[NewIR] add_n and combine support selected rows

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

@@ -172,7 +172,7 @@ class {op_name} : public ir::Op<{op_name}{interfaces}{traits}> {{
     'bool': 'ir::BoolAttribute',
 }
 
-_NO_NEED_GEN_OPS = {'add_n', 'split_grad'}
+_NO_NEED_GEN_OPS = {'add_n', 'add_n_', 'add_n_with_kernel', 'split_grad'}
 
 
 def to_phi_and_fluid_op_name(op_item):

paddle/fluid/ir/dialect/paddle_dialect/ir/pd_dialect.cc

@@ -48,7 +48,10 @@ void PaddleDialect::initialize() {
 #define GET_OP_LIST
 #include "paddle/fluid/ir/dialect/paddle_dialect/ir/pd_op.h"  // NOLINT
       >();
-  RegisterOps<paddle::dialect::AddNOp, paddle::dialect::SplitGradOp>();
+  RegisterOps<paddle::dialect::AddNOp,
+              paddle::dialect::AddN_Op,
+              paddle::dialect::AddNWithKernelOp,
+              paddle::dialect::SplitGradOp>();
 
   RegisterInterfaces<ParameterConvertInterface>();
 }

paddle/fluid/ir/dialect/paddle_dialect/ir/pd_manual_op.cc

@@ -57,13 +57,18 @@ void AddNOp::Verify() {
             "The size %d of inputs must be equal to 1.", input_size));
     if (auto vec_type = (*this)->operand(0).type().dyn_cast<ir::VectorType>()) {
       for (size_t i = 0; i < vec_type.size(); ++i) {
-        PADDLE_ENFORCE(vec_type[i].isa<paddle::dialect::DenseTensorType>(),
+        PADDLE_ENFORCE(vec_type[i].isa<paddle::dialect::DenseTensorType>() ||
+                           vec_type[i].isa<paddle::dialect::SelectedRowsType>(),
                        phi::errors::PreconditionNotMet(
                            "Type validation failed for the 0th input."));
       }
     } else {
       PADDLE_ENFORCE(
-          (*this)->operand(0).type().isa<paddle::dialect::DenseTensorType>(),
+          (*this)->operand(0).type().isa<paddle::dialect::DenseTensorType>() ||
+              (*this)
+                  ->operand(0)
+                  .type()
+                  .isa<paddle::dialect::SelectedRowsType>(),
           phi::errors::PreconditionNotMet(
               "Type validation failed for the 0th input."));
     }
@@ -81,7 +86,8 @@ void AddNOp::Verify() {
         phi::errors::PreconditionNotMet(
             "The size %d of outputs must be equal to 1.", output_size));
     PADDLE_ENFORCE(
-        (*this)->result(0).type().isa<paddle::dialect::DenseTensorType>(),
+        (*this)->result(0).type().isa<paddle::dialect::DenseTensorType>() ||
+            (*this)->result(0).type().isa<paddle::dialect::SelectedRowsType>(),
         phi::errors::PreconditionNotMet(
             "Type validation failed for the 0th output."));
   }
@@ -146,6 +152,262 @@ void AddNOp::InferMeta(phi::InferMetaContext *infer_meta) {
   fn(infer_meta);
 }
 
+OpInfoTuple AddN_Op::GetOpInfo() {
+  std::vector<paddle::dialect::OpInputInfo> inputs = {
+      paddle::dialect::OpInputInfo(
+          "inputs",
+          "ir::VectorType<paddle::dialect::DenseTensorType>",
+          false,
+          false,
+          false)};
+  std::vector<paddle::dialect::OpAttributeInfo> attributes = {};
+  std::vector<paddle::dialect::OpOutputInfo> outputs = {
+      paddle::dialect::OpOutputInfo(
+          "out", "paddle::dialect::DenseTensorType", false, false)};
+  paddle::dialect::OpRunTimeInfo run_time_info = paddle::dialect::OpRunTimeInfo(
+      "AddNInferMeta", {"inputs"}, {"add_n"}, {"inputs"}, {}, {}, {}, {});
+  return std::make_tuple(inputs, attributes, outputs, run_time_info, "add_n_");
+}
+
+void AddN_Op::Build(ir::Builder &builder,
+                    ir::OperationArgument &argument,
+                    ir::OpResult inputs_) {
+  VLOG(4) << "Builder construction inputs";
+  std::vector<ir::OpResult> argument_inputs = {inputs_};
+  argument.AddOperands(argument_inputs.begin(), argument_inputs.end());
+
+  VLOG(4) << "Builder construction attributes";
+
+  VLOG(4) << "Builder construction outputs";
+  ir::VectorType inputs = inputs_.type().dyn_cast<ir::VectorType>();
+  (void)inputs;
+  std::vector<phi::DenseTensor> vec_dense_inputs;
+  for (size_t i = 0; i < static_cast<size_t>(inputs.size()); i++) {
+    vec_dense_inputs.push_back(phi::DenseTensor(
+        std::make_unique<paddle::experimental::DefaultAllocator>(
+            paddle::platform::CPUPlace())
+            .get(),
+        phi::DenseTensorMeta(
+            paddle::dialect::TransToPhiDataType(
+                inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().dtype()),
+            inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().dims(),
+            inputs[i]
+                .dyn_cast<paddle::dialect::DenseTensorType>()
+                .data_layout(),
+            inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().lod(),
+            inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().offset())));
+  }
+  std::vector<phi::MetaTensor> vec_meta_inputs;
+  for (size_t i = 0; i < vec_dense_inputs.size(); i++) {
+    vec_meta_inputs.push_back(phi::MetaTensor(&vec_dense_inputs[i]));
+  }
+
+  std::vector<const phi::MetaTensor *> meta_inputs;
+  for (size_t i = 0; i < static_cast<size_t>(vec_meta_inputs.size()); i++) {
+    meta_inputs.push_back(&vec_meta_inputs[i]);
+  }
+  phi::DenseTensor dense_out;
+  phi::MetaTensor meta_out(&dense_out);
+
+  phi::AddNInferMeta(meta_inputs, &meta_out);
+
+  std::vector<ir::Type> argument_outputs;
+  ir::Type out_dense_tensor_type = paddle::dialect::DenseTensorType::get(
+      ir::IrContext::Instance(),
+      paddle::dialect::TransToIrDataType(dense_out.dtype()),
+      dense_out.dims(),
+      dense_out.layout(),
+      dense_out.lod(),
+      dense_out.offset());
+  argument_outputs.push_back(out_dense_tensor_type);
+  argument.AddOutputs(argument_outputs.begin(), argument_outputs.end());
+}
+
+void AddN_Op::Verify() {
+  VLOG(4) << "Start Verifying inputs, outputs and attributes for: AddN_Op.";
+  VLOG(4) << "Verifying inputs:";
+  {
+    auto input_size = num_operands();
+    PADDLE_ENFORCE_EQ(
+        input_size,
+        1u,
+        phi::errors::PreconditionNotMet(
+            "The size %d of inputs must be equal to 1.", input_size));
+    if (auto vec_type =
+            (*this)->operand_source(0).type().dyn_cast<ir::VectorType>()) {
+      for (size_t i = 0; i < vec_type.size(); ++i) {
+        PADDLE_ENFORCE(vec_type[i].isa<paddle::dialect::DenseTensorType>() ||
+                           vec_type[i].isa<paddle::dialect::SelectedRowsType>(),
+                       phi::errors::PreconditionNotMet(
+                           "Type validation failed for the 0th input."));
+      }
+    } else {
+      PADDLE_ENFORCE((*this)->operand_source(0)
+                             .type()
+                             .isa<paddle::dialect::DenseTensorType>() ||
+                         (*this)
+                             ->operand_source(0)
+                             .type()
+                             .isa<paddle::dialect::SelectedRowsType>(),
+                     phi::errors::PreconditionNotMet(
+                         "Type validation failed for the 0th input."));
+    }
+  }
+  VLOG(4) << "Verifying attributes:";
+  {
+    // Attributes num is 0, not need to check attributes type.
+  }
+  VLOG(4) << "Verifying outputs:";
+  {
+    auto output_size = num_results();
+    PADDLE_ENFORCE_EQ(
+        output_size,
+        1u,
+        phi::errors::PreconditionNotMet(
+            "The size %d of outputs must be equal to 1.", output_size));
+    PADDLE_ENFORCE(
+        (*this)->result(0).type().isa<paddle::dialect::DenseTensorType>() ||
+            (*this)->result(0).type().isa<paddle::dialect::SelectedRowsType>(),
+        phi::errors::PreconditionNotMet(
+            "Type validation failed for the 0th output."));
+  }
+  VLOG(4) << "End Verifying for: AddN_Op.";
+}
+
+void AddN_Op::InferMeta(phi::InferMetaContext *infer_meta) {
+  auto fn = PD_INFER_META(phi::AddNInferMeta);
+  fn(infer_meta);
+}
+
+OpInfoTuple AddNWithKernelOp::GetOpInfo() {
+  std::vector<paddle::dialect::OpInputInfo> inputs = {
+      paddle::dialect::OpInputInfo(
+          "inputs",
+          "ir::VectorType<paddle::dialect::DenseTensorType>",
+          false,
+          false,
+          false)};
+  std::vector<paddle::dialect::OpAttributeInfo> attributes = {};
+  std::vector<paddle::dialect::OpOutputInfo> outputs = {
+      paddle::dialect::OpOutputInfo(
+          "out", "paddle::dialect::DenseTensorType", false, false)};
+  paddle::dialect::OpRunTimeInfo run_time_info = paddle::dialect::OpRunTimeInfo(
+      "AddNInferMeta", {"inputs"}, {"add_n"}, {"inputs"}, {}, {}, {}, {});
+  return std::make_tuple(
+      inputs, attributes, outputs, run_time_info, "add_n_with_kernel");
+}
+
+void AddNWithKernelOp::Build(ir::Builder &builder,
+                             ir::OperationArgument &argument,
+                             ir::OpResult inputs_) {
+  VLOG(4) << "Builder construction inputs";
+  std::vector<ir::OpResult> argument_inputs = {inputs_};
+  argument.AddOperands(argument_inputs.begin(), argument_inputs.end());
+
+  VLOG(4) << "Builder construction attributes";
+
+  VLOG(4) << "Builder construction outputs";
+  ir::VectorType inputs = inputs_.type().dyn_cast<ir::VectorType>();
+  (void)inputs;
+  std::vector<phi::DenseTensor> vec_dense_inputs;
+  for (size_t i = 0; i < static_cast<size_t>(inputs.size()); i++) {
+    vec_dense_inputs.push_back(phi::DenseTensor(
+        std::make_unique<paddle::experimental::DefaultAllocator>(
+            paddle::platform::CPUPlace())
+            .get(),
+        phi::DenseTensorMeta(
+            paddle::dialect::TransToPhiDataType(
+                inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().dtype()),
+            inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().dims(),
+            inputs[i]
+                .dyn_cast<paddle::dialect::DenseTensorType>()
+                .data_layout(),
+            inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().lod(),
+            inputs[i].dyn_cast<paddle::dialect::DenseTensorType>().offset())));
+  }
+  std::vector<phi::MetaTensor> vec_meta_inputs;
+  for (size_t i = 0; i < vec_dense_inputs.size(); i++) {
+    vec_meta_inputs.push_back(phi::MetaTensor(&vec_dense_inputs[i]));
+  }
+
+  std::vector<const phi::MetaTensor *> meta_inputs;
+  for (size_t i = 0; i < static_cast<size_t>(vec_meta_inputs.size()); i++) {
+    meta_inputs.push_back(&vec_meta_inputs[i]);
+  }
+  phi::DenseTensor dense_out;
+  phi::MetaTensor meta_out(&dense_out);
+
+  phi::AddNInferMeta(meta_inputs, &meta_out);
+
+  std::vector<ir::Type> argument_outputs;
+  ir::Type out_dense_tensor_type = paddle::dialect::DenseTensorType::get(
+      ir::IrContext::Instance(),
+      paddle::dialect::TransToIrDataType(dense_out.dtype()),
+      dense_out.dims(),
+      dense_out.layout(),
+      dense_out.lod(),
+      dense_out.offset());
+  argument_outputs.push_back(out_dense_tensor_type);
+  argument.AddOutputs(argument_outputs.begin(), argument_outputs.end());
+}
+
+void AddNWithKernelOp::Verify() {
+  VLOG(4) << "Start Verifying inputs, outputs and attributes for: "
+             "AddNWithKernelOp.";
+  VLOG(4) << "Verifying inputs:";
+  {
+    auto input_size = num_operands();
+    PADDLE_ENFORCE_EQ(
+        input_size,
+        1u,
+        phi::errors::PreconditionNotMet(
+            "The size %d of inputs must be equal to 1.", input_size));
+    if (auto vec_type =
+            (*this)->operand_source(0).type().dyn_cast<ir::VectorType>()) {
+      for (size_t i = 0; i < vec_type.size(); ++i) {
+        PADDLE_ENFORCE(vec_type[i].isa<paddle::dialect::DenseTensorType>() ||
+                           vec_type[i].isa<paddle::dialect::SelectedRowsType>(),
+                       phi::errors::PreconditionNotMet(
+                           "Type validation failed for the 0th input."));
+      }
+    } else {
+      PADDLE_ENFORCE((*this)->operand_source(0)
+                             .type()
+                             .isa<paddle::dialect::DenseTensorType>() ||
+                         (*this)
+                             ->operand_source(0)
+                             .type()
+                             .isa<paddle::dialect::SelectedRowsType>(),
+                     phi::errors::PreconditionNotMet(
+                         "Type validation failed for the 0th input."));
+    }
+  }
+  VLOG(4) << "Verifying attributes:";
+  {
+    // Attributes num is 0, not need to check attributes type.
+  }
+  VLOG(4) << "Verifying outputs:";
+  {
+    auto output_size = num_results();
+    PADDLE_ENFORCE_EQ(
+        output_size,
+        1u,
+        phi::errors::PreconditionNotMet(
+            "The size %d of outputs must be equal to 1.", output_size));
+    PADDLE_ENFORCE(
+        (*this)->result(0).type().isa<paddle::dialect::DenseTensorType>() ||
+            (*this)->result(0).type().isa<paddle::dialect::SelectedRowsType>(),
+        phi::errors::PreconditionNotMet(
+            "Type validation failed for the 0th output."));
+  }
+  VLOG(4) << "End Verifying for: AddNWithKernelOp.";
+}
+
+void AddNWithKernelOp::InferMeta(phi::InferMetaContext *infer_meta) {
+  auto fn = PD_INFER_META(phi::AddNInferMeta);
+  fn(infer_meta);
+}
+
 const char *SplitGradOp::attributes_name[1] = {"axis"};
 
 OpInfoTuple SplitGradOp::GetOpInfo() {
@@ -364,3 +626,5 @@ void SplitGradOp::InferMeta(phi::InferMetaContext *infer_meta) {
 
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::AddNOp)
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::SplitGradOp)
+IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::AddN_Op)
+IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::AddNWithKernelOp)

paddle/fluid/ir/dialect/paddle_dialect/ir/pd_manual_op.h

@@ -24,6 +24,7 @@ paddle::dialect::AddNOp, paddle::dialect::SplitGradOp
 #include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/ir/dialect/paddle_dialect/interface/infermeta.h"
 #include "paddle/fluid/ir/dialect/paddle_dialect/interface/op_yaml_info.h"
+#include "paddle/fluid/ir/dialect/paddle_dialect/trait/inplace.h"
 #include "paddle/fluid/ir/dialect/paddle_dialect/utils/op_yaml_info_util.h"
 #include "paddle/fluid/ir/dialect/paddle_dialect/utils/utils.h"
 #include "paddle/ir/core/builder.h"
@@ -51,6 +52,47 @@ class AddNOp : public ir::Op<AddNOp, OpYamlInfoInterface> {
   static void InferMeta(phi::InferMetaContext *infer_meta);
 };
 
+class AddN_Op : public ir::Op<AddN_Op,
+                              paddle::dialect::OpYamlInfoInterface,
+                              paddle::dialect::InferMetaInterface,
+                              paddle::dialect::InplaceTrait> {
+ public:
+  using Op::Op;
+  static const char *name() { return "pd.add_n_"; }
+  static constexpr const char **attributes_name = nullptr;
+  static constexpr uint32_t attributes_num = 0;
+  static OpInfoTuple GetOpInfo();
+  static void Build(ir::Builder &builder,             // NOLINT
+                    ir::OperationArgument &argument,  // NOLINT
+                    ir::OpResult inputs_);
+
+  void Verify();
+  ir::Value inputs() { return operand_source(0); }
+  ir::OpResult out() { return result(0); }
+
+  static void InferMeta(phi::InferMetaContext *infer_meta);
+};
+
+class AddNWithKernelOp : public ir::Op<AddNWithKernelOp,
+                                       paddle::dialect::OpYamlInfoInterface,
+                                       paddle::dialect::InferMetaInterface> {
+ public:
+  using Op::Op;
+  static const char *name() { return "pd.add_n_with_kernel"; }
+  static constexpr const char **attributes_name = nullptr;
+  static constexpr uint32_t attributes_num = 0;
+  static OpInfoTuple GetOpInfo();
+  static void Build(ir::Builder &builder,             // NOLINT
+                    ir::OperationArgument &argument,  // NOLINT
+                    ir::OpResult inputs_);
+
+  void Verify();
+  ir::Value inputs() { return operand_source(0); }
+  ir::OpResult out() { return result(0); }
+
+  static void InferMeta(phi::InferMetaContext *infer_meta);
+};
+
 class SplitGradOp : public ir::Op<SplitGradOp, OpYamlInfoInterface> {
  public:
   using Op::Op;
@@ -79,5 +121,7 @@ class SplitGradOp : public ir::Op<SplitGradOp, OpYamlInfoInterface> {
 
 IR_DECLARE_EXPLICIT_TYPE_ID(paddle::dialect::AddNOp)
 IR_DECLARE_EXPLICIT_TYPE_ID(paddle::dialect::SplitGradOp)
+IR_DECLARE_EXPLICIT_TYPE_ID(paddle::dialect::AddN_Op)
+IR_DECLARE_EXPLICIT_TYPE_ID(paddle::dialect::AddNWithKernelOp)
 
 #endif