diff --git a/docs/api/api_python/mindspore.ops.rst b/docs/api/api_python/mindspore.ops.rst index 518dea7a052..00a8d00e973 100644 --- a/docs/api/api_python/mindspore.ops.rst +++ b/docs/api/api_python/mindspore.ops.rst @@ -454,6 +454,7 @@ Array操作 mindspore.ops.shape mindspore.ops.size mindspore.ops.slice + mindspore.ops.slogdet mindspore.ops.space_to_batch_nd mindspore.ops.sparse_segment_mean mindspore.ops.split @@ -469,6 +470,7 @@ Array操作 mindspore.ops.tensor_scatter_elements mindspore.ops.tensor_split mindspore.ops.tile + mindspore.ops.tril mindspore.ops.top_k mindspore.ops.transpose mindspore.ops.unbind diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.slogdet.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.slogdet.rst new file mode 100644 index 00000000000..61e22d0c8e4 --- /dev/null +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.slogdet.rst @@ -0,0 +1,6 @@ +mindspore.Tensor.slogdet +======================== + +.. py:method:: mindspore.Tensor.slogdet() + + 详情请参考 :func:`mindspore.ops.slogdet`。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.tril.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.tril.rst new file mode 100644 index 00000000000..7f8dad86f4e --- /dev/null +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.tril.rst @@ -0,0 +1,6 @@ +mindspore.Tensor.tril +===================== + +.. py:method:: mindspore.Tensor.tril(diagonal=0) + + 详情请参考 :func:`mindspore.ops.tril`。 diff --git a/docs/api/api_python/mindspore/mindspore.Tensor.rst b/docs/api/api_python/mindspore/mindspore.Tensor.rst index 42a904ffcd1..4bca1d08c6d 100644 --- a/docs/api/api_python/mindspore/mindspore.Tensor.rst +++ b/docs/api/api_python/mindspore/mindspore.Tensor.rst @@ -238,6 +238,7 @@ mindspore.Tensor mindspore.Tensor.sin mindspore.Tensor.sinc mindspore.Tensor.size + mindspore.Tensor.slogdet mindspore.Tensor.soft_shrink mindspore.Tensor.split mindspore.Tensor.sqrt @@ -263,6 +264,7 @@ mindspore.Tensor mindspore.Tensor.top_k mindspore.Tensor.trace mindspore.Tensor.transpose + mindspore.Tensor.tril mindspore.Tensor.triu mindspore.Tensor.true_divide mindspore.Tensor.unbind diff --git a/docs/api/api_python/ops/mindspore.ops.func_slogdet.rst b/docs/api/api_python/ops/mindspore.ops.func_slogdet.rst new file mode 100644 index 00000000000..af12ead6ebd --- /dev/null +++ b/docs/api/api_python/ops/mindspore.ops.func_slogdet.rst @@ -0,0 +1,20 @@ +mindspore.ops.slogdet +===================== + +.. py:function:: mindspore.ops.slogdet(x) + + 对一个或多个方阵行列式的绝对值取对数，返回其符号和值。 + + 参数： + - **x** (Tensor) - 输入Tensor，shape为 :math:`[..., M, M]` 。矩阵必须至少有两个维度，最后两个维度尺寸必须相同。支持的数据类型为float32、float64、complex64或complex128。 + + 返回： + Tensor，行列式的绝对值的对数的符号，shape为 `x.shape[:-2]` ，数据类型与 `x` 相同。 + + Tensor，行列式的绝对值的对数，shape为 `x.shape[:-2]` ，数据类型与 `x` 相同。 + + 异常： + - **TypeError** - `x` 不为 Tensor。 + - **TypeError** - `x` 的数据类型不为以下类型：float32、 float64、 complex64 和 complex128。 + - **ValueError** - `x` 的最后两个维度大小不同。 + - **ValueError** - `x` 的维数小于2。 diff --git a/docs/api/api_python/ops/mindspore.ops.func_tril.rst b/docs/api/api_python/ops/mindspore.ops.func_tril.rst new file mode 100644 index 00000000000..009e538f93a --- /dev/null +++ b/docs/api/api_python/ops/mindspore.ops.func_tril.rst @@ -0,0 +1,21 @@ +mindspore.ops.tril +=================== + +.. py:function:: mindspore.ops.tril(input_x, diagonal=0) + + 返回单个矩阵（二维Tensor）或批次输入矩阵的下三角形部分，其他位置的元素将被置零。 + 矩阵的下三角形部分定义为对角线本身和对角线以下的元素。 + + 参数： + - **input_x** (Tensor) - 输入Tensor。shape为 :math:`(x_1, x_2, ..., x_R)` ，其rank至少为2。 + 支持的数据类型有包括所有数值型和bool类型。 + - **diagonal** (int，可选) - 指定对角线位置，默认值：0，指定主对角线。 + + 返回： + Tensor，其数据类型和shape维度与 `input_x` 相同。 + + 异常： + - **TypeError** - 如果 `input_x` 不是Tensor。 + - **TypeError** - 如果 `diagonal` 不是int类型。 + - **TypeError** - 如果 `input_x` 的数据类型既不是数值型也不是bool。 + - **ValueError** - 如果 `input_x` 的秩小于2。 diff --git a/mindspore/ccsrc/pipeline/jit/resource.cc b/mindspore/ccsrc/pipeline/jit/resource.cc index 818954bf18a..610325aaa66 100644 --- a/mindspore/ccsrc/pipeline/jit/resource.cc +++ b/mindspore/ccsrc/pipeline/jit/resource.cc @@ -172,7 +172,9 @@ BuiltInTypeMap &GetMethodMap() { {"atan2", std::string("atan2")}, // P.Atan2 {"angle", std::string("angle")}, // C.reduce_any {"any", std::string("any_")}, // C.reduce_any - {"bincount", std::string("bincount")}, // C.reduce_any + {"bincount", std::string("bincount")}, // bincount + {"slogdet", std::string("slogdet")}, // slogdet + {"tril", std::string("tril")}, // tril {"__add__", std::string("add")}, // C.add {"__sub__", std::string("sub")}, // C.sub {"__mul__", std::string("mul")}, // C.mul diff --git a/mindspore/python/mindspore/_extends/parse/standard_method.py b/mindspore/python/mindspore/_extends/parse/standard_method.py index 073b7a1e902..79bb40c6105 100644 --- a/mindspore/python/mindspore/_extends/parse/standard_method.py +++ b/mindspore/python/mindspore/_extends/parse/standard_method.py @@ -284,6 +284,20 @@ def strides_(x): return strides +def slogdet(x): + r""" + For details, please refer to :func:`mindspore.ops.slogdet`. + """ + return F.slogdet(x) + + +def tril(x, diagonal=0): + r""" + For details, please refer to :func:`mindspore.ops.tril`. + """ + return F.tril(x, diagonal) + + def hasattr(x, attr): # pylint: disable=redefined-builtin """ Return whether an object has the attribute. diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py index 578852797e5..a7511d66a3d 100644 --- a/mindspore/python/mindspore/common/tensor.py +++ b/mindspore/python/mindspore/common/tensor.py @@ -1927,6 +1927,20 @@ class Tensor(Tensor_): new_shape = validator.prepare_shape_for_squeeze(self.shape, axis) return tensor_operator_registry.get('reshape')()(self, new_shape) + def slogdet(self): + """ + For details, please refer to :func:`mindspore.ops.slogdet`. + """ + self._init_check() + return tensor_operator_registry.get('slogdet')(self) + + def tril(self, diagonal=0): + """ + For details, please refer to :func:`mindspore.ops.tril`. + """ + self._init_check() + return tensor_operator_registry.get('tril')(self, diagonal) + def unsqueeze(self, dim): """ For details, please refer to :func:`mindspore.ops.unsqueeze`. diff --git a/mindspore/python/mindspore/ops/function/__init__.py b/mindspore/python/mindspore/ops/function/__init__.py index 679381a8a33..5dd9f8fe31d 100644 --- a/mindspore/python/mindspore/ops/function/__init__.py +++ b/mindspore/python/mindspore/ops/function/__init__.py @@ -73,6 +73,7 @@ from .array_func import ( scatter_nd_div, scatter_nd_max, scatter_nd_min, + tril, gather, gather_d, gather_elements, @@ -212,6 +213,7 @@ from .math_func import ( log, logdet, log_matrix_determinant, + slogdet, matrix_determinant, linspace, matrix_solve, diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py index 4087c4b249e..0bc184c78bf 100644 --- a/mindspore/python/mindspore/ops/function/array_func.py +++ b/mindspore/python/mindspore/ops/function/array_func.py @@ -45,6 +45,7 @@ from mindspore.ops.operations.array_ops import ( Lstsq, Mvlgamma, CountNonZero, + Tril ) from mindspore.ops.operations.array_ops import TensorScatterElements from mindspore.common import Tensor @@ -4660,6 +4661,66 @@ def split(x, split_size_or_sections, axis=0): return res +def tril(input_x, diagonal): + """ + Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices input, + the other elements of the result tensor out are set to 0. + The lower triangular part of the matrix is defined as the elements on and below the diagonal. + + Args: + input_x (Tensor): A Tensor with shape :math:`(x_1, x_2, ..., x_R)`. The rank must be at least 2. + Supporting all number types including bool. + diagonal (int, optional): An optional attribute indicates the diagonal to consider, default: 0, + indicating the main diagonal. + + Returns: + Tensor, the same shape and data type as the input `x`. + + Raises: + TypeError: If `x` is not a Tensor. + TypeError: If `diagonal` is not an int. + TypeError: If the type of `x` is neither number nor bool. + ValueError: If the rank of `x` is less than 2. + + Supported Platforms: + ``GPU`` ``CPU`` + + Examples: + >>> x = Tensor(np.array([[ 1, 2, 3, 4], + ... [ 5, 6, 7, 8], + ... [10, 11, 12, 13], + ... [14, 15, 16, 17]])) + >>> result = ops.tril(x) + >>> print(result) + [[ 1 0 0 0] + [ 5 6 0 0] + [10 11 12 0] + [14 15 16 17]] + >>> x = Tensor(np.array([[ 1, 2, 3, 4], + ... [ 5, 6, 7, 8], + ... [10, 11, 12, 13], + ... [14, 15, 16, 17]])) + >>> result = ops.tril(x, diagonal=1) + >>> print(result) + [[ 1 2 0 0] + [ 5 6 7 0] + [10 11 12 13] + [14 15 16 17]] + >>> x = Tensor(np.array([[ 1, 2, 3, 4], + ... [ 5, 6, 7, 8], + ... [10, 11, 12, 13], + ... [14, 15, 16, 17]])) + >>> result = ops.tril(x, diagonal=-1) + >>> print(result) + [[ 0 0 0 0] + [ 5 0 0 0] + [10 11 0 0] + [14 15 16 0]] + """ + tril_ = Tril(diagonal) + return tril_(input_x) + + @constexpr def _canonicalize_axis(axis, ndim): """ @@ -5747,6 +5808,7 @@ __all__ = [ 'scatter_div', 'scatter_update', 'select', + 'tril', 'nonzero', 'matrix_diag', 'matrix_diag_part', diff --git a/mindspore/python/mindspore/ops/function/math_func.py b/mindspore/python/mindspore/ops/function/math_func.py index fdc9592299e..6fd4f7ef067 100644 --- a/mindspore/python/mindspore/ops/function/math_func.py +++ b/mindspore/python/mindspore/ops/function/math_func.py @@ -2987,6 +2987,42 @@ def matrix_solve(matrix, rhs, adjoint=False): # pylint: disable=redefined-outer return matrix_solve_(matrix, rhs) +def slogdet(x): + r""" + Computes the sign and the log of the absolute value of the determinant of one or more square matrices. + + Args: + x (Tensor): A matrix to be calculated, its shape is :math:`[..., M, M]`. + The matrix must be at least two dimensions, and the last two + dimensions must be the same size. Data type must be float32, float64, complex64 or complex128. + + Returns: + Tensor. The signs of the log determinants. The shape is :math:`x.shape[:-2]`, + and the dtype is same as `x`. + + Tensor. The absolute values of the log determinants. The shape is :math:`x.shape[:-2]`, and + the dtype is same as `x`. + + Raises: + TypeError: If `x` is not a Tensor. + TypeError: If dtype of `x` not float32, float64, complex64 or complex128. + ValueError: If the last two dimensions of `x` is not same size. + ValueError: If the dimension of `x` is less than 2. + + Supported Platforms: + ``GPU`` ``CPU`` + + Examples: + >>> input_x = Tensor(np.array([[[-4.5, -1.5], [7.0, 6.0]], [[2.5, 0.5], [3.0, 9.0]]]), mindspore.float32) + >>> sign, output = ops.slogdet(input_x) + >>> print(sign) + [-1. 1.] + >>> print(output) + [2.80336046e+00 3.04452229e+00] + """ + return log_matrix_determinant_(x) + + def truncate_div(x, y): """ Divides the first input tensor by the second input tensor element-wise for integer types, negative numbers will @@ -9359,6 +9395,7 @@ __all__ = [ 'chain_matmul', 'hann_window', 'log2', + 'slogdet', 'xlogy', 'log10', 'log1p', diff --git a/mindspore/python/mindspore/ops/functional.py b/mindspore/python/mindspore/ops/functional.py index dd8785c7832..c40fd0cb04d 100644 --- a/mindspore/python/mindspore/ops/functional.py +++ b/mindspore/python/mindspore/ops/functional.py @@ -137,6 +137,8 @@ tensor_operator_registry.register('real', real) tensor_operator_registry.register('reciprocal', reciprocal) tensor_operator_registry.register('rsqrt', rsqrt) tensor_operator_registry.register('bincount', bincount) +tensor_operator_registry.register('slogdet', slogdet) +tensor_operator_registry.register('tril', tril) tensor_operator_registry.register('sqrt', sqrt) tensor_operator_registry.register('square', square) tensor_operator_registry.register('sub', sub) diff --git a/tests/st/ops/test_func_slogdet.py b/tests/st/ops/test_func_slogdet.py new file mode 100644 index 00000000000..d37aea2214d --- /dev/null +++ b/tests/st/ops/test_func_slogdet.py @@ -0,0 +1,73 @@ +# 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 +from mindspore import ops + + +class Net(nn.Cell): + def construct(self, x): + return ops.slogdet(x) + + +@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_slogdet(mode): + """ + Feature: slogdet + Description: Verify the result of slogdet + Expectation: success + """ + ms.set_context(mode=mode) + x = Tensor([[-1.8297, -0.8474, 1.0292], [-1.2167, 0.5574, -0.6753], [-0.6702, 0.2276, 1.2421]]) + net = Net() + output1, output2 = net(x) + expect_output1 = np.array(-1, dtype=np.float32) + expect_output2 = np.array(1.13549, dtype=np.float32) + assert np.allclose(output1.asnumpy(), expect_output1) + assert np.allclose(output2.asnumpy(), expect_output2) + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.platform_arm_cpu +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) +def test_slogdet_complex(mode): + """ + Feature: slogdet + Description: Verify the result of slogdet + Expectation: success + """ + ms.set_context(mode=mode) + x = Tensor([[-1.5 + 7.8j, 3 + 5.75j, 2 + 2.4j], + [-6.4 + 485.4j, 45 + 3.14j, 45 + 453j], + [-3.5 + 5.8j, 63 + 12.75j, -5 + 6.4j]], dtype=ms.complex64) + net = Net() + output1, output2 = net(x) + expect_output1 = np.array(0.749919+0.66153j, dtype=np.complex) + expect_output2 = np.array(12.0614+0j, dtype=np.complex) + assert np.allclose(output1.asnumpy(), expect_output1) + assert np.allclose(output2.asnumpy(), expect_output2) diff --git a/tests/st/ops/test_func_tril.py b/tests/st/ops/test_func_tril.py new file mode 100644 index 00000000000..2d0ee9a890b --- /dev/null +++ b/tests/st/ops/test_func_tril.py @@ -0,0 +1,47 @@ +# 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 +from mindspore import ops + + +class Net(nn.Cell): + def construct(self, x, diagonal=0): + return ops.tril(x, diagonal) + + +@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_tril(mode): + """ + Feature: tril + Description: Verify the result of tril + Expectation: success + """ + ms.set_context(mode=mode) + x = Tensor([[-1.8297, -0.8474, 1.0292], [-1.2167, 0.5574, -0.6753], [-0.6702, 0.2276, 1.2421]]) + net = Net() + output = net(x) + expect_output = np.array([[-1.8297, 0., 0.], [-1.2167, 0.5574, 0.], [-0.6702, 0.2276, 1.2421]], dtype=np.float32) + assert np.allclose(output.asnumpy(), expect_output) diff --git a/tests/st/tensor/test_slogdet.py b/tests/st/tensor/test_slogdet.py new file mode 100644 index 00000000000..254bea2e3c8 --- /dev/null +++ b/tests/st/tensor/test_slogdet.py @@ -0,0 +1,72 @@ +# 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.slogdet() + + +@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_slogdet(mode): + """ + Feature: slogdet + Description: Verify the result of slogdet + Expectation: success + """ + ms.set_context(mode=mode) + x = Tensor([[-1.8297, -0.8474, 1.0292], [-1.2167, 0.5574, -0.6753], [-0.6702, 0.2276, 1.2421]]) + net = Net() + output1, output2 = net(x) + expect_output1 = np.array(-1, dtype=np.float32) + expect_output2 = np.array(1.13549, dtype=np.float32) + assert np.allclose(output1.asnumpy(), expect_output1) + assert np.allclose(output2.asnumpy(), expect_output2) + + +@pytest.mark.level0 +@pytest.mark.platform_x86_cpu +@pytest.mark.platform_arm_cpu +@pytest.mark.platform_x86_gpu_training +@pytest.mark.env_onecard +@pytest.mark.parametrize('mode', [ms.GRAPH_MODE, ms.PYNATIVE_MODE]) +def test_slogdet_complex(mode): + """ + Feature: slogdet + Description: Verify the result of slogdet + Expectation: success + """ + ms.set_context(mode=mode) + x = Tensor([[-1.5 + 7.8j, 3 + 5.75j, 2 + 2.4j], + [-6.4 + 485.4j, 45 + 3.14j, 45 + 453j], + [-3.5 + 5.8j, 63 + 12.75j, -5 + 6.4j]], dtype=ms.complex64) + net = Net() + output1, output2 = net(x) + expect_output1 = np.array(0.749919+0.66153j, dtype=np.complex) + expect_output2 = np.array(12.0614+0j, dtype=np.complex) + assert np.allclose(output1.asnumpy(), expect_output1) + assert np.allclose(output2.asnumpy(), expect_output2) diff --git a/tests/st/tensor/test_tril.py b/tests/st/tensor/test_tril.py new file mode 100644 index 00000000000..2bbad569fd5 --- /dev/null +++ b/tests/st/tensor/test_tril.py @@ -0,0 +1,46 @@ +# 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, diagonal=0): + return x.tril(diagonal) + + +@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_tril(mode): + """ + Feature: tril + Description: Verify the result of tril + Expectation: success + """ + ms.set_context(mode=mode) + x = Tensor([[-1.8297, -0.8474, 1.0292], [-1.2167, 0.5574, -0.6753], [-0.6702, 0.2276, 1.2421]]) + net = Net() + output = net(x) + expect_output = np.array([[-1.8297, 0., 0.], [-1.2167, 0.5574, 0.], [-0.6702, 0.2276, 1.2421]], dtype=np.float32) + assert np.allclose(output.asnumpy(), expect_output)