tritonserver学习之六：自定义c++、python custom backend实践_triton c++

tritonserver学习之一：triton使用流程

tritonserver学习之二：tritonserver编译 

tritonserver学习之三：tritonserver运行流程

tritonserver学习之四：命令行解析

tritonserver学习之五：backend实现机制

tritonserver学习之七：cache管理器

 tritonserver学习之八：redis_caches实践

tritonserver学习之九：tritonserver grpc异步模式

1、环境准备(Ubuntu2004)

1.1 cmake安装

triton backend的编译，cmake的版本要3.17以上，从这里下载当前最新版本cmake，3.28版本：

https://githubfast.com/Kitware/CMake/releases/download/v3.28.1/cmake-3.28.1.tar.gz

进行环境检查：

tar zxvf cmake-3.28.1.tar.gz
cd cmake-3.28.1/
./bootstrap

你可能会遇到如下情况：

这种情况需要安装openssl：

sudo apt-get install libssl-dev

安装完成后，重新执行bootstrap则运行成功。 

运行make && sudo make install.

1.2 RapidJSON安装

clone代码:

git clone https://github.com/miloyip/rapidjson.git

最近github抽风，如果clone不下来就用如下命令：

git clone https://githubfast.com/miloyip/rapidjson.git

cd rapidjson
mkdir build
cd build
make && make install

2、c++ 自定义backend

2.1 自定义backend编译

c++自定义backend，上一篇文章：tritonserver学习之五：backend实现机制，有介绍，需要实现7个api，我们以backend代码库的recommended.cc为例，在调试过程中，我是复制了，路径如下：backend/examples/backends/liupeng，稍作改动加一些打印帮助理解：

// Copyright 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions
// are met:
//  * Redistributions of source code must retain the above copyright
//    notice, this list of conditions and the following disclaimer.
//  * Redistributions in binary form must reproduce the above copyright
//    notice, this list of conditions and the following disclaimer in the
//    documentation and/or other materials provided with the distribution.
//  * Neither the name of NVIDIA CORPORATION nor the names of its
//    contributors may be used to endorse or promote products derived
//    from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
// EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
// PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
// CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
// PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
// OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
// (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
// OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
#include "triton/backend/backend_common.h"
#include "triton/backend/backend_input_collector.h"
#include "triton/backend/backend_model.h"
#include "triton/backend/backend_model_instance.h"
#include "triton/backend/backend_output_responder.h"
#include "triton/core/tritonbackend.h"
 
namespace triton { namespace backend { namespace recommended {
 
//
// Backend that demonstrates the TRITONBACKEND API. This backend works
// for any model that has 1 input with any datatype and any shape and
// 1 output with the same shape and datatype as the input. The backend
// supports both batching and non-batching models.
//
// For each batch of requests, the backend returns the input tensor
// value in the output tensor.
//
 
/
 
extern "C" {
 
// Triton calls TRITONBACKEND_Initialize when a backend is loaded into
// Triton to allow the backend to create and initialize any state that
// is intended to be shared across all models and model instances that
// use the backend. The backend should also verify version
// compatibility with Triton in this function.
//
TRITONSERVER_Error*
TRITONBACKEND_Initialize(TRITONBACKEND_Backend* backend)
{
  const char* cname;
  RETURN_IF_ERROR(TRITONBACKEND_BackendName(backend, &cname));
  std::string name(cname);
 
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      (std::string("TRITONBACKEND_Initialize: ") + name).c_str());
 
  // Check the backend API version that Triton supports vs. what this
  // backend was compiled against. Make sure that the Triton major
  // version is the same and the minor version is >= what this backend
  // uses.
  uint32_t api_version_major, api_version_minor;
  RETURN_IF_ERROR(
      TRITONBACKEND_ApiVersion(&api_version_major, &api_version_minor));
 
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      (std::string("Triton TRITONBACKEND API version: ") +
       std::to_string(api_version_major) + "." +
       std::to_string(api_version_minor))
          .c_str());
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      (std::string("'") + name + "' TRITONBACKEND API version: " +
       std::to_string(TRITONBACKEND_API_VERSION_MAJOR) + "." +
       std::to_string(TRITONBACKEND_API_VERSION_MINOR))
          .c_str());
 
  if ((api_version_major != TRITONBACKEND_API_VERSION_MAJOR) ||
      (api_version_minor < TRITONBACKEND_API_VERSION_MINOR)) {
    return TRITONSERVER_ErrorNew(
        TRITONSERVER_ERROR_UNSUPPORTED,
        "triton backend API version does not support this backend");
  }
 
  // The backend configuration may contain information needed by the
  // backend, such as tritonserver command-line arguments. This
  // backend doesn't use any such configuration but for this example
  // print whatever is available.
  TRITONSERVER_Message* backend_config_message;
  RETURN_IF_ERROR(
      TRITONBACKEND_BackendConfig(backend, &backend_config_message));
 
  const char* buffer;
  size_t byte_size;
  RETURN_IF_ERROR(TRITONSERVER_MessageSerializeToJson(
      backend_config_message, &buffer, &byte_size));
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      (std::string("backend configuration:\n") + buffer).c_str());
 
  // This backend does not require any "global" state but as an
  // example create a string to demonstrate.
  std::string* state = new std::string("backend state");
  RETURN_IF_ERROR(
      TRITONBACKEND_BackendSetState(backend, reinterpret_cast<void*>(state)));
 
  return nullptr;  // success
}
 
// Triton calls TRITONBACKEND_Finalize when a backend is no longer
// needed.
//
TRITONSERVER_Error*
TRITONBACKEND_Finalize(TRITONBACKEND_Backend* backend)
{
  // Delete the "global" state associated with the backend.
  void* vstate;
  RETURN_IF_ERROR(TRITONBACKEND_BackendState(backend, &vstate));
  std::string* state = reinterpret_cast<std::string*>(vstate);
 
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      (std::string("TRITONBACKEND_Finalize: state is '") + *state + "'")
          .c_str());
 
  delete state;
  return nullptr;  // success
}
 
}  // extern "C"
 
/
 
//
// ModelState
//
// State associated with a model that is using this backend. An object
// of this class is created and associated with each
// TRITONBACKEND_Model. ModelState is derived from BackendModel class
// provided in the backend utilities that provides many common
// functions.
//
class ModelState : public BackendModel {
 public:
  static TRITONSERVER_Error* Create(
      TRITONBACKEND_Model* triton_model, ModelState** state);
  virtual ~ModelState() = default;
 
  // Name of the input and output tensor
  const std::string& InputTensorName() const { return input_name_; }
  const std::string& OutputTensorName() const { return output_name_; }
 
  // Datatype of the input and output tensor
  TRITONSERVER_DataType TensorDataType() const { return datatype_; }
 
  // Shape of the input and output tensor as given in the model
  // configuration file. This shape will not include the batch
  // dimension (if the model has one).
  const std::vector<int64_t>& TensorNonBatchShape() const { return nb_shape_; }
 
  // Shape of the input and output tensor, including the batch
  // dimension (if the model has one). This method cannot be called
  // until the model is completely loaded and initialized, including
  // all instances of the model. In practice, this means that backend
  // should only call it in TRITONBACKEND_ModelInstanceExecute.
  TRITONSERVER_Error* TensorShape(std::vector<int64_t>& shape);
 
  // Validate that this model is supported by this backend.
  TRITONSERVER_Error* ValidateModelConfig();
 
 private:
  ModelState(TRITONBACKEND_Model* triton_model);
 
  std::string input_name_;
  std::string output_name_;
 
  TRITONSERVER_DataType datatype_;
 
  bool shape_initialized_;
  std::vector<int64_t> nb_shape_;
  std::vector<int64_t> shape_;
};
 
ModelState::ModelState(TRITONBACKEND_Model* triton_model)
    : BackendModel(triton_model), shape_initialized_(false)
{
  // Validate that the model's configuration matches what is supported
  // by this backend.
  THROW_IF_BACKEND_MODEL_ERROR(ValidateModelConfig());
}
 
TRITONSERVER_Error*
ModelState::Create(TRITONBACKEND_Model* triton_model, ModelState** state)
{
  try {
    *state = new ModelState(triton_model);
  }
  catch (const BackendModelException& ex) {
    RETURN_ERROR_IF_TRUE(
        ex.err_ == nullptr, TRITONSERVER_ERROR_INTERNAL,
        std::string("unexpected nullptr in BackendModelException"));
    RETURN_IF_ERROR(ex.err_);
  }
 
  return nullptr;  // success
}
 
TRITONSERVER_Error*
ModelState::TensorShape(std::vector<int64_t>& shape)
{
  // This backend supports models that batch along the first dimension
  // and those that don't batch. For non-batch models the output shape
  // will be the shape from the model configuration. For batch models
  // the output shape will be the shape from the model configuration
  // prepended with [ -1 ] to represent the batch dimension. The
  // backend "responder" utility used below will set the appropriate
  // batch dimension value for each response. The shape needs to be
  // initialized lazily because the SupportsFirstDimBatching function
  // cannot be used until the model is completely loaded.
  if (!shape_initialized_) {
    bool supports_first_dim_batching;
    RETURN_IF_ERROR(SupportsFirstDimBatching(&supports_first_dim_batching));
    if (supports_first_dim_batching) {
      shape_.push_back(-1);
    }
 
    shape_.insert(shape_.end(), nb_shape_.begin(), nb_shape_.end());
    shape_initialized_ = true;
  }
 
  shape = shape_;
 
  return nullptr;  // success
}
 
TRITONSERVER_Error*
ModelState::ValidateModelConfig()
{
  // If verbose logging is enabled, dump the model's configuration as
  // JSON into the console output.
  if (TRITONSERVER_LogIsEnabled(TRITONSERVER_LOG_VERBOSE)) {
    common::TritonJson::WriteBuffer buffer;
    RETURN_IF_ERROR(ModelConfig().PrettyWrite(&buffer));
    LOG_MESSAGE(
        TRITONSERVER_LOG_VERBOSE,
        (std::string("model configuration:\n") + buffer.Contents()).c_str());
  }
 
  // ModelConfig is the model configuration as a TritonJson
  // object. Use the TritonJson utilities to parse the JSON and
  // determine if the configuration is supported by this backend.
  common::TritonJson::Value inputs, outputs;
  RETURN_IF_ERROR(ModelConfig().MemberAsArray("input", &inputs));
  RETURN_IF_ERROR(ModelConfig().MemberAsArray("output", &outputs));
 
  // The model must have exactly 1 input and 1 output.
  RETURN_ERROR_IF_FALSE(
      inputs.ArraySize() == 1, TRITONSERVER_ERROR_INVALID_ARG,
      std::string("model configuration must have 1 input"));
  RETURN_ERROR_IF_FALSE(
      outputs.ArraySize() == 1, TRITONSERVER_ERROR_INVALID_ARG,
      std::string("model configuration must have 1 output"));
 
  common::TritonJson::Value input, output;
  RETURN_IF_ERROR(inputs.IndexAsObject(0, &input));
  RETURN_IF_ERROR(outputs.IndexAsObject(0, &output));
 
  // Record the input and output name in the model state.
  const char* input_name;
  size_t input_name_len;
  RETURN_IF_ERROR(input.MemberAsString("name", &input_name, &input_name_len));
  input_name_ = std::string(input_name);
 
  const char* output_name;
  size_t output_name_len;
  RETURN_IF_ERROR(
      output.MemberAsString("name", &output_name, &output_name_len));
  output_name_ = std::string(output_name);
 
  // Input and output must have same datatype
  std::string input_dtype, output_dtype;
  RETURN_IF_ERROR(input.MemberAsString("data_type", &input_dtype));
  RETURN_IF_ERROR(output.MemberAsString("data_type", &output_dtype));
  RETURN_ERROR_IF_FALSE(
      input_dtype == output_dtype, TRITONSERVER_ERROR_INVALID_ARG,
      std::string("expected input and output datatype to match, got ") +
          input_dtype + " and " + output_dtype);
  datatype_ = ModelConfigDataTypeToTritonServerDataType(input_dtype);
 
  // Input and output must have same shape. Reshape is not supported
  // on either input or output so flag an error is the model
  // configuration uses it.
  triton::common::TritonJson::Value reshape;
  RETURN_ERROR_IF_TRUE(
      input.Find("reshape", &reshape), TRITONSERVER_ERROR_UNSUPPORTED,
      std::string("reshape not supported for input tensor"));
  RETURN_ERROR_IF_TRUE(
      output.Find("reshape", &reshape), TRITONSERVER_ERROR_UNSUPPORTED,
      std::string("reshape not supported for output tensor"));
 
  std::vector<int64_t> input_shape, output_shape;
  RETURN_IF_ERROR(backend::ParseShape(input, "dims", &input_shape));
  RETURN_IF_ERROR(backend::ParseShape(output, "dims", &output_shape));
 
  RETURN_ERROR_IF_FALSE(
      input_shape == output_shape, TRITONSERVER_ERROR_INVALID_ARG,
      std::string("expected input and output shape to match, got ") +
          backend::ShapeToString(input_shape) + " and " +
          backend::ShapeToString(output_shape));
 
  nb_shape_ = input_shape;
 
  return nullptr;  // success
}
 
extern "C" {
 
// Triton calls TRITONBACKEND_ModelInitialize when a model is loaded
// to allow the backend to create any state associated with the model,
// and to also examine the model configuration to determine if the
// configuration is suitable for the backend. Any errors reported by
// this function will prevent the model from loading.
//
TRITONSERVER_Error*
TRITONBACKEND_ModelInitialize(TRITONBACKEND_Model* model)
{
  // Create a ModelState object and associate it with the
  // TRITONBACKEND_Model. If anything goes wrong with initialization
  // of the model state then an error is returned and Triton will fail
  // to load the model.
  ModelState* model_state;
  RETURN_IF_ERROR(ModelState::Create(model, &model_state));
  RETURN_IF_ERROR(
      TRITONBACKEND_ModelSetState(model, reinterpret_cast<void*>(model_state)));
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      "============TRITONBACKEND_ModelInitialize============");
  return nullptr;  // success
}
 
// Triton calls TRITONBACKEND_ModelFinalize when a model is no longer
// needed. The backend should cleanup any state associated with the
// model. This function will not be called until all model instances
// of the model have been finalized.
//
TRITONSERVER_Error*
TRITONBACKEND_ModelFinalize(TRITONBACKEND_Model* model)
{
  void* vstate;
  RETURN_IF_ERROR(TRITONBACKEND_ModelState(model, &vstate));
  ModelState* model_state = reinterpret_cast<ModelState*>(vstate);
  delete model_state;
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      "============TRITONBACKEND_ModelFinalize============");
  return nullptr;  // success
}
 
}  // extern "C"
 
/
 
//
// ModelInstanceState
//
// State associated with a model instance. An object of this class is
// created and associated with each
// TRITONBACKEND_ModelInstance. ModelInstanceState is derived from
// BackendModelInstance class provided in the backend utilities that
// provides many common functions.
//
class ModelInstanceState : public BackendModelInstance {
 public:
  static TRITONSERVER_Error* Create(
      ModelState* model_state,
      TRITONBACKEND_ModelInstance* triton_model_instance,
      ModelInstanceState** state);
  virtual ~ModelInstanceState() = default;
 
  // Get the state of the model that corresponds to this instance.
  ModelState* StateForModel() const { return model_state_; }
 
 private:
  ModelInstanceState(
      ModelState* model_state,
      TRITONBACKEND_ModelInstance* triton_model_instance)
      : BackendModelInstance(model_state, triton_model_instance),
        model_state_(model_state)
  {
  }
 
  ModelState* model_state_;
};
 
TRITONSERVER_Error*
ModelInstanceState::Create(
    ModelState* model_state, TRITONBACKEND_ModelInstance* triton_model_instance,
    ModelInstanceState** state)
{
  try {
    *state = new ModelInstanceState(model_state, triton_model_instance);
  }
  catch (const BackendModelInstanceException& ex) {
    RETURN_ERROR_IF_TRUE(
        ex.err_ == nullptr, TRITONSERVER_ERROR_INTERNAL,
        std::string("unexpected nullptr in BackendModelInstanceException"));
    RETURN_IF_ERROR(ex.err_);
  }
 
  return nullptr;  // success
}
 
extern "C" {
 
// Triton calls TRITONBACKEND_ModelInstanceInitialize when a model
// instance is created to allow the backend to initialize any state
// associated with the instance.
//
TRITONSERVER_Error*
TRITONBACKEND_ModelInstanceInitialize(TRITONBACKEND_ModelInstance* instance)
{
  // Get the model state associated with this instance's model.
  TRITONBACKEND_Model* model;
  RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceModel(instance, &model));
 
  void* vmodelstate;
  RETURN_IF_ERROR(TRITONBACKEND_ModelState(model, &vmodelstate));
  ModelState* model_state = reinterpret_cast<ModelState*>(vmodelstate);
 
  // Create a ModelInstanceState object and associate it with the
  // TRITONBACKEND_ModelInstance.
  ModelInstanceState* instance_state;
  RETURN_IF_ERROR(
      ModelInstanceState::Create(model_state, instance, &instance_state));
  RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceSetState(
      instance, reinterpret_cast<void*>(instance_state)));
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      "============TRITONBACKEND_ModelInstanceInitialize============");
  return nullptr;  // success
}
 
// Triton calls TRITONBACKEND_ModelInstanceFinalize when a model
// instance is no longer needed. The backend should cleanup any state
// associated with the model instance.
//
TRITONSERVER_Error*
TRITONBACKEND_ModelInstanceFinalize(TRITONBACKEND_ModelInstance* instance)
{
  void* vstate;
  RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceState(instance, &vstate));
  ModelInstanceState* instance_state =
      reinterpret_cast<ModelInstanceState*>(vstate);
  delete instance_state;
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      "============TRITONBACKEND_ModelInstanceFinalize============");
  return nullptr;  // success
}
 
}  // extern "C"
 
/
 
extern "C" {
 
// When Triton calls TRITONBACKEND_ModelInstanceExecute it is required
// that a backend create a response for each request in the batch. A
// response may be the output tensors required for that request or may
// be an error that is returned in the response.
//
TRITONSERVER_Error*
TRITONBACKEND_ModelInstanceExecute(
    TRITONBACKEND_ModelInstance* instance, TRITONBACKEND_Request** requests,
    const uint32_t request_count)
{
  // Collect various timestamps during the execution of this batch or
  // requests. These values are reported below before returning from
  // the function.
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      "============TRITONBACKEND_ModelInstanceExecute============");
  uint64_t exec_start_ns = 0;
  SET_TIMESTAMP(exec_start_ns);
 
  // Triton will not call this function simultaneously for the same
  // 'instance'. But since this backend could be used by multiple
  // instances from multiple models the implementation needs to handle
  // multiple calls to this function at the same time (with different
  // 'instance' objects). Best practice for a high-performance
  // implementation is to avoid introducing mutex/lock and instead use
  // only function-local and model-instance-specific state.
  ModelInstanceState* instance_state;
  RETURN_IF_ERROR(TRITONBACKEND_ModelInstanceState(
      instance, reinterpret_cast<void**>(&instance_state)));
  ModelState* model_state = instance_state->StateForModel();
 
  // 'responses' is initialized as a parallel array to 'requests',
  // with one TRITONBACKEND_Response object for each
  // TRITONBACKEND_Request object. If something goes wrong while
  // creating these response objects, the backend simply returns an
  // error from TRITONBACKEND_ModelInstanceExecute, indicating to
  // Triton that this backend did not create or send any responses and
  // so it is up to Triton to create and send an appropriate error
  // response for each request. RETURN_IF_ERROR is one of several
  // useful macros for error handling that can be found in
  // backend_common.h.
 
  std::vector<TRITONBACKEND_Response*> responses;
  responses.reserve(request_count);
  for (uint32_t r = 0; r < request_count; ++r) {
    TRITONBACKEND_Request* request = requests[r];
    TRITONBACKEND_Response* response;
    RETURN_IF_ERROR(TRITONBACKEND_ResponseNew(&response, request));
    responses.push_back(response);
  }
 
  // At this point, the backend takes ownership of 'requests', which
  // means that it is responsible for sending a response for every
  // request. From here, even if something goes wrong in processing,
  // the backend must return 'nullptr' from this function to indicate
  // success. Any errors and failures must be communicated via the
  // response objects.
  //
  // To simplify error handling, the backend utilities manage
  // 'responses' in a specific way and it is recommended that backends
  // follow this same pattern. When an error is detected in the
  // processing of a request, an appropriate error response is sent
  // and the corresponding TRITONBACKEND_Response object within
  // 'responses' is set to nullptr to indicate that the
  // request/response has already been handled and no further processing
  // should be performed for that request. Even if all responses fail,
  // the backend still allows execution to flow to the end of the
  // function so that statistics are correctly reported by the calls
  // to TRITONBACKEND_ModelInstanceReportStatistics and
  // TRITONBACKEND_ModelInstanceReportBatchStatistics.
  // RESPOND_AND_SET_NULL_IF_ERROR, and
  // RESPOND_ALL_AND_SET_NULL_IF_ERROR are macros from
  // backend_common.h that assist in this management of response
  // objects.
 
  // The backend could iterate over the 'requests' and process each
  // one separately. But for performance reasons it is usually
  // preferred to create batched input tensors that are processed
  // simultaneously. This is especially true for devices like GPUs
  // that are capable of exploiting the large amount parallelism
  // exposed by larger data sets.
  //
  // The backend utilities provide a "collector" to facilitate this
  // batching process. The 'collector's ProcessTensor function will
  // combine a tensor's value from each request in the batch into a
  // single contiguous buffer. The buffer can be provided by the
  // backend or 'collector' can create and manage it. In this backend,
  // there is not a specific buffer into which the batch should be
  // created, so use ProcessTensor arguments that cause collector to
  // manage it. ProcessTensor does NOT support TRITONSERVER_TYPE_BYTES
  // data type.
 
  BackendInputCollector collector(
      requests, request_count, &responses, model_state->TritonMemoryManager(),
      false /* pinned_enabled */, nullptr /* stream*/);
 
  // To instruct ProcessTensor to "gather" the entire batch of input
  // tensors into a single contiguous buffer in CPU memory, set the
  // "allowed input types" to be the CPU ones (see tritonserver.h in
  // the triton-inference-server/core repo for allowed memory types).
  std::vector<std::pair<TRITONSERVER_MemoryType, int64_t>> allowed_input_types =
      {{TRITONSERVER_MEMORY_CPU_PINNED, 0}, {TRITONSERVER_MEMORY_CPU, 0}};
 
  const char* input_buffer;
  size_t input_buffer_byte_size;
  TRITONSERVER_MemoryType input_buffer_memory_type;
  int64_t input_buffer_memory_type_id;
 
  RESPOND_ALL_AND_SET_NULL_IF_ERROR(
      responses, request_count,
      collector.ProcessTensor(
          model_state->InputTensorName().c_str(), nullptr /* existing_buffer */,
          0 /* existing_buffer_byte_size */, allowed_input_types, &input_buffer,
          &input_buffer_byte_size, &input_buffer_memory_type,
          &input_buffer_memory_type_id));
 
  // Finalize the collector. If 'true' is returned, 'input_buffer'
  // will not be valid until the backend synchronizes the CUDA
  // stream or event that was used when creating the collector. For
  // this backend, GPU is not supported and so no CUDA sync should
  // be needed; so if 'true' is returned simply log an error.
  const bool need_cuda_input_sync = collector.Finalize();
  if (need_cuda_input_sync) {
    LOG_MESSAGE(
        TRITONSERVER_LOG_ERROR,
        "'recommended' backend: unexpected CUDA sync required by collector");
  }
 
  // 'input_buffer' contains the batched input tensor. The backend can
  // implement whatever logic is necessary to produce the output
  // tensor. This backend simply logs the input tensor value and then
  // returns the input tensor value in the output tensor so no actual
  // computation is needed.
 
  uint64_t compute_start_ns = 0;
  SET_TIMESTAMP(compute_start_ns);
 
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      (std::string("model ") + model_state->Name() + ": requests in batch " +
       std::to_string(request_count))
          .c_str());
  std::string tstr;
  IGNORE_ERROR(BufferAsTypedString(
      tstr, input_buffer, input_buffer_byte_size,
      model_state->TensorDataType()));
  LOG_MESSAGE(
      TRITONSERVER_LOG_INFO,
      (std::string("batched " + model_state->InputTensorName() + " value: ") +
       tstr)
          .c_str());
 
  const char* output_buffer = input_buffer;
  TRITONSERVER_MemoryType output_buffer_memory_type = input_buffer_memory_type;
  int64_t output_buffer_memory_type_id = input_buffer_memory_type_id;
 
  uint64_t compute_end_ns = 0;
  SET_TIMESTAMP(compute_end_ns);
 
  bool supports_first_dim_batching;
  RESPOND_ALL_AND_SET_NULL_IF_ERROR(
      responses, request_count,
      model_state->SupportsFirstDimBatching(&supports_first_dim_batching));
 
  std::vector<int64_t> tensor_shape;
  RESPOND_ALL_AND_SET_NULL_IF_ERROR(
      responses, request_count, model_state->TensorShape(tensor_shape));
 
  // Because the output tensor values are concatenated into a single
  // contiguous 'output_buffer', the backend must "scatter" them out
  // to the individual response output tensors.  The backend utilities
  // provide a "responder" to facilitate this scattering process.
  // BackendOutputResponder does NOT support TRITONSERVER_TYPE_BYTES
  // data type.
 
  // The 'responders's ProcessTensor function will copy the portion of
  // 'output_buffer' corresponding to each request's output into the
  // response for that request.
 
  BackendOutputResponder responder(
      requests, request_count, &responses, model_state->TritonMemoryManager(),
      supports_first_dim_batching, false /* pinned_enabled */,
      nullptr /* stream*/);
 
  responder.ProcessTensor(
      model_state->OutputTensorName().c_str(), model_state->TensorDataType(),
      tensor_shape, output_buffer, output_buffer_memory_type,
      output_buffer_memory_type_id);
 
  // Finalize the responder. If 'true' is returned, the output
  // tensors' data will not be valid until the backend synchronizes
  // the CUDA stream or event that was used when creating the
  // responder. For this backend, GPU is not supported and so no CUDA
  // sync should be needed; so if 'true' is returned simply log an
  // error.
  const bool need_cuda_output_sync = responder.Finalize();
  if (need_cuda_output_sync) {
    LOG_MESSAGE(
        TRITONSERVER_LOG_ERROR,
        "'recommended' backend: unexpected CUDA sync required by responder");
  }
 
  // Send all the responses that haven't already been sent because of
  // an earlier error.
  for (auto& response : responses) {
    if (response != nullptr) {
      LOG_IF_ERROR(
          TRITONBACKEND_ResponseSend(
              response, TRITONSERVER_RESPONSE_COMPLETE_FINAL, nullptr),
          "failed to send response");
    }
  }
 
  uint64_t exec_end_ns = 0;
  SET_TIMESTAMP(exec_end_ns);
 
#ifdef TRITON_ENABLE_STATS
  // For batch statistics need to know the total batch size of the
  // requests. This is not necessarily just the number of requests,
  // because if the model supports batching then any request can be a
  // batched request itself.
  size_t total_batch_size = 0;
  if (!supports_first_dim_batching) {
    total_batch_size = request_count;
  } else {
    for (uint32_t r = 0; r < request_count; ++r) {
      auto& request = requests[r];
      TRITONBACKEND_Input* input = nullptr;
      LOG_IF_ERROR(
          TRITONBACKEND_RequestInputByIndex(request, 0 /* index */, &input),
          "failed getting request input");
      if (input != nullptr) {
        const int64_t* shape = nullptr;
        LOG_IF_ERROR(
            TRITONBACKEND_InputProperties(
                input, nullptr, nullptr, &shape, nullptr, nullptr, nullptr),
            "failed getting input properties");
        if (shape != nullptr) {
          total_batch_size += shape[0];
        }
      }
    }
  }
#else
  (void)exec_start_ns;
  (void)exec_end_ns;
  (void)compute_start_ns;
  (void)compute_end_ns;
#endif  // TRITON_ENABLE_STATS
 
  // Report statistics for each request, and then release the request.
  for (uint32_t r = 0; r < request_count; ++r) {
    auto& request = requests[r];
 
#ifdef TRITON_ENABLE_STATS
    LOG_IF_ERROR(
        TRITONBACKEND_ModelInstanceReportStatistics(
            instance_state->TritonModelInstance(), request,
            (responses[r] != nullptr) /* success */, exec_start_ns,
            compute_start_ns, compute_end_ns, exec_end_ns),
        "failed reporting request statistics");
#endif  // TRITON_ENABLE_STATS
 
    LOG_IF_ERROR(
        TRITONBACKEND_RequestRelease(request, TRITONSERVER_REQUEST_RELEASE_ALL),
        "failed releasing request");
  }
 
#ifdef TRITON_ENABLE_STATS
  // Report batch statistics.
  LOG_IF_ERROR(
      TRITONBACKEND_ModelInstanceReportBatchStatistics(
          instance_state->TritonModelInstance(), total_batch_size,
          exec_start_ns, compute_start_ns, compute_end_ns, exec_end_ns),
      "failed reporting batch request statistics");
#endif  // TRITON_ENABLE_STATS
 
  return nullptr;  // success
}
 
}  // extern "C"
 
}}}  // namespace triton::backend::recommended

之后执行编译：

mkdir build
cmake -DCMAKE_INSTALL_PREFIX:PATH=`pwd`/install ..
make install

编译完成后，会生成动态库：

2.2 自定义backend的serve

模型的serve除了需要模型本身之外，还需要相应的配置文件，例如：

backend: "liupeng"
max_batch_size: 8
dynamic_batching {
  max_queue_delay_microseconds: 5000000
}
input [
  {
    name: "IN0"
    data_type: TYPE_INT32
    dims: [ 4 ]
  }
]
output [
  {
    name: "OUT0"
    data_type: TYPE_INT32
    dims: [ 4 ]
  }
]
instance_group [
  {
    kind: KIND_CPU
  }
]

 注意，backend的名称是有要求的，其名称必须为：libtriton_backendname.so红色部分。

1、配置文件准备完成后，之后按照如下目录形式放入到model_repository路径下：

2、启动triton镜像，并将 model_repository路径映射到容器中，命令行：

docker run --rm -p8000:8000 -p8001:8001 -p8002:8002 -it -v /root/tritonserver/server/docs/examples/model_repository:/models nvcr.io/nvidia/tritonserver:23.12-py3

3、启动triton：

tritonserver --model-repository=/models

在triton启动时，会执行 TRITONBACKEND_Initialize函数，有如下打印：

3、python自定义backend

3.1 python自定义backend代码

python自定义backend相比c++简单很多，只要实现三个api即可：

def initialize(self, args)
def execute(self, requests)
def finalize(self)

示例代码位于：

https://gitee.com/bd-super-sugar/python_backend/blob/main/examples/add_sub/model.py

代码如下：

# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#  * Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  * Redistributions in binary form must reproduce the above copyright
#    notice, this list of conditions and the following disclaimer in the
#    documentation and/or other materials provided with the distribution.
#  * Neither the name of NVIDIA CORPORATION nor the names of its
#    contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
import json
 
# triton_python_backend_utils is available in every Triton Python model. You
# need to use this module to create inference requests and responses. It also
# contains some utility functions for extracting information from model_config
# and converting Triton input/output types to numpy types.
import triton_python_backend_utils as pb_utils
 
 
class TritonPythonModel:
    """Your Python model must use the same class name. Every Python model
    that is created must have "TritonPythonModel" as the class name.
    """
 
    def initialize(self, args):
        """`initialize` is called only once when the model is being loaded.
        Implementing `initialize` function is optional. This function allows
        the model to initialize any state associated with this model.
        Parameters
        ----------
        args : dict
          Both keys and values are strings. The dictionary keys and values are:
          * model_config: A JSON string containing the model configuration
          * model_instance_kind: A string containing model instance kind
          * model_instance_device_id: A string containing model instance device ID
          * model_repository: Model repository path
          * model_version: Model version
          * model_name: Model name
        """
 
        # You must parse model_config. JSON string is not parsed here
        self.model_config = model_config = json.loads(args["model_config"])
 
        # Get OUTPUT0 configuration
        output0_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT0")
 
        # Get OUTPUT1 configuration
        output1_config = pb_utils.get_output_config_by_name(model_config, "OUTPUT1")
 
        # Convert Triton types to numpy types
        self.output0_dtype = pb_utils.triton_string_to_numpy(
            output0_config["data_type"]
        )
        self.output1_dtype = pb_utils.triton_string_to_numpy(
            output1_config["data_type"]
        )
 
    def execute(self, requests):
        """`execute` MUST be implemented in every Python model. `execute`
        function receives a list of pb_utils.InferenceRequest as the only
        argument. This function is called when an inference request is made
        for this model. Depending on the batching configuration (e.g. Dynamic
        Batching) used, `requests` may contain multiple requests. Every
        Python model, must create one pb_utils.InferenceResponse for every
        pb_utils.InferenceRequest in `requests`. If there is an error, you can
        set the error argument when creating a pb_utils.InferenceResponse
        Parameters
        ----------
        requests : list
          A list of pb_utils.InferenceRequest
        Returns
        -------
        list
          A list of pb_utils.InferenceResponse. The length of this list must
          be the same as `requests`
        """
 
        output0_dtype = self.output0_dtype
        output1_dtype = self.output1_dtype
 
        responses = []
 
        # Every Python backend must iterate over everyone of the requests
        # and create a pb_utils.InferenceResponse for each of them.
        for request in requests:
            # Get INPUT0
            in_0 = pb_utils.get_input_tensor_by_name(request, "INPUT0")
            # Get INPUT1
            in_1 = pb_utils.get_input_tensor_by_name(request, "INPUT1")
 
            out_0, out_1 = (
                in_0.as_numpy() + in_1.as_numpy(),
                in_0.as_numpy() - in_1.as_numpy(),
            )
 
            # Create output tensors. You need pb_utils.Tensor
            # objects to create pb_utils.InferenceResponse.
            out_tensor_0 = pb_utils.Tensor("OUTPUT0", out_0.astype(output0_dtype))
            out_tensor_1 = pb_utils.Tensor("OUTPUT1", out_1.astype(output1_dtype))
 
            # Create InferenceResponse. You can set an error here in case
            # there was a problem with handling this inference request.
            # Below is an example of how you can set errors in inference
            # response:
            #
            # pb_utils.InferenceResponse(
            #    output_tensors=..., TritonError("An error occurred"))
            inference_response = pb_utils.InferenceResponse(
                output_tensors=[out_tensor_0, out_tensor_1]
            )
            responses.append(inference_response)
 
        # You should return a list of pb_utils.InferenceResponse. Length
        # of this list must match the length of `requests` list.
        return responses
 
    def finalize(self):
        """`finalize` is called only once when the model is being unloaded.
        Implementing `finalize` function is OPTIONAL. This function allows
        the model to perform any necessary clean ups before exit.
        """
        print("Cleaning up...")

 对应的配置config.pbtxt如下：

name: "liupeng_python"
backend: "python"
 
input [
  {
    name: "INPUT0"
    data_type: TYPE_FP32
    dims: [ 4 ]
  }
]
input [
  {
    name: "INPUT1"
    data_type: TYPE_FP32
    dims: [ 4 ]
  }
]
output [
  {
    name: "OUTPUT0"
    data_type: TYPE_FP32
    dims: [ 4 ]
  }
]
output [
  {
    name: "OUTPUT1"
    data_type: TYPE_FP32
    dims: [ 4 ]
  }
]
 
instance_group [{ kind: KIND_CPU }]

3.2 模型的serve

模型目录组织结构如下：

 启动后，模型加载情况：

到此，python自定义backend已经加载成功。 

4、执行推理

以python自定义backend为例，client端代码：

# Copyright 2020-2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#  * Redistributions of source code must retain the above copyright
#    notice, this list of conditions and the following disclaimer.
#  * Redistributions in binary form must reproduce the above copyright
#    notice, this list of conditions and the following disclaimer in the
#    documentation and/or other materials provided with the distribution.
#  * Neither the name of NVIDIA CORPORATION nor the names of its
#    contributors may be used to endorse or promote products derived
#    from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
import sys
 
import numpy as np
import tritonclient.http as httpclient
from tritonclient.utils import *
 
model_name = "liupeng_python"
shape = [4]
 
with httpclient.InferenceServerClient("localhost:8000") as client:
    input0_data = np.random.rand(*shape).astype(np.float32)
    input1_data = np.random.rand(*shape).astype(np.float32)
    inputs = [
        httpclient.InferInput(
            "INPUT0", input0_data.shape, np_to_triton_dtype(input0_data.dtype)
        ),
        httpclient.InferInput(
            "INPUT1", input1_data.shape, np_to_triton_dtype(input1_data.dtype)
        ),
    ]
 
    inputs[0].set_data_from_numpy(input0_data)
    inputs[1].set_data_from_numpy(input1_data)
 
    outputs = [
        httpclient.InferRequestedOutput("OUTPUT0"),
        httpclient.InferRequestedOutput("OUTPUT1"),
    ]
 
    response = client.infer(model_name, inputs, request_id=str(1), outputs=outputs)
 
    result = response.get_response()
    output0_data = response.as_numpy("OUTPUT0")
    output1_data = response.as_numpy("OUTPUT1")
 
    print(
        "INPUT0 ({}) + INPUT1 ({}) = OUTPUT0 ({})".format(
            input0_data, input1_data, output0_data
        )
    )
    print(
        "INPUT0 ({}) - INPUT1 ({}) = OUTPUT1 ({})".format(
            input0_data, input1_data, output1_data
        )
    )
 
    if not np.allclose(input0_data + input1_data, output0_data):
        print("add_sub example error: incorrect sum")
        sys.exit(1)
 
    if not np.allclose(input0_data - input1_data, output1_data):
        print("add_sub example error: incorrect difference")
        sys.exit(1)
 
    print("PASS: liupeng_python")
    sys.exit(0)

安装好triton client依赖库后，执行该脚本，即可请求到【liupeng_python】模型上，简单期间，可以在triton的client镜像中运行。

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