qat 训练 模型convert_deploy的问题

[wqn628]

这里是我的模型定义：

class Net(torch.nn.Module):
    def __init__():
        pass
    def forward(self, input, input_length):
        x = self.input_layer(input)
        x = self.tdnn(x)
        chain_out = self.chain(x)    ##(B,F,T',1),T'=(T-8)//3
        chain_out = chain_out.transpose(1, 2).squeeze(3)
        out_lengths = self.output_lengths(input_length)

        return chain_out, out_lengths

    def inference(self, input):
        x = self.input_layer(input)
        x = self.tdnn(x)
        chain_out = self.chain(x)
        return chain_out   ###(B, F, T', 1);T' = (T-8//3) 

训练 过程用的是forward 函数，在部署的时候我想用inference 函数推理。因此，我在训练代码中做了如下修改：

    backend = BackendType.SNPE
    model = prepare_by_platform(model, backend, prepare_custom_config_dict={"preserve_attr": {"": ["inference"]}})
    enable_calibration(model)
    model = model.to(device)
    for epoch in range(10):
        train_one_epoch()
        model_name = 'mqbench_qmodel_{}'.format(epoch)
        #input_shape={'input': [1, 1, 29, 80], "length": [1]}
        input_shape={'input': [1, 1, 29, 80]}
        #model.forward = model.inference
        convert_deploy(model.eval(), backend, input_shape, output_path=model_dir, model_name=model_name)    

然而当执行到convert_deploy(model.eval(), backend, input_shape, output_path=model_dir, model_name=model_name) 这个函数时，错误发生了：

File "xpspeech/cloud/bin/train_mmi-ctdnn.mq.py", line 334, in main
    convert_deploy(model.eval(), backend, input_shape, output_path=model_dir, model_name=model_name)
  File "/dataset/workspace/wangqingnan/asr_tool/cfm-mmi/mqbench/convert_deploy.py", line 192, in convert_deploy
    deploy_model = deepcopy_graphmodule(model)
  File "/dataset/workspace/wangqingnan/asr_tool/cfm-mmi/mqbench/utils/utils.py", line 73, in deepcopy_graphmodule
    copied_gm = copy.deepcopy(gm)
  File "/dataset/workspace/miniconda3/envs/cfm-mq/lib/python3.8/copy.py", line 153, in deepcopy
    y = copier(memo)
  File "/dataset/workspace/miniconda3/envs/cfm-mq/lib/python3.8/site-packages/torch/quantization/fx/graph_module.py", line 20, in __deepcopy__
    fake_mod.__dict__ = copy.deepcopy(self.__dict__)
  File "/dataset/workspace/miniconda3/envs/cfm-mq/lib/python3.8/copy.py", line 270, in _reconstruct
    state = deepcopy(state, memo)
  File "/dataset/workspace/miniconda3/envs/cfm-mq/lib/python3.8/copy.py", line 146, in deepcopy
    y = copier(x, memo)
  File "/dataset/workspace/miniconda3/envs/cfm-mq/lib/python3.8/copy.py", line 230, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/dataset/workspace/miniconda3/envs/cfm-mq/lib/python3.8/copy.py", line 161, in deepcopy
    rv = reductor(4)
  File "/dataset/workspace/miniconda3/envs/cfm-mq/lib/python3.8/site-packages/torch/utils/hooks.py", line 26, in __getstate__
    return (self.hooks_dict_ref(), self.id)
RecursionError: maximum recursion depth exceeded while calling a Python object

函数convert_deploy的定义如下：

def convert_deploy(model: GraphModule, backend_type: BackendType,
                   input_shape_dict=None, dummy_input=None, output_path='./',
                   model_name='mqbench_qmodel', deploy_to_qlinear=False, **extra_kwargs):
    r"""Convert model to onnx model and quantization params depends on backend.

    Args:
        model (GraphModule): GraphModule prepared qat module.
        backend_type (BackendType): specific which backend should be converted to.
        input_shape_dict (dict): keys are model input name(should be forward function
                                 params name, values are list of tensor dims)
        output_path (str, optional): path to save convert results. Defaults to './'.
        model_name (str, optional): name of converted onnx model. Defaults to 'mqbench_qmodel'.

    >>> note on input_shape_dict:
        example: {'input_0': [1, 3, 224, 224]
                'input_1': [1, 3, 112, 112]
                }
        while **_forward function_** signature is like:
                def forward(self, input_0, input_1):
                    pass
    """
    kwargs = {
        'input_shape_dict': input_shape_dict,
        'dummy_input': dummy_input,
        'output_path': output_path,
        'model_name': model_name,
        'onnx_model_path': osp.join(output_path, '{}.onnx'.format(model_name)),
        'deploy_to_qlinear': deploy_to_qlinear
    }
    kwargs.update(extra_kwargs)
    deploy_model = deepcopy_graphmodule(model)
    for convert_function in BACKEND_DEPLOY_FUNCTION[backend_type]:
        convert_function(deploy_model, **kwargs)

想请教一下各位大佬，如果模型部署推理的时候 调的不是forward 函数而是其他函数的话，应该怎么做呀。
十分感谢，叨扰各位了

[www516717402]

• Custom operate

model = Net()
src_forward_func = model.forward
model.forward = mode.inference
# convert operate
xxxxxx
# replace previous func
model.forward = src_forward_func

• QAT

1. 在qat中上述方法不行，因为fx已经将py2py进行了转化，你只能去修改fx转化之后的model，比较麻烦
2. 建议将训练和推理相同的部分进行整合用fx转化，后面不同的地方用2分支表示

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