PaddlePaddle
diff --git a/‎paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.h
Copy file name to clipboard
+5-1Lines changed: 5 additions & 1 deletion b/‎paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.h
Copy file name to clipboard
+5-1Lines changed: 5 additions & 1 deletion
diff --git a/‎paddle/fluid/pir/dialect/operator/ir/manual_api.cc
Copy file name to clipboard
+17Lines changed: 17 additions & 0 deletions b/‎paddle/fluid/pir/dialect/operator/ir/manual_api.cc
Copy file name to clipboard
+17Lines changed: 17 additions & 0 deletions
diff --git a/‎paddle/fluid/pir/dialect/operator/ir/manual_api.h
Copy file name to clipboard
+4Lines changed: 4 additions & 0 deletions b/‎paddle/fluid/pir/dialect/operator/ir/manual_api.h
Copy file name to clipboard
+4Lines changed: 4 additions & 0 deletions
diff --git a/‎paddle/fluid/pir/dialect/operator/ir/manual_op.cc
Copy file name to clipboard
+249-6Lines changed: 249 additions & 6 deletions b/‎paddle/fluid/pir/dialect/operator/ir/manual_op.cc
Copy file name to clipboard
+249-6Lines changed: 249 additions & 6 deletions
@@ -213,9 +213,13 @@ void BuildPhiContext(pir::Operation* op,
         } else if (variable_array[i]->IsType<phi::SelectedRows>()) {
           inputs.emplace_back(InType(const_cast<phi::SelectedRows*>(
               &(variable_array[i]->Get<phi::SelectedRows>()))));
+        } else if (variable_array[i]->IsType<phi::TensorArray>()) {
+          inputs.emplace_back(InType(const_cast<phi::TensorArray*>(
+              &(variable_array[i]->Get<phi::TensorArray>()))));
         } else {
           PADDLE_THROW(phi::errors::Unimplemented(
-              "Only support Vector<DenseTensor> and vector<SelectedRows> now, "
+              "Only support Vector<DenseTensor> and vector<SelectedRows> "
+              "and vector<TensorArray> now "
               "not support vector<%d>.",
               variable_array[i]->Type()));
         }
 
@@ -133,6 +133,14 @@ pir::OpResult create_array(phi::DataType dtype) {
   return create_array_op.out();
 }
 
+pir::OpResult create_array_like(pir::Value input, float value) {
+  auto create_array_like_op =
+      ApiBuilder::Instance()
+          .GetBuilder()
+          ->Build<paddle::dialect::CreateArrayLikeOp>(input, value);
+  return create_array_like_op.out();
+}
+
 pir::OpResult array_length(pir::Value x) {
   auto array_length_op = ApiBuilder::Instance()
                              .GetBuilder()
@@ -165,6 +173,15 @@ std::tuple<pir::OpResult, pir::OpResult> array_to_tensor(pir::Value x,
   return std::make_tuple(array_to_tensor.result(0), array_to_tensor.result(1));
 }
 
+pir::OpResult add_n_array(const std::vector<pir::Value>& inputs) {
+  auto inputs_combine_op =
+      ApiBuilder::Instance().GetBuilder()->Build<pir::CombineOp>(inputs);
+  paddle::dialect::AddNArrayOp add_n_array_op =
+      ApiBuilder::Instance().GetBuilder()->Build<paddle::dialect::AddNArrayOp>(
+          inputs_combine_op.out());
+  return add_n_array_op.result(0);
+}
+
 pir::OpResult slice_array_dense(pir::Value input, pir::Value starts) {
   auto op = ApiBuilder::Instance()
                 .GetBuilder()
 
@@ -62,6 +62,8 @@ pir::OpResult zeros(const std::vector<int64_t>& shape,
 
 pir::OpResult create_array(phi::DataType dtype);
 
+pir::OpResult create_array_like(pir::Value input, float value);
+
 pir::OpResult array_length(pir::Value x);
 
 pir::OpResult array_read(pir::Value array, pir::Value i);
@@ -72,6 +74,8 @@ std::tuple<pir::OpResult, pir::OpResult> array_to_tensor(pir::Value x,
                                                          int axis,
                                                          bool use_stack);
 
+pir::OpResult add_n_array(const std::vector<pir::Value>& inputs);
+
 pir::OpResult slice_array_dense(pir::Value input, pir::Value starts);
 
 }  // namespace dialect
 
@@ -14,14 +14,16 @@
 #ifdef GET_OP_LIST
 #undef GET_OP_LIST
 paddle::dialect::AddNOp, paddle::dialect::AddN_Op,
-    paddle::dialect::AddNWithKernelOp, paddle::dialect::FusedGemmEpilogueOp,
+    paddle::dialect::AddNWithKernelOp, paddle::dialect::AddNArrayOp,
+    paddle::dialect::FusedGemmEpilogueOp,
     paddle::dialect::FusedGemmEpilogueGradOp, paddle::dialect::SplitGradOp,
     paddle::dialect::ExpandOp, paddle::dialect::CreateArrayOp,
-    paddle::dialect::ArrayLengthOp, paddle::dialect::ArrayReadOp,
-    paddle::dialect::ArrayWrite_Op, paddle::dialect::SliceArrayOp,
-    paddle::dialect::SliceArrayDenseOp, paddle::dialect::AssignArray_Op,
-    paddle::dialect::ArrayToTensorOp, paddle::dialect::SelectInputOp,
-    paddle::dialect::IncrementOp, paddle::dialect::Increment_Op
+    paddle::dialect::CreateArrayLikeOp, paddle::dialect::ArrayLengthOp,
+    paddle::dialect::ArrayReadOp, paddle::dialect::ArrayWrite_Op,
+    paddle::dialect::SliceArrayOp, paddle::dialect::SliceArrayDenseOp,
+    paddle::dialect::AssignArray_Op, paddle::dialect::ArrayToTensorOp,
+    paddle::dialect::SelectInputOp, paddle::dialect::IncrementOp,
+    paddle::dialect::Increment_Op
 #else
 
 #include "paddle/fluid/pir/dialect/operator/ir/manual_op.h"
@@ -421,6 +423,136 @@ void AddNWithKernelOp::InferMeta(phi::InferMetaContext *infer_meta) {
   fn(infer_meta);
 }
 
+OpInfoTuple AddNArrayOp::GetOpInfo() {
+  std::vector<paddle::dialect::OpInputInfo> inputs = {
+      OpInputInfo("inputs",
+                  "pir::VectorType<paddle::dialect::DenseTensorArrayType>",
+                  false,
+                  false,
+                  false,
+                  true)};
+  std::vector<paddle::dialect::OpAttributeInfo> attributes = {};
+  std::vector<paddle::dialect::OpOutputInfo> outputs = {OpOutputInfo(
+      "out", "paddle::dialect::DenseTensorArrayType", false, false)};
+  paddle::dialect::OpRunTimeInfo run_time_info =
+      OpRunTimeInfo("AddNTensorArrayInferMeta",
+                    {"inputs"},
+                    "add_n_array",
+                    {"inputs"},
+                    {},
+                    {},
+                    {},
+                    {});
+
+  return std::make_tuple(
+      inputs, attributes, outputs, run_time_info, "add_n_array");
+}
+
+void AddNArrayOp::VerifySig() {
+  VLOG(4) << "Start Verifying inputs, outputs and attributes for: AddNArrayOp.";
+  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(0).type().dyn_cast<pir::VectorType>()) {
+      for (size_t i = 0; i < vec_type.size(); ++i) {
+        PADDLE_ENFORCE(vec_type[i].isa<paddle::dialect::DenseTensorArrayType>(),
+                       phi::errors::PreconditionNotMet(
+                           "Type validation failed for the 0th input."));
+      }
+    } else {
+      PADDLE_ENFORCE((*this)
+                         ->operand(0)
+                         .type()
+                         .isa<paddle::dialect::DenseTensorArrayType>(),
+                     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::DenseTensorArrayType>(),
+        phi::errors::PreconditionNotMet(
+            "Type validation failed for the 0th output."));
+  }
+  VLOG(4) << "End Verifying for: AddNArrayOp.";
+}
+
+void AddNArrayOp::Build(pir::Builder &builder,             // NOLINT
+                        pir::OperationArgument &argument,  // NOLINT
+                        pir::Value inputs_) {
+  VLOG(4) << "Start build AddNArrayOp";
+
+  VLOG(4) << "Builder construction inputs";
+  argument.AddInput(inputs_);
+
+  VLOG(4) << "Builder construction attributes";
+
+  VLOG(4) << "Builder construction outputs";
+  pir::VectorType inputs = inputs_.type().dyn_cast<pir::VectorType>();
+
+  std::vector<paddle::dialect::IrTensor> vec_dense_inputs;
+  for (size_t i = 0; i < inputs.size(); i++) {
+    vec_dense_inputs.push_back(paddle::dialect::IrTensor(
+        TransToPhiDataType(
+            inputs[i]
+                .dyn_cast<paddle::dialect::DenseTensorArrayType>()
+                .dtype()),
+        {},
+        inputs[i]
+            .dyn_cast<paddle::dialect::DenseTensorArrayType>()
+            .data_layout(),
+        {}));
+  }
+
+  std::vector<paddle::dialect::IrMetaTensor> vec_meta_inputs;
+  for (size_t i = 0; i < vec_dense_inputs.size(); i++) {
+    vec_meta_inputs.push_back(
+        paddle::dialect::IrMetaTensor(&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]);
+  }
+
+  paddle::dialect::IrTensor dense_out;
+  paddle::dialect::IrMetaTensor meta_out(&dense_out);
+
+  phi::AddNTensorArrayInferMeta(
+      meta_inputs, &meta_out, phi::MetaConfig(false, false));
+  std::vector<pir::Type> argument_outputs;
+  pir::Type out_dense_tensor_type = paddle::dialect::DenseTensorArrayType::get(
+      pir::IrContext::Instance(),
+      TransToIrDataType(dense_out.dtype()),
+      dense_out.layout());
+
+  argument_outputs.push_back(out_dense_tensor_type);
+  argument.AddOutputs(argument_outputs.begin(), argument_outputs.end());
+  ::pir::PassStopGradientsDefaultly(argument);
+}
+
+void AddNArrayOp::InferMeta(phi::InferMetaContext *infer_meta) {
+  auto fn = PD_INFER_META(phi::AddNTensorArrayInferMeta);
+  fn(infer_meta);
+}
+
 const char *FusedGemmEpilogueOp::attributes_name[3] = {
     "trans_x", "trans_y", "activation"};
 
@@ -1156,6 +1288,114 @@ void CreateArrayOp::InferMeta(phi::InferMetaContext *infer_meta) {
   fn(infer_meta);
 }
 
+const char *CreateArrayLikeOp::attributes_name[1] = {"val"};
+
+OpInfoTuple CreateArrayLikeOp::GetOpInfo() {
+  std::vector<paddle::dialect::OpInputInfo> inputs = {
+      paddle::dialect::OpInputInfo("input",
+                                   "paddle::dialect::DenseTensorArrayType",
+                                   false,
+                                   false,
+                                   false,
+                                   false)};
+
+  std::vector<paddle::dialect::OpAttributeInfo> attributes = {
+      paddle::dialect::OpAttributeInfo("val", "pir::FloatAttribute", "")};
+
+  std::vector<paddle::dialect::OpOutputInfo> outputs = {OpOutputInfo(
+      "out", "paddle::dialect::DenseTensorArrayType", false, false)};
+
+  paddle::dialect::OpRunTimeInfo run_time_info =
+      OpRunTimeInfo("CreateArrayLikeInferMeta",
+                    {"input"},
+                    "create_array_like",
+                    {"input", "val"},
+                    {},
+                    {},
+                    {},
+                    {});
+
+  return std::make_tuple(
+      inputs, attributes, outputs, run_time_info, "create_array_like");
+}
+
+void CreateArrayLikeOp::Build(pir::Builder &builder,             // NOLINT
+                              pir::OperationArgument &argument,  // NOLINT
+                              pir::Value &input_,                // NOLINT
+                              float &val) {
+  VLOG(4) << "Start build CreateArrayLikeOp";
+  VLOG(4) << "Builder construction inputs";
+  std::vector<pir::Value> argument_inputs = {input_};
+  argument.AddInputs(argument_inputs);
+
+  VLOG(4) << "Builder construction attributes";
+  pir::Attribute attr_val =
+      pir::FloatAttribute::get(pir::IrContext::Instance(), val);
+  argument.AddAttribute("val", attr_val);
+  VLOG(4) << "Builder construction outputs";
+  paddle::dialect::DenseTensorArrayType input_type =
+      input_.type().dyn_cast<paddle::dialect::DenseTensorArrayType>();
+  paddle::dialect::IrTensor dense_input(
+      paddle::dialect::TransToPhiDataType(input_type.dtype()),
+      {},
+      input_type.data_layout(),
+      {});
+
+  paddle::dialect::IrMetaTensor meta_input(&dense_input);
+
+  paddle::dialect::IrTensor dense_out;
+  paddle::dialect::IrMetaTensor meta_out(&dense_out);
+
+  phi::CreateArrayLikeInferMeta(meta_input, &meta_out);
+
+  std::vector<pir::Type> argument_outputs;
+  pir::Type out_dense_tensor_type = paddle::dialect::DenseTensorArrayType::get(
+      pir::IrContext::Instance(),
+      paddle::dialect::TransToIrDataType(dense_out.dtype()),
+      dense_out.layout());
+  argument_outputs.push_back(out_dense_tensor_type);
+  argument.AddOutputs(argument_outputs.begin(), argument_outputs.end());
+  ::pir::PassStopGradientsDefaultly(argument);
+}
+
+void CreateArrayLikeOp::VerifySig() {
+  VLOG(4) << "Start Verifying inputs, outputs and attributes for: "
+             "CreateArrayLikeOp.";
+  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));
+  }
+  VLOG(4) << "Verifying attributes:";
+  {
+    auto &attributes = this->attributes();
+    PADDLE_ENFORCE(attributes.count("val") > 0, "val does not exist.");
+  }
+  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::DenseTensorArrayType>(),
+        phi::errors::PreconditionNotMet(
+            "Type validation failed for the 0th output."));
+  }
+  VLOG(4) << "End Verifying for: CreateArrayLikeOp.";
+}
+
+void CreateArrayLikeOp::InferMeta(phi::InferMetaContext *infer_meta) {
+  auto fn = PD_INFER_META(phi::CreateArrayLikeInferMeta);
+  fn(infer_meta);
+}
+
 OpInfoTuple ArrayLengthOp::GetOpInfo() {
   std::vector<paddle::dialect::OpInputInfo> inputs = {
       OpInputInfo("x",
@@ -1319,6 +1559,7 @@ void ArrayReadOp::Build(pir::Builder &builder,
       dense_out.lod());
   argument_outputs.push_back(out_type);
   argument.AddOutputs(argument_outputs.begin(), argument_outputs.end());
+  ::pir::PassStopGradientsDefaultly(argument);
 }
 
 void ArrayReadOp::Build(pir::Builder &builder,
@@ -2691,9 +2932,11 @@ 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)
+IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::AddNArrayOp)
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::FusedGemmEpilogueOp)
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::FusedGemmEpilogueGradOp)
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::CreateArrayOp)
+IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::CreateArrayLikeOp)
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::ArrayLengthOp)
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::ArrayReadOp)
 IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::ArrayWrite_Op)