【Paddle2ONNX】为Paddle2ONNX升级Opset版本到18

ZhengBicheng05222024-05-30 22:24

1 简介

在深度学习模型的部署过程中，模型格式的转换是一个至关重要的环节。Paddle2ONNX是一个开源工具，用于将飞桨（PaddlePaddle）模型转换为ONNX格式，以便在各种不同的深度学习框架中进行推理和部署。随着ONNX标准的不断更新，保持Paddle2ONNX与最新版本的兼容性显得尤为重要。本篇文章《【Paddle2ONNX】为Paddle2ONNX升级ONNX版本》将详细介绍如何为Paddle2ONNX项目升级其依赖的ONNX版本。

2 升级ONNX依赖实现过程

2.1 修改 .gitmodules

修改onnx的branch参数到最新的commit id

[submodule "third_party/onnx"]
	path = third_party/onnx
	url = https://github.com/onnx/onnx.git
	branch = ad834eb73ee0cd9b6fa9ea892caeed5fa17d7dc0

2.2 修改 CMakeLists.txt

Paddle2ONNX在CMakeLists.txt中规定了最大opset的数值，我们需要修改为18，关于opset与onnx版本的对应关系，可以查看 onnx版本与opset版本对应表

cmake 复制代码 
# if you build from other version of onnx this should be modified
# refer to https://github.com/onnx/onnx/blob/main/docs/Versioning.md#released-versions
add_definitions(-DMAX_ONNX_OPSET_VERSION=18)
add_definitions(-DPADDLE2ONNX_LIB)

2.3 添加对opset18转换的支持

在 paddle2onnx/mapper/mapper.h 中添加对opset18的支持

cpp 复制代码 
class Mapper {
  public:
    void Run() {
      int32_t opset_version = helper_->GetOpsetVersion();
      Assert(opset_version >= 7 && opset_version <= MAX_ONNX_OPSET_VERSION,
             "[Paddle2ONNX] Only support opset_version in range of [7, " +
             std::to_string(MAX_ONNX_OPSET_VERSION) + "].");
      if (IsExportAsCustomOp()) {
        return ExportAsCustomOp();
      }

      if (opset_version == 18) {
        Opset18();
      } else if (opset_version == 17) {
        Opset17();
      } else if (opset_version == 16) {
        Opset16();
      } else if (opset_version == 15) {
        Opset15();
      } else if (opset_version == 14) {
        Opset14();
      } else if (opset_version == 13) {
        Opset13();
      } else if (opset_version == 12) {
        Opset12();
      } else if (opset_version == 11) {
        Opset11();
      } else if (opset_version == 10) {
        Opset10();
      } else if (opset_version == 9) {
        Opset9();
      } else if (opset_version == 8) {
        Opset8();
      } else {
        Opset7();
      }
    }

    virtual void Opset18() { Opset17(); }
};

3 升级算子对Opset18的支持

3.1 升级 LayerNormalize 算子

当opset升级到17后，LayerNormalize 算子会出现问题，这里做的针对性的修复，主要是Scale 和Bias 两个参数的Shape 需要与 input_shape[norm_axis:] 一致

cpp 复制代码 
void LayerNormMapper::Opset17() {
  auto input_info = GetInput("X");
  auto output_info = GetOutput("Y");

  constexpr std::array<P2ODataType, 3> T = {P2ODataType::FP16, P2ODataType::FP32, P2ODataType::FP64};

  auto input_name = input_info[0].name;
  auto input_type = input_info[0].dtype;
  auto input_shape = input_info[0].shape;
  if (std::find(T.begin(), T.end(), input_type) == T.end()) {
    input_name = helper_->AutoCast(input_name, input_info[0].dtype, P2ODataType::FP32);
    input_type = P2ODataType::FP32;
  }

  bool has_input_Bias = HasInput("Bias");
  bool has_input_Scale = HasInput("Scale");
  if (has_input_Bias && has_input_Scale) {
    auto scale_info = GetInput("Scale");
    auto scale_name = scale_info[0].name;
    auto scale_type = scale_info[0].dtype;
    if (std::find(T.begin(), T.end(), scale_type) == T.end()) {
      scale_name = helper_->AutoCast(scale_name, scale_type, P2ODataType::FP32);
      scale_type = P2ODataType::FP32;
    }

    auto bias_info = GetInput("Bias");
    auto bias_name = bias_info[0].name;
    auto bias_type = bias_info[0].dtype;
    if (std::find(T.begin(), T.end(), bias_type) == T.end()) {
      bias_name = helper_->AutoCast(bias_name, bias_type, P2ODataType::FP32);
      bias_type = P2ODataType::FP32;
    }

    auto layer_norm_node = helper_->MakeNode(
      "LayerNormalization",
      {input_name, scale_name, bias_name},
      {output_info[0].name});
    AddAttribute(layer_norm_node, "axis", begin_norm_axis_);
    AddAttribute(layer_norm_node, "epsilon", epsilon_);
    return;
  }

  if (has_input_Scale) {
    auto scale_info = GetInput("Scale");
    auto scale_name = scale_info[0].name;
    auto scale_type = scale_info[0].dtype;
    if (std::find(T.begin(), T.end(), scale_type) == T.end()) {
      scale_name = helper_->AutoCast(scale_name, scale_type, P2ODataType::FP32);
      scale_type = P2ODataType::FP32;
    }

    auto layer_norm_node = helper_->MakeNode(
      "LayerNormalization",
      {input_name, scale_name},
      {output_info[0].name});
    AddAttribute(layer_norm_node, "axis", begin_norm_axis_);
    AddAttribute(layer_norm_node, "epsilon", epsilon_);
    return;
  }

  std::vector<int64_t> normalized_shape;
  for (int64_t i = begin_norm_axis_;i < input_shape.size();i++) {
    normalized_shape.emplace_back(input_shape[i]);
  }

  if (has_input_Bias) {
    auto bias_info = GetInput("Bias");
    auto bias_name = bias_info[0].name;
    auto bias_type = bias_info[0].dtype;
    if (std::find(T.begin(), T.end(), bias_type) == T.end()) {
      bias_name = helper_->AutoCast(bias_name, bias_type, P2ODataType::FP32);
      bias_type = P2ODataType::FP32;
    }

    std::string scale_name = helper_->Constant(normalized_shape, GetOnnxDtype(P2ODataType::FP32), static_cast<float>(1.0));
    auto layer_norm_node = helper_->MakeNode(
      "LayerNormalization",
      {input_name, scale_name, bias_name},
      {output_info[0].name});
    AddAttribute(layer_norm_node, "axis", begin_norm_axis_);
    AddAttribute(layer_norm_node, "epsilon", epsilon_);
    return;
  }

  if (!has_input_Bias && !has_input_Scale) {
    std::string scale_name = helper_->Constant(normalized_shape, GetOnnxDtype(P2ODataType::FP32), static_cast<float>(1.0));
    auto layer_norm_node = helper_->MakeNode(
      "LayerNormalization",
      {input_name, scale_name},
      {output_info[0].name});
    AddAttribute(layer_norm_node, "axis", begin_norm_axis_);
    AddAttribute(layer_norm_node, "epsilon", epsilon_);
  }
}

3.2 升级并拆分Reduce算子

在OP版本升级到18后，Reduce算子的输入发生了变化，因此需要对Reduce系列的算子进行全面的升级，为了不将所有的代码耦合在一起，我将他们做了拆分，具体代码如下:

cpp 复制代码 
#include "paddle2onnx/mapper/tensor/reduce_logsumexp.h"

namespace paddle2onnx {
REGISTER_MAPPER(logsumexp, ReduceLogSumExpMapper)

int32_t ReduceLogSumExpMapper::GetMinOpset(bool verbose) {
  constexpr int op_version = 11;
  Logger(verbose, op_version) << RequireOpset(op_version) << std::endl;
  return op_version;
}

void ReduceLogSumExpMapper::Opset18() {
  GetAttr("keepdim", &keep_dim_);
  GetAttr("reduce_all", &reduce_all_);
  GetAttr("axis", &dim_);

  auto x_info = GetInput("X");
  std::string dims;
  if (!reduce_all_) {
    dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, dim_);
  } else {
    dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, Arange(0, x_info[0].Rank()));
  }

  std::string input_name = x_info[0].name;
  auto input_tpye = x_info[0].dtype;
  if (x_info[0].dtype == P2ODataType::BOOL) {
    input_name = helper_->AutoCast(input_name, input_tpye, P2ODataType::INT32);
    input_tpye = P2ODataType::INT32;
  }
  auto reduce_node = helper_->MakeNode("ReduceLogSumExp", {input_name, dims});

  // Add attribute
  AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
  auto out_node_name = reduce_node->output(0);

  bool reduce_all_axes = dim_.size() == x_info[0].Rank();
  if (reduce_all_) {
    reduce_all_axes = true;
  }
  if (!keep_dim_ && reduce_all_axes) {
    out_node_name = helper_->Reshape(out_node_name, {-1});
  }
  auto out_info = GetOutput("Out");
  helper_->AutoCast(out_node_name, out_info[0].name, input_tpye, out_info[0].dtype);
}
}  // namespace paddle2onnx
cpp 复制代码 
#include "paddle2onnx/mapper/tensor/reduce_max.h"

namespace paddle2onnx {
REGISTER_MAPPER(reduce_max, ReduceMaxMapper)
REGISTER_MAPPER(reduce_any, ReduceMaxMapper)

int32_t ReduceMaxMapper::GetMinOpset(bool verbose) {
  int op_version = 11;

  auto x_info = GetInput("X");
  if (x_info[0].dtype == P2ODataType::FP64) {
    op_version = 12;
  }

  Logger(verbose, op_version) << RequireOpset(op_version) << std::endl;
  return op_version;
}

void ReduceMaxMapper::Opset18() {
  GetAttr("keep_dim", &keep_dim_);
  GetAttr("reduce_all", &reduce_all_);
  GetAttr("in_dtype", &in_dtype_);
  GetAttr("out_dtype", &out_dtype_);
  GetAttr("dim", &dim_);

  auto x_info = GetInput("X");
  std::string dims;
  if (!reduce_all_) {
    dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, dim_);
  } else {
    dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, Arange(0, x_info[0].Rank()));
  }

  std::string input_name = x_info[0].name;
  auto input_tpye = x_info[0].dtype;
  if (x_info[0].dtype == P2ODataType::BOOL) {
    input_name = helper_->AutoCast(input_name, input_tpye, P2ODataType::INT32);
    input_tpye = P2ODataType::INT32;
  }
  auto reduce_node = helper_->MakeNode("ReduceMax", {input_name, dims});

  // Add attribute
  AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
  auto out_node_name = reduce_node->output(0);

  bool reduce_all_axes = dim_.size() == x_info[0].Rank();
  if (reduce_all_) {
    reduce_all_axes = true;
  }
  if (!keep_dim_ && reduce_all_axes) {
    out_node_name = helper_->Reshape(out_node_name, {-1});
  }
  auto out_info = GetOutput("Out");
  helper_->AutoCast(out_node_name, out_info[0].name, input_tpye, out_info[0].dtype);
}

void ReduceMaxMapper::Opset12() {
  // The implementation logic of Opset12 is the same as that of Opset11, with the difference being that Opset12 supports input data types as double.
  Opset11();
}
} // namespace paddle2onnx
cpp 复制代码 
#include "paddle2onnx/mapper/tensor/reduce_min.h"

namespace paddle2onnx {
REGISTER_MAPPER(reduce_min, ReduceMinMapper)
REGISTER_MAPPER(reduce_all, ReduceMinMapper)

int32_t ReduceMinMapper::GetMinOpset(bool verbose) {
  int op_version = 11;

  auto x_info = GetInput("X");
  if (x_info[0].dtype == P2ODataType::FP64) {
    op_version = 12;
  }

  Logger(verbose, op_version) << RequireOpset(op_version) << std::endl;
  return op_version;
}

void ReduceMinMapper::Opset18() {
  GetAttr("keep_dim", &keep_dim_);
  GetAttr("reduce_all", &reduce_all_);
  GetAttr("in_dtype", &in_dtype_);
  GetAttr("out_dtype", &out_dtype_);
  GetAttr("dim", &dim_);

  auto x_info = GetInput("X");
  std::string dims;
  if (!reduce_all_) {
    dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, dim_);
  } else {
    dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, Arange(0, x_info[0].Rank()));
  }

  auto input_node_name = x_info[0].name;
  auto input_tpye = x_info[0].dtype;
  if (x_info[0].dtype == P2ODataType::BOOL) {
    input_node_name = helper_->AutoCast(x_info[0].name, x_info[0].dtype, P2ODataType::INT32);
    input_tpye = P2ODataType::INT32;
  }

  // Add attribute
  auto reduce_node = helper_->MakeNode("ReduceMin", {input_node_name, dims});
  AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));

  auto out_node_name = reduce_node->output(0);
  bool reduce_all_axes = dim_.size() == x_info[0].Rank();
  if (reduce_all_) {
    reduce_all_axes = true;
  }
  if (!keep_dim_ && reduce_all_axes) {
    out_node_name = helper_->Reshape(out_node_name, {-1});
  }
  auto out_info = GetOutput("Out");
  helper_->AutoCast(out_node_name, out_info[0].name, input_tpye, out_info[0].dtype);
}

void ReduceMinMapper::Opset12() {
  // The implementation logic of Opset12 is the same as that of Opset11, with the difference being that Opset12 supports input data types as double.
  Opset11();
}
}  // namespace paddle2onnx
cpp 复制代码 
#include "paddle2onnx/mapper/tensor/reduce_mean.h"

namespace paddle2onnx {
REGISTER_MAPPER(reduce_mean, ReduceMeanMapper)

int32_t ReduceMeanMapper::GetMinOpset(bool verbose) {
  constexpr int op_version = 11;
  Logger(verbose, op_version) << RequireOpset(op_version) << std::endl;
  return op_version;
}

void ReduceMeanMapper::Opset18() {
  auto axis_name_ = "dim";
  GetAttr("keep_dim", &keep_dim_);
  GetAttr("reduce_all", &reduce_all_);
  GetAttr("in_dtype", &in_dtype_);
  GetAttr("out_dtype", &out_dtype_);
  if (IsAttrVar(axis_name_)) {
    auto info = GetAttrVar(axis_name_);
    TryGetValue(info[0], &dim_);
  } else {
    GetAttr(axis_name_, &dim_);
  }

  auto x_info = GetInput("X");
  std::string dims;
  if (IsAttrVar(axis_name_)) {
    auto info = GetAttrVar(axis_name_);
    dims = helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
  } else {
    if (!reduce_all_) {
      dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, dim_);
    } else {
      dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, Arange(0, x_info[0].Rank()));
    }
  }

  // Add attribute
  auto reduce_node = helper_->MakeNode("ReduceMean", {x_info[0].name, dims});
  AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
  auto out_node_name = reduce_node->output(0);

  bool reduce_all_axes = dim_.size() == x_info[0].Rank();
  if (reduce_all_) {
    reduce_all_axes = true;
  }
  if (!keep_dim_ && reduce_all_axes) {
    out_node_name = helper_->Reshape(out_node_name, {-1});
  }
  auto out_info = GetOutput("Out");
  helper_->AutoCast(out_node_name, out_info[0].name, x_info[0].dtype, out_info[0].dtype);
}
}  // namespace paddle2onnx
cpp 复制代码 
#include "paddle2onnx/mapper/tensor/reduce_sum.h"

namespace paddle2onnx {
REGISTER_MAPPER(reduce_sum, ReduceMapperSum)

int32_t ReduceMapperSum::GetMinOpset(bool verbose) {
  constexpr int op_version = 13;
  Logger(verbose, op_version) << RequireOpset(op_version) << std::endl;
  return op_version;
}

void ReduceMapperSum::Opset13() {
  auto axis_name_ = "dim";
  GetAttr("keep_dim", &keep_dim_);
  GetAttr("reduce_all", &reduce_all_);
  GetAttr("in_dtype", &in_dtype_);
  GetAttr("out_dtype", &out_dtype_);
  if (IsAttrVar(axis_name_)) {
    auto info = GetAttrVar(axis_name_);
    TryGetValue(info[0], &dim_);
  } else {
    GetAttr(axis_name_, &dim_);
  }

  auto x_info = GetInput("X");
  std::string dims;
  if (IsAttrVar(axis_name_)) {
    auto info = GetAttrVar(axis_name_);
    dims = helper_->AutoCast(info[0].name, info[0].dtype, P2ODataType::INT64);
  } else {
    if (!reduce_all_) {
      dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, dim_);
    } else {
      dims = helper_->Constant(ONNX_NAMESPACE::TensorProto::INT64, Arange(0, x_info[0].Rank()));
    }
  }

  // Add attribute
  auto reduce_node = helper_->MakeNode("ReduceSum", {x_info[0].name, dims});
  AddAttribute(reduce_node, "keepdims", static_cast<int64_t>(keep_dim_));
  auto out_node_name = reduce_node->output(0);

  bool reduce_all_axes = dim_.size() == x_info[0].Rank();
  if (reduce_all_) {
    reduce_all_axes = true;
  }
  if (!keep_dim_ && reduce_all_axes) {
    out_node_name = helper_->Reshape(out_node_name, {-1});
  }
  auto out_info = GetOutput("Out");
  helper_->AutoCast(out_node_name, out_info[0].name, x_info[0].dtype, out_info[0].dtype);
}
}  // namespace paddle2onnx

由于篇幅的限制，这里仅放出了部分核心代码，更多代码请到 Paddle2ONNX PR 1250 内查看。

4 升级CI机制

原先的CI机制对所有的opset都进行验证，这在高等级opset上是不合适的，所以对原先的CI机制进行了升级，仅对指定的opset进行验证

4.1 升级 onnxbase

python 复制代码 
class APIOnnx(object):
    def run(self):
        """
        1. use dygraph layer to make exp
        2. dygraph layer to onnx
        3. use onnx to make res
        4. compare diff
        """
        self._mkdir()
        self.set_input_spec()
        for place in self.places:
            paddle.set_device(place)
            exp = self._mk_dygraph_exp(self._func)
            res_fict = {}

            assert len(self.ops) <= 1, "Need to make sure the number of ops in config is 1."

            # Save Paddle Inference model
            if os.path.exists(self.name):
                shutil.rmtree(self.name)
            paddle.jit.save(self._func, os.path.join(self.name, "model"), self.input_spec)

            # Get PaddleInference model path
            pdmodel_path = os.path.join(self.name, "model.pdmodel")
            pdiparams_path = os.path.join(self.name, "model.pdiparams")
            if len(self.ops) > 0:
                self.dev_check_ops(self.ops[0], pdmodel_path)

            original_model_file = pdmodel_path
            params_file = pdiparams_path
            if not os.path.exists(params_file):
                params_file = ""

            # clip extra
            model_file = os.path.join(self.name, "cliped_model.pdmodel")
            self.clip_extra_program_only(original_model_file, model_file)

            for v in self._version:
                onnx_model_str = c_p2o.export(
                    model_file, params_file, v, False, True, True, True,
                    True, {}, "onnxruntime", "", "", False)
                with open(os.path.join(self.name, self.name + '_' + str(v) + ".onnx"), "wb") as f:
                    f.write(onnx_model_str)
                res_fict[str(v)] = self._mk_onnx_res(ver=v)

            for v in self._version:
                compare(res_fict[str(v)], exp, delta=self.delta, rtol=self.rtol)

4.2 升级单测代码

由于篇幅的限制，更多代码请到 Paddle2ONNX PR 1250 内查看。

5 参考链接

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