Remove excessive logging in mlir pass

[jackalcooper]

No description provided.

[github-actions]

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/10324/

[github-actions]

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.4ms (= 4344.9ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.8ms (= 5775.3ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.33 (= 57.8ms / 43.4ms)

OneFlow resnet50 time: 25.8ms (= 2579.9ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 37.4ms (= 3739.2ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.45 (= 37.4ms / 25.8ms)

OneFlow resnet50 time: 18.8ms (= 3767.1ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 35.8ms (= 7160.0ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.90 (= 35.8ms / 18.8ms)

OneFlow resnet50 time: 18.5ms (= 3692.0ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 32.3ms (= 6459.9ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.75 (= 32.3ms / 18.5ms)

OneFlow resnet50 time: 17.3ms (= 3454.0ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 30.6ms (= 6117.5ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.77 (= 30.6ms / 17.3ms)

OneFlow swin dataloader time: 0.202s (= 40.458s / 200, num_workers=1)
PyTorch swin dataloader time: 0.128s (= 25.634s / 200, num_workers=1)
Relative speed: 0.634 (= 0.128s / 0.202s)

OneFlow swin dataloader time: 0.056s (= 11.206s / 200, num_workers=4)
PyTorch swin dataloader time: 0.032s (= 6.423s / 200, num_workers=4)
Relative speed: 0.573 (= 0.032s / 0.056s)

OneFlow swin dataloader time: 0.029s (= 5.884s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.419s / 200, num_workers=8)
Relative speed: 0.581 (= 0.017s / 0.029s)

❌ OneFlow resnet50 time: 47.5ms (= 4753.9ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 65.3ms (= 6534.1ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.37 (= 65.3ms / 47.5ms)

OneFlow resnet50 time: 31.1ms (= 3108.2ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 46.0ms (= 4598.0ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.48 (= 46.0ms / 31.1ms)

OneFlow resnet50 time: 24.1ms (= 4817.9ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 40.6ms (= 8120.6ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.69 (= 40.6ms / 24.1ms)

OneFlow resnet50 time: 21.8ms (= 4364.1ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 39.0ms (= 7807.1ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.79 (= 39.0ms / 21.8ms)

OneFlow resnet50 time: 21.0ms (= 4197.6ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 35.1ms (= 7022.0ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.67 (= 35.1ms / 21.0ms)