Add cusparse and unittest (#38431)

    将cuSparse的handle与DeviceContext进行绑定，避免op中进行创建和销毁
    添加对cuSparse中dense和sparse转换的API进行封装
    添加对封装的API的单测

paddle/fluid/operators/math/CMakeLists.txt

@@ -111,6 +111,15 @@ if(WITH_ROCM)
     hip_test(selected_rows_functor_gpu_test SRCS selected_rows_functor_test.cu.cc DEPS selected_rows_functor math_function)
 endif()
 cc_test(concat_test SRCS concat_test.cc DEPS concat_and_split)
+
+if(WITH_GPU AND (NOT WITH_ROCM))
+#currenty not yet support ROCM 
+#the generic conversion APIs of dense and sparse are only supported after cuda11.2
+    if((NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_LESS 11.2)) 
+        cc_test(cusparse_conversion_api_test SRCS cusparse_conversion_api_test.cc DEPS tensor)
+    endif()
+endif()
+
 cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
 if(WITH_TESTING AND TEST im2col_test)
     set_tests_properties(im2col_test PROPERTIES TIMEOUT 120)

paddle/fluid/operators/math/cusparse_conversion_api_test.cc

+/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <vector>
+
+#include "glog/logging.h"
+#include "gtest/gtest.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/operators/math/sparse.h"
+
+template <typename T>
+void TestNNZ(const std::vector<T>& dense_data, const int correct_nnz,
+             const int rows, const int cols) {
+  paddle::platform::CUDADeviceContext* context =
+      new paddle::platform::CUDADeviceContext(paddle::platform::CUDAPlace());
+  auto sparse =
+      paddle::operators::math::GetSparse<paddle::platform::CUDADeviceContext,
+                                         T>(*context);
+
+  paddle::framework::Tensor dense, nnz_tensor;
+  auto dense_dims = paddle::framework::make_ddim({rows, cols});
+  auto nnz_dims = paddle::framework::make_ddim({dense_dims[0] + 1});
+  dense.mutable_data<T>(dense_dims, paddle::platform::CUDAPlace());
+  paddle::framework::TensorFromVector<T>(dense_data, *context, &dense);
+  int32_t* nnz_ptr =
+      nnz_tensor.mutable_data<int32_t>(nnz_dims, paddle::platform::CUDAPlace());
+  sparse.nnz(rows, cols, dense.data<T>(), nnz_ptr, nnz_ptr + 1);
+  std::vector<int32_t> nnz_vec(dense_dims[0] + 1);
+  paddle::framework::TensorToVector<int32_t>(nnz_tensor, *context, &nnz_vec);
+  delete context;
+  CHECK_EQ(correct_nnz, nnz_vec[0]);
+}
+
+TEST(sparse, nnz) {
+  std::vector<float> dense_data = {0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 3.2, 0.0, 0.0};
+  TestNNZ<float>(dense_data, 4, 3, 3);
+}
+
+TEST(sparse, nnz_double) {
+  std::vector<double> dense_data = {0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 3.2, 0.0};
+  TestNNZ<double>(dense_data, 4, 4, 2);
+}
+
+template <typename T>
+void TestDenseToSparse(const std::vector<T>& correct_dense_data,
+                       const std::vector<int64_t>& correct_rows,
+                       const std::vector<int64_t>& correct_cols,
+                       const std::vector<T>& correct_values,
+                       const int correct_nnz, const int rows, const int cols,
+                       const std::string& mode) {
+  paddle::platform::CUDADeviceContext* context =
+      new paddle::platform::CUDADeviceContext(paddle::platform::CUDAPlace());
+  // get sparse
+  auto sparse =
+      paddle::operators::math::GetSparse<paddle::platform::CUDADeviceContext,
+                                         T>(*context);
+
+  // create tensor and copy vector to tensor
+  paddle::framework::Tensor dense_tensor, rows_tensor, cols_tensor,
+      values_tensor, actual_dense_tensor;
+  auto dense_dims = paddle::framework::make_ddim({rows, cols});
+  T* dense_data =
+      dense_tensor.mutable_data<T>(dense_dims, paddle::platform::CUDAPlace());
+  T* actual_dense_data = actual_dense_tensor.mutable_data<T>(
+      dense_dims, paddle::platform::CUDAPlace());
+  paddle::framework::TensorFromVector<T>(correct_dense_data, *context,
+                                         &dense_tensor);
+
+  auto nnz_dims = paddle::framework::make_ddim({correct_nnz});
+  auto crows_dims = paddle::framework::make_ddim({rows + 1});
+  int64_t* rows_data = nullptr;
+  if (mode == "COO") {
+    rows_data = rows_tensor.mutable_data<int64_t>(
+        nnz_dims, paddle::platform::CUDAPlace());
+  } else {
+    rows_data = rows_tensor.mutable_data<int64_t>(
+        crows_dims, paddle::platform::CUDAPlace());
+  }
+  int64_t* cols_data = cols_tensor.mutable_data<int64_t>(
+      nnz_dims, paddle::platform::CUDAPlace());
+  T* values_data =
+      values_tensor.mutable_data<T>(nnz_dims, paddle::platform::CUDAPlace());
+
+  // test dense_to_sparse
+  if (mode == "COO") {
+    sparse.DenseToSparseCoo(rows, cols, dense_data, rows_data, cols_data,
+                            values_data);
+  } else {
+    sparse.DenseToSparseCsr(rows, cols, dense_data, rows_data, cols_data,
+                            values_data);
+  }
+
+  std::vector<int64_t> actual_rows(correct_nnz), actual_crows(rows + 1),
+      actual_cols(correct_nnz);
+  std::vector<T> actual_values(correct_nnz), actual_dense_vec(rows * cols);
+  if (mode == "COO") {
+    paddle::framework::TensorToVector<int64_t>(rows_tensor, *context,
+                                               &actual_rows);
+  } else {
+    paddle::framework::TensorToVector<int64_t>(rows_tensor, *context,
+                                               &actual_crows);
+  }
+  paddle::framework::TensorToVector<int64_t>(cols_tensor, *context,
+                                             &actual_cols);
+  paddle::framework::TensorToVector<T>(values_tensor, *context, &actual_values);
+
+  for (int i = 0; i < correct_nnz; i++) {
+    if (mode == "COO") {
+      CHECK_EQ(correct_rows[i], actual_rows[i]);
+    }
+    CHECK_EQ(correct_cols[i], actual_cols[i]);
+    CHECK_EQ(correct_values[i], actual_values[i]);
+  }
+  if (mode == "CSR") {
+    for (int i = 0; i < rows + 1; i++) {
+      CHECK_EQ(correct_rows[i], actual_crows[i]);
+    }
+  }
+
+  // test sparse_to_dense
+  if (mode == "COO") {
+    sparse.SparseCooToDense(rows, cols, correct_nnz, rows_data, cols_data,
+                            values_data, actual_dense_data);
+  } else {
+    sparse.SparseCsrToDense(rows, cols, correct_nnz, rows_data, cols_data,
+                            values_data, actual_dense_data);
+  }
+  paddle::framework::TensorToVector<T>(actual_dense_tensor, *context,
+                                       &actual_dense_vec);
+  for (uint64_t i = 0; i < correct_dense_data.size(); i++) {
+    CHECK_EQ(correct_dense_data[i], actual_dense_vec[i]);
+  }
+
+  delete context;
+}
+
+TEST(sparse, dense_to_sparse) {
+  std::vector<float> dense_data = {0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 3.2, 0.0, 0.0};
+  std::vector<float> values = {1.0, 2.0, 3.0, 3.2};
+  std::vector<int64_t> rows = {0, 1, 1, 2};
+  std::vector<int64_t> crows = {0, 1, 3, 4};
+  std::vector<int64_t> cols = {1, 0, 2, 0};
+  TestDenseToSparse<float>(dense_data, rows, cols, values, 4, 3, 3, "COO");
+  TestDenseToSparse<float>(dense_data, crows, cols, values, 4, 3, 3, "CSR");
+}
+
+TEST(sparse, dense_to_sparse_double) {
+  std::vector<double> dense_data = {0.0, 1.0, 0.0, 2.0, 0.0, 3.0, 3.2, 0.0};
+  std::vector<double> values = {1.0, 2.0, 3.0, 3.2};
+  std::vector<int64_t> rows = {0, 1, 2, 3};
+  std::vector<int64_t> crows = {0, 1, 2, 3, 4};
+  std::vector<int64_t> cols = {1, 1, 1, 0};
+  TestDenseToSparse<double>(dense_data, rows, cols, values, 4, 4, 2, "COO");
+  TestDenseToSparse<double>(dense_data, crows, cols, values, 4, 4, 2, "CSR");
+}
+
+TEST(sparse, dense_to_sparse_fp16) {
+  using float16 = paddle::platform::float16;
+  std::vector<float16> dense_data = {float16(0.0), float16(1.0), float16(0.0),
+                                     float16(2.0), float16(0.0), float16(3.0),
+                                     float16(3.2), float16(0.0)};
+  std::vector<float16> values = {float16(1.0), float16(2.0), float16(3.0),
+                                 float16(3.2)};
+  std::vector<int64_t> rows = {0, 1, 2, 3};
+  std::vector<int64_t> crows = {0, 1, 2, 3, 4};
+  std::vector<int64_t> cols = {1, 1, 1, 0};
+  TestDenseToSparse<float16>(dense_data, rows, cols, values, 4, 4, 2, "COO");
+  TestDenseToSparse<float16>(dense_data, crows, cols, values, 4, 4, 2, "CSR");
+}

paddle/fluid/operators/math/sparse.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/framework/tensor.h"
+
+namespace paddle {
+namespace framework {
+class ExecutionContext;
+class Tensor;
+}  // namespace framework
+}  // namespace paddle
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename DeviceContext>
+class Sparse {
+ public:
+  explicit Sparse(const DeviceContext& context) : context_(context) {}
+
+  template <typename T>
+  void nnz(const int M, const int N, const T* dense, int* nnz,
+           int* nnzPerRowColumn) const;
+
+  template <typename T>
+  void DenseToSparseCoo(const int M, const int N, const T* dense, int64_t* rows,
+                        int64_t* cols, T* values) const;
+
+  template <typename T>
+  void DenseToSparseCsr(const int M, const int N, const T* dense,
+                        int64_t* crows, int64_t* cols, T* values) const;
+
+  template <typename T>
+  void SparseCooToDense(const int64_t M, const int64_t N, const int64_t nnz,
+                        const int64_t* rows, const int64_t* cols,
+                        const T* values, T* dense) const;
+  template <typename T>
+  void SparseCsrToDense(const int64_t M, const int64_t N, const int64_t nnz,
+                        const int64_t* crows, const int64_t* cols,
+                        const T* values, T* dense) const;
+
+ private:
+  const DeviceContext& context_;
+};
+
+template <typename DeviceContext, typename T>
+class SparseT : private Sparse<DeviceContext> {
+ public:
+  using Sparse<DeviceContext>::Sparse;
+
+  template <typename... ARGS>
+  void nnz(ARGS... args) const {
+    Base()->template nnz<T>(args...);
+  }
+
+  template <typename... ARGS>
+  void DenseToSparseCoo(ARGS... args) const {
+    Base()->template DenseToSparseCoo<T>(args...);
+  }
+  template <typename... ARGS>
+  void DenseToSparseCsr(ARGS... args) const {
+    Base()->template DenseToSparseCsr<T>(args...);
+  }
+  template <typename... ARGS>
+  void SparseCooToDense(ARGS... args) const {
+    Base()->template SparseCooToDense<T>(args...);
+  }
+  template <typename... ARGS>
+  void SparseCsrToDense(ARGS... args) const {
+    Base()->template SparseCsrToDense<T>(args...);
+  }
+
+ private:
+  const Sparse<DeviceContext>* Base() const {
+    return static_cast<const Sparse<DeviceContext>*>(this);
+  }
+};
+
+template <typename DeviceContext, typename T>
+inline SparseT<DeviceContext, T> GetSparse(
+    const framework::ExecutionContext& exe_ctx) {
+  return SparseT<DeviceContext, T>(
+      exe_ctx.template device_context<DeviceContext>());
+}
+
+template <typename DeviceContext, typename T>
+inline SparseT<DeviceContext, T> GetSparse(const DeviceContext& dev_ctx) {
+  return SparseT<DeviceContext, T>(dev_ctx);
+}
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle
+
+#if defined(PADDLE_WITH_CUDA)
+#if CUDA_VERSION >= 11020
+#include "paddle/fluid/operators/math/sparse_impl.cu.h"
+#endif
+#endif

paddle/fluid/operators/math/sparse_impl.cu.h

+//   Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#pragma once
+
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/platform/dynload/cusparse.h"
+
+#include "paddle/fluid/platform/device/gpu/gpu_info.h"
+
+namespace paddle {
+namespace operators {
+namespace math {
+
+template <typename T>
+cudaDataType_t GetGpuDataType() {
+  if (std::is_same<T, float>::value) {
+    return CUDA_R_32F;
+  } else if (std::is_same<T, double>::value) {
+    return CUDA_R_64F;
+  } else if (std::is_same<T, platform::float16>::value) {
+    return CUDA_R_16F;
+  }
+}
+
+template <>
+template <typename T>
+void Sparse<platform::CUDADeviceContext>::nnz(const int M, const int N,
+                                              const T* dense, int* nnz,
+                                              int* nnzPerRowColumn) const {}
+
+template <>
+template <>
+void Sparse<platform::CUDADeviceContext>::nnz(const int M, const int N,
+                                              const float* dense, int* nnz,
+                                              int* nnzPerRowColumn) const {
+  cusparseMatDescr_t descr = 0;
+  PADDLE_ENFORCE_GPU_SUCCESS(
+      paddle::platform::dynload::cusparseCreateMatDescr(&descr));
+  PADDLE_ENFORCE_GPU_SUCCESS(paddle::platform::dynload::cusparseSetMatType(
+      descr, CUSPARSE_MATRIX_TYPE_GENERAL));
+  PADDLE_ENFORCE_GPU_SUCCESS(paddle::platform::dynload::cusparseSetMatIndexBase(
+      descr, CUSPARSE_INDEX_BASE_ZERO));
+
+  context_.CusparseCall([&](cusparseHandle_t handle) {
+    PADDLE_ENFORCE_GPU_SUCCESS(paddle::platform::dynload::cusparseSnnz(
+        handle, CUSPARSE_DIRECTION_ROW, M, N, descr, dense, M, nnzPerRowColumn,
+        nnz));
+  });
+}
+
+template <>
+template <>
+void Sparse<platform::CUDADeviceContext>::nnz(const int M, const int N,
+                                              const double* dense, int* nnz,
+                                              int* nnzPerRowColumn) const {
+  cusparseMatDescr_t descr = 0;
+  PADDLE_ENFORCE_GPU_SUCCESS(
+      paddle::platform::dynload::cusparseCreateMatDescr(&descr));
+  PADDLE_ENFORCE_GPU_SUCCESS(paddle::platform::dynload::cusparseSetMatType(
+      descr, CUSPARSE_MATRIX_TYPE_GENERAL));
+  PADDLE_ENFORCE_GPU_SUCCESS(paddle::platform::dynload::cusparseSetMatIndexBase(
+      descr, CUSPARSE_INDEX_BASE_ZERO));
+
+  context_.CusparseCall([&](cusparseHandle_t handle) {
+    PADDLE_ENFORCE_GPU_SUCCESS(paddle::platform::dynload::cusparseDnnz(
+        handle, CUSPARSE_DIRECTION_ROW, M, N, descr, dense, M, nnzPerRowColumn,
+        nnz));
+  });
+}
+
+template <typename T>
+inline void DenseToSparse(const platform::CUDADeviceContext& context,
+                          const int M, const int N, const T* dense,
+                          int64_t* rows, int64_t* cols, T* values,
+                          const cusparseFormat_t format) {
+  cusparseSpMatDescr_t matB;
+  cusparseDnMatDescr_t matA;
+
+  cudaDataType_t dtype = GetGpuDataType<T>();
+
+  PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCreateDnMat(
+      &matA, M, N, N, const_cast<void*>(reinterpret_cast<const void*>(dense)),
+      dtype, CUSPARSE_ORDER_ROW));
+
+  if (format == CUSPARSE_FORMAT_COO) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCreateCoo(
+        &matB, M, N, 0, nullptr, nullptr, nullptr, CUSPARSE_INDEX_64I,
+        CUSPARSE_INDEX_BASE_ZERO, dtype));
+  } else if (format == CUSPARSE_FORMAT_CSR) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCreateCsr(
+        &matB, M, N, 0, rows, nullptr, nullptr, CUSPARSE_INDEX_64I,
+        CUSPARSE_INDEX_64I, CUSPARSE_INDEX_BASE_ZERO, dtype));
+  } else {
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "the sparse format [%s] is not supported", format));
+  }
+
+  size_t buffer_size = 0;
+  context.CusparseCall([&](cusparseHandle_t handle) {
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cusparseDenseToSparse_bufferSize(
+            handle, matA, matB, CUSPARSE_DENSETOSPARSE_ALG_DEFAULT,
+            &buffer_size));
+  });
+  framework::Tensor buffer;
+  float* buffer_data = buffer.mutable_data<float>(
+      {static_cast<int64_t>(buffer_size)}, context.GetPlace());
+
+  context.CusparseCall([&](cusparseHandle_t handle) {
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cusparseDenseToSparse_analysis(
+            handle, matA, matB, CUSPARSE_DENSETOSPARSE_ALG_DEFAULT,
+            buffer_data));
+  });
+
+  if (format == CUSPARSE_FORMAT_COO) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCooSetPointers(
+        matB, rows, cols, reinterpret_cast<void*>(values)));
+  } else if (format == CUSPARSE_FORMAT_CSR) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCsrSetPointers(
+        matB, rows, cols, reinterpret_cast<void*>(values)));
+  } else {
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "the sparse format [%s] is not supported", format));
+  }
+  context.CusparseCall([&](cusparseHandle_t handle) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseDenseToSparse_convert(
+        handle, matA, matB, CUSPARSE_DENSETOSPARSE_ALG_DEFAULT, buffer_data));
+  });
+}
+template <>
+template <typename T>
+void Sparse<platform::CUDADeviceContext>::DenseToSparseCoo(
+    const int M, const int N, const T* dense, int64_t* rows, int64_t* cols,
+    T* values) const {
+  DenseToSparse<T>(context_, M, N, dense, rows, cols, values,
+                   CUSPARSE_FORMAT_COO);
+}
+
+template <>
+template <typename T>
+void Sparse<platform::CUDADeviceContext>::DenseToSparseCsr(
+    const int M, const int N, const T* dense, int64_t* crows, int64_t* cols,
+    T* values) const {
+  DenseToSparse<T>(context_, M, N, dense, crows, cols, values,
+                   CUSPARSE_FORMAT_CSR);
+}
+
+template <typename T>
+void SparseToDense(const platform::CUDADeviceContext& context, const int64_t M,
+                   const int64_t N, const int64_t nnz, const int64_t* rows,
+                   const int64_t* cols, const T* values, T* dense,
+                   const cusparseFormat_t format) {
+  cusparseSpMatDescr_t matA;
+  cusparseDnMatDescr_t matB;
+
+  cudaDataType_t dtype = GetGpuDataType<T>();
+  if (format == CUSPARSE_FORMAT_COO) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCreateCoo(
+        &matA, M, N, nnz,
+        const_cast<void*>(reinterpret_cast<const void*>(rows)),
+        const_cast<void*>(reinterpret_cast<const void*>(cols)),
+        const_cast<void*>(reinterpret_cast<const void*>(values)),
+        CUSPARSE_INDEX_64I, CUSPARSE_INDEX_BASE_ZERO, dtype));
+  } else if (format == CUSPARSE_FORMAT_CSR) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCreateCsr(
+        &matA, M, N, nnz,
+        const_cast<void*>(reinterpret_cast<const void*>(rows)),
+        const_cast<void*>(reinterpret_cast<const void*>(cols)),
+        const_cast<void*>(reinterpret_cast<const void*>(values)),
+        CUSPARSE_INDEX_64I, CUSPARSE_INDEX_64I, CUSPARSE_INDEX_BASE_ZERO,
+        dtype));
+  } else {
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "the sparse format [%s] is not supported", format));
+  }
+
+  PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseCreateDnMat(
+      &matB, M, N, N, reinterpret_cast<void*>(dense), dtype,
+      CUSPARSE_ORDER_ROW));
+
+  size_t buffer_size = 0;
+  context.CusparseCall([&](cusparseHandle_t handle) {
+    PADDLE_ENFORCE_GPU_SUCCESS(
+        platform::dynload::cusparseSparseToDense_bufferSize(
+            handle, matA, matB, CUSPARSE_SPARSETODENSE_ALG_DEFAULT,
+            &buffer_size));
+  });
+  framework::Tensor buffer;
+  float* buffer_data = buffer.mutable_data<float>(
+      {static_cast<int64_t>(buffer_size)}, context.GetPlace());
+
+  context.CusparseCall([&](cusparseHandle_t handle) {
+    PADDLE_ENFORCE_GPU_SUCCESS(platform::dynload::cusparseSparseToDense(
+        handle, matA, matB, CUSPARSE_SPARSETODENSE_ALG_DEFAULT, buffer_data));
+  });
+}
+
+template <>
+template <typename T>
+void Sparse<platform::CUDADeviceContext>::SparseCooToDense(
+    const int64_t M, const int64_t N, const int64_t nnz, const int64_t* rows,
+    const int64_t* cols, const T* values, T* dense) const {
+  SparseToDense<T>(context_, M, N, nnz, rows, cols, values, dense,
+                   CUSPARSE_FORMAT_COO);
+}
+
+template <>
+template <typename T>
+void Sparse<platform::CUDADeviceContext>::SparseCsrToDense(
+    const int64_t M, const int64_t N, const int64_t nnz, const int64_t* crows,
+    const int64_t* cols, const T* values, T* dense) const {
+  SparseToDense<T>(context_, M, N, nnz, crows, cols, values, dense,
+                   CUSPARSE_FORMAT_CSR);
+}
+
+}  // namespace math
+}  // namespace operators
+}  // namespace paddle

paddle/fluid/platform/device/gpu/cuda/cusparse_helper.h

+/* Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#pragma once
+
+#include <mutex>  // NOLINT
+
+#include "paddle/fluid/platform/dynload/cusparse.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/platform/macros.h"
+
+namespace paddle {
+namespace platform {
+
+class CusparseHandleHolder {
+ public:
+  explicit CusparseHandleHolder(cudaStream_t stream) {
+// ROCM is not yet supported
+#if defined(PADDLE_WITH_CUDA)
+// The generic APIs is supported from CUDA10.1
+#if CUDA_VERSION >= 10010
+    PADDLE_RETRY_CUDA_SUCCESS(dynload::cusparseCreate(&handle_));
+    PADDLE_RETRY_CUDA_SUCCESS(dynload::cusparseSetStream(handle_, stream));
+#endif
+#endif
+  }
+  const cusparseHandle_t& GetCusparseHandle() const { return handle_; }
+
+  ~CusparseHandleHolder() PADDLE_MAY_THROW {
+#if defined(PADDLE_WITH_CUDA)
+#if CUDA_VERSION >= 10010
+    PADDLE_RETRY_CUDA_SUCCESS(dynload::cusparseDestroy(handle_));
+#endif
+#endif
+  }
+
+  template <typename Callback>
+  inline void Call(Callback&& callback) const {
+    std::lock_guard<std::mutex> guard(mtx_);
+    callback(handle_);
+  }
+
+ private:
+  DISABLE_COPY_AND_ASSIGN(CusparseHandleHolder);
+
+  cusparseHandle_t handle_;
+  mutable std::mutex mtx_;
+};
+
+}  // namespace platform
+}  // namespace paddle

paddle/fluid/platform/device/gpu/gpu_helper.h

@@ -19,6 +19,7 @@
 #include "paddle/fluid/platform/device/gpu/rocm/rocm_helper.h"
 #else
 #include "paddle/fluid/platform/device/gpu/cuda/cuda_helper.h"
+#include "paddle/fluid/platform/device/gpu/cuda/cusparse_helper.h"
 #endif
 
 #define CUDA_KERNEL_LOOP(i, num) CUDA_KERNEL_LOOP_TYPE(i, num, int)

paddle/fluid/platform/device_context.cc

@@ -484,6 +484,7 @@ CUDAContext::CUDAContext(const CUDAPlace& place,
   InitCuBlasContext();
   InitCuDNNContext();
 #ifndef PADDLE_WITH_HIP
+  InitCuSparseContext();
   InitCuSolverContext();
 #endif
 }
@@ -513,6 +514,7 @@ CUDAContext::~CUDAContext() {
   DestoryCuDNNContext();
   DestoryCuBlasContext();
 #ifndef PADDLE_WITH_HIP
+  DestoryCuSparseContext();
   DestoryCuSolverContext();
 #endif
 }
@@ -630,6 +632,9 @@ rocblas_handle CUDADeviceContext::cublas_handle() const {
 cublasHandle_t CUDADeviceContext::cublas_handle() const {
   return context()->CublasHandle()->GetCublasHandle();
 }
+cusparseHandle_t CUDADeviceContext::cusparse_handle() const {
+  return context()->CusparseHandle()->GetCusparseHandle();
+}
 #endif
 
 CudnnWorkspaceHandle CUDADeviceContext::cudnn_workspace_handle() const {

paddle/fluid/platform/device_context.h

@@ -358,6 +358,12 @@ class CUDAContext {
     return cublas_tensor_core_handle_;
   }
 
+#ifndef PADDLE_WITH_HIP
+  const std::unique_ptr<CusparseHandleHolder>& CusparseHandle() const {
+    return cusparse_handle_;
+  }
+#endif
+
   /*! \brief  Call cublas function safely. */
   template <typename Callback>
   inline void CublasCall(Callback&& callback) const {
@@ -368,6 +374,14 @@ class CUDAContext {
     }
   }
 
+#ifndef PADDLE_WITH_HIP
+  /*! \brief  Call cusparse function safely. */
+  template <typename Callback>
+  inline void CusparseCall(Callback&& callback) const {
+    cusparse_handle_->Call(std::forward<Callback>(callback));
+  }
+#endif
+
   /*! \brief  Check whether tensor core is supported */
   bool tensor_core_available() const;
 
@@ -406,6 +420,12 @@ class CUDAContext {
   }
 #endif
 
+#ifndef PADDLE_WITH_HIP
+  void InitCuSparseContext() {
+    cusparse_handle_.reset(new CusparseHandleHolder(RawStream()));
+  }
+#endif
+
   void InitCuDNNContext() {
     if (dynload::HasCUDNN()) {
 #ifdef PADDLE_WITH_HIP
@@ -478,6 +498,10 @@ class CUDAContext {
     cublas_tf32_tensor_core_handle_.reset();
   }
 
+#ifndef PADDLE_WITH_HIP
+  void DestoryCuSparseContext() { cusparse_handle_.reset(); }
+#endif
+
 #ifndef PADDLE_WITH_HIP
   void DestoryCuSolverContext() {
     if (cusolver_dn_handle_) {
@@ -501,6 +525,7 @@ class CUDAContext {
   std::unique_ptr<CublasHandleHolder> cublas_tf32_tensor_core_handle_;
 #ifndef PADDLE_WITH_HIP
   cusolverDnHandle_t cusolver_dn_handle_;
+  std::unique_ptr<CusparseHandleHolder> cusparse_handle_;
 #endif
   DISABLE_COPY_AND_ASSIGN(CUDAContext);
 };
@@ -540,6 +565,14 @@ class CUDADeviceContext : public DeviceContext {
     return context()->CublasCall(callback);
   }
 
+#ifndef PADDLE_WITH_HIP
+  /*! \brief  Call cusparse function safely. */
+  template <typename Callback>
+  inline void CusparseCall(Callback&& callback) const {
+    return context()->CusparseCall(callback);
+  }
+#endif
+
   /*! \brief  Check whether tensor core is supported */
   bool tensor_core_available() const;
 
@@ -562,6 +595,7 @@ class CUDADeviceContext : public DeviceContext {
   rocblas_handle cublas_handle() const;
 #else
   cublasHandle_t cublas_handle() const;
+  cusparseHandle_t cusparse_handle() const;
 #endif
 
   /*! \brief  Return a cudnn workspace handle to call multiple cudnn

paddle/fluid/platform/dynload/cusparse.cc

@@ -30,6 +30,10 @@ CUSPARSE_ROUTINE_EACH(DEFINE_WRAP);
 #ifdef CUBLAS_BLAS_ROUTINE_EACH_R2
 CUSPARSE_ROUTINE_EACH_R2(DEFINE_WRAP);
 #endif
+
+#ifdef CUSPARSE_ROUTINE_EACH_11020
+CUSPARSE_ROUTINE_EACH_11020(DEFINE_WRAP);
+#endif
 }  // namespace dynload
 }  // namespace platform
 }  // namespace paddle

paddle/fluid/platform/dynload/cusparse.h

@@ -41,21 +41,41 @@ extern void *cusparse_dso_handle;
   };                                                                 \
   extern DynLoad__##__name __name
 
-#if !defined(PADDLE_WITH_ARM) && !defined(_WIN32)
-// APIs available after CUDA 11.0
-#if CUDA_VERSION >= 11000
+#if !defined(PADDLE_WITH_ARM)
+// The generic APIs is supported from CUDA10.1
+#if CUDA_VERSION >= 10010
 #define CUSPARSE_ROUTINE_EACH(__macro) \
   __macro(cusparseCreate);             \
-  __macro(cusparseCreateCsr);          \
-  __macro(cusparseCreateDnMat);        \
-  __macro(cusparseSpMM_bufferSize);    \
-  __macro(cusparseSpMM);               \
-  __macro(cusparseDestroySpMat);       \
-  __macro(cusparseDestroyDnMat);       \
-  __macro(cusparseDestroy);
+  __macro(cusparseSetStream);          \
+  __macro(cusparseCreateMatDescr);     \
+  __macro(cusparseDestroy);            \
+  __macro(cusparseSnnz);               \
+  __macro(cusparseDnnz);               \
+  __macro(cusparseSetMatType);         \
+  __macro(cusparseSetMatIndexBase);
 
 CUSPARSE_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP);
 
+// APIs available after CUDA 11.2
+#if CUDA_VERSION >= 11020
+#define CUSPARSE_ROUTINE_EACH_11020(__macro) \
+  __macro(cusparseCreateCsr);                \
+  __macro(cusparseCreateCoo);                \
+  __macro(cusparseCreateDnMat);              \
+  __macro(cusparseSpMM_bufferSize);          \
+  __macro(cusparseSpMM);                     \
+  __macro(cusparseDestroySpMat);             \
+  __macro(cusparseDestroyDnMat);             \
+  __macro(cusparseCooSetPointers);           \
+  __macro(cusparseCsrSetPointers);           \
+  __macro(cusparseDenseToSparse_bufferSize); \
+  __macro(cusparseDenseToSparse_analysis);   \
+  __macro(cusparseDenseToSparse_convert);    \
+  __macro(cusparseSparseToDense_bufferSize); \
+  __macro(cusparseSparseToDense);
+
+CUSPARSE_ROUTINE_EACH_11020(DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
+
 // APIs available after CUDA 11.3
 #if CUDA_VERSION >= 11030
 #define CUSPARSE_ROUTINE_EACH_R2(__macro) \
@@ -67,6 +87,7 @@ CUSPARSE_ROUTINE_EACH_R2(DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP)
 #endif
 #endif
 #endif
+#endif
 
 #undef DECLARE_DYNAMIC_LOAD_CUSPARSE_WRAP
 }  // namespace dynload

paddle/fluid/platform/enforce.h

@@ -33,6 +33,7 @@ limitations under the License. */
 #include <cudnn.h>
 #include <cufft.h>
 #include <curand.h>
+#include <cusparse.h>
 #include <thrust/system/cuda/error.h>
 #include <thrust/system_error.h>
 #include "paddle/fluid/platform/external_error.pb.h"
@@ -707,6 +708,7 @@ DEFINE_EXTERNAL_API_TYPE(cudaError_t, cudaSuccess, CUDA);
 DEFINE_EXTERNAL_API_TYPE(curandStatus_t, CURAND_STATUS_SUCCESS, CURAND);
 DEFINE_EXTERNAL_API_TYPE(cudnnStatus_t, CUDNN_STATUS_SUCCESS, CUDNN);
 DEFINE_EXTERNAL_API_TYPE(cublasStatus_t, CUBLAS_STATUS_SUCCESS, CUBLAS);
+DEFINE_EXTERNAL_API_TYPE(cusparseStatus_t, CUSPARSE_STATUS_SUCCESS, CUSPARSE);
 DEFINE_EXTERNAL_API_TYPE(cusolverStatus_t, CUSOLVER_STATUS_SUCCESS, CUSOLVER);
 DEFINE_EXTERNAL_API_TYPE(cufftResult_t, CUFFT_SUCCESS, CUFFT);
 DEFINE_EXTERNAL_API_TYPE(CUresult, CUDA_SUCCESS, CU);
@@ -750,6 +752,10 @@ inline const char* GetErrorMsgUrl(T status) {
       break;
     case platform::proto::ApiType::CUFFT:
       return "https://docs.nvidia.com/cuda/cufft/index.html#cufftresult";
+    case platform::proto::ApiType::CUSPARSE:
+      return "https://docs.nvidia.com/cuda/cusparse/"
+             "index.html#cusparseStatus_t";
+      break;
     default:
       return "Unknown type of External API, can't get error message URL!";
       break;
@@ -837,6 +843,7 @@ template std::string GetExternalErrorMsg<cudaError_t>(cudaError_t);
 template std::string GetExternalErrorMsg<curandStatus_t>(curandStatus_t);
 template std::string GetExternalErrorMsg<cudnnStatus_t>(cudnnStatus_t);
 template std::string GetExternalErrorMsg<cublasStatus_t>(cublasStatus_t);
+template std::string GetExternalErrorMsg<cusparseStatus_t>(cusparseStatus_t);
 template std::string GetExternalErrorMsg<cusolverStatus_t>(cusolverStatus_t);
 template std::string GetExternalErrorMsg<cufftResult_t>(cufftResult_t);
 template std::string GetExternalErrorMsg<CUresult>(CUresult);
@@ -889,6 +896,17 @@ inline std::string build_nvidia_error_msg(cublasStatus_t stat) {
   return sout.str();
 }
 
+/*************** CUSPARSE ERROR ***************/
+inline bool is_error(cusparseStatus_t stat) {
+  return stat != CUSPARSE_STATUS_SUCCESS;
+}
+
+inline std::string build_nvidia_error_msg(cusparseStatus_t stat) {
+  std::ostringstream sout;
+  sout << "CUSparse error(" << stat << "). " << GetExternalErrorMsg(stat);
+  return sout.str();
+}
+
 /*************** CUSOLVER ERROR ***************/
 inline bool is_error(cusolverStatus_t stat) {
   return stat != CUSOLVER_STATUS_SUCCESS;

paddle/fluid/platform/external_error.proto

@@ -26,6 +26,7 @@ enum ApiType {
   NCCL = 5;
   CUFFT = 6;
   CU = 7;
+  CUSPARSE = 8;
 }
 
 message MessageDesc {
@@ -45,4 +46,4 @@ message AllMessageDesc {
 message ExternalErrorDesc {
   // Error messages of different kind of external third party API
   repeated AllMessageDesc errors = 1;
-}
+}
\ No newline at end of file