onnx报错解决-bert

一、定义

1. UserWarning: Provided key output for dynamic axes is not a valid input/output name warnings.warn(

2. 案例

3. 实体识别bert 案例

4. 转transformers 模型到onnx 接口解读

二、实现

https://huggingface.co/docs/transformers/main_classes/onnx#transformers.onnx.FeaturesManager

1. UserWarning: Provided key output for dynamic axes is not a valid input/output name warnings.warn(

代码：

with torch.no_grad():
    symbolic_names = {0: 'batch_size', 1: 'max_seq_len'}
    torch.onnx.export(model,
                      (inputs["input_ids"], inputs["token_type_ids"], inputs["attention_mask"]),
                      "./saves/bertclassify.onnx",
                      opset_version=14,
                      input_names=["input_ids", "token_type_ids", "attention_mask"],        
                       output_names=["logits"],                                             
                      dynamic_axes =    {'input_ids': symbolic_names,
                                        'attention_mask': symbolic_names,
                                        'token_type_ids': symbolic_names,
                                        'logits': symbolic_names
                                         }
                      )

改正后：原因： input_names 名字顺序与模型定义不一致导致。为了避免错误产生，应该标准化。如下2所示。

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path)
model.eval()
onnx_config = FeaturesManager._SUPPORTED_MODEL_TYPE['bert']['sequence-classification']("./saves")
dummy_inputs = onnx_config.generate_dummy_inputs(tokenizer, framework='pt')
from itertools import chain
with torch.no_grad():
    symbolic_names = {0: 'batch_size', 1: 'max_seq_len'}
    torch.onnx.export(model,
                      (inputs["input_ids"],inputs["attention_mask"], inputs["token_type_ids"]),
                      "./saves/bertclassify.onnx",
                      opset_version=14,
                      input_names=["input_ids", "attention_mask", "token_type_ids"],      
                       output_names=["logits"],                                            
                      dynamic_axes =    {
        name: axes for name, axes in chain(onnx_config.inputs.items(), onnx_config.outputs.items())
    }
)
# #验证是否成功
import onnx
onnx_model=onnx.load("./saves/bertclassify.onnx")
onnx.checker.check_model(onnx_model)
print("无报错，转换成功")

# #推理
import onnxruntime
ort_session=onnxruntime.InferenceSession("./saves/bertclassify.onnx", providers=['CPUExecutionProvider'])    #加载模型
ort_input={"input_ids":inputs["input_ids"].cpu().numpy(),"token_type_ids":inputs["token_type_ids"].cpu().numpy(),
           "attention_mask":inputs["attention_mask"].cpu().numpy()}
output_on = ort_session.run(["logits"], ort_input)[0]   #推理


print(output_org.detach().numpy())
print(output_on)
assert np.allclose(output_org.detach().numpy(), output_on, 10-5)  #无报错

标准化：

output_onnx_path = "./saves/bertclassify.onnx"
from itertools import chain
dummy_inputs = onnx_config.generate_dummy_inputs(tokenizer, framework='pt')

torch.onnx.export(
    model,
    (dummy_inputs,),
    f=output_onnx_path,
    input_names=list(onnx_config.inputs.keys()),
    output_names=list(onnx_config.outputs.keys()),
    dynamic_axes={
        name: axes for name, axes in chain(onnx_config.inputs.items(), onnx_config.outputs.items())
    },
    do_constant_folding=True,
    opset_version=14,
)

全部：

import torch
devices=torch.device("cpu")
from transformers.onnx.features import FeaturesManager
import torch
from transformers import AutoTokenizer, BertForSequenceClassification
import numpy as np
model_path = "./saves"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = BertForSequenceClassification.from_pretrained(model_path)

words = ["你叫什么名字"]
inputs = tokenizer(words, return_tensors='pt', padding=True)
model.eval()

onnx_config = FeaturesManager._SUPPORTED_MODEL_TYPE['bert']['sequence-classification']("./saves")
dummy_inputs = onnx_config.generate_dummy_inputs(tokenizer, framework='pt')
from itertools import chain

output_org = model(**inputs).logits

torch.onnx.export(
    model,
    (dummy_inputs,),
    f=output_onnx_path,
    input_names=list(onnx_config.inputs.keys()),
    output_names=list(onnx_config.outputs.keys()),
    dynamic_axes={
        name: axes for name, axes in chain(onnx_config.inputs.items(), onnx_config.outputs.items())
    },
    do_constant_folding=True,
    opset_version=14,
)

# #验证是否成功
import onnx
onnx_model=onnx.load("./saves/bertclassify.onnx")
onnx.checker.check_model(onnx_model)
print("无报错，转换成功")

# #推理
import onnxruntime
ort_session=onnxruntime.InferenceSession("./saves/bertclassify.onnx", providers=['CPUExecutionProvider'])    #加载模型
ort_input={"input_ids":inputs["input_ids"].cpu().numpy(),"token_type_ids":inputs["token_type_ids"].cpu().numpy(),
           "attention_mask":inputs["attention_mask"].cpu().numpy()}
output_on = ort_session.run(["logits"], ort_input)[0]   #推理


print(output_org.detach().numpy())
print(output_on)
assert np.allclose(output_org.detach().numpy(), output_on, 10-5)  #无报错

无任何警告产生

2. 实体识别案例

import onnxruntime
from itertools import chain
from transformers.onnx.features import FeaturesManager

config = ner_config
tokenizer = ner_tokenizer
model = ner_model
output_onnx_path = "bert-ner.onnx"

onnx_config = FeaturesManager._SUPPORTED_MODEL_TYPE['bert']['sequence-classification'](config)
dummy_inputs = onnx_config.generate_dummy_inputs(tokenizer, framework='pt')

torch.onnx.export(
    model,
    (dummy_inputs,),
    f=output_onnx_path,
    input_names=list(onnx_config.inputs.keys()),
    output_names=list(onnx_config.outputs.keys()),
    dynamic_axes={
        name: axes for name, axes in chain(onnx_config.inputs.items(), onnx_config.outputs.items())
    },
    do_constant_folding=True,
    opset_version=onnx_config.default_onnx_opset,       #默认，报错改为14
)

1. 转transformers 模型到onnx 接口解读

Huggingface:导出transformers模型到onnx_ONNX_程序员架构进阶_InfoQ写作社区

https://zhuanlan.zhihu.com/p/684444410