paddle_api.h

// 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.
#pragma once

#include <cassert>
#include <memory>
#include <string>
#include <vector>

namespace paddle {

// Data type.
enum PaddleDType {
  FLOAT32,
  INT64,
  // TODO(Superjomn) support more data types if needed.
};

/*
 * Memory menage for PaddleTensor.
 * The PaddleBuf holds a buffer for data input or output. The memory can be
 * allocated by user or by PaddleBuf itself, but in any case, the PaddleBuf
 * should be reused for better performance.
 *
 * For user allocated memory, the following API can be used:
 * - PaddleBuf(void* data, size_t length) to set an external memory by
 * specifying
 *   the memory address and length.
 * - Reset(void* data, size_t length) to reset the PaddleBuf with an external
 * memory.
 * ATTENTION, for user allocated memory, deallocation should be done by users
 * externally after the program finished. The PaddleBuf won't do any allocation
 * or deallocation.
 *
 * To have the PaddleBuf allocate and manage the memory:
 * - PaddleBuf(size_t length) will allocate a memory of size `length`.
 * - Resize(size_t length) resize the memory to no less than `length`, ATTENTION
 *   if the allocated memory is larger than `length`, nothing will done.
 */
class PaddleBuf {
 public:
  // PaddleBuf allocate memory internally, and manage it.
  explicit PaddleBuf(size_t length)
      : data_(new char[length]), length_(length), memory_owned_(true) {}
  // Set external memory, the PaddleBuf won't manage it.
  PaddleBuf(void* data, size_t length)
      : data_(data), length_(length), memory_owned_{false} {}
  // Copy only available when memory is managed externally.
  explicit PaddleBuf(const PaddleBuf&);

  // Resize the memory.
  void Resize(size_t length);
  // Reset to external memory, with address and length set.
  void Reset(void* data, size_t length);
  // Tell whether the buffer is empty.
  bool empty() const { return length_ == 0; }
  // Get the memory address.
  void* data() const { return data_; }
  // Get the memory length.
  size_t length() const { return length_; }

  ~PaddleBuf() { Free(); }
  PaddleBuf& operator=(const PaddleBuf&);
  PaddleBuf& operator=(PaddleBuf&&);
  PaddleBuf() = default;
  PaddleBuf(PaddleBuf&& other);

 private:
  void Free();
  void* data_{nullptr};  // pointer to the data memory.
  size_t length_{0};     // number of memory bytes.
  bool memory_owned_{true};
};

// Basic input and output data structure for PaddlePredictor.
struct PaddleTensor {
  PaddleTensor() = default;
  std::string name;  // variable name.
  std::vector<int> shape;
  PaddleBuf data;  // blob of data.
  PaddleDType dtype;
  std::vector<std::vector<size_t>> lod;  // Tensor+LoD equals LoDTensor
};

enum class PaddlePlace { kUNK = -1, kCPU, kGPU };
// Tensor without copy, currently only supports AnalysisPredictor.
class ZeroCopyTensor {
 public:
  void Reshape(const std::vector<int>& shape);

  // Get the memory in CPU or GPU with specific data type, should Reshape first
  // to tell the data size.
  // Once can directly call this data to feed the data.
  // This is for write the input tensor.
  template <typename T>
  T* mutable_data(PaddlePlace place);
  // Get the memory directly, will return the place and memory size by pointer.
  // This is for reading the output tensor.
  template <typename T>
  T* data(PaddlePlace* place, int* size) const;

  std::vector<int64_t> shape() const;

  void SetLoD(const std::vector<std::vector<size_t>>& x);
  std::vector<std::vector<size_t>> lod() const;
  const std::string& name() const { return name_; }

 protected:
  explicit ZeroCopyTensor(void* scope) : scope_{scope} {}
  void SetName(const std::string& name) { name_ = name; }
  void* FindTensor() const;

 private:
  std::string name_;
  bool input_or_output_;
  friend class AnalysisPredictor;
  void* scope_{nullptr};
};

/*
 * A simple Inference API for Paddle.
 */
class PaddlePredictor {
 public:
  struct Config;
  PaddlePredictor() = default;
  PaddlePredictor(const PaddlePredictor&) = delete;
  PaddlePredictor& operator=(const PaddlePredictor&) = delete;

  // Predict an record.
  // The caller should be responsible for allocating and releasing the memory of
  // `inputs`. `inputs` should be available until Run returns. Caller should be
  // responsible for the output tensor's buffer, either allocated or passed from
  // outside.
  virtual bool Run(const std::vector<PaddleTensor>& inputs,
                   std::vector<PaddleTensor>* output_data,
                   int batch_size = -1) = 0;

  // Zero copy input and output optimization.
  // Get the input or output tensors, and operate on their memory directly,
  // without copy.
  virtual std::unique_ptr<ZeroCopyTensor> GetInputTensor(
      const std::string& name) {
    return nullptr;
  }
  virtual std::unique_ptr<ZeroCopyTensor> GetOutputTensor(
      const std::string& name) {
    return nullptr;
  }
  virtual bool ZeroCopyRun() { return false; }

  // Clone a predictor that share the model weights, the Cloned predictor should
  // be thread-safe.
  virtual std::unique_ptr<PaddlePredictor> Clone() = 0;

  // Destroy the Predictor.
  virtual ~PaddlePredictor() = default;

  // The common configs for all the predictors.
  struct Config {
    std::string model_dir;  // path to the model directory.
  };
};

struct NativeConfig : public PaddlePredictor::Config {
  // GPU related fields.
  bool use_gpu{false};
  int device{0};
  float fraction_of_gpu_memory{-1.f};  // Change to a float in (0,1] if needed.

  // Specify the exact path of program and parameter files.
  std::string prog_file;
  std::string param_file;

  // Specify the variable's name of each input if input tensors don't follow the
  // `feeds` and `fetches` of the phase `save_inference_model`.
  bool specify_input_name{false};

  // Set and get the number of cpu math library threads.
  void SetCpuMathLibraryNumThreads(int cpu_math_library_num_threads) {
    cpu_math_library_num_threads_ = cpu_math_library_num_threads;
  }
  int cpu_math_library_num_threads() const {
    return cpu_math_library_num_threads_;
  }

 protected:
  // number of cpu math library (such as MKL, OpenBlas) threads for each
  // instance.
  int cpu_math_library_num_threads_{1};
};

// A factory to help create different predictors.
//
// Usage:
//
// NativeConfig config;
// ... // change the configs.
// auto native_predictor = CreatePaddlePredictor(config);
//
// FOR EXTENSION DEVELOPER:
// Different predictors are designated by config type. Similar configs can be
// merged, but there shouldn't be a huge config containing different fields for
// more than one kind of predictors.
template <typename ConfigT>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);

// NOTE The following APIs are too trivial, we will discard it in the following
// versions.
enum class PaddleEngineKind {
  kNative = 0,         // Use the native Fluid facility.
  kAutoMixedTensorRT,  // Automatically mix Fluid with TensorRT.
  kAnalysis,           // More optimization.
  kAnakin              // Use Anakin for inference, not mature yet.
};

template <typename ConfigT, PaddleEngineKind engine>
std::unique_ptr<PaddlePredictor> CreatePaddlePredictor(const ConfigT& config);

int PaddleDtypeSize(PaddleDType dtype);

}  // namespace paddle