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Your PR requires formatting changes to meet the project's style guidelines. Click here to view the suggested changes.diff --git a/lib/cudnn/src/util.jl b/lib/cudnn/src/util.jl
index 8923ff9b5..c7ec0c2bd 100644
--- a/lib/cudnn/src/util.jl
+++ b/lib/cudnn/src/util.jl
@@ -4,13 +4,13 @@ using BFloat16s: BFloat16
cptr(x,a::DenseCuArray{Float64})=Float64[x]
cptr(x,a::DenseCuArray{Float32})=Float32[x]
cptr(x,a::DenseCuArray{Float16})=Float32[x]
-cptr(x,a::DenseCuArray{BFloat16})=Float32[x]
+cptr(x, a::DenseCuArray{BFloat16}) = Float32[x]
# Conversion between Julia and cuDNN datatypes
cudnnDataType(::Type{Float16})=CUDNN_DATA_HALF
cudnnDataType(::Type{Float32})=CUDNN_DATA_FLOAT
cudnnDataType(::Type{Float64})=CUDNN_DATA_DOUBLE
-cudnnDataType(::Type{BFloat16})=CUDNN_DATA_BFLOAT16
+cudnnDataType(::Type{BFloat16}) = CUDNN_DATA_BFLOAT16
cudnnDataType(::Type{Int8}) = CUDNN_DATA_INT8
cudnnDataType(::Type{UInt8}) = CUDNN_DATA_UINT8
cudnnDataType(::Type{Int32}) = CUDNN_DATA_INT32
@@ -21,7 +21,7 @@ cudnnDataType(::Type{Int32}) = CUDNN_DATA_INT32
juliaDataType(a)=(a==CUDNN_DATA_HALF ? Float16 :
a==CUDNN_DATA_FLOAT ? Float32 :
a==CUDNN_DATA_DOUBLE ? Float64 :
- a==CUDNN_DATA_BFLOAT16 ? BFloat16 :
+ a == CUDNN_DATA_BFLOAT16 ? BFloat16 :
a==CUDNN_DATA_INT8 ? Int8 :
a==CUDNN_DATA_UINT8 ? UInt8 :
a==CUDNN_DATA_INT32 ? Int32 : error())
diff --git a/lib/cudnn/test/activation.jl b/lib/cudnn/test/activation.jl
index e25cf4c7c..33f76ed94 100644
--- a/lib/cudnn/test/activation.jl
+++ b/lib/cudnn/test/activation.jl
@@ -62,9 +62,9 @@ activationtest(alpha=2)
activationtest(beta=2)
if capability(device()) >= v"8.0"
- (ax,ay) = randn.(BFloat16, (10,10))
- (cx,cy) = CuArray.((ax,ay))
- activationtest(mode=CUDNN_ACTIVATION_SIGMOID)
- activationtest(mode=CUDNN_ACTIVATION_RELU)
- activationtest(mode=CUDNN_ACTIVATION_TANH)
+ (ax, ay) = randn.(BFloat16, (10, 10))
+ (cx, cy) = CuArray.((ax, ay))
+ activationtest(mode = CUDNN_ACTIVATION_SIGMOID)
+ activationtest(mode = CUDNN_ACTIVATION_RELU)
+ activationtest(mode = CUDNN_ACTIVATION_TANH)
end
diff --git a/lib/cudnn/test/softmax.jl b/lib/cudnn/test/softmax.jl
index 2102d6d02..74befeeae 100644
--- a/lib/cudnn/test/softmax.jl
+++ b/lib/cudnn/test/softmax.jl
@@ -46,8 +46,8 @@ softmaxtest(algo=CUDNN_SOFTMAX_ACCURATE)
softmaxtest(algo=CUDNN_SOFTMAX_LOG)
if capability(device()) >= v"8.0"
- ax,ay = randn(BFloat16,10,10),randn(BFloat16,10,10)
- cx,cy = CuArray.((ax,ay))
+ ax, ay = randn(BFloat16, 10, 10), randn(BFloat16, 10, 10)
+ cx, cy = CuArray.((ax, ay))
softmaxtest()
- softmaxtest(algo=CUDNN_SOFTMAX_LOG)
+ softmaxtest(algo = CUDNN_SOFTMAX_LOG)
end |
Contributor
Author
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Hm, duplicate of #1092? That one doesn't define the |
Member
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1.12 failure unrelated, retried CI |
Member
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Second CI fail also seems unrelated, rerunning. If that succeeds you should rebase on top of |
Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #2987 +/- ##
===========================================
+ Coverage 76.53% 89.22% +12.68%
===========================================
Files 148 148
Lines 12860 12950 +90
===========================================
+ Hits 9842 11554 +1712
+ Misses 3018 1396 -1622 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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AntonOresten
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Thanks, Katharine. I’ve updated the BFloat16s compat entry to align with CUDA.jl’s |
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CUDA.jl Benchmarks
Details
| Benchmark suite | Current: a23ee46 | Previous: 5d9474a | Ratio |
|---|---|---|---|
latency/precompile |
55341296562.5 ns |
55510377029.5 ns |
1.00 |
latency/ttfp |
7795931886 ns |
7790703567 ns |
1.00 |
latency/import |
4120273995 ns |
4122189304 ns |
1.00 |
integration/volumerhs |
9609087 ns |
9624973 ns |
1.00 |
integration/byval/slices=1 |
146813 ns |
147064 ns |
1.00 |
integration/byval/slices=3 |
425709 ns |
425893 ns |
1.00 |
integration/byval/reference |
144869 ns |
145082 ns |
1.00 |
integration/byval/slices=2 |
286216 ns |
286384 ns |
1.00 |
integration/cudadevrt |
103582 ns |
103602 ns |
1.00 |
kernel/indexing |
14109 ns |
14225 ns |
0.99 |
kernel/indexing_checked |
14715 ns |
14969 ns |
0.98 |
kernel/occupancy |
719.9230769230769 ns |
732.5227272727273 ns |
0.98 |
kernel/launch |
2487 ns |
2249.4444444444443 ns |
1.11 |
kernel/rand |
14840 ns |
18642 ns |
0.80 |
array/reverse/1d |
19661 ns |
19990 ns |
0.98 |
array/reverse/2dL_inplace |
66712 ns |
66917 ns |
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array/reverse/1dL |
69754 ns |
70158 ns |
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array/reverse/2d |
21612 ns |
21954 ns |
0.98 |
array/reverse/1d_inplace |
9523 ns |
9677 ns |
0.98 |
array/reverse/2d_inplace |
10951 ns |
11077 ns |
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array/reverse/2dL |
73712 ns |
74051.5 ns |
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array/reverse/1dL_inplace |
66734 ns |
66880 ns |
1.00 |
array/copy |
20406 ns |
20660 ns |
0.99 |
array/iteration/findall/int |
156259 ns |
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array/iteration/findall/bool |
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array/iteration/findfirst/int |
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array/iteration/findfirst/bool |
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array/iteration/scalar |
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array/iteration/logical |
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51356 ns |
50981 ns |
1.01 |
array/iteration/findmin/2d |
95639 ns |
96704 ns |
0.99 |
array/reductions/reduce/Int64/1d |
42833 ns |
43491 ns |
0.98 |
array/reductions/reduce/Int64/dims=1 |
44548.5 ns |
52642.5 ns |
0.85 |
array/reductions/reduce/Int64/dims=2 |
61336 ns |
61484 ns |
1.00 |
array/reductions/reduce/Int64/dims=1L |
88872 ns |
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1.00 |
array/reductions/reduce/Int64/dims=2L |
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87977 ns |
0.99 |
array/reductions/reduce/Float32/1d |
35926 ns |
37248.5 ns |
0.96 |
array/reductions/reduce/Float32/dims=1 |
47245 ns |
43278 ns |
1.09 |
array/reductions/reduce/Float32/dims=2 |
59452 ns |
60066 ns |
0.99 |
array/reductions/reduce/Float32/dims=1L |
52057 ns |
52282 ns |
1.00 |
array/reductions/reduce/Float32/dims=2L |
71472 ns |
72365.5 ns |
0.99 |
array/reductions/mapreduce/Int64/1d |
42652 ns |
43561 ns |
0.98 |
array/reductions/mapreduce/Int64/dims=1 |
44200 ns |
44306 ns |
1.00 |
array/reductions/mapreduce/Int64/dims=2 |
61326 ns |
61482 ns |
1.00 |
array/reductions/mapreduce/Int64/dims=1L |
88603 ns |
89001 ns |
1.00 |
array/reductions/mapreduce/Int64/dims=2L |
87623 ns |
88320 ns |
0.99 |
array/reductions/mapreduce/Float32/1d |
36241 ns |
38092.5 ns |
0.95 |
array/reductions/mapreduce/Float32/dims=1 |
45113 ns |
41962 ns |
1.08 |
array/reductions/mapreduce/Float32/dims=2 |
59389 ns |
60039 ns |
0.99 |
array/reductions/mapreduce/Float32/dims=1L |
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52636 ns |
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array/reductions/mapreduce/Float32/dims=2L |
71604 ns |
72310 ns |
0.99 |
array/broadcast |
19787 ns |
20127 ns |
0.98 |
array/copyto!/gpu_to_gpu |
12685 ns |
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1.00 |
array/copyto!/cpu_to_gpu |
213773 ns |
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0.98 |
array/copyto!/gpu_to_cpu |
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1.00 |
array/accumulate/Int64/1d |
124050 ns |
124778 ns |
0.99 |
array/accumulate/Int64/dims=1 |
83524.5 ns |
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array/accumulate/Int64/dims=2 |
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array/accumulate/Int64/dims=1L |
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array/accumulate/Int64/dims=2L |
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array/accumulate/Float32/1d |
108765 ns |
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0.99 |
array/accumulate/Float32/dims=1 |
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array/accumulate/Float32/dims=2 |
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array/accumulate/Float32/dims=1L |
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array/accumulate/Float32/dims=2L |
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array/construct |
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1295.5 ns |
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array/random/randn/Float32 |
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array/random/randn!/Float32 |
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array/random/rand!/Int64 |
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array/random/rand!/Float32 |
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8909.666666666666 ns |
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array/random/rand/Int64 |
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30055 ns |
1.27 |
array/random/rand/Float32 |
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array/permutedims/4d |
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array/permutedims/2d |
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array/permutedims/3d |
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array/sorting/1d |
2757516.5 ns |
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array/sorting/by |
3344110 ns |
3344541 ns |
1.00 |
array/sorting/2d |
1080569 ns |
1081521 ns |
1.00 |
cuda/synchronization/stream/auto |
1007 ns |
1036.5 ns |
0.97 |
cuda/synchronization/stream/nonblocking |
7651.9 ns |
7410.8 ns |
1.03 |
cuda/synchronization/stream/blocking |
808.6 ns |
820.6336633663366 ns |
0.99 |
cuda/synchronization/context/auto |
1154.2 ns |
1154.3 ns |
1.00 |
cuda/synchronization/context/nonblocking |
7125.8 ns |
7124.4 ns |
1.00 |
cuda/synchronization/context/blocking |
936.6078431372549 ns |
887.4107142857143 ns |
1.06 |
This comment was automatically generated by workflow using github-action-benchmark.
Contributor
Author
|
The cuDNN run on CUDA 13 appears to fail due to running on SM75. |
Contributor
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rebase on master? |
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Done! |
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CI failures look related. |
Contributor
Author
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Hi Tim. Test outcome seems to depend on compute capability / arch: Tests for cuDNN on CUDA 13.0 ran on an A100 (passed): But the tests for cuDNN on CUDA 12.0 happened to run on a Turing GPU (failed): See also my earlier comment:
Should the tests be conditionally skipped instead? Is there a way to require a certain compute capability for e.g. cuDNN? |
Member
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Yes, the tests should take care not to cover unsupported code paths by e.g. inspecting |
Contributor
Author
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I put the tests behind a conditional block, so they should pass on SM75 now. |
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This PR defines methods for making cuDNN work with
BFloat16s.BFloat16.In the following example, I show how the new methods fixes the
BFloat16backward pass ofFlux.logitcrossentropy:Before
Note: Core.BFloat16 === BFloat16s.BFloat16, but I didn't explicitly import in this REPL session.
After defining cudnnDataType(::Type{BFloat16})
After defining scalingParameter(::Type{BFloat16}, val)
I also define a
cptrmethod for consistency, but it appears the function isn't used anywhere.Tests are added for softmax, activations, and pooling. I initially also tested convolutions, normalization, RNNs, and MHA but they don't appear to support BFloat16.
Along with my proposed fix in FluxML/Optimisers.jl#215, this has allowed me to train LLMs in BFloat16 with Flux.jl in Julia v1.12. I am still tinkering with Optimisers.jl, but these together would be a significant unlock for my lab.