op_gen.py 61.2 KB
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# Copyright (c) 2023 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.

import argparse
import os

import yaml

# =====================================
# String Template for h file code gen
# =====================================
NAMESPACE_GARD_TEMPLATE = """namespace {namespace} {{
{input}
}} // namespace {namespace}"""

H_FILE_TEMPLATE = """#ifdef GET_OP_LIST
#undef GET_OP_LIST
{op_declare}
#else
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// This file is generated by "paddle/fluid/ir/dialect/op_gen.py"
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#include <vector>

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#include "paddle/ir/core/builder.h"
#include "paddle/ir/core/operation_utils.h"
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#include "paddle/ir/core/op_base.h"
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#include "paddle/fluid/ir/dialect/utils.h"
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#include "paddle/fluid/ir/interface/op_yaml_info.h"
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#include "paddle/fluid/ir/interface/infershape.h"
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#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/phi/core/infermeta_utils.h"

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{input}
#endif
"""

GET_OP_LIST_TEMPALTE = """{}
"""

OP_DECLARE_TEMPLATE = """
class {op_name} : public ir::Op<{op_name}{interfaces}{traits}> {{
 public:
  using Op::Op;
  static const char *name() {{ return "{dialect_op_name}"; }}
  {attribute_declare}
  static constexpr uint32_t attributes_num = {attribute_num};
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  static OpInfoTuple GetOpInfo();
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  static void Build({build_args});
  static void Verify(const std::vector<ir::OpResult> &inputs, const std::vector<ir::Type> &outputs, const ir::AttributeMap &attributes);
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{get_inputs_and_outputs}
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{exclusive_interface}
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}};
"""
op_0_attribute_declare_str = (
    "static constexpr const char **attributes_name = nullptr;"
)
op_n_attribute_declare_str = (
    "static const char *attributes_name[{attribute_num}];"
)

OP_GET_INPUT_TEMPLATE = """  ir::OpOperand {input_name}() {{ return operation()->GetOperandByIndex({input_index}); }}
"""
OP_GET_OUTPUT_TEMPLATE = """  ir::OpResult {output_name}() {{ return operation()->GetResultByIndex({output_index}); }}
"""

# =====================================
# String Template for cc file code gen
# =====================================
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CC_FILE_TEMPLATE = """// This file is generated by "paddle/fluid/ir/dialect/op_gen.py"

#include "{h_file}"
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#include "paddle/fluid/ir/dialect/pd_type.h"
#include "paddle/fluid/ir/dialect/pd_attribute.h"
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#include "paddle/ir/core/builtin_attribute.h"
#include "paddle/ir/core/builtin_type.h"
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#include "paddle/ir/core/builtin_op.h"
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#include "paddle/ir/core/ir_context.h"
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#include "paddle/phi/core/enforce.h"
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#include "paddle/phi/core/dense_tensor.h"
#include "paddle/phi/infermeta/binary.h"
#include "paddle/phi/infermeta/multiary.h"
#include "paddle/phi/infermeta/nullary.h"
#include "paddle/phi/infermeta/unary.h"
#include "paddle/phi/infermeta/ternary.h"
#include "paddle/phi/infermeta/backward.h"

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{input}
"""

OP_N_ATTRIBUTE_DEFINED_TEMPLATE = """
const char *{op_name}::attributes_name[{attribute_num}] = {{ {attribute_names} }};
"""

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# get op info
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OP_INFO_TEMPLATE = """
OpInfoTuple {op_name}::GetOpInfo() {{
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  std::vector<paddle::dialect::OpInputInfo> inputs = {{ {inputs} }};
  std::vector<paddle::dialect::OpAttributeInfo> attributes = {{ {attributes} }};
  std::vector<paddle::dialect::OpOutputInfo> outputs = {{ {outputs} }};
  paddle::dialect::OpRunTimeInfo run_time_info = OpRunTimeInfo("{infer_meta_func}", {{"{infer_meta_param}"}}, {{"{kernel_func}"}}, {{"{kernel_param}"}});
  return std::make_tuple(inputs, attributes, outputs, run_time_info);
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}}
"""
CONSTRUCT_INPUT_INFO_TEMPLATE = (
    """OpInputInfo("{name}", "{typename}", {optional}, {no_need_buffer})"""
)
CONSTRUCT_OUTPUT_INFO_TEMPLATE = (
    """OpOutputInfo("{name}", "{typename}", {optional}, {intermediate})"""
)
CONSTRUCT_ATTRIBUTE_INFO_TEMPLATE = (
    """OpAttributeInfo("{name}", "{typename}", "{data_type}")"""
)

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# build
OP_BUILD_TEMPLATE = """
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void {op_name}::Build({build_args}) {{
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{build_inputs}
{build_attributes}
{build_outputs}
}}
"""

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# verify
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OP_VERIFY_TEMPLATE = """
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void {op_name}::Verify(const std::vector<ir::OpResult> &inputs, const std::vector<ir::Type> &outputs, const ir::AttributeMap &attributes) {{
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  VLOG(4) << "Verifying inputs, outputs and attributes for: {op_name}.";

  // Verify inputs type:
  PADDLE_ENFORCE_EQ(inputs.size(), {inputs_size},
                    phi::errors::PreconditionNotMet("The size %d of inputs must be equal to {inputs_size}.", inputs.size()));
  {inputs_type_check}
  // Verify outputs type:
  PADDLE_ENFORCE_EQ(outputs.size(), {outputs_size},
                    phi::errors::PreconditionNotMet("The size %d of outputs must be equal to {outputs_size}.", outputs.size()));
  {outputs_type_check}
  // Verify if attributes contain attribute name in attributes_name:
  {attributes_check}
}}
"""

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GRAD_OP_VERIFY_TEMPLATE = """
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void {op_name}::Verify(const std::vector<ir::OpResult> &inputs, const std::vector<ir::Type> &outputs, const ir::AttributeMap &attributes) {{
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  (void)inputs;
  (void)outputs;
  (void)attributes;
}}
"""

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INPUT_TYPE_CHECK_TEMPLATE = """PADDLE_ENFORCE_EQ(inputs[{index}].type().isa<{standard}>(), true,
                    phi::errors::PreconditionNotMet("Type validation failed for the {index}th input."));
  """
INPUT_VECTORTYPE_CHECK_TEMPLATE = """if (inputs[{index}].type().isa<ir::VectorType>()) {{
    for (size_t i = 0; i < inputs[{index}].type().dyn_cast<ir::VectorType>().size(); i++) {{
      PADDLE_ENFORCE_EQ(inputs[{index}].type().dyn_cast<ir::VectorType>()[i].isa<{standard}>(), true,
                        phi::errors::PreconditionNotMet("Type validation failed for the {index}th input."));
    }}
  }} else {{
    PADDLE_ENFORCE_EQ(inputs[{index}].type().isa<{standard}>(), true,
                      phi::errors::PreconditionNotMet("Type validation failed for the {index}th input."));
  }}
  """
INPUT_OPTIONAL_TYPE_CHECK_TEMPLATE = """if (inputs[{index}]) {{
    PADDLE_ENFORCE_EQ(inputs[{index}].type().isa<{standard}>(), true,
                      phi::errors::PreconditionNotMet("Type validation failed for the {index}th input."));
  }}
  """
INPUT_OPTIONAL_VECTORTYPE_CHECK_TEMPLATE = """if (inputs[{index}]) {{
    if (inputs[{index}].type().isa<ir::VectorType>()) {{
      for (size_t i = 0; i < inputs[{index}].type().dyn_cast<ir::VectorType>().size(); i++) {{
        PADDLE_ENFORCE_EQ(inputs[{index}].type().dyn_cast<ir::VectorType>()[i].isa<{standard}>(), true,
                          phi::errors::PreconditionNotMet("Type validation failed for the {index}th input."));
      }}
    }} else {{
      PADDLE_ENFORCE_EQ(inputs[{index}].type().isa<{standard}>(), true,
                        phi::errors::PreconditionNotMet("Type validation failed for the {index}th input."));
    }}
  }}
  """

OUTPUT_TYPE_CHECK_TEMPLATE = """PADDLE_ENFORCE_EQ(outputs[{index}].isa<{standard}>(), true,
                    phi::errors::PreconditionNotMet("Type validation failed for the {index}th output."));
  """
OUTPUT_VECTORTYPE_CHECK_TEMPLATE = """if (outputs[{index}].isa<ir::VectorType>()) {{
    for (size_t i = 0; i < outputs[{index}].dyn_cast<ir::VectorType>().size(); i++) {{
      PADDLE_ENFORCE_EQ(outputs[{index}].dyn_cast<ir::VectorType>()[i].isa<{standard}>(), true,
                        phi::errors::PreconditionNotMet("Type validation failed for the {index}th output."));
    }}
  }} else {{
    PADDLE_ENFORCE_EQ(outputs[{index}].isa<{standard}>(), true,
                      phi::errors::PreconditionNotMet("Type validation failed for the {index}th output."));
  }}
  """
OUTPUT_OPTIONAL_TYPE_CHECK_TEMPLATE = """if (outputs[{index}]) {{
    PADDLE_ENFORCE_EQ(outputs[{index}].isa<{standard}>(), true,
                      phi::errors::PreconditionNotMet("Type validation failed for the {index}th output."));
  }}
  """
OUTPUT_OPTIONAL_VECTORTYPE_CHECK_TEMPLATE = """if (outputs[{index}]) {{
    if (outputs[{index}].isa<ir::VectorType>()) {{
      for (size_t i = 0; i < outputs[{index}].dyn_cast<ir::VectorType>().size(); i++) {{
        PADDLE_ENFORCE_EQ(outputs[{index}].dyn_cast<ir::VectorType>()[i].isa<{standard}>(), true,
                          phi::errors::PreconditionNotMet("Type validation failed for the {index}th output."));
      }}
    }} else {{
      PADDLE_ENFORCE_EQ(outputs[{index}].isa<{standard}>(), true,
                        phi::errors::PreconditionNotMet("Type validation failed for the {index}th output."));
    }}
  }}
  """

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ATTRIBUTE_CHECK_TEMPLATE = """PADDLE_ENFORCE_EQ(attributes.count("{attribute_name}")>0 && attributes.at("{attribute_name}").isa<{standard}>(), true,
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                    phi::errors::PreconditionNotMet("Type of attribute: {attribute_name} is not right."));
  """
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ATTRIBUTE_VECTOR_CHECK_TEMPLATE = """PADDLE_ENFORCE_EQ(attributes.count("{attribute_name}")>0 && attributes.at("{attribute_name}").isa<ir::ArrayAttribute>(), true,
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                    phi::errors::PreconditionNotMet("Type of attribute: {attribute_name} is not right."));
  for (size_t i = 0; i < attributes.at("{attribute_name}").dyn_cast<ir::ArrayAttribute>().size(); i++) {{
    PADDLE_ENFORCE_EQ(attributes.at("{attribute_name}").dyn_cast<ir::ArrayAttribute>()[i].isa<{standard}>(), true,
                      phi::errors::PreconditionNotMet("Type of attribute: {attribute_name} is not right."));
  }}
  """
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OP_INFER_SHAPE_TEMPLATE = """
void {op_name}::InferShape( phi::InferMetaContext *infer_meta ) {{
  auto fn = PD_INFER_META(phi::{infer_meta_func});
  fn(infer_meta);
}}
"""
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def to_phi_and_fluid_op_name(op_item):
    # Templat: - op : phi_name (fluid_name)
    names = op_item.split('(')
    if len(names) == 1:
        phi_fluid_name = names[0].strip()
        return phi_fluid_name, phi_fluid_name
    else:
        phi_name = names[0].strip()
        fluid_name = names[1].split(')')[0].strip()
        return phi_name, fluid_name


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scalar_type_maps = {
    'int': 'ir::Int32_tAttribute',
    'int64_t': 'ir::Int64_tAttribute',
    'float': 'ir::FloatAttribute',
    'dobule': 'ir::DoubleAttribute',
    'bool': 'ir::BoolAttribute',
}


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# =====================================
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# Parse Op Compat From Yaml
# =====================================
class OpCompatParser:
    def __init__(self, ops_compat_yaml_file):
        self.ops_compat_yaml_file = ops_compat_yaml_file
        with open(self.ops_compat_yaml_file, "r") as f:
            self.ops_compat = yaml.safe_load(f)

    def get_compat(self, op_name):
        for compat in self.ops_compat:
            phi_name, fluid_name = to_phi_and_fluid_op_name(compat['op'])
            if op_name == phi_name:
                return compat
        return None


# =====================================
# Parse Op Information From Yaml
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# =====================================
class OpInfoParser:
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    def __init__(self, op_yaml_item, op_compat_item):
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        self.op_yaml_item = op_yaml_item
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        self.op_compat_item = op_compat_item
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        self.op_phi_name = self.parse_op_phi_name()
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        # parse inputs
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        self.input_name_list = self.parse_input_name_list()
        self.input_type_list = self.parse_input_type_list()
        self.input_optional_list = self.parse_input_optional_list()
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        self.input_no_need_buffer_list = self.parse_input_no_need_buffer_list()
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        self.cross_check(
            self.input_name_list, self.input_type_list, self.input_optional_list
        )
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        # parse outputs
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        self.output_name_list = self.parse_output_name_list()
        self.output_type_list = self.parse_output_type_list()
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        self.output_size_list = self.parse_output_size_list()
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        self.output_optional_list = self.parse_output_optional_list()
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        self.output_intermediate_list = self.parse_output_intermediate_list()
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        self.cross_check(
            self.output_name_list,
            self.output_type_list,
            self.output_optional_list,
        )
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        # parse attributes
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        self.attr_types_map = {
            'IntArray': ['paddle::dialect::IntArrayAttribute', 'IntArray'],
            'Scalar': ['paddle::dialect::ScalarAttribute', 'Scalar'],
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            'Scalar(int)': ['ir::Int32_tAttribute', 'int'],
            'Scalar(int64_t)': ['ir::Int64_tAttribute', 'int64_t'],
            'Scalar(float)': ['ir::FloatAttribute', 'float'],
            'Scalar(dobule)': ['ir::DoubleAttribute', 'dobule'],
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            'Scalar[]': [
                'ir::ArrayAttribute<paddle::dialect::ScalarAttribute>',
                'std::vector<Scalar>',
            ],
            'int': ['ir::Int32_tAttribute', 'int'],
            'int32_t': ['ir::Int32_tAttribute', 'int32_t'],
            'int64_t': ['ir::Int64_tAttribute', 'int64_t'],
            'long': ['ir::LongAttribute', 'long'],
            'size_t': ['ir::Size_tAttribute', 'size_t'],
            'float': ['ir::FloatAttribute', 'float'],
            'float[]': [
                'ir::ArrayAttribute<ir::FloatAttribute>',
                'std::vector<float>',
            ],
            'double': ['ir::DoubleAttribute', 'double'],
            'bool': ['ir::BoolAttribute', 'bool'],
            'bool[]': [
                'ir::ArrayAttribute<ir::BoolAttribute>',
                'std::vecot<bool>',
            ],
            'str': ['ir::StrAttribute', 'std::string'],
            'str[]': [
                'ir::ArrayAttribute<ir::StrAttribute>',
                'std::vector<std::string>',
            ],
            'Place': ['paddle::dialect::PlaceAttribute', 'Place'],
            'DataLayout': [
                'paddle::dialect::DataLayoutAttribute',
                'DataLayout',
            ],
            'DataType': ['paddle::dialect::DataTypeAttribute', 'DataType'],
            'int64_t[]': [
                'ir::ArrayAttribute<ir::Int64_tAttribute>',
                'std::vector<int64_t>',
            ],
            'int[]': [
                'ir::ArrayAttribute<ir::Int32_tAttribute>',
                'std::vector<int>',
            ],
        }
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        self.attribute_name_list = self.parse_attribute_name_list()
        self.attribute_type_list = self.parse_attribute_type_list()
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        self.attribute_build_arg_type_list = (
            self.parse_attribute_build_arg_type_list()
        )
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        self.attribute_data_type_list = self.parse_attribute_data_type_list()
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        self.attribute_default_value_list = (
            self.parse_attribute_default_value_list()
        )
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        self.cross_check(self.attribute_name_list, self.attribute_type_list)

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        # parse mutable attributes (as inputs)
        (
            self.mutable_attribute_name_list,
            self.mutable_attribute_type_list,
        ) = self.parse_mutable_attribute()

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        # parse infermeta && kernel
        self.infer_meta_map = self.parse_infer_meta_map()
        self.kernel_map = self.parse_kernel_map()
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        if 'infer_meta' in self.op_yaml_item:
            self.infer_shape_func = self.op_yaml_item['infer_meta']["func"]
        else:
            self.infer_shape_func = None

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    def cross_check(self, name_list, type_list, optional_list=None):
        assert len(name_list) == len(
            type_list
        ), "name list size != type list size."
        if optional_list is not None:
            assert len(type_list) == len(
                optional_list
            ), "type list size != optional list size."

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    def parse_op_phi_name(self):
        if self.parse_op_inplace_info() is None:
            return [self.op_yaml_item['name']]
        else:
            if self.op_yaml_item['name'][-1] == "_":
                return [self.op_yaml_item['name']]
            else:
                return [
                    self.op_yaml_item['name'],
                    self.op_yaml_item['name'] + "_",
                ]

    def parse_op_inplace_info(self):
        if 'inplace' in self.op_yaml_item:
            return self.op_yaml_item['inplace']
        return None

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    def parse_mutable_attribute(self):
        """
        {'axis': 'paddle::dialect::ScalarAttribute', 'rotl': 'paddle::dialect::IntArrayAttribute'}
        """
        mutable_attribute_name_list = []
        mutable_attribute_type_list = []
        # scalar
        if (self.op_compat_item is not None) and (
            'scalar' in self.op_compat_item
        ):
            for scalar_attr in self.op_compat_item['scalar'].keys():
                if 'data_type' in self.op_compat_item['scalar'][scalar_attr]:
                    if (
                        self.op_compat_item['scalar'][scalar_attr]['data_type']
                        == "std::string"
                    ):
                        # see isclose and allclose in op_compat.yaml
                        mutable_attribute_name_list.append(scalar_attr)
                        mutable_attribute_type_list.append(
                            ["ir::StrAttribute", "std::string"]
                        )
                    else:
                        mutable_attribute_name_list.append(scalar_attr)
                        mutable_attribute_type_list.append(
                            [
                                "paddle::dialect::ScalarAttribute",
                                self.op_compat_item['scalar'][scalar_attr][
                                    'data_type'
                                ],
                            ]
                        )
                # See eye in op_compat.yaml
                else:
                    mutable_attribute_name_list.append(scalar_attr)
                    mutable_attribute_type_list.append(
                        [
                            "paddle::dialect::ScalarAttribute",
                            self.attribute_data_type_list[
                                self.attribute_name_list.index(scalar_attr)
                            ],
                        ]
                    )

        # int_array
        if (self.op_compat_item is not None) and (
            'int_array' in self.op_compat_item
        ):
            for int_array_attr in self.op_compat_item['int_array']:
                mutable_attribute_name_list.append(int_array_attr)
                mutable_attribute_type_list.append(
                    [
                        "paddle::dialect::IntArrayAttribute",
                        self.op_compat_item['int_array'][int_array_attr][
                            'data_type'
                        ],
                    ]
                )
        return mutable_attribute_name_list, mutable_attribute_type_list

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    def parse_input_name_list(self):
        name_list = []
        for input_info in self.op_yaml_item['inputs']:
            name_list.append(input_info['name'])
        return name_list

    def parse_input_type_list(self):
        input_types_map = {
            'Tensor': 'paddle::dialect::DenseTensorType',
            'Tensor[]': 'ir::VectorType<paddle::dialect::DenseTensorType>',
        }
        type_list = []
        for input_info in self.op_yaml_item['inputs']:
            assert (
                input_info['typename'] in input_types_map
            ), f"{self.op_phi_name} : Input type error: the input type only support Tensor and Tensor[], but now is {input_info['typename']}."
            type_list.append(input_types_map[input_info['typename']])
        return type_list

    def parse_input_optional_list(self):
        optional_list = []
        for input_info in self.op_yaml_item['inputs']:
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            if input_info['optional']:
                optional_list.append("true")
            else:
                optional_list.append("false")
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        return optional_list

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    def parse_input_no_need_buffer_list(self):
        no_need_buffer_list = []
        for input_info in self.op_yaml_item['inputs']:
            if input_info['no_need_buffer']:
                no_need_buffer_list.append("true")
            else:
                no_need_buffer_list.append("false")
        return no_need_buffer_list

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    def parse_output_name_list(self):
        name_list = []
        for output_info in self.op_yaml_item['outputs']:
            name_list.append(output_info['name'])
        return name_list

    def parse_output_type_list(self):
        output_type_map = {
            'Tensor': 'paddle::dialect::DenseTensorType',
            'Tensor[]': 'ir::VectorType<paddle::dialect::DenseTensorType>',
        }
        type_list = []
        for output_info in self.op_yaml_item['outputs']:
            assert (
                output_info['typename'] in output_type_map
            ), f"{self.op_phi_name} : Output type error: the output type only support Tensor and Tensor[], but now is {output_info['typename']}."
            type_list.append(output_type_map[output_info['typename']])
        return type_list

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    def parse_output_size_list(self):
        size_list = []
        for output_info in self.op_yaml_item['outputs']:
            if 'size' in output_info:
                size_list.append(output_info['size'])
            else:
                size_list.append(None)
        return size_list

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    def parse_output_optional_list(self):
        optional_list = []
        for output_info in self.op_yaml_item['outputs']:
            if 'optional' in output_info:
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                if output_info['optional']:
                    optional_list.append("true")
                else:
                    optional_list.append("false")
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            else:
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                optional_list.append("false")
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        return optional_list

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    def parse_output_intermediate_list(self):
        intermediate_list = []
        for output_info in self.op_yaml_item['outputs']:
            if 'intermediate' in output_info:
                if output_info['intermediate']:
                    intermediate_list.append("true")
                else:
                    intermediate_list.append("false")
            else:
                intermediate_list.append("false")
        return intermediate_list

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    def parse_attribute_name_list(self):
        name_list = []
        for attribute_info in self.op_yaml_item['attrs']:
            name_list.append(attribute_info['name'])
        return name_list

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    def parse_attribute_build_arg_type_list(self):
        type_list = []
        for attribute_info in self.op_yaml_item['attrs']:
            assert (
                attribute_info['typename'] in self.attr_types_map
            ), f"{self.op_phi_name} : Attr type error."

            # Scalar & IntArray has data_type
            temp_type = self.attr_types_map[attribute_info['typename']][1]
            if 'Scalar' in temp_type:
                if 'data_type' in attribute_info:
                    temp_type = attribute_info['data_type']
            if 'IntArray' in temp_type:
                if 'data_type' in attribute_info:
                    temp_type = attribute_info['data_type']
            type_list.append(self.get_phi_dtype_name(temp_type))
        return type_list

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    def parse_attribute_type_list(self):
        type_list = []
        for attribute_info in self.op_yaml_item['attrs']:
            assert (
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                attribute_info['typename'] in self.attr_types_map
582
            ), f"{self.op_phi_name} : Attr type error."
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            type_list.append(self.attr_types_map[attribute_info['typename']][0])
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        return type_list

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    def parse_attribute_data_type_list(self):
        data_type_list = []
        for attribute_info in self.op_yaml_item['attrs']:
            if 'data_type' in attribute_info:
                data_type_list.append(attribute_info['data_type'])
            else:
                data_type_list.append("")
        return data_type_list

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    def parse_attribute_default_value_list(self):
        default_value_list = []
        for attribute_info in self.op_yaml_item['attrs']:
            if 'default_value' in attribute_info:
                default_value = attribute_info['default_value']
                default_value_list.append(
                    self.get_phi_dtype_name(default_value)
                )
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            else:
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                default_value_list.append(None)
        return default_value_list
606

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    def parse_infer_meta_map(self):
        if 'infer_meta' in self.op_yaml_item:
            return self.op_yaml_item['infer_meta']
        else:
            return None

    def parse_kernel_map(self):
        if 'kernel' in self.op_yaml_item:
            return self.op_yaml_item['kernel']
        else:
            return None

    def get_phi_dtype_name(self, name):
        name = name.replace('Scalar', 'phi::Scalar')
        name = name.replace('IntArray', 'phi::IntArray')
        name = name.replace('DataLayout', 'phi::DataLayout')
        name = name.replace('DataType', 'phi::DataType')
        if name.startswith(
            (
                "Place",
                "CPUPlace",
                "GPUPlace",
                "GPUPinnedPlace",
                "XPUPlace",
                "IPUPlace",
                "CustomPlace",
            )
        ):
            return "phi::" + name
        return name
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def to_pascal_case(s):
    words = s.split("_")
    if s[-1] == "_":
        return "".join([word.capitalize() for word in words]) + "_"
    else:
        return "".join([word.capitalize() for word in words]) + ""


# =====================================
648
# Generate Op Definition Files
649
# =====================================
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def GenBuildInputArgsStr(
    op_input_name_list,
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    op_mutable_attribute_name_list,
    op_non_mutable_attribute_name_list,
    op_non_mutable_attribute_build_arg_type_list,
    op_non_mutable_attribute_default_value_list,
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    for_func_define=True,
):
    '''
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    Example: ir::OperationArgument &argument, ir::OpResult x_, phi::DataType dtype=phi::DataType::UNDEFINED, phi::Place place={}
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    '''
661
    # add inputs
662
    build_args_str = "ir::OperationArgument &argument"
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    if len(op_input_name_list) > 0:
        for input_name in op_input_name_list:
            build_args_str += ", ir::OpResult " + input_name + "_"
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    # add mutable attributes as inputs
    if len(op_mutable_attribute_name_list) > 0:
        for mutable_attr in op_mutable_attribute_name_list:
            build_args_str += ", ir::OpResult " + mutable_attr + "_"

    # add non-mutable attributes
    for attr_idx in range(len(op_non_mutable_attribute_name_list)):
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        build_args_str += (
            ", "
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            + op_non_mutable_attribute_build_arg_type_list[attr_idx]
676
            + " "
677
            + op_non_mutable_attribute_name_list[attr_idx]
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        )
        if for_func_define:
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            if (
                op_non_mutable_attribute_default_value_list[attr_idx]
                is not None
            ):
                default_value = op_non_mutable_attribute_default_value_list[
                    attr_idx
                ]
                if (
                    op_non_mutable_attribute_build_arg_type_list[attr_idx]
                    != "std::string"
                ):
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                    if default_value[0] == "'" or default_value[0] == '"':
                        default_value = default_value[1:]
                    if default_value[-1] == "'" or default_value[-1] == '"':
                        default_value = default_value[0:-1]
                build_args_str += "=" + default_value
    return build_args_str


699
def GenBuildInputs(op_input_name_list, op_mutable_attribute_name_list):
700
    BUILD_INPUT_TEMPLATE = """  std::vector<ir::OpResult> argument_inputs = {{{inputs_args}}};
701
  argument.AddOperands(argument_inputs.begin(), argument_inputs.end());
702
"""
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    build_input_str = '  VLOG(4) << "Builder construction inputs";\n'
    input_name_list = op_input_name_list + op_mutable_attribute_name_list
    if len(input_name_list) > 0:
        inputs_args_str = ""
        inputs_args_str += "_, ".join(input_name_list) + "_"
        build_input_str += BUILD_INPUT_TEMPLATE.format(
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            inputs_args=inputs_args_str
        )
    return build_input_str


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def GenBuildAttributes(
    op_non_mutable_attribute_name_list, op_non_mutable_attribute_type_list
):
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    INTARRAY_STR_TEMPLATE = """  ir::Attribute attr_{attr_name} = {op_attribute_type}::get(ir::IrContext::Instance(), phi::IntArray({attr}));
"""
719
    SCALAR_STR_TEMPLATE = """  ir::Attribute attr_{attr_name} = TransToIrAttribute({attr}, ir::IrContext::Instance());
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"""
    STR_TEMPLATE = """  ir::Attribute attr_{attr_name} = {op_attribute_type}::get(ir::IrContext::Instance(), {attr});
"""
    ARRAY_ATTRIBUTE_TEMPLATE = """  std::vector<ir::Attribute> vec_{attr_name};
  for (size_t i = 0; i < static_cast<size_t>({attr_size}); i++) {{
    {create_attribute}
    vec_{attr_name}.push_back(attr_{attr_name});
  }}
  ir::Attribute attr_{attr_name} = ir::ArrayAttribute::get(ir::IrContext::Instance(), vec_{attr_name});
"""
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    attr_str = '  VLOG(4) << "Builder construction attributes";\n'
    for idx in range(len(op_non_mutable_attribute_name_list)):
        if "ir::ArrayAttribute<" in op_non_mutable_attribute_type_list[idx]:
            inner_attribute_type = op_non_mutable_attribute_type_list[idx][
                19:-1
            ]
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            if inner_attribute_type == "paddle::dialect::IntArrayAttribute":
                attr_str += ARRAY_ATTRIBUTE_TEMPLATE.format(
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                    attr_name=op_non_mutable_attribute_name_list[idx],
                    attr_size=op_non_mutable_attribute_name_list[idx]
                    + ".size()",
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                    create_attribute=INTARRAY_STR_TEMPLATE.format(
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                        attr_name=op_non_mutable_attribute_name_list[idx],
743
                        op_attribute_type=inner_attribute_type,
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                        attr=op_non_mutable_attribute_name_list[idx] + "[i]",
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                    ),
                )
            elif inner_attribute_type == "paddle::dialect::ScalarAttribute":
                attr_str += ARRAY_ATTRIBUTE_TEMPLATE.format(
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                    attr_name=op_non_mutable_attribute_name_list[idx],
                    attr_size=op_non_mutable_attribute_name_list[idx]
                    + ".size()",
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                    create_attribute=SCALAR_STR_TEMPLATE.format(
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                        attr_name=op_non_mutable_attribute_name_list[idx],
                        attr=op_non_mutable_attribute_name_list[idx] + "[i]",
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                    ),
                )
            else:
                attr_str += ARRAY_ATTRIBUTE_TEMPLATE.format(
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                    attr_name=op_non_mutable_attribute_name_list[idx],
                    attr_size=op_non_mutable_attribute_name_list[idx]
                    + ".size()",
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                    create_attribute=STR_TEMPLATE.format(
763
                        attr_name=op_non_mutable_attribute_name_list[idx],
764
                        op_attribute_type=inner_attribute_type,
765
                        attr=op_non_mutable_attribute_name_list[idx] + "[i]",
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                    ),
                )
        elif (
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            op_non_mutable_attribute_type_list[idx]
            == "paddle::dialect::IntArrayAttribute"
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        ):
            attr_str += INTARRAY_STR_TEMPLATE.format(
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                attr_name=op_non_mutable_attribute_name_list[idx],
                op_attribute_type=op_non_mutable_attribute_type_list[idx],
                attr=op_non_mutable_attribute_name_list[idx],
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            )

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        elif (
            op_non_mutable_attribute_type_list[idx]
            == "paddle::dialect::ScalarAttribute"
        ):
782
            attr_str += SCALAR_STR_TEMPLATE.format(
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                attr_name=op_non_mutable_attribute_name_list[idx],
                attr=op_non_mutable_attribute_name_list[idx],
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            )
        else:
            attr_str += STR_TEMPLATE.format(
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                attr_name=op_non_mutable_attribute_name_list[idx],
                op_attribute_type=op_non_mutable_attribute_type_list[idx],
                attr=op_non_mutable_attribute_name_list[idx],
791
            )
792
        attr_str += """  argument.AddAttribute("{attr_name}", attr_{attr_name});\n""".format(
793
            attr_name=op_non_mutable_attribute_name_list[idx]
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        )

    return attr_str


def GenBuildOutputs(
    op_input_name_list,
    op_input_type_list,
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    op_mutable_attribute_name_list,
    op_mutable_attribute_type_list,
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    op_output_name_list,
    op_output_type_list,
    op_output_size_list,
    op_infer_meta_map,
):
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    build_output_str = '  VLOG(4) << "Builder construction outputs";\n'
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    CREATE_INPUT_METATENSOR_TEMPLATE = """  phi::DenseTensor dense_{name};
  dense_{name}.set_meta(
    phi::DenseTensorMeta(TransToPhiDataType({name}.dtype()),
                         {name}.dims(),
                         {name}.data_layout(),
                         {name}.lod(),
                         {name}.offset())
    );
  phi::MetaTensor meta_{name}(&dense_{name});
"""
    CREATE_INPUT_VEC_METATENSOR_TEMPLATE = """  std::vector<phi::DenseTensor> vec_dense_{name}({name}.size(), phi::DenseTensor());
  std::vector<phi::MetaTensor> vec_meta_{name};
  for (size_t i=0; i < static_cast<size_t>({name}.size()); i++) {{
    vec_dense_{name}[i].set_meta(
        phi::DenseTensorMeta(TransToPhiDataType({name}[i].dyn_cast<paddle::dialect::DenseTensorType>().dtype()),
                             {name}[i].dyn_cast<paddle::dialect::DenseTensorType>().dims(),
                             {name}[i].dyn_cast<paddle::dialect::DenseTensorType>().data_layout(),
                             {name}[i].dyn_cast<paddle::dialect::DenseTensorType>().lod(),
                             {name}[i].dyn_cast<paddle::dialect::DenseTensorType>().offset())
        );
    vec_meta_{name}.push_back(phi::MetaTensor(&vec_dense_{name}[i]));
  }}
  std::vector<const phi::MetaTensor*> meta_{name};
  for (size_t i=0; i < static_cast<size_t>(vec_meta_{name}.size()); i++) {{
    meta_{name}.push_back(&vec_meta_{name}[i]);
  }}
 """
837
    CREATE_INTARRAY_MUTABLE_ATTRIBUE_TEMPLATE = """  std::vector<int64_t> {name} = {name}_.owner()->dyn_cast<ir::ConstantOp>().value().dyn_cast<paddle::dialect::IntArrayAttribute>().data().GetData(); (void){name};\n"""
838
    CREATE_SCALAR_MUTABLE_ATTRIBUE_TEMPLATE = """  {dtype} {name} = {name}_.owner()->dyn_cast<ir::ConstantOp>().value().dyn_cast<{ir_type}>().data(); (void){name};\n"""
839 840
    CREATE_STRING_MUTABLE_ATTRIBUE_TEMPLATE = """  std::string {name} = {name}_.owner()->dyn_cast<ir::ConstantOp>().value().dyn_cast<ir::StrAttribute>().data(); (void){name};\n"""

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    CREATE_OUTPUT_METATENSOR_TEMPLATE = """  phi::DenseTensor dense_{name};
  phi::MetaTensor meta_{name}(&dense_{name});
"""
    CREATE_OUTPUT_VEC_METATENSOR_TEMPLATE = """  std::vector<phi::DenseTensor> vec_dense_{name}(({output_size}), phi::DenseTensor());
  std::vector<phi::MetaTensor> vec_meta_{name};
  for (size_t i=0; i < static_cast<size_t>({output_size}); i++) {{
    vec_meta_{name}.push_back(phi::MetaTensor(&vec_dense_{name}[i]));
  }}
  std::vector<phi::MetaTensor*> meta_{name};
  for (size_t i=0; i < static_cast<size_t>(vec_meta_{name}.size()); i++) {{
    meta_{name}.push_back(&vec_meta_{name}[i]);
  }}
"""
    # Prepar input type
    for idx in range(len(op_input_name_list)):
        # is a vector<Tensor>
        if 'ir::VectorType' in op_input_type_list[idx]:
            build_output_str += "  ir::VectorType {name} = {name}_.type().dyn_cast<ir::VectorType>(); (void){name};\n".format(
                name=op_input_name_list[idx]
            )
        # is a Tensor
        else:
            build_output_str += "  paddle::dialect::DenseTensorType {name} = {name}_.type().dyn_cast<paddle::dialect::DenseTensorType>(); (void){name};\n".format(
                name=op_input_name_list[idx]
            )
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    # Prepare mutable attributes
    for idx in range(len(op_mutable_attribute_name_list)):
        attr_dtype = op_mutable_attribute_type_list[idx]
        # int_array
        if attr_dtype[0] == "paddle::dialect::IntArrayAttribute":
            build_output_str += (
                CREATE_INTARRAY_MUTABLE_ATTRIBUE_TEMPLATE.format(
                    name=op_mutable_attribute_name_list[idx]
                )
            )
        # scalar
        elif attr_dtype[0] == "paddle::dialect::ScalarAttribute":
            build_output_str += CREATE_SCALAR_MUTABLE_ATTRIBUE_TEMPLATE.format(
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                name=op_mutable_attribute_name_list[idx],
                dtype=attr_dtype[1],
                ir_type=scalar_type_maps[attr_dtype[1]],
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            )
        # string
        elif attr_dtype[0] == "ir::StrAttribute":
            build_output_str += CREATE_STRING_MUTABLE_ATTRIBUE_TEMPLATE.format(
                name=op_mutable_attribute_name_list[idx]
            )
        else:
            assert "mutable attribtue type is not right."
    build_output_str += "\n"
891

892
    # Prepare inputs_meta_tensor & attributes for infer meta
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    infer_meta_args = []
    for idx in range(len(op_infer_meta_map['param'])):
        # is input
        if op_infer_meta_map['param'][idx] in op_input_name_list:
            if (
                "meta_" + op_infer_meta_map['param'][idx]
            ) not in infer_meta_args:
                # is a vector<Tensor>
                if (
                    'ir::VectorType'
                    in op_input_type_list[
                        op_input_name_list.index(
                            op_infer_meta_map['param'][idx]
                        )
                    ]
                ):
                    build_output_str += (
                        CREATE_INPUT_VEC_METATENSOR_TEMPLATE.format(
                            name=op_infer_meta_map['param'][idx]
                        )
                    )
                # is a Tensor
                else:
                    build_output_str += CREATE_INPUT_METATENSOR_TEMPLATE.format(
                        name=op_infer_meta_map['param'][idx]
                    )

            infer_meta_args.append("meta_" + op_infer_meta_map['param'][idx])
        # is attribute
        else:
            infer_meta_args.append(op_infer_meta_map['param'][idx])

925
    # Prepare outputs_meta_tensor for infer meta
926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976
    for idx in range(len(op_output_name_list)):
        # is a vector<Tensor>
        if 'ir::VectorType' in op_output_type_list[idx]:
            build_output_str += CREATE_OUTPUT_VEC_METATENSOR_TEMPLATE.format(
                name=op_output_name_list[idx],
                output_size=op_output_size_list[idx],
            )
            infer_meta_args.append(f"meta_{op_output_name_list[idx]}")
        # is a Tensor
        else:
            build_output_str += CREATE_OUTPUT_METATENSOR_TEMPLATE.format(
                name=op_output_name_list[idx]
            )
            infer_meta_args.append(f"&meta_{op_output_name_list[idx]}")

    # Execute infer meta function
    CREATE_INFER_META_FUNC_TEMPLATE = """
  phi::{func}({args});
"""
    build_output_str += CREATE_INFER_META_FUNC_TEMPLATE.format(
        func=op_infer_meta_map['func'], args=", ".join(infer_meta_args)
    )

    # use dense_{name} or vec_dense_{name} to create Outputs type
    build_output_str += "\n  std::vector<ir::Type> argument_outputs;"

    CREATE_OUTPUT_DENSE_TENSOR_TEMPLATE = """
  ir::Type {name}_dense_tensor_type = paddle::dialect::DenseTensorType::get(ir::IrContext::Instance(), TransToIrDataType(dense_{name}.dtype()), dense_{name}.dims(), dense_{name}.layout(), dense_{name}.lod(), dense_{name}.offset());
  argument_outputs.push_back({name}_dense_tensor_type);
"""
    CREATE_OUTPUT_VEC_DENSE_TENSOR_TEMPLATE = """
  std::vector<ir::Type> {name}_types;
  for (size_t i=0; i < static_cast<size_t>({output_size}); i++) {{
    {name}_types.push_back(paddle::dialect::DenseTensorType::get(ir::IrContext::Instance(), TransToIrDataType(vec_dense_{name}[i].dtype()), vec_dense_{name}[i].dims(), vec_dense_{name}[i].layout(), vec_dense_{name}[i].lod(), vec_dense_{name}[i].offset()));
  }}
  ir::Type {name}_vector_type = ir::VectorType::get(ir::IrContext::Instance(), {name}_types);
  argument_outputs.push_back({name}_vector_type);
"""
    for idx in range(len(op_output_name_list)):
        # is a vector<Tensor>
        if 'ir::VectorType' in op_output_type_list[idx]:
            build_output_str += CREATE_OUTPUT_VEC_DENSE_TENSOR_TEMPLATE.format(
                name=op_output_name_list[idx],
                output_size=op_output_size_list[idx],
            )
        # is a Tensor
        else:
            build_output_str += CREATE_OUTPUT_DENSE_TENSOR_TEMPLATE.format(
                name=op_output_name_list[idx]
            )

977
    build_output_str += "  argument.AddTypes(argument_outputs.begin(), argument_outputs.end());\n"
978 979 980 981

    return build_output_str


982 983
def OpGenerator(
    op_yaml_files,
984
    op_compat_yaml_file,
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    namespaces,
    dialect_name,
    op_def_h_file,
    op_def_cc_file,
):
    # (1) Prepare: Delete existing old files: pd_op.h.tmp, pd_op.cc.tmp
    if os.path.exists(op_def_h_file):
        os.remove(op_def_h_file)
    if os.path.exists(op_def_cc_file):
        os.remove(op_def_cc_file)

    # (2) Prepare: Get all op item in all op_yaml_files
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    op_compat_parser = OpCompatParser(op_compat_yaml_file)

999 1000 1001 1002 1003 1004 1005
    op_yaml_items = []
    for yaml_file in op_yaml_files:
        with open(yaml_file, "r") as f:
            ops = yaml.safe_load(f)
            op_yaml_items = op_yaml_items + ops
    op_info_items = []
    for op in op_yaml_items:
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        op_info_items.append(
            OpInfoParser(op, op_compat_parser.get_compat(op['name']))
        )
1009 1010 1011 1012 1013 1014

    # (3) CodeGen: Traverse op_info_items and generate
    ops_name_list = []  # all op class name store in this list
    ops_declare_list = []  # all op class declare store in this list
    ops_defined_list = []  # all op class defined store in this list
    for op_info in op_info_items:
1015
        # get op inputs info
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        op_input_name_list = op_info.input_name_list
        op_input_type_list = op_info.input_type_list
        op_input_optional_list = op_info.input_optional_list
1019
        op_input_no_need_buffer_list = op_info.input_no_need_buffer_list
1020
        # get op outputs info
1021 1022
        op_output_name_list = op_info.output_name_list
        op_output_type_list = op_info.output_type_list
1023
        op_output_size_list = op_info.output_size_list
1024
        op_output_optional_list = op_info.output_optional_list
1025
        op_output_intermediate_list = op_info.output_intermediate_list
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        # get op mutable attribute
        op_mutable_attribute_name_list = op_info.mutable_attribute_name_list
        op_mutable_attribute_type_list = op_info.mutable_attribute_type_list
        # get op attribute
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        op_attribute_name_list = op_info.attribute_name_list
        op_attribute_type_list = op_info.attribute_type_list
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        op_attribute_data_type_list = op_info.attribute_data_type_list
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        op_attribute_build_arg_type_list = op_info.attribute_build_arg_type_list
        op_attribute_default_value_list = op_info.attribute_default_value_list
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        op_non_mutable_attribute_name_list = []
        op_non_mutable_attribute_type_list = []
        op_non_mutable_attribute_data_type_list = []
        op_non_mutable_attribute_build_arg_type_list = []
        op_non_mutable_attribute_default_value_list = []
        for idx in range(len(op_attribute_name_list)):
            if (
                op_attribute_name_list[idx]
                not in op_mutable_attribute_name_list
            ):
                op_non_mutable_attribute_name_list.append(
                    op_attribute_name_list[idx]
                )
                op_non_mutable_attribute_type_list.append(
                    op_attribute_type_list[idx]
                )
                op_non_mutable_attribute_data_type_list.append(
                    op_attribute_data_type_list[idx]
                )
                op_non_mutable_attribute_build_arg_type_list.append(
                    op_attribute_build_arg_type_list[idx]
                )
                op_non_mutable_attribute_default_value_list.append(
                    op_attribute_default_value_list[idx]
                )
        # others
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        op_infer_meta_map = op_info.infer_meta_map
        op_kernel_map = op_info.kernel_map
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        op_interfaces = ["OpYamlInfoInterface"]
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        op_traits = []

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        exclusive_interface_str = ""
        if op_info.infer_shape_func:
            op_interfaces += ["InferShapeInterface"]
            exclusive_interface_str += (
                "  static void InferShape( phi::InferMetaContext *infer_meta );"
            )

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        # If op has inplace info, we will generate inplace op and non-inplace op.
        for op_name in op_info.op_phi_name:
            op_class_name = to_pascal_case(op_name) + "Op"
            op_dialect_name = dialect_name + "." + op_name

            # gen interface/trait str
            op_interfaces_str = ""
            if len(op_interfaces) > 0:
                op_interfaces_str = "," + ",".join(op_interfaces)
            op_traits_str = ""
            if len(op_traits) > 0:
                op_traits_str = "," + ",".join(op_traits)

            op_get_inputs_outputs_str = ""
            for idx in range(len(op_input_name_list)):
                op_get_inputs_outputs_str += OP_GET_INPUT_TEMPLATE.format(
                    input_name=op_input_name_list[idx],
                    input_index=idx,
                )
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            for idx in range(len(op_mutable_attribute_name_list)):
                op_get_inputs_outputs_str += OP_GET_INPUT_TEMPLATE.format(
                    input_name=op_mutable_attribute_name_list[idx],
                    input_index=idx + len(op_input_name_list),
                )
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            for idx in range(len(op_output_name_list)):
                op_get_inputs_outputs_str += OP_GET_OUTPUT_TEMPLATE.format(
                    output_name=op_output_name_list[idx],
                    output_index=idx,
                )
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            # gen build str
            build_define_input_args_str = ""
            build_declare_input_args_str = ""
            build_func_declare_str = ""
            if op_infer_meta_map is not None:
                build_define_input_args_str = GenBuildInputArgsStr(
                    op_input_name_list,
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                    op_mutable_attribute_name_list,
                    op_non_mutable_attribute_name_list,
                    op_non_mutable_attribute_build_arg_type_list,
                    op_non_mutable_attribute_default_value_list,
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                    True,
                )
                build_declare_input_args_str = GenBuildInputArgsStr(
                    op_input_name_list,
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                    op_mutable_attribute_name_list,
                    op_non_mutable_attribute_name_list,
                    op_non_mutable_attribute_build_arg_type_list,
                    op_non_mutable_attribute_default_value_list,
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                    False,
                )
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                build_inputs_str = GenBuildInputs(
                    op_input_name_list, op_mutable_attribute_name_list
                )
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                build_attributes_str = GenBuildAttributes(
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                    op_non_mutable_attribute_name_list,
                    op_non_mutable_attribute_type_list,
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                )
                build_outputs_str = GenBuildOutputs(
                    op_input_name_list,
                    op_input_type_list,
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                    op_mutable_attribute_name_list,
                    op_mutable_attribute_type_list,
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                    op_output_name_list,
                    op_output_type_list,
                    op_output_size_list,
                    op_infer_meta_map,
                )
                build_func_declare_str = OP_BUILD_TEMPLATE.format(
                    op_name=op_class_name,
                    build_args=build_declare_input_args_str,
                    build_inputs=build_inputs_str,
                    build_attributes=build_attributes_str,
                    build_outputs=build_outputs_str,
                )
            else:
                build_func_declare_str = OP_BUILD_TEMPLATE.format(
                    op_name=op_class_name,
                    build_args=build_declare_input_args_str,
                    build_inputs="",
                    build_attributes="",
                    build_outputs="",
                )

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            # gen op_declare_str/op_defined_str
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            if len(op_non_mutable_attribute_name_list) == 0:
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                op_declare_str = OP_DECLARE_TEMPLATE.format(
                    op_name=op_class_name,
                    dialect_op_name=op_dialect_name,
                    interfaces=op_interfaces_str,
                    traits=op_traits_str,
                    attribute_declare=op_0_attribute_declare_str,
                    attribute_num=0,
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                    build_args=build_define_input_args_str,
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                    get_inputs_and_outputs=op_get_inputs_outputs_str,
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                    exclusive_interface=exclusive_interface_str,
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                )
                op_defined_str = ""
            else:
                op_declare_str = OP_DECLARE_TEMPLATE.format(
                    op_name=op_class_name,
                    dialect_op_name=op_dialect_name,
                    interfaces=op_interfaces_str,
                    traits=op_traits_str,
                    attribute_declare=op_n_attribute_declare_str.format(
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                        attribute_num=len(op_non_mutable_attribute_name_list)
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                    ),
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                    attribute_num=len(op_non_mutable_attribute_name_list),
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                    build_args=build_define_input_args_str,
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                    get_inputs_and_outputs=op_get_inputs_outputs_str,
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                    exclusive_interface=exclusive_interface_str,
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                )
                attribute_names_str = (
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                    '"' + '", "'.join(op_non_mutable_attribute_name_list) + '"'
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                )
                op_defined_str = OP_N_ATTRIBUTE_DEFINED_TEMPLATE.format(
                    op_name=op_class_name,
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                    attribute_num=len(op_non_mutable_attribute_name_list),
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                    attribute_names=attribute_names_str,
                )
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            # generate get op info funciton: inputs
            inputs_info_str = ""
            if len(op_input_name_list) > 0:
                input_info_list = []
                for idx in range(len(op_input_name_list)):
                    input_info_list.append(
                        CONSTRUCT_INPUT_INFO_TEMPLATE.format(
                            name=op_input_name_list[idx],
                            typename=op_input_type_list[idx],
                            optional=op_input_optional_list[idx],
                            no_need_buffer=op_input_no_need_buffer_list[idx],
                        )
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                    )
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                inputs_info_str = ", ".join(input_info_list)

            # generate get op info funciton: outputs
            outputs_info_str = ""
            if len(op_output_name_list) > 0:
                output_info_list = []
                for idx in range(len(op_output_name_list)):
                    output_info_list.append(
                        CONSTRUCT_OUTPUT_INFO_TEMPLATE.format(
                            name=op_output_name_list[idx],
                            typename=op_output_type_list[idx],
                            optional=op_output_optional_list[idx],
                            intermediate=op_output_intermediate_list[idx],
                        )
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                    )
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                outputs_info_str = ", ".join(output_info_list)

            # generate get op info funciton: attributes
            attribute_info_str = ""
            if len(op_attribute_name_list) > 0:
                attribute_info_list = []
                for idx in range(len(op_attribute_name_list)):
                    attribute_info_list.append(
                        CONSTRUCT_ATTRIBUTE_INFO_TEMPLATE.format(
                            name=op_attribute_name_list[idx],
                            typename=op_attribute_type_list[idx],
                            data_type=op_attribute_data_type_list[idx],
                        )
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                    )
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                attribute_info_str = ", ".join(attribute_info_list)
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            # generate runtiem info
            infer_meta_func_str = ""
            infer_meta_param_str = ""
            if op_infer_meta_map is not None:
                infer_meta_func_str = op_infer_meta_map['func']
                infer_meta_param_str = '", "'.join(op_infer_meta_map['param'])
            kernel_func_str = ""
            kernel_param_str = ""
            if op_kernel_map is not None:
                kernel_func_str = '", "'.join(op_kernel_map['func'])
                kernel_param_str = '", "'.join(op_kernel_map['param'])

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            op_info_func_str = OP_INFO_TEMPLATE.format(
                op_name=op_class_name,
                inputs=inputs_info_str,
                attributes=attribute_info_str,
                outputs=outputs_info_str,
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                infer_meta_func=infer_meta_func_str,
                infer_meta_param=infer_meta_param_str,
                kernel_func=kernel_func_str,
                kernel_param=kernel_param_str,
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            )
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            # generate op verify function: inputs_type_check_str
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            if (
                len(op_input_type_list) + len(op_mutable_attribute_name_list)
            ) == 0:
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                inputs_type_check_str = (
                    "// Inputs num is 0, not need to check inputs type."
                )
            else:
                inputs_type_check_str = ""
            for idx in range(len(op_input_type_list)):
                input_type = op_input_type_list[idx]
                is_optional = op_input_optional_list[idx]
                is_vector = False
                if input_type.startswith("ir::VectorType<"):
                    is_vector = True
                    input_type = input_type[15:-1]
                check_str = ""
                if is_optional == "true":
                    if is_vector:
                        check_str = (
                            INPUT_OPTIONAL_VECTORTYPE_CHECK_TEMPLATE.format(
                                index=idx, standard=input_type
                            )
                        )
                    else:
                        check_str = INPUT_OPTIONAL_TYPE_CHECK_TEMPLATE.format(
                            index=idx, standard=input_type
                        )
                else:
                    if is_vector:
                        check_str = INPUT_VECTORTYPE_CHECK_TEMPLATE.format(
                            index=idx, standard=input_type
                        )
                    else:
                        check_str = INPUT_TYPE_CHECK_TEMPLATE.format(
                            index=idx, standard=input_type
                        )
                inputs_type_check_str += check_str

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            for idx in range(len(op_mutable_attribute_name_list)):
                mutable_attribute_type = op_mutable_attribute_type_list[idx][0]
                check_str = ""
                if mutable_attribute_type == "paddle::dialect::ScalarAttribute":
                    check_str = INPUT_TYPE_CHECK_TEMPLATE.format(
                        index=idx + len(op_input_type_list),
                        standard="paddle::dialect::DenseTensorType",
                    )
                else:
                    check_str = INPUT_VECTORTYPE_CHECK_TEMPLATE.format(
                        index=idx + len(op_input_type_list),
                        standard="paddle::dialect::DenseTensorType",
                    )
                inputs_type_check_str += check_str

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            # generate op verify function: outputs_type_check_str
            if len(op_output_type_list) == 0:
                outputs_type_check_str = (
                    "// Outputs num is 0, not need to check outputs type."
                )
            else:
                outputs_type_check_str = ""
            for idx in range(len(op_output_type_list)):
                output_type = op_output_type_list[idx]
                is_optional = op_output_optional_list[idx]
                is_vector = False
                if output_type.startswith("ir::VectorType<"):
                    is_vector = True
                    output_type = output_type[15:-1]
                check_str = ""
                if is_optional == "true":
                    if is_vector:
                        check_str = (
                            OUTPUT_OPTIONAL_VECTORTYPE_CHECK_TEMPLATE.format(
                                index=idx, standard=output_type
                            )
                        )
                    else:
                        check_str = OUTPUT_OPTIONAL_TYPE_CHECK_TEMPLATE.format(
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                            index=idx, standard=output_type
                        )
                else:
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                    if is_vector:
                        check_str = OUTPUT_VECTORTYPE_CHECK_TEMPLATE.format(
                            index=idx, standard=output_type
                        )
                    else:
                        check_str = OUTPUT_TYPE_CHECK_TEMPLATE.format(
                            index=idx, standard=output_type
                        )
                outputs_type_check_str += check_str

            # generate op verify function: attributes_check_str
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            if len(op_non_mutable_attribute_name_list) == 0:
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                attributes_check_str = (
                    "// Attributes num is 0, not need to check attributes type."
                )
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            else:
1358
                attributes_check_str = ""
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            for idx in range(len(op_non_mutable_attribute_name_list)):
                attribute_name = op_non_mutable_attribute_name_list[idx]
                attribute_type = op_non_mutable_attribute_type_list[idx]
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                if attribute_type.startswith("ir::ArrayAttribute<"):
                    attribute_type = attribute_type[19:-1]
                    attributes_check_str += (
                        ATTRIBUTE_VECTOR_CHECK_TEMPLATE.format(
                            attribute_name=attribute_name,
                            standard=attribute_type,
                        )
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                    )
                else:
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                    attributes_check_str += ATTRIBUTE_CHECK_TEMPLATE.format(
                        attribute_name=attribute_name, standard=attribute_type
1373 1374
                    )

1375
            # generate op verify function
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            if "GradOp" in op_class_name or "Grad_Op" in op_class_name:
                op_verify_str = GRAD_OP_VERIFY_TEMPLATE.format(
                    op_name=op_class_name,
                )
            else:
                op_verify_str = OP_VERIFY_TEMPLATE.format(
                    op_name=op_class_name,
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                    inputs_size=len(op_input_type_list)
                    + len(op_mutable_attribute_type_list),
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                    outputs_size=len(op_output_type_list),
                    inputs_type_check=inputs_type_check_str,
                    outputs_type_check=outputs_type_check_str,
                    attributes_check=attributes_check_str,
                )
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            op_infer_shape_str = ""
            if op_info.infer_shape_func:
                op_infer_shape_str = OP_INFER_SHAPE_TEMPLATE.format(
                    op_name=op_class_name,
                    infer_meta_func=op_info.infer_shape_func,
                )

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            ops_name_list.append(op_class_name)
            ops_declare_list.append(op_declare_str)
            ops_defined_list.append(op_defined_str)
            ops_defined_list.append(op_info_func_str)
1402
            ops_defined_list.append(build_func_declare_str)
1403
            ops_defined_list.append(op_verify_str)
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            ops_defined_list.append(op_infer_shape_str)
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    # (4) Generate head file str
    op_namespaces_prev = ""
    for name in namespaces:
        op_namespaces_prev += name + "::"
    ops_name_with_namespace_list = []
    for name in ops_name_list:
        ops_name_with_namespace_list.append(op_namespaces_prev + name)
    op_list_str = GET_OP_LIST_TEMPALTE.format(
        ", ".join(ops_name_with_namespace_list)
    )  # Add GET_OP_LIST
    head_file_str = ""
    head_file_str += "".join(ops_declare_list)  # Add op class
    for name in reversed(namespaces):
        head_file_str = NAMESPACE_GARD_TEMPLATE.format(
            namespace=name, input=head_file_str
        )  # Add namespaces
    head_file_str = H_FILE_TEMPLATE.format(
        op_declare=op_list_str, input=head_file_str
    )  # Add head

    # (5) Generate source file str
    source_file_str = "".join(ops_defined_list)  # Add op define
    for name in reversed(namespaces):
        source_file_str = NAMESPACE_GARD_TEMPLATE.format(
            namespace=name, input=source_file_str
        )  # Add namespaces
    source_file_str = CC_FILE_TEMPLATE.format(
1433
        h_file=op_def_h_file[:-4], input=source_file_str
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    )  # Add head

    # (5) Generate pd_op.h.tmp, pd_op.cc.tmp
    with open(op_def_h_file, 'a') as f:
        f.write(head_file_str)
    with open(op_def_cc_file, 'a') as f:
        f.write(source_file_str)


# =====================================
# Script parameter parsing
# =====================================
def ParseArguments():
    parser = argparse.ArgumentParser(
        description='Generate Dialect OP Definition Files By Yaml'
    )
    parser.add_argument('--op_yaml_files', type=str)
    parser.add_argument('--op_compat_yaml_file', type=str)
    parser.add_argument('--namespaces', type=str)
    parser.add_argument('--dialect_name', type=str)
    parser.add_argument('--op_def_h_file', type=str)
    parser.add_argument('--op_def_cc_file', type=str)
    return parser.parse_args()


# =====================================
# Main
# =====================================
if __name__ == "__main__":
    # parse arguments
    args = ParseArguments()
    op_yaml_files = args.op_yaml_files.split(",")
    op_compat_yaml_file = args.op_compat_yaml_file
    namespaces = []
    if args.namespaces is not None:
        namespaces = args.namespaces.split(",")
    dialect_name = args.dialect_name
    op_def_h_file = args.op_def_h_file
    op_def_cc_file = args.op_def_cc_file

    # auto code generate
    OpGenerator(
        op_yaml_files,
1477
        op_compat_yaml_file,
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        namespaces,
        dialect_name,
        op_def_h_file,
        op_def_cc_file,
    )