[Sparse]BatchNorm use inplace (#48254)

paddle/fluid/operators/generator/generate_sparse_op.py

@@ -82,6 +82,8 @@ def main(op_yaml_path, backward_yaml_path, output_op_path, output_arg_map_path):
     backward_op_dict = to_named_dict(backward_ops)
 
     for op in ops:
+        if op['name'][-1] == '_':
+            op['name'] = op['name'][:-1]
         op['op_name'] = SPARSE_OP_PREFIX + op['name']
         op['name'] = op['op_name']
         if op["backward"] is not None:

paddle/phi/api/yaml/sparse_backward.yaml

@@ -101,7 +101,7 @@
            atanh_csr_grad {sparse_csr, sparse_csr -> sparse_csr}
 
 - backward_op : batch_norm_grad
-  forward : batch_norm (Tensor x, Tensor mean, Tensor variance, Tensor scale, Tensor bias, bool is_test, float momentum, float epsilon, str data_layout, bool use_global_stats, bool trainable_statistics) -> Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
+  forward : batch_norm_ (Tensor x, Tensor mean, Tensor variance, Tensor scale, Tensor bias, bool is_test, float momentum, float epsilon, str data_layout, bool use_global_stats, bool trainable_statistics) -> 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)
   output : Tensor(x_grad), Tensor(scale_grad), Tensor(bias_grad)
   infer_meta :

paddle/phi/api/yaml/sparse_ops.yaml

@@ -87,7 +87,7 @@
     layout : x
   backward : atanh_grad
 
-- op : batch_norm
+- op : batch_norm_
   args : (Tensor x, Tensor mean, Tensor variance, Tensor scale, Tensor bias, bool is_test, float momentum, float epsilon, str data_layout, bool use_global_stats, bool trainable_statistics)
   output : Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
   infer_meta :
@@ -95,7 +95,7 @@
   kernel :
     func : batch_norm_coo {sparse_coo, dense, dense, dense, dense -> sparse_coo, dense, dense, dense, dense, dense}
     data_type : x
-  view : (mean -> mean_out), (variance -> variance_out)
+  inplace : (mean -> mean_out), (variance -> variance_out)
   backward : batch_norm_grad
 
 - op : cast

paddle/phi/kernels/sparse/batch_norm_kernel.h

@@ -23,16 +23,16 @@ namespace phi {
 namespace sparse {
 
 template <typename T, typename Context>
-void BatchNormKernel(const Context& dev_ctx,
+void BatchNormCooKernel(const Context& dev_ctx,
                         const SparseCooTensor& x,
-                     const DenseTensor& scale,
-                     const DenseTensor& bias,
                         const DenseTensor& mean,
                         const DenseTensor& variance,
+                        const DenseTensor& scale,
+                        const DenseTensor& bias,
+                        bool is_test,
                         float momentum,
                         float epsilon,
                         const std::string& data_layout,
-                     bool is_test,
                         bool use_global_stats,
                         bool trainable_statistics,
                         SparseCooTensor* y,

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

@@ -138,7 +138,7 @@ class BatchNorm(paddle.nn.BatchNorm1D):
         data_format = 'NCHW' if self._data_format[1] == 'C' else 'NHWC'
 
         if in_dynamic_mode():
-            batch_norm_out, _, _, _, _, _ = _C_ops.sparse_batch_norm(
+            batch_norm_out, _, _, _, _, _ = _C_ops.sparse_batch_norm_(
                 input,
                 self._mean,
                 self._variance,