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深度学习框架OneFlow是如何和ONNX交互的?

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oneflow-onnx 工具开发者:daquexian, bbuf

 

0x0. 介绍

 

在开始阅读本篇文章之前,如果你对ONNX不是很了解介意先阅读我之前写的这几篇介绍ONNX文章:

 

ONNX初探

 

ONNX 再探

 

onnx2pytorch和onnx-simplifier新版介绍

 

以及大老师的:

 

onnx simplifier 和 optimizer

 

然后,这篇文章不会继续探索ONNX本身的东西,而是聊聊另外一个有趣的话题,即深度学习框架是如何和ONNX进行交互的?我最近配合大老师基于OneFlow深度学习框架做了一些和ONNX有关的工作,感觉自己对OneFlow和ONNX的交互过程也算熟悉一些了。因此,在这篇文章我将分享OneFlow和ONNX交互的具体实现思路以及介绍oneflow-onnx这个开源工具的一些特性。让读者了解OneFlow的模型是如何转换为ONNX模型,以及ONNX模型是如何转回OneFlow的模型(X2OneFlow)的。个人认为OneFlow目前和ONNX交互的做法是比较优雅并且具有较好扩展性的,因此我们将这项工作转换成了开源成果并分享实现思路,github地址为: https://github.com/Oneflow-Inc/oneflow_convert_tools
。这个工具作为OneFlow 生态系统的一部分会被我们持续维护,同时这个工具也被我们制作成了一个wheel包,感兴趣的用户只需要pip安装oneflow-onnx即可快速体验。在下面的第二节以及工程的README也有详细的安装步骤。

 

oneflow-onnx工具包含两个功能,一个是将OneFlow导出ONNX,另外一个是将各个训练框架导出的ONNX模型转换为OneFlow的模型。本工程已经适配了TensorFlow/Pytorch/PaddlePaddle框架的预训练模型通过导出ONNX转换为OneFlow(我们将这一功能叫作X2OneFlow)。更多使用示例以及相关文档和源码均可以在开源 https://github.com/Oneflow-Inc/oneflow_convert_tools
工程中获得。

 

0x1. 算子支持和模型支持

 

OneFlow2ONNX

 

OneFlow2ONNX 支持的OP列表

 

目前OneFlow2ONNX 支持60+的ONNX OP,我们在下面的列表中列出了目前OneFlow支持导出的全部ONNX OP

序号 OP 序号 OP 序号 OP 序号 OP
1 GatherND 2 Transpose 3 Add 4 Sub
5 Mul 6 Div 7 Sum 8 LeakyRelu
9 Softplus 10 Softplus 11 Abs 12 Ceil
13 Elu 14 Exp 15 Floor 16 Log
17 Neg 18 Sigmoid 19 Sqrt 20 Tanh
21 Reciprocal 22 Relu 23 Acos 24 Asin
25 Atan 26 Cos 27 Sin 28 Tan
29 Acosh 30 Asinh 31 Atanh 32 Cosh
33 Sinh 34 Min 35 Max 36 Clip
37 Softmax 38 Sign 39 MatMul 40 Erf
41 FloorMod 42 Round 43 Not 44 And
45 Or 46 Equal 47 NotEqual 48 Greater
49 Less 50 Pad 51 AveragePool 52 MaxPool
53 Conv 54 QuantizeLinear 56 ReduceMin 57 BatchNormalization
58 ReduceSum 59 ReduceProd 60 ArgMax 61 ArgMin
62 Reshape 63 Squeeze 64 Transpose 65 Concat
66 Cast 67 Identity 68 Mul

 

OneFlow2ONNX 模型测试库

 

目前OneFlow2ONNX 支持60+的ONNX OP,我们在下面的模型列表中测试了OneFlow2ONNX的转换。

 

模型 来源 operator version
AlexNet OneFlow-AlexNet 10
MobileNetV2 Oneflow-MobileNetV2 10
ResNet50 OneFlow-ResNet50 10

 

X2OneFlow

 

X2OneFlow 支持的OP列表

 

目前X2OneFlow 支持40+的ONNX OP,30+的Tensorflow/Pytorch/PaddlePaddle OP,覆盖了大部分CV分类模型常用的操作。OP的单元测试代码会逐渐移步到工程的examples目录下,并支持更多的OP。

 

ONNX

序号 OP 序号 OP 序号 OP 序号 OP
1 Conv 2 BatchNormalization 3 MaxPool 4 AveragePool
5 Concat 6 ReLU 7 AdaptiveMaxPool 8 Softmax
9 Unsqueeze 10 Transpose 11 Clip 12 Gather
13 Slice 14 Split 15 Flatten 16 Add
17 Sub 18 Mul 19 Div 20 Sqrt
21 Pow 22 Tanh 23 Sigmoid 24 Cast
25 Pad 26 ReduceMean 27 Reshape 28 AdaptiveAvgPool
29 Squeeze 30 Expand 31 Gather 32 Slice
33 Split 34 Min 35 Max 36 Constant
37 HardSigmoid 38 Gemm 39 MatMul 40 Erf
41 Cast 42 GlobalMaxPool 43 GlobalAveragePool 44 ReduceMax
45 Identity

 

TensorFlow

序号 OP 序号 OP 序号 OP 序号 OP
1 relu 2 concatenate 3 expand_dims 4 transpose
5 batchnorm 6 slice 7 gather 8 clip_by_value
9 conv2d 10 depthwiseconv2d 11 flatten 12 add
13 sub 14 mul 15 div 16 pow
17 sqrt 18 tanh 19 sigmoid 20 erf
21 cast 22 pad 23 maxpool 24 avgpool
25 globalavgpool 26 globalmaxpool 27 reduce_mean 28 reshape
29 softmax 30 relu6

 

分组卷积存在问题,已给TensorFlow2ONNX团队PR。

 

Pytorch

 

序号 OP 序号 OP 序号 OP 序号 OP
1 relu 2 cat 3 unsqueeze 4 transpose
5 batchnorm 6 slice 7 gather 8 clamp
9 conv2d 10 depthwiseconv2d 11 flatten 12 add
13 sub 14 mul 15 div 16 pow
17 sqrt 18 tanh 19 sigmoid 20 erf
21 cast 22 pad 23 maxpool 24 avgpool
25 globalavgpool 26 globalmaxpool 27 reduce_mean 28 reshape
29 softmax 30 relu6 31 CrossEntropyLoss

 

PaddlePaddle

 

序号 OP 序号 OP 序号 OP 序号 OP
1 relu 2 concatenate 3 expand_dims 4 transpose
5 batchnorm 6 slice 7 gather 8 clip_by_value
9 conv2d 10 depthwiseconv2d 11 flatten 12 add
13 sub 14 mul 15 div 16 pow
17 sqrt 18 tanh 19 sigmoid 20 erf
21 cast 22 pad 23 maxpool 24 avgpool
25 adaptiveavgpool 26 adptivemaxpool 27 reduce_mean 28 reshape
29 softmax 30 relu6

 

相关issue:

 

https://github.com/PaddlePaddle/Paddle2ONNX/issues/221

 

https://github.com/PaddlePaddle/Paddle2ONNX/issues/220

 

X2OneFlow模型测试库

 

目前X2OneFlow 支持40+的ONNX OP,30+的Tensorflow/Pytorch/PaddlePaddle OP,覆盖了大部分CV分类模型常用的操作。我们在如下模型列表中测试了X2OneFlow的转换。

 

Pytorch

 

模型 是否支持
AlexNet Yes
VGGNet Yes
GoogleNet Yes
ResNet Yes
ResNext Yes
Yes
MobileNetV1 Yes
MobileNetV2 Yes
MobileNetV3 Yes
RegNet Yes
DenseNet Yes
EfficientNet Yes
InceptionNet Yes
ShuffleNetV1 Yes
ShuffleNetV2 Yes
SqueezeNet Yes

 

TensorFlow

模型 是否支持
VGGNet Yes
ResNet Yes
ResNetV2 Yes
XceptionNet Yes
MobileNetV1 Yes
MobileNetV2 Yes
MobileNetV3 Yes
DenseNet Yes
EfficientNet Yes
InceptionNet Yes

 

PaddlePaddle

 

模型 是否支持
AlexNet Yes
VGGNet Yes
GoogleNet Yes
ResNet Yes
ResNext Yes
SE_ResNext Yes
SENet Yes
MobileNetV1 Yes
MobileNetV2 Yes
MobileNetV3 Yes
RegNet Yes
DenseNet No(msg: “op_name: Concat_58 already exist in job: job_eval”)
EfficientNet Yes
InceptionNet Yes
ShuffleNetV2 Yes
SqueezeNet Yes
DPNNet Yes
DarkNet Yes
GhostNet Yes
RepVGG Yes
XceptionNet Yes
Xception_DeepLab Yes
Vision_Transformer No(”op_name: Constant_20 already exist in job: job_eval”)
Res2Net No(split op bug,working)
Unet No(OneFlow的上采样OP和Paddle未对齐)

 

模型的测试代码均可以在工程的examples中找到。

 

0x2. 快速体验

 

用户环境配置

 

python>=3.5
onnx>=1.8.0
onnx-simplifier>=0.3.3
onnxoptimizer>=0.2.5
onnxruntime>=1.6.0
oneflow>=0.3.4

 

如果你想使用X2OneFlow(X代表TensorFlow/Pytorch/PaddlePaddle)需要安装对应的深度学习框架,需要安装对应的深度学习框架,依赖如下:

 

pytorch>=1.7.0
paddlepaddle>=2.0.0
tensorflow>=2.0.0

 

安装

 

安装方式1

 

pip install oneflow_onnx

 

安装方式2

 

git clone https://github.com/Oneflow-Inc/oneflow_convert_tools
cd oneflow_onnx
python3 setup.py install

 

使用方法见工程的samples下的示例。

 

0x3. OneFlow-ONNX思路分享

 

我们将在这一节分享一下OneFlow的模型是如何被转换为ONNX的,这里我们以将OneFlow定义的AlexNet导出ONNX模型为例来分析源码。首先我们 https://github.com/Oneflow-Inc/oneflow_convert_tools/blob/main/examples/oneflow2onnx/test_alexnet.py#L133
进到这里,可以看到下面调用代码:

 

def test_alexnet():
    @flow.global_function()
def alexnet_eval_job(x: tp.Numpy.Placeholder((1, 227, 227, 3))):
return alexnet(x, None, False)
    convert_to_onnx_and_check(alexnet_eval_job, flow_weight_dir=None, onnx_model_path="/tmp")

 

这里通过 flow.global_function()
定义了一个预测用于eval的AlexNet job
,网络的完整定义可以通过上面的链接访问,可以看到这里通过 convert_to_onnx_and_check
函数将OneFlow定义的AlexNet转换为了ONNX模型,我们跟进这个函数,就来到了这里: https://github.com/Oneflow-Inc/oneflow_convert_tools/blob/main/oneflow_onnx/oneflow2onnx/util.py#L65-L73
,代码为:

 

 while not os.path.exists(os.path.join(flow_weight_dir, "snapshot_done")):
pass
 onnx_model_dir = onnx_model_path
 onnx_model_path = os.path.join(onnx_model_dir, "model.onnx")
 flow.onnx.export(
     job_func,
     flow_weight_dir,
     onnx_model_path,
     opset=opset,
     external_data=external_data,
 )

 

可以看到完成ONNX模型转换的核心函数就是这个 flow.onnx.export
函数,我们继续跳转到这个函数 https://github.com/Oneflow-Inc/oneflow_convert_tools/blob/main/oneflow_onnx/oneflow2onnx/flow2onnx.py#L229-L281
,代码如下:

 

def Export(
    job_func: Callable,
    model_save_dir: Text,
    onnx_filename: Text,
    continue_on_error: bool = False,
    opset: Optional[int] = None,
    extra_opset: Optional[int] = None,
    shape_override: Optional[Dict[Text, List[int]]] = None,
    external_data: bool = False,
):
r"""Export a oneflow model into ONNX format.
    Args:
        job_func: OneFlow的作业函数
        model_save_dir: 包含OneFlow定义的模型权重的文件夹. 这个模型权重是用oneflow的check_point.save接口保存的。
        onnx_filename: 输出ONNX模型文件名,字符串类型
        continue_on_error: 如果某个OP无法处理(即没有映射),是否继续
        opset: ONNX Opset版本号,默认为10
        extra_opset: 额外Opset的列表,例如自定义操作使用的Opset
        shape_override: 带有输入信息的字典,覆盖OneFlow给定的输入形状
        external_data: 将权重另存为ONNX外部数据,通常是为了绕过protobuf的2GB文件大小限制。
    """
assert os.getenv("ENABLE_USER_OP") != "False"
# 确定模型的路径是存在的
assert os.path.isdir(model_save_dir)
# 通过c_api_util.GetJobSet()方法获取当前的所有job
    job_set = c_api_util.GetJobSet()
# 我们要转的模型被定义在job_func中,所以我们先记录下它的名字
    job_name = job_func.__name__
# 编译job_set,找到定义模型的job
for job in job_set.job:
# TODO(OYY) Modify the interface before modifying it
if job.job_conf.job_name == job_name:
# job找到了,可以开始进行下面的步骤,我们在外面详细解释
            onnx_graph = ProcessFlowGraph(
                job,
                model_save_dir,
                continue_on_error=continue_on_error,
                opset=opset,
                extra_opset=extra_opset,
                shape_override=shape_override,
            )
            onnx_graph = optimizer.OptimizeGraph(onnx_graph)
            model_proto = onnx_graph.MakeModel(
                job_name, onnx_filename, external_data=external_data
            )
with open(onnx_filename, "wb") as f:
try:
                    f.write(model_proto.SerializeToString())
except ValueError as e:
raise ValueError(
"Error occured when running model_proto.SerializeToString(). If the model is larger than 2GB, please specify external_data=True when calling flow.onnx.export. Original error message:\n{}".format(
                            e
                        )
                    )
return
raise ValueError('Cannot find job "{}" in jobset'.format(job_name))

 

可以看到这个函数首先编译了OneFlow中的job_set,然后找到了我们最开始定义AlexNet模型的那个job,然后就进入了 ProcessFlowGraph
函数,这个函数主要做了三件事情并最终获得了 初版的合法ONNX模型
(初版的意思是还没有经过优化以及填ONNX节点的权重),我们跟进这个函数,代码如下。

 

def ProcessFlowGraph(
    flow_graph,
    model_save_dir,
    continue_on_error=False,
    opset=None,
    extra_opset=None,
    shape_override=None,
):
# 这个函数用来获取导出的ONNX的Opset Version,OneFlow里面最高为10
    opset = util.FindOpset(opset)
    logger.info("Using opset <onnx, %s>", opset)
# 判断当前的ONNX版本是否支持上面的Opset Version
if opset > schemas.get_max_supported_opset_version():
        logger.warning(
"Currently installed onnx package %s is too low to support opset %s, "
"please upgrade onnx package to avoid potential conversion issue.",
            util.get_onnx_version(),
            opset,
        )
if shape_override is None:
        shape_override = {}
# 用于将oneflow 的各个 node 转换为 onnx node 的格式,保持 op 类型、输入输出和属性值不变,这一步产生的还不是合法的 onnx 模型
    (onnx_nodes, op_cnt, attr_cnt, dtypes, output_shapes,) = FlowToOnnxNaive(
        flow_graph, shape_override
    )
# 构造一个 Graph 类,用于后续方便的修改 onnx 网络结构
    g = Graph(onnx_nodes, model_save_dir, output_shapes, dtypes, opset, extra_opset,)
# create ops mapping for the desired opsets
    ops_mapping = handler.flow_op.CreateMapping(g.opset, g.extra_opset)
# some nodes may already copied into inner Graph, so remove them from main Graph.
    TopologicalSort(g, continue_on_error)
# FlowOnnxMapping 函数调用各个转换函数(通过 @flow_op 注册)逐个转换 op,转换后产生的是合法的 onnx 模型
    mapped_op, unmapped_op, exceptions = FlowOnnxMapping(g, ops_mapping)
if unmapped_op:
        logger.error("Unsupported ops: %s", unmapped_op)
if exceptions and not continue_on_error:
raise exceptions[0]
# onnx requires topological sorting
    TopologicalSort(g, continue_on_error)
    g.UpdateProto()
    logger.debug(
"Summay Stats:\n"
"\toneflow ops: {}\n"
"\toneflow attr: {}\n"
"\tonnx mapped: {}\n"
"\tonnx unmapped: {}".format(op_cnt, attr_cnt, mapped_op, unmapped_op)
    )
return g

 

FlowToOnnxNaive
这个函数用于将oneflow 的各个 node 转换为 onnx node 的格式,保持 op 类型、输入输出和属性值不变,最后将转换后的ONNX节点(这个地方这些ONNX节点还不是真正的合法ONNX节点,要后面执行一对一转换之后才是合法的ONNX节点)全部返回。接下来利用这些ONNX节点来构造Graph类,方便后续对ONNX模型进行修改。Graph类的实现在 https://github.com/Oneflow-Inc/oneflow_convert_tools/blob/18e041d92654cfc8b03e16c906c451a405c99fd2/oneflow_onnx/onnx_wrapper.py
,这个文件主要是定义了onnx graph和node的wrapper,包含各种修改 onnx 图结构的 api,这里复用了tensorflow-onnx项目的相关代码。注意构造Graph类之后还并没有构造ONNX模型,因为OneFlow的OP还没有一对一的转换为ONNX的OP。

 

接下来,我们调用 handler.flow_op.CreateMapping(g.opset, g.extra_opset)
这个函数,代码实现如下:

 

def CreateMapping(max_onnx_opset_version, extra_opsets):
"""Create the final mapping dictionary by stacking domains and opset versions.
        :param max_onnx_opset_version: The highest onnx opset the resulting graph may use.
        :param extra_opsets: Extra opsets the resulting graph may use.
        """
        mapping = {constants.ONNX_DOMAIN: max_onnx_opset_version}
if extra_opsets:
for extra_opset in extra_opsets:
                mapping[extra_opset.domain] = extra_opset.version
        ops_mapping = {}
for domain, opsets in flow_op.get_opsets().items():
for target_opset, op_map in enumerate(opsets):
                print('='*100)
                print(target_opset)
                print(op_map)
                m = mapping.get(domain)
if m:
if target_opset <= m and op_map:
                        ops_mapping.update(op_map)
        flow_op._MAPPING = ops_mapping
return ops_mapping

 

这个函数做的事情就是将每个ONNX Opset版本号(也就是for循环中的 domain
)和(OneFlow OP和ONNX OP的mapper,这个mapper是如何获得的请看后文)关联起来并返回,我们打印一下 target_opset
op_map
就可以理解了。以AlexNet为例打印如下:

 

====================================================================================================
0
{}
====================================================================================================
1
{'add_n': (<bound method AddN.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.AddN'>>, 'Sum', {}), 'leaky_relu': (<bound method DirectOpSinceOpset1.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOpSinceOpset1'>>, 'LeakyRelu', {}), 'softplus': (<bound method DirectOpSinceOpset1.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOpSinceOpset1'>>, 'Softplus', {}), 'abs': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Abs', {}), 'ceil': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Ceil', {}), 'elu': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Elu', {}), 'exp': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Exp', {}), 'floor': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Floor', {}), 'log': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Log', {}), 'neg': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Neg', {}), 'sigmoid': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sigmoid_v2': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sqrt': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sqrt', {}), 'tanh': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Tanh', {}), 'reciprocal': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Reciprocal', {}), 'relu': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Relu', {}), 'broadcast_maximum': (<bound method MinMaxOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MinMaxOp'>>, 'Max', {}), 'broadcast_minimum': (<bound method MinMaxOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MinMaxOp'>>, 'Min', {}), 'clip_by_scalar': (<bound method ClipByValueOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_min': (<bound method ClipByValueOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_max': (<bound method ClipByValueOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'softmax': (<bound method Softmax.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Softmax'>>, 'Softmax', {}), 'square': (<bound method Square.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Square'>>, None, {}), 'rsqrt': (<bound method Rsqrt.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Rsqrt'>>, None, {}), 'squared_difference': (<bound method SquaredDifference.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.SquaredDifference'>>, None, {}), 'sign': (<bound method Sign.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Sign'>>, 'Sign', {}), 'matmul': (<bound method MatMul.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MatMul'>>, 'MatMul', {}), 'batch_matmul': (<bound method MatMul.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.MatMul'>>, 'MatMul', {}), 'erf': (<bound method Erf.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Erf'>>, 'Erf', {}), 'logical_not': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Not', {}), 'broadcast_logical_or': (<bound method BroadcastOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Or', {}), 'broadcast_logical_and': (<bound method BroadcastOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'And', {}), 'input': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.DirectOp'>>, None, {}), 'return': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.DirectOp'>>, None, {}), 'variable': (<bound method DirectOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.DirectOp'>>, None, {}), 'distribute_split': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'distribute_concat': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'distribute_clone': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'distribute_add': (<bound method BoxingOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.misc.BoxingOp'>>, 'Identity', {}), 'conv2d': (<bound method ConvOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.ConvOp'>>, None, {}), 'max_pool_2d': (<bound method PoolOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'MaxPool', {}), 'avg_pool_2d': (<bound method PoolOp.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'AveragePool', {}), 'reduce_prod': (<bound method ReduceOpBase.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceProd', {}), 'reduce_sum': (<bound method ReduceOpBase.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceSum', {}), 'reduce_min': (<bound method ReduceOpBase.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceMin', {}), 'argmax': (<bound method ArgMax.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMax', {}), 'argmin': (<bound method ArgMax.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMin', {}), 'squeeze': (<bound method Squeeze.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Squeeze'>>, 'Squeeze', {}), 'transpose': (<bound method Transpose.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Transpose'>>, 'Transpose', {}), 'concat': (<bound method Concat.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Concat'>>, 'Concat', {}), 'identity': (<bound method Identity.Version_1 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Identity'>>, 'Identity', {})}
====================================================================================================
2
{'pad': (<bound method Pad.Version_2 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.Pad'>>, 'Pad', {})}
====================================================================================================
3
{}
====================================================================================================
4
{}
====================================================================================================
5
{'reshape': (<bound method Reshape.Version_5 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Reshape'>>, 'Reshape', {})}
====================================================================================================
6
{'broadcast_div': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Div', {}), 'scalar_div_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Div', {}), 'multiply': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Mul', {}), 'broadcast_mul': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Mul', {}), 'scalar_mul_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Mul', {}), 'broadcast_sub': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Sub', {}), 'scalar_sub_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Sub', {}), 'broadcast_add': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Add', {}), 'scalar_add_by_tensor': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Add', {}), 'scalar_add': (<bound method ScalarBinaryOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ScalarBinaryOp'>>, 'Add', {}), 'scalar_mul': (<bound method ScalarBinaryOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ScalarBinaryOp'>>, 'Mul', {}), '_add': (<bound method BiasAdd.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BiasAdd'>>, 'Add', {}), 'abs': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Abs', {}), 'ceil': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Ceil', {}), 'elu': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Elu', {}), 'exp': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Exp', {}), 'floor': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Floor', {}), 'log': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Log', {}), 'neg': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Neg', {}), 'sigmoid': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sigmoid_v2': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sigmoid', {}), 'sqrt': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Sqrt', {}), 'tanh': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Tanh', {}), 'reciprocal': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Reciprocal', {}), 'relu': (<bound method DirectOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.DirectOp'>>, 'Relu', {}), 'broadcast_logical_or': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'Or', {}), 'broadcast_logical_and': (<bound method BroadcastOp.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.BroadcastOp'>>, 'And', {}), 'normalization': (<bound method BatchNorm.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.BatchNorm'>>, None, {}), 'cast': (<bound method Cast.Version_6 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Cast'>>, 'Cast', {})}
====================================================================================================
7
{'acos': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Acos', {}), 'asin': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Asin', {}), 'atan': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Atan', {}), 'cos': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Cos', {}), 'sin': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Sin', {}), 'tan': (<bound method TrigOpSinceOpset7.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset7'>>, 'Tan', {}), 'broadcast_floor_mod': (<bound method FloorMod.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.FloorMod'>>, 'FloorMod', {}), 'broadcast_equal': (<bound method Equal.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'Equal', {}), 'broadcast_not_equal': (<bound method Equal.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'NotEqual', {}), 'broadcast_greater': (<bound method GreaterLess.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLess'>>, 'Greater', {}), 'broadcast_less': (<bound method GreaterLess.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLess'>>, 'Less', {}), 'broadcast_less_equal': (<bound method GreaterLessEqual.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLessEqual'>>, 'Greater', {}), 'broadcast_greater_equal': (<bound method GreaterLessEqual.Version_7 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.GreaterLessEqual'>>, 'Less', {})}
====================================================================================================
8
{}
====================================================================================================
9
{'acosh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Acosh', {}), 'asinh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Asinh', {}), 'atanh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Atanh', {}), 'cosh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Cosh', {}), 'sinh': (<bound method TrigOpSinceOpset9.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.TrigOpSinceOpset9'>>, 'Sinh', {}), 'sign': (<bound method Sign.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Sign'>>, 'Sign', {}), 'erf': (<bound method Erf.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Erf'>>, 'Erf', {}), 'normalization': (<bound method BatchNorm.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.BatchNorm'>>, None, {}), 'cast': (<bound method Cast.Version_9 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Cast'>>, 'Cast', {})}
====================================================================================================
10
{'max_pool_2d': (<bound method PoolOp.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'MaxPool', {}), 'avg_pool_2d': (<bound method PoolOp.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'AveragePool', {}), 'min_max_observer': (<bound method MinMaxObserver.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.MinMaxObserver'>>, None, {}), 'moving_average_min_max_observer': (<bound method MovingAverageMinMaxObserver.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.MovingAverageMinMaxObserver'>>, None, {}), 'fake_quantization': (<bound method FakeQuantization.Version_10 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.FakeQuantization'>>, 'QuantizeLinear', {})}
====================================================================================================
11
{'clip_by_scalar': (<bound method ClipByValueOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_min': (<bound method ClipByValueOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'clip_by_scalar_max': (<bound method ClipByValueOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.ClipByValueOp'>>, 'Clip', {}), 'softmax': (<bound method Softmax.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Softmax'>>, 'Softmax', {}), 'round': (<bound method Round.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Round'>>, 'Round', {}), 'broadcast_equal': (<bound method Equal.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'Equal', {}), 'broadcast_not_equal': (<bound method Equal.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.math.Equal'>>, 'NotEqual', {}), 'conv2d': (<bound method ConvOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.ConvOp'>>, None, {}), 'max_pool_2d': (<bound method PoolOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'MaxPool', {}), 'avg_pool_2d': (<bound method PoolOp.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.PoolOp'>>, 'AveragePool', {}), 'pad': (<bound method Pad.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.nn.Pad'>>, 'Pad', {}), 'reduce_prod': (<bound method ReduceOpBase.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceProd', {}), 'reduce_sum': (<bound method ReduceOpBase.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceSum', {}), 'reduce_min': (<bound method ReduceOpBase.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ReduceOpBase'>>, 'ReduceMin', {}), 'argmax': (<bound method ArgMax.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMax', {}), 'argmin': (<bound method ArgMax.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.reduce.ArgMax'>>, 'ArgMin', {}), 'squeeze': (<bound method Squeeze.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Squeeze'>>, 'Squeeze', {}), 'concat': (<bound method Concat.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.Concat'>>, 'Concat', {}), 'gather_nd': (<bound method GatherND.Version_11 of <class 'oneflow_onnx.oneflow2onnx.handlers.array.GatherND'>>, 'GatherND', {})}
====================================================================================================
12
{}
====================================================================================================
13
{'min_max_observer': (<bound method MinMaxObserver.Version_13 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.MinMaxObserver'>>, None, {}), 'fake_quantization': (<bound method FakeQuantization.Version_13 of <class 'oneflow_onnx.oneflow2onnx.handlers.quantize.FakeQuantization'>>, 'QuantizeLinear', {})}

 

可以看到对于ONNX的每一个Opset Version的OP都对应了OneFlow实现的OP,需要特别注意的是这个OP Mapper过程在是在 https://github.com/Oneflow-Inc/oneflow_convert_tools/tree/main/oneflow_onnx/oneflow2onnx/handlers
这里完成的,只要安装了oneflow-onnx这个包或者编译了oneflow-onnx工程源码,Python就会自动将OneFlow的OP和ONNX的OP进行映射,这是通过 @flow_op(["avg_pool_2d"], onnx_op="AveragePool")
装饰器来实现的, flow_op
装饰器的具体实现在 https://github.com/Oneflow-Inc/oneflow_convert_tools/blob/main/oneflow_onnx/oneflow2onnx/handler.py#L34
这里。

 

完成了ONNX每个Opset版本的OP和OneFlow OP的mapper之后,我们需要对 Graph
里面的ONNX节点(注意现在的ONNX节点并不是合法的ONNX节点,因为还没有执行一对一的转换,只是复制了OneFlow OP的类型、输入输出和属性值)先执行拓扑排序,然后再一对一的转换。这个地方很有意思,为什幺要进行拓扑排序呢?

 

我们首先需要了解一下拓扑序算法,拓扑排序要解决的问题是给一个图的所有节点排序。

 

以下对拓扑排序的解释引自oi.wiki。

 

我们可以拿大学选课的例子来描述这个过程,比如学习大学课程中有:单变量微积分,线性代数,离散数学概述,概率论与统计学概述,语言基础,算法导论,机器学习。当我们想要学习 算法导论 的时候,就必须先学会 离散数学概述 和 概率论与统计学概述,不然在课堂就会听的一脸懵逼。当然还有一个更加前的课程 单变量微积分。这些课程就相当于几个顶点, 顶点之间的有向边就相当于学习课程的顺序。显然拓扑排序不是那幺的麻烦,不然你是如何选出合适的学习顺序。下面将介绍如何将这个过程抽象出来,用算法来实现。

 

但是如果某一天排课的老师打瞌睡了,说想要学习 算法导论,还得先学 机器学习,而 机器学习 的前置课程又是 算法导论,然后你就一万脸懵逼了,我到底应该先学哪一个?当然我们在这里不考虑什幺同时学几个课程的情况。在这里,算法导论 和 机器学习 间就出现了一个环,显然你现在没办法弄清楚你需要学什幺了,于是你也没办法进行拓扑排序了。因而如果有向图中存在环路,那幺我们就没办法进行 拓扑排序 了。

 

因此我们可以说 在一个 DAG(有向无环图),我们将图中的顶点以线性方式进行排序,使得对于任何的顶点到的有向边, 都可以有在的前面。

 

还有给定一个 DAG,如果从到有边,则认为依赖于。如果到有路径(可达),则称间接依赖于。

 

拓扑排序的目标是将所有节点排序,使得排在前面的节点不能依赖于排在后面的节点。伪代码实现如下:

 

void TopologicalSort(Graph G){
 InitStack(S);
for(i = 0;i < G.vexnum; i++){
if(indegrdd[i]==0)
   Push(S, i);
 }
int count =0;
while(!Empty(S)){
  Pop(S,i);
  print[count++] = i;
for(p = G.vertices[i].firstarc; p; p = p->nextarc){
   v = p->adjvex;
if(!(--indegree[v]))
    Push(S, v);
  }
 }
if(count < G.vexnum)
return false;
else
return true;
}

 

上面加粗的这句话即是拓扑排序的核心。一般深度学习模型也是一个DAG(有向无环图),我们这里同样使用了拓扑排序算法使得我们在一对一转换OP时和真实的网络结构是完全一致的。另外考虑到这里可能插入了一些新的节点如Identity可能会破坏原Graph的拓扑序,以及时刻需要判断计算图是否是一个完整合法的DAG,使用拓扑排序都是没有坏处的。

 

完成拓扑排序之后我们就可以执行 FlowOnnxMapping
完成OneFlow OP和ONNX OP的一对一转换了,代码如下:

 

def FlowOnnxMapping(g, ops_mapping):
    logger.debug("Mapping Oneflow node to ONNX node(s)")
    mapped_op = collections.Counter()
    unmapped_op = collections.Counter()
    exceptions = []
    ops = list(g.get_nodes())
for node in ops:
        logger.debug("Process node: %s\n%s", node.name, node.summary)
if node.skip_conversion:
            logger.debug("explicitly skip node " + node.name)
continue
        op = node.op_type
        map_info = ops_mapping.get(op)
if map_info is None:
            unmapped_op[op] += 1
            logger.error("oneflow op [%s: %s] is not supported", node.name, op)
continue
        mapped_op[op] += 1
        func, onnx_op, kwargs = map_info
if onnx_op is not None:
            node.op_type = onnx_op
try:
            func(g, node, **kwargs)
            node.skip_conversion = True
except Exception as ex:
            logger.error(
"Failed to convert node %s\n%s", node.name, node.summary, exc_info=1
            )
            exceptions.append(ex)
return mapped_op, unmapped_op, exceptions

 

执行完这个函数会返回map上的OP容器,以及没有map上的OP容器,当然如果 Graph
中有OP没有map上也就是转换失败会抛出错误信息给用户。在转换完成之后,我们调用 Graph
中的每个 Node
UpdateProto()
构造函数将之前的假ONNX节点信息更新成真的ONNX节点信息。

 

接下来,我们调用各种 optimizer 优化网络结构,例如尽可能消除 nhwc->nchw 带来的 transpose op(Export 函数内的 optimizer.OptimizeGraph),即 https://github.com/Oneflow-Inc/oneflow_convert_tools/blob/main/oneflow_onnx/oneflow2onnx/flow2onnx.py#L264
。在oneflow-onnx里面主要有以下几种optimizer:

oneflow-onnx里面的optimizer,获得更优的ONNX模型

这些 optimizer 继承自 tensorflow-onnx,我们后续会将其中的一部分用 onnx 原生的 optimizer 替代。

 

在优化了ONNX模型之后,最后调用下面的函数取磁盘中保存的 oneflow 权重,赋给 onnx 模型对象,并返回 protobuf 格式的 onnx 模型对象。至此就完成了创建合法的ONNX模型。

 

model_proto = onnx_graph.MakeModel(
                job_name, onnx_filename, external_data=external_data
            )

 

我们的X2OneFlow分为X2ONNX和ONNX2Oneflow两个步骤,其中ONNX2OneFlow和OneFlow2ONNX共用了一套基础代码,所以需要修改的地方仅仅是将 handles
里面的注册OP转换的装饰器改个方向即可,这里不再赘述。

 

想了解更多细节可以看我们的源码 https://github.com/Oneflow-Inc/oneflow_convert_tools/tree/main/oneflow_onnx

 

0x4. 总结

 

在这篇文章中,我们分享了最近维护OneFlow和ONNX做的一系列工作,这项工作目前已经开源并在持续维护中,欢迎对OneFlow框架以及ONNX感兴趣的小伙伴体验和PR。点击阅读原文快速关注和体验oneflow-onnx工具。

 

0x5. 相关链接

 

OneFlow:https://github.com/Oneflow-Inc/oneflow

 

https://github.com/onnx/tensorflow-onnx

 

https://github.com/OI-wiki/OI-wiki

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