Using cuDNN 1.4.5 leads to CUDNN_STATUS_EXECUTION_FAILED for BatchNorm forward pass

[andrejcarlo]

Describe the bug

When performing a forward pass using BatchNorm on the GPU, the following error occurs: ERROR: CUDNNError: CUDNN_STATUS_EXECUTION_FAILED (code 5000)

Below is the relevant stack trace:

ERROR: CUDNNError: CUDNN_STATUS_EXECUTION_FAILED (code 5000)
Stacktrace:
  [1] throw_api_error(res::cuDNN.cudnnStatus_t)
    @ cuDNN ~/.julia/packages/cuDNN/TvswS/src/libcudnn.jl:15
  [2] check
    @ ~/.julia/packages/cuDNN/TvswS/src/libcudnn.jl:26 [inlined]
  [3] cudnnBatchNormalizationForwardInference
    @ ~/.julia/packages/GPUToolbox/XaIIx/src/ccalls.jl:33 [inlined]
  [4] cudnnBNForward!(y::CuArray{…}, g::CuArray{…}, b::CuArray{…}, x::CuArray{…}, running_mean::CuArray{…}, running_var::CuArray{…}, momentum::Float32; cache::Nothing, alpha::Int64, beta::Int64, eps::Float32, training::Bool, affine::Bool, track_stats::Bool)
    @ NNlibCUDACUDNNExt ~/.julia/packages/NNlib/1TYHL/ext/NNlibCUDACUDNNExt/batchnorm.jl:88
  [5] cudnnBNForward!
    @ ~/.julia/packages/NNlib/1TYHL/ext/NNlibCUDACUDNNExt/batchnorm.jl:40 [inlined]
  [6] #batchnorm#64
    @ ~/.julia/packages/NNlib/1TYHL/ext/NNlibCUDACUDNNExt/batchnorm.jl:37 [inlined]
  [7] batchnorm
    @ ~/.julia/packages/NNlib/1TYHL/ext/NNlibCUDACUDNNExt/batchnorm.jl:35 [inlined]
  [8] #batchnorm#63
    @ ~/.julia/packages/NNlib/1TYHL/ext/NNlibCUDACUDNNExt/batchnorm.jl:31 [inlined]
  [9] (::BatchNorm{…})(x::CuArray{…}, cache::Nothing)
    @ FluxCUDAcuDNNExt ~/.julia/packages/Flux/uRn8o/ext/FluxCUDAcuDNNExt/FluxCUDAcuDNNExt.jl:13
 [10] (::BatchNorm{typeof(identity), CuArray{…}, Float32, CuArray{…}})(x::CuArray{Float32, 2, CUDA.DeviceMemory})
    @ FluxCUDAcuDNNExt ~/.julia/packages/Flux/uRn8o/ext/FluxCUDAcuDNNExt/FluxCUDAcuDNNExt.jl:10
 [11] top-level scope
    @ ~/minimal_example.jl:7

To reproduce

The Minimal Working Example (MWE) for this bug:

using Flux
using CUDA 
device_to_run = CUDA.functional() && Flux.gpu

noisy = rand(Float32, 2, 1000) |> device_to_run
model = BatchNorm(2) |> device_to_run
model(noisy)

Project.toml

[deps]
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Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
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cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"

[compat]
CUDA = "5.8.2"
Flux = "0.16"
cuDNN = "=1.4.5"

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[[deps.SuiteSparse]]
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Expected behavior

A clear and concise description of what you expected to happen.

Version info

Details on Julia:

julia> versioninfo()
Julia Version 1.11.6
Commit 9615af0f269 (2025-07-09 12:58 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 12 × Intel(R) Core(TM) i7-8750H CPU @ 2.20GHz
  WORD_SIZE: 64
  LLVM: libLLVM-16.0.6 (ORCJIT, skylake)
Threads: 1 default, 0 interactive, 1 GC (on 12 virtual cores)

Details on CUDA:

julia> CUDA.versioninfo()
CUDA toolchain: 
- runtime 12.9, artifact installation
- driver 570.181.0 for 12.8
- compiler 12.9

CUDA libraries: 
- CUBLAS: 12.9.1
- CURAND: 10.3.10
- CUFFT: 11.4.1
- CUSOLVER: 11.7.5
- CUSPARSE: 12.5.10
- CUPTI: 2025.2.1 (API 28.0.0)
- NVML: 12.0.0+570.181

Julia packages: 
- CUDA: 5.8.3
- CUDA_Driver_jll: 13.0.1+0
- CUDA_Compiler_jll: 0.2.1+0
- CUDA_Runtime_jll: 0.19.1+0

Toolchain:
- Julia: 1.11.6
- LLVM: 16.0.6

1 device:
  0: Quadro P1000 (sm_61, 3.476 GiB / 4.000 GiB available)

Additional context

Current fix: Downgrading cuDNN for one patch version ( 1.4.5 to 1.4.4 ) my minimal example works fine. Took me a while to find this :)

[kshyatt]

I think this might be occurring because 1.4.4 was the last version we tagged before upgrading the JLL to CUDNN 9.12? I wonder if that version had a bug as they've already released 9.13...

[kshyatt]

@andrejcarlo the new JLL has released and should be usable from CUDNN 1.4.5. Can you try that and see if it resolves this?

[andrejcarlo]

Tested again by upgrading CUDNN from 1.4.4 to 1.4.5, which led to CUDNN_jll v9.10.0+0 ⇒ v9.13.0+0. Unfortunately, the problem persists in the same manner.

[grantbruer]

I had the same problem on the Quadro P2200. I believe the issue is that it installs the wrong version of the cuDNN library. We require cuDNN version < 9 to run with our GPUs. As best as I can tell, the cuDNN version is tied directly to the CUDA.jl version, so I backtracked through CUDA.jl's history to find that version 5.2 installs cuDNN v8.9.

You can do something like this:

import Pkg
Pkg.add(; name = "CUDA", version=v"5.2")
Pkg.pin(; name = "CUDA", version=v"5.2")

(probably not the simplest way to do it)

If anyone has a better way to set the cuDNN version, let me know.
This caused me a lot of trouble with a self-hosted Github runner because I thought it would automatically choose a compatible version or use my local installed version. Unfortunately, there are no environment variables to control this, so I had to inject some code into the runner's startup.jl to pin the CUDA.jl version after it installed its copy of Julia.

[grantbruer]

Correction: I looked at this more closely and realized that it worked for you on 1.4.4. We need cuDNN version < 9.11 for our GPUs (https://docs.nvidia.com/deeplearning/cudnn/backend/v9.11.1/reference/support-matrix.html).

cuDNN.jl v1.4.4 uses cuDNN v9.10, where cuDNN.jl v1.4.5 uses cuDNN v9.12. I guess there's not an automatic system in place to determine at install time which cuDNN is compatible with the GPUs, so we need to manually set the version lower.