From df02f35e6e70eee60342ab2d50a82ac73c2ed246 Mon Sep 17 00:00:00 2001 From: yuzhenhua Date: Tue, 22 Nov 2022 14:41:45 +0800 Subject: [PATCH] tensor api: H --- .../mindspore/Tensor/mindspore.Tensor.H.rst | 7 ++ .../api_python/mindspore/mindspore.Tensor.rst | 1 + docs/api/api_python_en/Tensor_list.rst | 1 + mindspore/ccsrc/pipeline/jit/resource.cc | 1 + .../_extends/parse/standard_method.py | 8 ++ mindspore/python/mindspore/common/tensor.py | 11 +++ tests/st/tensor/test_H.py | 97 +++++++++++++++++++ 7 files changed, 126 insertions(+) create mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.H.rst create mode 100644 tests/st/tensor/test_H.py diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.H.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.H.rst new file mode 100644 index 00000000000..aeaa0ed35e3 --- /dev/null +++ b/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)。 diff --git a/docs/api/api_python/mindspore/mindspore.Tensor.rst b/docs/api/api_python/mindspore/mindspore.Tensor.rst index d371912be9c..47da3c6261c 100644 --- a/docs/api/api_python/mindspore/mindspore.Tensor.rst +++ b/docs/api/api_python/mindspore/mindspore.Tensor.rst @@ -122,6 +122,7 @@ 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 diff --git a/docs/api/api_python_en/Tensor_list.rst b/docs/api/api_python_en/Tensor_list.rst index 3288312d64a..06fd4bda1b6 100644 --- a/docs/api/api_python_en/Tensor_list.rst +++ b/docs/api/api_python_en/Tensor_list.rst @@ -128,6 +128,7 @@ mindspore.Tensor.greater mindspore.Tensor.greater_equal mindspore.Tensor.gt + mindspore.Tensor.H mindspore.Tensor.half mindspore.Tensor.hardshrink mindspore.Tensor.has_init diff --git a/mindspore/ccsrc/pipeline/jit/resource.cc b/mindspore/ccsrc/pipeline/jit/resource.cc index ac0de647e19..6578f1dcdc3 100644 --- a/mindspore/ccsrc/pipeline/jit/resource.cc +++ b/mindspore/ccsrc/pipeline/jit/resource.cc @@ -494,6 +494,7 @@ 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_ diff --git a/mindspore/python/mindspore/_extends/parse/standard_method.py b/mindspore/python/mindspore/_extends/parse/standard_method.py index de7b8ba2935..2fa406dc8c3 100644 --- a/mindspore/python/mindspore/_extends/parse/standard_method.py +++ b/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`. diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py index 561c99d6d04..42bedd7de58 100644 --- a/mindspore/python/mindspore/common/tensor.py +++ b/mindspore/python/mindspore/common/tensor.py @@ -443,6 +443,17 @@ 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.""" diff --git a/tests/st/tensor/test_H.py b/tests/st/tensor/test_H.py new file mode 100644 index 00000000000..4e97171ab1d --- /dev/null +++ b/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)