!46602 for Tensor api: H

Merge pull request !46602 from 于振华/tensor_api_H_1208

docs/api/api_python/mindspore/Tensor/mindspore.Tensor.H.rst

@@ -0,0 +1,7 @@
+mindspore.Tensor.H
+==================
+
+.. py:method:: mindspore.Tensor.H
+    :property:
+
+    返回共轭和转置的矩阵（2-D张量）的视图。如果x是复数矩阵，x.H等价于self.swapaxes(0, 1).conj()，如果是实数矩阵则等价于self.swapaxes(0, 1)。

docs/api/api_python/mindspore/mindspore.Tensor.rst

@@ -123,6 +123,7 @@ mindspore.Tensor
     mindspore.Tensor.greater
     mindspore.Tensor.greater_equal
     mindspore.Tensor.gt
+    mindspore.Tensor.H
     mindspore.Tensor.half
     mindspore.Tensor.hardshrink
     mindspore.Tensor.has_init

docs/api/api_python_en/Tensor_list.rst

@@ -129,6 +129,7 @@
     mindspore.Tensor.greater
     mindspore.Tensor.greater_equal
     mindspore.Tensor.gt
+    mindspore.Tensor.H
     mindspore.Tensor.half
     mindspore.Tensor.hardshrink
     mindspore.Tensor.has_init

mindspore/ccsrc/pipeline/jit/resource.cc

@@ -503,6 +503,7 @@ BuiltInTypeMap &GetAttrMap() {
        {"dtype", prim::kPrimDType},             // C.dtype_
        {"size", std::string("size_")},          // C.size_
        {"ndim", std::string("ndim_")},          // C.ndim_
+       {"H", std::string("H")},                 // C.H
        {"T", std::string("T_")},                // C.T_
        {"itemsize", std::string("itemsize_")},  // C.itemsize_
        {"nbytes", std::string("nbytes_")},      // C.nbytes_

mindspore/python/mindspore/_extends/parse/standard_method.py

@@ -219,6 +219,14 @@ def bincount(x, weights=None, minlength=0):
     return F.bincount(x, weights, minlength)
 
 
+def H(x):
+    """Returns a view of a matrix (2-D tensor) conjugated and transposed."""
+    output = x.swapaxes(0, 1)
+    if x.dtype in (mstype.complex64, mstype.complex128):
+        return output.conj()
+    return output
+
+
 def size_(x):
     """
     Return the number of elements in tensor `x`.

mindspore/python/mindspore/common/tensor.py

@@ -449,6 +449,17 @@ class Tensor(Tensor_):
         """Return the number of tensor dimensions."""
         return len(self._shape)
 
+    @property
+    def H(self):
+        """
+        Returns a view of a matrix (2-D tensor) conjugated and transposed.
+        x.H is equivalent to x.swapaxes(0, 1).conj() for complex matrices and x.swapaxes(0, 1) for real matrices.
+        """
+        output = self.swapaxes(0, 1)
+        if self.dtype in (mstype.complex64, mstype.complex128):
+            return output.conj()
+        return output
+
     @property
     def has_init(self):
         """Whether tensor is initialized."""

tests/st/tensor/test_H.py

@@ -0,0 +1,97 @@
+# Copyright 2022 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 as ms
+import mindspore.nn as nn
+from mindspore import Tensor
+
+
+class Net(nn.Cell):
+    def construct(self, x):
+        return x.H
+
+
+@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])
+@pytest.mark.skip(reason="No support")
+def test_tensor_H_complex(mode):
+    """
+    Feature: tensor.positive
+    Description: Verify the result of positive
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    x = Tensor([[-1.5 + 7.8j, 3 + 5.75j]])
+    net = Net()
+    output = net(x)
+    expect_output = [[-1.5000-7.8000j],
+                     [3.0000-5.7500j]]
+    assert np.allclose(output.asnumpy(), expect_output)
+
+
+@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_tensor_positive_01(mode):
+    """
+    Feature: tensor.positive
+    Description: Verify the result of positive
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    x = Tensor(np.array([[12., -51, 4], [6, 167, -68]]), ms.float32)
+    net = Net()
+    output = net(x)
+    expect_output = [[12.0, 6.0],
+                     [-51.0, 167.0],
+                     [4.0, -68.0]]
+    assert np.allclose(output.asnumpy(), expect_output)
+
+
+@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_tensor_positive_02(mode):
+    """
+    Feature: tensor.positive
+    Description: Verify the result of positive
+    Expectation: success
+    """
+    ms.set_context(mode=mode)
+    x = Tensor(np.array([[12., -51, 4], [6, 167, -68], [-4, 24, -41], [-4, 24, -41]]), ms.float32)
+    net = Net()
+    output = net(x)
+    expect_output = [[12.0, 6., -4.0, -4.0],
+                     [-51.0, 167.0, 24.0, 24.0],
+                     [4.0, -68.0, -41.0, -41.0]]
+    assert np.allclose(output.asnumpy(), expect_output)