[Prim][PIR] Recover pir bn

paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py

@@ -21,6 +21,7 @@
 decomp_interface_declare_gen_op_list = [
     "add_n",
     "batch_norm",
+    "batch_norm_",
     "dropout",
     "full_like",
     "gelu",

paddle/fluid/pir/dialect/op_generator/op_gen.py

@@ -1177,6 +1177,8 @@ def AutoCodeGen(op_info_items, all_op_info_items, namespaces, dialect_name):
         )
         op_interfaces_tmp = op_interfaces
         exclusive_interface_str_tmp = exclusive_interface_str
+        decomp_interface_str = "paddle::dialect::DecompInterface"
+        decomp_interface_declare_str = "\n  static std::vector<std::vector<pir::OpResult>> Decomp(pir::Operation* op);"
 
         # If op has inplace info, we will generate inplace op and non-inplace op.
         for op_name in op_info.op_phi_name:
@@ -1212,10 +1214,13 @@ def AutoCodeGen(op_info_items, all_op_info_items, namespaces, dialect_name):
                     and kernel_func_name in decomp_interface_declare_gen_op_list
                     and dialect_name != "onednn_op"
                 ):
-                    op_interfaces = op_interfaces + [
-                        "paddle::dialect::DecompInterface"
-                    ]
-                    exclusive_interface_str += "\n  static std::vector<std::vector<pir::OpResult>> Decomp(pir::Operation* op);"
+                    if decomp_interface_str not in op_interfaces:
+                        op_interfaces = op_interfaces + [decomp_interface_str]
+                    if (
+                        decomp_interface_declare_str
+                        not in exclusive_interface_str
+                    ):
+                        exclusive_interface_str += decomp_interface_declare_str
                 else:
                     op_interfaces = op_interfaces_tmp
                     exclusive_interface_str = exclusive_interface_str_tmp

paddle/fluid/pir/dialect/operator/ir/manual_op_decomp.cc

@@ -60,7 +60,7 @@ std::vector<std::vector<pir::OpResult>> BatchNormOp::Decomp(
   float epsilon =
       op->attribute("epsilon").dyn_cast<pir::FloatAttribute>().data();
   const std::string& data_layout =
-      op->attribute("data_layout").dyn_cast<pir::StrAttribute>().AsString();
+      op->attribute("data_format").dyn_cast<pir::StrAttribute>().AsString();
   bool use_global_stats =
       op->attribute("use_global_stats").dyn_cast<pir::BoolAttribute>().data();
   bool trainable_statistics = op->attribute("trainable_statistics")
@@ -117,5 +117,93 @@ std::vector<std::vector<pir::OpResult>> BatchNormOp::Decomp(
   return res;
 }
 
+std::vector<std::vector<pir::OpResult>> BatchNorm_Op::Decomp(
+    pir::Operation* op) {
+  VLOG(4) << "Decomp call batch_norm_'s decomp interface begin";
+  BatchNorm_Op op_obj = op->dyn_cast<BatchNorm_Op>();
+  (void)op_obj;
+
+  FLAGS_tensor_operants_mode = "static";
+
+  VLOG(6) << "Decomp Prepare inputs of batch_norm_";
+
+  Tensor x(std::make_shared<primitive::LazyTensor>(op_obj.x()));
+  Tensor mean(std::make_shared<primitive::LazyTensor>(op_obj.mean()));
+  Tensor variance(std::make_shared<primitive::LazyTensor>(op_obj.variance()));
+  paddle::optional<Tensor> scale;
+  if (!IsEmptyValue(op_obj.scale())) {
+    scale = paddle::make_optional<Tensor>(
+        Tensor(std::make_shared<primitive::LazyTensor>(op_obj.scale())));
+  }
+  paddle::optional<Tensor> bias;
+  if (!IsEmptyValue(op_obj.bias())) {
+    bias = paddle::make_optional<Tensor>(
+        Tensor(std::make_shared<primitive::LazyTensor>(op_obj.bias())));
+  }
+
+  VLOG(6) << "Decomp prepare attributes of batch_norm_";
+  bool is_test = op->attribute("is_test").dyn_cast<pir::BoolAttribute>().data();
+  float momentum =
+      op->attribute("momentum").dyn_cast<pir::FloatAttribute>().data();
+  float epsilon =
+      op->attribute("epsilon").dyn_cast<pir::FloatAttribute>().data();
+  const std::string& data_layout =
+      op->attribute("data_format").dyn_cast<pir::StrAttribute>().AsString();
+  bool use_global_stats =
+      op->attribute("use_global_stats").dyn_cast<pir::BoolAttribute>().data();
+  bool trainable_statistics = op->attribute("trainable_statistics")
+                                  .dyn_cast<pir::BoolAttribute>()
+                                  .data();
+
+  VLOG(6) << "Decomp call batch_norm_'s forward composite rule prepare";
+
+  auto org_res = op->results();
+  std::vector<std::vector<pir::OpResult>> res(org_res.size());
+
+  VLOG(6) << "Decomp call batch_norm_'s forward composite rule begin";
+
+  std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor> op_res =
+      paddle::primitive::details::batch_norm_decomp<primitive::LazyTensor>(
+          x,
+          mean,
+          variance,
+          scale,
+          bias,
+          is_test,
+          momentum,
+          epsilon,
+          data_layout,
+          use_global_stats,
+          trainable_statistics);
+
+  VLOG(6) << "Decomp call batch_norm_'s forward composite rule end";
+
+  res[0].push_back(std::static_pointer_cast<primitive::LazyTensor>(
+                       std::get<0>(op_res).impl())
+                       ->value()
+                       .dyn_cast<pir::OpResult>());
+  res[1].push_back(std::static_pointer_cast<primitive::LazyTensor>(
+                       std::get<1>(op_res).impl())
+                       ->value()
+                       .dyn_cast<pir::OpResult>());
+  res[2].push_back(std::static_pointer_cast<primitive::LazyTensor>(
+                       std::get<2>(op_res).impl())
+                       ->value()
+                       .dyn_cast<pir::OpResult>());
+  res[3].push_back(std::static_pointer_cast<primitive::LazyTensor>(
+                       std::get<3>(op_res).impl())
+                       ->value()
+                       .dyn_cast<pir::OpResult>());
+  res[4].push_back(std::static_pointer_cast<primitive::LazyTensor>(
+                       std::get<4>(op_res).impl())
+                       ->value()
+                       .dyn_cast<pir::OpResult>());
+  pir::OpResult reserve_space;
+  res[5].push_back(reserve_space);
+
+  VLOG(4) << "Decomp call batch_norm_'s decomp interface end";
+  return res;
+}
+
 }  // namespace dialect
 }  // namespace paddle

paddle/fluid/primitive/base/decomp_trans.cc

@@ -32,8 +32,11 @@ using Program = pir::Program;
 
 // some outputs like xshape will no longer used after decomp, and those outputs
 // will skip checking.
-std::unordered_set<std::string> decomp_op_contain_none = {
-    "pd_op.squeeze", "pd_op.unsqueeze", "pd_op.flatten", "pd_op.batch_norm"};
+std::unordered_set<std::string> decomp_op_contain_none = {"pd_op.squeeze",
+                                                          "pd_op.unsqueeze",
+                                                          "pd_op.flatten",
+                                                          "pd_op.batch_norm",
+                                                          "pd_op.batch_norm_"};
 
 static bool find_value(const std::vector<int64_t>& vec, int64_t value) {
   if (std::find(vec.begin(), vec.end(), value) != vec.end()) {
@@ -275,14 +278,15 @@ std::vector<std::vector<pir::OpResult>> call_decomp_rule(pir::Operation* op) {
 }
 
 void DecompProgram::decomp_program() {
-  std::ostringstream orig_prog_stream;
   std::unordered_map<pir::OpResult, int> orig_vars_dict;
   for (size_t i = 0; i < src_vars_.size(); i++) {
     orig_vars_dict[src_vars_[i]] = static_cast<int>(i);
   }
+  std::ostringstream orig_prog_stream;
   program_->Print(orig_prog_stream);
-  VLOG(4) << "[Prim] Origin program bofore decomp :\n"
+  VLOG(4) << "[Prim] Origin program before decomp :\n"
           << orig_prog_stream.str();
+
   if (!paddle::prim::PrimCommonUtils::IsFwdPrimEnabled()) {
     return;
   }

paddle/fluid/primitive/composite/composite.h

@@ -206,13 +206,13 @@ std::tuple<Tensor, Tensor, Tensor, Tensor, Tensor, Tensor> batch_norm_decomp(
     y = x_hat * reshape<T>(new_scale, stats_shape) +
         reshape<T>(new_bias, stats_shape);
   }
-  if (need_cast) {
-    y = cast<T>(y, org_dtype);
-  }
   Tensor reserve_space;
 
   auto batch_mean_ = assign<T>(batch_mean);
   auto inv_std_ = assign<T>(inv_std);
+  if (need_cast) {
+    y = cast<T>(y, org_dtype);
+  }
   if (!use_run_stat) {
     return std::make_tuple(
         y, run_mean_, run_var_, batch_mean_, inv_std_, reserve_space);

paddle/phi/infermeta/multiary.cc

@@ -727,14 +727,24 @@ void BatchNormInferMeta(const MetaTensor& x,
                           C,
                           bias.dims()[0]));
   }
+  auto dtype = x.dtype();
+  if (dtype == phi::DataType::FLOAT16 || dtype == phi::DataType::BFLOAT16 ||
+      dtype == phi::DataType::UINT16) {
+    dtype = phi::DataType::FLOAT32;
+  }
+
   y->set_dims(x_dims);
   mean_out->set_dims({C});
+  mean_out->set_dtype(mean.dtype());
   variance_out->set_dims({C});
+  variance_out->set_dtype(variance.dtype());
   if (saved_mean) {
     saved_mean->set_dims({C});
+    saved_mean->set_dtype(dtype);
   }
   if (saved_variance) {
     saved_variance->set_dims({C});
+    saved_variance->set_dtype(dtype);
   }
   if (reserve_space) {
     reserve_space->set_dims({-1});

test/legacy_test/test_batch_norm_op_prim_nchw.py

@@ -278,6 +278,7 @@ def initConfig(self):
         self.epsilon = 1e-05
         self.data_format = "NCHW"
         self.use_global_stats = None
+        self.check_prim_pir = True
 
 
 class TestBatchNormOpNCHWTestModeFp64(TestBatchNormOp):

test/legacy_test/test_batch_norm_op_prim_nhwc.py

@@ -124,6 +124,7 @@ def initConfig(self):
         self.epsilon = 1e-05
         self.data_format = "NHWC"
         self.use_global_stats = None
+        self.check_prim_pir = True
 
 
 class TestBatchNormOpNHWCFp16(TestBatchNormOp):
@@ -161,8 +162,6 @@ def initConfig(self):
         self.epsilon = 1e-05
         self.data_format = "NHWC"
         self.use_global_stats = None
-        # Todo(CZ): open this
-        self.check_prim_pir = False
 
 
 class TestBatchNormOpNHWCShape2(TestBatchNormOp):