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CUDA programming in Julia.
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Julia keeps allocations alive in presence of try/catch #2197

Open IanButterworth opened 9 months ago

IanButterworth commented 9 months ago

Describe the bug

I have a case where the inclusion of a try-catch in a for loop is causing CUDA OOM.

Rather nastily the try-catch was actually introduced by a @info "epoch $epoch_num" log, which wraps the evaluation of the log message in a try-catch because of the interpolation. The issue doesn't happen for @info "epoch"

https://github.com/IanButterworth/julia/blob/61a36549f961eacd74d431f5e987f3e7f789c643/base/logging.jl#L344-L380

To reproduce

The Minimal Working Example (MWE) for this bug:

Instantiate https://github.com/IanButterworth/EfficientNet-Training/tree/main

This is with CUDA.jl dev-ed and logging added as suggested by @maleadt 

julia> include("effnet_train.jl")

julia> @time train(limit=3, gpu_gc=false, gpu_stats=true)
[ Info: loading CIFAR-10 dataset
[ Info: loading EfficientNetv2 model
[ Info: starting training
Effective GPU memory usage: 99.55% (11.688 GiB/11.741 GiB)
Memory pool usage: 8.505 GiB (10.406 GiB reserved)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 8.292 GiB)
Effective GPU memory usage: 99.55% (11.688 GiB/11.741 GiB)
Memory pool usage: 8.492 GiB (10.406 GiB reserved)
training epoch 1/45   4%|███▊                                                                                           |  ETA: 0:00:09WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 8.279 GiB)
Effective GPU memory usage: 99.55% (11.688 GiB/11.741 GiB)
Memory pool usage: 8.492 GiB (10.406 GiB reserved)
training epoch 1/45   6%|█████▊                                                                                         |  ETA: 0:00:09WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 8.279 GiB)
Effective GPU memory usage: 99.54% (11.687 GiB/11.741 GiB)
Memory pool usage: 8.492 GiB (10.406 GiB reserved)
training epoch 1/45 100%|███████████████████████████████████████████████████████████████████████████████████████████████| Time: 0:00:00
[ Info: epoch 1 complete. Testing...
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 9.974 GiB)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 9.917 GiB)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 9.818 GiB)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 9.834 GiB)
[ Info: (train_loss = 2.303f0, train_acc = 0.1, test_loss = 2.303f0, test_acc = 0.099)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 5.953 GiB)
Effective GPU memory usage: 99.54% (11.687 GiB/11.741 GiB)
Memory pool usage: 6.301 GiB (10.406 GiB reserved)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 5.875 GiB)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 3.002 GiB)
Effective GPU memory usage: 99.54% (11.687 GiB/11.741 GiB)
Memory pool usage: 5.703 GiB (10.406 GiB reserved)
training epoch 2/45   4%|███▊                                                                                           |  ETA: 0:00:09WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 5.240 GiB)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 1.578 GiB)
WARNING: CUDA memory allocation failed; attempting to free up memory...
... waiting for pending frees (freed 0 bytes)
... waiting for all pending frees (freed 0 bytes)
... running a quick GC (freed 167.542 MiB)
... running a full GC (freed 11.719 MiB)
... releasing reserved memory (freed 0 bytes)

┌ Error: Out of GPU memory trying to allocate 128.000 MiB
│ Effective GPU memory usage: 99.54% (11.687 GiB/11.741 GiB)
│ Memory pool usage: 4.157 GiB (10.406 GiB reserved)
└ @ Main ~/Documents/GitHub/EfficientNet-Training/effnet_train.jl:104
  3.465019 seconds (2.09 M allocations: 1.532 GiB, 27.75% gc time)

The inner loop can be simplified to this reproducer

for epoch in 1:epochs
    for (x, y) in train_loader
        x = x |> device; y = y |> device
        gs, _ = gradient(model, x) do m, _x
            logitcrossentropy(m(_x), y)
        end
        # state, model = Optimisers.update(state, model, gs)
        model = Flux.Functors.fmap(copy, model; exclude = Optimisers.maywrite)
        state = Flux.Functors.fmap(copy, state; exclude = Optimisers.maywrite)
        @show CUDA.MemoryInfo().pool_used_bytes
    end
    try
        true
    catch
    end
end

Commenting out the try-catch (or the 2 loop @info logs in the original code), or will avoid the OOM entirely.

Manifest.toml

``` # This file is machine-generated - editing it directly is not advised julia_version = "1.9.4" manifest_format = "2.0" project_hash = "4bc608d3c54e7666344bf4c7441aba4973b01068" [[deps.AbstractFFTs]] deps = ["LinearAlgebra"] git-tree-sha1 = "d92ad398961a3ed262d8bf04a1a2b8340f915fef" uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" version = "1.5.0" weakdeps = ["ChainRulesCore", "Test"] [deps.AbstractFFTs.extensions] AbstractFFTsChainRulesCoreExt = "ChainRulesCore" AbstractFFTsTestExt = "Test" [[deps.Adapt]] deps = ["LinearAlgebra", "Requires"] git-tree-sha1 = "02f731463748db57cc2ebfbd9fbc9ce8280d3433" uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" version = "3.7.1" weakdeps = ["StaticArrays"] [deps.Adapt.extensions] AdaptStaticArraysExt = "StaticArrays" [[deps.ArgCheck]] git-tree-sha1 = "a3a402a35a2f7e0b87828ccabbd5ebfbebe356b4" uuid = "dce04be8-c92d-5529-be00-80e4d2c0e197" version = "2.3.0" [[deps.ArgTools]] uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" version = "1.1.1" [[deps.Artifacts]] uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" [[deps.Atomix]] deps = ["UnsafeAtomics"] git-tree-sha1 = "c06a868224ecba914baa6942988e2f2aade419be" uuid = "a9b6321e-bd34-4604-b9c9-b65b8de01458" version = "0.1.0" [[deps.AtomsBase]] deps = ["LinearAlgebra", "PeriodicTable", "Printf", "Requires", "StaticArrays", "Unitful", "UnitfulAtomic"] git-tree-sha1 = "995c2b6b17840cd87b722ce9c6cdd72f47bab545" uuid = "a963bdd2-2df7-4f54-a1ee-49d51e6be12a" version = "0.3.5" [[deps.BFloat16s]] deps = ["LinearAlgebra", "Printf", "Random", "Test"] git-tree-sha1 = "dbf84058d0a8cbbadee18d25cf606934b22d7c66" uuid = "ab4f0b2a-ad5b-11e8-123f-65d77653426b" version = "0.4.2" [[deps.BSON]] git-tree-sha1 = "2208958832d6e1b59e49f53697483a84ca8d664e" uuid = "fbb218c0-5317-5bc6-957e-2ee96dd4b1f0" version = "0.3.7" [[deps.BangBang]] deps = ["Compat", "ConstructionBase", "InitialValues", "LinearAlgebra", "Requires", "Setfield", "Tables"] git-tree-sha1 = "e28912ce94077686443433c2800104b061a827ed" uuid = "198e06fe-97b7-11e9-32a5-e1d131e6ad66" version = "0.3.39" [deps.BangBang.extensions] BangBangChainRulesCoreExt = "ChainRulesCore" BangBangDataFramesExt = "DataFrames" BangBangStaticArraysExt = "StaticArrays" BangBangStructArraysExt = "StructArrays" BangBangTypedTablesExt = "TypedTables" [deps.BangBang.weakdeps] ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0" StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" StructArrays = "09ab397b-f2b6-538f-b94a-2f83cf4a842a" TypedTables = "9d95f2ec-7b3d-5a63-8d20-e2491e220bb9" [[deps.Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" [[deps.Baselet]] git-tree-sha1 = "aebf55e6d7795e02ca500a689d326ac979aaf89e" uuid = "9718e550-a3fa-408a-8086-8db961cd8217" version = "0.1.1" [[deps.BitFlags]] git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" version = "0.1.8" [[deps.BufferedStreams]] git-tree-sha1 = "4ae47f9a4b1dc19897d3743ff13685925c5202ec" uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d" version = "1.2.1" [[deps.CEnum]] git-tree-sha1 = "eb4cb44a499229b3b8426dcfb5dd85333951ff90" uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82" version = "0.4.2" [[deps.CSV]] deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"] git-tree-sha1 = "44dbf560808d49041989b8a96cae4cffbeb7966a" uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" version = "0.10.11" [[deps.CUDA]] deps = ["AbstractFFTs", "Adapt", "BFloat16s", "CEnum", "CUDA_Driver_jll", "CUDA_Runtime_Discovery", "CUDA_Runtime_jll", "Crayons", "DataFrames", "ExprTools", "GPUArrays", "GPUCompiler", "KernelAbstractions", "LLVM", "LLVMLoopInfo", "LazyArtifacts", "Libdl", "LinearAlgebra", "Logging", "NVTX", "Preferences", "PrettyTables", "Printf", "Random", "Random123", "RandomNumbers", "Reexport", "Requires", "SparseArrays", "Statistics", "UnsafeAtomicsLLVM"] git-tree-sha1 = "76582ae19006b1186e87dadd781747f76cead72c" uuid = "052768ef-5323-5732-b1bb-66c8b64840ba" version = "5.1.1" weakdeps = ["ChainRulesCore", "SpecialFunctions"] [deps.CUDA.extensions] ChainRulesCoreExt = "ChainRulesCore" SpecialFunctionsExt = "SpecialFunctions" [[deps.CUDA_Driver_jll]] deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl", "Pkg"] git-tree-sha1 = "1e42ef1bdb45487ff28de16182c0df4920181dc3" uuid = "4ee394cb-3365-5eb0-8335-949819d2adfc" version = "0.7.0+0" [[deps.CUDA_Runtime_Discovery]] deps = ["Libdl"] git-tree-sha1 = "bcc4a23cbbd99c8535a5318455dcf0f2546ec536" uuid = "1af6417a-86b4-443c-805f-a4643ffb695f" version = "0.2.2" [[deps.CUDA_Runtime_jll]] deps = ["Artifacts", "CUDA_Driver_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] git-tree-sha1 = "9704e50c9158cf8896c2776b8dbc5edd136caf80" uuid = "76a88914-d11a-5bdc-97e0-2f5a05c973a2" version = "0.10.1+0" [[deps.CUDNN_jll]] deps = ["Artifacts", "CUDA_Runtime_jll", "JLLWrappers", "LazyArtifacts", "Libdl", "TOML"] git-tree-sha1 = "75923dce4275ead3799b238e10178a68c07dbd3b" uuid = "62b44479-cb7b-5706-934f-f13b2eb2e645" version = "8.9.4+0" [[deps.ChainRules]] deps = ["Adapt", "ChainRulesCore", "Compat", "Distributed", "GPUArraysCore", "IrrationalConstants", "LinearAlgebra", "Random", "RealDot", "SparseArrays", "SparseInverseSubset", "Statistics", "StructArrays", "SuiteSparse"] git-tree-sha1 = "006cc7170be3e0fa02ccac6d4164a1eee1fc8c27" uuid = "082447d4-558c-5d27-93f4-14fc19e9eca2" version = "1.58.0" [[deps.ChainRulesCore]] deps = ["Compat", "LinearAlgebra"] git-tree-sha1 = "e0af648f0692ec1691b5d094b8724ba1346281cf" uuid = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" version = "1.18.0" weakdeps = ["SparseArrays"] [deps.ChainRulesCore.extensions] ChainRulesCoreSparseArraysExt = "SparseArrays" [[deps.Chemfiles]] deps = ["AtomsBase", "Chemfiles_jll", "DocStringExtensions", "PeriodicTable", "Unitful", "UnitfulAtomic"] git-tree-sha1 = "82fe5e341c793cb51149d993307da9543824b206" uuid = "46823bd8-5fb3-5f92-9aa0-96921f3dd015" version = "0.10.41" [[deps.Chemfiles_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "f3743181e30d87c23d9c8ebd493b77f43d8f1890" uuid = "78a364fa-1a3c-552a-b4bb-8fa0f9c1fcca" version = "0.10.4+0" [[deps.CodecZlib]] deps = ["TranscodingStreams", "Zlib_jll"] git-tree-sha1 = "cd67fc487743b2f0fd4380d4cbd3a24660d0eec8" uuid = "944b1d66-785c-5afd-91f1-9de20f533193" version = "0.7.3" [[deps.ColorSchemes]] deps = ["ColorTypes", "ColorVectorSpace", "Colors", "FixedPointNumbers", "PrecompileTools", "Random"] git-tree-sha1 = "67c1f244b991cad9b0aa4b7540fb758c2488b129" uuid = "35d6a980-a343-548e-a6ea-1d62b119f2f4" version = "3.24.0" [[deps.ColorTypes]] deps = ["FixedPointNumbers", "Random"] git-tree-sha1 = "eb7f0f8307f71fac7c606984ea5fb2817275d6e4" uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f" version = "0.11.4" [[deps.ColorVectorSpace]] deps = ["ColorTypes", "FixedPointNumbers", "LinearAlgebra", "Requires", "Statistics", "TensorCore"] git-tree-sha1 = "a1f44953f2382ebb937d60dafbe2deea4bd23249" uuid = "c3611d14-8923-5661-9e6a-0046d554d3a4" version = "0.10.0" weakdeps = ["SpecialFunctions"] [deps.ColorVectorSpace.extensions] SpecialFunctionsExt = "SpecialFunctions" [[deps.Colors]] deps = ["ColorTypes", "FixedPointNumbers", "Reexport"] git-tree-sha1 = "fc08e5930ee9a4e03f84bfb5211cb54e7769758a" uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" version = "0.12.10" [[deps.CommonSubexpressions]] deps = ["MacroTools", "Test"] git-tree-sha1 = "7b8a93dba8af7e3b42fecabf646260105ac373f7" uuid = "bbf7d656-a473-5ed7-a52c-81e309532950" version = "0.3.0" [[deps.Compat]] deps = ["UUIDs"] git-tree-sha1 = "886826d76ea9e72b35fcd000e535588f7b60f21d" uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" version = "4.10.1" weakdeps = ["Dates", "LinearAlgebra"] [deps.Compat.extensions] CompatLinearAlgebraExt = "LinearAlgebra" [[deps.CompilerSupportLibraries_jll]] deps = ["Artifacts", "Libdl"] uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae" version = "1.0.5+0" [[deps.CompositionsBase]] git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad" uuid = "a33af91c-f02d-484b-be07-31d278c5ca2b" version = "0.1.2" [deps.CompositionsBase.extensions] CompositionsBaseInverseFunctionsExt = "InverseFunctions" [deps.CompositionsBase.weakdeps] InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" [[deps.ConcurrentUtilities]] deps = ["Serialization", "Sockets"] git-tree-sha1 = "8cfa272e8bdedfa88b6aefbbca7c19f1befac519" uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" version = "2.3.0" [[deps.ConstructionBase]] deps = ["LinearAlgebra"] git-tree-sha1 = "c53fc348ca4d40d7b371e71fd52251839080cbc9" uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9" version = "1.5.4" [deps.ConstructionBase.extensions] ConstructionBaseIntervalSetsExt = "IntervalSets" ConstructionBaseStaticArraysExt = "StaticArrays" [deps.ConstructionBase.weakdeps] IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953" StaticArrays = "90137ffa-7385-5640-81b9-e52037218182" [[deps.ContextVariablesX]] deps = ["Compat", "Logging", "UUIDs"] git-tree-sha1 = "25cc3803f1030ab855e383129dcd3dc294e322cc" uuid = "6add18c4-b38d-439d-96f6-d6bc489c04c5" version = "0.1.3" [[deps.Contour]] git-tree-sha1 = "d05d9e7b7aedff4e5b51a029dced05cfb6125781" uuid = "d38c429a-6771-53c6-b99e-75d170b6e991" version = "0.6.2" [[deps.Crayons]] git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15" uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" version = "4.1.1" [[deps.DataAPI]] git-tree-sha1 = "8da84edb865b0b5b0100c0666a9bc9a0b71c553c" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" version = "1.15.0" [[deps.DataDeps]] deps = ["HTTP", "Libdl", "Reexport", "SHA", "p7zip_jll"] git-tree-sha1 = "6e8d74545d34528c30ccd3fa0f3c00f8ed49584c" uuid = "124859b0-ceae-595e-8997-d05f6a7a8dfe" version = "0.7.11" [[deps.DataFrames]] deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", 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FreeType = "b38be410-82b0-50bf-ab77-7b57e271db43" ImageInTerminal = "d8c32880-2388-543b-8c61-d9f865259254" IntervalSets = "8197267c-284f-5f27-9208-e0e47529a953" Term = "22787eb5-b846-44ae-b979-8e399b8463ab" Unitful = "1986cc42-f94f-5a68-af5c-568840ba703d" [[deps.Unitful]] deps = ["Dates", "LinearAlgebra", "Random"] git-tree-sha1 = "3c793be6df9dd77a0cf49d80984ef9ff996948fa" uuid = "1986cc42-f94f-5a68-af5c-568840ba703d" version = "1.19.0" [deps.Unitful.extensions] ConstructionBaseUnitfulExt = "ConstructionBase" InverseFunctionsUnitfulExt = "InverseFunctions" [deps.Unitful.weakdeps] ConstructionBase = "187b0558-2788-49d3-abe0-74a17ed4e7c9" InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" [[deps.UnitfulAtomic]] deps = ["Unitful"] git-tree-sha1 = "903be579194534af1c4b4778d1ace676ca042238" uuid = "a7773ee8-282e-5fa2-be4e-bd808c38a91a" version = "1.0.0" [[deps.UnsafeAtomics]] git-tree-sha1 = "6331ac3440856ea1988316b46045303bef658278" uuid = "013be700-e6cd-48c3-b4a1-df204f14c38f" version = "0.2.1" [[deps.UnsafeAtomicsLLVM]] deps = ["LLVM", "UnsafeAtomics"] git-tree-sha1 = "323e3d0acf5e78a56dfae7bd8928c989b4f3083e" uuid = "d80eeb9a-aca5-4d75-85e5-170c8b632249" version = "0.1.3" [[deps.WeakRefStrings]] deps = ["DataAPI", "InlineStrings", "Parsers"] git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23" uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5" version = "1.4.2" [[deps.WorkerUtilities]] git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7" uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60" version = "1.6.1" [[deps.ZipFile]] deps = ["Libdl", "Printf", "Zlib_jll"] git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a" uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" version = "0.10.1" [[deps.Zlib_jll]] deps = ["Libdl"] uuid = "83775a58-1f1d-513f-b197-d71354ab007a" version = "1.2.13+0" [[deps.Zygote]] deps = ["AbstractFFTs", "ChainRules", "ChainRulesCore", "DiffRules", "Distributed", "FillArrays", "ForwardDiff", "GPUArrays", "GPUArraysCore", "IRTools", "InteractiveUtils", "LinearAlgebra", "LogExpFunctions", "MacroTools", "NaNMath", "PrecompileTools", "Random", "Requires", "SparseArrays", "SpecialFunctions", "Statistics", "ZygoteRules"] git-tree-sha1 = "5ded212acd815612df112bb895ef3910c5a03f57" uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" version = "0.6.67" [deps.Zygote.extensions] ZygoteColorsExt = "Colors" ZygoteDistancesExt = "Distances" ZygoteTrackerExt = "Tracker" [deps.Zygote.weakdeps] Colors = "5ae59095-9a9b-59fe-a467-6f913c188581" Distances = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7" Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" [[deps.ZygoteRules]] deps = ["ChainRulesCore", "MacroTools"] git-tree-sha1 = "9d749cd449fb448aeca4feee9a2f4186dbb5d184" uuid = "700de1a5-db45-46bc-99cf-38207098b444" version = "0.2.4" [[deps.cuDNN]] deps = ["CEnum", "CUDA", "CUDA_Runtime_Discovery", "CUDNN_jll"] git-tree-sha1 = "c092c26591a851083ed3358890d0d916c58dde62" uuid = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd" version = "1.2.1" [[deps.libaec_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl"] git-tree-sha1 = "eddd19a8dea6b139ea97bdc8a0e2667d4b661720" uuid = "477f73a3-ac25-53e9-8cc3-50b2fa2566f0" version = "1.0.6+1" [[deps.libblastrampoline_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" version = "5.8.0+0" [[deps.libevent_jll]] deps = ["Artifacts", "JLLWrappers", "Libdl", "OpenSSL_jll"] git-tree-sha1 = "f04ec6d9a186115fb38f858f05c0c4e1b7fc9dcb" uuid = "1080aeaf-3a6a-583e-a51c-c537b09f60ec" version = "2.1.13+1" [[deps.nghttp2_jll]] deps = ["Artifacts", "Libdl"] uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" version = "1.52.0+1" [[deps.p7zip_jll]] deps = ["Artifacts", "Libdl"] uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0" version = "17.4.0+0" [[deps.prrte_jll]] deps = ["Artifacts", "Hwloc_jll", "JLLWrappers", "Libdl", "PMIx_jll", "libevent_jll"] git-tree-sha1 = "5adb2d7a18a30280feb66cad6f1a1dfdca2dc7b0" uuid = "eb928a42-fffd-568d-ab9c-3f5d54fc65b9" version = "3.0.2+0" ```

Expected behavior

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

No OOM, which is the case if you GC.gc() each loop (turn that on via train(limit=3, gpu_gc=true, gpu_stats=true)

Version info

Details on Julia:

julia> versioninfo()
Julia Version 1.9.4
Commit 8e5136fa297 (2023-11-14 08:46 UTC)
Build Info:
  Official https://julialang.org/ release
Platform Info:
  OS: Linux (x86_64-linux-gnu)
  CPU: 32 × AMD Ryzen 9 5950X 16-Core Processor
  WORD_SIZE: 64
  LIBM: libopenlibm
  LLVM: libLLVM-14.0.6 (ORCJIT, znver3)
  Threads: 5 on 32 virtual cores

Details on CUDA:

CUDA runtime 12.3, artifact installation
CUDA driver 12.0
NVIDIA driver 525.147.5

CUDA libraries: 
- CUBLAS: 12.3.4
- CURAND: 10.3.4
- CUFFT: 11.0.12
- CUSOLVER: 11.5.4
- CUSPARSE: 12.2.0
- CUPTI: 21.0.0
- NVML: 12.0.0+525.147.5

Julia packages: 
- CUDA: 5.1.1
- CUDA_Driver_jll: 0.7.0+0
- CUDA_Runtime_jll: 0.10.1+0

Toolchain:
- Julia: 1.9.4
- LLVM: 14.0.6

1 device:
  0: NVIDIA GeForce RTX 3080 (sm_86, 10.301 GiB / 12.000 GiB available)

Additional context

Add any other context about the problem here.

CUDA.jl debug prints added

diff --git a/src/pool.jl b/src/pool.jl
index 9d476d591..4a134e6c5 100644
--- a/src/pool.jl
+++ b/src/pool.jl
@@ -363,25 +363,35 @@ function retry_reclaim(f, isfailed)
     state = active_state()
     is_stream_ordered = stream_ordered(state.device)

+    println(Core.stdout, "WARNING: CUDA memory allocation failed; attempting to free up memory...")
+
     phase = 1
     while true
+      before_memory = used_memory()
       if is_stream_ordered
         if phase == 1
+          print(Core.stdout, "... waiting for pending frees")
           synchronize(state.stream)
         elseif phase == 2
+          print(Core.stdout, "... waiting for all pending frees")
           device_synchronize()
         elseif phase == 3
+          print(Core.stdout, "... running a quick GC")
           GC.gc(false)
           device_synchronize()
         elseif phase == 4
+          print(Core.stdout, "... running a full GC")
           GC.gc(true)
           device_synchronize()
         elseif phase == 5
           # in case we had a release threshold configured
+          print(Core.stdout, "... releasing reserved memory")
           trim(memory_pool(state.device))
         else
           break
         end
+        after_memory = used_memory()
+        println(Core.stdout, " (freed $(Base.format_bytes(before_memory - after_memory)))")
       else
         if phase == 1
           GC.gc(false)
IanButterworth commented 9 months ago

@maleadt reported that in julia 1.10 on their setup the OOM happens sooner

❯ JULIA_CUDA_HARD_MEMORY_LIMIT=12GB jl --project effnet_train.jl
[ Info: loading CIFAR-10 dataset
[ Info: loading EfficientNetv2 model
[ Info: starting training

┌ Error: Out of GPU memory trying to allocate 4.578 GiB
│ Effective GPU memory usage: 24.97% (11.808 GiB/47.296 GiB)
│ Memory pool usage: 4.017 GiB (11.188 GiB reserved)
│ Memory limit: hard = 11.176 GiB
└ @ Main ~/Julia/tmp/EfficientNet-Training/effnet_train.jl:104
IanButterworth commented 9 months ago

I have been trying to look for differences in GC collections with GC logging on. On the left here is with the try-catch, on the right is without. The GC operations seem to happen in the same sequence. You can see pool_used_bytes jumps up on the left, and doesn't on the right, but on the right there's no big GC collect, which I was expecting to see

image

maleadt commented 9 months ago

On Slack, we determined that this is likely due to objects being kept alive in upsilon nodes due to the try/catch. Unclear why calling GC.gc helps for that, but this does look like a Julia bug...