Add CPU and GPU eigh op implementation

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 "eigh_op.cu")
         list(REMOVE_ITEM hip_srcs "multinomial_op.cu")
         list(REMOVE_ITEM hip_srcs "decode_jpeg_op.cu")
         hip_library(${TARGET} SRCS ${cc_srcs} ${hip_cc_srcs} ${miopen_cu_cc_srcs} ${miopen_cu_srcs} ${mkldnn_cc_srcs} ${hip_srcs} DEPS ${op_library_DEPS}

paddle/fluid/operators/eigh_op.cc

@@ -0,0 +1,167 @@
+/* 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/eigh_op.h"
+
+namespace paddle {
+namespace operators {
+
+using framework::Tensor;
+
+class EighOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Eigh");
+    OP_INOUT_CHECK(ctx->HasOutput("Eigenvalues"), "Output", "Eigenvalues",
+                   "Eigh");
+    OP_INOUT_CHECK(ctx->HasOutput("Eigenvectors"), "Output", "Eigenvectors",
+                   "Eigh");
+
+    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(
+            "Eigh 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;
+    if (rank > 2) {
+      for (auto i = 0; i < rank - 1; i++) {
+        values_dim.emplace_back(input_dim[i]);
+      }
+    } else {
+      values_dim = {input_dim[1]};
+    }
+
+    ctx->SetOutputDim("Eigenvalues", framework::make_ddim(values_dim));
+    ctx->SetOutputDim("Eigenvectors", input_dim);
+  }
+};
+
+class EignOpMaker : 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``.");
+    AddAttr<std::string>(
+        "UPLO",
+        "(string, default 'L'), 'L' represents the lower triangular matrix,"
+        "'U' represents the upper triangular matrix.")
+        .SetDefault("L");
+    AddComment(R"DOC(
+Eigh Operator.
+
+Computes the eigenvalues and eigenvectors of a complex Hermitian
+ (conjugate symmetric) or a real symmetric matrix.
+
+)DOC");
+  }
+};
+
+class EighGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("Eigenvalues"), "Input", "Eigenvalues",
+                   "EighGrad");
+    OP_INOUT_CHECK(ctx->HasInput("Eigenvectors"), "Input", "Eigenvectors",
+                   "EighGrad");
+    OP_INOUT_CHECK(ctx->HasInputs(framework::GradVarName("Eigenvalues")),
+                   "Input", "Eigenvalues@GRAD", "EighGrad");
+    OP_INOUT_CHECK(ctx->HasInputs(framework::GradVarName("Eigenvectors")),
+                   "Input", "Eigenvectors@GRAD", "EighGrad");
+    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, framework::GradVarName("Eigenvectors")),
+        ctx.device_context());
+  }
+};
+
+template <typename T>
+class EighGradOpMaker : 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("Eigenvalues", this->Output("Eigenvalues"));
+    op->SetInput("Eigenvectors", this->Output("Eigenvectors"));
+    op->SetInput(framework::GradVarName("Eigenvalues"),
+                 this->OutputGrad("Eigenvalues"));
+    op->SetInput(framework::GradVarName("Eigenvectors"),
+                 this->OutputGrad("Eigenvectors"));
+    op->SetAttrMap(this->Attrs());
+    op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(eigh, ops::EighOp, ops::EignOpMaker,
+                  ops::EighGradOpMaker<paddle::framework::OpDesc>,
+                  ops::EighGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OPERATOR(eigh_grad, ops::EighGradOp);
+
+REGISTER_OP_CPU_KERNEL(
+    eigh, ops::EighKernel<paddle::platform::CPUDeviceContext, float, float>,
+    ops::EighKernel<paddle::platform::CPUDeviceContext, double, double>,
+    ops::EighKernel<paddle::platform::CPUDeviceContext, float,
+                    paddle::platform::complex<float>>,
+    ops::EighKernel<paddle::platform::CPUDeviceContext, double,
+                    paddle::platform::complex<double>>);
+
+REGISTER_OP_CPU_KERNEL(
+    eigh_grad,
+    ops::EighGradKernel<paddle::platform::CPUDeviceContext, float, float>,
+    ops::EighGradKernel<paddle::platform::CPUDeviceContext, double, double>,
+    ops::EighGradKernel<paddle::platform::CPUDeviceContext, float,
+                        paddle::platform::complex<float>>,
+    ops::EighGradKernel<paddle::platform::CPUDeviceContext, double,
+                        paddle::platform::complex<double>>);

paddle/fluid/operators/eigh_op.cu

@@ -0,0 +1,53 @@
+/* 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/eigh_op.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename ValueType, typename T>
+class EighGPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    auto input_var = ctx.Input<Tensor>("X");
+    auto output_w_var = ctx.Output<Tensor>("Eigenvalues");
+    auto output_v_var = ctx.Output<Tensor>("Eigenvectors");
+    std::string lower = ctx.Attr<std::string>("UPLO");
+    bool is_lower = (lower == "L");
+    math::MatrixEighFunctor<ValueType, T> functor;
+    functor(ctx, *input_var, output_w_var, output_v_var, is_lower, true);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_CUDA_KERNEL(
+    eigh, ops::EighGPUKernel<float, float>, ops::EighGPUKernel<double, double>,
+    ops::EighGPUKernel<float, paddle::platform::complex<float>>,
+    ops::EighGPUKernel<double, paddle::platform::complex<double>>);
+
+REGISTER_OP_CUDA_KERNEL(
+    eigh_grad,
+    ops::EighGradKernel<paddle::platform::CUDADeviceContext, float, float>,
+    ops::EighGradKernel<paddle::platform::CUDADeviceContext, double, double>,
+    ops::EighGradKernel<paddle::platform::CUDADeviceContext, float,
+                        paddle::platform::complex<float>>,
+    ops::EighGradKernel<paddle::platform::CUDADeviceContext, double,
+                        paddle::platform::complex<double>>);

paddle/fluid/operators/eigh_op.h

@@ -0,0 +1,80 @@
+// 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, size_t D, int MajorType = Eigen::RowMajor,
+          typename IndexType = Eigen::DenseIndex>
+using EigenTensor = framework::EigenTensor<T, D, MajorType, IndexType>;
+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 EighKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto input_var = ctx.Input<Tensor>("X");
+    auto output_w_var = ctx.Output<Tensor>("Eigenvalues");
+    auto output_v_var = ctx.Output<Tensor>("Eigenvectors");
+    std::string lower = ctx.Attr<std::string>("UPLO");
+    bool is_lower = (lower == "L");
+    math::MatrixEighFunctorCPU<DeviceContext, ValueType, T> functor;
+    functor(ctx, *input_var, output_w_var, output_v_var, is_lower, true);
+  }
+};
+
+template <typename DeviceContext, typename ValueType, typename T>
+class EighGradKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto& x_grad = *ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    x_grad.mutable_data<T>(ctx.GetPlace());
+    auto& output_w_var = *ctx.Input<Tensor>("Eigenvalues");
+    auto& output_v_var = *ctx.Input<Tensor>("Eigenvectors");
+    auto& output_w_grad =
+        *ctx.Input<Tensor>(framework::GradVarName("Eigenvalues"));
+    auto& output_v_grad =
+        *ctx.Input<Tensor>(framework::GradVarName("Eigenvectors"));
+
+    auto& dims = output_v_var.dims();
+    const int m = dims[dims.size() - 1];
+    auto dito =
+        math::DeviceIndependenceTensorOperations<DeviceContext, T, ValueType>(
+            ctx);
+    auto tV = dito.Transpose(dito.Conj(output_v_var));
+    auto W = dito.Sub_(dito.Unsqueeze(output_w_var, -2),
+                       dito.Unsqueeze(output_w_var, -1));
+    Tensor result = dito.Matmul(tV, output_v_grad);
+    result.mutable_data<T>(dims, ctx.GetPlace());
+    std::vector<int> out_shape = framework::vectorize<int>(dims);
+    auto constant = dito.Fill(out_shape, 0.5);
+    result = dito.Sub(result, dito.Conj(dito.Transpose(result)));
+    result = dito.Mul(result, constant);
+    result = dito.Div_(result, W);
+    result = dito.DiagFill(m, m, m, 0, output_w_grad, result);
+    x_grad = dito.Matmul(output_v_var, dito.Matmul(result, tV));
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle