add dummy interfaces for import timm

[marigoold]

python3 -c "import timm" 输出如下，然后成功返回

/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:19: UserWarning: The oneflow.jit interface is just to align the torch.jit interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:134: UserWarning: The oneflow.jit.Final interface is just to align the torch.jit.Final interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:31: UserWarning: The oneflow.jit.script interface is just to align the torch.jit.script interface and has no practical significance.
  warnings.warn(
/home/wangyi/miniconda3/envs/py10/lib/python3.10/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: 'load_library is not implemented'If you don't plan on using image functionality from `torchvision.io`, you can ignore this warning. Otherwise, there might be something wrong with your environment. Did you have `libjpeg` or `libpng` installed before building `torchvision` from source?
  warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:49: UserWarning: The oneflow.jit.unused interface is just to align the torch.jit.unused interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/onnx/symbolic_helper.py:21: UserWarning: The oneflow.onnx.parse_args interface is just to align the torch.onnx.parse_args interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:57: UserWarning: The oneflow.jit._script_if_tracing interface is just to align the torch.jit._script_if_tracing interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/onnx/__init__.py:26: UserWarning: The oneflow.onnx.register_custom_op_symbolic interface is just to align the torch.onnx.register_custom_op_symbolic interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:65: UserWarning: The oneflow.jit._overload_method interface is just to align the torch.jit._overload_method interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:134: UserWarning: The oneflow.jit.Final interface is just to align the torch.jit.Final interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:31: UserWarning: The oneflow.jit.script interface is just to align the torch.jit.script interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:38: UserWarning: The oneflow.jit.ignore interface is just to align the torch.jit.ignore interface and has no practical significance.
  warnings.warn(
/data/home/wangyi/workspace/oneflow-public/python/oneflow/jit/__init__.py:140: UserWarning: The oneflow.jit.interface interface is just to align the torch.jit.interface interface and has no practical significance.
  warnings.warn(

[levi131]

LGTM

[daquexian]

开 lazy mock 还需要这些空接口吗？

[marigoold]

开 lazy mock 还需要这些空接口吗？

有些是装饰器函数，比如

     13 @parse_args("v", "v", "f")
---> 14 def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
     15     boxes = opset11.unsqueeze(g, boxes, 0)
     16     scores = opset11.unsqueeze(g, opset11.unsqueeze(g, scores, 0), 0)

有些是 __init__.py 里面就会调用的，比如

  File "/home/wangyi/miniconda3/envs/py10/lib/python3.10/site-packages/torchvision/ops/__init__.py", line 30, in <module>
_register_custom_op()

有些是类型声明的，比如 return_indices: torch.jit.Final[bool]

[github-actions]

CI failed when running job: Build cpu. PR label automerge has been removed

[daquexian]

开 lazy mock 还需要这些空接口吗？

有些是装饰器函数，比如

     13 @parse_args("v", "v", "f")
---> 14 def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
     15     boxes = opset11.unsqueeze(g, boxes, 0)
     16     scores = opset11.unsqueeze(g, opset11.unsqueeze(g, scores, 0), 0)

有些是 __init__.py 里面就会调用的，比如

  File "/home/wangyi/miniconda3/envs/py10/lib/python3.10/site-packages/torchvision/ops/__init__.py", line 30, in <module>
_register_custom_op()

有些是类型声明的，比如 return_indices: torch.jit.Final[bool]

开了 lazy mock 之后这些接口即使不存在应该也都不会报错？

lazy mock 是 这里 的 lazy=True 参数

[github-actions]

Code got formatted by CI. Please request CI again if you still want to have this PR merged. If the PR is from a forked repo, please download the patch files from the GitHub Actions web page and apply them locally.

[marigoold]

开 lazy mock 还需要这些空接口吗？

有些是装饰器函数，比如

     13 @parse_args("v", "v", "f")
---> 14 def symbolic_multi_label_nms(g, boxes, scores, iou_threshold):
     15     boxes = opset11.unsqueeze(g, boxes, 0)
     16     scores = opset11.unsqueeze(g, opset11.unsqueeze(g, scores, 0), 0)

有些是 __init__.py 里面就会调用的，比如

  File "/home/wangyi/miniconda3/envs/py10/lib/python3.10/site-packages/torchvision/ops/__init__.py", line 30, in <module>
_register_custom_op()

有些是类型声明的，比如 return_indices: torch.jit.Final[bool]

开了 lazy mock 之后这些接口即使不存在应该也都不会报错？

lazy mock 是 这里 的 lazy=True 参数

我试了下还真是 😅 用户当时使用的时候是没开 lazy，会直接报错

[daquexian]

我试了下还真是 😅 用户当时使用的时候是没开 lazy，会直接报错

可以把 lazy 设成默认的，或者更新下文档推荐用户使用 lazy（现在文档里说 lazy 是为了调试方便，这个看法过时了）

[github-actions]

View latest API docs preview at: https://oneflow-staging.oss-cn-beijing.aliyuncs.com/docs/Oneflow-Inc/oneflow/pr/10395/

[github-actions]

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.2ms (= 4324.2ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.3ms (= 5729.2ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.32 (= 57.3ms / 43.2ms)

OneFlow resnet50 time: 26.2ms (= 2624.8ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 38.5ms (= 3848.5ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.47 (= 38.5ms / 26.2ms)

OneFlow resnet50 time: 18.7ms (= 3739.0ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 36.7ms (= 7334.9ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.96 (= 36.7ms / 18.7ms)

OneFlow resnet50 time: 17.9ms (= 3574.0ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 30.7ms (= 6149.3ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.72 (= 30.7ms / 17.9ms)

OneFlow resnet50 time: 17.1ms (= 3420.7ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 28.6ms (= 5729.6ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.67 (= 28.6ms / 17.1ms)

OneFlow swin dataloader time: 0.200s (= 40.095s / 200, num_workers=1)
PyTorch swin dataloader time: 0.129s (= 25.811s / 200, num_workers=1)
Relative speed: 0.644 (= 0.129s / 0.200s)

OneFlow swin dataloader time: 0.056s (= 11.296s / 200, num_workers=4)
PyTorch swin dataloader time: 0.032s (= 6.428s / 200, num_workers=4)
Relative speed: 0.569 (= 0.032s / 0.056s)

OneFlow swin dataloader time: 0.030s (= 6.055s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.398s / 200, num_workers=8)
Relative speed: 0.561 (= 0.017s / 0.030s)

❌ OneFlow resnet50 time: 49.0ms (= 4899.3ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 65.9ms (= 6586.1ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.34 (= 65.9ms / 49.0ms)

OneFlow resnet50 time: 36.4ms (= 3640.3ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 44.4ms (= 4444.9ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.22 (= 44.4ms / 36.4ms)

OneFlow resnet50 time: 28.0ms (= 5599.0ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 39.6ms (= 7926.5ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.42 (= 39.6ms / 28.0ms)

OneFlow resnet50 time: 25.3ms (= 5063.4ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 38.4ms (= 7673.7ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.52 (= 38.4ms / 25.3ms)

OneFlow resnet50 time: 23.8ms (= 4766.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 36.0ms (= 7192.2ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.51 (= 36.0ms / 23.8ms)