(mode):
+    """
+    Feature: ops.any
+    Description: Verify the result of any
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    x = Tensor([[True, True],
+                [False, True],
+                [True, False],
+                [False, False]], ms.bool_)
+    net = Net()
+    output = net(x)
+    expect_output = Tensor([[True],
+                            [True],
+                            [True],
+                            [False]], ms.bool_)
+    assert all(output == expect_output)