add paddle.linalg.eigvalsh API (#35615)

* add eigvalsh with is_test

* add eigvalsh op

* fix backward bug

* forward and backward, float and complex, unittest

* remove eigvalsh_helper.h

* remove changes of cusolver.h

* fix unittest

* fix unittest bug

* update code following eigh

* fix test

* update lapack

* pull develop

* update funcor

* fix unittest bug

* fix details

* add tensor_method_func

* fix notes

cmake/operators.cmake

@@ -185,6 +185,7 @@ function(op_library TARGET)
         list(REMOVE_ITEM hip_srcs "cholesky_op.cu")
         list(REMOVE_ITEM hip_srcs "matrix_rank_op.cu")
         list(REMOVE_ITEM hip_srcs "svd_op.cu")
+        list(REMOVE_ITEM hip_srcs "eigvalsh_op.cu")
         list(REMOVE_ITEM hip_srcs "qr_op.cu")
         list(REMOVE_ITEM hip_srcs "eigh_op.cu")
         list(REMOVE_ITEM hip_srcs "multinomial_op.cu")

paddle/fluid/operators/eigvalsh_op.cc

+/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+
+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. */
+
+#include "paddle/fluid/operators/eigvalsh_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class EigvalshOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Eigvalsh");
+    OP_INOUT_CHECK(ctx->HasOutput("Eigenvalues"), "Output", "Eigenvalues",
+                   "Eigvalsh");
+
+    auto input_dim = ctx->GetInputDim("X");
+    auto rank = input_dim.size();
+
+    PADDLE_ENFORCE_GE(rank, 2,
+                      platform::errors::InvalidArgument(
+                          "The Input(X) should have at least 2 dimensions."
+                          "But received a %d dimension tensor.",
+                          rank));
+    PADDLE_ENFORCE_EQ(
+        input_dim[rank - 2], input_dim[rank - 1],
+        platform::errors::InvalidArgument(
+            "Eigvalsh op is designed for square matrix, consequently"
+            "inner-most 2 dimensions of Input(X) should be symmetric."
+            "But received X's shape[-2] = %d and shape[-1] = %d.",
+            input_dim[rank - 2], input_dim[rank - 1]));
+
+    std::vector<int64_t> values_dim;
+
+    for (auto i = 0; i < rank - 1; i++) {
+      values_dim.emplace_back(input_dim[i]);
+    }
+
+    ctx->SetOutputDim("Eigenvalues", framework::make_ddim(values_dim));
+
+    if (ctx->HasOutput("Eigenvectors")) {
+      ctx->SetOutputDim("Eigenvectors", input_dim);
+    }
+  }
+};
+
+class EigvalshOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X",
+             "(Tensor), Hermitian or real symmetric matrices."
+             "Its shape should be [*, N, N] where * is zero or"
+             "more batch dimensions. The data type is float32 ,"
+             "float64, complex64, complex128.");
+    AddOutput("Eigenvalues",
+              "(Tensor), The eigenvalues in ascending order."
+              "The data type is float32 or float64.");
+    AddOutput(
+        "Eigenvectors",
+        "(Tensor), The column is the normalized eigenvector "
+        "corresponding to the eigenvalue. The data type is the same as ``X``."
+        "Eigenvectors are required to calculate gradient when backward.");
+    AddAttr<std::string>(
+        "UPLO",
+        "(string, default 'L'), 'L' represents the lower triangular matrix,"
+        "'U' represents the upper triangular matrix.")
+        .SetDefault("L");
+    AddAttr<bool>("is_test",
+                  "(bool, default false) Set to true for inference only, false "
+                  "for training.")
+        .SetDefault(false);
+    AddComment(R"DOC(
+Eigvalsh Operator.
+
+Computes the eigenvalues of a complex Hermitian
+ (conjugate symmetric) or a real symmetric matrix.
+
+)DOC");
+  }
+};
+
+class EigvalshGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("Eigenvectors"), "Input", "Eigenvectors",
+                   "EigvalshGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Eigenvalues")),
+                   "Input", "Eigenvalues@GRAD", "EigvalshGrad");
+    auto dims = ctx->GetInputDim("Eigenvectors");
+    auto x_grad_name = framework::GradVarName("X");
+    if (ctx->HasOutput(x_grad_name)) {
+      ctx->SetOutputDim(x_grad_name, dims);
+    }
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "Eigenvectors"),
+        ctx.device_context());
+  }
+};
+
+template <typename T>
+class EigvalshGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> op) const override {
+    op->SetType(this->ForwardOpType() + "_grad");
+    op->SetInput("Eigenvectors", this->Output("Eigenvectors"));
+    op->SetInput(framework::GradVarName("Eigenvalues"),
+                 this->OutputGrad("Eigenvalues"));
+    op->SetAttrMap(this->Attrs());
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(eigvalsh, ops::EigvalshOp, ops::EigvalshOpMaker,
+                  ops::EigvalshGradOpMaker<paddle::framework::OpDesc>,
+                  ops::EigvalshGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(eigvalsh_grad, ops::EigvalshGradOp);
+
+REGISTER_OP_CPU_KERNEL(
+    eigvalsh,
+    ops::EigvalshKernel<paddle::platform::CPUDeviceContext, float, float>,
+    ops::EigvalshKernel<paddle::platform::CPUDeviceContext, double, double>,
+    ops::EigvalshKernel<paddle::platform::CPUDeviceContext, float,
+                        paddle::platform::complex<float>>,
+    ops::EigvalshKernel<paddle::platform::CPUDeviceContext, double,
+                        paddle::platform::complex<double>>);
+
+REGISTER_OP_CPU_KERNEL(
+    eigvalsh_grad,
+    ops::EigvalshGradKernel<paddle::platform::CPUDeviceContext, float, float>,
+    ops::EigvalshGradKernel<paddle::platform::CPUDeviceContext, double, double>,
+    ops::EigvalshGradKernel<paddle::platform::CPUDeviceContext, float,
+                            paddle::platform::complex<float>>,
+    ops::EigvalshGradKernel<paddle::platform::CPUDeviceContext, double,
+                            paddle::platform::complex<double>>);

paddle/fluid/operators/eigvalsh_op.cu

+/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+
+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. */
+
+#include "paddle/fluid/operators/eigvalsh_op.h"
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    eigvalsh,
+    ops::EigvalshKernel<paddle::platform::CUDADeviceContext, float, float>,
+    ops::EigvalshKernel<paddle::platform::CUDADeviceContext, double, double>,
+    ops::EigvalshKernel<paddle::platform::CUDADeviceContext, float,
+                        paddle::platform::complex<float>>,
+    ops::EigvalshKernel<paddle::platform::CUDADeviceContext, double,
+                        paddle::platform::complex<double>>);
+
+REGISTER_OP_CUDA_KERNEL(
+    eigvalsh_grad,
+    ops::EigvalshGradKernel<paddle::platform::CUDADeviceContext, float, float>,
+    ops::EigvalshGradKernel<paddle::platform::CUDADeviceContext, double,
+                            double>,
+    ops::EigvalshGradKernel<paddle::platform::CUDADeviceContext, float,
+                            paddle::platform::complex<float>>,
+    ops::EigvalshGradKernel<paddle::platform::CUDADeviceContext, double,
+                            paddle::platform::complex<double>>);

paddle/fluid/operators/eigvalsh_op.h

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// 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.
+
+#pragma once
+
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/eigen_values_vectors.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenVector = framework::EigenVector<T, MajorType, IndexType>;
+
+template <typename DeviceContext, typename ValueType, typename T>
+class EigvalshKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto input = ctx.Input<Tensor>("X");
+    auto output_w = ctx.Output<Tensor>("Eigenvalues");
+
+    std::string lower = ctx.Attr<std::string>("UPLO");
+    bool is_lower = (lower == "L");
+    bool is_test = ctx.Attr<bool>("is_test");
+    math::MatrixEighFunctor<DeviceContext, T> functor;
+    if (is_test) {
+      functor(ctx, *input, output_w, nullptr, is_lower, false);
+    } else {
+      auto output_v = ctx.Output<Tensor>("Eigenvectors");
+      functor(ctx, *input, output_w, output_v, is_lower, true);
+    }
+  }
+};
+
+template <typename DeviceContext, typename ValueType, typename T>
+class EigvalshGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& x_grad = *ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto& output_v = *ctx.Input<Tensor>("Eigenvectors");
+    auto& output_w_grad =
+        *ctx.Input<Tensor>(framework::GradVarName("Eigenvalues"));
+
+    auto dito =
+        math::DeviceIndependenceTensorOperations<DeviceContext, T, ValueType>(
+            ctx);
+    auto tV = dito.Transpose(dito.Conj(output_v));
+
+    // compute elementwise multiply of output_v and output_w_grad
+    x_grad.mutable_data<T>(output_v.dims(), ctx.GetPlace());
+    auto output_v_vector = EigenVector<T>::Flatten(output_v);
+    auto output_w_grad_vector = EigenVector<ValueType>::Flatten(output_w_grad);
+    auto result_vector = EigenVector<T>::Flatten(x_grad);
+    auto& place = *ctx.template device_context<DeviceContext>().eigen_device();
+    std::vector<int> broadcast_factor;
+    broadcast_factor.push_back(output_v.dims().at(output_v.dims().size() - 1));
+    result_vector.device(place) =
+        output_v_vector * output_w_grad_vector.broadcast(broadcast_factor);
+
+    x_grad = dito.Matmul(x_grad, tV);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/__init__.py

@@ -101,6 +101,7 @@ from .tensor.linalg import histogram  # noqa: F401
 from .tensor.linalg import bincount  # noqa: F401
 from .tensor.linalg import mv  # noqa: F401
 from .tensor.logic import equal  # noqa: F401
+from .tensor.linalg import eigvalsh  # noqa: F401
 from .tensor.logic import greater_equal  # noqa: F401
 from .tensor.logic import greater_than  # noqa: F401
 from .tensor.logic import is_empty  # noqa: F401

python/paddle/fluid/tests/unittests/test_eigvalsh_op.py

+#   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import paddle
+from op_test import OpTest
+from gradient_checker import grad_check
+
+
+class TestEigvalshOp(OpTest):
+    def setUp(self):
+        paddle.enable_static()
+        self.op_type = "eigvalsh"
+        self.init_input()
+        self.init_config()
+        np.random.seed(123)
+        out_w, out_v = np.linalg.eigh(self.x_np, self.UPLO)
+        self.inputs = {"X": self.x_np}
+        self.attrs = {"UPLO": self.UPLO, "is_test": False}
+        self.outputs = {'Eigenvalues': out_w, 'Eigenvectors': out_v}
+
+    def init_config(self):
+        self.UPLO = 'L'
+
+    def init_input(self):
+        self.x_shape = (10, 10)
+        self.x_type = np.float64
+        self.x_np = np.random.random(self.x_shape).astype(self.x_type)
+
+    def test_check_output(self):
+        # Vectors in posetive or negative is equivalent
+        self.check_output(no_check_set=['Eigenvectors'])
+
+    def test_grad(self):
+        self.check_grad(["X"], ["Eigenvalues"])
+
+
+class TestEigvalshUPLOCase(TestEigvalshOp):
+    def init_config(self):
+        self.UPLO = 'U'
+
+
+class TestEigvalshGPUCase(unittest.TestCase):
+    def setUp(self):
+        self.x_shape = [32, 32]
+        self.dtype = "float32"
+        np.random.seed(123)
+        self.x_np = np.random.random(self.x_shape).astype(self.dtype)
+        self.rtol = 1e-5
+        self.atol = 1e-5
+
+    def test_check_output_gpu(self):
+        if paddle.is_compiled_with_cuda():
+            paddle.disable_static(place=paddle.CUDAPlace(0))
+            input_real_data = paddle.to_tensor(self.x_np)
+            expected_w = np.linalg.eigvalsh(self.x_np)
+            actual_w = paddle.linalg.eigvalsh(input_real_data)
+            np.testing.assert_allclose(
+                actual_w, expected_w, rtol=self.rtol, atol=self.atol)
+
+
+class TestEigvalshAPI(unittest.TestCase):
+    def setUp(self):
+        self.init_input_shape()
+        self.dtype = "float32"
+        self.UPLO = 'L'
+        self.rtol = 1e-6
+        self.atol = 1e-6
+        self.place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda() \
+            else paddle.CPUPlace()
+        np.random.seed(123)
+        self.real_data = np.random.random(self.x_shape).astype(self.dtype)
+        self.complex_data = np.random.random(self.x_shape).astype(
+            self.dtype) + 1J * np.random.random(self.x_shape).astype(self.dtype)
+        self.trans_dims = list(range(len(self.x_shape) - 2)) + [
+            len(self.x_shape) - 1, len(self.x_shape) - 2
+        ]
+
+    def init_input_shape(self):
+        self.x_shape = [5, 5]
+
+    def compare_result(self, actual_w, expected_w):
+        np.testing.assert_allclose(
+            actual_w, expected_w, rtol=self.rtol, atol=self.atol)
+
+    def check_static_float_result(self):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, startup_prog):
+            input_x = paddle.static.data(
+                'input_x', shape=self.x_shape, dtype=self.dtype)
+            output_w = paddle.linalg.eigvalsh(input_x)
+            exe = paddle.static.Executor(self.place)
+            expected_w = exe.run(main_prog,
+                                 feed={"input_x": self.real_data},
+                                 fetch_list=[output_w])
+
+            actual_w = np.linalg.eigvalsh(self.real_data)
+            self.compare_result(actual_w, expected_w[0])
+
+    def check_static_complex_result(self):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, startup_prog):
+            x_dtype = np.complex64 if self.dtype == "float32" else np.complex128
+            input_x = paddle.static.data(
+                'input_x', shape=self.x_shape, dtype=x_dtype)
+            output_w = paddle.linalg.eigvalsh(input_x)
+            exe = paddle.static.Executor(self.place)
+            expected_w = exe.run(main_prog,
+                                 feed={"input_x": self.complex_data},
+                                 fetch_list=[output_w])
+            actual_w = np.linalg.eigvalsh(self.complex_data)
+            self.compare_result(actual_w, expected_w[0])
+
+    def test_in_static_mode(self):
+        paddle.enable_static()
+        self.check_static_float_result()
+        self.check_static_complex_result()
+
+    def test_in_dynamic_mode(self):
+        paddle.disable_static(self.place)
+        input_real_data = paddle.to_tensor(self.real_data)
+        expected_w = np.linalg.eigvalsh(self.real_data)
+        actual_w = paddle.linalg.eigvalsh(input_real_data)
+        self.compare_result(actual_w, expected_w)
+
+        input_complex_data = paddle.to_tensor(self.complex_data)
+        expected_w = np.linalg.eigvalsh(self.complex_data)
+        actual_w = paddle.linalg.eigvalsh(input_complex_data)
+        self.compare_result(actual_w, expected_w)
+
+    def test_eigvalsh_grad(self):
+        paddle.disable_static(self.place)
+        x = paddle.to_tensor(self.complex_data, stop_gradient=False)
+        w = paddle.linalg.eigvalsh(x)
+        (w.sum()).backward()
+        np.testing.assert_allclose(
+            abs(x.grad.numpy()),
+            abs(x.grad.numpy().conj().transpose(self.trans_dims)),
+            rtol=self.rtol,
+            atol=self.atol)
+
+
+class TestEigvalshBatchAPI(TestEigvalshAPI):
+    def init_input_shape(self):
+        self.x_shape = [2, 5, 5]
+
+
+class TestEigvalshAPIError(unittest.TestCase):
+    def test_error(self):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog, startup_prog):
+            #input maxtrix must greater than 2 dimensions
+            input_x = paddle.static.data(
+                name='x_1', shape=[12], dtype='float32')
+            self.assertRaises(ValueError, paddle.linalg.eigvalsh, input_x)
+
+            #input matrix must be square matrix
+            input_x = paddle.static.data(
+                name='x_2', shape=[12, 32], dtype='float32')
+            self.assertRaises(ValueError, paddle.linalg.eigvalsh, input_x)
+
+            #uplo must be in 'L' or 'U'
+            input_x = paddle.static.data(
+                name='x_3', shape=[4, 4], dtype="float32")
+            uplo = 'R'
+            self.assertRaises(ValueError, paddle.linalg.eigvalsh, input_x, uplo)
+
+            #x_data cannot be integer
+            input_x = paddle.static.data(
+                name='x_4', shape=[4, 4], dtype="int32")
+            self.assertRaises(TypeError, paddle.linalg.eigvalsh, input_x)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/white_list/no_check_set_white_list.py

@@ -33,5 +33,6 @@ no_check_set_white_list = [
     'softmax_with_cross_entropy',
     'svd',
     'eigh',
+    'eigvalsh',
     'class_center_sample',
 ]

python/paddle/linalg.py

@@ -23,6 +23,7 @@ from .tensor.linalg import eigvals  # noqa: F401
 from .tensor.linalg import multi_dot  # noqa: F401
 from .tensor.linalg import matrix_rank
 from .tensor.linalg import svd
+from .tensor.linalg import eigvalsh
 from .tensor.linalg import qr
 from .tensor.linalg import eigh  # noqa: F401
 from .tensor.linalg import det
@@ -44,6 +45,7 @@ __all__ = [
     'det',
     'slogdet',
     'eigh',
+    'eigvalsh',
     'pinv',
     'solve'
 ]

python/paddle/tensor/__init__.py

@@ -52,6 +52,7 @@ from .linalg import qr  # noqa: F401
 from .linalg import eigvals  # noqa: F401
 from .linalg import multi_dot  # noqa: F401
 from .linalg import svd  # noqa: F401
+from .linalg import eigvalsh  # noqa: F401
 from .linalg import eigh  # noqa: F401
 from .linalg import pinv  # noqa: F401
 from .linalg import solve  # noqa: F401
@@ -240,6 +241,7 @@ tensor_method_func  = [ #noqa
            'matrix_power',
            'qr',
            'eigvals',
+           'eigvalsh',
            'abs',
            'acos',
            'all',

python/paddle/tensor/linalg.py

@@ -14,8 +14,8 @@
 
 import numpy as np
 from ..fluid.layer_helper import LayerHelper
+from ..fluid.framework import in_dygraph_mode, _varbase_creator, Variable, _dygraph_tracer
 from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype
-from ..fluid.framework import in_dygraph_mode, _varbase_creator, Variable
 
 from ..fluid.layers import transpose, cast  # noqa: F401
 from ..fluid import layers
@@ -2313,3 +2313,70 @@ def solve(x, y, name=None):
         type="solve", inputs={"X": x,
                               "Y": y}, outputs={"Out": out})
     return out
+
+
+def eigvalsh(x, UPLO='L', name=None):
+    """
+    Computes the eigenvalues of a 
+    complex Hermitian (conjugate symmetric) or a real symmetric matrix.
+
+    Args:
+        x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x
+            should be one of float32, float64, complex64, complex128.
+        UPLO(str, optional): Lower triangular part of a (‘L’, default) or the upper triangular part (‘U’).
+        name(str, optional): The default value is None.  Normally there is no need for user to set this
+            property.  For more information, please refer to :ref:`api_guide_Name`.
+
+    Returns:
+        Tensor: The tensor eigenvalues in ascending order.
+
+    Examples:
+        .. code-block:: python
+
+            import numpy as np
+            import paddle
+
+            x_data = np.array([[1, -2j], [2j, 5]])
+            x = paddle.to_tensor(x_data)
+            out_value = paddle.eigvalsh(x, UPLO='L')
+            print(out_value)
+            #[0.17157288, 5.82842712]
+    """
+    if in_dygraph_mode():
+        is_test = x.stop_gradient
+        values, _ = _C_ops.eigvalsh(x, 'UPLO', UPLO, 'is_test', is_test)
+        return values
+
+    def __check_input(x, UPLO):
+        x_shape = list(x.shape)
+        if len(x.shape) < 2:
+            raise ValueError(
+                "Input(input) only support >=2 tensor, but received "
+                "length of Input(input) is %s." % len(x.shape))
+        if x_shape[-1] != x_shape[-2]:
+            raise ValueError(
+                "The input matrix must be batches of square matrices. But received x's dimention: {}".
+                format(x_shape))
+        if UPLO is not 'L' and UPLO is not 'U':
+            raise ValueError(
+                "UPLO must be L or U. But received UPLO is: {}".format(UPLO))
+
+    __check_input(x, UPLO)
+
+    helper = LayerHelper('eigvalsh', **locals())
+    check_variable_and_dtype(x, 'dtype',
+                             ['float32', 'float64', 'complex64', 'complex128'],
+                             'eigvalsh')
+
+    out_value = helper.create_variable_for_type_inference(dtype=x.dtype)
+    out_vector = helper.create_variable_for_type_inference(dtype=x.dtype)
+
+    is_test = x.stop_gradient
+    helper.append_op(
+        type='eigvalsh',
+        inputs={'X': x},
+        outputs={'Eigenvalues': out_value,
+                 'Eigenvectors': out_vector},
+        attrs={'UPLO': UPLO,
+               'is_test': is_test})
+    return out_value