[cherry-pick] FixEighOP; Unified MatrixEighFunctor function (#35812) (#35919)

cherry-pick #35812，修复Eigh OP

paddle/fluid/operators/eigh_op.cc

@@ -47,12 +47,9 @@ class EighOp : public framework::OperatorWithKernel {
             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]};
+
+    for (auto i = 0; i < rank - 1; i++) {
+      values_dim.emplace_back(input_dim[i]);
     }
 
     ctx->SetOutputDim("Eigenvalues", framework::make_ddim(values_dim));
@@ -99,9 +96,9 @@ class EighGradOp : public framework::OperatorWithKernel {
                    "EighGrad");
     OP_INOUT_CHECK(ctx->HasInput("Eigenvectors"), "Input", "Eigenvectors",
                    "EighGrad");
-    OP_INOUT_CHECK(ctx->HasInputs(framework::GradVarName("Eigenvalues")),
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Eigenvalues")),
                    "Input", "Eigenvalues@GRAD", "EighGrad");
-    OP_INOUT_CHECK(ctx->HasInputs(framework::GradVarName("Eigenvectors")),
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Eigenvectors")),
                    "Input", "Eigenvectors@GRAD", "EighGrad");
     auto dims = ctx->GetInputDim("Eigenvectors");
     auto x_grad_name = framework::GradVarName("X");

paddle/fluid/operators/eigh_op.cu

@@ -14,34 +14,14 @@ 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>>);
+    eigh, ops::EighKernel<paddle::platform::CUDADeviceContext, float, float>,
+    ops::EighKernel<paddle::platform::CUDADeviceContext, double, double>,
+    ops::EighKernel<paddle::platform::CUDADeviceContext, float,
+                    paddle::platform::complex<float>>,
+    ops::EighKernel<paddle::platform::CUDADeviceContext, double,
+                    paddle::platform::complex<double>>);
 
 REGISTER_OP_CUDA_KERNEL(
     eigh_grad,

paddle/fluid/operators/eigh_op.h

@@ -22,24 +22,17 @@ 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");
+    auto input = ctx.Input<Tensor>("X");
+    auto output_w = ctx.Output<Tensor>("Eigenvalues");
+    auto output_v = 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);
+    math::MatrixEighFunctor<DeviceContext, ValueType, T> functor;
+    functor(ctx, *input, output_w, output_v, is_lower, true);
   }
 };
 
@@ -49,30 +42,30 @@ class EighGradKernel : public framework::OpKernel<T> {
   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 = *ctx.Input<Tensor>("Eigenvalues");
+    auto& output_v = *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();
+    auto& dims = output_v.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));
+    auto tV = dito.Transpose(dito.Conj(output_v));
+    auto W = dito.template Sub<ValueType>(dito.Unsqueeze(output_w, -2),
+                                          dito.Unsqueeze(output_w, -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.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));
+    x_grad = dito.Matmul(output_v, dito.Matmul(result, tV));
   }
 };
 

paddle/fluid/operators/math/eigen_values_vectors.h

@@ -16,7 +16,6 @@
 
 #include "Eigen/Core"
 #include "paddle/fluid/memory/memory.h"
-#include "paddle/fluid/operators/math/complex_functors.h"
 #include "paddle/fluid/operators/svd_helper.h"
 #ifdef PADDLE_WITH_CUDA
 #include "paddle/fluid/platform/dynload/cusolver.h"
@@ -26,10 +25,6 @@ namespace paddle {
 namespace operators {
 namespace math {
 
-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 InputMatrixMap = Eigen::Map<
@@ -67,7 +62,7 @@ inline void ComputeFloatEigenvaluesAndVectors(ValueType *x_data,
 
     eigenvalues = eigen_solver.eigenvalues().transpose();
     if (has_vectors) {
-      eigenvectors = eigen_solver.eigenvectors().transpose();
+      eigenvectors = eigen_solver.eigenvectors();
     }
   }
 }
@@ -103,7 +98,7 @@ inline void ComputeComplexEigenvaluesAndVectors(T *x_data,
 
     eigenvalues = eigen_solver.eigenvalues().transpose();
     if (has_vectors) {
-      eigenvectors = eigen_solver.eigenvectors().transpose();
+      eigenvectors = eigen_solver.eigenvectors();
     }
   }
 }
@@ -117,11 +112,18 @@ inline int64_t GetBatchSize(framework::DDim dims) {
   return batch_size;
 }
 
+template <typename DeviceContext, typename ValueType, typename T>
+struct MatrixEighFunctor {
+  void operator()(const framework::ExecutionContext &ctx, const Tensor &input,
+                  Tensor *eigen_values, Tensor *eigen_vectors, bool is_lower,
+                  bool has_vectors);
+};
+
 // Calculates the eigenvalues ​​and eigenvectors of Hermitian or real
 // symmetric matrices, and uses the variable has_vectors to
 // control whether to return the eigenvectors.
-template <typename DeviceContext, typename ValueType, typename T>
-struct MatrixEighFunctorCPU {
+template <typename ValueType, typename T>
+struct MatrixEighFunctor<platform::CPUDeviceContext, ValueType, T> {
  public:
   void operator()(const framework::ExecutionContext &ctx, const Tensor &input,
                   Tensor *eigen_values, Tensor *eigen_vectors, bool is_lower,
@@ -134,7 +136,8 @@ struct MatrixEighFunctorCPU {
     for (int64_t i = 0; i < dim_size - 2; i++) {
       batch_size *= dims[i];
     }
-    auto dito = DeviceIndependenceTensorOperations<DeviceContext, T>(ctx);
+    auto dito =
+        DeviceIndependenceTensorOperations<platform::CPUDeviceContext, T>(ctx);
     Tensor input_tensor;
     TensorCopy(input, ctx.GetPlace(), &input_tensor);
     if (!is_lower) {
@@ -157,9 +160,6 @@ struct MatrixEighFunctorCPU {
       ComputeFloatEigenvaluesAndVectors<ValueType>(
           x_data, value_data, vector_data, batch_size, rows, rows, has_vectors);
     }
-    if (has_vectors) {
-      *eigen_vectors = dito.Transpose(*eigen_vectors);
-    }
   }
 };
 
@@ -169,7 +169,7 @@ struct MatrixEighFunctorCPU {
 // symmetric matrices on GPU, and uses the variable has_vectors
 // to control whether to return the eigenvectors.
 template <typename ValueType, typename T>
-struct MatrixEighFunctor {
+struct MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T> {
  public:
   void operator()(const framework::ExecutionContext &ctx, const Tensor &input,
                   Tensor *eigen_values, Tensor *eigen_vectors, bool is_lower,
@@ -278,7 +278,8 @@ struct MatrixEighFunctor {
 
 #define EVDBUFFER_INSTANCE(ValueType, T, C, CastType)                          \
   template <>                                                                  \
-  inline void MatrixEighFunctor<ValueType, T>::EvdBuffer(                      \
+  inline void                                                                  \
+  MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T>::EvdBuffer(     \
       cusolverDnHandle_t handle, cusolverEigMode_t jobz,                       \
       cublasFillMode_t uplo, int n, const T *A, int lda, const ValueType *W,   \
       int *lwork) const {                                                      \
@@ -292,7 +293,8 @@ FUNC_WITH_TYPES(EVDBUFFER_INSTANCE);
 
 #define EVD_INSTANCE(ValueType, T, C, CastType)                           \
   template <>                                                             \
-  inline void MatrixEighFunctor<ValueType, T>::Evd(                       \
+  inline void                                                             \
+  MatrixEighFunctor<platform::CUDADeviceContext, ValueType, T>::Evd(      \
       cusolverDnHandle_t handle, cusolverEigMode_t jobz,                  \
       cublasFillMode_t uplo, int n, T *A, int lda, ValueType *W, T *work, \
       int lwork, int *devInfo) const {                                    \

paddle/fluid/operators/svd_helper.h

@@ -289,10 +289,20 @@ struct DeviceIndependenceTensorOperations {
   framework::Tensor Div(const framework::Tensor& x,
                         const framework::Tensor& y) {
     framework::Tensor ret;
-    std::vector<int> out_shape = GetBroadcastShape({&x, &y});
-    ret.Resize(framework::make_ddim(out_shape));
-    ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(
-        context, &x, &y, -1, DivFunctor<T>(), &ret);
+    if (x.type() != y.type()) {
+      ret.mutable_data<T>(x.dims(), context.GetPlace());
+      auto x_vector = EigenVector<T>::Flatten(x);
+      auto y_vector = EigenVector<ValueType>::Flatten(y);
+      auto out_vector = EigenVector<T>::Flatten(ret);
+      auto& place =
+          *context.template device_context<DeviceContext>().eigen_device();
+      out_vector.device(place) = x_vector / y_vector;
+    } else {
+      std::vector<int> out_shape = GetBroadcastShape({&x, &y});
+      ret.Resize(framework::make_ddim(out_shape));
+      ElementwiseComputeEx<DivFunctor<T>, DeviceContext, T>(
+          context, &x, &y, -1, DivFunctor<T>(), &ret);
+    }
     return ret;
   }
   framework::Tensor Add(const framework::Tensor& x,
@@ -330,7 +340,8 @@ struct DeviceIndependenceTensorOperations {
     NameInTensorMap inputs({{"X", {&x}}});
     return CreateOpRunAndReturnTensor("reduce_max", inputs, attrs, out_dim);
   }
-
+  // Support float and complex type subtraction，the default is T type
+  template <typename InT = T>
   framework::Tensor Sub(const framework::Tensor& x,
                         const framework::Tensor& y) {
     framework::Tensor ret;
@@ -340,18 +351,18 @@ struct DeviceIndependenceTensorOperations {
 #if defined(__NVCC__) || defined(__HIPCC__)
       // For GPU, there is no need to define XxxInverseFunctor and call
       // ElementwiseComputeEx in two branches.
-      ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-          context, &x, &y, -1, SubFunctor<T>(), &ret);
+      ElementwiseComputeEx<SubFunctor<InT>, DeviceContext, InT>(
+          context, &x, &y, -1, SubFunctor<InT>(), &ret);
 #endif
     } else {
       if (x.dims().size() >= y.dims().size()) {
-        ElementwiseComputeEx<SubFunctor<T>, DeviceContext, T>(
-            context, &x, &y, -1, SubFunctor<T>(), &ret);
+        ElementwiseComputeEx<SubFunctor<InT>, DeviceContext, InT>(
+            context, &x, &y, -1, SubFunctor<InT>(), &ret);
       } else {
-        ElementwiseComputeEx<InverseSubFunctor<T>, DeviceContext, T>(
-            // This is copyed from elementwise_sub, which means we
-            // need reverse will xrank < yrank
-            context, &x, &y, -1, InverseSubFunctor<T>(), &ret);
+        // This is copyed from elementwise_sub, which means we
+        // need reverse will xrank < yrank
+        ElementwiseComputeEx<InverseSubFunctor<InT>, DeviceContext, InT>(
+            context, &x, &y, -1, InverseSubFunctor<InT>(), &ret);
       }
     }
     return ret;
@@ -461,37 +472,6 @@ struct DeviceIndependenceTensorOperations {
     return out;
   }
 
-  // Support x and y are different data types
-  Tensor Div_(const Tensor& x, const Tensor& y) {
-    Tensor out;
-    out.mutable_data<T>(x.dims(), context.GetPlace());
-    auto x_vector = EigenVector<T>::Flatten(x);
-    auto y_vector = EigenVector<ValueType>::Flatten(y);
-    auto out_vector = EigenVector<T>::Flatten(out);
-    auto& place =
-        *context.template device_context<DeviceContext>().eigen_device();
-    out_vector.device(place) = x_vector / y_vector;
-    return out;
-  }
-
-  framework::Tensor Sub_(const framework::Tensor& x,
-                         const framework::Tensor& y) {
-    framework::Tensor ret;
-    std::vector<int> out_shape = GetBroadcastShape({&x, &y});
-    ret.Resize(framework::make_ddim(out_shape));
-    if (x.dims().size() >= y.dims().size()) {
-      ElementwiseComputeEx<SubFunctor<ValueType>, DeviceContext, ValueType>(
-          context, &x, &y, -1, SubFunctor<ValueType>(), &ret);
-    } else {
-      ElementwiseComputeEx<InverseSubFunctor<ValueType>, DeviceContext,
-                           ValueType>(
-          // This is copyed from elementwise_sub, which means we
-          // need reverse will xrank < yrank
-          context, &x, &y, -1, InverseSubFunctor<ValueType>(), &ret);
-    }
-    return ret;
-  }
-
  private:
   const framework::ExecutionContext& context;
   BlasT<DeviceContext, T> GetBlas() {

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

@@ -140,7 +140,7 @@ class TestEighAPI(unittest.TestCase):
         self.check_static_complex_result()
 
     def test_in_dynamic_mode(self):
-        paddle.disable_static(self.place)
+        paddle.disable_static()
         input_real_data = paddle.to_tensor(self.real_data)
         expected_w, expected_v = np.linalg.eigh(self.real_data)
         actual_w, actual_v = paddle.linalg.eigh(input_real_data)
@@ -152,7 +152,7 @@ class TestEighAPI(unittest.TestCase):
         self.compare_result(actual_w, actual_v.numpy(), expected_w, expected_v)
 
     def test_eigh_grad(self):
-        paddle.disable_static(self.place)
+        paddle.disable_static()
         x = paddle.to_tensor(self.complex_data, stop_gradient=False)
         w, v = paddle.linalg.eigh(x)
         (w.sum() + paddle.abs(v).sum()).backward()