Dev ccl kernel support bool

[hjchen2]

No description provided.

[github-actions]

Speed stats:

GPU Name: GeForce GTX 1080 

OneFlow resnet50 time: 136.5ms (= 13653.1ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 140.3ms (= 14029.9ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.03 (= 140.3ms / 136.5ms)

OneFlow resnet50 time: 78.5ms (= 7848.8ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 85.4ms (= 8543.6ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.09 (= 85.4ms / 78.5ms)

OneFlow resnet50 time: 54.7ms (= 10948.3ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 52.9ms (= 10582.8ms / 200, input_shape=[4, 3, 224, 224])
❌ Relative speed: 0.97 (= 52.9ms / 54.7ms)

OneFlow resnet50 time: 37.6ms (= 7514.5ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 47.9ms (= 9573.8ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.27 (= 47.9ms / 37.6ms)

OneFlow resnet50 time: 37.6ms (= 7515.1ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 41.2ms (= 8237.0ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.10 (= 41.2ms / 37.6ms)

OneFlow resnet50 time: 147.1ms (= 14706.1ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 154.3ms (= 15430.9ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.05 (= 154.3ms / 147.1ms)

OneFlow resnet50 time: 89.4ms (= 8936.8ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 98.5ms (= 9846.2ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.10 (= 98.5ms / 89.4ms)

OneFlow resnet50 time: 66.1ms (= 13221.9ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 78.2ms (= 15641.0ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.18 (= 78.2ms / 66.1ms)

OneFlow resnet50 time: 54.9ms (= 10975.9ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 66.7ms (= 13334.3ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.21 (= 66.7ms / 54.9ms)

OneFlow resnet50 time: 61.7ms (= 12333.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 62.4ms (= 12479.2ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.01 (= 62.4ms / 61.7ms)

[github-actions]

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

[github-actions]

Speed stats:

GPU Name: GeForce GTX 1080 

OneFlow resnet50 time: 136.4ms (= 13642.2ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 141.1ms (= 14108.9ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.03 (= 141.1ms / 136.4ms)

OneFlow resnet50 time: 78.3ms (= 7828.7ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 84.1ms (= 8405.2ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.07 (= 84.1ms / 78.3ms)

OneFlow resnet50 time: 55.6ms (= 11117.2ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 56.8ms (= 11354.9ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.02 (= 56.8ms / 55.6ms)

OneFlow resnet50 time: 41.4ms (= 8270.3ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 46.3ms (= 9267.6ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.12 (= 46.3ms / 41.4ms)

OneFlow resnet50 time: 40.6ms (= 8121.0ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 40.2ms (= 8048.8ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 0.99 (= 40.2ms / 40.6ms)

OneFlow resnet50 time: 146.8ms (= 14683.2ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 157.5ms (= 15753.3ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.07 (= 157.5ms / 146.8ms)

OneFlow resnet50 time: 89.2ms (= 8924.8ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 96.8ms (= 9682.8ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.08 (= 96.8ms / 89.2ms)

OneFlow resnet50 time: 65.5ms (= 13107.0ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 69.4ms (= 13888.8ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.06 (= 69.4ms / 65.5ms)

OneFlow resnet50 time: 50.7ms (= 10140.8ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 61.6ms (= 12322.5ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.22 (= 61.6ms / 50.7ms)

OneFlow resnet50 time: 59.7ms (= 11931.3ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 56.7ms (= 11349.5ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 0.95 (= 56.7ms / 59.7ms)

[jackalcooper]

好像从这个pr开始多机超时的几率变大了
发现之前也有