fix device_type2sub_tsk_gph_builder_

[Flowingsun007]

support multi devices training in graph mode(e.g. ascend npu)

[github-actions]

View latest API docs preview at: https://oneflow-staging.oss-cn-beijing.aliyuncs.com/docs/Oneflow-Inc/oneflow/pr/10563/

[github-actions]

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.8ms (= 4376.1ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 59.1ms (= 5913.3ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.35 (= 59.1ms / 43.8ms)

OneFlow resnet50 time: 26.4ms (= 2643.8ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 37.5ms (= 3754.1ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.42 (= 37.5ms / 26.4ms)

OneFlow resnet50 time: 18.2ms (= 3642.5ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 37.3ms (= 7453.6ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 2.05 (= 37.3ms / 18.2ms)

OneFlow resnet50 time: 17.6ms (= 3513.0ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 32.2ms (= 6446.2ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.83 (= 32.2ms / 17.6ms)

OneFlow resnet50 time: 16.8ms (= 3361.0ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 29.1ms (= 5826.6ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.73 (= 29.1ms / 16.8ms)

OneFlow swin dataloader time: 0.201s (= 40.248s / 200, num_workers=1)
PyTorch swin dataloader time: 0.130s (= 25.999s / 200, num_workers=1)
Relative speed: 0.646 (= 0.130s / 0.201s)

OneFlow swin dataloader time: 0.057s (= 11.300s / 200, num_workers=4)
PyTorch swin dataloader time: 0.032s (= 6.461s / 200, num_workers=4)
Relative speed: 0.572 (= 0.032s / 0.057s)

OneFlow swin dataloader time: 0.030s (= 6.089s / 200, num_workers=8)
PyTorch swin dataloader time: 0.016s (= 3.260s / 200, num_workers=8)
Relative speed: 0.535 (= 0.016s / 0.030s)

❌ OneFlow resnet50 time: 49.4ms (= 4942.8ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 64.8ms (= 6483.7ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.31 (= 64.8ms / 49.4ms)

OneFlow resnet50 time: 36.9ms (= 3692.4ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 46.0ms (= 4600.6ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.25 (= 46.0ms / 36.9ms)

OneFlow resnet50 time: 27.8ms (= 5565.8ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 42.2ms (= 8448.9ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.52 (= 42.2ms / 27.8ms)

OneFlow resnet50 time: 25.0ms (= 5009.8ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 38.3ms (= 7658.7ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.53 (= 38.3ms / 25.0ms)

OneFlow resnet50 time: 25.1ms (= 5028.7ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 35.9ms (= 7180.4ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.43 (= 35.9ms / 25.1ms)