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[hotfix] doubled bias in FusedMLP #317

Merged
merged 1 commit into from
Jun 1, 2022
Merged

[hotfix] doubled bias in FusedMLP #317

merged 1 commit into from
Jun 1, 2022

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blefaudeux
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@blefaudeux blefaudeux commented May 31, 2022 •
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What does this PR do?

Fixes the FusedMLP block having twice the bias layers, found randomly when working on the weight inits (#312)

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@facebook-github-bot facebook-github-bot added the CLA Signed This label is managed by the Facebook bot. Authors need to sign the CLA before a PR can be reviewed. label May 31, 2022
@blefaudeux
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Perf numbers without the doubled bias:

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.0 drop - fw 0.19 1.26 1.27 5.97
fused - gelu - no bias - 0.0 drop - fw 0.19 1.26 1.27 5.52

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.0 drop - fw 0.36 2.56 2.54 10.13
fused - gelu - no bias - 0.0 drop - fw 0.36 2.54 2.54 10.14

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.1 drop - fw 0.24 1.44 1.45 5.98
fused - gelu - no bias - 0.1 drop - fw 0.21 1.36 1.36 5.53

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.1 drop - fw 0.44 2.86 2.85 10.63
fused - gelu - no bias - 0.1 drop - fw 0.39 2.66 2.64 10.60

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.0 drop - fw 0.23 1.43 1.42 5.88
fused - gelu - bias - 0.0 drop - fw 0.23 1.43 1.43 5.92

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.0 drop - fw 0.43 2.83 2.83 10.76
fused - gelu - bias - 0.0 drop - fw 0.43 2.83 2.83 10.97

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.1 drop - fw 0.27 1.59 1.59 5.95
fused - gelu - bias - 0.1 drop - fw 0.22 1.36 1.36 5.54

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.1 drop - fw 0.50 3.10 3.12 11.60
fused - gelu - bias - 0.1 drop - fw 0.40 2.65 2.64 10.37

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.0 drop - fw+bw 0.60 4.26 4.27 16.46
fused - gelu - no bias - 0.0 drop - fw+bw 0.60 4.71 4.26 16.28

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.0 drop - fw+bw 1.14 9.02 8.45 31.93
fused - gelu - no bias - 0.0 drop - fw+bw 1.15 8.88 8.37 34.53

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.1 drop - fw+bw 0.67 4.56 4.58 16.84
fused - gelu - no bias - 0.1 drop - fw+bw 0.66 4.37 4.69 19.42

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - no bias - 0.1 drop - fw+bw 1.29 9.11 9.12 32.54
fused - gelu - no bias - 0.1 drop - fw+bw 1.18 8.49 8.69 34.63

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.0 drop - fw+bw 0.67 4.48 4.71 17.17
fused - gelu - bias - 0.0 drop - fw+bw 0.79 4.62 4.62 16.83

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.0 drop - fw+bw 1.24 9.81 8.81 32.42
fused - gelu - bias - 0.0 drop - fw+bw 1.35 9.20 9.01 32.51

--- Type: torch.float16 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.1 drop - fw+bw 0.75 5.26 5.16 17.36
fused - gelu - bias - 0.1 drop - fw+bw 0.96 4.55 4.57 16.64

--- Type: torch.float32 ---

Units: runtime in ms, lower is better. BMK - mul: 8 x 256 x 512 - 4 8 x 512 x 1024 - 4 4 x 1024 x 1024 - 4 2 x 2048 x 2048 - 4
standard - gelu - bias - 0.1 drop - fw+bw 1.40 9.47 9.72 33.54
fused - gelu - bias - 0.1 drop - fw+bw 1.30 8.56 8.56 32.66

If/when I get the time to revisit the fused linear this could probably be improved

@blefaudeux blefaudeux requested review from dianaml0 and fmassa June 1, 2022 00:01
blefaudeux
blefaudeux commented Jun 1, 2022
View reviewed changes
xformers/components/feedforward/fused_mlp.py
@@ -46,16 +46,26 @@ def __init__(
dim_mlp = hidden_layer_multiplier * dim_model

self.mlp = nn.Sequential(
nn.Linear(in_features=dim_model, out_features=dim_mlp, bias=bias),
nn.Linear(
in_features=dim_model, out_features=dim_mlp, bias=False
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the gist of it, this was a typo, the bias is handled in the next layer already

@blefaudeux
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insta-land @danthe3rd / @fmassa / @dianaml0 , I hope that's ok, semi-obvious typo

blefaudeux
blefaudeux commented Jun 1, 2022
View reviewed changes
xformers/benchmarks/benchmark_mlp.py
@@ -88,7 +88,7 @@ def mlp_fused():
),
]:
time = triton.testing.do_bench(testcase.function)[0]
key = f"B={B}, M={M}, K={K}, HLM={hlm}"
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minor presentation changes

blefaudeux
blefaudeux commented Jun 1, 2022
View reviewed changes
xformers/benchmarks/benchmark_mlp.py
@@ -19,8 +19,8 @@
(8, 512, 1024),
(4, 1024, 1024),
(2, 2048, 2048),
(1, 2048, 12288),
(2, 4096, 4096),
(1, 2048, 4096),
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trying to make this fit on a smaller GPU

@blefaudeux blefaudeux changed the title [hotfix] dual bias in FusedMLP [hotfix] doubled bias in FusedMLP Jun 1, 2022
@blefaudeux blefaudeux requested a review from danthe3rd June 1, 2022 00:06
@blefaudeux blefaudeux merged commit cbf4526 into main Jun 1, 2022
@blefaudeux blefaudeux deleted the hotfix_fused_mlp branch June 3, 2022 03:52
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