Add Sparse Op: copy_sparse_coo and copy_sparse_csr (#41193)

paddle/fluid/eager/grad_node_info.cc

@@ -19,6 +19,7 @@
 
 #include "paddle/phi/common/data_type.h"
 #include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
 
 #include "paddle/fluid/framework/convert_utils.h"
 #include "paddle/fluid/framework/data_type.h"
@@ -124,12 +125,19 @@ void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
     return;
   }
 
+  phi::DenseTensor* dense_tensor = nullptr;
   // Record TensorMeta
   if (phi::DenseTensor::classof(fwd_out.impl().get())) {
     // Only Copy Meta
-    phi::DenseTensor* dense_tensor =
-        static_cast<phi::DenseTensor*>(fwd_out.impl().get());
-
+    dense_tensor = static_cast<phi::DenseTensor*>(fwd_out.impl().get());
+  } else if (phi::SparseCooTensor::classof(fwd_out.impl().get())) {
+    phi::SparseCooTensor* coo_tensor =
+        static_cast<phi::SparseCooTensor*>(fwd_out.impl().get());
+    dense_tensor = coo_tensor->mutable_non_zero_elements();
+  } else {
+    VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
+               "non-DenseTensor argument.";
+  }
   PADDLE_ENFORCE_NE(
       dense_tensor->meta().dtype, phi::DataType::UNDEFINED,
       paddle::platform::errors::Fatal(
@@ -143,10 +151,6 @@ void GradNodeBase::SetGradInMeta(const paddle::experimental::Tensor& fwd_out,
           paddle::framework::TransToProtoVarType(dense_tensor->type()))) {
     need_complex_to_real_ = true;
   }
-  } else {
-    VLOG(6) << "Unable to initialize the DenseTensorMeta of GradSlotMeta with "
-               "non-DenseTensor argument.";
-  }
 }
 
 void GradNodeBase::SetGradInMeta(

paddle/phi/api/lib/kernel_dispatch.h

@@ -25,6 +25,8 @@ limitations under the License. */
 #include "paddle/phi/common/data_type.h"
 #include "paddle/phi/common/layout.h"
 #include "paddle/phi/core/selected_rows.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+#include "paddle/phi/core/sparse_csr_tensor.h"
 
 // TODO(chenweihang): split Key, Kernel, Factory into diff files
 #include "paddle/phi/core/kernel_factory.h"
@@ -41,7 +43,9 @@ phi::DeviceContext* GetDeviceContextByBackend(phi::Backend backend);
 
 enum class KernelType {
   DENSE_TENSOR_KENREL,   // kernel for DenseTensor
-  SELECTED_ROWS_KENREL  // kernel for SelectedRows
+  SELECTED_ROWS_KENREL,  // kernel for SelectedRows
+  SPARSE_COO_KERNEL,     // kernel for SparseCooTensor
+  SPARSE_CSR_KERNEL      // kernel for SparseCsrTensor
 };
 
 // TODO(chenweihang): support DataLayout and DataType selected
@@ -130,6 +134,10 @@ struct KernelTypeParser : ArgsIterator<KernelTypeParser> {
   void operator()(const Tensor& x) {
     if (phi::SelectedRows::classof(x.impl().get())) {
       kernel_type = KernelType::SELECTED_ROWS_KENREL;
+    } else if (phi::SparseCooTensor::classof(x.impl().get())) {
+      kernel_type = KernelType::SPARSE_COO_KERNEL;
+    } else if (phi::SparseCsrTensor::classof(x.impl().get())) {
+      kernel_type = KernelType::SPARSE_CSR_KERNEL;
     }
   }
 

paddle/phi/api/lib/tensor_method.cc

@@ -177,6 +177,40 @@ void Tensor::copy_(const Tensor &src,
                  target_place,
                  blocking,
                  static_cast<phi::SelectedRows *>(impl_.get()));
+  } else if (kernel_type == KernelType::SPARSE_COO_KERNEL) {
+    auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+        "copy_sparse_coo", {kernel_backend, kernel_layout, kernel_data_type});
+    VLOG(6) << "copy API kernel key: " << kernel_key;
+    VLOG(6) << "copy API kernel: " << kernel;
+    using kernel_signature = void (*)(const platform::DeviceContext &,
+                                      const phi::SparseCooTensor &,
+                                      phi::Place,
+                                      bool,
+                                      phi::SparseCooTensor *);
+    this->set_impl(std::make_shared<phi::SparseCooTensor>());
+    auto *kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
+    (*kernel_fn)(*dev_ctx,
+                 (*(std::static_pointer_cast<phi::SparseCooTensor>(src.impl_))),
+                 target_place,
+                 blocking,
+                 static_cast<phi::SparseCooTensor *>(impl_.get()));
+  } else if (kernel_type == KernelType::SPARSE_CSR_KERNEL) {
+    auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
+        "copy_sparse_csr", {kernel_backend, kernel_layout, kernel_data_type});
+    VLOG(6) << "copy API kernel key: " << kernel_key;
+    VLOG(6) << "copy API kernel: " << kernel;
+    using kernel_signature = void (*)(const platform::DeviceContext &,
+                                      const phi::SparseCsrTensor &,
+                                      phi::Place,
+                                      bool,
+                                      phi::SparseCsrTensor *);
+    this->set_impl(std::make_shared<phi::SparseCsrTensor>());
+    auto *kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
+    (*kernel_fn)(*dev_ctx,
+                 (*(std::static_pointer_cast<phi::SparseCsrTensor>(src.impl_))),
+                 target_place,
+                 blocking,
+                 static_cast<phi::SparseCsrTensor *>(impl_.get()));
   } else {
     PADDLE_THROW(phi::errors::InvalidArgument(
         "We currently only support dense tensor copy for now and if u need to "

paddle/phi/core/sparse_coo_tensor.cc

@@ -16,6 +16,11 @@ limitations under the License. */
 
 namespace phi {
 
+SparseCooTensor::SparseCooTensor() {
+  DenseTensor non_zero_indices, non_zero_elements;
+  this->SetMember(non_zero_indices, non_zero_elements, {1}, true);
+}
+
 SparseCooTensor::SparseCooTensor(const DenseTensor& non_zero_indices,
                                  const DenseTensor& non_zero_elements,
                                  const DDim& dims)

paddle/phi/core/sparse_coo_tensor.h

@@ -30,6 +30,7 @@ namespace phi {
 class SparseCooTensor : public TensorBase,
                         public TypeInfoTraits<TensorBase, SparseCooTensor> {
  public:
+  SparseCooTensor();
   /// \brief Create the sparse coo tensor
   /// \param non_zero_indices The indices of non zero elements in original dense
   /// tensor.
@@ -145,6 +146,8 @@ class SparseCooTensor : public TensorBase,
   void* AllocateFrom(Allocator* allocator,
                      DataType dtype,
                      size_t requested_size = 0) override;
+
+  /// \brief set the dims of original dense tensor
   void set_dims(const DDim& dims) { this->dims_ = dims; }
 
  private:

paddle/phi/core/sparse_csr_tensor.cc

@@ -16,6 +16,14 @@ limitations under the License. */
 
 namespace phi {
 
+SparseCsrTensor::SparseCsrTensor() {
+  DenseTensor crows, cols, values;
+  this->non_zero_crows_ = crows;
+  this->non_zero_cols_ = cols;
+  this->non_zero_elements_ = values;
+  this->dims_ = phi::make_ddim({1, 1});
+}
+
 inline void check_shape(const DDim& dims) {
   bool valid = dims.size() == 2 || dims.size() == 3;
 

paddle/phi/core/sparse_csr_tensor.h

@@ -33,6 +33,7 @@ class CompatibleDenseTensorUtils;
 class SparseCsrTensor : public TensorBase,
                         public TypeInfoTraits<TensorBase, SparseCsrTensor> {
  public:
+  SparseCsrTensor();
   /// \brief Because sparse csr tensor is a resource handle, we provide a
   /// default
   /// move constructor to support move semantics.
@@ -143,6 +144,9 @@ class SparseCsrTensor : public TensorBase,
   /// return a mutable pointer of non_zero_elements.
   DenseTensor* mutable_non_zero_elements() { return &non_zero_elements_; }
 
+  /// \brief set the dims of original dense tensor
+  void set_dims(const DDim& dims) { this->dims_ = dims; }
+
  private:
   // save the compressed rows information of non zero elements
   DenseTensor non_zero_crows_;

paddle/phi/kernels/copy_kernel.h

@@ -15,7 +15,6 @@ limitations under the License. */
 #pragma once
 
 #include "paddle/phi/core/dense_tensor.h"
-#include "paddle/phi/core/sparse_csr_tensor.h"
 
 namespace phi {
 

paddle/phi/kernels/sparse/copy_kernel.cc

+/* Copyright (c) 2022 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/phi/kernels/sparse/copy_kernel.h"
+
+#include "paddle/phi/backends/cpu/cpu_context.h"
+#include "paddle/phi/backends/gpu/gpu_context.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+#include "paddle/phi/core/sparse_csr_tensor.h"
+#include "paddle/phi/kernels/copy_kernel.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename Context>
+void CopyCoo(const Context& dev_ctx,
+             const SparseCooTensor& src,
+             Place dst_place,
+             bool blocking,
+             SparseCooTensor* dst) {
+  phi::Copy<Context>(dev_ctx,
+                     src.non_zero_indices(),
+                     dst_place,
+                     blocking,
+                     dst->mutable_non_zero_indices());
+
+  phi::Copy<Context>(dev_ctx,
+                     src.non_zero_elements(),
+                     dst_place,
+                     blocking,
+                     dst->mutable_non_zero_elements());
+  dst->set_dims(src.dims());
+}
+
+template <typename Context>
+void CopyCsr(const Context& dev_ctx,
+             const SparseCsrTensor& src,
+             Place dst_place,
+             bool blocking,
+             SparseCsrTensor* dst) {
+  phi::Copy<Context>(dev_ctx,
+                     src.non_zero_crows(),
+                     dst_place,
+                     blocking,
+                     dst->mutable_non_zero_crows());
+
+  phi::Copy<Context>(dev_ctx,
+                     src.non_zero_cols(),
+                     dst_place,
+                     blocking,
+                     dst->mutable_non_zero_cols());
+
+  phi::Copy<Context>(dev_ctx,
+                     src.non_zero_elements(),
+                     dst_place,
+                     blocking,
+                     dst->mutable_non_zero_elements());
+  dst->set_dims(src.dims());
+}
+
+}  // namespace sparse
+}  // namespace phi
+
+PD_REGISTER_GENERAL_KERNEL(copy_sparse_coo,
+                           CPU,
+                           ALL_LAYOUT,
+                           phi::sparse::CopyCoo<phi::CPUContext>,
+                           ALL_DTYPE) {}
+
+PD_REGISTER_GENERAL_KERNEL(copy_sparse_csr,
+                           CPU,
+                           ALL_LAYOUT,
+                           phi::sparse::CopyCsr<phi::CPUContext>,
+                           ALL_DTYPE) {}
+
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+PD_REGISTER_GENERAL_KERNEL(copy_sparse_coo,
+                           GPU,
+                           ALL_LAYOUT,
+                           phi::sparse::CopyCoo<phi::GPUContext>,
+                           ALL_DTYPE) {}
+PD_REGISTER_GENERAL_KERNEL(copy_sparse_csr,
+                           GPU,
+                           ALL_LAYOUT,
+                           phi::sparse::CopyCsr<phi::GPUContext>,
+                           ALL_DTYPE) {}
+#endif

paddle/phi/kernels/sparse/copy_kernel.h

+/* Copyright (c) 2022 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/phi/api/lib/utils/storage.h"
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+#include "paddle/phi/core/sparse_csr_tensor.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename Context>
+void CopyCoo(const Context& dev_ctx,
+             const SparseCooTensor& src,
+             Place dst_place,
+             bool blocking,
+             SparseCooTensor* dst);
+
+template <typename Context>
+void CopyCsr(const Context& dev_ctx,
+             const SparseCsrTensor& src,
+             Place dst_place,
+             bool blocking,
+             SparseCsrTensor* dst);
+
+}  // namespace sparse
+}  // namespace phi

paddle/phi/kernels/sparse/cpu/convolution.h

@@ -153,8 +153,9 @@ void UpdateRulebookAndOutIndex(const Context& dev_ctx,
   const int64_t sparse_dim = 4;
   DenseTensorMeta indices_meta(
       DataType::INT32, {sparse_dim, out_non_zero_num}, DataLayout::NCHW);
-  DenseTensorMeta values_meta(
-      x.dtype(), {out_non_zero_num, out_channels}, x.layout());
+  DenseTensorMeta values_meta(x.dtype(),
+                              {out_non_zero_num, out_channels},
+                              x.non_zero_elements().layout());
   phi::DenseTensor out_indices = phi::Empty(dev_ctx, std::move(indices_meta));
   phi::DenseTensor out_values = phi::Empty(dev_ctx, std::move(values_meta));
   int* out_indices_ptr = out_indices.data<int>();

paddle/phi/kernels/sparse/cpu/sparse_utils_kernel.cc

@@ -121,7 +121,8 @@ void SparseCsrToCooKernel(const Context& dev_ctx,
   const auto place = dev_ctx.GetPlace();
   DenseTensorMeta indices_meta(
       DataType::INT64, {sparse_dim, non_zero_num}, DataLayout::NCHW);
-  DenseTensorMeta values_meta(x.dtype(), {non_zero_num}, x.layout());
+  DenseTensorMeta values_meta(
+      x.dtype(), {non_zero_num}, x.non_zero_elements().layout());
   phi::DenseTensor indices = phi::Empty(dev_ctx, std::move(indices_meta));
   phi::DenseTensor values = phi::Empty(dev_ctx, std::move(values_meta));
   int64_t* coo_indices = indices.mutable_data<int64_t>(place);
@@ -174,7 +175,8 @@ void SparseCooToCsrKernel(const Context& dev_ctx,
   DenseTensorMeta crows_meta(
       DataType::INT64, {batchs * (rows + 1)}, DataLayout::NCHW);
   DenseTensorMeta cols_meta(DataType::INT64, {non_zero_num}, DataLayout::NCHW);
-  DenseTensorMeta values_meta(x.dtype(), {non_zero_num}, x.layout());
+  DenseTensorMeta values_meta(
+      x.dtype(), {non_zero_num}, x.non_zero_elements().layout());
   phi::DenseTensor non_zero_crows(
       phi::make_intrusive<paddle::experimental::SharedStorage>(place),
       std::move(crows_meta));

paddle/phi/kernels/sparse/gpu/convolution.cu.h

@@ -349,7 +349,10 @@ int ProductRuleBook(const Context& dev_ctx,
   int kernel_size = kernel_sizes[0] * kernel_sizes[1] * kernel_sizes[2];
   const int rulebook_rows = 3;
   const int rulebook_cols = kernel_size * non_zero_num;
-  rulebook->ResizeAndAllocate({rulebook_rows, rulebook_cols});
+  DenseTensorMeta rulebook_meta(
+      DataType::INT32, {rulebook_rows, rulebook_cols}, DataLayout::NCHW);
+  rulebook->set_meta(rulebook_meta);
+  dev_ctx.Alloc(rulebook, rulebook->dtype(), rulebook->numel() * sizeof(int));
   int* rulebook_ptr = rulebook->data<int>();
 
   const auto x_dims = x.dims();
@@ -608,8 +611,9 @@ int ProductRuleBook(const Context& dev_ctx,
   const int64_t sparse_dim = 4;
   DenseTensorMeta indices_meta(
       DataType::INT32, {sparse_dim, out_non_zero_num}, DataLayout::NCHW);
-  DenseTensorMeta values_meta(
-      x.dtype(), {out_non_zero_num, kernel_sizes[4]}, x.layout());
+  DenseTensorMeta values_meta(x.dtype(),
+                              {out_non_zero_num, kernel_sizes[4]},
+                              x.non_zero_elements().layout());
   phi::DenseTensor out_indices = phi::Empty(dev_ctx, std::move(indices_meta));
   phi::DenseTensor out_values = phi::Empty(dev_ctx, std::move(values_meta));
 

paddle/phi/kernels/sparse/gpu/sparse_utils_kernel.cu

@@ -257,7 +257,8 @@ void SparseCsrToCooKernel(const Context& dev_ctx,
   const auto place = dev_ctx.GetPlace();
   DenseTensorMeta indices_meta(
       DataType::INT64, {sparse_dim, non_zero_num}, DataLayout::NCHW);
-  DenseTensorMeta values_meta(x.dtype(), {non_zero_num}, x.layout());
+  DenseTensorMeta values_meta(
+      x.dtype(), {non_zero_num}, x.non_zero_elements().layout());
   DenseTensorMeta offsets_meta(DataType::INT32, {batchs}, DataLayout::NCHW);
   DenseTensor indices = phi::Empty(dev_ctx, std::move(indices_meta));
   DenseTensor values = phi::Empty(dev_ctx, std::move(values_meta));
@@ -385,7 +386,8 @@ void SparseCooToCsrKernel(const Context& dev_ctx,
   DenseTensorMeta crows_meta(
       DataType::INT64, {batchs * (rows + 1)}, DataLayout::NCHW);
   DenseTensorMeta cols_meta(DataType::INT64, {non_zero_num}, DataLayout::NCHW);
-  DenseTensorMeta values_meta(x.dtype(), {non_zero_num}, x.layout());
+  DenseTensorMeta values_meta(
+      x.dtype(), {non_zero_num}, x.non_zero_elements().layout());
   phi::DenseTensor non_zero_crows(
       phi::make_intrusive<paddle::experimental::SharedStorage>(place),
       std::move(crows_meta));

paddle/phi/tests/kernels/test_sparse_conv3d_dev_api.cc

@@ -218,11 +218,8 @@ void TestConv3dBase(const std::vector<int>& indices,
                             correct_out_indices.size() * sizeof(int));
   ASSERT_EQ(cmp_indices2, 0);
 
-  DenseTensor h_features_tensor = phi::Empty(
-      dev_ctx_cpu,
-      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
-                      {d_out.nnz()},
-                      d_out.layout()));
+  DenseTensor h_features_tensor =
+      phi::EmptyLike<T>(dev_ctx_cpu, d_out.non_zero_elements());
 
   phi::Copy(dev_ctx_gpu,
             d_out.non_zero_elements(),
@@ -243,15 +240,11 @@ void TestConv3dBase(const std::vector<int>& indices,
                               strides,
                               1,
                               subm);
-    DenseTensor h_features_grad = phi::Empty(
-        dev_ctx_cpu,
-        DenseTensorMeta(grads[0].dtype(), grads[0].dims(), grads[0].layout()));
+    DenseTensor h_features_grad = phi::EmptyLike<T>(dev_ctx_cpu, grads[0]);
     phi::Copy(dev_ctx_gpu, grads[0], phi::CPUPlace(), true, &h_features_grad);
     f_verify(h_features_grad.data<T>(), features_grad);
 
-    DenseTensor h_kernel_grad = phi::Empty(
-        dev_ctx_cpu,
-        DenseTensorMeta(grads[1].dtype(), grads[1].dims(), grads[1].layout()));
+    DenseTensor h_kernel_grad = phi::EmptyLike<T>(dev_ctx_cpu, grads[1]);
     phi::Copy(dev_ctx_gpu, grads[1], phi::CPUPlace(), true, &h_kernel_grad);
     f_verify(h_kernel_grad.data<T>(), kernel_grad);
   }

paddle/phi/tests/kernels/test_sparse_pool_dev_api.cc

@@ -56,6 +56,10 @@ void TestMaxPoolBase(const std::vector<int>& indices,
       paddle::memory::allocation::AllocatorFacade::Instance()
           .GetAllocator(paddle::platform::CPUPlace())
           .get());
+  dev_ctx_cpu.SetHostAllocator(
+      paddle::memory::allocation::AllocatorFacade::Instance()
+          .GetAllocator(phi::CPUPlace())
+          .get());
   dev_ctx_cpu.Init();
 
   const int in_channels = x_dims[4];
@@ -138,11 +142,8 @@ void TestMaxPoolBase(const std::vector<int>& indices,
   phi::Copy(
       dev_ctx_gpu, indices_tensor, phi::GPUPlace(), true, &d_indices_tensor);
 
-  DenseTensor d_features_tensor = phi::Empty(
-      dev_ctx_gpu,
-      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
-                      {non_zero_num, in_channels},
-                      DataLayout::NHWC));
+  DenseTensor d_features_tensor =
+      phi::EmptyLike<T>(dev_ctx_gpu, features_tensor);
   phi::Copy(
       dev_ctx_gpu, features_tensor, phi::GPUPlace(), true, &d_features_tensor);
 
@@ -178,11 +179,8 @@ void TestMaxPoolBase(const std::vector<int>& indices,
                             correct_out_indices.size() * sizeof(int));
   ASSERT_EQ(cmp_indices2, 0);
 
-  DenseTensor h_features_tensor = phi::Empty(
-      dev_ctx_cpu,
-      DenseTensorMeta(paddle::experimental::CppTypeToDataType<T>::Type(),
-                      {d_out.nnz()},
-                      d_out.layout()));
+  DenseTensor h_features_tensor =
+      phi::EmptyLike<T>(dev_ctx_cpu, d_out.non_zero_elements());
 
   phi::Copy(dev_ctx_gpu,
             d_out.non_zero_elements(),
@@ -198,9 +196,7 @@ void TestMaxPoolBase(const std::vector<int>& indices,
                                                 d_out,
                                                 d_out.non_zero_elements(),
                                                 kernel_sizes);
-    DenseTensor h_features_grad = phi::Empty(
-        dev_ctx_cpu,
-        DenseTensorMeta(x_grad.dtype(), x_grad.dims(), x_grad.layout()));
+    DenseTensor h_features_grad = phi::EmptyLike<T>(dev_ctx_cpu, x_grad);
     phi::Copy(dev_ctx_gpu, x_grad, phi::CPUPlace(), true, &h_features_grad);
     f_verify(h_features_grad.data<T>(), features_grad);
   }

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

+# Copyright (c) 2022 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 paddle import _C_ops
+from paddle.fluid import core
+from paddle.fluid.framework import _test_eager_guard
+
+
+class TestSparseCopy(unittest.TestCase):
+    def test_copy_sparse_coo(self):
+        with _test_eager_guard():
+            np_x = [[0, 1.0, 0], [2.0, 0, 0], [0, 3.0, 0]]
+            np_values = [1.0, 2.0, 3.0]
+            dense_x = paddle.to_tensor(np_x, dtype='float32')
+            coo_x = dense_x.to_sparse_coo(2)
+
+            np_x_2 = [[0, 3.0, 0], [2.0, 0, 0], [0, 3.0, 0]]
+            dense_x_2 = paddle.to_tensor(np_x_2, dtype='float32')
+            coo_x_2 = dense_x_2.to_sparse_coo(2)
+            coo_x_2.copy_(coo_x, True)
+            assert np.array_equal(np_values,
+                                  coo_x_2.non_zero_elements().numpy())
+
+    def test_copy_sparse_csr(self):
+        with _test_eager_guard():
+            np_x = [[0, 1.0, 0], [2.0, 0, 0], [0, 3.0, 0]]
+            np_values = [1.0, 2.0, 3.0]
+            dense_x = paddle.to_tensor(np_x, dtype='float32')
+            csr_x = dense_x.to_sparse_csr()
+
+            np_x_2 = [[0, 3.0, 0], [2.0, 0, 0], [0, 3.0, 0]]
+            dense_x_2 = paddle.to_tensor(np_x_2, dtype='float32')
+            csr_x_2 = dense_x_2.to_sparse_csr()
+            csr_x_2.copy_(csr_x, True)
+            assert np.array_equal(np_values,
+                                  csr_x_2.non_zero_elements().numpy())