flip memory coalescing for last dim case #10310

marigoold · 2023-08-07T06:01:53Z

针对 dim = -1 时候访存无法合并的情况做了优化。
实现方式是先 flip 后写到 shared memory，然后统一从 shm 中顺序写到 global memory 中，此时可以合并访存。
对比：

import oneflow as flow
x = flow.randn(32, 2048).cuda()

flow._oneflow_internal.profiler.RangePush("flip_prof")
for i in range(100):
    y = flow.flip(x, [-1])
flow._oneflow_internal.profiler.RangePop()

import torch
x = torch.randn(32, 2048).cuda()

torch.cuda.nvtx.range_push("flip_prof")
for i in range(100):
    y = torch.flip(x, [-1])
torch.cuda.nvtx.range_pop()

nsys 结果对比

torch kernel：

oneflow kernel（优化后）：

oneflow kernel（优化前）：

github-actions · 2023-08-07T06:09:50Z

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.

…nc/oneflow into optimize_flip_last_dim

github-actions · 2023-08-07T07:16:33Z

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/10310/

github-actions · 2023-08-07T07:22:15Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.8ms (= 4382.0ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.2ms (= 5723.5ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.31 (= 57.2ms / 43.8ms)

OneFlow resnet50 time: 25.9ms (= 2586.5ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 38.3ms (= 3834.2ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.48 (= 38.3ms / 25.9ms)

OneFlow resnet50 time: 18.7ms (= 3747.8ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 35.5ms (= 7092.7ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.89 (= 35.5ms / 18.7ms)

OneFlow resnet50 time: 19.2ms (= 3833.0ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 32.4ms (= 6477.1ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.69 (= 32.4ms / 19.2ms)

OneFlow resnet50 time: 17.2ms (= 3439.4ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 29.2ms (= 5848.9ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.70 (= 29.2ms / 17.2ms)

OneFlow swin dataloader time: 0.202s (= 40.326s / 200, num_workers=1)
PyTorch swin dataloader time: 0.128s (= 25.619s / 200, num_workers=1)
Relative speed: 0.635 (= 0.128s / 0.202s)

OneFlow swin dataloader time: 0.054s (= 10.783s / 200, num_workers=4)
PyTorch swin dataloader time: 0.033s (= 6.523s / 200, num_workers=4)
Relative speed: 0.605 (= 0.033s / 0.054s)

OneFlow swin dataloader time: 0.032s (= 6.300s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.318s / 200, num_workers=8)
Relative speed: 0.527 (= 0.017s / 0.032s)

❌ OneFlow resnet50 time: 47.7ms (= 4765.0ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 64.1ms (= 6407.5ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.34 (= 64.1ms / 47.7ms)

OneFlow resnet50 time: 32.5ms (= 3254.7ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 44.1ms (= 4405.7ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.35 (= 44.1ms / 32.5ms)

OneFlow resnet50 time: 24.0ms (= 4809.3ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 41.2ms (= 8243.3ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.71 (= 41.2ms / 24.0ms)

OneFlow resnet50 time: 22.8ms (= 4557.6ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 36.8ms (= 7364.0ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.62 (= 36.8ms / 22.8ms)

OneFlow resnet50 time: 21.1ms (= 4217.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 34.1ms (= 6815.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.62 (= 34.1ms / 21.1ms)

github-actions · 2023-08-07T08:13:20Z

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…nc/oneflow into optimize_flip_last_dim

github-actions · 2023-08-14T17:40:31Z

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/10310/

github-actions · 2023-08-14T17:45:12Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.8ms (= 4382.5ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 61.2ms (= 6115.3ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.40 (= 61.2ms / 43.8ms)

OneFlow resnet50 time: 26.0ms (= 2595.7ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 37.8ms (= 3777.2ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.46 (= 37.8ms / 26.0ms)

OneFlow resnet50 time: 18.7ms (= 3742.1ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 35.3ms (= 7058.7ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.89 (= 35.3ms / 18.7ms)

OneFlow resnet50 time: 18.3ms (= 3664.2ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 34.0ms (= 6793.4ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.85 (= 34.0ms / 18.3ms)

OneFlow resnet50 time: 18.9ms (= 3784.8ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 29.2ms (= 5837.6ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.54 (= 29.2ms / 18.9ms)

OneFlow swin dataloader time: 0.201s (= 40.190s / 200, num_workers=1)
PyTorch swin dataloader time: 0.129s (= 25.828s / 200, num_workers=1)
Relative speed: 0.643 (= 0.129s / 0.201s)

OneFlow swin dataloader time: 0.055s (= 10.987s / 200, num_workers=4)
PyTorch swin dataloader time: 0.033s (= 6.547s / 200, num_workers=4)
Relative speed: 0.596 (= 0.033s / 0.055s)

OneFlow swin dataloader time: 0.031s (= 6.145s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.312s / 200, num_workers=8)
Relative speed: 0.539 (= 0.017s / 0.031s)

❌ OneFlow resnet50 time: 47.8ms (= 4783.5ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 64.1ms (= 6409.2ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.34 (= 64.1ms / 47.8ms)

OneFlow resnet50 time: 30.6ms (= 3058.8ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 43.3ms (= 4331.9ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.42 (= 43.3ms / 30.6ms)

OneFlow resnet50 time: 24.3ms (= 4858.6ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 41.8ms (= 8367.8ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.72 (= 41.8ms / 24.3ms)

OneFlow resnet50 time: 21.8ms (= 4369.9ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 38.9ms (= 7775.8ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.78 (= 38.9ms / 21.8ms)

OneFlow resnet50 time: 20.9ms (= 4180.4ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 34.4ms (= 6871.7ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.64 (= 34.4ms / 20.9ms)

github-actions · 2023-08-15T02:01:52Z

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/10310/

github-actions · 2023-08-15T02:06:55Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.6ms (= 4364.1ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 58.8ms (= 5883.0ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.35 (= 58.8ms / 43.6ms)

OneFlow resnet50 time: 26.1ms (= 2608.5ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 38.5ms (= 3853.6ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.48 (= 38.5ms / 26.1ms)

OneFlow resnet50 time: 18.8ms (= 3754.3ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 35.8ms (= 7154.7ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.91 (= 35.8ms / 18.8ms)

OneFlow resnet50 time: 18.9ms (= 3782.3ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 32.0ms (= 6400.6ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.69 (= 32.0ms / 18.9ms)

OneFlow resnet50 time: 17.5ms (= 3508.3ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 30.0ms (= 6005.6ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.71 (= 30.0ms / 17.5ms)

OneFlow swin dataloader time: 0.203s (= 40.613s / 200, num_workers=1)
PyTorch swin dataloader time: 0.129s (= 25.815s / 200, num_workers=1)
Relative speed: 0.636 (= 0.129s / 0.203s)

OneFlow swin dataloader time: 0.056s (= 11.164s / 200, num_workers=4)
PyTorch swin dataloader time: 0.033s (= 6.505s / 200, num_workers=4)
Relative speed: 0.583 (= 0.033s / 0.056s)

OneFlow swin dataloader time: 0.031s (= 6.195s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.345s / 200, num_workers=8)
Relative speed: 0.540 (= 0.017s / 0.031s)

❌ OneFlow resnet50 time: 47.4ms (= 4741.2ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 66.3ms (= 6626.5ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.40 (= 66.3ms / 47.4ms)

OneFlow resnet50 time: 31.0ms (= 3103.2ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 44.9ms (= 4494.4ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.45 (= 44.9ms / 31.0ms)

OneFlow resnet50 time: 24.4ms (= 4875.6ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 42.2ms (= 8430.8ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.73 (= 42.2ms / 24.4ms)

OneFlow resnet50 time: 22.2ms (= 4431.4ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 36.6ms (= 7328.2ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.65 (= 36.6ms / 22.2ms)

OneFlow resnet50 time: 21.1ms (= 4214.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 34.0ms (= 6806.1ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.62 (= 34.0ms / 21.1ms)

github-actions · 2023-08-15T03:17:03Z

Speed stats:

github-actions · 2023-08-15T03:19:18Z

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.

github-actions · 2023-08-15T03:51:26Z

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

github-actions · 2023-08-15T03:51:45Z

Speed stats:

github-actions · 2023-08-15T04:01:19Z

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

github-actions · 2023-08-15T04:01:50Z

Speed stats:

github-actions · 2023-08-15T04:10:29Z

View latest API docs preview at: https://staging.oneflow.info/docs/Oneflow-Inc/oneflow/pr/10310/

github-actions · 2023-08-15T04:16:00Z

Speed stats:

GPU Name: NVIDIA GeForce RTX 3080 Ti 

❌ OneFlow resnet50 time: 43.6ms (= 4362.4ms / 100, input_shape=[16, 3, 224, 224])
PyTorch resnet50 time: 57.5ms (= 5751.3ms / 100, input_shape=[16, 3, 224, 224])
✔️ Relative speed: 1.32 (= 57.5ms / 43.6ms)

OneFlow resnet50 time: 25.9ms (= 2589.8ms / 100, input_shape=[8, 3, 224, 224])
PyTorch resnet50 time: 38.8ms (= 3876.3ms / 100, input_shape=[8, 3, 224, 224])
✔️ Relative speed: 1.50 (= 38.8ms / 25.9ms)

OneFlow resnet50 time: 19.8ms (= 3967.2ms / 200, input_shape=[4, 3, 224, 224])
PyTorch resnet50 time: 36.2ms (= 7248.5ms / 200, input_shape=[4, 3, 224, 224])
✔️ Relative speed: 1.83 (= 36.2ms / 19.8ms)

OneFlow resnet50 time: 17.2ms (= 3433.1ms / 200, input_shape=[2, 3, 224, 224])
PyTorch resnet50 time: 31.8ms (= 6368.4ms / 200, input_shape=[2, 3, 224, 224])
✔️ Relative speed: 1.85 (= 31.8ms / 17.2ms)

OneFlow resnet50 time: 17.5ms (= 3493.4ms / 200, input_shape=[1, 3, 224, 224])
PyTorch resnet50 time: 28.7ms (= 5749.8ms / 200, input_shape=[1, 3, 224, 224])
✔️ Relative speed: 1.65 (= 28.7ms / 17.5ms)

OneFlow swin dataloader time: 0.200s (= 39.964s / 200, num_workers=1)
PyTorch swin dataloader time: 0.129s (= 25.896s / 200, num_workers=1)
Relative speed: 0.648 (= 0.129s / 0.200s)

OneFlow swin dataloader time: 0.055s (= 10.928s / 200, num_workers=4)
PyTorch swin dataloader time: 0.033s (= 6.501s / 200, num_workers=4)
Relative speed: 0.595 (= 0.033s / 0.055s)

OneFlow swin dataloader time: 0.030s (= 6.011s / 200, num_workers=8)
PyTorch swin dataloader time: 0.017s (= 3.325s / 200, num_workers=8)
Relative speed: 0.553 (= 0.017s / 0.030s)

❌ OneFlow resnet50 time: 47.6ms (= 4764.9ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 63.5ms (= 6350.8ms / 100, input_shape=[16, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.33 (= 63.5ms / 47.6ms)

OneFlow resnet50 time: 31.1ms (= 3112.3ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 44.8ms (= 4477.5ms / 100, input_shape=[8, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.44 (= 44.8ms / 31.1ms)

OneFlow resnet50 time: 24.3ms (= 4866.6ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 41.5ms (= 8295.7ms / 200, input_shape=[4, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.70 (= 41.5ms / 24.3ms)

OneFlow resnet50 time: 22.5ms (= 4501.8ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 36.7ms (= 7342.9ms / 200, input_shape=[2, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.63 (= 36.7ms / 22.5ms)

OneFlow resnet50 time: 21.8ms (= 4364.2ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
PyTorch resnet50 time: 34.0ms (= 6798.9ms / 200, input_shape=[1, 3, 224, 224], ddp, world size=2)
✔️ Relative speed: 1.56 (= 34.0ms / 21.8ms)

memory coalescing for last dim flip

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marigoold requested a review from liujuncheng as a code owner August 7, 2023 06:01

marigoold added enhancement automerge op api labels Aug 7, 2023

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Merge branch 'master' into optimize_flip_last_dim

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marigoold added 2 commits August 7, 2023 14:44

refine, fix bug of final block unactive thread

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Merge branch 'optimize_flip_last_dim' of https://github.com/Oneflow-I…

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flip memory coalescing for last dim case #10310

flip memory coalescing for last dim case #10310

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flip memory coalescing for last dim case #10310

flip memory coalescing for last dim case #10310

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