cherry-pick: add kron op (#24105) (#24159)

* cherrypick: add kron op (#24105)
* add kron op and fluid.layers.kron
* add paddle.complex.kron

paddle/fluid/operators/kron_op.cc

+/* Copyright (c) 2018 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 <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include "paddle/fluid/operators/kron_op.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace paddle {
+namespace operators {
+
+class KronOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "kron");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "kron");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "kron");
+
+    auto dim_x = ctx->GetInputDim("X");
+    auto dim_y = ctx->GetInputDim("Y");
+    auto rank_x = dim_x.size();
+    auto rank_y = dim_y.size();
+    auto rank = (rank_x > rank_y) ? rank_x : rank_y;
+
+    std::vector<int64_t> dim_out;
+    dim_out.reserve(rank);
+    for (int i = 0; i < rank; i++) {
+      int64_t dim_xi = (i < rank - rank_x) ? 1 : dim_x.at(i - (rank - rank_x));
+      int64_t dim_yi = (i < rank - rank_y) ? 1 : dim_y.at(i - (rank - rank_y));
+      dim_out.push_back(dim_xi == -1 || dim_yi == -1 ? -1 : dim_xi * dim_yi);
+    }
+    ctx->SetOutputDim("Out", framework::make_ddim(dim_out));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
+  }
+};
+
+class KronOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor), the first operand of kron op");
+    AddInput("Y", "(Tensor), the second operand of kron op");
+    AddOutput("Out", "(Tensor), the output of kron op.");
+    AddComment(R"DOC(
+          Kron Operator.
+
+          This operator computes the Kronecker product of two tensors, a
+          composite tensor made of blocks of the second tensor scaled by the 
+          first.
+
+          This operator assumes that the rank of the two tensors, $X$ and $Y$
+          are the same, if necessary prepending the smallest with ones. If the 
+          shape of $X$ is [$r_0$, $r_1$, ..., $r_N$] and the shape of $Y$ is 
+          [$s_0$, $s_1$, ..., $s_N$], then the shape of the output tensor is 
+          [$r_{0}s_{0}$, $r_{1}s_{1}$, ..., $r_{N}s_{N}$]. The elements are 
+          products of elements from $X$ and $Y$.
+
+          The equation is:
+          $$
+          output[k_{0}, k_{1}, ..., k_{N}] = X[i_{0}, i_{1}, ..., i_{N}] *
+          Y[j_{0}, j_{1}, ..., j_{N}]
+          $$
+
+          where
+          $$
+          k_{t} = i_{t} * s_{t} + j_{t}, t = 0, 1, ..., N
+          $$
+        )DOC");
+  }
+};
+
+class KronGradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "kron_grad");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "kron_grad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "kron_grad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "kron_grad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("Y")), "Output",
+                   framework::GradVarName("Y"), "kron_grad");
+
+    auto x_grad_name = framework::GradVarName("X");
+    auto y_grad_name = framework::GradVarName("Y");
+    ctx->SetOutputDim(x_grad_name, ctx->GetInputDim("X"));
+    ctx->ShareLoD("X", /*->*/ x_grad_name);
+    ctx->SetOutputDim(y_grad_name, ctx->GetInputDim("Y"));
+    ctx->ShareLoD("Y", /*->*/ y_grad_name);
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    auto out_grad_name = framework::GradVarName("Out");
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, out_grad_name),
+        ctx.GetPlace());
+  }
+};
+
+template <typename T>
+class KronGradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> grad_op) const override {
+    grad_op->SetType("kron_grad");
+
+    grad_op->SetInput("X", this->Input("X"));
+    grad_op->SetInput("Y", this->Input("Y"));
+    grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+
+    grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    grad_op->SetOutput(framework::GradVarName("Y"), this->InputGrad("Y"));
+
+    grad_op->SetAttrMap(this->Attrs());
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(kron, ops::KronOp, ops::KronOpMaker,
+                  ops::KronGradOpMaker<paddle::framework::OpDesc>,
+                  ops::KronGradOpMaker<paddle::imperative::OpBase>);
+REGISTER_OP_CPU_KERNEL(
+    kron, ops::KronKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::KronKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::KronKernel<paddle::platform::CPUDeviceContext,
+                    paddle::platform::float16>,
+    ops::KronKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::KronKernel<paddle::platform::CPUDeviceContext, int64_t>);
+
+REGISTER_OPERATOR(kron_grad, ops::KronGradOp);
+REGISTER_OP_CPU_KERNEL(
+    kron_grad, ops::KronGradKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::KronGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::KronGradKernel<paddle::platform::CPUDeviceContext,
+                        paddle::platform::float16>,
+    ops::KronGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::KronGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/kron_op.cu

+/* Copyright (c) 2018 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/kron_op.h"
+#include "paddle/fluid/platform/float16.h"
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(
+    kron, ops::KronKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::KronKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::KronKernel<paddle::platform::CUDADeviceContext,
+                    paddle::platform::float16>,
+    ops::KronKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::KronKernel<paddle::platform::CUDADeviceContext, int64_t>);
+
+REGISTER_OP_CUDA_KERNEL(
+    kron_grad, ops::KronGradKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::KronGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::KronGradKernel<paddle::platform::CUDADeviceContext,
+                        paddle::platform::float16>,
+    ops::KronGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::KronGradKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/kron_op.h

+/* Copyright (c) 2018 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 <algorithm>
+#include <vector>
+#include "paddle/fluid/framework/eigen.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/platform/for_range.h"
+#if __NVCC__
+#include "paddle/fluid/operators/reduce_ops/cub_reduce.h"
+#include "thrust/device_vector.h"
+#endif
+
+namespace paddle {
+namespace operators {
+
+// Process an element in the output, used with a parallel-for
+template <typename T>
+struct KronElemFunctor {
+  KronElemFunctor(const T* a, const T* b, T* out, const int64_t* shape_b,
+                  const int64_t* stride_a, const int64_t* stride_b,
+                  const int64_t* stride_out, int ndims)
+      : a_(a),
+        b_(b),
+        out_(out),
+        shape_b_(shape_b),
+        stride_a_(stride_a),
+        stride_b_(stride_b),
+        stride_out_(stride_out),
+        ndims_(ndims) {}
+
+  HOSTDEVICE void operator()(int64_t idx) const {
+    // it computes 1 element in the output
+    int64_t index = idx;
+    int64_t index_a = 0;
+    int64_t index_b = 0;
+    for (int i = 0; i < ndims_; i++) {
+      auto pos_i = index / stride_out_[i];
+      index = index % stride_out_[i];
+      auto pos_ai = pos_i / shape_b_[i];
+      auto pos_bi = pos_i % shape_b_[i];
+      index_a += stride_a_[i] * pos_ai;
+      index_b += stride_b_[i] * pos_bi;
+    }
+    out_[idx] = a_[index_a] * b_[index_b];
+  }
+
+ private:
+  const T* a_;
+  const T* b_;
+  T* out_;
+  const int64_t* shape_b_;
+  const int64_t* stride_a_;
+  const int64_t* stride_b_;
+  const int64_t* stride_out_;
+  const int ndims_;
+};
+
+template <typename DeviceContext, typename T>
+struct KronOpFunctor {
+  void operator()(const DeviceContext& dev_ctx, const framework::Tensor& x,
+                  const framework::Tensor& y, framework::Tensor* out) {
+    int ndims = out->dims().size();
+    int64_t numel = out->numel();
+
+    const framework::DDim& dim_x = x.dims();
+    const framework::DDim& dim_y = y.dims();
+    const framework::DDim& dim_out = out->dims();
+    const framework::DDim stride_x = framework::stride(dim_x);
+    const framework::DDim stride_y = framework::stride(dim_y);
+    const framework::DDim stride_out = framework::stride(dim_out);
+
+    const int64_t *p_stride_x = nullptr, *p_stride_y = nullptr,
+                  *p_stride_out = nullptr, *p_shape_y = nullptr;
+#if __NVCC__
+    thrust::device_vector<int64_t> d_stride_x(ndims);
+    thrust::device_vector<int64_t> d_stride_y(ndims);
+    thrust::device_vector<int64_t> d_stride_out(ndims);
+    thrust::device_vector<int64_t> d_shape_y(ndims);
+    thrust::copy(stride_x.Get(), stride_x.Get() + ndims, d_stride_x.begin());
+    thrust::copy(stride_y.Get(), stride_y.Get() + ndims, d_stride_y.begin());
+    thrust::copy(stride_out.Get(), stride_out.Get() + ndims,
+                 d_stride_out.begin());
+    thrust::copy(dim_y.Get(), dim_y.Get() + ndims, d_shape_y.begin());
+
+    p_stride_x = thrust::raw_pointer_cast(d_stride_x.data());
+    p_stride_y = thrust::raw_pointer_cast(d_stride_y.data());
+    p_stride_out = thrust::raw_pointer_cast(d_stride_out.data());
+    p_shape_y = thrust::raw_pointer_cast(d_shape_y.data());
+#else
+    p_stride_x = stride_x.Get();
+    p_stride_y = stride_y.Get();
+    p_stride_out = stride_out.Get();
+    p_shape_y = dim_y.Get();
+#endif
+
+    platform::ForRange<DeviceContext> for_range(dev_ctx, numel);
+    KronElemFunctor<T> functor(x.data<T>(), y.data<T>(), out->data<T>(),
+                               p_shape_y, p_stride_x, p_stride_y, p_stride_out,
+                               ndims);
+    for_range(functor);
+  }
+};
+
+template <typename T>
+struct KronGradElemFunctor {
+  KronGradElemFunctor(const T* dout, const T* A, const T* B, T* dout_a,
+                      T* dout_b, const int64_t* stride_dout,
+                      const int64_t* stride_a, const int64_t* stride_b,
+                      const int64_t* shape_b, const int64_t numel_a,
+                      const int64_t numel_b, const int ndims)
+      : dout_(dout),
+        A_(A),
+        B_(B),
+        dout_a_(dout_a),
+        dout_b_(dout_b),
+        stride_dout_(stride_dout),
+        stride_a_(stride_a),
+        stride_b_(stride_b),
+        shape_b_(shape_b),
+        numel_a_(numel_a),
+        numel_b_(numel_b),
+        ndims_(ndims) {}
+
+  HOSTDEVICE void operator()(int64_t idx) {
+    int64_t index = idx;
+    int64_t index_a = 0;
+    int64_t index_b = 0;
+    for (int i = 0; i < ndims_; i++) {
+      auto pos_i = index / stride_dout_[i];
+      index = index % stride_dout_[i];
+      auto pos_ai = pos_i / shape_b_[i];
+      auto pos_bi = pos_i % shape_b_[i];
+      index_a += stride_a_[i] * pos_ai;
+      index_b += stride_b_[i] * pos_bi;
+    }
+
+    size_t index_out_a = index_a * numel_b_ + index_b;
+    size_t index_out_b = index_b * numel_a_ + index_a;
+
+    dout_a_[index_out_a] = dout_[idx] * B_[index_b];
+    dout_b_[index_out_b] = dout_[idx] * A_[index_a];
+  }
+
+ private:
+  const T* dout_;
+  const T* A_;
+  const T* B_;
+  T* dout_a_;
+  T* dout_b_;
+  const int64_t* stride_dout_;
+  const int64_t* stride_a_;
+  const int64_t* stride_b_;
+  const int64_t* shape_b_;
+  const int64_t numel_a_;
+  const int64_t numel_b_;
+  const int ndims_;
+};
+
+template <typename T>
+struct IdentityFunctor {
+  HOSTDEVICE explicit inline IdentityFunctor() {}
+
+  HOSTDEVICE inline T operator()(const T& x) const { return x; }
+};
+
+template <typename DeviceContext, typename T>
+struct KronGradOpFunctor {
+  void operator()(const DeviceContext& dev_ctx, const framework::Tensor& dout,
+                  const framework::Tensor& x, const framework::Tensor& y,
+                  framework::Tensor* dx, framework::Tensor* dy) {
+    int ndims = dout.dims().size();
+    int64_t numel = dout.numel();
+    int64_t numel_x = x.numel();
+    int64_t numel_y = y.numel();
+
+    const framework::DDim& dim_x = x.dims();
+    const framework::DDim& dim_y = y.dims();
+    const framework::DDim& dim_dout = dout.dims();
+
+    const framework::DDim stride_x = framework::stride(dim_x);
+    const framework::DDim stride_y = framework::stride(dim_y);
+    const framework::DDim stride_dout = framework::stride(dim_dout);
+
+    const int64_t* p_stride_x = nullptr;
+    const int64_t* p_stride_y = nullptr;
+    const int64_t* p_stride_dout = nullptr;
+    const int64_t* p_shape_y = nullptr;
+#if __NVCC__
+    thrust::device_vector<int64_t> d_stride_x(ndims);
+    thrust::device_vector<int64_t> d_stride_y(ndims);
+    thrust::device_vector<int64_t> d_stride_dout(ndims);
+    thrust::device_vector<int64_t> d_shape_y(ndims);
+    thrust::copy(stride_x.Get(), stride_x.Get() + ndims, d_stride_x.begin());
+    thrust::copy(stride_y.Get(), stride_y.Get() + ndims, d_stride_y.begin());
+    thrust::copy(stride_dout.Get(), stride_dout.Get() + ndims,
+                 d_stride_dout.begin());
+    thrust::copy(dim_y.Get(), dim_y.Get() + ndims, d_shape_y.begin());
+
+    p_stride_x = thrust::raw_pointer_cast(d_stride_x.data());
+    p_stride_y = thrust::raw_pointer_cast(d_stride_y.data());
+    p_stride_dout = thrust::raw_pointer_cast(d_stride_dout.data());
+    p_shape_y = thrust::raw_pointer_cast(d_shape_y.data());
+#else
+    p_stride_x = stride_x.Get();
+    p_stride_y = stride_y.Get();
+    p_stride_dout = stride_dout.Get();
+    p_shape_y = dim_y.Get();
+#endif
+    // dout_x: dout * kron(ones(X), Y) re-aranged in shape (numel_x, numel_y)
+    // dout_y: dout * kron(X, ones(Y)) re-aranged in shaoe (numel_y, numel_x)
+    framework::Tensor dout_x;
+    dout_x.mutable_data<T>({numel_x, numel_y}, dev_ctx.GetPlace());
+    framework::Tensor dout_y;
+    dout_y.mutable_data<T>({numel_y, numel_x}, dev_ctx.GetPlace());
+
+    platform::ForRange<DeviceContext> for_range(dev_ctx, numel);
+    KronGradElemFunctor<T> func(dout.data<T>(), x.data<T>(), y.data<T>(),
+                                dout_x.data<T>(), dout_y.data<T>(),
+                                p_stride_dout, p_stride_x, p_stride_y,
+                                p_shape_y, numel_x, numel_y, ndims);
+    for_range(func);
+
+// reduce_sum along aixs 1
+#if __NVCC__
+    auto stream = dev_ctx.stream();  // it is a cuda device_context
+    TensorReduce<T, T, cub::Sum, IdentityFunctor<T>>(
+        dout_x, dx, {1}, static_cast<T>(0), cub::Sum(), IdentityFunctor<T>(),
+        stream);
+    TensorReduce<T, T, cub::Sum, IdentityFunctor<T>>(
+        dout_y, dy, {1}, static_cast<T>(0), cub::Sum(), IdentityFunctor<T>(),
+        stream);
+#else
+    auto eigen_dout_x = framework::EigenMatrix<T>::Reshape(dout_x, 1);
+    auto eigen_dout_y = framework::EigenMatrix<T>::Reshape(dout_y, 1);
+    auto eigen_vec_dx = framework::EigenVector<T>::Flatten(*dx);
+    auto eigen_vec_dy = framework::EigenVector<T>::Flatten(*dy);
+    auto* place = dev_ctx.eigen_device();
+    Eigen::array<int, 1> reduce_dim = {1};
+    eigen_vec_dx.device(*place) = eigen_dout_x.sum(reduce_dim);
+    eigen_vec_dy.device(*place) = eigen_dout_y.sum(reduce_dim);
+#endif
+  }
+};
+
+inline framework::Tensor UnsqueezeTo(const framework::Tensor& src, int ndims) {
+  const framework::DDim& shape = src.dims();
+  int rank = shape.size();
+  framework::Tensor res;
+  res.ShareDataWith(src);
+  PADDLE_ENFORCE_LE(
+      rank, ndims,
+      platform::errors::InvalidArgument(
+          "The input Tensor's rank should be less than or equal to ndims"
+          "Received input Tensor's rank = %d, ndims = %d",
+          rank, ndims));
+  if (rank < ndims) {
+    std::vector<int64_t> new_dim(ndims, 1);
+    for (int i = ndims - rank; i < ndims; i++) {
+      new_dim[i] = shape[i - ndims + rank];
+    }
+    res.Resize(framework::make_ddim(new_dim));
+  }
+  return res;
+}
+
+template <typename DeviceContext, typename T>
+class KronKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& ctx) const {
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* y = ctx.Input<framework::Tensor>("Y");
+
+    auto* out = ctx.Output<framework::Tensor>("Out");
+    out->mutable_data<T>(ctx.GetPlace());
+
+    int ndims = out->dims().size();
+    framework::Tensor xx = UnsqueezeTo(*x, ndims);
+    framework::Tensor yy = UnsqueezeTo(*y, ndims);
+
+    KronOpFunctor<DeviceContext, T> func;
+    func(dev_ctx, xx, yy, out);
+  }
+};
+
+template <typename DeviceContext, typename T>
+class KronGradKernel : public framework::OpKernel<T> {
+ public:
+  virtual void Compute(const framework::ExecutionContext& ctx) const {
+    auto& dev_ctx = ctx.template device_context<DeviceContext>();
+    auto* x = ctx.Input<framework::Tensor>("X");
+    auto* y = ctx.Input<framework::Tensor>("Y");
+    auto* dout = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
+
+    auto* dx = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
+    auto* dy = ctx.Output<framework::Tensor>(framework::GradVarName("Y"));
+    dx->mutable_data<T>(ctx.GetPlace());
+    dy->mutable_data<T>(ctx.GetPlace());
+
+    int ndims = dout->dims().size();
+    framework::Tensor xx = UnsqueezeTo(*x, ndims);
+    framework::Tensor dxx = UnsqueezeTo(*dx, ndims);
+    framework::Tensor yy = UnsqueezeTo(*y, ndims);
+    framework::Tensor dyy = UnsqueezeTo(*dy, ndims);
+
+    KronGradOpFunctor<DeviceContext, T> func;
+    func(dev_ctx, *dout, xx, yy, &dxx, &dyy);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/complex/tensor/math.py

@@ -12,12 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from paddle.common_ops_import import *
 from ..helper import is_complex, is_real, complex_variable_exists
 from ...fluid.framework import ComplexVariable
 from ...fluid import layers
 
 __all__ = [
-    'elementwise_add', 'elementwise_sub', 'elementwise_mul', 'elementwise_div'
+    'elementwise_add', 'elementwise_sub', 'elementwise_mul', 'elementwise_div',
+    'kron'
 ]
 
 
@@ -37,6 +39,9 @@ def elementwise_add(x, y, axis=-1, name=None):
             with any number of dimensions. The supported data types include float32 
             and float64 when it is a Variable. Otherwise the supported data types 
             are complex64 or complex128.
+        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`.
 
     Examples:
         .. code-block:: python
@@ -85,6 +90,9 @@ def elementwise_sub(x, y, axis=-1, name=None):
             with any number of dimensions. The supported data types include float32 
             and float64 when it is a Variable. Otherwise the supported data types 
             are complex64 or complex128.
+        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`.
 
     Examples:
         .. code-block:: python
@@ -133,6 +141,9 @@ def elementwise_mul(x, y, axis=-1, name=None):
             with any number of dimensions. The supported data types include float32 
             and float64 when it is a Variable. Otherwise the supported data types 
             are complex64 or complex128.
+        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`.
 
     Examples:
         .. code-block:: python
@@ -184,6 +195,9 @@ def elementwise_div(x, y, axis=-1, name=None):
             with any number of dimensions. The supported data types include float32 
             and float64 when it is a Variable. Otherwise the supported data types 
             are complex64 or complex128.
+        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`.
 
     Examples:
         .. code-block:: python
@@ -214,3 +228,71 @@ def elementwise_div(x, y, axis=-1, name=None):
         e,
         axis=axis,
         name=name)
+
+
+def kron(x, y, name=None):
+    """
+    The kronecker product of two complex tensors. At least one of inputs :attr:`x` 
+    and :attr:`y` must be a ComplexVariable. See the detailed description for 
+    the function and other arguments in :ref:`api_paddle_tensor_kron` . 
+
+    Let $x = a + ib$, and $y = c + id$, the euqation is 
+
+    .. math::
+       kron(x, y) = kron(a, c) - kron(b, d) + i(kron(a, d) + kron(b, c))
+
+    Args:
+        x (Variable|ComplexVariable): The first input Variable or ComplexVariable 
+            with any number of dimensions. The supported data types include float32 
+            and float64 when it is a Variable. Otherwise the supported data types 
+            are complex64 or complex128.
+        y (Variable|ComplexVariable): The second input Variable or ComplexVariable 
+            with any number of dimensions. The supported data types include float32 
+            and float64 when it is a Variable. Otherwise the supported data types 
+            are complex64 or complex128.
+        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:
+        ComplexVariable: The kronecker product, data type: complex64 or complex128, depending on the data type of x and y. If the data types of x and y are float32/complex64, the data type of the output is complex64, else if the data types of x and y are float64/complex128, the data type of the output is complex128.
+
+    Examples:
+        .. code-block:: python
+    
+            import numpy as np
+            import paddle
+            import paddle.fluid.dygraph as dg
+
+            a = np.array([[1.0+1.0j, 2.0+1.0j], [3.0+1.0j, 4.0+1.0j]])
+            b = np.array([[5.0+2.0j, 6.0+2.0j], [7.0+2.0j, 8.0+2.0j]])
+
+            place = fluid.CPUPlace()
+            with dg.guard(place):
+                x = dg.to_variable(a)
+                y = dg.to_variable(b)
+                out = paddle.complex.kron(x, y)
+                print(out.numpy())
+            # [[ 3. +7.j  4. +8.j  8. +9.j 10.+10.j]
+            #  [ 5. +9.j  6.+10.j 12.+11.j 14.+12.j]
+            #  [13.+11.j 16.+12.j 18.+13.j 22.+14.j]
+            #  [19.+13.j 22.+14.j 26.+15.j 30.+16.j]]
+    """
+    complex_variable_exists([x, y], "kron")
+
+    # X = A + Bi, Y = C+Di
+    # kron(X, Y) = kron(A, C) - kron(B, D) + (kron(A, D) + kron(B, C))i
+    (a, b) = (x.real, x.imag) if is_complex(x) else (x, None)
+    (c, d) = (y.real, y.imag) if is_complex(y) else (y, None)
+
+    if is_real(b) and is_real(d):
+        real = layers.kron(a, c) - layers.kron(b, d)
+        imag = layers.kron(a, d) + layers.kron(b, c)
+    elif is_real(b):
+        real = layers.kron(a, c)
+        imag = layers.kron(b, c)
+    else:
+        # is_real(d)
+        real = layers.kron(a, c)
+        imag = layers.kron(a, d)
+    return ComplexVariable(real, imag)

python/paddle/fluid/layers/tensor.py

@@ -30,11 +30,32 @@ import numpy
 import warnings
 
 __all__ = [
-    'create_tensor', 'create_parameter', 'create_global_var', 'cast',
-    'tensor_array_to_tensor', 'concat', 'sums', 'assign',
-    'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
-    'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite',
-    'range', 'linspace', 'zeros_like', 'ones_like', 'diag', 'eye'
+    'create_tensor',
+    'create_parameter',
+    'create_global_var',
+    'cast',
+    'tensor_array_to_tensor',
+    'concat',
+    'sums',
+    'assign',
+    'fill_constant_batch_size_like',
+    'fill_constant',
+    'argmin',
+    'argmax',
+    'argsort',
+    'ones',
+    'zeros',
+    'reverse',
+    'has_inf',
+    'has_nan',
+    'isfinite',
+    'range',
+    'linspace',
+    'zeros_like',
+    'ones_like',
+    'diag',
+    'eye',
+    'kron',
 ]
 
 
@@ -1559,3 +1580,69 @@ def ones_like(x, out=None):
         attrs={'value': 1.0},
         outputs={'Out': [out]})
     return out
+
+
+@templatedoc(op_type="kron")
+def kron(x, y, out=None, name=None):
+    """${comment}
+
+    Args:
+        x (Variable): the fist operand of kron op, data type: float16, float32, 
+            float64, int32 or int64.
+        y (Variable): the second operand of kron op, data type: float16, 
+            float32, float64, int32 or int64. Its data type should be the same 
+            with x.
+        out (Variable, optional): Optional output which can be any created 
+            Variable that meets the requirements to store the result of 
+            operation. If out is None, a new Varibale will be create to store 
+            the result. Defaults to None.
+        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:
+        Variable: The output of kron op, data type: float16, float32, float64, int32 or int64. Its data is the same with x.
+
+    Examples:
+        .. code-block:: python
+        
+          import paddle
+          from paddle import fluid
+          import paddle.fluid.dygraph as dg
+          import numpy as np
+
+          a = np.arange(1, 5).reshape(2, 2).astype(np.float32)
+          b = np.arange(1, 10).reshape(3, 3).astype(np.float32)
+
+          place = fluid.CPUPlace()
+          with dg.guard(place):
+              a_var = dg.to_variable(a)
+              b_var = dg.to_variable(b)
+              c_var = fluid.layers.kron(a_var, b_var)
+              c_np = c_var.numpy()
+          print(c_np)
+
+          #[[ 1.  2.  3.  2.  4.  6.]
+          # [ 4.  5.  6.  8. 10. 12.]
+          # [ 7.  8.  9. 14. 16. 18.]
+          # [ 3.  6.  9.  4.  8. 12.]
+          # [12. 15. 18. 16. 20. 24.]
+          # [21. 24. 27. 28. 32. 36.]]
+    """
+    if in_dygraph_mode():
+        return core.ops.kron(x, y)
+
+    helper = LayerHelper('kron', **locals())
+    check_variable_and_dtype(
+        x, 'x', ['float16', 'float32', 'float64', 'int32', 'int64'], 'kron')
+    check_variable_and_dtype(
+        y, 'y', ['float16', 'float32', 'float64', 'int32', 'int64'], 'kron')
+
+    if out is None:
+        out = helper.create_variable_for_type_inference(dtype=x.dtype)
+    else:
+        check_variable_and_dtype(
+            out, 'out', ['float16', 'float32', 'float64', 'int32', 'int64'],
+            'kron')
+    helper.append_op(type="kron", inputs={"X": x, "Y": y}, outputs={"Out": out})
+    return out

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

+# Copyright (c) 2020 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 paddle import fluid, tensor
+import paddle.complex as cpx
+import paddle.fluid.dygraph as dg
+import numpy as np
+import unittest
+
+
+class ComplexKronTestCase(unittest.TestCase):
+    def __init__(self, methodName='runTest', x=None, y=None):
+        super(ComplexKronTestCase, self).__init__(methodName)
+        self.x = x
+        self.y = y
+
+    def setUp(self):
+        self.ref_result = np.kron(self.x, self.y)
+
+    def runTest(self):
+        place = fluid.CPUPlace()
+        self.test_identity(place)
+
+        if fluid.is_compiled_with_cuda():
+            place = fluid.CUDAPlace(0)
+            self.test_identity(place)
+
+    def test_identity(self, place):
+        with dg.guard(place):
+            x_var = dg.to_variable(self.x)
+            y_var = dg.to_variable(self.y)
+            out_var = cpx.kron(x_var, y_var)
+            np.testing.assert_allclose(out_var.numpy(), self.ref_result)
+
+
+def load_tests(loader, standard_tests, pattern):
+    suite = unittest.TestSuite()
+    suite.addTest(
+        ComplexKronTestCase(
+            x=np.random.randn(2, 2) + 1j * np.random.randn(2, 2),
+            y=np.random.randn(3, 3) + 1j * np.random.randn(3, 3)))
+    suite.addTest(
+        ComplexKronTestCase(
+            x=np.random.randn(2, 2),
+            y=np.random.randn(3, 3) + 1j * np.random.randn(3, 3)))
+    suite.addTest(
+        ComplexKronTestCase(
+            x=np.random.randn(2, 2) + 1j * np.random.randn(2, 2),
+            y=np.random.randn(3, 3)))
+
+    suite.addTest(
+        ComplexKronTestCase(
+            x=np.random.randn(2, 2) + 1j * np.random.randn(2, 2),
+            y=np.random.randn(2, 2, 3)))
+    return suite
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+# Copyright (c) 2020 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
+from op_test import OpTest
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.dygraph as dg
+
+
+class TestKronOp(OpTest):
+    def setUp(self):
+        self.op_type = "kron"
+        self.dtype = self._init_dtype()
+        x = np.random.uniform(size=(10, 10)).astype(self.dtype)
+        y = np.random.uniform(size=(10, 10)).astype(self.dtype)
+        out_ref = np.kron(x, y)
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': out_ref}
+
+    def _init_dtype(self):
+        return "float64"
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        self.check_grad(['X', 'Y'], 'Out')
+
+
+class TestKronOp2(TestKronOp):
+    def setUp(self):
+        self.op_type = "kron"
+        self.dtype = self._init_dtype()
+        x = np.random.uniform(size=(5, 5, 4)).astype(self.dtype)
+        y = np.random.uniform(size=(10, 10)).astype(self.dtype)
+        out_ref = np.kron(x, y)
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': out_ref}
+
+
+class TestKronOp3(TestKronOp):
+    def setUp(self):
+        self.op_type = "kron"
+        self.dtype = self._init_dtype()
+        x = np.random.uniform(size=(10, 10)).astype(self.dtype)
+        y = np.random.uniform(size=(5, 5, 4)).astype(self.dtype)
+        out_ref = np.kron(x, y)
+        self.inputs = {'X': x, 'Y': y}
+        self.outputs = {'Out': out_ref}
+
+
+class TestKronLayer(unittest.TestCase):
+    def test_case(self):
+        a = np.random.randn(10, 10).astype(np.float64)
+        b = np.random.randn(10, 10).astype(np.float64)
+
+        place = fluid.CPUPlace()
+        with dg.guard(place):
+            a_var = dg.to_variable(a)
+            b_var = dg.to_variable(b)
+            c_var = fluid.layers.kron(a_var, b_var)
+            np.testing.assert_allclose(c_var.numpy(), np.kron(a, b))
+
+    def test_case_with_output(self):
+        a = np.random.randn(10, 10).astype(np.float64)
+        b = np.random.randn(10, 10).astype(np.float64)
+
+        main = fluid.Program()
+        start = fluid.Program()
+        with fluid.unique_name.guard():
+            with fluid.program_guard(main, start):
+                a_var = fluid.data("a", [-1, -1], dtype="float64")
+                b_var = fluid.data("b", [-1, -1], dtype="float64")
+                out_var = fluid.layers.create_tensor("float64", "c")
+                fluid.layers.kron(a_var, b_var, out=out_var)
+
+        place = fluid.CPUPlace()
+        exe = fluid.Executor(place)
+        exe.run(start)
+        c, = exe.run(main, feed={'a': a, 'b': b}, fetch_list=[out_var])
+        np.testing.assert_allclose(c, np.kron(a, b))
+
+
+if __name__ == '__main__':
+    unittest.main()