Add matrix_rank Op and its GPU and CPU kernel

cmake/operators.cmake

@@ -183,6 +183,7 @@ function(op_library TARGET)
         list(REMOVE_ITEM miopen_cu_cc_srcs "affine_grid_cudnn_op.cu.cc")
         list(REMOVE_ITEM miopen_cu_cc_srcs "grid_sampler_cudnn_op.cu.cc")
         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 "multinomial_op.cu")
         list(REMOVE_ITEM hip_srcs "decode_jpeg_op.cu")

paddle/fluid/operators/matrix_rank_op.cc

@@ -0,0 +1,256 @@
+// 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/matrix_rank_op.h"
+#include <memory>
+#include <string>
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+#include "paddle/fluid/operators/svd_helper.h"
+
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
+
+namespace paddle {
+namespace operators {
+using DDim = framework::DDim;
+
+namespace detail {
+static DDim GetInputBatchDim(const DDim& dim_x) {
+  auto x_vec = framework::vectorize(dim_x);
+  if (x_vec.size() == 2) {
+    return framework::make_ddim({1});
+  }
+  x_vec.erase(x_vec.end() - 2, x_vec.end());
+  return framework::make_ddim(x_vec);
+}
+}  // namespace detail
+
+class MatrixRankeOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "MatrixRank");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "MatrixRank");
+    auto dim_x = ctx->GetInputDim("X");
+    PADDLE_ENFORCE_GE(dim_x.size(), 2,
+                      platform::errors::InvalidArgument(
+                          "The dims of input must be greater than 2"));
+
+    bool hermitian = ctx->Attrs().Get<bool>("hermitian");
+    if (hermitian) {
+      int rows = dim_x[dim_x.size() - 2];
+      int cols = dim_x[dim_x.size() - 1];
+      PADDLE_ENFORCE_EQ(rows, cols,
+                        platform::errors::InvalidArgument(
+                            "if hermitian == true, matrix should be n*n"));
+    }
+
+    DDim dim_x_batch = detail::GetInputBatchDim(dim_x);
+    if (ctx->Attrs().Get<bool>(
+            "use_default_tol")) {  // user not input TolTensor and tol
+      ctx->SetOutputDim("Out", dim_x_batch);
+    } else if (ctx->HasInput("TolTensor")) {
+      auto dim_tol = ctx->GetInputDim("TolTensor");
+      if (dim_x_batch == dim_tol) {
+        ctx->SetOutputDim("Out", dim_x_batch);
+      } else {
+        int max_dim = std::max(dim_x_batch.size(), dim_tol.size());
+        int axis = std::abs(dim_x_batch.size() - dim_tol.size());
+        std::vector<int> x_batch_dims_array(max_dim);
+        std::vector<int> tol_dims_array(max_dim);
+        std::vector<int> out_dims_array(max_dim);
+        GetBroadcastDimsArrays(dim_x_batch, dim_tol, x_batch_dims_array.data(),
+                               tol_dims_array.data(), out_dims_array.data(),
+                               max_dim, axis);
+        for (auto& it : out_dims_array) {
+          VLOG(3) << "out dims: " << it;
+        }
+        ctx->SetOutputDim("Out", framework::make_ddim(out_dims_array));
+      }
+    } else {
+      ctx->SetOutputDim("Out", dim_x_batch);
+    }
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    framework::LibraryType library{framework::LibraryType::kPlain};
+    framework::DataLayout layout = framework::DataLayout::kAnyLayout;
+    auto data_type = OperatorWithKernel::IndicateVarDataType(ctx, "X");
+    return framework::OpKernelType(data_type, ctx.GetPlace(), layout, library);
+  }
+};
+
+class MatrixRankeOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor), The input tensor of matrix_rank op.");
+    AddInput("TolTensor", "(optional) Tol tensor, shape is same as X batch.")
+        .AsDispensable();
+    AddOutput("Out", "(Tensor), The output tensor of matrix_rank op.");
+    AddAttr<float>("tol", "(float, optional). tol").SetDefault(0.0f);
+    AddAttr<bool>("use_default_tol",
+                  "represent whether user input TolTensor/tol, if input "
+                  "TolTensor/tol use_default_tol=true, otherwise "
+                  "use_default_tol=false")
+        .SetDefault(true);
+    AddAttr<bool>("hermitian", "(bool, optional). whether is hermitian matrix")
+        .SetDefault(false);
+    AddComment(R"DOC(MatrixRank Operator.
+    This operator is used to perform MatrixRank operation for batched matrics.
+    $$out = matrix_rank(X, tol, hermitian)$$
+    )DOC");
+  }
+};
+
+template <typename T>
+void BatchEigenvalues(const T* x_data, T* eigenvalues_data, int batches,
+                      int rows, int cols, int k) {
+  // Eigen::Matrix API need non-const pointer.
+  T* input = const_cast<T*>(x_data);
+  int stride = rows * cols;
+  for (int i = 0; i < batches; i++) {
+    auto m = Eigen::Map<
+        Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>(
+        input + i * stride, rows, rows);
+    Eigen::SelfAdjointEigenSolver<
+        Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+        eigen_solver(m);
+    auto eigenvalues = eigen_solver.eigenvalues().cwiseAbs();
+    for (int j = 0; j < k; j++) {
+      *(eigenvalues_data + i * k + j) = eigenvalues[j];
+    }
+  }
+}
+
+template <typename T>
+void BatchSVD(const T* x_data, T* eigenvalues_data, int batches, int rows,
+              int cols, int k) {
+  // Eigen::Matrix API need non-const pointer.
+  T* input = const_cast<T*>(x_data);
+  int stride = rows * cols;
+  Eigen::BDCSVD<
+      Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>
+      svd;
+  for (int i = 0; i < batches; i++) {
+    auto m = Eigen::Map<
+        Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic, Eigen::RowMajor>>(
+        input + i * stride, rows, cols);
+    svd.compute(m);
+    auto res_s = svd.singularValues();
+    for (int j = 0; j < k; j++) {
+      eigenvalues_data[i * k + j] = res_s[j];
+    }
+  }
+}
+
+template <typename T>
+class MatrixRankCPUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    const Tensor* x = context.Input<Tensor>("X");
+    auto* x_data = x->data<T>();
+    auto* out = context.Output<Tensor>("Out");
+    out->mutable_data<int64_t>(context.GetPlace());
+    bool hermitian = context.Attr<bool>("hermitian");
+
+    auto dim_x = x->dims();
+    auto dim_out = out->dims();
+    int rows = dim_x[dim_x.size() - 2];
+    int cols = dim_x[dim_x.size() - 1];
+    int k = std::min(rows, cols);
+    auto numel = x->numel();
+    int batches = numel / (rows * cols);
+
+    bool use_default_tol = context.Attr<bool>("use_default_tol");
+    const Tensor* atol_tensor = nullptr;
+    Tensor temp_tensor;
+    T rtol_T = 0;
+    if (use_default_tol) {
+      framework::TensorFromVector<T>(std::vector<T>{0},
+                                     context.device_context(), &temp_tensor);
+      atol_tensor = &temp_tensor;
+      rtol_T = std::numeric_limits<T>::epsilon() * std::max(rows, cols);
+    } else if (context.HasInput("TolTensor")) {
+      atol_tensor = context.Input<Tensor>("TolTensor");
+    } else {
+      framework::TensorFromVector<T>(std::vector<T>{context.Attr<float>("tol")},
+                                     context.device_context(), &temp_tensor);
+      atol_tensor = &temp_tensor;
+    }
+
+    Tensor eigenvalue_tensor;
+    auto* eigenvalue_data = eigenvalue_tensor.mutable_data<T>(
+        detail::GetEigenvalueDim(dim_x, k), context.GetPlace());
+    if (hermitian) {
+      BatchEigenvalues<T>(x_data, eigenvalue_data, batches, rows, cols, k);
+    } else {
+      BatchSVD<T>(x_data, eigenvalue_data, batches, rows, cols, k);
+    }
+
+    auto dito_T =
+        math::DeviceIndependenceTensorOperations<platform::CPUDeviceContext, T>(
+            context);
+    std::vector<int> max_eigenvalue_shape = framework::vectorize<int>(
+        detail::RemoveLastDim(eigenvalue_tensor.dims()));
+    Tensor max_eigenvalue_tensor =
+        dito_T.ReduceMax(eigenvalue_tensor, max_eigenvalue_shape);
+
+    Tensor temp_rtol_tensor;
+    framework::TensorFromVector<T>(std::vector<T>{rtol_T}, &temp_rtol_tensor);
+    Tensor rtol_tensor = dito_T.Mul(temp_rtol_tensor, max_eigenvalue_tensor);
+    Tensor tol_tensor;
+    tol_tensor.mutable_data<T>(dim_out, context.GetPlace());
+    ElementwiseComputeEx<GreaterElementFunctor<T>, platform::CPUDeviceContext,
+                         T, T>(context, atol_tensor, &rtol_tensor, -1,
+                               GreaterElementFunctor<T>(), &tol_tensor);
+
+    tol_tensor.Resize(detail::NewAxisDim(tol_tensor.dims(), 1));
+
+    Tensor compare_result;
+    compare_result.mutable_data<int>(detail::NewAxisDim(dim_out, k),
+                                     context.GetPlace());
+
+    int axis = -1;
+    if (eigenvalue_tensor.dims().size() >= tol_tensor.dims().size()) {
+      ElementwiseComputeEx<GreaterThanFunctor<T>, platform::CPUDeviceContext, T,
+                           int>(context, &eigenvalue_tensor, &tol_tensor, axis,
+                                GreaterThanFunctor<T>(), &compare_result);
+    } else {
+      ElementwiseComputeEx<LessThanFunctor<T>, platform::CPUDeviceContext, T,
+                           int>(context, &eigenvalue_tensor, &tol_tensor, axis,
+                                LessThanFunctor<T>(), &compare_result);
+    }
+    auto dito_int =
+        math::DeviceIndependenceTensorOperations<platform::CPUDeviceContext,
+                                                 int64_t>(context);
+    std::vector<int> result_shape = framework::vectorize<int>(dim_out);
+    Tensor result = dito_int.ReduceSum(compare_result, result_shape);
+    out->ShareDataWith(result);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(matrix_rank, ops::MatrixRankeOp, ops::MatrixRankeOpMaker);
+
+REGISTER_OP_CPU_KERNEL(matrix_rank, ops::MatrixRankCPUKernel<float>,
+                       ops::MatrixRankCPUKernel<double>);