Call sparse op from python (#40608)

* call sparse api from python

paddle/fluid/eager/auto_code_generator/final_state_generator/eager_gen.py

@@ -730,7 +730,7 @@ def GenerateNodeCreationCodes(
         else:
             # Tuple api_result
             if IsPlainTensorType(rtype):
-                output_autograd_meta = f"    egr::AutogradMeta* {output_autograd_meta_name} = egr::EagerUtils::autograd_meta(&api_result[{pos}]);"
+                output_autograd_meta = f"    egr::AutogradMeta* {output_autograd_meta_name} = egr::EagerUtils::autograd_meta(&std::get<{pos}>(api_result));"
             else:
                 assert IsVectorTensorType(rtype)
                 output_autograd_meta = f"    std::vector<egr::AutogradMeta*> {output_autograd_meta_vec_name} = egr::EagerUtils::autograd_meta(&api_result[{pos}]);\n"
@@ -767,8 +767,11 @@ def GenerateNodeCreationCodes(
             else:
                 set_tensor_wrappers = f"        grad_node->SetTensorWrapper{name}({name}, true);"
         else:
-            if IsVectorTensorType(atype):
-                tw_name = f"api_result[{pos}]"
+            if num_fwd_outputs > 1:
+                # Aligned with forward output position
+                assert name in forward_outputs_position_map.keys()
+                fwd_output_pos = forward_outputs_position_map[name][1]
+                tw_name = f"std::get<{fwd_output_pos}>(api_result)"
             else:
                 tw_name = f"api_result"
 
@@ -805,8 +808,8 @@ def GenerateNodeCreationCodes(
             set_retain_grad = f"        egr::EagerUtils::CheckAndRetainGrad(api_result);"
             set_grad_in_meta = f"        grad_node->SetGradInMeta(api_result, {pos});"
         else:
-            set_retain_grad = f"        egr::EagerUtils::CheckAndRetainGrad(api_result[{pos}]);"
-            set_grad_in_meta = f"        grad_node->SetGradInMeta(api_result[{pos}], {pos});"
+            set_retain_grad = f"        egr::EagerUtils::CheckAndRetainGrad(std::get<{pos}>(api_result));"
+            set_grad_in_meta = f"        grad_node->SetGradInMeta(std::get<{pos}>(api_result), {pos});"
 
         set_out_rank_list.append(set_out_rank)
         set_history_list.append(set_history)
@@ -934,7 +937,7 @@ def GenerateForwardDefinition(fwd_api_name, bwd_api_name,
             returns_list[0] = f"api_result"
         else:
             # Tuple api_result
-            returns_list[pos] = f"api_result[{pos}]"
+            returns_list[pos] = f"std::get<{pos}>(api_result)"
 
         if IsPlainTensorType(rtype):
             returns_type_list[pos] = "paddle::experimental::Tensor"
@@ -1084,7 +1087,7 @@ def GenerateNodeCCFile(filepath, node_definition_str):
 #include "paddle/fluid/eager/api/generated/eager_generated/backwards/nodes.h"
 #include "paddle/fluid/eager/to_static/run_program_op_node.h"
 
-#include "paddle/phi/api/include/sparse_api.h"
+#include "paddle/phi/api/backward/sparse_bw_api.h"
 """
     file_contents += node_definition_str
     with open(filepath, 'a') as f:

paddle/fluid/eager/auto_code_generator/final_state_generator/python_c_gen.py

@@ -337,7 +337,7 @@ class PythonCSingleFunctionGenerator:
                 "paddle::experimental::", namespace, forward_api_name)
         else:
             fwd_function_name = FUNCTION_NAME_TEMPLATE.format(
-                "", namespace, GetForwardFunctionName(forward_api_name))
+                "::", namespace, GetForwardFunctionName(forward_api_name))
 
         # Generate Record Event for performance profiling
         pythonc_record_event_str = RECORD_EVENT_TEMPLATE.format(

paddle/fluid/pybind/eager_method.cc

@@ -36,6 +36,8 @@ limitations under the License. */
 #include "paddle/phi/common/data_type.h"
 #include "paddle/phi/core/compat/convert_utils.h"
 #include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+#include "paddle/phi/core/sparse_csr_tensor.h"
 
 namespace paddle {
 namespace pybind {
@@ -718,6 +720,98 @@ static PyObject* set_grad_type(TensorObject* self, PyObject* args,
   EAGER_CATCH_AND_THROW_RETURN_NULL
 }
 
+static PyObject* tensor_method_get_non_zero_indices(TensorObject* self,
+                                                    PyObject* args,
+                                                    PyObject* kwargs) {
+  EAGER_TRY
+  PADDLE_ENFORCE(self->tensor.is_sparse_coo_tensor(),
+                 paddle::platform::errors::Fatal(
+                     "this method is only effective for SparseCooTensor"));
+  auto sparse_coo_tensor =
+      std::dynamic_pointer_cast<phi::SparseCooTensor>(self->tensor.impl());
+  paddle::experimental::Tensor tensor(std::make_shared<phi::DenseTensor>(
+      sparse_coo_tensor->non_zero_indices()));
+  return ToPyObject(tensor);
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
+static PyObject* tensor_method_get_non_zero_elements(TensorObject* self,
+                                                     PyObject* args,
+                                                     PyObject* kwargs) {
+  EAGER_TRY
+  PADDLE_ENFORCE(
+      self->tensor.is_sparse_coo_tensor() ||
+          self->tensor.is_sparse_csr_tensor(),
+      paddle::platform::errors::Fatal("this method is only effective for "
+                                      "SparseCooTensor or SparseCsrTensor"));
+  if (self->tensor.is_sparse_coo_tensor()) {
+    auto sparse_coo_tensor =
+        std::dynamic_pointer_cast<phi::SparseCooTensor>(self->tensor.impl());
+    paddle::experimental::Tensor tensor(std::make_shared<phi::DenseTensor>(
+        sparse_coo_tensor->non_zero_elements()));
+    return ToPyObject(tensor);
+  } else {
+    auto sparse_csr_tensor =
+        std::dynamic_pointer_cast<phi::SparseCsrTensor>(self->tensor.impl());
+    paddle::experimental::Tensor tensor(std::make_shared<phi::DenseTensor>(
+        sparse_csr_tensor->non_zero_elements()));
+    return ToPyObject(tensor);
+  }
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
+static PyObject* tensor_method_get_non_zero_crows(TensorObject* self,
+                                                  PyObject* args,
+                                                  PyObject* kwargs) {
+  EAGER_TRY
+  PADDLE_ENFORCE(self->tensor.is_sparse_csr_tensor(),
+                 paddle::platform::errors::Fatal(
+                     "this method is only effective for SparseCsrTensor"));
+  auto sparse_csr_tensor =
+      std::dynamic_pointer_cast<phi::SparseCsrTensor>(self->tensor.impl());
+  paddle::experimental::Tensor tensor(
+      std::make_shared<phi::DenseTensor>(sparse_csr_tensor->non_zero_crows()));
+  return ToPyObject(tensor);
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
+static PyObject* tensor_method_get_non_zero_cols(TensorObject* self,
+                                                 PyObject* args,
+                                                 PyObject* kwargs) {
+  EAGER_TRY
+  PADDLE_ENFORCE(self->tensor.is_sparse_csr_tensor(),
+                 paddle::platform::errors::Fatal(
+                     "this method is only effective for SparseCsrTensor"));
+  auto sparse_csr_tensor =
+      std::dynamic_pointer_cast<phi::SparseCsrTensor>(self->tensor.impl());
+  paddle::experimental::Tensor tensor(
+      std::make_shared<phi::DenseTensor>(sparse_csr_tensor->non_zero_cols()));
+  return ToPyObject(tensor);
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
+static PyObject* tensor_method_is_sparse(TensorObject* self, PyObject* args,
+                                         PyObject* kwargs) {
+  EAGER_TRY
+  return ToPyObject(self->tensor.is_sparse_coo_tensor() ||
+                    self->tensor.is_sparse_csr_tensor());
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
+static PyObject* tensor_method_is_sparse_coo(TensorObject* self, PyObject* args,
+                                             PyObject* kwargs) {
+  EAGER_TRY
+  return ToPyObject(self->tensor.is_sparse_coo_tensor());
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
+static PyObject* tensor_method_is_sparse_csr(TensorObject* self, PyObject* args,
+                                             PyObject* kwargs) {
+  EAGER_TRY
+  return ToPyObject(self->tensor.is_sparse_csr_tensor());
+  EAGER_CATCH_AND_THROW_RETURN_NULL
+}
+
 static PyObject* tensor__inplace_version(TensorObject* self, PyObject* args,
                                          PyObject* kwargs) {
   EAGER_TRY
@@ -775,6 +869,26 @@ PyMethodDef variable_methods[] = {
      METH_VARARGS | METH_KEYWORDS, NULL},
     {"_set_grad_type", (PyCFunction)(void (*)(void))set_grad_type,
      METH_VARARGS | METH_KEYWORDS, NULL},
+    /***the method of sparse tensor****/
+    {"non_zero_indices",
+     (PyCFunction)(void (*)(void))tensor_method_get_non_zero_indices,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    {"non_zero_elements",
+     (PyCFunction)(void (*)(void))tensor_method_get_non_zero_elements,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    {"non_zero_crows",
+     (PyCFunction)(void (*)(void))tensor_method_get_non_zero_crows,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    {"non_zero_cols",
+     (PyCFunction)(void (*)(void))tensor_method_get_non_zero_cols,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    {"is_sparse", (PyCFunction)(void (*)(void))tensor_method_is_sparse,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    {"is_sparse_coo", (PyCFunction)(void (*)(void))tensor_method_is_sparse_coo,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    {"is_sparse_csr", (PyCFunction)(void (*)(void))tensor_method_is_sparse_csr,
+     METH_VARARGS | METH_KEYWORDS, NULL},
+    /***the method of sparse tensor****/
     {"_inplace_version", (PyCFunction)(void (*)(void))tensor__inplace_version,
      METH_VARARGS | METH_KEYWORDS, NULL},
     {NULL, NULL, 0, NULL}};

paddle/phi/api/include/tensor.h

@@ -225,6 +225,22 @@ class PADDLE_API Tensor final {
    */
   bool is_selected_rows() const;
 
+  /**
+   * @brief Determine whether tensor is SparseCooTensor
+   *
+   * @return true
+   * @return false
+   */
+  bool is_sparse_coo_tensor() const;
+
+  /**
+   * @brief Determine whether tensor is SparseCsrTensor
+   *
+   * @return true
+   * @return false
+   */
+  bool is_sparse_csr_tensor() const;
+
   /* Part 3: Device and Backend methods */
 
   /**

paddle/phi/api/lib/sparse_api_custom_impl.cc

@@ -25,25 +25,24 @@ namespace paddle {
 namespace experimental {
 namespace sparse {
 
-Tensor to_sparse_coo_impl(const Tensor& x,
-                          Backend backend,
-                          const int64_t sparse_dim) {
+Tensor to_sparse_coo_impl(const Tensor& x, const int64_t sparse_dim) {
   if (x.layout() == phi::DataLayout::SPARSE_COO) {
     return x;
   }
+
   // 1. Get kernel signature and kernel
-  auto kernel_key_set = ParseKernelKeyByInputArgs(x);
-  kernel_key_set.backend_set = kernel_key_set.backend_set | BackendSet(backend);
-  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
   std::string kernel_name = "dense_to_sparse_coo";
   if (x.layout() == phi::DataLayout::SPARSE_CSR) {
     kernel_name = "sparse_csr_to_coo";
   }
 
+  auto kernel_key_set = ParseKernelKeyByInputArgs(x);
+  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
+
   auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
       kernel_name, kernel_key);
 
-  VLOG(6) << "to API kernel key: " << kernel_key;
+  VLOG(6) << "add API kernel key: " << kernel_key;
   VLOG(6) << "to API kernel: " << kernel;
 
   // 2. Get Device Context
@@ -62,18 +61,18 @@ Tensor to_sparse_coo_impl(const Tensor& x,
 
   // 4. InferMeta
   auto indices_meta =
-      phi::DenseTensorMeta(phi::DataType::INT64, {-1}, phi::DataLayout::NCHW);
-  auto elements_meta = phi::DenseTensorMeta(x.dtype(), {-1}, x.layout());
+      phi::DenseTensorMeta(phi::DataType::INT64, {1}, phi::DataLayout::NCHW);
+  auto elements_meta = phi::DenseTensorMeta(x.dtype(), {1}, x.layout());
 
   // 5. Prepare outputs
   // create empty SparseCooTensor
   phi::DenseTensor non_zero_indices(
       phi::make_intrusive<paddle::experimental::SharedStorage>(
-          phi::TransToPhiPlace(backend)),
+          phi::TransToPhiPlace(kernel_key.backend())),
       std::move(indices_meta));
   phi::DenseTensor non_zero_elements(
       phi::make_intrusive<paddle::experimental::SharedStorage>(
-          phi::TransToPhiPlace(backend)),
+          phi::TransToPhiPlace(kernel_key.backend())),
       std::move(elements_meta));
   auto coo = std::make_shared<phi::SparseCooTensor>(
       non_zero_indices, non_zero_elements, x.dims());
@@ -88,23 +87,23 @@ Tensor to_sparse_coo_impl(const Tensor& x,
   return out;
 }
 
-Tensor to_sparse_csr_impl(const Tensor& x, Backend backend) {
+Tensor to_sparse_csr_impl(const Tensor& x) {
   if (x.layout() == phi::DataLayout::SPARSE_CSR) {
     return x;
   }
   // 1. Get kernel signature and kernel
-  auto kernel_key_set = ParseKernelKeyByInputArgs(x);
-  kernel_key_set.backend_set = kernel_key_set.backend_set | BackendSet(backend);
-  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
   std::string kernel_name = "dense_to_sparse_csr";
   if (x.layout() == phi::DataLayout::SPARSE_COO) {
     kernel_name = "sparse_coo_to_csr";
   }
 
+  auto kernel_key_set = ParseKernelKeyByInputArgs(x);
+  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
+
   auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
       kernel_name, kernel_key);
 
-  VLOG(6) << "to API kernel key: " << kernel_key;
+  VLOG(6) << "add API kernel key: " << kernel_key;
   VLOG(6) << "to API kernel: " << kernel;
 
   // 2. Get Device Context
@@ -122,24 +121,24 @@ Tensor to_sparse_csr_impl(const Tensor& x, Backend backend) {
 
   // 4. InferMeta
   auto crows_meta =
-      phi::DenseTensorMeta(phi::DataType::INT64, {-1}, phi::DataLayout::NCHW);
+      phi::DenseTensorMeta(phi::DataType::INT64, {1}, phi::DataLayout::NCHW);
   auto cols_meta =
-      phi::DenseTensorMeta(phi::DataType::INT64, {-1}, phi::DataLayout::NCHW);
-  auto elements_meta = phi::DenseTensorMeta(x.dtype(), {-1}, x.layout());
+      phi::DenseTensorMeta(phi::DataType::INT64, {1}, phi::DataLayout::NCHW);
+  auto elements_meta = phi::DenseTensorMeta(x.dtype(), {1}, x.layout());
 
   // 5. Prepare outputs
   // create empty SparseCooTensor
   phi::DenseTensor non_zero_crows(
       phi::make_intrusive<paddle::experimental::SharedStorage>(
-          phi::TransToPhiPlace(backend)),
+          phi::TransToPhiPlace(kernel_key.backend())),
       std::move(crows_meta));
   phi::DenseTensor non_zero_cols(
       phi::make_intrusive<paddle::experimental::SharedStorage>(
-          phi::TransToPhiPlace(backend)),
+          phi::TransToPhiPlace(kernel_key.backend())),
       std::move(cols_meta));
   phi::DenseTensor non_zero_elements(
       phi::make_intrusive<paddle::experimental::SharedStorage>(
-          phi::TransToPhiPlace(backend)),
+          phi::TransToPhiPlace(kernel_key.backend())),
       std::move(elements_meta));
   auto csr = std::make_shared<phi::SparseCsrTensor>(
       non_zero_crows, non_zero_cols, non_zero_elements, x.dims());
@@ -154,24 +153,25 @@ Tensor to_sparse_csr_impl(const Tensor& x, Backend backend) {
   return out;
 }
 
-Tensor to_dense_impl(const Tensor& x, Backend backend) {
+Tensor to_dense_impl(const Tensor& x) {
   if (x.layout() != phi::DataLayout::SPARSE_CSR &&
       x.layout() != phi::DataLayout::SPARSE_COO) {
     return x;
   }
+
   // 1. Get kernel signature and kernel
-  auto kernel_key_set = ParseKernelKeyByInputArgs(x);
-  kernel_key_set.backend_set = kernel_key_set.backend_set | BackendSet(backend);
-  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
   std::string kernel_name = "sparse_coo_to_dense";
   if (x.layout() == phi::DataLayout::SPARSE_CSR) {
     kernel_name = "sparse_csr_to_dense";
   }
 
+  auto kernel_key_set = ParseKernelKeyByInputArgs(x);
+  auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
+
   auto kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
       kernel_name, kernel_key);
 
-  VLOG(6) << "to API kernel key: " << kernel_key;
+  VLOG(6) << "add API kernel key: " << kernel_key;
   VLOG(6) << "to API kernel: " << kernel;
 
   // 2. Get Device Context
@@ -194,7 +194,7 @@ Tensor to_dense_impl(const Tensor& x, Backend backend) {
   // create empty SparseCooTensor
   auto dense_out = std::make_shared<phi::DenseTensor>(
       phi::make_intrusive<paddle::experimental::SharedStorage>(
-          phi::TransToPhiPlace(backend)),
+          phi::TransToPhiPlace(kernel_key.backend())),
       std::move(dense_meta));
 
   kernel_context.EmplaceBackOutput(dense_out.get());

paddle/phi/api/lib/sparse_api_custom_impl.h

@@ -21,13 +21,11 @@ namespace paddle {
 namespace experimental {
 namespace sparse {
 
-Tensor to_dense_impl(const Tensor& x, Backend backend);
+Tensor to_dense_impl(const Tensor& x);
 
-Tensor to_sparse_coo_impl(const Tensor& x,
-                          Backend backend,
-                          const int64_t sparse_dim);
+Tensor to_sparse_coo_impl(const Tensor& x, const int64_t sparse_dim);
 
-Tensor to_sparse_csr_impl(const Tensor& x, Backend backend);
+Tensor to_sparse_csr_impl(const Tensor& x);
 
 }  // namespace sparse
 }  // namespace experimental

paddle/phi/api/lib/tensor.cc

@@ -25,6 +25,8 @@ limitations under the License. */
 #include "paddle/phi/core/compat/convert_utils.h"
 #include "paddle/phi/core/dense_tensor.h"
 #include "paddle/phi/core/selected_rows.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+#include "paddle/phi/core/sparse_csr_tensor.h"
 #include "paddle/phi/core/tensor_base.h"
 #include "paddle/phi/core/tensor_meta.h"
 #include "paddle/phi/core/tensor_utils.h"
@@ -132,6 +134,12 @@ bool Tensor::is_dense_tensor() const {
 bool Tensor::is_selected_rows() const {
   return phi::SelectedRows::classof(impl_.get());
 }
+bool Tensor::is_sparse_coo_tensor() const {
+  return phi::SparseCooTensor::classof(impl_.get());
+}
+bool Tensor::is_sparse_csr_tensor() const {
+  return phi::SparseCsrTensor::classof(impl_.get());
+}
 /* Part 3: Device and Backend methods */
 
 PlaceType Tensor::place() const {

paddle/phi/tests/api/test_sparse_utils_api.cc

@@ -53,8 +53,7 @@ TEST(API, to_sparse_coo) {
 
   // 1. test dense_to_sparse_coo
   paddle::experimental::Tensor x(dense_x);
-  auto out = paddle::experimental::sparse::to_sparse_coo(
-      x, phi::Backend::CPU, sparse_dim);
+  auto out = paddle::experimental::sparse::to_sparse_coo(x, sparse_dim);
   auto coo = std::dynamic_pointer_cast<phi::SparseCooTensor>(out.impl());
   ASSERT_EQ(coo->nnz(), non_zero_num);
   int cmp_indices = memcmp(coo->non_zero_indices().data<int64_t>(),
@@ -91,8 +90,7 @@ TEST(API, to_sparse_coo) {
   auto csr =
       std::make_shared<phi::SparseCsrTensor>(crows, cols, values, dense_dims);
   paddle::experimental::Tensor csr_x(csr);
-  auto out2 = paddle::experimental::sparse::to_sparse_coo(
-      csr_x, phi::Backend::CPU, sparse_dim);
+  auto out2 = paddle::experimental::sparse::to_sparse_coo(csr_x, sparse_dim);
 
   auto coo2 = std::dynamic_pointer_cast<phi::SparseCooTensor>(out.impl());
   ASSERT_EQ(coo2->nnz(), non_zero_num);
@@ -132,7 +130,7 @@ TEST(API, to_sparse_csr) {
 
   // 1. test dense_to_sparse_csr
   paddle::experimental::Tensor x(dense_x);
-  auto out = paddle::experimental::sparse::to_sparse_csr(x, phi::Backend::CPU);
+  auto out = paddle::experimental::sparse::to_sparse_csr(x);
   auto csr = std::dynamic_pointer_cast<phi::SparseCsrTensor>(out.impl());
   auto check = [&](const phi::SparseCsrTensor& csr) {
     ASSERT_EQ(csr.non_zero_cols().numel(), non_zero_num);
@@ -170,8 +168,7 @@ TEST(API, to_sparse_csr) {
   auto coo =
       std::make_shared<phi::SparseCooTensor>(indices, values, dense_dims);
   paddle::experimental::Tensor coo_x(coo);
-  auto out2 =
-      paddle::experimental::sparse::to_sparse_csr(coo_x, phi::Backend::CPU);
+  auto out2 = paddle::experimental::sparse::to_sparse_csr(coo_x);
 
   auto csr2 = std::dynamic_pointer_cast<phi::SparseCsrTensor>(out.impl());
   check(*csr2);
@@ -212,7 +209,7 @@ TEST(API, to_dense) {
       std::make_shared<phi::SparseCooTensor>(indices, values, dense_dims);
 
   paddle::experimental::Tensor coo_x(coo);
-  auto out = paddle::experimental::sparse::to_dense(coo_x, phi::Backend::CPU);
+  auto out = paddle::experimental::sparse::to_dense(coo_x);
   auto dense_out = std::dynamic_pointer_cast<phi::DenseTensor>(out.impl());
   int cmp1 =
       memcmp(dense_out->data<float>(), &dense_data[0][0], 9 * sizeof(float));
@@ -237,7 +234,7 @@ TEST(API, to_dense) {
   auto csr =
       std::make_shared<phi::SparseCsrTensor>(crows, cols, values, dense_dims);
   paddle::experimental::Tensor csr_x(csr);
-  auto out2 = paddle::experimental::sparse::to_dense(csr_x, phi::Backend::CPU);
+  auto out2 = paddle::experimental::sparse::to_dense(csr_x);
 
   auto dense_out2 = std::dynamic_pointer_cast<phi::DenseTensor>(out.impl());
   int cmp2 =

python/paddle/fluid/tests/unittests/test_sparse_utils_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.framework import _test_eager_guard
+
+
+class TestSparseUtils(unittest.TestCase):
+    def test_to_sparse_coo(self):
+        with _test_eager_guard():
+            x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
+            non_zero_indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
+            non_zero_elements = [1, 2, 3, 4, 5]
+            dense_x = paddle.to_tensor(x)
+            #TODO(zhangkaihuo): change to test the corresponding API
+            out = _C_ops.final_state_to_sparse_coo(dense_x, 2)
+            print(out)
+            assert np.array_equal(out.non_zero_indices().numpy(),
+                                  non_zero_indices)
+            assert np.array_equal(out.non_zero_elements().numpy(),
+                                  non_zero_elements)
+
+            dense_tensor = _C_ops.final_state_to_dense(out)
+            assert np.array_equal(dense_tensor.numpy(), x)
+
+    def test_to_sparse_csr(self):
+        with _test_eager_guard():
+            x = [[0, 1, 0, 2], [0, 0, 3, 0], [4, 5, 0, 0]]
+            non_zero_crows = [0, 2, 3, 5]
+            non_zero_cols = [1, 3, 2, 0, 1]
+            non_zero_elements = [1, 2, 3, 4, 5]
+            dense_x = paddle.to_tensor(x)
+            out = _C_ops.final_state_to_sparse_csr(dense_x)
+            print(out)
+            assert np.array_equal(out.non_zero_crows().numpy(), non_zero_crows)
+            assert np.array_equal(out.non_zero_cols().numpy(), non_zero_cols)
+            assert np.array_equal(out.non_zero_elements().numpy(),
+                                  non_zero_elements)
+
+            dense_tensor = _C_ops.final_state_to_dense(out)
+            assert np.array_equal(dense_tensor.numpy(), x)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/tensor/to_string.py

@@ -263,14 +263,7 @@ def to_string(var, prefix='Tensor'):
         data=data)
 
 
-def tensor_to_string(tensor, prefix='Tensor'):
-    indent = len(prefix) + 1
-
-    _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient},\n{indent}{data})"
-
-    if not tensor._is_initialized():
-        return "Tensor(Not initialized)"
-
+def _format_dense_tensor(tensor, indent):
     np_tensor = tensor.numpy()
 
     if len(tensor.shape) == 0:
@@ -288,7 +281,49 @@ def tensor_to_string(tensor, prefix='Tensor'):
 
     data = _format_tensor(
         np_tensor, sumary, indent=indent, max_width=max_width, signed=signed)
+    return data
 
+
+def sparse_tensor_to_string(tensor, prefix='Tensor'):
+    indent = len(prefix) + 1
+    _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient}, \n{indent}{data})"
+    if tensor.is_sparse_coo():
+        indices_tensor = tensor.non_zero_indices()
+        elements_tensor = tensor.non_zero_elements()
+        indices_data = _format_dense_tensor(indices_tensor, indent)
+        elements_data = _format_dense_tensor(elements_tensor, indent)
+        data = 'non_zero_indices=' + indices_data + ',\nnon_zero_elements=' + elements_data
+    else:
+        crows_tensor = tensor.non_zero_crows()
+        cols_tensor = tensor.non_zero_cols()
+        elements_tensor = tensor.non_zero_elements()
+        crows_data = _format_dense_tensor(crows_tensor, indent)
+        cols_data = _format_dense_tensor(cols_tensor, indent)
+        elements_data = _format_dense_tensor(elements_tensor, indent)
+        data = 'non_zero_crows=' + crows_data + ',\nnon_zero_cols=' + cols_data + ',\nnon_zero_elements=' + elements_data
+
+    return _template.format(
+        prefix=prefix,
+        shape=tensor.shape,
+        dtype=tensor.dtype,
+        place=tensor._place_str,
+        stop_gradient=tensor.stop_gradient,
+        indent=' ' * indent,
+        data=data)
+
+
+def tensor_to_string(tensor, prefix='Tensor'):
+    indent = len(prefix) + 1
+
+    _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient},\n{indent}{data})"
+
+    if not tensor._is_initialized():
+        return "Tensor(Not initialized)"
+
+    if tensor.is_sparse():
+        return sparse_tensor_to_string(tensor, prefix)
+    else:
+        data = _format_dense_tensor(tensor, indent)
         return _template.format(
             prefix=prefix,
             shape=tensor.shape,

python/paddle/utils/code_gen/sparse_api.yaml

@@ -4,18 +4,19 @@
   kernel :
     func : sparse_conv3d
     layout : x
+  backward : conv3d_grad
 
 - api : to_dense
-  args : (Tensor x, Backend backend)
+  args : (Tensor x)
   output : Tensor(out@DenseTensor)
-  invoke : to_dense_impl(x, backend)
+  invoke : to_dense_impl(x)
 
 - api : to_sparse_coo
-  args : (Tensor x, Backend backend, int64 sparse_dim)
+  args : (Tensor x, int64 sparse_dim)
   output : Tensor(out@SparseCooTensor)
-  invoke : to_sparse_coo_impl(x, backend, sparse_dim)
+  invoke : to_sparse_coo_impl(x, sparse_dim)
 
 - api : to_sparse_csr
-  args : (Tensor x, Backend backend)
+  args : (Tensor x)
   output : Tensor(out@SparseCsrTensor)
-  invoke : to_sparse_csr_impl(x, backend)
+  invoke : to_sparse_csr_impl(x)

python/paddle/utils/code_gen/sparse_api_gen.py

@@ -192,9 +192,7 @@ def source_include(header_file_path):
 
 
 def api_register():
-    return """
-PD_REGISTER_API(Test);
-"""
+    return ""
 
 
 def api_namespace():

python/paddle/utils/code_gen/sparse_bw_api_gen.py

@@ -115,9 +115,7 @@ def source_include(header_file_path):
 
 
 def api_register():
-    return """
-PD_REGISTER_API(Test);
-"""
+    return ""
 
 
 def api_namespace():