[Sparse] Add a batch_norm kernel (#46359)

paddle/fluid/operators/sparse_manual_op.cc

@@ -20,6 +20,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/phi/core/infermeta_utils.h"
 #include "paddle/phi/infermeta/binary.h"
+#include "paddle/phi/infermeta/multiary.h"
 #include "paddle/phi/infermeta/sparse/binary.h"
 #include "paddle/phi/infermeta/sparse/unary.h"
 #include "paddle/phi/infermeta/unary.h"
@@ -185,6 +186,47 @@ DECLARE_INFER_SHAPE_FUNCTOR(sparse_add,
                             SparseAddInferShapeFunctor,
                             PD_INFER_META(phi::UnchangedInferMeta));
 
+class SparseBatchNormOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("x", "(Tensor), input 0 of sparse_batch_norm op.");
+    AddInput("scale", "(Tensor), input 1 of sparse_batch_norm op.");
+    AddInput("bias", "(Tensor), input 2 of sparse_batch_norm op.");
+    AddInput("mean", "(Tensor), input 3 of sparse_batch_norm op.");
+    AddInput("variance", "(Tensor), input 4 of sparse_batch_norm op.");
+    AddOutput("y", "(Tensor), output 0 of sparse_batch_norm op.");
+    AddOutput("mean_out", "(Tensor), output 1 of sparse_batch_norm op.");
+    AddOutput("variance_out", "(Tensor), output 2 of sparse_batch_norm op.");
+    AddOutput("saved_mean", "(Tensor), output 3 of sparse_batch_norm op.");
+    AddOutput("saved_variance", "(Tensor), output 4 of sparse_batch_norm op.");
+    AddOutput("reserve_space", "(Tensor), output 5 of sparse_batch_norm op.");
+    AddAttr<float>("momentum",
+                   "(float), attribute 0 for sparse_batch_norm op.");
+    AddAttr<float>("epsilon", "(float), attribute 1 for sparse_batch_norm op.");
+    AddAttr<std::string>("data_layout",
+                         "(string), attribute 2 for sparse_batch_norm op.");
+    AddAttr<bool>("is_test", "(bool), attribute 3 for sparse_batch_norm op.");
+    AddAttr<bool>("use_global_stats",
+                  "(bool), attribute 4 for sparse_batch_norm op.");
+    AddAttr<bool>("trainable_statistics",
+                  "(bool), attribute 4 for sparse_batch_norm op.");
+    AddAttr<bool>("fuse_with_relu",
+                  "(bool), attribute 4 for sparse_batch_norm op.");
+    AddComment(R"DOC(
+TODO: Documentation of sparse_conv3d op.
+)DOC");
+  }
+};
+
+class SparseBatchNormOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+};
+
+DECLARE_INFER_SHAPE_FUNCTOR(sparse_batch_norm,
+                            SparseBatchNormInferShapeFunctor,
+                            PD_INFER_META(phi::BatchNormInferMeta));
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -224,3 +266,8 @@ REGISTER_OPERATOR(sparse_add,
                   ops::SparseAddOp,
                   ops::SparseAddOpMaker,
                   ops::SparseAddInferShapeFunctor);
+
+REGISTER_OPERATOR(sparse_batch_norm,
+                  ops::SparseBatchNormOp,
+                  ops::SparseBatchNormOpMaker,
+                  ops::SparseBatchNormInferShapeFunctor);

paddle/phi/api/yaml/sparse_backward.yaml

@@ -100,6 +100,18 @@
     func : atanh_coo_grad {sparse_coo, sparse_coo -> sparse_coo},
            atanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
 
+- backward_op : batch_norm_grad
+  forward : batch_norm (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu) -> Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
+  args : (Tensor x, Tensor scale, Tensor bias, Tensor mean_out, Tensor variance_out, Tensor saved_mean, Tensor saved_variance, Tensor reserve_space, Tensor out_grad, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
+  output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
+  infer_meta :
+    func : GeneralTernaryGradInferMeta
+    param : [x, scale, bias]
+  kernel :
+    func : batch_norm_coo_grad {sparse_coo, dense, dense, dense, dense, dense, dense, dense, sparse_coo -> sparse_coo, dense, dense}
+    data_type : out_grad
+  optional : mean_out, variance_out, reserve_space
+
 - backward_op : cast_grad
   forward : cast(Tensor x, DataType index_dtype, DataType value_dtype) -> Tensor(out)
   args : (Tensor x, Tensor out_grad, DataType value_dtype)

paddle/phi/api/yaml/sparse_ops.yaml

@@ -87,6 +87,16 @@
     layout : x
   backward : atanh_grad
 
+- op : batch_norm
+  args : (Tensor x, Tensor scale, Tensor bias, Tensor mean, Tensor variance, float momentum, float epsilon, str data_layout, bool is_test, bool use_global_stats, bool trainable_statistics, bool fuse_with_relu)
+  output : Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
+  infer_meta :
+    func : BatchNormInferMeta
+  kernel :
+    func : batch_norm_coo {sparse_coo, dense, dense, dense, dense -> sparse_coo, dense, dense, dense, dense, dense}
+    data_type : x
+  backward : batch_norm_grad
+
 - op : cast
   args : (Tensor x, DataType index_dtype=DataType::UNDEFINED, DataType value_dtype=DataType::UNDEFINED)
   output : Tensor(out)

paddle/phi/infermeta/multiary.cc

@@ -638,6 +638,7 @@ void BatchNormInferMeta(const MetaTensor& x,
     saved_variance->set_dims({C});
   }
   y->share_lod(x);
+  y->set_dtype(x.dtype());
 }
 
 void BatchNormInferInferMeta(const MetaTensor& x,

paddle/phi/kernels/sparse/batch_norm_grad_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/batch_norm_grad_kernel.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/batch_norm_grad_kernel.h"
+#include "paddle/phi/kernels/empty_kernel.h"
+#include "paddle/phi/kernels/sparse/empty_kernel.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename T, typename Context>
+void BatchNormCooGradKernel(const Context& dev_ctx,
+                            const SparseCooTensor& x,
+                            const DenseTensor& scale,
+                            const DenseTensor& bias,
+                            const paddle::optional<DenseTensor>& mean,
+                            const paddle::optional<DenseTensor>& variance,
+                            const DenseTensor& saved_mean,
+                            const DenseTensor& saved_variance,
+                            const paddle::optional<DenseTensor>& reserve_space,
+                            const SparseCooTensor& y_grad,
+                            float momentum,
+                            float epsilon,
+                            const std::string& data_layout,
+                            bool is_test,
+                            bool use_global_stats,
+                            bool trainable_statistics,
+                            bool fuse_with_relu,
+                            SparseCooTensor* x_grad,
+                            DenseTensor* scale_grad,
+                            DenseTensor* bias_grad) {
+  EmptyLikeCooKernel<T, Context>(dev_ctx, x, x_grad);
+  *scale_grad = phi::EmptyLike<T, Context>(dev_ctx, scale);
+  *bias_grad = phi::EmptyLike<T, Context>(dev_ctx, bias);
+  phi::BatchNormGradKernel<T, Context>(dev_ctx,
+                                       x.values(),
+                                       scale,
+                                       bias,
+                                       mean,
+                                       variance,
+                                       saved_mean,
+                                       saved_variance,
+                                       reserve_space,
+                                       y_grad.values(),
+                                       momentum,
+                                       epsilon,
+                                       data_layout,
+                                       is_test,
+                                       use_global_stats,
+                                       trainable_statistics,
+                                       fuse_with_relu,
+                                       x_grad->mutable_values(),
+                                       scale_grad,
+                                       bias_grad);
+}
+
+}  // namespace sparse
+}  // namespace phi
+
+PD_REGISTER_KERNEL(batch_norm_coo_grad,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::sparse::BatchNormCooGradKernel,
+                   float,
+                   double) {
+  kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
+}
+
+#if defined(PADDLE_WITH_HIP)
+PD_REGISTER_KERNEL(batch_norm_coo_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::sparse::BatchNormCooGradKernel,
+                   float,
+                   phi::dtype::float16) {
+  kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
+}
+#endif
+
+#if defined(PADDLE_WITH_CUDA)
+PD_REGISTER_KERNEL(batch_norm_coo_grad,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::sparse::BatchNormCooGradKernel,
+                   float,
+                   double,
+                   phi::dtype::float16) {
+  kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
+  if (kernel_key.dtype() == phi::DataType::FLOAT16) {
+    kernel->OutputAt(0).SetDataType(phi::DataType::FLOAT32);  // x_grad
+    kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);  // scale_grad
+    kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);  // bias_grad
+  }
+}
+#endif

paddle/phi/kernels/sparse/batch_norm_grad_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 <string>
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename T, typename Context>
+void BatchNormCooGradKernel(const Context& dev_ctx,
+                            const SparseCooTensor& x,
+                            const DenseTensor& scale,
+                            const DenseTensor& bias,
+                            const paddle::optional<DenseTensor>& mean,
+                            const paddle::optional<DenseTensor>& variance,
+                            const DenseTensor& saved_mean,
+                            const DenseTensor& saved_variance,
+                            const paddle::optional<DenseTensor>& reserve_space,
+                            const SparseCooTensor& y_grad,
+                            float momentum,
+                            float epsilon,
+                            const std::string& data_layout,
+                            bool is_test,
+                            bool use_global_stats,
+                            bool trainable_statistics,
+                            bool fuse_with_relu,
+                            SparseCooTensor* x_grad,
+                            DenseTensor* scale_grad,
+                            DenseTensor* bias_grad);
+
+}  // namespace sparse
+}  // namespace phi

paddle/phi/kernels/sparse/batch_norm_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/batch_norm_kernel.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/batch_norm_kernel.h"
+#include "paddle/phi/kernels/sparse/empty_kernel.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename T, typename Context>
+void BatchNormCooKernel(const Context& dev_ctx,
+                        const SparseCooTensor& x,
+                        const DenseTensor& scale,
+                        const DenseTensor& bias,
+                        const DenseTensor& mean,
+                        const DenseTensor& variance,
+                        float momentum,
+                        float epsilon,
+                        const std::string& data_layout,
+                        bool is_test,
+                        bool use_global_stats,
+                        bool trainable_statistics,
+                        bool fuse_with_relu,
+                        SparseCooTensor* y,
+                        DenseTensor* mean_out,
+                        DenseTensor* variance_out,
+                        DenseTensor* saved_mean,
+                        DenseTensor* saved_variance,
+                        DenseTensor* reserve_space) {
+  EmptyLikeCooKernel<T, Context>(dev_ctx, x, y);
+  phi::BatchNormKernel<T, Context>(dev_ctx,
+                                   x.values(),
+                                   scale,
+                                   bias,
+                                   mean,
+                                   variance,
+                                   momentum,
+                                   epsilon,
+                                   data_layout,
+                                   is_test,
+                                   use_global_stats,
+                                   trainable_statistics,
+                                   fuse_with_relu,
+                                   y->mutable_values(),
+                                   mean_out,
+                                   variance_out,
+                                   saved_mean,
+                                   saved_variance,
+                                   reserve_space);
+  y->SetIndicesDict(x.GetIndicesDict());
+}
+
+}  // namespace sparse
+}  // namespace phi
+
+PD_REGISTER_KERNEL(batch_norm_coo,
+                   CPU,
+                   ALL_LAYOUT,
+                   phi::sparse::BatchNormCooKernel,
+                   float,
+                   double) {
+  kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
+}
+
+#if defined(PADDLE_WITH_HIP)
+PD_REGISTER_KERNEL(batch_norm_coo,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::sparse::BatchNormCooKernel,
+                   float,
+                   phi::dtype::float16) {
+  kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
+  kernel->InputAt(1).SetDataType(phi::DataType::FLOAT32);
+  kernel->InputAt(2).SetDataType(phi::DataType::FLOAT32);
+  kernel->InputAt(3).SetDataType(phi::DataType::FLOAT32);
+  kernel->InputAt(4).SetDataType(phi::DataType::FLOAT32);
+  kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
+  kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
+  kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32);
+  kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32);
+}
+#endif
+
+#if defined(PADDLE_WITH_CUDA)
+PD_REGISTER_KERNEL(batch_norm_coo,
+                   GPU,
+                   ALL_LAYOUT,
+                   phi::sparse::BatchNormCooKernel,
+                   float,
+                   double,
+                   phi::dtype::float16) {
+  kernel->InputAt(0).SetDataLayout(phi::DataLayout::SPARSE_COO);
+  if (kernel_key.dtype() == phi::DataType::FLOAT16) {
+    kernel->InputAt(1).SetDataType(phi::DataType::FLOAT32);
+    kernel->InputAt(2).SetDataType(phi::DataType::FLOAT32);
+    kernel->InputAt(3).SetDataType(phi::DataType::FLOAT32);
+    kernel->InputAt(4).SetDataType(phi::DataType::FLOAT32);
+    kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32);
+    kernel->OutputAt(2).SetDataType(phi::DataType::FLOAT32);
+    kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32);
+    kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32);
+  }
+}
+#endif

paddle/phi/kernels/sparse/batch_norm_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 <string>
+
+#include "paddle/phi/core/dense_tensor.h"
+#include "paddle/phi/core/sparse_coo_tensor.h"
+
+namespace phi {
+namespace sparse {
+
+template <typename T, typename Context>
+void BatchNormKernel(const Context& dev_ctx,
+                     const SparseCooTensor& x,
+                     const DenseTensor& scale,
+                     const DenseTensor& bias,
+                     const DenseTensor& mean,
+                     const DenseTensor& variance,
+                     float momentum,
+                     float epsilon,
+                     const std::string& data_layout,
+                     bool is_test,
+                     bool use_global_stats,
+                     bool trainable_statistics,
+                     bool fuse_with_relu,
+                     SparseCooTensor* y,
+                     DenseTensor* mean_out,
+                     DenseTensor* variance_out,
+                     DenseTensor* saved_mean,
+                     DenseTensor* saved_variance,
+                     DenseTensor* reserve_space);
+
+}  // namespace sparse
+}  // namespace phi

paddle/phi/ops/compat/sparse_manual_op_sig.cc

@@ -82,6 +82,29 @@ KernelSignature SparseAddOpArgumentMapping(const ArgumentMappingContext& ctx) {
   }
 }
 
+KernelSignature SparseBatchNormOpArgumentMapping(
+    const ArgumentMappingContext& ctx) {
+  if (ctx.IsSparseCooTensorInput("x")) {
+    return KernelSignature("batch_norm_coo",
+                           {"x", "scale", "bias", "mean", "variance"},
+                           {"momentum",
+                            "epsilon",
+                            "data_layout",
+                            "is_test",
+                            "use_global_stats",
+                            "trainable_statistics",
+                            "fuse_with_relu"},
+                           {"y",
+                            "mean_out",
+                            "variance_out",
+                            "saved_mean",
+                            "saved_variance",
+                            "reserve_space"});
+  } else {
+    return KernelSignature("unregistered", {}, {}, {});
+  }
+}
+
 }  // namespace phi
 
 PD_REGISTER_BASE_KERNEL_NAME(sparse_sparse_coo_tensor, sparse_coo_tensor);
@@ -106,3 +129,7 @@ PD_REGISTER_ARG_MAPPING_FN(sparse_conv3d, phi::SparseConv3dOpArgumentMapping);
 
 PD_REGISTER_BASE_KERNEL_NAME(sparse_add, add_coo_coo);
 PD_REGISTER_ARG_MAPPING_FN(sparse_add, phi::SparseAddOpArgumentMapping);
+
+PD_REGISTER_BASE_KERNEL_NAME(sparse_batch_norm, batch_norm_coo);
+PD_REGISTER_ARG_MAPPING_FN(sparse_batch_norm,
+                           phi::SparseBatchNormOpArgumentMapping);

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

@@ -16,6 +16,7 @@ import unittest
 import numpy as np
 import paddle
 from paddle.incubate.sparse import nn
+import paddle.incubate.sparse as sparse
 import paddle.fluid as fluid
 import copy
 
@@ -117,5 +118,49 @@ class TestSyncBatchNorm(unittest.TestCase):
                 self.assertEqual(isinstance(model[idx], nn.SyncBatchNorm), True)
 
 
+class TestStatic(unittest.TestCase):
+
+    def test(self):
+        paddle.enable_static()
+        indices = paddle.static.data(name='indices',
+                                     shape=[4, 4],
+                                     dtype='int32')
+        values = paddle.static.data(name='values',
+                                    shape=[4, 1],
+                                    dtype='float32')
+        channels = 1
+        dense_shape = [1, 1, 3, 4, channels]
+        sp_x = sparse.sparse_coo_tensor(indices, values, dense_shape)
+
+        sparse_batch_norm = paddle.incubate.sparse.nn.BatchNorm(channels)
+        sp_y = sparse_batch_norm(sp_x)
+        out = sp_y.to_dense()
+
+        exe = paddle.static.Executor()
+        indices_data = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 2], [1, 3, 2, 3]]
+        values_data = np.array([[1.0], [2.0], [3.0], [4.0]]).astype('float32')
+        bias_data = np.array([1.0]).astype('float32')
+        weight_data = np.array([2.0]).astype('float32')
+        mean_data = np.array([1.0]).astype('float32')
+        variance_data = np.array([2.0]).astype('float32')
+
+        fetch = exe.run(feed={
+            'indices': indices_data,
+            'values': values_data,
+            'batch_norm_0.b_0': bias_data,
+            'batch_norm_0.w_0': weight_data,
+            'batch_norm_0.w_1': mean_data,
+            'batch_norm_0.w_2': variance_data
+        },
+                        fetch_list=[out],
+                        return_numpy=True)
+        correct_out = np.array([[[[[0.0], [-1.6832708], [0.0], [0.1055764]],
+                                  [[0.0], [0.0], [1.8944236], [0.0]],
+                                  [[0.0], [0.0], [0.0],
+                                   [3.683271]]]]]).astype('float32')
+        np.testing.assert_allclose(correct_out, fetch[0], rtol=1e-5)
+        paddle.disable_static()
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/incubate/sparse/nn/layer/norm.py

@@ -12,23 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-#
-# 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.
-
 import paddle
 import warnings
 from paddle.nn.layer.norm import _BatchNormBase
 from paddle.framework import no_grad
+from paddle import _C_ops, in_dynamic_mode
+from paddle.fluid.layer_helper import LayerHelper
 
 
 class BatchNorm(paddle.nn.BatchNorm1D):
@@ -131,34 +120,70 @@ class BatchNorm(paddle.nn.BatchNorm1D):
             raise ValueError('sparse BatchNorm only support layout of "NDHWC"')
 
     def forward(self, input):
-        values = input.values()
         self._check_data_format(self._data_format)
 
-        if len(values.shape) != 2:
-            raise ValueError('expected 2D input.values() (got {}D)'.format(
-                len(values.shape)))
-
         if self.training:
             warnings.warn(
                 "When training, we now always track global mean and variance.")
 
-        batch_norm_out = paddle.nn.functional.batch_norm(
-            values,
-            self._mean,
-            self._variance,
-            weight=self.weight,
-            bias=self.bias,
-            training=self.training,
-            momentum=self._momentum,
-            epsilon=self._epsilon,
-            data_format='NC',
-            use_global_stats=self._use_global_stats)
-
-        return paddle.incubate.sparse.sparse_coo_tensor(
-            input.indices(),
-            batch_norm_out,
-            shape=input.shape,
-            stop_gradient=input.stop_gradient)
+        if self._use_global_stats == None:
+            self._use_global_stats = not self.training
+            trainable_statistics = False
+        else:
+            trainable_statistics = not self._use_global_stats
+
+        data_format = 'NCHW' if self._data_format[1] == 'C' else 'NHWC'
+
+        if in_dynamic_mode():
+            batch_norm_out, _, _, _, _, _ = _C_ops.sparse_batch_norm(
+                input, self.weight, self.bias, self._mean, self._variance,
+                self._momentum, self._epsilon, data_format, not self.training,
+                self._use_global_stats, trainable_statistics, False)
+            return batch_norm_out
+        else:
+            inputs = {
+                'x': input,
+                'scale': self.weight,
+                'bias': self.bias,
+                'mean': self._mean,
+                'variance': self._variance
+            }
+            attrs = {
+                'momentum': self._momentum,
+                'epsilon': self._epsilon,
+                'data_layout': data_format,
+                'is_test': not self.training,
+                'use_global_stats': self._use_global_stats,
+                'trainable_statistics': trainable_statistics,
+                'fuse_with_relu': False
+            }
+            op_type = 'sparse_batch_norm'
+            helper = LayerHelper(op_type)
+            dtype = input.dtype
+            mean_out = helper.create_variable_for_type_inference(
+                dtype=dtype, stop_gradient=True)
+            variance_out = helper.create_variable_for_type_inference(
+                dtype=dtype, stop_gradient=True)
+            saved_mean = helper.create_variable_for_type_inference(
+                dtype=dtype, stop_gradient=True)
+            saved_variance = helper.create_variable_for_type_inference(
+                dtype=dtype, stop_gradient=True)
+            reserve_space = helper.create_variable_for_type_inference(
+                dtype=dtype, stop_gradient=True)
+            y = helper.create_sparse_variable_for_type_inference(dtype)
+            outputs = {
+                "y": y,
+                "mean_out": mean_out,
+                "variance_out": variance_out,
+                "saved_mean": saved_mean,
+                "saved_variance": saved_variance,
+                "reserve_space": reserve_space
+            }
+            helper.append_op(type=op_type,
+                             inputs=inputs,
+                             outputs=outputs,
+                             attrs=attrs)
+            return y
 
 
 class SyncBatchNorm(paddle.nn.SyncBatchNorm):