oneflow backend 对接 torch compile ，运行 faster rcnn about oneflow

[crazy-JiangDongHua]

首先遇到第一大问题，oneflow 编译前端传递的计算图失败了

[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] WON'T CONVERT forward /data/home/jiangdonghua/miniconda3/envs/oneflow-dev-gcc9/lib/python3.9/site-packages/torchvision/models/detection/backbone_utils.py line 56 
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] due to: 
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] Traceback (most recent call last):
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING]   File "/data/home/jiangdonghua/miniconda3/envs/oneflow-dev-gcc9/lib/python3.9/site-packages/onefx/graph.py", line 1176, in _target_to_str
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING]     assert isinstance(target, str)
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] torch._dynamo.exc.BackendCompilerFailed: backend='oneflow_backend' raised:
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] AssertionError: 
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] 
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] Set TORCH_LOGS="+dynamo" and TORCHDYNAMO_VERBOSE=1 for more information
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING] 
[2024-02-29 03:15:49,828] torch._dynamo.convert_frame: [WARNING]

通过调试发现，原因是 torch.nn.functional.maxpool2d 的实现采用了一个 boolean dispatch，这个函数返回闭包内实现的一个函数 fn，其主要作用为，如果参数 return_indices 值为 True，则调用 max_pool2d_with_indices 函数，否则调用 _max_pool2d 函数。所以 max_pool2d 在 fx 的计算图追踪过程中，记录下来调用的是 torch.nn.fuctional.boolean_dispatch.<locals>.fn 函数。

max_pool2d = boolean_dispatch(
    arg_name="return_indices",
    arg_index=6,
    default=False,
    if_true=max_pool2d_with_indices,
    if_false=_max_pool2d,
    module_name=__name__,
    func_name="max_pool2d",
)

在 torch 模型转 oneflow 模型的过程中，对 torch.nn.fuctional.boolean_dispatch.<local>.fn 函数的调用会替换成对 oneflow.nn.fuctional.boolean_dispatch.<local>.fn 函数的调用，但是 oneflow 不是这么实现的，没有这个函数，应该直接调用 oneflow.nn.fuctional.max_pool2d 函数。知道问题以后，通过在转过过程添加一个特判就可以解决了。

[crazy-JiangDongHua]

然后出现了另外一个问题，在编译完成后，模型推理过程中，产生了 conv TypeError 如下：

TypeError: conv2d() received an invalid combination of arguments - got (Tensor, Parameter, Parameter, tuple, tuple, tuple, int), but expected one of:
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, tuple of ints padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (Tensor, Parameter, Parameter, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)
 * (Tensor input, Tensor weight, Tensor bias, tuple of ints stride, str padding, tuple of ints dilation, int groups)
      didn't match because some of the arguments have invalid types: (Tensor, Parameter, Parameter, tuple of (int, int), tuple of (int, int), tuple of (int, int), int)

经过检查发现这个 conv 是一个普通的 torch.nn.Conv2d, 打印 stride ，padding ，dialtion 都是正常的，并且同样形式的 conv 在前面可以通过检查，

stride: (1, 1), padding: (1, 1), dilation: (1, 1)

这个问题卡了很久，依然没有头绪。。.

[mosout]

这个问题很明显，穿进conv的参数是(Tensor, Parameter, Parameter, tuple, tuple, tuple, int) 里面的weight和bias是parameter，需要把他们转化为tensor

[crazy-JiangDongHua]

经过调试发现，原来是 oneflow backend 在处理输出时，将 oneflow.Tensor 转为 torch.Tensor 的过程中漏判了输出为 flow._oneflow_internal.TensorTuple 的情况，导致将 oneflow.Tensor 传入 torch 执行导致了错误。通过添加对 flow._oneflow_internal.TensorTuple 的处理解决了这个问题。现在 faster rcnn 在关闭动态 shape 的情况下能跑通了。

[mosout]

这个报出的错误看起来和产生的原因差的有点远，后面在提PR修这个问题的时候，check一下如果不是tensor/tuple/将要增加的dict类型的话就显式抛出一个异常以便于完善

[crazy-JiangDongHua]

这个报出的错误看起来和产生的原因差的有点远，后面在提PR修这个问题的时候，check一下如果不是tensor/tuple/将要增加的dict类型的话就显式抛出一个异常以便于完善

好的，我给添加一下。