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README
Apache-2.0

Compass Unified Parser

Compass Unified Parser 是为将多种不同框架的模型转化成一种浮点中间表示(IR)而设计的, 这种IR是由安谋中国设计的一种标准的IR,用于周易系列的神经网络编译器。

Parser 的处理流程和设计理念

Parser 的主要目标是将一个训练好的模型转换成浮点IR喂给OPT(优化器)。如下是Parser的主要处理流程:

  1. 一个模型通过统一的配置文件传给Parser。
  2. 配置解析器解析配置文件,并且根据不同的配置将提交一个任务给相应的模型读取器。
  3. 支持的模型读取器会接管输入模型,完成模型的读取:
    • 解析模型文件(例如 protobuf/flattenbuf/json 或一些私有格式)然后建立一个原始图表征。
    • 将原始图表征里面的节点转换为内部的统一节点, 例如:
      • 合并若干的TensorFlow 节点到一个GRUSeq节点。
      • 将caffe的 detectionoutput 节点转换为 detectboxnms 节点。
  4. 模型读取器会生成一个内部统一图表征,然后交给前端优化器。
  5. 前端优化器主要操作对象是统一图表征。它将会合并或者消除一些节点, 例如:
    • 合并convadd 到一个节点。
    • 合并 conv/fc 节点和 batchnorm 节点 到一个节点。
    • 消除一些无用的节点,例如:一个交换维度未变的transpose节点。
  6. 优化之后,Parser至少会进行一次形状推导来获取所有张量的形状。
  7. 进行一些额外的处理,例如:
    • 为一些模型添加一些后处理节点。
  8. 序列化为IR文件。

GraphNode 的设计

在Parser里面,和其他框架类似,我们使用 GraphNode 来表征一个模型,使用一个链表来表征一个图。 Graph 只保存所有节点,节点间的拓扑关系是保存在一个 Node 和另一个 Node连接上。 Node 表征 IR里的层概念(layer), Node 可以通过调用 serialize 方法来序列化成一个字串。

关于Parser设计

  • Parser 只支持一个固定形状的图(静态图),在整个解析转换过程中,会进行若干次的形状推导。
  • 每一次图操作之后,例如合并、转换、消除节点之后,我们都希望进行一次形状推导,除非你清楚并且保证所有形状是无误的。
    • 进行形状推导是因为图操作可能会改变图拓扑,也可能会导致形状的变化。
    • 如果某些参数依赖于形状,那么请将这些参数的处理放到形状推导之后或在推导阶段。
  • 优化的处理只指出统一图表征,不支持原始图表征。因此,所有框架的模型都能受益于这些优化处理。

快速入门

安装向导

Parser 是 Compass AIPUBuilder(NN-Compiler) 编译器的一部分。 你可以参考如下Compass AIPUBuilder的指引来安装AIPUBuilder。 完成AIPUBuilder的安装后, Parser 也会被安装并且可以直接使用。

你也可以通过Compass_Integration中的指引来编译一个包含Parser的AIPUBuilder。关于AIPUBuilder的使用说明,请参考MiniPkg里面的说明书:Zhouyi_Compass_Software_Programming_Guide_61010011_0205_01_en.pdf。

初除此之外,Parser可以单独运行。只要满足如下的依赖,就可以直接运行 main.py文件来运行Parser。

安装依赖

  • python (3.8 or higher)
  • numpy
  • onnx (> 12)
  • protobuf
  • flatbuffers
  • tensorflow (== 2.6)
  • torch

运行Parser

Parser是以配置文件为输入驱动的,你可以使用如下实例来运行Parser

python3 main.py -c my_config.ini

配置文件格式

所有的选项必须在 Common 段里面:

  • input_shape [required]

    输入张量的形状。常间的模型只有一个输入张量,如:input_shape=[1,224,224,3] 如果你有多个输入张量,使用英文逗号分隔,如: input_shape=[1,224,224,3],[1,112,112,3]

  • model_name [required]

    输入模型的名称

  • model_type [optional]

    输入模型的框架,默认是tensorflow,目前支持:

    • tensorflow
    • tflite
    • onnx
    • caffe
  • model_domain [required]

    模型的分类,例如:

    • image_classification
    • object_detection
    • keyword_spotting
    • speech_recognition
  • detection_postprocess [requiredmodel_domainobject_detection]

    如果你的模型是object_detection,并且你使用的是官方的模型,你可以选择如下两种后处理方式,我们将在结束出添加相应的后处理节点:

    • caffe_fasterrcnn
    • ssd
    • ssd_resnet
    • yolo2
    • yolo3_tiny
    • yolo3_full
  • input_model [required]

    输入模型的文件路径,当前支持tensorflow frozen pb, tflite, caffe and onnx 格式。

  • input [required]

    输入节点(或张量)的名称,如果有多个输入,使用英文逗号,分隔。

  • output [required]

    输出节点(或张量)名称,如果有多个输出,使用英文逗号,分隔。

配置文件示例

[Common]
input_shape = [1,224,224,3]
model_name = resnet50
model_domain = image_classification
detection_postprocess =
input_model = resnet50/frozen.pb
input = Placeholder
output = resnet_v1_50/predictions/Reshape

更多示例请参考 examples

运行示例

首先,你需要下载相应的原始模型。你可以通过examples下面的download_model.sh脚本来下载。

sh examples/tensorflow/download_model.sh

然后配置example.cfg文件里的相应的输入输出

[Common]
model_type = tensorflow
model_name = gru_l
model_domain = image_classification
input_model = ./GRU_L.pb
input = Mfcc:0
input_shape = [1, 49, 10]
output = labels_softmax:0
output_dir = ./

运行 run_example.py

  • --framework [optional]

    指定相应的示例,默认是tensorflow。

  • --input_data [optional]

    指定相应的输入数据,如果没有指定将使用随机数据。

python3 run_example.py --framework [specify example] --input_data [specify feed data]

贡献指引

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简介

NPU Parser 将多种不同框架的模型转化成一种浮点中间表示(IR),用于周易系列的神经网络编译器。 展开 收起
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