eager_op_function_generator.cc

// 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.

#include <algorithm>
#include <fstream>
#include <iostream>
#include <set>
#include <string>
#ifndef _WIN32
#include <unistd.h>
#endif

#include "paddle/fluid/framework/op_info.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/framework/operator.h"
#include "paddle/fluid/framework/variable.h"
#include "paddle/fluid/pybind/pybind.h"
#include "paddle/fluid/string/string_helper.h"
#ifdef PADDLE_WITH_ASCEND_CL
#include "paddle/fluid/framework/fleet/ascend_wrapper.h"
#endif
#include "paddle/fluid/pybind/op_function_generator.h"

std::set<std::string> gen_list = {
    "sigmoid", "matmul_v2", "reduce_sum", "elementwise_add", "rsqrt",
    "multihead_matmul", "addmm", "gru", "round", "push_dense", "rank_attention",
    "fused_embedding_fc_lstm", "where_index", "bicubic_interp", "arg_min",
    "tile", "bilinear_tensor_product", "ctc_align",
    "pow2_decay_with_linear_warmup", "marker", "split", "fc",
    "load", "elementwise_max", "adadelta",
    "tan",
    "fsp", "where", "logical_xor", "multiclass_nms3", "one_hot_v2",
    "sequence_softmax", "affine_channel", "triangular_solve",
    "sequence_topk_avg_pooling", "space_to_depth", "reverse",
    "fused_embedding_eltwise_layernorm", "expand_v2", "lgamma", "solve",
    "deformable_psroi_pooling", "instance_norm", "decode_jpeg", "gather_nd",
    "reduce_prod", "matrix_rank", "asin", "lstmp", "iou_similarity",
    "huber_loss", "one_hot", "sequence_slice", "lookup_table", "softplus",
    "depthwise_conv2d", "fused_fc_elementwise_layernorm",
    "sigmoid_cross_entropy_with_logits", "exp", "scatter", "equal_all",
    "searchsorted", "fusion_squared_mat_sub", "unique", "log", "conv_shift",
    "smooth_l1_loss", "linear_interp_v2",
    "temporal_shift", "nce", "mv", "proximal_gd", "memcpy_h2d",
    "add_position_encoding", "cosh", "hash", "grad_add", "sign", "prelu",
    "linspace", "fill_diagonal", "logsigmoid", "load_combine", "fetch_v2",
    "randperm", "sequence_scatter", "partial_sum", "relu6", "conv3d",
    "lstm_unit", "not_equal", "transpose2", "uniform_random_batch_size_like",
    "unfold", "lrn", "softmax_with_cross_entropy", "isfinite_v2", "bernoulli",
    "max_pool3d_with_index", "gaussian_random", "flatten2",
    "cvm", "adamax", "masked_select", "range", "bitwise_not", "trace",
    "multinomial", "modified_huber_loss", "roll", "squared_l2_distance",
    "conv3d_transpose", "share_data", "fake_quantize_abs_max",
    "unique_with_counts", "fill", "concat", "fill_zeros_like",
    "hierarchical_sigmoid", "isinf_v2", "squeeze", "multiclass_nms2",
    "bpr_loss", "fft_c2c", "bicubic_interp_v2", "reshape", "coalesce_tensor",
    "roi_align", "reshape2", "reduce_any", "unstack", "scatter_nd_add",
    "sequence_reshape", "bilateral_slice", "fill_any_like", "empty",
    "pad_constant_like", "pool2d", "size", "imag", "eigh", "stack",
    "dgc_momentum",
    "generate_proposals_v2", "bitwise_or", "gru_unit",
    "sampling_id", "unsqueeze2",
    "sequence_enumerate", "fusion_seqconv_eltadd_relu", "bce_loss",
    "generate_proposal_labels", "im2sequence", "isinf", "adagrad",
    "linear_chain_crf", "retinanet_target_assign", "fusion_group",
    "teacher_student_sigmoid_loss", "random_crop", "lookup_table_v2",
    "detection_map", "l1_norm", "sqrt", "fused_elemwise_activation",
    "slogdeterminant", "share_buffer", "bitwise_and", "diag_embed", "unbind",
    "dropout",
    "beam_search", "log_loss", "greater_than", "kron", "sigmoid_focal_loss",
    "rmsprop", "conv2d", "uniform_random_inplace", "maxout", "linear_interp",
    "auc", "logical_or",
    "acos", "unpool", "cumprod", "sample_logits", "crop_tensor",
    "deformable_conv", "generate_mask_labels", "locality_aware_nms",
    "expand_as", "matrix_power", "greater_equal", "generate_proposals",
    "bilinear_interp", "inplace_abn", "softshrink", "mul", "data_norm",
    "get_tensor_from_selected_rows", "spp", "floor", "gelu",
    "retinanet_detection_output", "push_dense", "silu", "sequence_erase",
    "real", "nearest_interp_v2", "dgc_clip_by_norm", "squeeze2",
    "strided_slice", "conj", "precision_recall", "save",
    "fusion_seqexpand_concat_fc", "fake_quantize_range_abs_max",
    "depthwise_conv2d_transpose", "positive_negative_pair", "square",
    "var_conv_2d", "log1p", "fused_softmax_mask_upper_triangle", "clip_by_norm",
    "atan2", "box_decoder_and_assign", "fft_r2c", "roi_pool", "overlap_add",
    "fill_constant_batch_size_like", "fill_any", "dequantize_log",
    "max_pool2d_with_index", "pad3d", "norm", "viterbi_decode", "mish",
    "box_coder", "flatten", "elementwise_mod", "margin_cross_entropy",
    "logical_and", "pow", "stanh", "label_smooth", "merged_momentum",
    "ascend_trigger", "fused_feedforward", "rpn_target_assign",
    "roi_perspective_transform", "expand", "prroi_pool", "pool3d", "memcpy",
    "distribute_fpn_proposals", "frame", "bincount", "shape", "group_norm",
    "resnet_unit", "sequence_expand_as", "cos_sim", "eigvals", "save_combine",
    "class_center_sample", "read_file", "isfinite", "arg_max", "equal",
    "fake_dequantize_max_abs", "qr", "anchor_generator", "layer_norm",
    "merge_selected_rows", "less_equal",
    "fusion_lstm", "lars_momentum", "hard_sigmoid", "isnan",
    "elementwise_floordiv", "correlation", "histogram", "gather_tree",
    "segment_pool", 
    "fusion_repeated_fc_relu", "nop",
    "expand_as_v2", "filter_by_instag", "nll_loss", "dot", "scale", "ncclBcast",
    "shuffle_batch", "ncclReduce", "diag", "multiplex", "leaky_relu",
    "allclose",
    "elementwise_pow", "prior_box", "p_norm", "unique_consecutive", "lod_reset",
    "pad", "sequence_conv", "log10", "set_value", "bitwise_xor", "center_loss",
    "randint", "attention_lstm", "uniform_random", "slice", "meshgrid",
    "hard_swish", "sin", "mean_iou", "pad2d", "inverse", "spectral_norm",
    "shuffle_channel", "psroi_pool", "seed", "ceil", "eig", "reduce_min", "cos",
    "ncclAllReduce", "cudnn_lstm", "digamma", "assign_value", "increment",
    "tdm_sampler", "fused_softmax_mask", "sequence_reverse", "eigvalsh",
    "diagonal", "trunc", "log2", "tanh", "yolov3_loss", "graph_send_recv",
    "atan", "less_than", "unsqueeze", "crf_decoding", "log_softmax", "ftrl",
    "matrix_nms", "top_k_v2", "cast", "tanh_shrink", "hard_shrink",
    "multiclass_nms", "fusion_transpose_flatten_concat", "sequence_unpad",
    "fused_elemwise_add_activation", "frobenius_norm", "crop", "cross_entropy2",
    "skip_layernorm", "tdm_child", "fused_embedding_seq_pool", "erf",
    "conv2d_inception_fusion", "trilinear_interp", "logsumexp",
    "fusion_seqpool_concat", "alloc_float_status", "sequence_concat",
    "fusion_seqpool_cvm_concat", "similarity_focus", "argsort",
    "sequence_expand",
    "fused_bn_add_activation", "bilinear_interp_v2", "clip",
    "deformable_conv_v1", "hinge_loss", "determinant", "conv2d_transpose",
    "memcpy_d2h", "softsign",
    "broadcast_tensors", "grid_sampler", "fft_c2r", "pyramid_hash",
    "multi_dot", "sequence_pool", "transpose", "top_k", "dist", "affine_grid",
    "gaussian_random_batch_size_like", "fake_channel_wise_dequantize_max_abs",
    "reciprocal", "sequence_mask", "fill_diagonal_tensor", "abs",
    "partial_concat", "elu", "index_select", "row_conv", "cross",
    "elementwise_mul", "decayed_adagrad", "bipartite_match",
    "fake_quantize_moving_average_abs_max", "mine_hard_examples",
    "target_assign", "lstm", "truncated_gaussian_random", "match_matrix_tensor",
    "elementwise_div", "kldiv_loss", "cumsum", "sum", "proximal_adagrad",
    "shard_index", "selu", "mean", "gumbel_softmax", "sequence_pad",
    "tree_conv", "assign", "flatten_contiguous_range", "tril_triu", "brelu",
    "celu", "reduce_mean", "sinh", "rank_loss", "reduce_max", "fusion_gru",
    "fill_zeros_like2", "expm1", "squared_l2_norm", "elementwise_sub",
    "margin_rank_loss", "faster_tokenizer", "relu", "is_empty", "reduce_all",
    "edit_distance", "bmm", "yolo_box", "soft_relu", "density_prior_box", "eye",
    "swish", "cross_entropy", "dpsgd", "cholesky", "batch_fc", "nearest_interp",
    "gather", "trilinear_interp_v2", "box_clip", "isnan_v2", "softmax",
    "conv2d_fusion", "fused_batch_norm_act",
    "index_sample", "elementwise_min", "logical_not", "collect_fpn_proposals",
    "pixel_shuffle", "thresholded_relu", "polygon_box_transform",
    "lookup_table_dequant", "warpctc", "fake_channel_wise_quantize_abs_max",
    "dequantize_abs_max", "svd", "flip"};

// clang-format off
const char* OUT_INITIALIZER_TEMPLATE =
    R"({"%s", {std::shared_ptr<imperative::VarBase>(new imperative::VarBase("auto_"+std::to_string(VarBaseUniqueNameID++)+"_"))}})";
const char* OUT_DUPLICABLE_INITIALIZER_TEMPLATE = R"({"%s", ConstructDuplicableOutput(%s)})";

const char* INPUT_INITIALIZER_TEMPLATE = R"({"%s", {%s}})";
const char* INPUT_LIST_INITIALIZER_TEMPLATE = R"({"%s", %s})";

const char* INPUT_INITIALIZER_TEMPLATE_WITH_NULL = R"(
    if (%s != nullptr) {
      ins["%s"] = {%s};
    }
)";

const char* INPUT_INITIALIZER_TEMPLATE_WITH_NULL_LIST = R"(
    if (%s.size() != 0) {
      ins["%s"] = %s;
    }
)";

const char* OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL = R"(
    outs["%s"] = {%s};
)";

const char* OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL_LIST = R"(
    outs["%s"] = %s;
)";
// if inputs is list, no need {}
const char* ARG_OUT_NUM = R"(%sNum)";
const char* ARG_OUT_NUM_TYPE = R"(size_t )";

const char* IN_VAR_TYPE = R"(py::handle)";
const char* IN_VAR_LIST_TYPE = R"(py::handle)";

const char* OUT_VAR_TYPE = R"(std::shared_ptr<imperative::VarBase>)";
const char* OUT_VAR_LIST_TYPE = R"(std::vector<std::shared_ptr<imperative::VarBase>>)";

const char* CAST_VAR_TEMPLATE = R"(
    auto %s = GetEagerTensorFromArgs("%s", "%s", args, %d, %s);)";

const char* CAST_VAR_LIST_TEMPLATE = R"(
    auto %s = GetEagerTensorListFromArgs("%s", "%s", args, %d, %s);)";

const char* CAST_SIZE_T_TEMPLATE = R"(
    auto %s = GetUnsignedLongFromArgs("%s", "%s", args, %d, %s);)";

const char* ARG_TEMPLATE = R"(const %s& %s)";

const char* RETURN_TUPLE_TYPE = R"(std::tuple<%s>)";
const char* RETURN_TUPLE_TEMPLATE = R"(std::make_tuple(%s))";
const char* RETURN_LIST_TEMPLATE = R"(outs["%s"])";
const char* RETURN_TEMPLATE = R"(outs["%s"][0])";

const char* FUNCTION_ARGS = R"(%s, const py::args& args)";
const char* FUNCTION_ARGS_NO_INPUT = R"(const py::args& args)";

const char* HANDLE_VIEW_BETWEEN_INPUT_AND_OUTPUT = R"(
    if (ins.count("%s") && outs.count("%s")) {
      HandleViewBetweenInputAndOutput(ins["%s"][0], outs["%s"][0]);
    })";

const char* OP_FUNCTION_TEMPLATE =
R"(
static PyObject * %s(PyObject *self, PyObject *args, PyObject *kwargs)
{
  PyThreadState *tstate = nullptr;
  try
  {
    %s
    framework::AttributeMap attrs;
    ConstructAttrMapFromPyArgs("%s", args, %d, PyTuple_GET_SIZE(args) , attrs);
    tstate = PyEval_SaveThread();
    %s
    PyEval_RestoreThread(tstate);
    tstate = nullptr;
    %s
  }
  catch(...) {
    if (tstate) {
      PyEval_RestoreThread(tstate);
    }
    ThrowExceptionToPython(std::current_exception());
    return nullptr;
  }
})";

const char* PYBIND_ITEM_TEMPLATE = R"(  {"%s", (PyCFunction)(void(*)(void))%s, METH_VARARGS | METH_KEYWORDS, "C++ interface function for %s in dygraph."},)";

// clang-format on
static inline bool FindInsMap(const std::string& op_type,
                              const std::string& in_name) {
  return op_ins_map[op_type].count(in_name);
}

static inline bool FindOutsMap(const std::string& op_type,
                               const std::string& out_name) {
  return op_outs_map[op_type].count(out_name);
}

static inline bool FindPassingOutsMap(const std::string& op_type,
                                      const std::string& out_name) {
  return op_passing_outs_map[op_type].count(out_name);
}

static inline bool FindViewOpMap(const std::string& op_type) {
  return view_op_map.count(op_type);
}

static inline std::string TempName(const std::string& name) {
  return name + '_';
}

std::string GenerateOpFunctionsBody(
    const paddle::framework::proto::OpProto* op_proto, std::string func_name,
    bool use_inplace_strategy = false,
    std::map<std::string, std::string> inplace_map = {}) {
  auto& op_type = op_proto->type();
  std::string input_args = "";
  std::string call_api_str = "auto out = " + op_type + "_dygraph_function(";
  std::string ins_initializer_with_null = "";
  std::string py_arg = "";
  int arg_idx = 0;
  int input_args_num = 0;
  std::string ins_cast_str = "";
  std::string view_strategy_str = "";
  for (auto& input : op_proto->inputs()) {
    auto& in_name = input.name();
    // skip those dispensable inputs, like ResidualData in conv2d
    if (input.dispensable() && !FindInsMap(op_type, in_name)) {
      continue;
    }
    const auto in_type = input.duplicable() ? IN_VAR_LIST_TYPE : IN_VAR_TYPE;
    auto input_arg =
        paddle::string::Sprintf(ARG_TEMPLATE, in_type, TempName(in_name));
    input_args += input_arg;
    input_args += ",";
    input_args_num++;
    const auto in_cast_type =
        input.duplicable() ? CAST_VAR_LIST_TEMPLATE : CAST_VAR_TEMPLATE;
    auto dispensable = input.dispensable() ? "true" : "false";
    ins_cast_str += paddle::string::Sprintf(in_cast_type, in_name, op_type,
                                            in_name, arg_idx++, dispensable);

    call_api_str += in_name + ", ";
  }

  if (!input_args.empty() && input_args.back() == ',') {
    input_args.pop_back();
  }

  // Generate outs initializer
  std::string outs_initializer = "{";
  std::string outs_initializer_with_null = "";
  std::string return_str = "";

  int outs_num = 0;
  for (auto& output : op_proto->outputs()) {
    auto& out_name = output.name();

    // skip those dispensable oututs
    if (output.dispensable() && !FindOutsMap(op_type, out_name)) {
      continue;
    }
    const auto out_type =
        output.duplicable() ? OUT_VAR_LIST_TYPE : OUT_VAR_TYPE;

    if (FindPassingOutsMap(op_type, out_name)) {
      if (input_args != "") {
        input_args += ",";
      }
      input_args += out_type;
      input_args += out_name;
      input_args_num++;

      if (output.dispensable()) {
        const auto out_template =
            output.duplicable() ? OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL_LIST
                                : OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL;
        outs_initializer_with_null +=
            paddle::string::Sprintf(out_template, out_name, out_name);
      } else {
        const auto out_template = output.duplicable()
                                      ? INPUT_LIST_INITIALIZER_TEMPLATE
                                      : INPUT_INITIALIZER_TEMPLATE;
        outs_initializer +=
            paddle::string::Sprintf(out_template, out_name, out_name);
        outs_initializer += ",";
      }

      const auto in_cast_type =
          output.duplicable() ? CAST_VAR_LIST_TEMPLATE : CAST_VAR_TEMPLATE;
      auto dispensable = output.dispensable() ? "true" : "false";
      ins_cast_str += paddle::string::Sprintf(in_cast_type, out_name, op_type,
                                              out_name, arg_idx++, dispensable);

      // call_api_str += out_name + ", ";
    } else {
      // There are few Operators that have duplicable output, like `Out` in
      // split op. We need to specify the number of variables for the
      // duplicable output, as the argument OutNum;
      if (output.duplicable()) {
        if (input_args != "") {
          input_args += ",";
        }
        auto out_num_str = paddle::string::Sprintf(ARG_OUT_NUM, out_name);
        input_args += ARG_OUT_NUM_TYPE;
        input_args += out_num_str;
        input_args_num++;
        outs_initializer += paddle::string::Sprintf(
            OUT_DUPLICABLE_INITIALIZER_TEMPLATE, out_name, out_num_str);

        auto dispensable = output.dispensable() ? "true" : "false";
        ins_cast_str +=
            paddle::string::Sprintf(CAST_SIZE_T_TEMPLATE, out_num_str, op_type,
                                    out_num_str, arg_idx++, dispensable);
        call_api_str += out_num_str + ", ";
      } else {
        outs_initializer +=
            paddle::string::Sprintf(OUT_INITIALIZER_TEMPLATE, out_name);
      }
      outs_initializer += ",";
    }

    // return_str += paddle::string::Sprintf(return_template, out_name);
    // return_str += ",";
    outs_num += 1;
  }
  call_api_str += "attrs);";
  if (outs_initializer.back() == ',') {
    outs_initializer.pop_back();
    // return_str.pop_back();
  }
  outs_initializer += "}";
  if (FindViewOpMap(op_type)) {
    std::string viwe_input_name = view_op_map[op_type].first;
    std::string viwe_output_name = view_op_map[op_type].second;
    view_strategy_str += paddle::string::Sprintf(
        HANDLE_VIEW_BETWEEN_INPUT_AND_OUTPUT, viwe_input_name, viwe_output_name,
        viwe_input_name, viwe_output_name);
  }

  return_str = "return ToPyObject(out);";

  std::string function_args = "";
  if (input_args == "") {
    function_args = FUNCTION_ARGS_NO_INPUT;
  } else {
    function_args = paddle::string::Sprintf(FUNCTION_ARGS, input_args);
  }

  // generate op funtcion body
  auto op_function_str = paddle::string::Sprintf(
      OP_FUNCTION_TEMPLATE, func_name, ins_cast_str, op_type, input_args_num,
      call_api_str, return_str);

  return op_function_str;
}

static std::tuple<std::vector<std::string>, std::vector<std::string>>
GenerateOpFunctions() {
  auto& op_info_map = paddle::framework::OpInfoMap::Instance().map();

  std::vector<std::string> op_function_list, bind_function_list;
  auto& all_kernels = paddle::framework::OperatorWithKernel::AllOpKernels();

  for (auto& pair : op_info_map) {
    auto& op_info = pair.second;
    auto op_proto = op_info.proto_;
    if (op_proto == nullptr) {
      continue;
    }
    auto& op_type = op_proto->type();
    // Skip ooerator which is not inherit form OperatorWithKernel, like while,
    // since only OperatorWithKernel can run in dygraph mode.
    // if the pten lib contains op kernel, we still generate ops method
    if (!all_kernels.count(op_type) &&
        !pten::KernelFactory::Instance().HasCompatiblePtenKernel(op_type)) {
      continue;
    }
    if (!gen_list.count(op_type)) {
      continue;
    }
    std::string func_name = "eager_api_" + op_type;
    std::string op_function_str = GenerateOpFunctionsBody(op_proto, func_name);

    // generate pybind item
    auto bind_function_str = paddle::string::Sprintf(
        PYBIND_ITEM_TEMPLATE, op_type, func_name, op_type);

    op_function_list.emplace_back(std::move(op_function_str));
    bind_function_list.emplace_back(std::move(bind_function_str));
  }
  return std::make_tuple(op_function_list, bind_function_list);
}

int main(int argc, char* argv[]) {
  if (argc != 2) {
    std::cerr << "argc must be 2" << std::endl;
    return -1;
  }

#ifdef PADDLE_WITH_ASCEND_CL
  auto ascend_ptr = paddle::framework::AscendInstance::GetInstance();
  ascend_ptr->InitGEForUT();
#endif

  std::vector<std::string> headers{
      "\"pybind11/detail/common.h\"",
      "\"paddle/fluid/pybind/op_function_common.h\"",
      "\"paddle/fluid/pybind/exception.h\"", "<Python.h>"};

  std::ofstream out(argv[1], std::ios::out);

  out << "#pragma once\n\n";

  for (auto& header : headers) {
    out << "#include  " + header + "\n";
  }

  out << "\n\n";

  auto op_funcs = GenerateOpFunctions();

  out << "namespace paddle {\n"
      << "namespace pybind {\n\n";
  out << paddle::string::join_strings(std::get<0>(op_funcs), '\n');
  out << "\n\n";

  out << "static PyMethodDef ExtestMethods[] = {\n"
      << paddle::string::join_strings(std::get<1>(op_funcs), '\n')
      << "\n  {nullptr,nullptr,0,nullptr}"
      << "};\n\n";

  out << "inline void BindEagerOpFunctions(pybind11::module *module) {\n"
      << "  InitOpsAttrTypeMap();\n"
      << "  auto m = module->def_submodule(\"ops\");\n"
      << "  if (PyModule_AddFunctions(m.ptr(), ExtestMethods) < 0) {\n"
      << "    PADDLE_THROW(platform::errors::Fatal (\"Add functions to "
         "core.eager.ops failed!\"));\n"
      << "  }\n\n"
      << "}\n\n"
      << "} // namespace pybind\n"
      << "} // namespace paddle\n";

  out.close();

#ifdef PADDLE_WITH_ASCEND_CL
  ge::GEFinalize();
#endif

  return 0;
}