elementwise_op.cc

/* Copyright (c) 2018 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 "paddle/fluid/inference/tensorrt/convert/op_converter.h"
#include "paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h"

namespace paddle {
namespace inference {
namespace tensorrt {

static bool CheckDims(const nvinfer1::Dims& dims_x,
                      const nvinfer1::Dims& dims_y) {
  if (dims_x.nbDims != dims_y.nbDims) {
    return false;
  }
  for (int i = 0; i < dims_x.nbDims; i++) {
    if (dims_x.d[i] != dims_y.d[i]) {
      return false;
    }
  }
  return true;
}

class ElementwiseWeightOpConverter : public OpConverter {
 public:
  ElementwiseWeightOpConverter() {}
  void operator()(const framework::proto::OpDesc& op,
                  const framework::Scope& scope, bool test_mode) override {
    // Here the two nullptr looks strange, that's because the
    // framework::OpDesc's constructor is strange.
    nvinfer1::ILayer* layer = nullptr;
    framework::OpDesc op_desc(op, nullptr);
    VLOG(3) << "Convert a fluid elementwise op to TensorRT IScaleLayer";

    auto* X = engine_->GetITensor(op_desc.Input("X").front());
    auto* Y_v = scope.FindVar(op_desc.Input("Y").front());
    PADDLE_ENFORCE_NOT_NULL(
        Y_v, platform::errors::NotFound("Variable %s not found in scope.",
                                        op_desc.Input("Y").front().c_str()));
    auto* Y_t = Y_v->GetMutable<framework::LoDTensor>();
    float* weight_data = nullptr;
    auto output_name = op_desc.Output("Out")[0];
    weight_data = engine_->GetWeightCPUData(op_desc.Input("Y").front(), Y_t);
    nvinfer1::Dims dims_x = X->getDimensions();
    std::vector<int> dims_y = phi::vectorize<int>(Y_t->dims());

    auto regist_eltwise_weight = [&](nvinfer1::ScaleMode scale_mode) {
      nvinfer1::IShuffleLayer* expand_layer = nullptr;
      nvinfer1::IShuffleLayer* squeeze_layer = nullptr;
      int dynamic_shape_offset = engine_->with_dynamic_shape() ? 1 : 0;
      auto input_dim = X->getDimensions();
      // reshape
      if (input_dim.nbDims < 3 + dynamic_shape_offset) {
        nvinfer1::Dims expand_shape;
        expand_shape.nbDims = 3 + dynamic_shape_offset;
        for (int i = 0; i < expand_shape.nbDims; i++) {
          if (i < input_dim.nbDims) {
            expand_shape.d[i] = input_dim.d[i] < 0 ? 0 : input_dim.d[i];
          } else {
            expand_shape.d[i] = 1;
          }
        }
        expand_layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *X);
        expand_layer->setReshapeDimensions(expand_shape);
        X = expand_layer->getOutput(0);
        expand_layer->getOutput(0)->setName(
            ("elementwise_reshape_out: " + output_name).c_str());
        expand_layer->setName(
            ("Elewise: Shuffle: (Output: " + output_name + ")").c_str());
      }
      // eltwise_ops
      TensorRTEngine::Weight shift_weights{nvinfer1::DataType::kFLOAT, nullptr,
                                           0};
      TensorRTEngine::Weight scale_weights{nvinfer1::DataType::kFLOAT, nullptr,
                                           0};
      TensorRTEngine::Weight power_weights{nvinfer1::DataType::kFLOAT, nullptr,
                                           0};
      if (op_type_ == "add") {
        shift_weights = TensorRTEngine::Weight(
            nvinfer1::DataType::kFLOAT, static_cast<void*>(weight_data),
            static_cast<size_t>(Y_t->numel()));
      } else if (op_type_ == "sub") {
        for (int i = 0; i < Y_t->numel(); i++) {
          weight_data[i] = -weight_data[i];
        }
        shift_weights = TensorRTEngine::Weight(
            nvinfer1::DataType::kFLOAT, static_cast<void*>(weight_data),
            static_cast<size_t>(Y_t->numel()));
      } else if (op_type_ == "mul") {
        scale_weights = TensorRTEngine::Weight(
            nvinfer1::DataType::kFLOAT, static_cast<void*>(weight_data),
            static_cast<size_t>(Y_t->numel()));
      } else if (op_type_ == "div") {
        for (int i = 0; i < Y_t->numel(); i++) {
          weight_data[i] = 1.f / weight_data[i];
        }
        scale_weights = TensorRTEngine::Weight(
            nvinfer1::DataType::kFLOAT, static_cast<void*>(weight_data),
            static_cast<size_t>(Y_t->numel()));
      } else if (op_type_ == "pow") {
        power_weights = TensorRTEngine::Weight(
            nvinfer1::DataType::kFLOAT, static_cast<void*>(weight_data),
            static_cast<size_t>(Y_t->numel()));
      }
      nvinfer1::IScaleLayer* scale_layer = TRT_ENGINE_ADD_LAYER(
          engine_, ScaleNd, *X, scale_mode, shift_weights.get(),
          scale_weights.get(), power_weights.get(), dynamic_shape_offset);
      layer = scale_layer;
      // reshape
      if (input_dim.nbDims < 3 + dynamic_shape_offset) {
        nvinfer1::Dims squeeze_shape;
        squeeze_shape.nbDims = input_dim.nbDims;
        for (int i = 0; i < squeeze_shape.nbDims; i++) {
          squeeze_shape.d[i] = input_dim.d[i] < 0 ? 0 : input_dim.d[i];
        }
        squeeze_layer =
            TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *(layer->getOutput(0)));
        squeeze_layer->setReshapeDimensions(squeeze_shape);
        RreplenishLayerAndOutput(squeeze_layer, "elementwise_" + op_type_,
                                 {output_name}, test_mode);
      } else {
        RreplenishLayerAndOutput(layer, "elementwise_" + op_type_,
                                 {output_name}, test_mode);
      }
    };

    // dynamic shape
    if (engine_->with_dynamic_shape()) {
      if (dims_y.size() == 1 && dims_y[0] == dims_x.d[1]) {
        regist_eltwise_weight(nvinfer1::ScaleMode::kCHANNEL);
      } else if (dims_y.size() == 1 && dims_y[0] == 1) {
        regist_eltwise_weight(nvinfer1::ScaleMode::kUNIFORM);
      } else if (dims_y.size() == static_cast<size_t>(dims_x.nbDims)) {
        regist_eltwise_weight(nvinfer1::ScaleMode::kELEMENTWISE);
      } else {
        PADDLE_THROW(platform::errors::InvalidArgument(
            "The size of input_y's dims is %d, but TensorRT dynamic shape "
            "only support size = 1 or size = input_x.size() for Elementwise "
            "op!",
            dims_y.size()));
      }
      return;
    }

    // static shape with dynamic batch
    std::vector<int> no_batch_dims;
    int start_index = 0;
    for (; start_index < dims_x.nbDims; start_index++) {
      no_batch_dims.push_back(dims_x.d[start_index]);
    }
    if (dims_y.size() == 1 && dims_y[0] == no_batch_dims[0]) {
      regist_eltwise_weight(nvinfer1::ScaleMode::kCHANNEL);
    } else if (dims_y.size() == 1 && dims_y[0] == 1) {
      regist_eltwise_weight(nvinfer1::ScaleMode::kUNIFORM);
    } else if (dims_y.size() == no_batch_dims.size() + 1) {
      regist_eltwise_weight(nvinfer1::ScaleMode::kELEMENTWISE);
    } else {
      PADDLE_THROW(platform::errors::InvalidArgument(
          "The size of input_y's dims is %d, but TensorRT dynamic shape "
          "only support size = 1 or size = input_x.size() for Elementwise "
          "op!",
          dims_y.size()));
    }
  }

 protected:
  std::string op_type_;
};

class ElementwiseTensorOpConverter : public OpConverter {
 public:
  ElementwiseTensorOpConverter() {}
  void operator()(const framework::proto::OpDesc& op,
                  const framework::Scope& scope, bool test_mode) override {
    auto op_pair = ops.find(op_type_);
    PADDLE_ENFORCE_NE(op_pair, ops.end(),
                      platform::errors::InvalidArgument(
                          "Elementwise op's type(%s) is not supported. Please "
                          "check if the op_type is correct.",
                          op_type_));

    // Here the two nullptr looks strange, that's because the
    // framework::OpDesc's constructor is strange.
    framework::OpDesc op_desc(op, nullptr);
    nvinfer1::ILayer* layer = nullptr;

    auto* X = engine_->GetITensor(op_desc.Input("X").front());
    auto* Y = engine_->GetITensor(op_desc.Input("Y").front());
    std::vector<nvinfer1::ITensor*> itensors;
    itensors.push_back(X);
    itensors.push_back(Y);
    nvinfer1::Dims dims_x = X->getDimensions();
    nvinfer1::Dims dims_y = Y->getDimensions();

    int axis = BOOST_GET_CONST(int, op_desc.GetAttr("axis"));
    auto output_name = op_desc.Output("Out")[0];

    auto common_func = [&](nvinfer1::ILayer* layer) {
      RreplenishLayerAndOutput(layer, "elementwise", {output_name}, test_mode);
    };
    if (dims_x.nbDims == dims_y.nbDims) {
      // The two input tensor should have the same dims
      VLOG(3) << "Convert a fluid elementwise op to TensorRT IElementWiseLayer";
      nvinfer1::IElementWiseLayer* elet_layer =
          TRT_ENGINE_ADD_LAYER(engine_, ElementWise, *X, *Y, op_pair->second);

      layer = elet_layer;
    } else {
      VLOG(3) << "Convert a fluid elementwise op to TensorRT "
                 "ElementWisePluginLayer";
      if (engine_->with_dynamic_shape()) {
#if IS_TRT_VERSION_GE(6000)
        plugin::ElementwisePluginDynamic* plugin =
            new plugin::ElementwisePluginDynamic(op_type_, axis);
        layer = engine_->AddDynamicPlugin(itensors.data(), 2, plugin);
#else
        PADDLE_THROW(platform::errors::Fatal(
            "You are running the TRT Dynamic Shape mode, need to confirm that "
            "your TRT version is no less than 6.0"));
#endif
      } else {
        plugin::ElementWisePlugin* plugin =
            new plugin::ElementWisePlugin(op_type_, dims_x, dims_y, axis);

        std::vector<nvinfer1::ITensor*> inputs{X, Y};
        auto* plugin_layer = engine_->AddPlugin(
            inputs.data(), inputs.size(),
            reinterpret_cast<plugin::PluginTensorRT*>(plugin));
        layer = plugin_layer;
      }
    }
    common_func(layer);
  }

 protected:
  static const std::unordered_map<std::string, nvinfer1::ElementWiseOperation>
      ops;
  std::string op_type_;
};

const std::unordered_map<std::string, nvinfer1::ElementWiseOperation>
    ElementwiseTensorOpConverter::ops = {
        {"add", nvinfer1::ElementWiseOperation::kSUM},
        {"mul", nvinfer1::ElementWiseOperation::kPROD},
        {"sub", nvinfer1::ElementWiseOperation::kSUB},
        {"div", nvinfer1::ElementWiseOperation::kDIV},
        {"min", nvinfer1::ElementWiseOperation::kMIN},
        {"pow", nvinfer1::ElementWiseOperation::kPOW},
        {"max", nvinfer1::ElementWiseOperation::kMAX},
};

class ElementwiseWeightAddOpConverter : public ElementwiseWeightOpConverter {
 public:
  ElementwiseWeightAddOpConverter() { op_type_ = "add"; }
};

class ElementwiseWeightMulOpConverter : public ElementwiseWeightOpConverter {
 public:
  ElementwiseWeightMulOpConverter() { op_type_ = "mul"; }
};

class ElementwiseWeightSubOpConverter : public ElementwiseWeightOpConverter {
 public:
  ElementwiseWeightSubOpConverter() { op_type_ = "sub"; }
};

class ElementwiseWeightDivOpConverter : public ElementwiseWeightOpConverter {
 public:
  ElementwiseWeightDivOpConverter() { op_type_ = "div"; }
};

class ElementwiseWeightPowOpConverter : public ElementwiseWeightOpConverter {
 public:
  ElementwiseWeightPowOpConverter() { op_type_ = "pow"; }
};

class ElementwiseTensorAddOpConverter : public ElementwiseTensorOpConverter {
 public:
  ElementwiseTensorAddOpConverter() { op_type_ = "add"; }
};

class ElementwiseTensorMulOpConverter : public ElementwiseTensorOpConverter {
 public:
  ElementwiseTensorMulOpConverter() { op_type_ = "mul"; }
};

class ElementwiseTensorSubOpConverter : public ElementwiseTensorOpConverter {
 public:
  ElementwiseTensorSubOpConverter() { op_type_ = "sub"; }
};

class ElementwiseTensorDivOpConverter : public ElementwiseTensorOpConverter {
 public:
  ElementwiseTensorDivOpConverter() { op_type_ = "div"; }
};

class ElementwiseTensorMinOpConverter : public ElementwiseTensorOpConverter {
 public:
  ElementwiseTensorMinOpConverter() { op_type_ = "min"; }
};

class ElementwiseTensorMaxOpConverter : public ElementwiseTensorOpConverter {
 public:
  ElementwiseTensorMaxOpConverter() { op_type_ = "max"; }
};

class ElementwiseTensorPowOpConverter : public ElementwiseTensorOpConverter {
 public:
  ElementwiseTensorPowOpConverter() { op_type_ = "pow"; }
};

}  // namespace tensorrt
}  // namespace inference
}  // namespace paddle

REGISTER_TRT_OP_CONVERTER(elementwise_add_weight,
                          ElementwiseWeightAddOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_mul_weight,
                          ElementwiseWeightMulOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_sub_weight,
                          ElementwiseWeightSubOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_div_weight,
                          ElementwiseWeightDivOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_pow_weight,
                          ElementwiseWeightPowOpConverter);

REGISTER_TRT_OP_CONVERTER(elementwise_add_tensor,
                          ElementwiseTensorAddOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_sub_tensor,
                          ElementwiseTensorSubOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_div_tensor,
                          ElementwiseTensorDivOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_mul_tensor,
                          ElementwiseTensorMulOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_max_tensor,
                          ElementwiseTensorMaxOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_min_tensor,
                          ElementwiseTensorMinOpConverter);
REGISTER_TRT_OP_CONVERTER(elementwise_pow_tensor,
                          ElementwiseTensorPowOpConverter);