[Auto Parallel] Add spmd rule No.4、13 for (batch_norm,sync_batch_norm) and their backward ops. (#72918)

* add unary ops which have spmd_rule but not add in yaml file.

* Add spmd_rule for batch_norm ops.

* Add spmd_rule for batch_norm and batch_norm_grad.

* fix bug

* fix bug.

* fix bug.

* add spmd_rule for sync_natch_norm

* add spmd_rule for sync_natch_norm

* fix bug.

* fix bug.

* Add partial status.

* fix ci bug.

* fix CI bug.

* apply review.

* fix bug.

Author: Glencsa

paddle/phi/infermeta/spmd_rules/batch_norm.cc

@@ -0,0 +1,57 @@
+/* Copyright (c) 2025 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/common/scalar.h"
+#include "paddle/phi/core/distributed/auto_parallel/dist_meta_tensor.h"
+#include "paddle/phi/core/distributed/type_defs.h"
+
+namespace phi {
+namespace distributed {
+SpmdInfo BatchNormInferSpmd(const DistMetaTensor& x,
+                            const DistMetaTensor& mean,
+                            const DistMetaTensor& variance,
+                            const DistMetaTensor& scale,
+                            const DistMetaTensor& bias,
+                            const bool is_test = false,
+                            const float momentum = 0.9,
+                            const float epsilon = 1e-05,
+                            const std::string& data_format = "NCHW",
+                            const bool use_global_stats = false,
+                            const bool trainable_statistics = false);
+SpmdInfo BatchNormInferSpmdStatic(const DistMetaTensor& x,
+                                  const DistMetaTensor& mean,
+                                  const DistMetaTensor& variance,
+                                  const DistMetaTensor& scale,
+                                  const DistMetaTensor& bias);
+
+SpmdInfo BatchNormGradInferSpmd(const DistMetaTensor& x,
+                                const DistMetaTensor& scale,
+                                const DistMetaTensor& bias,
+                                const DistMetaTensor& mean_out,
+                                const DistMetaTensor& variance_out,
+                                const DistMetaTensor& saved_mean,
+                                const DistMetaTensor& saved_variance,
+                                const DistMetaTensor& reserve_space,
+                                const DistMetaTensor& out_grad,
+                                const float momentum = 0.9,
+                                const float epsilon = 1e-05,
+                                const std::string& data_format = "NCHW",
+                                const bool is_test = false,
+                                const bool use_global_stats = false,
+                                const bool trainable_statistics = false);
+
+}  // namespace distributed
+}  // namespace phi

paddle/phi/infermeta/spmd_rules/batch_norm.h

@@ -66,7 +66,6 @@ PD_REGISTER_SPMD_RULE(
     fused_rotary_position_embedding,
     PD_INFER_SPMD(phi::distributed::FusedRopeInferSpmd),
     PD_INFER_SPMD(phi::distributed::FusedRopeInferSpmdReverse));
-
 // replicated rule /* for unittest */
 PD_REGISTER_SPMD_RULE(
     replicated,
@@ -525,6 +524,9 @@ PD_REGISTER_SPMD_RULE(
 PD_REGISTER_SPMD_RULE(mean_all,
                       PD_INFER_SPMD(phi::distributed::MeanAllInferSpmd),
                       PD_INFER_SPMD(phi::distributed::MeanAllGradInferSpmd));
+// batch_norm
+PD_REGISTER_SPMD_RULE(
+    batch_norm, PD_INFER_SPMD(phi::distributed::BatchNormInferSpmdStatic));
 
 // layer_norm
 PD_REGISTER_SPMD_RULE(

paddle/phi/infermeta/spmd_rules/rules.cc

@@ -19,6 +19,7 @@ limitations under the License. */
 #include "paddle/phi/infermeta/spmd_rules/argmax.h"
 #include "paddle/phi/infermeta/spmd_rules/argmin.h"
 #include "paddle/phi/infermeta/spmd_rules/argsort.h"
+#include "paddle/phi/infermeta/spmd_rules/batch_norm.h"
 #include "paddle/phi/infermeta/spmd_rules/c_embedding.h"
 #include "paddle/phi/infermeta/spmd_rules/c_softmax_with_cross_entropy.h"
 #include "paddle/phi/infermeta/spmd_rules/c_softmax_with_multi_label_cross_entropy.h"

paddle/phi/infermeta/spmd_rules/rules.h

@@ -76,6 +76,7 @@
   infer_meta :
     func : GeneralTernaryGradInferMeta
     param : [x, scale, bias]
+    spmd_rule : BatchNormGradInferSpmd
   kernel :
     func : batch_norm_grad
     data_type : out_grad

paddle/phi/ops/yaml/inconsistent/dygraph_backward.yaml

@@ -50,6 +50,7 @@
   output : Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
   infer_meta:
     func : BatchNormInferMeta
+    spmd_rule : BatchNormInferSpmd
   kernel :
     func : batch_norm
     data_type : x

paddle/phi/ops/yaml/inconsistent/dygraph_ops.yaml

@@ -88,6 +88,7 @@
   infer_meta :
     func : GeneralTernaryGradInferMeta
     param : [x, scale, bias]
+    spmd_rule : BatchNormGradInferSpmd
   kernel :
     func : batch_norm_grad
     data_type : out_grad

paddle/phi/ops/yaml/inconsistent/static_backward.yaml

@@ -71,6 +71,7 @@
   output : Tensor(out), Tensor(mean_out), Tensor(variance_out), Tensor(saved_mean), Tensor(saved_variance), Tensor(reserve_space)
   infer_meta:
     func : BatchNormInferMeta
+    spmd_rule : BatchNormInferSpmd
   kernel :
     func : batch_norm
     data_type : x

paddle/phi/ops/yaml/inconsistent/static_ops.yaml

@@ -2812,6 +2812,113 @@ TEST(MeanAll, Ctor) {
   check_dim_mapping(backward_info.first[1], {});
   check_dim_mapping(backward_info.second[0], {-1, -1});
 }
+TEST(BatchNorm, Ctor) {
+  std::vector<int64_t> mesh_shape = {2, 2};
+  std::vector<int64_t> process_ids = {0, 1, 2, 3};
+  std::vector<std::string> dim_names = {"x", "y"};
+  ProcessMesh process_mesh(mesh_shape, process_ids, dim_names);
+
+  // test forward
+  // data_format = NCHW
+  // [0, 1, -1, -1],[-1],[-1],[-1],[-1] ->[-1 , 1, -1, -1],[1],[1],[1],[1],[-1]
+  auto x_dist_attr = TensorDistAttr();
+  x_dist_attr.set_process_mesh(process_mesh);
+  x_dist_attr.set_dims_mapping({0, 1, -1, -1});
+  x_dist_attr.set_dynamic_dims({false, false, false, false});
+  auto one_dim_dist_attr = TensorDistAttr();
+  one_dim_dist_attr.set_process_mesh(process_mesh);
+  one_dim_dist_attr.set_dims_mapping({-1});
+  one_dim_dist_attr.set_dynamic_dims({false});
+
+  phi::distributed::DistMetaTensor x = phi::distributed::DistMetaTensor(
+      common::make_ddim({16, 16, 16, 16}), x_dist_attr);
+  phi::distributed::DistMetaTensor mean = phi::distributed::DistMetaTensor(
+      common::make_ddim({16}), one_dim_dist_attr);
+  phi::distributed::DistMetaTensor variance = phi::distributed::DistMetaTensor(
+      common::make_ddim({16}), one_dim_dist_attr);
+  phi::distributed::DistMetaTensor scale = phi::distributed::DistMetaTensor(
+      common::make_ddim({16}), one_dim_dist_attr);
+  phi::distributed::DistMetaTensor bias = phi::distributed::DistMetaTensor(
+      common::make_ddim({16}), one_dim_dist_attr);
+  phi::distributed::SpmdInfo forward_info =
+      phi::distributed::BatchNormInferSpmdStatic(
+          x, mean, variance, scale, bias);
+
+  EXPECT_EQ(forward_info.first.size(), 5UL);
+  EXPECT_EQ(forward_info.second.size(), 6UL);
+  check_dim_mapping(forward_info.first[0], {-1, 1, -1, -1});
+  check_dim_mapping(forward_info.first[1], {1});
+  check_dim_mapping(forward_info.first[2], {1});
+  check_dim_mapping(forward_info.first[3], {-1});
+  check_dim_mapping(forward_info.first[4], {-1});
+  check_dim_mapping(forward_info.second[0], {-1, 1, -1, -1});
+  check_dim_mapping(forward_info.second[1], {1});
+  check_dim_mapping(forward_info.second[2], {1});
+  check_dim_mapping(forward_info.second[3], {1});
+  check_dim_mapping(forward_info.second[4], {1});
+  check_dim_mapping(forward_info.second[5], {-1});
+
+  // test backward
+  // data_format = NCHW
+  // [0, 1, -1, -1],[-1],[-1],[-1],[-1],[-1],[-1],[-1],[0, 1, -1, -1]
+  // ->[-1,1,-1,-1],[-1],[-1]
+  // dst_input: [-1, 1, -1, -1],[-1],[-1],[1],[1],[1],[1],[-1],[-1, 1, -1, -1]
+
+  x = phi::distributed::DistMetaTensor(common::make_ddim({16, 16, 16, 16}),
+                                       x_dist_attr);
+  phi::distributed::DistMetaTensor out_grad = phi::distributed::DistMetaTensor(
+      common::make_ddim({16, 16, 16, 16}), x_dist_attr);
+  phi::distributed::DistMetaTensor mean_out = phi::distributed::DistMetaTensor(
+      common::make_ddim({16}), one_dim_dist_attr);
+  phi::distributed::DistMetaTensor variance_out =
+      phi::distributed::DistMetaTensor(common::make_ddim({16}),
+                                       one_dim_dist_attr);
+  scale = phi::distributed::DistMetaTensor(common::make_ddim({16}),
+                                           one_dim_dist_attr);
+  bias = phi::distributed::DistMetaTensor(common::make_ddim({16}),
+                                          one_dim_dist_attr);
+  phi::distributed::DistMetaTensor saved_mean =
+      phi::distributed::DistMetaTensor(common::make_ddim({16}),
+                                       one_dim_dist_attr);
+  phi::distributed::DistMetaTensor saved_variance =
+      phi::distributed::DistMetaTensor(common::make_ddim({16}),
+                                       one_dim_dist_attr);
+  phi::distributed::DistMetaTensor reserve_space =
+      phi::distributed::DistMetaTensor(common::make_ddim({16}),
+                                       one_dim_dist_attr);
+  phi::distributed::SpmdInfo backward_info =
+      phi::distributed::BatchNormGradInferSpmd(x,
+                                               scale,
+                                               bias,
+                                               mean_out,
+                                               variance_out,
+                                               saved_mean,
+                                               saved_variance,
+                                               reserve_space,
+                                               out_grad,
+                                               0.9,
+                                               0.1,
+                                               "NCHW",
+                                               false,
+                                               false,
+                                               false);
+
+  EXPECT_EQ(backward_info.first.size(), 9UL);
+  EXPECT_EQ(backward_info.second.size(), 3UL);
+  check_dim_mapping(backward_info.first[0], {-1, 1, -1, -1});
+  check_dim_mapping(backward_info.first[1], {-1});
+  check_dim_mapping(backward_info.first[2], {-1});
+  check_dim_mapping(backward_info.first[3], {1});
+  check_dim_mapping(backward_info.first[4], {1});
+  check_dim_mapping(backward_info.first[5], {1});
+  check_dim_mapping(backward_info.first[6], {1});
+  check_dim_mapping(backward_info.first[7], {-1});
+  check_dim_mapping(backward_info.first[8], {-1, 1, -1, -1});
+
+  check_dim_mapping(backward_info.second[0], {-1, 1, -1, -1});
+  check_dim_mapping(backward_info.second[1], {-1});
+  check_dim_mapping(backward_info.second[2], {-1});
+}
 TEST(Topk, Ctor) {
   std::vector<int64_t> mesh_shape = {2, 2};
   std::vector<int64_t> process_ids = {0, 1, 2, 3};