api_gen.py

# Copyright (c) 2021 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 os
import yaml
import argparse
import re

from api_base import BaseAPI, PREFIX_TENSOR_NAME


class ForwardAPI(BaseAPI):
    def __init__(self, api_item_yaml):
        super(ForwardAPI, self).__init__(api_item_yaml)
        self.is_dygraph_api, self.intermediate_outs = self.parse_intermediate(
            api_item_yaml)

    def get_api_func_name(self):
        if self.is_dygraph_api:
            return self.api + '_intermediate'
        else:
            return self.api

    def parse_intermediate(self, api_item_yaml):
        if 'intermediate' in api_item_yaml:
            intermediate_outs = [
                item.strip()
                for item in api_item_yaml['intermediate'].split(',')
            ]
            return True, intermediate_outs
        else:
            return False, []

    def get_return_type(self, out_type_list):
        return out_type_list[0] if len(
            out_type_list) == 1 else "std::tuple<" + ",".join(
                out_type_list) + ">"

    def gene_return_type_code(self):
        if self.is_dygraph_api or len(self.intermediate_outs) == 0:
            return self.outputs['return_type']
        else:
            return_out_list = []
            for i, name in enumerate(self.outputs['names']):
                if name.split('@')[0] not in self.intermediate_outs:
                    return_out_list.append(self.outputs['types'][i])
            return return_out_list[0] if len(
                return_out_list) == 1 else "std::tuple<" + ",".join(
                    return_out_list) + ">"

    def gene_return_code(self):
        if self.is_dygraph_api or len(self.intermediate_outs) == 0:
            return "return api_output;"
        else:
            return_out_list = []
            for i, name in enumerate(self.outputs['names']):
                if name.split('@')[0] not in self.intermediate_outs:
                    return_out_list.append(i)
            if len(return_out_list) == 1:
                return f"return std::get<{return_out_list[0]}>(api_output);"
            else:
                selected_code = [
                    f"std::get<{i}>(api_output)" for i in return_out_list
                ]
            return 'return {' + ", ".join(selected_code) + '};'

    def gene_output(self,
                    output_type_list,
                    set_out_func,
                    code_indent,
                    inplace_flag=False):
        kernel_output = ""
        output_names = []
        output_create = ""

        if len(output_type_list) == 1:
            kernel_output = 'kernel_out'
            output_names.append('kernel_out')
            inplace_assign = " = " + self.inplace_map[self.outputs['names'][
                0]] if inplace_flag and self.inplace_map is not None and self.outputs[
                    'names'][0] in self.inplace_map else ""
            output_create = f"""
{code_indent}  {self.outputs['return_type']} api_output{inplace_assign};"""

            if self.outputs['return_type'] == 'std::vector<Tensor>':
                assert self.outputs['out_size_expr'] is not None, \
                     f"{api_name}: The out size expr : '{{expr}}' should be set when output has Tensor[]. You can refer 'split' api."
                output_create = output_create + f"""
{code_indent}  auto kernel_out = {set_out_func}({self.outputs['out_size_expr']}, kernel_backend, &api_output);"""

            else:
                output_create = output_create + f"""
{code_indent}  auto kernel_out = {set_out_func}(kernel_backend, &api_output);"""

            if not inplace_flag and self.view_map is not None and self.outputs[
                    'names'][0] in self.view_map:
                output_create = output_create + f"""
{code_indent}  kernel_out->ShareBufferWith(*{PREFIX_TENSOR_NAME}{self.view_map[self.outputs['names'][0]]});
{code_indent}  kernel_out->ShareInplaceVersionCounterWith(*{PREFIX_TENSOR_NAME}{self.view_map[self.outputs['names'][0]]});
{code_indent}  VLOG(3) << "Perform View between Output and Input Tensor, share allocation and inplace version.";"""

        elif len(output_type_list) > 1:
            output_create = f"""
{code_indent}  {self.outputs['return_type']} api_output;"""

            for i in range(len(output_type_list)):
                kernel_output = kernel_output + f'kernel_out_{i}, '
                output_names.append(f'kernel_out_{i}')
                if inplace_flag and self.inplace_map is not None and self.outputs[
                        'names'][i] in self.inplace_map:
                    output_create = output_create + f"""
{code_indent}  std::get<{i}>(api_output) = {self.inplace_map[self.outputs['names'][i]]};"""

                if output_type_list[i] == 'std::vector<Tensor>':
                    assert self.outputs['out_size_expr'][i] is not None, \
                        f"{api_name}: The out size expr : '{{expr}}' should be set when output has Tensor[]. You can refer 'split' api."
                    output_create = output_create + f"""
{code_indent}  auto kernel_out_{i} = {set_out_func}({self.outputs['out_size_expr'][i]}, kernel_backend, &std::get<{i}>(api_output));"""

                else:
                    output_create = output_create + f"""
{code_indent}  auto kernel_out_{i} = {set_out_func}(kernel_backend, &std::get<{i}>(api_output));"""

                if not inplace_flag and self.view_map is not None and self.outputs[
                        'names'][i] in self.view_map:
                    output_create = output_create + f"""
{code_indent}  kernel_out_{i}->ShareBufferWith(*{PREFIX_TENSOR_NAME}{self.view_map[self.outputs['names'][i]]});
{code_indent}  kernel_out_{i}->ShareInplaceVersionCounterWith(*{PREFIX_TENSOR_NAME}{self.view_map[self.outputs['names'][i]]});
{code_indent}  VLOG(3) << "Perform View between Output and Input Tensor, share allocation and inplace version.";"""

            kernel_output = kernel_output[:-2]
        else:
            raise ValueError(
                "{} : Output error: the output should not be empty.".format(
                    self.api))

        return kernel_output, output_names, output_create


def header_include():
    return """
#include <tuple>

#include "paddle/phi/api/include/tensor.h"
#include "paddle/phi/common/scalar.h"
#include "paddle/phi/common/int_array.h"
#include "paddle/utils/optional.h"
"""


def source_include(header_file_path):
    return f"""
#include "{header_file_path}"
#include <memory>

#include "glog/logging.h"

#include "paddle/phi/api/lib/api_custom_impl.h"
#include "paddle/phi/api/lib/api_gen_utils.h"
#include "paddle/phi/api/lib/data_transform.h"
#include "paddle/phi/api/lib/kernel_dispatch.h"
#include "paddle/phi/api/lib/utils/storage.h"
#include "paddle/phi/core/kernel_registry.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/fluid/platform/profiler/event_tracing.h"

DECLARE_bool(conv2d_disable_cudnn);
"""


def api_namespace():
    return ("""
namespace paddle {
namespace experimental {

""", """

}  // namespace experimental
}  // namespace paddle
""")


def generate_api(api_yaml_path, header_file_path, source_file_path):

    with open(api_yaml_path, 'r') as f:
        apis = yaml.load(f, Loader=yaml.FullLoader)
    header_file = open(header_file_path, 'w')
    source_file = open(source_file_path, 'w')

    namespace = api_namespace()

    header_file.write("#pragma once\n")
    header_file.write(header_include())
    header_file.write(namespace[0])

    include_header_file = "paddle/phi/api/include/api.h"
    source_file.write(source_include(include_header_file))
    source_file.write(namespace[0])

    for api in apis:
        foward_api = ForwardAPI(api)
        if foward_api.is_dygraph_api:
            foward_api.is_dygraph_api = False

        header_file.write(foward_api.gene_api_declaration())
        source_file.write(foward_api.gene_api_code())

    header_file.write(namespace[1])
    source_file.write(namespace[1])

    header_file.close()
    source_file.close()


def main():
    parser = argparse.ArgumentParser(
        description='Generate PaddlePaddle C++ API files')
    parser.add_argument(
        '--api_yaml_path',
        help='path to api yaml file',
        default='python/paddle/utils/code_gen/api.yaml')

    parser.add_argument(
        '--api_header_path',
        help='output of generated api header code file',
        default='paddle/phi/api/include/api.h')

    parser.add_argument(
        '--api_source_path',
        help='output of generated api source code file',
        default='paddle/phi/api/lib/api.cc')

    options = parser.parse_args()

    api_yaml_path = options.api_yaml_path
    header_file_path = options.api_header_path
    source_file_path = options.api_source_path

    generate_api(api_yaml_path, header_file_path, source_file_path)


if __name__ == '__main__':
    main()