Rename pooling ops

[Flowingsun007]

• 新版对齐torch的pooling op命名为max_pool_xd、avg_pool_xd；老板对齐tf的pool op统一加上tf前缀用来标识
• 将avg pool和max pool的命名及实现分开，更易辨别区分;
• 统一用pool命名而不是pooling

[github-actions]

Speed stats:

[github-actions]

CI failed when running job: cuda-speed-test. PR label automerge has been removed

[github-actions]

CI failed when running job: cuda-benchmark. PR label automerge has been removed

[github-actions]

Speed stats:

[github-actions]

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/8111/

[github-actions]

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/8111/

[github-actions]

CI failed when running job: cuda-misc. PR label automerge has been removed

[github-actions]

Speed stats:

[github-actions]

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/8111/

[github-actions]

Speed stats:

GPU Name: NVIDIA GeForce GTX 1080 

❌ OneFlow resnet50 time: 129.2ms (= 12923.1ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 143.2ms (= 14317.4ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.11 (= 143.2ms / 129.2ms)

OneFlow resnet50 time: 78.5ms (= 7852.9ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 86.7ms (= 8673.9ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.10 (= 86.7ms / 78.5ms)

OneFlow resnet50 time: 53.4ms (= 10675.7ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 58.4ms (= 11681.7ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.09 (= 58.4ms / 53.4ms)

OneFlow resnet50 time: 43.4ms (= 8686.7ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 45.4ms (= 9079.7ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.05 (= 45.4ms / 43.4ms)

OneFlow resnet50 time: 38.5ms (= 7699.4ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 40.0ms (= 7996.1ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.04 (= 40.0ms / 38.5ms)

OneFlow swin dataloader time: 0.253s (= 50.554s / 200, num_workers=1)
PyTorch swin dataloader time: 0.151s (= 30.159s / 200, num_workers=1)
Relative speed: 0.597 (= 0.151s / 0.253s)

OneFlow swin dataloader time: 0.067s (= 13.454s / 200, num_workers=4)
PyTorch swin dataloader time: 0.042s (= 8.325s / 200, num_workers=4)
Relative speed: 0.619 (= 0.042s / 0.067s)

OneFlow swin dataloader time: 0.059s (= 11.744s / 200, num_workers=8)
PyTorch swin dataloader time: 0.022s (= 4.357s / 200, num_workers=8)
Relative speed: 0.371 (= 0.022s / 0.059s)

❌ OneFlow resnet50 time: 145.2ms (= 14519.5ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 168.8ms (= 16876.5ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.16 (= 168.8ms / 145.2ms)

OneFlow resnet50 time: 97.7ms (= 9765.0ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 110.7ms (= 11065.7ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.13 (= 110.7ms / 97.7ms)

OneFlow resnet50 time: 76.2ms (= 15238.1ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 87.7ms (= 17539.2ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
❌ Relative speed: 1.15 (= 87.7ms / 76.2ms)

OneFlow resnet50 time: 62.8ms (= 12566.3ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 74.5ms (= 14904.0ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.19 (= 74.5ms / 62.8ms)

OneFlow resnet50 time: 57.3ms (= 11457.0ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 70.9ms (= 14176.3ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.24 (= 70.9ms / 57.3ms)