diff --git a/.travis.yml b/.travis.yml index df8161c7..a9cd86ea 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,7 +6,7 @@ os: # - osx julia: - - 1.0 + - 1.1 - nightly matrix: diff --git a/Manifest.toml b/Manifest.toml index ab7777d2..17eb544e 100644 --- a/Manifest.toml +++ b/Manifest.toml @@ -174,6 +174,12 @@ git-tree-sha1 = "dd169c636d1d3656a9faca772f5bd7c226a61254" uuid = "0c68f7d7-f131-5f86-a1c3-88cf8149b2d7" version = "1.0.1" +[[IRTools]] +deps = ["InteractiveUtils", "MacroTools", "Test"] +git-tree-sha1 = "e23faa71b8f54c3fdc99b230b9c2906cafdddca5" +uuid = "7869d1d1-7146-5819-86e3-90919afe41df" +version = "0.2.3" + [[InteractiveUtils]] deps = ["Markdown"] uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" @@ -226,10 +232,9 @@ uuid = "e89f7d12-3494-54d1-8411-f7d8b9ae1f27" version = "0.5.0" [[Missings]] -deps = ["SparseArrays", "Test"] -git-tree-sha1 = "f0719736664b4358aa9ec173077d4285775f8007" +git-tree-sha1 = "29858ce6c8ae629cf2d733bffa329619a1c843d0" uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28" -version = "0.4.1" +version = "0.4.2" [[Mmap]] uuid = "a63ad114-7e13-5084-954f-fe012c677804" @@ -254,9 +259,9 @@ version = "1.1.0" [[Parsers]] deps = ["Dates", "Test"] -git-tree-sha1 = "db2b35dedab3c0e46dc15996d170af07a5ab91c9" +git-tree-sha1 = "ef0af6c8601db18c282d092ccbd2f01f3f0cd70b" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" -version = "0.3.6" +version = "0.3.7" [[Pkg]] deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"] @@ -314,10 +319,10 @@ deps = ["LinearAlgebra", "Random"] uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" [[SpecialFunctions]] -deps = ["BinDeps", "BinaryProvider", "Libdl", "Test"] -git-tree-sha1 = "0b45dc2e45ed77f445617b99ff2adf0f5b0f23ea" +deps = ["BinDeps", "BinaryProvider", "Libdl"] +git-tree-sha1 = "3bdd374b6fd78faf0119b8c5d538788dbf910c6e" uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "0.7.2" +version = "0.8.0" [[StaticArrays]] deps = ["LinearAlgebra", "Random", "Statistics"] @@ -350,12 +355,6 @@ git-tree-sha1 = "dfcdbbfb2d0370716c815cbd6f8a364efb6f42cf" uuid = "0796e94c-ce3b-5d07-9a54-7f471281c624" version = "0.5.6" -[[Tracker]] -deps = ["Adapt", "DiffRules", "ForwardDiff", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics", "Test"] -git-tree-sha1 = "1aa443d3b4bfa91a8aec32f169a479cb87309910" -uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" -version = "0.2.3" - [[TranscodingStreams]] deps = ["Random", "Test"] git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c" @@ -386,3 +385,17 @@ deps = ["BinaryProvider", "Libdl", "Printf"] git-tree-sha1 = "580ce62b6c14244916cc28ad54f8a2e2886f843d" uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" version = "0.8.3" + +[[Zygote]] +deps = ["DiffRules", "FFTW", "FillArrays", "ForwardDiff", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Random", "Requires", "SpecialFunctions", "Statistics", "ZygoteRules"] +git-tree-sha1 = "9186cb0b3b59219e4aba0840614d6a9d7282012e" +repo-rev = "master" +repo-url = "https://github.com/FluxML/Zygote.jl.git" +uuid = "e88e6eb3-aa80-5325-afca-941959d7151f" +version = "0.3.4" + +[[ZygoteRules]] +deps = ["MacroTools"] +git-tree-sha1 = "def5f96ac2895fd9b48435f6b97020979ee0a4c6" +uuid = "700de1a5-db45-46bc-99cf-38207098b444" +version = "0.1.0" diff --git a/Project.toml b/Project.toml index 944cd11a..2fcdc943 100644 --- a/Project.toml +++ b/Project.toml @@ -21,18 +21,20 @@ Reexport = "189a3867-3050-52da-a836-e630ba90ab69" SHA = "ea8e919c-243c-51af-8825-aaa63cd721ce" Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" -Tracker = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" +Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" +Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" [compat] CUDAapi = "1.1" CuArrays = "1.2" NNlib = "0.6" -Tracker = "0.2" -julia = "0.7, 1" +Zygote = "0.3" +julia = "1.1" [extras] +Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" [targets] -test = ["Test"] +test = ["Test", "Documenter"] diff --git a/REQUIRE b/REQUIRE deleted file mode 100644 index 3e8e9066..00000000 --- a/REQUIRE +++ /dev/null @@ -1,13 +0,0 @@ -julia 1.0 -Juno -MacroTools 0.3.3 -NNlib -Requires -Adapt 0.4 -CodecZlib -Colors -ZipFile -AbstractTrees -Reexport -StatsBase -Tracker diff --git a/docs/Manifest.toml b/docs/Manifest.toml index 6445e42f..bf9d220a 100644 --- a/docs/Manifest.toml +++ b/docs/Manifest.toml @@ -1,205 +1,56 @@ # This file is machine-generated - editing it directly is not advised -[[AbstractTrees]] -deps = ["Markdown", "Test"] -git-tree-sha1 = "6621d9645702c1c4e6970cc6a3eae440c768000b" -uuid = "1520ce14-60c1-5f80-bbc7-55ef81b5835c" -version = "0.2.1" - -[[Adapt]] -deps = ["LinearAlgebra", "Test"] -git-tree-sha1 = "53d8fec4f662088c1202530e338a11a919407f3b" -uuid = "79e6a3ab-5dfb-504d-930d-738a2a938a0e" -version = "0.4.2" - [[Base64]] uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" -[[BinDeps]] -deps = ["Compat", "Libdl", "SHA", "URIParser"] -git-tree-sha1 = "12093ca6cdd0ee547c39b1870e0c9c3f154d9ca9" -uuid = "9e28174c-4ba2-5203-b857-d8d62c4213ee" -version = "0.8.10" - -[[BinaryProvider]] -deps = ["Libdl", "Pkg", "SHA", "Test"] -git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e" -uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232" -version = "0.5.3" - -[[CSTParser]] -deps = ["LibGit2", "Test", "Tokenize"] -git-tree-sha1 = "437c93bc191cd55957b3f8dee7794b6131997c56" -uuid = "00ebfdb7-1f24-5e51-bd34-a7502290713f" -version = "0.5.2" - -[[CodecZlib]] -deps = ["BinaryProvider", "Libdl", "Test", "TranscodingStreams"] -git-tree-sha1 = "36bbf5374c661054d41410dc53ff752972583b9b" -uuid = "944b1d66-785c-5afd-91f1-9de20f533193" -version = "0.5.2" - -[[ColorTypes]] -deps = ["FixedPointNumbers", "Random", "Test"] -git-tree-sha1 = "f73b0e10f2a5756de7019818a41654686da06b09" -uuid = "3da002f7-5984-5a60-b8a6-cbb66c0b333f" -version = "0.7.5" - -[[Colors]] -deps = ["ColorTypes", "FixedPointNumbers", "InteractiveUtils", "Printf", "Reexport", "Test"] -git-tree-sha1 = "9f0a0210450acb91c730b730a994f8eef1d3d543" -uuid = "5ae59095-9a9b-59fe-a467-6f913c188581" -version = "0.9.5" - -[[CommonSubexpressions]] -deps = ["Test"] -git-tree-sha1 = "efdaf19ab11c7889334ca247ff4c9f7c322817b0" -uuid = "bbf7d656-a473-5ed7-a52c-81e309532950" -version = "0.2.0" - -[[Compat]] -deps = ["Base64", "Dates", "DelimitedFiles", "Distributed", "InteractiveUtils", "LibGit2", "Libdl", "LinearAlgebra", "Markdown", "Mmap", "Pkg", "Printf", "REPL", "Random", "Serialization", "SharedArrays", "Sockets", "SparseArrays", "Statistics", "Test", "UUIDs", "Unicode"] -git-tree-sha1 = "84aa74986c5b9b898b0d1acaf3258741ee64754f" -uuid = "34da2185-b29b-5c13-b0c7-acf172513d20" -version = "2.1.0" - -[[Crayons]] -deps = ["Test"] -git-tree-sha1 = "f621b8ef51fd2004c7cf157ea47f027fdeac5523" -uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" -version = "4.0.0" - -[[DataStructures]] -deps = ["InteractiveUtils", "OrderedCollections", "Random", "Serialization", "Test"] -git-tree-sha1 = "ca971f03e146cf144a9e2f2ce59674f5bf0e8038" -uuid = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" -version = "0.15.0" - [[Dates]] deps = ["Printf"] uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" -[[DelimitedFiles]] -deps = ["Mmap"] -uuid = "8bb1440f-4735-579b-a4ab-409b98df4dab" - -[[DiffResults]] -deps = ["Compat", "StaticArrays"] -git-tree-sha1 = "34a4a1e8be7bc99bc9c611b895b5baf37a80584c" -uuid = "163ba53b-c6d8-5494-b064-1a9d43ac40c5" -version = "0.0.4" - -[[DiffRules]] -deps = ["Random", "Test"] -git-tree-sha1 = "dc0869fb2f5b23466b32ea799bd82c76480167f7" -uuid = "b552c78f-8df3-52c6-915a-8e097449b14b" -version = "0.0.10" - [[Distributed]] deps = ["Random", "Serialization", "Sockets"] uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" [[DocStringExtensions]] deps = ["LibGit2", "Markdown", "Pkg", "Test"] -git-tree-sha1 = "4d30e889c9f106a51ffa4791a88ffd4765bf20c3" +git-tree-sha1 = "0513f1a8991e9d83255e0140aace0d0fc4486600" uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" -version = "0.7.0" +version = "0.8.0" [[Documenter]] -deps = ["Base64", "DocStringExtensions", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "Pkg", "REPL", "Random", "Test", "Unicode"] -git-tree-sha1 = "13a6d15102410d8e70146533b759fc48d844a1d0" +deps = ["Base64", "DocStringExtensions", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "REPL", "Test", "Unicode"] +git-tree-sha1 = "c61d6eedbc3c4323c08b64af12d29c8ee0fcbb5f" uuid = "e30172f5-a6a5-5a46-863b-614d45cd2de4" -version = "0.22.3" - -[[FixedPointNumbers]] -deps = ["Test"] -git-tree-sha1 = "b8045033701c3b10bf2324d7203404be7aef88ba" -uuid = "53c48c17-4a7d-5ca2-90c5-79b7896eea93" -version = "0.5.3" - -[[Flux]] -deps = ["AbstractTrees", "Adapt", "CodecZlib", "Colors", "DelimitedFiles", "Juno", "LinearAlgebra", "MacroTools", "NNlib", "Pkg", "Printf", "Random", "Reexport", "Requires", "SHA", "Statistics", "StatsBase", "Tracker", "ZipFile"] -path = ".." -uuid = "587475ba-b771-5e3f-ad9e-33799f191a9c" -version = "0.8.2+" - -[[ForwardDiff]] -deps = ["CommonSubexpressions", "DiffResults", "DiffRules", "InteractiveUtils", "LinearAlgebra", "NaNMath", "Random", "SparseArrays", "SpecialFunctions", "StaticArrays", "Test"] -git-tree-sha1 = "4c4d727f1b7e0092134fabfab6396b8945c1ea5b" -uuid = "f6369f11-7733-5829-9624-2563aa707210" -version = "0.10.3" +version = "0.23.2" [[InteractiveUtils]] deps = ["Markdown"] uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" [[JSON]] -deps = ["Dates", "Distributed", "Mmap", "Sockets", "Test", "Unicode"] -git-tree-sha1 = "1f7a25b53ec67f5e9422f1f551ee216503f4a0fa" +deps = ["Dates", "Mmap", "Parsers", "Unicode"] +git-tree-sha1 = "b34d7cef7b337321e97d22242c3c2b91f476748e" uuid = "682c06a0-de6a-54ab-a142-c8b1cf79cde6" -version = "0.20.0" - -[[Juno]] -deps = ["Base64", "Logging", "Media", "Profile", "Test"] -git-tree-sha1 = "4e4a8d43aa7ecec66cadaf311fbd1e5c9d7b9175" -uuid = "e5e0dc1b-0480-54bc-9374-aad01c23163d" -version = "0.7.0" +version = "0.21.0" [[LibGit2]] uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" -[[Libdl]] -uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" - -[[LinearAlgebra]] -deps = ["Libdl"] -uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" - [[Logging]] uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" -[[MacroTools]] -deps = ["CSTParser", "Compat", "DataStructures", "Test"] -git-tree-sha1 = "daecd9e452f38297c686eba90dba2a6d5da52162" -uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09" -version = "0.5.0" - [[Markdown]] deps = ["Base64"] uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" -[[Media]] -deps = ["MacroTools", "Test"] -git-tree-sha1 = "75a54abd10709c01f1b86b84ec225d26e840ed58" -uuid = "e89f7d12-3494-54d1-8411-f7d8b9ae1f27" -version = "0.5.0" - -[[Missings]] -deps = ["Dates", "InteractiveUtils", "SparseArrays", "Test"] -git-tree-sha1 = "d1d2585677f2bd93a97cfeb8faa7a0de0f982042" -uuid = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28" -version = "0.4.0" - [[Mmap]] uuid = "a63ad114-7e13-5084-954f-fe012c677804" -[[NNlib]] -deps = ["Libdl", "LinearAlgebra", "Requires", "Statistics", "TimerOutputs"] -git-tree-sha1 = "0c667371391fc6bb31f7f12f96a56a17098b3de8" -uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" -version = "0.6.0" - -[[NaNMath]] -deps = ["Compat"] -git-tree-sha1 = "ce3b85e484a5d4c71dd5316215069311135fa9f2" -uuid = "77ba4419-2d1f-58cd-9bb1-8ffee604a2e3" -version = "0.3.2" - -[[OrderedCollections]] -deps = ["Random", "Serialization", "Test"] -git-tree-sha1 = "c4c13474d23c60d20a67b217f1d7f22a40edf8f1" -uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" -version = "1.1.0" +[[Parsers]] +deps = ["Dates", "Test"] +git-tree-sha1 = "db2b35dedab3c0e46dc15996d170af07a5ab91c9" +uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" +version = "0.3.6" [[Pkg]] deps = ["Dates", "LibGit2", "Markdown", "Printf", "REPL", "Random", "SHA", "UUIDs"] @@ -209,10 +60,6 @@ uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" deps = ["Unicode"] uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" -[[Profile]] -deps = ["Printf"] -uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79" - [[REPL]] deps = ["InteractiveUtils", "Markdown", "Sockets"] uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" @@ -221,106 +68,22 @@ uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" deps = ["Serialization"] uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" -[[Reexport]] -deps = ["Pkg"] -git-tree-sha1 = "7b1d07f411bc8ddb7977ec7f377b97b158514fe0" -uuid = "189a3867-3050-52da-a836-e630ba90ab69" -version = "0.2.0" - -[[Requires]] -deps = ["Test"] -git-tree-sha1 = "f6fbf4ba64d295e146e49e021207993b6b48c7d1" -uuid = "ae029012-a4dd-5104-9daa-d747884805df" -version = "0.5.2" - [[SHA]] uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" [[Serialization]] uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" -[[SharedArrays]] -deps = ["Distributed", "Mmap", "Random", "Serialization"] -uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383" - [[Sockets]] uuid = "6462fe0b-24de-5631-8697-dd941f90decc" -[[SortingAlgorithms]] -deps = ["DataStructures", "Random", "Test"] -git-tree-sha1 = "03f5898c9959f8115e30bc7226ada7d0df554ddd" -uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c" -version = "0.3.1" - -[[SparseArrays]] -deps = ["LinearAlgebra", "Random"] -uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" - -[[SpecialFunctions]] -deps = ["BinDeps", "BinaryProvider", "Libdl", "Test"] -git-tree-sha1 = "0b45dc2e45ed77f445617b99ff2adf0f5b0f23ea" -uuid = "276daf66-3868-5448-9aa4-cd146d93841b" -version = "0.7.2" - -[[StaticArrays]] -deps = ["InteractiveUtils", "LinearAlgebra", "Random", "Statistics", "Test"] -git-tree-sha1 = "3841b39ed5f047db1162627bf5f80a9cd3e39ae2" -uuid = "90137ffa-7385-5640-81b9-e52037218182" -version = "0.10.3" - -[[Statistics]] -deps = ["LinearAlgebra", "SparseArrays"] -uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" - -[[StatsBase]] -deps = ["DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"] -git-tree-sha1 = "8a0f4b09c7426478ab677245ab2b0b68552143c7" -uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91" -version = "0.30.0" - [[Test]] deps = ["Distributed", "InteractiveUtils", "Logging", "Random"] uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" -[[TimerOutputs]] -deps = ["Crayons", "Printf", "Test", "Unicode"] -git-tree-sha1 = "b80671c06f8f8bae08c55d67b5ce292c5ae2660c" -uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f" -version = "0.5.0" - -[[Tokenize]] -deps = ["Printf", "Test"] -git-tree-sha1 = "3e83f60b74911d3042d3550884ca2776386a02b8" -uuid = "0796e94c-ce3b-5d07-9a54-7f471281c624" -version = "0.5.3" - -[[Tracker]] -deps = ["Adapt", "DiffRules", "ForwardDiff", "LinearAlgebra", "MacroTools", "NNlib", "NaNMath", "Printf", "Random", "Requires", "SpecialFunctions", "Statistics", "Test"] -git-tree-sha1 = "0bec1b68c63a0e8a58d3944261cbf4cc9577c8a1" -uuid = "9f7883ad-71c0-57eb-9f7f-b5c9e6d3789c" -version = "0.2.0" - -[[TranscodingStreams]] -deps = ["Random", "Test"] -git-tree-sha1 = "a25d8e5a28c3b1b06d3859f30757d43106791919" -uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" -version = "0.9.4" - -[[URIParser]] -deps = ["Test", "Unicode"] -git-tree-sha1 = "6ddf8244220dfda2f17539fa8c9de20d6c575b69" -uuid = "30578b45-9adc-5946-b283-645ec420af67" -version = "0.4.0" - [[UUIDs]] deps = ["Random", "SHA"] uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" [[Unicode]] uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" - -[[ZipFile]] -deps = ["BinaryProvider", "Libdl", "Printf", "Test"] -git-tree-sha1 = "5f6f663890dfb9bad6af75a86a43f67904e5050e" -uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea" -version = "0.8.1" diff --git a/docs/Project.toml b/docs/Project.toml index c882d475..dfa65cd1 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -1,4 +1,2 @@ [deps] Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" -Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" -NNlib = "872c559c-99b0-510c-b3b7-b6c96a88d5cd" diff --git a/docs/make.jl b/docs/make.jl index 51fe4bf3..b950e959 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -1,12 +1,13 @@ +using Pkg; +Pkg.activate(joinpath(@__DIR__, "..")); Pkg.instantiate() +Pkg.activate(); Pkg.instantiate() + +pushfirst!(LOAD_PATH, joinpath(@__DIR__, "..")) + using Documenter, Flux, NNlib makedocs(modules=[Flux, NNlib], - doctest = true, - analytics = "UA-36890222-9", sitename = "Flux", - # Uncomment below for local build - #format = Documenter.HTML(prettyurls = false), - assets = ["assets/flux.css"], pages = ["Home" => "index.md", "Building Models" => ["Basics" => "models/basics.md", @@ -20,8 +21,9 @@ makedocs(modules=[Flux, NNlib], "GPU Support" => "gpu.md", "Saving & Loading" => "saving.md", "Performance Tips" => "performance.md", - "Internals" => - ["Backpropagation" => "internals/tracker.md"], - "Community" => "community.md"]) + "Community" => "community.md"], + format = Documenter.HTML(assets = ["assets/flux.css"], + analytics = "UA-36890222-9", + prettyurls = haskey(ENV, "CI"))) deploydocs(repo = "github.com/FluxML/Flux.jl.git") diff --git a/docs/src/community.md b/docs/src/community.md index 143c45bd..c8f277e9 100644 --- a/docs/src/community.md +++ b/docs/src/community.md @@ -1,5 +1,5 @@ # Community -All Flux users are welcome to join our community on the [Julia forum](https://discourse.julialang.org/), the [slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866) (channel #machine-learning), or Flux's [Gitter](https://gitter.im/FluxML/Lobby). If you have questions or issues we'll try to help you out. +All Flux users are welcome to join our community on the [Julia forum](https://discourse.julialang.org/), or the [slack](https://discourse.julialang.org/t/announcing-a-julia-slack/4866) (channel #machine-learning). If you have questions or issues we'll try to help you out. If you're interested in hacking on Flux, the [source code](https://github.com/FluxML/Flux.jl) is open and easy to understand -- it's all just the same Julia code you work with normally. You might be interested in our [intro issues](https://github.com/FluxML/Flux.jl/issues?q=is%3Aopen+is%3Aissue+label%3A%22help+wanted%22) to get started. diff --git a/docs/src/gpu.md b/docs/src/gpu.md index 0ac3a938..aed33f4e 100644 --- a/docs/src/gpu.md +++ b/docs/src/gpu.md @@ -1,14 +1,6 @@ # GPU Support -## Installation - -To get GPU support for NVIDIA graphics cards, you need to install `CuArrays.jl` - -**Steps needed** - -1. Install [NVIDIA toolkit](https://developer.nvidia.com/cuda-downloads) -2. Install [NVIDIA cuDNN library](https://developer.nvidia.com/cudnn) -3. In Julia's terminal run `]add CuArrays` +NVIDIA GPU support should work out of the box on systems with CUDA and CUDNN installed. For more details see the [CuArrays](https://github.com/JuliaGPU/CuArrays.jl) readme. ## GPU Usage diff --git a/docs/src/internals/tracker.md b/docs/src/internals/tracker.md deleted file mode 100644 index 456a9129..00000000 --- a/docs/src/internals/tracker.md +++ /dev/null @@ -1,184 +0,0 @@ -# Flux.Tracker - -Backpropagation, or reverse-mode automatic differentiation, is handled by the `Flux.Tracker` module. - -```julia -julia> using Flux.Tracker -``` - -Here we discuss some more advanced uses of this module, as well as covering its internals. - -## Taking Gradients - -In the [basics section](../models/basics.md) we covered basic usage of the `gradient` function. - -```julia -using Flux.Tracker - -Tracker.gradient((a, b) -> a*b, 2, 3) # (3.0 (tracked), 2.0 (tracked)) -``` - -`gradient` is actually just a thin wrapper around the backpropagator-based interface, `forward`. - -```julia -using Flux.Tracker: forward - -y, back = forward((a, b) -> a*b, 2, 3) # (6.0 (tracked), Flux.Tracker.#9) - -back(1) # (3.0 (tracked), 2.0 (tracked)) -``` - -The `forward` function returns two results. The first, `y`, is the original value of the function (perhaps with tracking applied). The second, `back`, is a new function which, given a sensitivity, returns the sensitivity of the inputs to `forward` (we call this a "backpropagator"). One use of this interface is to provide custom sensitivities when outputs are not scalar. - -```julia -julia> y, back = forward((a, b) -> a.*b, [1,2,3],[4,5,6]) -(param([4.0, 10.0, 18.0]), Flux.Tracker.#9) - -julia> back([1,1,1]) -(param([4.0, 5.0, 6.0]), param([1.0, 2.0, 3.0])) -``` - -We can also take gradients in-place. This can be useful if you only care about first-order gradients. - -```julia -a, b = param(2), param(3) - -c = a*b # 6.0 (tracked) - -Tracker.back!(c) - -Tracker.grad(a), Tracker.grad(b) # (3.0, 2.0) -``` - -## Tracked Arrays - -The `param` function converts a normal Julia array into a new object that, while behaving like an array, tracks extra information that allows us to calculate derivatives. For example, say we multiply two parameters: - -```julia -julia> W = param([1 2; 3 4]) -Tracked 2×2 Array{Float64,2}: - 1.0 2.0 - 3.0 4.0 - -julia> x = param([5, 6]) -Tracked 2-element Array{Float64,1}: - 5.0 - 6.0 - -julia> y = W*x -Tracked 2-element Array{Float64,1}: - 17.0 - 39.0 -``` - -The output `y` is also a `TrackedArray` object. We can now backpropagate sensitivities to `W` and `x` via the `back!` function, and see the gradients accumulated in the `W` and `x` tracked arrays: - -```julia -julia> Tracker.back!(y, [1, -1]) - -julia> W.grad -2×2 Array{Float64,2}: - 5.0 6.0 --5.0 -6.0 - -julia> x.grad -2-element Array{Float64,1}: - -2.0 - -2.0 -``` - -You may sometimes want to drop derivative information and just get the plain value back. You can do this by calling `Tracker.data(W)`. - -## Custom Gradients - -We can hook in to the processes above to implement custom gradients for a function or kernel. For a toy example, imagine a custom implementation of `minus`: - -```julia -minus(a, b) = a - b -``` - -Firstly, we must tell the tracker system to stop when it sees a call to `minus`, and record it. We can do this using dispatch: - -```julia -using Flux.Tracker: TrackedArray, track, @grad - -minus(a::TrackedArray, b::TrackedArray) = track(minus, a, b) -``` - -`track` takes care of building a new `Tracked` object and recording the operation on the tape. We just need to provide a gradient definition. - -```julia -@grad function minus(a, b) - return minus(data(a), data(b)), Δ -> (Δ, -Δ) -end -``` - -This is essentially just a way of overloading the `forward` function we saw above. We strip tracking from `a` and `b` so that we are calling the original definition of `minus` (otherwise, we'd just try to track the call again and hit an infinite regress). - -Note that in the backpropagator we don't call `data(a)`; we *do* in fact want to track this, since nest AD will take a derivative through the backpropagator itself. For example, the gradient of `*` might look like this. - -```julia -@grad a * b = data(a)*data(b), Δ -> (Δ*b, a*Δ) -``` - -We can then calculate the first derivative of `minus` as follows: - -```julia -a = param([1,2,3]) -b = param([3,2,1]) - -c = minus(a, b) # [-2.0 (tracked), 0.0 (tracked), 2.0 (tracked)] - -Tracker.back!(c, 1) -Tracker.grad(a) # [1.00, 1.00, 1.00] -Tracker.grad(b) # [-1.00, -1.00, -1.00] -``` - -For multi-argument functions with custom gradients, you likely want to catch not just `minus(::TrackedArray, ::TrackedArray)` but also `minus(::Array, TrackedArray)` and so on. To do so, just define those extra signatures as needed: - -```julia -minus(a::AbstractArray, b::TrackedArray) = Tracker.track(minus, a, b) -minus(a::TrackedArray, b::AbstractArray) = Tracker.track(minus, a, b) -``` - -## Tracked Internals - -All `Tracked*` objects (`TrackedArray`, `TrackedReal`) are light wrappers around the `Tracked` type, which you can access via the `.tracker` field. - -```julia -julia> x.tracker -Flux.Tracker.Tracked{Array{Float64,1}}(0x00000000, Flux.Tracker.Call{Nothing,Tuple{}}(nothing, ()), true, [5.0, 6.0], [-2.0, -2.0]) -``` - -The `Tracker` stores the gradient of a given object, which we've seen before. - -```julia -julia> x.tracker.grad -2-element Array{Float64,1}: - -2.0 - -2.0 -``` - -The tracker also contains a `Call` object, which simply represents a function call that was made at some point during the forward pass. For example, the `+` call would look like this: - -```julia -julia> Tracker.Call(+, 1, 2) -Flux.Tracker.Call{Base.#+,Tuple{Int64,Int64}}(+, (1, 2)) -``` - -In the case of the `y` we produced above, we can see that it stores the call that produced it -- that is, `W*x`. - -```julia -julia> y.tracker.f -Flux.Tracker.Call{...}(*, (param([1.0 2.0; 3.0 4.0]), param([5.0, 6.0]))) -``` - -Notice that because the arguments to the call may also be tracked arrays, storing their own calls, this means that `Tracker` ends up forming a data structure that records everything that happened during the forward pass (often known as a *tape*). - -When we call `back!(y, [1, -1])`, the sensitivities `[1, -1]` simply get forwarded to `y`'s call (`*`), effectively calling - -```julia -Tracker.back(*, [1, -1], W, x) -``` - -which in turn calculates the sensitivities of the arguments (`W` and `x`) and back-propagates through their calls. This is recursive, so it will walk the entire program graph and propagate gradients to the original model parameters. diff --git a/docs/src/models/basics.md b/docs/src/models/basics.md index 3b7b2a8e..ddd81992 100644 --- a/docs/src/models/basics.md +++ b/docs/src/models/basics.md @@ -5,55 +5,56 @@ Flux's core feature is taking gradients of Julia code. The `gradient` function takes another Julia function `f` and a set of arguments, and returns the gradient with respect to each argument. (It's a good idea to try pasting these examples in the Julia terminal.) ```jldoctest basics -julia> using Flux.Tracker +julia> using Flux julia> f(x) = 3x^2 + 2x + 1; -julia> df(x) = Tracker.gradient(f, x; nest = true)[1]; # df/dx = 6x + 2 +julia> df(x) = gradient(f, x)[1]; # df/dx = 6x + 2 julia> df(2) -14.0 (tracked) +14 -julia> d2f(x) = Tracker.gradient(df, x; nest = true)[1]; # d²f/dx² = 6 +julia> d2f(x) = gradient(df, x)[1]; # d²f/dx² = 6 julia> d2f(2) -6.0 (tracked) +6 ``` -(We'll learn more about why these numbers show up as `(tracked)` below.) - -When a function has many parameters, we can pass them all in explicitly: +When a function has many parameters, we can get gradients of each one at the same time: ```jldoctest basics -julia> f(W, b, x) = W * x + b; +julia> f(x, y) = sum((x .- y).^2); -julia> Tracker.gradient(f, 2, 3, 4) -(4.0 (tracked), 1.0 (tracked), 2.0 (tracked)) +julia> gradient(f, [2, 1], [2, 0]) +([0, 2], [0, -2]) ``` -But machine learning models can have *hundreds* of parameters! Flux offers a nice way to handle this. We can tell Flux to treat something as a parameter via `param`. Then we can collect these together and tell `gradient` to collect the gradients of all `params` at once. +But machine learning models can have *hundreds* of parameters! To handle this, Flux lets you work with collections of parameters, via `params`. You can get the gradient of all parameters used in a program without explicitly passing them in. ```jldoctest basics julia> using Flux -julia> W = param(2) -2.0 (tracked) +julia> x = [2, 1]; -julia> b = param(3) -3.0 (tracked) +julia> y = [2, 0]; -julia> f(x) = W * x + b; +julia> gs = gradient(params(x, y)) do + f(x, y) + end +Grads(...) -julia> grads = Tracker.gradient(() -> f(4), params(W, b)); +julia> gs[x] +2-element Array{Int64,1}: + 0 + 2 -julia> grads[W] -4.0 (tracked) - -julia> grads[b] -1.0 (tracked) +julia> gs[y] +2-element Array{Int64,1}: + 0 + -2 ``` -There are a few things to notice here. Firstly, `W` and `b` now show up as *tracked*. Tracked things behave like normal numbers or arrays, but keep records of everything you do with them, allowing Flux to calculate their gradients. `gradient` takes a zero-argument function; no arguments are necessary because the `params` tell it what to differentiate. +Here, `gradient` takes a zero-argument function; no arguments are necessary because the `params` tell it what to differentiate. This will come in really handy when dealing with big, complicated models. For now, though, let's start with something simple. @@ -76,26 +77,20 @@ x, y = rand(5), rand(2) # Dummy data loss(x, y) # ~ 3 ``` -To improve the prediction we can take the gradients of `W` and `b` with respect to the loss and perform gradient descent. Let's tell Flux that `W` and `b` are parameters, just like we did above. +To improve the prediction we can take the gradients of `W` and `b` with respect to the loss and perform gradient descent. ```julia -using Flux.Tracker +using Flux -W = param(W) -b = param(b) - -gs = Tracker.gradient(() -> loss(x, y), params(W, b)) +gs = gradient(() -> loss(x, y), params(W, b)) ``` -Now that we have gradients, we can pull them out and update `W` to train the model. The `update!(W, Δ)` function applies `W = W + Δ`, which we can use for gradient descent. +Now that we have gradients, we can pull them out and update `W` to train the model. ```julia -using Flux.Tracker: update! +W̄ = gs[W] -Δ = gs[W] - -# Update the parameter and reset the gradient -update!(W, -0.1Δ) +W .-= 0.1 .* W̄ loss(x, y) # ~ 2.5 ``` @@ -111,12 +106,12 @@ It's common to create more complex models than the linear regression above. For ```julia using Flux -W1 = param(rand(3, 5)) -b1 = param(rand(3)) +W1 = rand(3, 5) +b1 = rand(3) layer1(x) = W1 * x .+ b1 -W2 = param(rand(2, 3)) -b2 = param(rand(2)) +W2 = rand(2, 3) +b2 = rand(2) layer2(x) = W2 * x .+ b2 model(x) = layer2(σ.(layer1(x))) @@ -128,8 +123,8 @@ This works but is fairly unwieldy, with a lot of repetition – especially as we ```julia function linear(in, out) - W = param(randn(out, in)) - b = param(randn(out)) + W = randn(out, in) + b = randn(out) x -> W * x .+ b end @@ -150,7 +145,7 @@ struct Affine end Affine(in::Integer, out::Integer) = - Affine(param(randn(out, in)), param(randn(out))) + Affine(randn(out, in), randn(out)) # Overload call, so the object can be used as a function (m::Affine)(x) = m.W * x .+ m.b diff --git a/docs/src/models/layers.md b/docs/src/models/layers.md index f2bd8046..8b725bfb 100644 --- a/docs/src/models/layers.md +++ b/docs/src/models/layers.md @@ -59,7 +59,6 @@ swish These layers don't affect the structure of the network but may improve training times or reduce overfitting. ```@docs -Flux.testmode! BatchNorm Dropout AlphaDropout diff --git a/docs/src/models/recurrence.md b/docs/src/models/recurrence.md index 1ae7cbd8..2516c548 100644 --- a/docs/src/models/recurrence.md +++ b/docs/src/models/recurrence.md @@ -101,26 +101,4 @@ m = Chain(LSTM(10, 15), Dense(15, 5)) m.(seq) ``` -## Truncating Gradients - -By default, calculating the gradients in a recurrent layer involves its entire history. For example, if we call the model on 100 inputs, we'll have to calculate the gradient for those 100 calls. If we then calculate another 10 inputs we have to calculate 110 gradients – this accumulates and quickly becomes expensive. - -To avoid this we can *truncate* the gradient calculation, forgetting the history. - -```julia -truncate!(m) -``` - -Calling `truncate!` wipes the slate clean, so we can call the model with more inputs without building up an expensive gradient computation. - -`truncate!` makes sense when you are working with multiple chunks of a large sequence, but we may also want to work with a set of independent sequences. In this case the hidden state should be completely reset to its original value, throwing away any accumulated information. `reset!` does this for you. - -In general, when training with recurrent layers in your model, you'll want to call `reset!` or `truncate!` for each loss calculation: - -```julia -function loss(x,y) - l = Flux.mse(m(x), y) - Flux.reset!(m) - return l -end -``` +Finally, we can reset the hidden state of the cell back to its initial value using `reset!(m)`. diff --git a/docs/src/models/regularisation.md b/docs/src/models/regularisation.md index 370a53d9..e1d88d77 100644 --- a/docs/src/models/regularisation.md +++ b/docs/src/models/regularisation.md @@ -15,6 +15,8 @@ loss(x, y) = crossentropy(softmax(m(x)), y) We can regularise this by taking the (L2) norm of the parameters, `m.W` and `m.b`. ```julia +using LinearAlgebra + penalty() = norm(m.W) + norm(m.b) loss(x, y) = crossentropy(softmax(m(x)), y) + penalty() ``` @@ -48,15 +50,17 @@ loss(rand(28^2), rand(10)) One can also easily add per-layer regularisation via the `activations` function: ```julia +julia> using Flux: activations + julia> c = Chain(Dense(10,5,σ),Dense(5,2),softmax) -Chain(Dense(10, 5, NNlib.σ), Dense(5, 2), NNlib.softmax) +Chain(Dense(10, 5, σ), Dense(5, 2), softmax) julia> activations(c, rand(10)) 3-element Array{Any,1}: - param([0.71068, 0.831145, 0.751219, 0.227116, 0.553074]) - param([0.0330606, -0.456104]) - param([0.61991, 0.38009]) + Float32[0.84682214, 0.6704139, 0.42177814, 0.257832, 0.36255655] + Float32[0.1501253, 0.073269576] + Float32[0.5192045, 0.48079553] julia> sum(norm, ans) -2.639678767773633 (tracked) +2.1166067f0 ``` diff --git a/docs/src/saving.md b/docs/src/saving.md index 73777422..f71c4350 100644 --- a/docs/src/saving.md +++ b/docs/src/saving.md @@ -53,7 +53,7 @@ julia> using Flux julia> model = Chain(Dense(10,5,relu),Dense(5,2),softmax) Chain(Dense(10, 5, NNlib.relu), Dense(5, 2), NNlib.softmax) -julia> weights = Tracker.data.(params(model)); +julia> weights = params(model); julia> using BSON: @save diff --git a/docs/src/training/optimisers.md b/docs/src/training/optimisers.md index a8f0f2db..4a8d09cb 100644 --- a/docs/src/training/optimisers.md +++ b/docs/src/training/optimisers.md @@ -3,25 +3,25 @@ Consider a [simple linear regression](../models/basics.md). We create some dummy data, calculate a loss, and backpropagate to calculate gradients for the parameters `W` and `b`. ```julia -using Flux, Flux.Tracker +using Flux -W = param(rand(2, 5)) -b = param(rand(2)) +W = rand(2, 5)) +b = rand(2) -predict(x) = W*x .+ b +predict(x) = (W * x) .+ b loss(x, y) = sum((predict(x) .- y).^2) x, y = rand(5), rand(2) # Dummy data l = loss(x, y) # ~ 3 θ = Params([W, b]) -grads = Tracker.gradient(() -> loss(x, y), θ) +grads = gradient(() -> loss(x, y), θ) ``` We want to update each parameter, using the gradient, in order to improve (reduce) the loss. Here's one way to do that: ```julia -using Flux.Tracker: grad, update! +using Flux: update! η = 0.1 # Learning Rate for p in (W, b) diff --git a/src/Flux.jl b/src/Flux.jl index 416bd71f..9d1fbfc5 100644 --- a/src/Flux.jl +++ b/src/Flux.jl @@ -3,19 +3,15 @@ module Flux # Zero Flux Given using Base: tail -using MacroTools, Juno, Reexport, Statistics, Random +using Zygote, MacroTools, Juno, Reexport, Statistics, Random using MacroTools: @forward +@reexport using NNlib +using Zygote: Params, @adjoint, gradient, forward +export gradient export Chain, Dense, Maxout, RNN, LSTM, GRU, Conv, CrossCor, ConvTranspose, MaxPool, MeanPool, DepthwiseConv, Dropout, AlphaDropout, LayerNorm, BatchNorm, InstanceNorm, GroupNorm, - SkipConnection, - params, mapleaves, cpu, gpu, f32, f64 - -@reexport using NNlib - -using Tracker -using Tracker: data -export Tracker, TrackedArray, TrackedVector, TrackedMatrix, param + SkipConnection, params, mapleaves, cpu, gpu, f32, f64 include("optimise/Optimise.jl") using .Optimise @@ -49,6 +45,8 @@ include("layers/normalise.jl") include("data/Data.jl") +include("deprecations.jl") + if has_cuarrays() include("cuda/cuda.jl") end diff --git a/src/cuda/cudnn.jl b/src/cuda/cudnn.jl index f951de9d..448ea140 100644 --- a/src/cuda/cudnn.jl +++ b/src/cuda/cudnn.jl @@ -1,13 +1,10 @@ using CuArrays: libcudnn using CuArrays.CUDNN: @check, handle, cudnnStatus_t, cudnnTensorDescriptor_t, cudnnBatchNormMode_t, cudnnHandle_t, cudnnDataType, TensorDesc, FilterDesc - import CuArrays.CUDAdrv: CuPtr, CU_NULL using LinearAlgebra -import ..Flux: data - mutable struct DropoutDesc ptr::Ptr{Nothing} states::CuVector{UInt8} @@ -198,36 +195,8 @@ end # Flux Interface -(BN::Flux.BatchNorm)(x::Union{CuParam{T,2},CuParam{T,4},CuParam{T,5}}, cache = nothing) where T<:Union{Float32, Float64} = - BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; cache = cache, alpha = 1, beta = 0, eps = BN.ϵ, training = BN.active)) +(BN::Flux.BatchNorm)(x::Union{CuArray{T,2},CuArray{T,4},CuArray{T,5}}, cache = nothing) where T<:Union{Float32, Float64} = + BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum; cache = cache, alpha = 1, beta = 0, eps = BN.ϵ, training = Flux.istraining())) -batchnorm(g::TrackedArray, b::TrackedArray, x::TrackedArray, running_mean::CuArray{T}, - running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = - track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) - -batchnorm(g::TrackedArray, b::TrackedArray, x::CuArray{T}, running_mean::CuArray{T}, - running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = - track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) - -batchnorm(g::TrackedArray, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T}, - running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = - track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) - -batchnorm(g::CuArray{T}, b::TrackedArray, x::CuArray{T}, running_mean::CuArray{T}, - running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = - track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) - -batchnorm(g::CuArray{T}, b::TrackedArray, x::TrackedArray, running_mean::CuArray{T}, - running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = - track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) - -batchnorm(g::TrackedArray, b::CuArray{T}, x::CuArray{T}, running_mean::CuArray{T}, - running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = - track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) - -batchnorm(g::CuArray{T}, b::CuArray{T}, x::TrackedArray, running_mean::CuArray{T}, - running_var::CuArray{T}, momentum; kw...) where T<:Union{Float32, Float64} = - track(batchnorm, g, b, x, running_mean, running_var, momentum; kw...) - -@grad batchnorm(g, b, x, running_mean, running_var, momentum; kw...) = - batchnorm(data.((g, b, x))..., running_mean, running_var, momentum; kw...), Δ -> (nobacksies(:batchnorm, ∇batchnorm(data.((g, b, x, Δ))..., running_mean, running_var, momentum; kw...))..., nothing, nothing, nothing) +@adjoint batchnorm(g, b, x, running_mean, running_var, momentum; kw...) = + batchnorm(g, b, x, running_mean, running_var, momentum; kw...), Δ -> (∇batchnorm(g, b, x, Δ, running_mean, running_var, momentum; kw...)..., nothing, nothing, nothing) diff --git a/src/cuda/curnn.jl b/src/cuda/curnn.jl index de257a66..ca8b5140 100644 --- a/src/cuda/curnn.jl +++ b/src/cuda/curnn.jl @@ -225,7 +225,6 @@ end # Interface import ..Flux: Flux, relu -import ..Tracker: TrackedArray using CuArrays.CUDAnative using CuArrays: @cuindex, cudims @@ -240,17 +239,16 @@ function LinearAlgebra.copy_transpose!(dst::CuArray, src::CuArray) return dst end -CuParam{T,N} = Union{CuArray{T,N},TrackedArray{T,N,CuArray{T,N}}} -CuRNN{T} = Flux.RNNCell{<:Union{typeof(tanh),typeof(relu)},<:CuParam{T,2},<:CuParam{T,1}} -CuGRU{T} = Flux.GRUCell{<:CuParam{T,2},<:CuParam{T,1}} -CuLSTM{T} = Flux.LSTMCell{<:CuParam{T,2},<:CuParam{T,1}} +CuRNN{T} = Flux.RNNCell{<:Union{typeof(tanh),typeof(relu)},<:CuArray{T,2},<:CuArray{T,1}} +CuGRU{T} = Flux.GRUCell{<:CuArray{T,2},<:CuArray{T,1}} +CuLSTM{T} = Flux.LSTMCell{<:CuArray{T,2},<:CuArray{T,1}} CuRNNs{T} = Union{CuRNN{T},CuGRU{T},CuLSTM{T}} function copyparams!(m::CuRNNs, d::RNNDesc) Wi, Wh = d.weights - copy_transpose!(Wi, Flux.data(m.Wi)) - copy_transpose!(Wh, Flux.data(m.Wh)) - copy_transpose!(d.bias, Flux.data(m.b)) + copy_transpose!(Wi, m.Wi) + copy_transpose!(Wh, m.Wh) + copy_transpose!(d.bias, m.b) return end @@ -271,59 +269,58 @@ function desc(rnn) return d end -import Flux.Tracker -import Flux.Tracker: data, istracked, track, unbroadcast, @grad, nobacksies +using ..Flux: @adjoint -istrain(m::CuRNNs, args...) = any(x -> x isa TrackedArray, (m.Wi, m.Wh, m.b, args...)) - -function (m::CuRNN{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64} - result = istrain(m, h, x) ? - track(m, x, h, m.Wi, m.Wh, m.b) : - forward(desc(m), x, h) - return result[2], result[1] +function (m::CuRNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64} + y, h′ = forward(desc(m), x, h) + return h′, y end -function (m::CuGRU{T})(h::CuParam{T}, x::CuParam{T}) where T <: Union{Float32,Float64} - result = istrain(m, h, x) ? - track(m, x, h, m.Wi, m.Wh, m.b) : - forward(desc(m), x, h) - return result[2], result[1] +function (m::CuGRU{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64} + y, h′ = forward(desc(m), x, h) + return h′, y end -function (m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x::CuParam{T}) where T <: Union{Float32,Float64} - result = istrain(m, h, x) ? - track(m, x, h[1], h[2], m.Wi, m.Wh, m.b) : - forward(desc(m), x, h[1], h[2]) - return (result[2], result[3]), result[1] +function (m::CuLSTM{T})(h::NTuple{2,CuArray{T}}, x::CuArray{T}) where T <: Union{Float32,Float64} + y, h′, c′ = forward(desc(m), x, h[1], h[2]) + return (h′, c′), y end -(m::CuRNN{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x)) -(m::CuGRU{T})(h::CuParam{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x)) -(m::CuLSTM{T})(h::NTuple{2,CuParam{T}}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x)) +(m::CuRNN{T})(h::CuArray{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x)) +(m::CuGRU{T})(h::CuArray{T}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x)) +(m::CuLSTM{T})(h::NTuple{2,CuArray{T}}, x) where T <: Union{Float32,Float64} = m(h, CuArray{T}(x)) -@grad function (m::Union{CuRNN,CuGRU})(x, h, Wi, Wh, b) - reserve, result = forwardTrain(desc(m), data(x), data(h)) - result, function (Δ) - y, ho = result - dy, dho = Δ - h_ = hBatch(x, data(h)) - dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve) - (dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve) - nobacksies(:RNN, (dx, unbroadcast(h, dh), transpose(dWi), transpose(dWh), db)) +trim(x, Δ) = reshape(Δ, ntuple(i -> size(Δ, i), Val(ndims(x)))) + +unbroadcast(x::AbstractArray, Δ) = + size(x) == size(Δ) ? Δ : + length(x) == length(Δ) ? trim(x, Δ) : + trim(x, sum(Δ, dims = ntuple(i -> size(x, i) == 1 ? i : ndims(Δ)+1, Val(ndims(Δ))))) + +for RNN in (CuRNN, CuGRU) + @eval @adjoint function (m::$RNN{T})(h::CuArray{T}, x::CuArray{T}) where T <: Union{Float32,Float64} + reserve, (y, ho) = forwardTrain(desc(m), x, h) + (ho, y), function (Δ) + dho, dy = Δ + h_ = hBatch(x, h) + dx, dh = backwardData(descs[m], y, dy, dho, h_, reserve) + (dWi, dWh), db = backwardWeights(descs[m], x, h_, y, reserve) + dm = Ref{Any}((σ=nothing,Wi=transpose(dWi),Wh=transpose(dWh),b=db,h=nothing)) + (dm, unbroadcast(h, dh), dx) + end end end -@grad function (m::CuLSTM)(x, h, c, Wi, Wh, b) - reserve, result = forwardTrain(desc(m), data.((x, h, c))...) - result, function (Δ) - y, ho = result - dy, dho, dco = Δ - h_ = hBatch(x, data(h)) - c_ = hBatch(x, data(c)) +@adjoint function (m::CuLSTM)((h, c)::Tuple{CuArray{T},CuArray{T}}, x::CuArray{T}) where T <: Union{Float32,Float64} + reserve, (y, ho, co) = forwardTrain(desc(m), x, h, c) + ((ho, co), y), function (Δ) + dhc, dy = Δ + dho, dco = dhc === nothing ? (nothing, nothing) : dhc + h_ = hBatch(x, h) + c_ = hBatch(x, c) dx, dh, dc = backwardData(descs[m], y, dy, dho, dco, h_, c_, reserve) - (dWi, dWh), db = backwardWeights(descs[m], data(x), h_, y, reserve) - nobacksies(:RNN, - (dx, unbroadcast(h, dh), unbroadcast(c, dc), - transpose(dWi), transpose(dWh), db)) + (dWi, dWh), db = backwardWeights(descs[m], x, h_, y, reserve) + dm = Ref{Any}((Wi=transpose(dWi),Wh=transpose(dWh),b=db,h=nothing,c=nothing)) + (dm, (unbroadcast(h, dh), unbroadcast(c, dc)), dx) end end diff --git a/src/data/iris.jl b/src/data/iris.jl index 3da90330..d78606d8 100644 --- a/src/data/iris.jl +++ b/src/data/iris.jl @@ -1,14 +1,10 @@ - """ - - Iris - Fisher's classic iris dataset. -Measurements from 3 different species of iris: setosa, versicolor and +Measurements from 3 different species of iris: setosa, versicolor and virginica. There are 50 examples of each species. -There are 4 measurements for each example: sepal length, sepal width, petal +There are 4 measurements for each example: sepal length, sepal width, petal length and petal width. The measurements are in centimeters. The module retrieves the data from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/ml/datasets/iris). @@ -35,10 +31,12 @@ end labels() -Get the labels of the iris dataset, a 150 element array of strings listing the +Get the labels of the iris dataset, a 150 element array of strings listing the species of each example. ```jldoctest +julia> using Flux + julia> labels = Flux.Data.Iris.labels(); julia> summary(labels) @@ -58,11 +56,13 @@ end features() -Get the features of the iris dataset. This is a 4x150 matrix of Float64 -elements. It has a row for each feature (sepal length, sepal width, +Get the features of the iris dataset. This is a 4x150 matrix of Float64 +elements. It has a row for each feature (sepal length, sepal width, petal length, petal width) and a column for each example. ```jldoctest +julia> using Flux + julia> features = Flux.Data.Iris.features(); julia> summary(features) @@ -81,6 +81,5 @@ function features() iris = readdlm(deps("iris.data"), ',') Matrix{Float64}(iris[1:end, 1:4]') end + end - - diff --git a/src/deprecations.jl b/src/deprecations.jl new file mode 100644 index 00000000..ccaac27a --- /dev/null +++ b/src/deprecations.jl @@ -0,0 +1,2 @@ +@deprecate param(x) x +@deprecate data(x) x diff --git a/src/layers/basic.jl b/src/layers/basic.jl index 83eeee21..0cebead1 100644 --- a/src/layers/basic.jl +++ b/src/layers/basic.jl @@ -89,7 +89,7 @@ Dense(W, b) = Dense(W, b, identity) function Dense(in::Integer, out::Integer, σ = identity; initW = glorot_uniform, initb = zeros) - return Dense(param(initW(out, in)), param(initb(out)), σ) + return Dense(initW(out, in), initb(out), σ) end @treelike Dense @@ -129,7 +129,7 @@ struct Diagonal{T} end Diagonal(in::Integer; initα = ones, initβ = zeros) = - Diagonal(param(initα(in)), param(initβ(in))) + Diagonal(initα(in), initβ(in)) @treelike Diagonal @@ -204,7 +204,6 @@ A 'ResNet'-type skip-connection with identity shortcut would simply be SkipConnection(layer, (a,b) -> a + b) ``` """ - struct SkipConnection layers connection #user can pass arbitrary connections here, such as (a,b) -> a + b diff --git a/src/layers/conv.jl b/src/layers/conv.jl index ff547b41..4361a389 100644 --- a/src/layers/conv.jl +++ b/src/layers/conv.jl @@ -14,11 +14,11 @@ Example: Applying Conv layer to a 1-channel input using a 2x2 window size, size = (2,2) in = 1 - out = 16 + out = 16 Conv((2, 2), 1=>16, relu) -Data should be stored in WHCN order (width, height, # channels, # batches). -In other words, a 100×100 RGB image would be a `100×100×3×1` array, +Data should be stored in WHCN order (width, height, # channels, # batches). +In other words, a 100×100 RGB image would be a `100×100×3×1` array, and a batch of 50 would be a `100×100×3×50` array. Takes the keyword arguments `pad`, `stride` and `dilation`. @@ -42,7 +42,7 @@ end Conv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N = - Conv(param(init(k..., ch...)), param(zeros(ch[2])), σ, + Conv(init(k..., ch...), zeros(ch[2]), σ, stride = stride, pad = pad, dilation = dilation) @treelike Conv @@ -74,8 +74,10 @@ end Standard convolutional transpose layer. `size` should be a tuple like `(2, 2)`. `in` and `out` specify the number of input and output channels respectively. + Data should be stored in WHCN order. In other words, a 100×100 RGB image would be a `100×100×3` array, and a batch of 50 would be a `100×100×3×50` array. + Takes the keyword arguments `pad`, `stride` and `dilation`. """ struct ConvTranspose{N,M,F,A,V} @@ -97,7 +99,7 @@ end ConvTranspose(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N = -ConvTranspose(param(init(k..., reverse(ch)...)), param(zeros(ch[2])), σ, +ConvTranspose(init(k..., reverse(ch)...), zeros(ch[2]), σ, stride = stride, pad = pad, dilation = dilation) @treelike ConvTranspose @@ -169,8 +171,8 @@ function DepthwiseConv(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N @assert ch[2] % ch[1] == 0 "Output channels must be integer multiple of input channels" return DepthwiseConv( - param(init(k..., div(ch[2], ch[1]), ch[1])), - param(zeros(ch[2])), + init(k..., div(ch[2], ch[1]), ch[1]), + zeros(ch[2]), σ; stride = stride, pad = pad, @@ -198,25 +200,26 @@ end (a::DepthwiseConv{<:Any,<:Any,W})(x::AbstractArray{<:Real}) where {T <: Union{Float32,Float64}, W <: AbstractArray{T}} = a(T.(x)) + """ CrossCor(size, in=>out) CrossCor(size, in=>out, relu) - + Standard cross convolutional layer. `size` should be a tuple like `(2, 2)`. `in` and `out` specify the number of input and output channels respectively. - + Example: Applying CrossCor layer to a 1-channel input using a 2x2 window size, giving us a 16-channel output. Output is activated with ReLU. - + size = (2,2) in = 1 - out = 16 + out = 16 CrossCor((2, 2), 1=>16, relu) - -Data should be stored in WHCN order (width, height, # channels, # batches). -In other words, a 100×100 RGB image would be a `100×100×3×1` array, + +Data should be stored in WHCN order (width, height, # channels, # batches). +In other words, a 100×100 RGB image would be a `100×100×3×1` array, and a batch of 50 would be a `100×100×3×50` array. - + Takes the keyword arguments `pad`, `stride` and `dilation`. """ struct CrossCor{N,M,F,A,V} @@ -238,7 +241,7 @@ end CrossCor(k::NTuple{N,Integer}, ch::Pair{<:Integer,<:Integer}, σ = identity; init = glorot_uniform, stride = 1, pad = 0, dilation = 1) where N = - CrossCor(param(init(k..., ch...)), param(zeros(ch[2])), σ, + CrossCor(init(k..., ch...), zeros(ch[2]), σ, stride = stride, pad = pad, dilation = dilation) @treelike CrossCor diff --git a/src/layers/normalise.jl b/src/layers/normalise.jl index 4e82c863..61a62adf 100644 --- a/src/layers/normalise.jl +++ b/src/layers/normalise.jl @@ -1,17 +1,20 @@ -""" - testmode!(m) - testmode!(m, false) +istraining() = false -Put layers like [`Dropout`](@ref) and [`BatchNorm`](@ref) into testing mode -(or back to training mode with `false`). -""" -function testmode!(m, val::Bool=true) - prefor(x -> _testmode!(x, val), m) - return m +@adjoint istraining() = true, _ -> nothing + +_dropout_shape(s, ::Colon) = size(s) +_dropout_shape(s, dims) = tuple((i ∉ dims ? 1 : si for (i, si) ∈ enumerate(size(s)))...) + +_dropout_kernel(y::T, p, q) where {T} = y > p ? T(1 / q) : T(0) + +dropout(x, p; dims = :) = x + +@adjoint function dropout(x, p; dims = :) + y = rand!(similar(x, _dropout_shape(x, dims))) + y .= _dropout_kernel.(y, p, 1 - p) + return x .* y, Δ -> (Δ .* y, nothing) end -_testmode!(m, test) = nothing - """ Dropout(p, dims = :) @@ -19,79 +22,52 @@ A Dropout layer. For each input, either sets that input to `0` (with probability `p`) or scales it by `1/(1-p)`. The `dims` argument is to specified the unbroadcasted dimensions, i.e. `dims=1` does dropout along columns and `dims=2` along rows. This is used as a regularisation, i.e. it reduces overfitting during training. see also [`dropout`](@ref). - -Does nothing to the input once in [`testmode!`](@ref). """ -mutable struct Dropout{F} +mutable struct Dropout{F,D} p::F - dims::Union{Colon, Int, NTuple{N, Int} where N} - active::Bool + dims::D end function Dropout(p; dims = :) @assert 0 ≤ p ≤ 1 - Dropout{typeof(p)}(p, dims, true) + Dropout{typeof(p),typeof(dims)}(p, dims) end -_dropout_shape(s, ::Colon) = size(s) -_dropout_shape(s, dims) = tuple((i ∉ dims ? 1 : si for (i, si) ∈ enumerate(size(s)))...) +(a::Dropout)(x) = dropout(x, a.p; dims = a.dims) -_dropout_kernel(y::T, p, q) where {T} = y > p ? T(1 / q) : T(0) - - -""" - dropout(x, p; dims = :) - -The dropout function. For each input, either sets that input to `0` (with probability -`p`) or scales it by `1/(1-p)`. The `dims` argument is to specified the unbroadcasted - dimensions, i.e. `dims=1` does dropout along columns and `dims=2` along rows. This is - used as a regularisation, i.e. it reduces overfitting during training. -""" -function dropout(x, p; dims = :) - y = similar(x, _dropout_shape(x, dims)) - rand!(y) - y .= _dropout_kernel.(y, p, 1 - p) - return x .* y +function Base.show(io::IO, d::Dropout) + print(io, "Dropout(", d.p) + d.dims != (:) && print(io, ", dims = $(repr(d.dims))") + print(io, ")") end -function (a::Dropout)(x) - a.active || return x - return dropout(x, a.p; dims = a.dims) -end - -_testmode!(a::Dropout, test) = (a.active = !test) - """ AlphaDropout(p) -A dropout layer. It is used in Self-Normalizing Neural Networks. +A dropout layer. It is used in Self-Normalizing Neural Networks. (https://papers.nips.cc/paper/6698-self-normalizing-neural-networks.pdf) The AlphaDropout layer ensures that mean and variance of activations remains the same as before. """ mutable struct AlphaDropout{F} p::F - active::Bool -end - -function AlphaDropout(p) - @assert 0 ≤ p ≤ 1 - AlphaDropout(p,true) + function AlphaDropout(p) + @assert 0 ≤ p ≤ 1 + new{typeof(p)}(p) + end end function (a::AlphaDropout)(x) - a.active || return x + istraining() || return x λ = eltype(x)(1.0507009873554804934193349852946) α = eltype(x)(1.6732632423543772848170429916717) α1 = eltype(x)(-λ*α) noise = randn(eltype(x), size(x)) - x = @. x*(noise > (1 - a.p)) + α1 * (noise <= (1 - a.p)) + x = @. x*(noise > (1 - a.p)) + α1 * (noise < (1 - a.p)) A = (a.p + a.p * (1 - a.p) * α1 ^ 2)^0.5 B = -A * α1 * (1 - a.p) x = @. A * x + B return x end -_testmode!(a::AlphaDropout, test) = (a.active = !test) - """ LayerNorm(h::Integer) @@ -151,25 +127,23 @@ mutable struct BatchNorm{F,V,W,N} σ²::W # moving std ϵ::N momentum::N - active::Bool end BatchNorm(chs::Integer, λ = identity; initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) = - BatchNorm(λ, param(initβ(chs)), param(initγ(chs)), - zeros(chs), ones(chs), ϵ, momentum, true) + BatchNorm(λ, initβ(chs), initγ(chs), + zeros(chs), ones(chs), ϵ, momentum) function (BN::BatchNorm)(x) size(x, ndims(x)-1) == length(BN.β) || error("BatchNorm expected $(length(BN.β)) channels, got $(size(x, ndims(x)-1))") dims = length(size(x)) channels = size(x, dims-1) - affine_shape = ones(Int, dims) - affine_shape[end-1] = channels - m = prod(size(x)[1:end-2]) * size(x)[end] + affine_shape = ntuple(i->i == ndims(x) - 1 ? size(x, i) : 1, ndims(x)) + m = div(prod(size(x)), channels) γ = reshape(BN.γ, affine_shape...) β = reshape(BN.β, affine_shape...) - if !BN.active + if !istraining() μ = reshape(BN.μ, affine_shape...) σ² = reshape(BN.σ², affine_shape...) ϵ = BN.ϵ @@ -178,11 +152,12 @@ function (BN::BatchNorm)(x) axes = [1:dims-2; dims] # axes to reduce along (all but channels axis) μ = mean(x, dims = axes) σ² = sum((x .- μ) .^ 2, dims = axes) ./ m - ϵ = data(convert(T, BN.ϵ)) + ϵ = convert(T, BN.ϵ) # update moving mean/std - mtm = data(convert(T, BN.momentum)) - BN.μ = (1 - mtm) .* BN.μ .+ mtm .* reshape(data(μ), :) - BN.σ² = (1 - mtm) .* BN.σ² .+ (mtm * m / (m - 1)) .* reshape(data(σ²), :) + mtm = BN.momentum + S = eltype(BN.μ) + BN.μ = (1 - mtm) .* BN.μ .+ mtm .* S.(reshape(μ, :)) + BN.σ² = (1 - mtm) .* BN.σ² .+ (mtm * m / (m - 1)) .* S.(reshape(σ², :)) end let λ = BN.λ @@ -192,12 +167,10 @@ function (BN::BatchNorm)(x) end children(BN::BatchNorm) = - (BN.λ, BN.β, BN.γ, BN.μ, BN.σ², BN.ϵ, BN.momentum, BN.active) + (BN.λ, BN.β, BN.γ, BN.μ, BN.σ², BN.ϵ, BN.momentum) mapchildren(f, BN::BatchNorm) = # e.g. mapchildren(cu, BN) - BatchNorm(BN.λ, f(BN.β), f(BN.γ), f(BN.μ), f(BN.σ²), BN.ϵ, BN.momentum, BN.active) - -_testmode!(BN::BatchNorm, test) = (BN.active = !test) + BatchNorm(BN.λ, f(BN.β), f(BN.γ), f(BN.μ), f(BN.σ²), BN.ϵ, BN.momentum) function Base.show(io::IO, l::BatchNorm) print(io, "BatchNorm($(join(size(l.β), ", "))") @@ -244,13 +217,12 @@ mutable struct InstanceNorm{F,V,W,N} σ²::W # moving std ϵ::N momentum::N - active::Bool end InstanceNorm(chs::Integer, λ = identity; initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) = - InstanceNorm(λ, param(initβ(chs)), param(initγ(chs)), - zeros(chs), ones(chs), ϵ, momentum, true) + InstanceNorm(λ, initβ(chs), initγ(chs), + zeros(chs), ones(chs), ϵ, momentum) function (in::InstanceNorm)(x) size(x, ndims(x)-1) == length(in.β) || @@ -261,28 +233,26 @@ function (in::InstanceNorm)(x) dims = length(size(x)) c = size(x, dims-1) bs = size(x, dims) - affine_shape = ones(Int, dims) - affine_shape[end-1] = c - affine_shape[end] = bs - m = prod(size(x)[1:end-2]) + affine_shape = ntuple(i->i == ndims(x) - 1 || i == ndims(x) ? size(x, i) : 1, ndims(x)) + m = div(prod(size(x)), c*bs) γ, β = expand_inst(in.γ, affine_shape), expand_inst(in.β, affine_shape) - if !in.active + if !istraining() μ = expand_inst(in.μ, affine_shape) σ² = expand_inst(in.σ², affine_shape) ϵ = in.ϵ else T = eltype(x) - ϵ = data(convert(T, in.ϵ)) + ϵ = convert(T, in.ϵ) axes = 1:dims-2 # axes to reduce along (all but channels and batch size axes) μ = mean(x, dims = axes) σ² = mean((x .- μ) .^ 2, dims = axes) - + S = eltype(in.μ) # update moving mean/std - mtm = data(convert(T, in.momentum)) - in.μ = dropdims(mean(repeat((1 - mtm) .* in.μ, outer=[1, bs]) .+ mtm .* reshape(data(μ), (c, bs)), dims = 2), dims=2) - in.σ² = dropdims(mean((repeat((1 - mtm) .* in.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (c, bs))), dims = 2), dims=2) + mtm = in.momentum + in.μ = dropdims(mean(repeat((1 - mtm) .* in.μ, outer=[1, bs]) .+ mtm .* S.(reshape(μ, (c, bs))), dims = 2), dims=2) + in.σ² = dropdims(mean((repeat((1 - mtm) .* in.σ², outer=[1, bs]) .+ (mtm * m / (m - 1)) .* S.(reshape(σ², (c, bs)))), dims = 2), dims=2) end let λ = in.λ @@ -292,12 +262,10 @@ function (in::InstanceNorm)(x) end children(in::InstanceNorm) = - (in.λ, in.β, in.γ, in.μ, in.σ², in.ϵ, in.momentum, in.active) + (in.λ, in.β, in.γ, in.μ, in.σ², in.ϵ, in.momentum) mapchildren(f, in::InstanceNorm) = # e.g. mapchildren(cu, in) - InstanceNorm(in.λ, f(in.β), f(in.γ), f(in.μ), f(in.σ²), in.ϵ, in.momentum, in.active) - -_testmode!(in::InstanceNorm, test) = (in.active = !test) + InstanceNorm(in.λ, f(in.β), f(in.γ), f(in.μ), f(in.σ²), in.ϵ, in.momentum) function Base.show(io::IO, l::InstanceNorm) print(io, "InstanceNorm($(join(size(l.β), ", "))") @@ -306,11 +274,11 @@ function Base.show(io::IO, l::InstanceNorm) end """ -Group Normalization. +Group Normalization. This layer can outperform Batch-Normalization and Instance-Normalization. GroupNorm(chs::Integer, G::Integer, λ = identity; - initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), + initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) ``chs`` is the number of channels, the channel dimension of your input. @@ -322,12 +290,11 @@ The number of channels must be an integer multiple of the number of groups. Example: ``` m = Chain(Conv((3,3), 1=>32, leakyrelu;pad = 1), - GroupNorm(32,16)) # 32 channels, 16 groups (G = 16), thus 2 channels per group used + GroupNorm(32,16)) # 32 channels, 16 groups (G = 16), thus 2 channels per group used ``` Link : https://arxiv.org/pdf/1803.08494.pdf """ - mutable struct GroupNorm{F,V,W,N,T} G::T # number of groups λ::F # activation function @@ -337,13 +304,12 @@ mutable struct GroupNorm{F,V,W,N,T} σ²::W # moving std ϵ::N momentum::N - active::Bool end GroupNorm(chs::Integer, G::Integer, λ = identity; initβ = (i) -> zeros(Float32, i), initγ = (i) -> ones(Float32, i), ϵ = 1f-5, momentum = 0.1f0) = - GroupNorm(G, λ, param(initβ(chs)), param(initγ(chs)), - zeros(G,1), ones(G,1), ϵ, momentum, true) + GroupNorm(G, λ, initβ(chs), initγ(chs), + zeros(G,1), ones(G,1), ϵ, momentum) function(gn::GroupNorm)(x) size(x,ndims(x)-1) == length(gn.β) || error("Group Norm expected $(length(gn.β)) channels, but got $(size(x,ndims(x)-1)) channels") @@ -355,20 +321,17 @@ function(gn::GroupNorm)(x) channels = size(x, dims-1) batches = size(x,dims) channels_per_group = div(channels,groups) - affine_shape = ones(Int, dims) + affine_shape = ntuple(i->i == ndims(x) - 1 ? size(x, i) : 1, ndims(x)) # Output reshaped to (W,H...,C/G,G,N) - affine_shape[end-1] = channels - - μ_affine_shape = ones(Int,dims + 1) - μ_affine_shape[end-1] = groups + μ_affine_shape = ntuple(i->i == ndims(x) ? groups : 1, ndims(x) + 1) m = prod(size(x)[1:end-2]) * channels_per_group γ = reshape(gn.γ, affine_shape...) β = reshape(gn.β, affine_shape...) - + y = reshape(x,((size(x))[1:end-2]...,channels_per_group,groups,batches)) - if !gn.active + if !istraining() og_shape = size(x) μ = reshape(gn.μ, μ_affine_shape...) # Shape : (1,1,...C/G,G,1) σ² = reshape(gn.σ², μ_affine_shape...) # Shape : (1,1,...C/G,G,1) @@ -379,31 +342,29 @@ function(gn::GroupNorm)(x) axes = [(1:ndims(y)-2)...] # axes to reduce along (all but channels axis) μ = mean(y, dims = axes) σ² = mean((y .- μ) .^ 2, dims = axes) - - ϵ = data(convert(T, gn.ϵ)) - # update moving mean/std - mtm = data(convert(T, gn.momentum)) - gn.μ = mean((1 - mtm) .* gn.μ .+ mtm .* reshape(data(μ), (groups,batches)),dims=2) - gn.σ² = mean((1 - mtm) .* gn.σ² .+ (mtm * m / (m - 1)) .* reshape(data(σ²), (groups,batches)),dims=2) + ϵ = convert(T, gn.ϵ) + # update moving mean/std + mtm = gn.momentum + S = eltype(gn.μ) + gn.μ = mean((1 - mtm) .* gn.μ .+ mtm .* S.(reshape(μ, (groups,batches))),dims=2) + gn.σ² = mean((1 - mtm) .* gn.σ² .+ (mtm * m / (m - 1)) .* S.(reshape(σ², (groups,batches))),dims=2) end let λ = gn.λ x̂ = (y .- μ) ./ sqrt.(σ² .+ ϵ) - # Reshape x̂ + # Reshape x̂ x̂ = reshape(x̂,og_shape) λ.(γ .* x̂ .+ β) end end children(gn::GroupNorm) = - (gn.λ, gn.β, gn.γ, gn.μ, gn.σ², gn.ϵ, gn.momentum, gn.active) + (gn.λ, gn.β, gn.γ, gn.μ, gn.σ², gn.ϵ, gn.momentum) mapchildren(f, gn::GroupNorm) = # e.g. mapchildren(cu, BN) - GroupNorm(gn.G,gn.λ, f(gn.β), f(gn.γ), f(gn.μ), f(gn.σ²), gn.ϵ, gn.momentum, gn.active) - -_testmode!(gn::GroupNorm, test) = (gn.active = !test) + GroupNorm(gn.G,gn.λ, f(gn.β), f(gn.γ), f(gn.μ), f(gn.σ²), gn.ϵ, gn.momentum) function Base.show(io::IO, l::GroupNorm) print(io, "GroupNorm($(join(size(l.β), ", "))") diff --git a/src/layers/recurrent.jl b/src/layers/recurrent.jl index 61bbec4e..b5eea4a4 100644 --- a/src/layers/recurrent.jl +++ b/src/layers/recurrent.jl @@ -42,21 +42,6 @@ end Base.show(io::IO, m::Recur) = print(io, "Recur(", m.cell, ")") -_truncate(x::AbstractArray) = Tracker.data(x) -_truncate(x::Tuple) = _truncate.(x) - -""" - truncate!(rnn) - -Truncates the gradient of the hidden state in recurrent layers. The value of the -state is preserved. See also `reset!`. - -Assuming you have a `Recur` layer `rnn`, this is roughly equivalent to - - rnn.state = Tracker.data(rnn.state) -""" -truncate!(m) = prefor(x -> x isa Recur && (x.state = _truncate(x.state)), m) - """ reset!(rnn) @@ -83,8 +68,8 @@ end RNNCell(in::Integer, out::Integer, σ = tanh; init = glorot_uniform) = - RNNCell(σ, param(init(out, in)), param(init(out, out)), - param(init(out)), param(zeros(out))) + RNNCell(σ, init(out, in), init(out, out), + init(out), zeros(out)) function (m::RNNCell)(h, x) σ, Wi, Wh, b = m.σ, m.Wi, m.Wh, m.b @@ -122,9 +107,9 @@ end function LSTMCell(in::Integer, out::Integer; init = glorot_uniform) - cell = LSTMCell(param(init(out*4, in)), param(init(out*4, out)), param(init(out*4)), - param(zeros(out)), param(zeros(out))) - cell.b.data[gate(out, 2)] .= 1 + cell = LSTMCell(init(out * 4, in), init(out * 4, out), init(out * 4), + zeros(out), zeros(out)) + cell.b[gate(out, 2)] .= 1 return cell end @@ -168,8 +153,8 @@ mutable struct GRUCell{A,V} end GRUCell(in, out; init = glorot_uniform) = - GRUCell(param(init(out*3, in)), param(init(out*3, out)), - param(init(out*3)), param(zeros(out))) + GRUCell(init(out * 3, in), init(out * 3, out), + init(out * 3), zeros(out)) function (m::GRUCell)(h, x) b, o = m.b, size(h, 1) diff --git a/src/layers/stateless.jl b/src/layers/stateless.jl index 23fd1651..4c216672 100644 --- a/src/layers/stateless.jl +++ b/src/layers/stateless.jl @@ -49,8 +49,3 @@ function normalise(x::AbstractArray; dims=1) σ′ = std(x, dims = dims, mean = μ′, corrected=false) return (x .- μ′) ./ σ′ end - -function normalise(x::AbstractArray, dims) - Base.depwarn("`normalise(x::AbstractArray, dims)` is deprecated, use `normalise(a, dims=dims)` instead.", :normalise) - normalise(x, dims = dims) -end diff --git a/src/onehot.jl b/src/onehot.jl index 8193e3f8..fe93c5c5 100644 --- a/src/onehot.jl +++ b/src/onehot.jl @@ -54,17 +54,19 @@ it will error. ## Examples ```jldoctest +julia> using Flux: onehot + julia> onehot(:b, [:a, :b, :c]) 3-element Flux.OneHotVector: - false - true - false + 0 + 1 + 0 julia> onehot(:c, [:a, :b, :c]) 3-element Flux.OneHotVector: - false - false - true + 0 + 0 + 1 ``` """ function onehot(l, labels) @@ -88,12 +90,13 @@ Create an [`OneHotMatrix`](@ref) with a batch of labels based on possible `label ## Examples ```jldoctest -julia> onehotbatch([:b, :a, :b], [:a, :b, :c]) -3×3 Flux.OneHotMatrix: - false true false - true false true - false false false +julia> using Flux: onehotbatch +julia> onehotbatch([:b, :a, :b], [:a, :b, :c]) +3×3 Flux.OneHotMatrix{Array{Flux.OneHotVector,1}}: + 0 1 0 + 1 0 1 + 0 0 0 ``` """ onehotbatch(ls, labels, unk...) = @@ -106,9 +109,9 @@ Base.argmax(xs::OneHotVector) = xs.ix Inverse operations of [`onehot`](@ref). -## Examples - ```jldoctest +julia> using Flux: onecold + julia> onecold([true, false, false], [:a, :b, :c]) :a @@ -124,15 +127,6 @@ onecold(y::AbstractMatrix, labels...) = onecold(y::OneHotMatrix, labels...) = mapreduce(x -> Flux.onecold(x, labels...), |, y.data, dims = 2, init = 0) -function argmax(xs...) - Base.depwarn("`argmax(...)` is deprecated, use `onecold(...)` instead.", :argmax) - return onecold(xs...) -end - -# Ambiguity hack - -a::TrackedMatrix * b::OneHotVector = invoke(*, Tuple{AbstractMatrix,OneHotVector}, a, b) -a::TrackedMatrix * b::OneHotMatrix = invoke(*, Tuple{AbstractMatrix,OneHotMatrix}, a, b) - -onecold(x::TrackedVector, l...) = onecold(data(x), l...) -onecold(x::TrackedMatrix, l...) = onecold(data(x), l...) +# TODO probably still want this as a custom adjoint Zygote +# onecold(x::TrackedVector, l...) = onecold(data(x), l...) +# onecold(x::TrackedMatrix, l...) = onecold(data(x), l...) diff --git a/src/optimise/Optimise.jl b/src/optimise/Optimise.jl index 9a210316..68c18a6f 100644 --- a/src/optimise/Optimise.jl +++ b/src/optimise/Optimise.jl @@ -7,6 +7,5 @@ export train!, include("optimisers.jl") include("train.jl") -include("deprecations.jl") end diff --git a/src/optimise/deprecations.jl b/src/optimise/deprecations.jl deleted file mode 100644 index 26e127dc..00000000 --- a/src/optimise/deprecations.jl +++ /dev/null @@ -1,126 +0,0 @@ -using Base: depwarn -using Flux: Params - -check_decay(opt, decay) = decay == 0 ? opt : Optimiser(opt, InvDecay(decay)) - -# legacy update rule -updaterule(opt, ps) = () -> _update_params!(opt, ps) - -function SGD(params::Union{AbstractArray, Params}, η = 0.1; decay = 0.) - depwarn("SGD(params) is deprecated; use Descent(η::Float64) instead", :SGD) - - ps = params - opt = Descent(η) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function Momentum(params::Union{AbstractArray, Params}, η = 0.01; ρ = 0.9, decay = 0.) - depwarn("Momentum(params) is deprecated; use Momentum(η::Float64) instead", :Momentum) - - ps = params - opt = Momentum(η, ρ) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function Nesterov(params::Union{AbstractArray, Params}, η = 0.001; ρ = 0.9, decay = 0.) - depwarn("Nesterov(params) is deprecated; use Nesterov(η::Float64) instead", :Nesterov) - - ps = params - opt = Nesterov(η, ρ) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function RMSProp(params::Union{AbstractArray, Params}, η = 0.001; ρ = 0.9, decay = 0.) - depwarn("RMSProp(params) is deprecated; use RMSProp(η::Float64) instead", :RMSProp) - - ps = params - opt = RMSProp(η, ρ) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function ADAM(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.) - depwarn("ADAM(params) is deprecated; use ADAM(η::Float64) instead", :ADAM) - - ps = params - β = (β1, β2) - opt = ADAM(η, β) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function ADAGrad(params::Union{AbstractArray, Params}, η::Float64 = 0.1; decay = 0.) - depwarn("ADAGrad(params) is deprecated; use ADAGrad(η::Float64) instead", :ADAGrad) - - ps = params - opt = ADAGrad(η) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function ADADelta(params::Union{AbstractArray, Params}, ρ::Float64 = 0.9; decay = 0.) - depwarn("ADADelta(params) is deprecated; use ADADelta(η::Float64) instead", :ADADelta) - - ps = params - opt = ADADelta(ρ) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function AdaMax(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.) - depwarn("AdaMax(params) is deprecated; use AdaMax(η::Float64) instead", :AdaMax) - - ps = params - β = (β1, β2) - opt = AdaMax(η, β) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function AMSGrad(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.) - depwarn("AMSGrad(params) is deprecated; use AMSGrad(η::Float64) instead", :AMSGrad) - - ps = params - β = (β1, β2) - opt = AMSGrad(η, β) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function NADAM(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.) - depwarn("NADAM(params) is deprecated; use NADAM(η::Float64) instead", :NADAM) - - ps = params - β = (β1, β2) - opt = NADAM(η, β) - opt = check_decay(opt, decay) - updaterule(opt, ps) -end - -function ADAMW(params::Union{AbstractArray, Params}, η = 0.001; β1 = 0.9, β2 = 0.999, decay = 0.) - depwarn("ADAMW(params) is deprecated; use ADAMW(η::Float64) instead", :ADAMW) - - ps = params - β = (β1, β2) - opt = ADAMW(η, β) - opt = check_decay(opt, decay) - decay != 0 && (opt = Optimiser(opt, WeightDecay(decay))) - updaterule(opt, ps) -end - -# Old training loop - -struct OldOptimiser - func -end - -_update_params!(opt::OldOptimiser, ps) = opt.func() - -# Train function -function train!(loss, data, opt; cb = () -> ()) - depwarn("train!(loss, data, opt) is deprecated; use train!(loss, params, data, opt) instead", :train!) - train!(loss, (), data, OldOptimiser(opt); cb = cb) -end diff --git a/src/optimise/optimisers.jl b/src/optimise/optimisers.jl index a3f4cdbd..58cd5ff7 100644 --- a/src/optimise/optimisers.jl +++ b/src/optimise/optimisers.jl @@ -37,7 +37,7 @@ Momentum(η = 0.01, ρ = 0.9) = Momentum(η, ρ, IdDict()) function apply!(o::Momentum, x, Δ) η, ρ = o.eta, o.rho - v = get!(o.velocity, x, zero(x))::typeof(data(x)) + v = get!(o.velocity, x, zero(x))::typeof(x) @. v = ρ * v - η * Δ @. Δ = -v end @@ -57,7 +57,7 @@ Nesterov(η = 0.001, ρ = 0.9) = Nesterov(η, ρ, IdDict()) function apply!(o::Nesterov, x, Δ) η, ρ = o.eta, o.rho - v = get!(o.velocity, x, zero(x))::typeof(data(x)) + v = get!(o.velocity, x, zero(x))::typeof(x) d = @. ρ^2 * v - (1+ρ) * η * Δ @. v = ρ*v - η*Δ @. Δ = -d @@ -80,7 +80,7 @@ RMSProp(η = 0.001, ρ = 0.9) = RMSProp(η, ρ, IdDict()) function apply!(o::RMSProp, x, Δ) η, ρ = o.eta, o.rho - acc = get!(o.acc, x, zero(x))::typeof(data(x)) + acc = get!(o.acc, x, zero(x))::typeof(x) @. acc = ρ * acc + (1 - ρ) * Δ^2 @. Δ *= η / (√acc + ϵ) end @@ -177,7 +177,7 @@ ADAGrad(η = 0.1) = ADAGrad(η, IdDict()) function apply!(o::ADAGrad, x, Δ) η = o.eta - acc = get!(o.acc, x, fill(ϵ, size(x)))::typeof(data(x)) + acc = get!(o.acc, x, fill(ϵ, size(x)))::typeof(x) @. acc += Δ^2 @. Δ *= η / (√acc + ϵ) end @@ -352,5 +352,5 @@ WeightDecay() = WeightDecay(0) function apply!(o::WeightDecay, x, Δ) wd = o.wd - @. Δ += wd * data(x) + @. Δ += wd * x end diff --git a/src/optimise/train.jl b/src/optimise/train.jl index ab8be578..ae0f334c 100644 --- a/src/optimise/train.jl +++ b/src/optimise/train.jl @@ -1,32 +1,29 @@ using Juno -import Flux.Tracker: Params, gradient, data, update! -import Base.depwarn +import Zygote: Params, gradient + +function update!(x::AbstractArray, x̄) + x .+= x̄ + return x +end function update!(opt, x, x̄) - update!(x, -apply!(opt, x, data(x̄))) + x .-= apply!(opt, x, x̄) end function update!(opt, xs::Params, gs) for x in xs + gs[x] == nothing && continue update!(opt, x, gs[x]) end end -# Added as an internal API but everyone started using it. -function _update_params!(opt, xs) - depwarn("`_update_params!` is deprecated, use `update!` instead.", :stop) - for x in xs - update!(opt, x, Tracker.grad(x)) - x.tracker.grad = Tracker.zero_grad!(x.tracker.grad) - end -end - # Callback niceties call(f, xs...) = f(xs...) runall(f) = f runall(fs::AbstractVector) = () -> foreach(call, fs) struct StopException <: Exception end + """ stop() @@ -72,10 +69,7 @@ function train!(loss, ps, data, opt; cb = () -> ()) loss(d...) end update!(opt, ps, gs) - if cb() == :stop - depwarn("Use of `:stop` is deprecated; use `Flux.stop()` instead", :stop) - break - end + cb() catch ex if ex isa StopException break diff --git a/src/treelike.jl b/src/treelike.jl index 2ca6d614..42b10f23 100644 --- a/src/treelike.jl +++ b/src/treelike.jl @@ -1,5 +1,5 @@ import Adapt: adapt, adapt_storage -import .Tracker: IdSet +import Zygote: IdSet children(x) = () mapchildren(f, x) = x @@ -40,7 +40,7 @@ end function params(m) ps = Params() prefor(p -> - Tracker.istracked(p) && Tracker.isleaf(p) && + p isa AbstractArray{<:Real} && !any(p′ -> p′ === p, ps) && push!(ps, p), m) return ps @@ -52,7 +52,7 @@ function loadparams!(m, xs) for (p, x) in zip(params(m), xs) size(p) == size(x) || error("Expected param size $(size(p)), got $(size(x))") - copyto!(data(p), data(x)) + copyto!(p, x) end end @@ -81,8 +81,6 @@ f64(m) = paramtype(Float64, m) function mapparams(f, m) mapleaves(m) do x - Tracker.istracked(x) ? param(f(Tracker.data(x))) : - x isa Union{AbstractArray,Number} ? f(x) : - x + x isa Union{AbstractArray,Number} ? f(x) : x end end diff --git a/test/cuda/cuda.jl b/test/cuda/cuda.jl index 96d04c28..3508e561 100644 --- a/test/cuda/cuda.jl +++ b/test/cuda/cuda.jl @@ -1,4 +1,4 @@ -using Flux, Flux.Tracker, CuArrays, Test +using Flux, CuArrays, Test using Flux: gpu @info "Testing GPU Support" @@ -7,11 +7,11 @@ using Flux: gpu CuArrays.allowscalar(false) -x = param(randn(5, 5)) +x = randn(5, 5) cx = gpu(x) -@test cx isa TrackedArray && cx.data isa CuArray +@test cx isa CuArray -@test Flux.onecold(param(gpu([1.,2.,3.]))) == 3 +@test Flux.onecold(gpu([1.0, 2.0, 3.0])) == 3 x = Flux.onehotbatch([1, 2, 3], 1:3) cx = gpu(x) @@ -21,24 +21,26 @@ cx = gpu(x) m = Chain(Dense(10, 5, tanh), Dense(5, 2), softmax) cm = gpu(m) -@test all(p isa TrackedArray && p.data isa CuArray for p in params(cm)) -@test cm(gpu(rand(10, 10))) isa TrackedArray{Float32,2,CuArray{Float32,2}} +@test all(p isa CuArray for p in params(cm)) +@test cm(gpu(rand(10, 10))) isa CuArray{Float32,2} x = [1,2,3] cx = gpu(x) @test Flux.crossentropy(x,x) ≈ Flux.crossentropy(cx,cx) -xs = param(rand(5,5)) +xs = rand(5, 5) ys = Flux.onehotbatch(1:5,1:5) @test collect(cu(xs) .+ cu(ys)) ≈ collect(xs .+ ys) c = gpu(Conv((2,2),3=>4)) +x = gpu(rand(10, 10, 3, 2)) l = c(gpu(rand(10,10,3,2))) -Flux.back!(sum(l)) +@test gradient(x -> sum(c(x)), x)[1] isa CuArray c = gpu(CrossCor((2,2),3=>4)) +x = gpu(rand(10, 10, 3, 2)) l = c(gpu(rand(10,10,3,2))) -Flux.back!(sum(l)) +@test gradient(x -> sum(c(x)), x)[1] isa CuArray end diff --git a/test/cuda/cudnn.jl b/test/cuda/cudnn.jl index 9a154961..a7fc244e 100644 --- a/test/cuda/cudnn.jl +++ b/test/cuda/cudnn.jl @@ -1,48 +1,44 @@ -using Flux, Flux.Tracker, CuArrays, Test -using Flux.Tracker: TrackedArray, data +using Flux, CuArrays, Test +using Flux: forward @testset "CUDNN BatchNorm" begin @testset "4D Input" begin - x = TrackedArray(Float64.(collect(reshape(1:12, 2, 2, 3, 1)))) + x = Float64.(collect(reshape(1:12, 2, 2, 3, 1))) m = BatchNorm(3) cx = gpu(x) cm = gpu(m) - y = m(x) - cy = cm(cx) + y, back = forward((m, x) -> m(x), m, x) + cy, cback = forward((m, x) -> m(x), cm, cx) - @test cy isa TrackedArray{Float32,4,CuArray{Float32,4}} + @test cpu(cy) ≈ y - @test cpu(data(cy)) ≈ data(y) + Δ = randn(size(y)) + dm, dx = back(Δ) + cdm, cdx = cback(gpu(Δ)) - g = rand(size(y)...) - Flux.back!(y, g) - Flux.back!(cy, gpu(g)) - - @test m.γ.grad ≈ cpu(cm.γ.grad) - @test m.β.grad ≈ cpu(cm.β.grad) - @test x.grad ≈ cpu(x.grad) + @test dm[].γ ≈ cpu(cdm[].γ) + @test dm[].β ≈ cpu(cdm[].β) + @test dx ≈ cpu(cdx) end @testset "2D Input" begin - x = TrackedArray(Float64.(collect(reshape(1:12, 3, 4)))) + x = Float64.(collect(reshape(1:12, 3, 4))) m = BatchNorm(3) cx = gpu(x) cm = gpu(m) - y = m(x) - cy = cm(cx) + y, back = forward((m, x) -> m(x), m, x) + cy, cback = forward((m, x) -> m(x), cm, cx) - @test cy isa TrackedArray{Float32,2,CuArray{Float32,2}} + @test cpu(cy) ≈ y - @test cpu(data(cy)) ≈ data(y) + Δ = randn(size(y)) + dm, dx = back(Δ) + cdm, cdx = cback(gpu(Δ)) - g = rand(size(y)...) - Flux.back!(y, g) - Flux.back!(cy, gpu(g)) - - @test m.γ.grad ≈ cpu(cm.γ.grad) - @test m.β.grad ≈ cpu(cm.β.grad) - @test x.grad ≈ cpu(x.grad) + @test dm[].γ ≈ cpu(cdm[].γ) + @test dm[].β ≈ cpu(cdm[].β) + @test dx ≈ cpu(cdx) end end diff --git a/test/cuda/curnn.jl b/test/cuda/curnn.jl index 3f5e1819..c1bc804e 100644 --- a/test/cuda/curnn.jl +++ b/test/cuda/curnn.jl @@ -1,46 +1,54 @@ using Flux, CuArrays, Test +using Flux: forward @testset "RNN" begin - @testset for R in [RNN, GRU, LSTM] + @testset for R in [RNN, GRU, LSTM], batch_size in (1, 5) rnn = R(10, 5) curnn = mapleaves(gpu, rnn) - @testset for batch_size in (1, 5) - Flux.reset!(rnn) - Flux.reset!(curnn) - x = batch_size == 1 ? - param(rand(10)) : - param(rand(10,batch_size)) - cux = gpu(x) - y = (rnn(x); rnn(x)) - cuy = (curnn(cux); curnn(cux)) - @test y.data ≈ collect(cuy.data) - @test haskey(Flux.CUDA.descs, curnn.cell) + Flux.reset!(rnn) + Flux.reset!(curnn) + x = batch_size == 1 ? + rand(10) : + rand(10, batch_size) + cux = gpu(x) - Δ = randn(size(y)) + y, back = forward((r, x) -> (r(x)), rnn, x) + cuy, cuback = forward((r, x) -> (r(x)), curnn, cux) - Flux.back!(y, Δ) - Flux.back!(cuy, gpu(Δ)) + @test y ≈ collect(cuy) + @test haskey(Flux.CUDA.descs, curnn.cell) - @test x.grad ≈ collect(cux.grad) - @test rnn.cell.Wi.grad ≈ collect(curnn.cell.Wi.grad) - @test rnn.cell.Wh.grad ≈ collect(curnn.cell.Wh.grad) - @test rnn.cell.b.grad ≈ collect(curnn.cell.b.grad) - @test rnn.cell.h.grad ≈ collect(curnn.cell.h.grad) - if isdefined(rnn.cell, :c) - @test rnn.cell.c.grad ≈ collect(curnn.cell.c.grad) + ȳ = randn(size(y)) + m̄, x̄ = back(ȳ) + cum̄, cux̄ = cuback(gpu(ȳ)) + + m̄[].cell[].Wi + + m̄[].state + cum̄[].state + + @test x̄ ≈ collect(cux̄) + @test m̄[].cell[].Wi ≈ collect(cum̄[].cell[].Wi) + @test m̄[].cell[].Wh ≈ collect(cum̄[].cell[].Wh) + @test m̄[].cell[].b ≈ collect(cum̄[].cell[].b) + if m̄[].state isa Tuple + for (x, cx) in zip(m̄[].state, cum̄[].state) + @test x ≈ collect(cx) end - - Flux.reset!(rnn) - Flux.reset!(curnn) - ohx = batch_size == 1 ? - Flux.onehot(rand(1:10), 1:10) : - Flux.onehotbatch(rand(1:10, batch_size), 1:10) - cuohx = gpu(ohx) - y = (rnn(ohx); rnn(ohx)) - cuy = (curnn(cuohx); curnn(cuohx)) - - @test y.data ≈ collect(cuy.data) + else + @test m̄[].state ≈ collect(cum̄[].state) end + + Flux.reset!(rnn) + Flux.reset!(curnn) + ohx = batch_size == 1 ? + Flux.onehot(rand(1:10), 1:10) : + Flux.onehotbatch(rand(1:10, batch_size), 1:10) + cuohx = gpu(ohx) + y = (rnn(ohx); rnn(ohx)) + cuy = (curnn(cuohx); curnn(cuohx)) + + @test y ≈ collect(cuy) end end diff --git a/test/layers/conv.jl b/test/layers/conv.jl index 5b2e2392..aa3925f1 100644 --- a/test/layers/conv.jl +++ b/test/layers/conv.jl @@ -25,9 +25,9 @@ end @testset "asymmetric padding" begin r = ones(Float32, 28, 28, 1, 1) m = Conv((3, 3), 1=>1, relu; pad=(0,1,1,2)) - m.weight.data[:] .= 1.0 - m.bias.data[:] .= 0.0 - y_hat = Flux.data(m(r))[:,:,1,1] + m.weight[:] .= 1.0 + m.bias[:] .= 0.0 + y_hat = m(r)[:,:,1,1] @test size(y_hat) == (27, 29) @test y_hat[1, 1] ≈ 6.0 @test y_hat[2, 2] ≈ 9.0 @@ -41,7 +41,7 @@ end r = zeros(Float32, 28, 28, 3, 5) m1 = DepthwiseConv((2, 2), 3=>15) @test size(m1(r), 3) == 15 - + m3 = DepthwiseConv((2, 3), 3=>9) @test size(m3(r), 3) == 9 @@ -62,7 +62,7 @@ end y = CrossCor(w, [0.0]) @test sum(w .* x[1:2, 1:2, :, :]) == y(x)[1, 1, 1, 1] - + r = zeros(Float32, 28, 28, 1, 5) m = Chain( CrossCor((2, 2), 1=>16, relu), @@ -102,4 +102,3 @@ end true end end - diff --git a/test/layers/normalisation.jl b/test/layers/normalisation.jl index 72c2d52b..cda0cc59 100644 --- a/test/layers/normalisation.jl +++ b/test/layers/normalisation.jl @@ -1,29 +1,29 @@ -using Flux: testmode! -using Flux.Tracker: data +using Flux, Test, Statistics +using Zygote: forward + +trainmode(f, x...) = forward(f, x...)[1] +trainmode(f) = (x...) -> trainmode(f, x...) @testset "Dropout" begin x = [1.,2.,3.] - @test x == testmode!(Dropout(0.1))(x) - @test x == Dropout(0)(x) - @test zero(x) == Dropout(1)(x) + @test x == Dropout(0.1)(x) + @test x == trainmode(Dropout(0), x) + @test zero(x) == trainmode(Dropout(1), x) x = rand(100) m = Dropout(0.9) - y = m(x) + y = trainmode(m, x) @test count(a->a==0, y) > 50 - testmode!(m) y = m(x) @test count(a->a==0, y) == 0 - testmode!(m, false) - y = m(x) + y = trainmode(m, x) @test count(a->a==0, y) > 50 - x = rand(100) + x = rand(Float32, 100) m = Chain(Dense(100,100), Dropout(0.9)) - y = m(x) + y = trainmode(m, x) @test count(a->a == 0, y) > 50 - testmode!(m) y = m(x) @test count(a->a == 0, y) == 0 @@ -39,18 +39,16 @@ using Flux.Tracker: data end @testset "BatchNorm" begin - let m = BatchNorm(2), x = param([1 3 5; - 2 4 6]) + let m = BatchNorm(2), x = [1.0 3.0 5.0; + 2.0 4.0 6.0] - @test m.β.data == [0, 0] # initβ(2) - @test m.γ.data == [1, 1] # initγ(2) + @test m.β == [0, 0] # initβ(2) + @test m.γ == [1, 1] # initγ(2) # initial m.σ is 1 # initial m.μ is 0 - @test m.active - - # @test m(x).data ≈ [-1 -1; 0 0; 1 1]' - m(x) + y = trainmode(m, x) + @test isapprox(y, [-1.22474 0 1.22474; -1.22474 0 1.22474], atol = 1.0e-5) # julia> x # 2×3 Array{Float64,2}: # 1.0 3.0 5.0 @@ -69,41 +67,32 @@ end # 2×1 Array{Float64,2}: # 1.3 # 1.3 - @test m.σ² ≈ .1 .* var(x.data, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.] + @test m.σ² ≈ .1 .* var(x, dims = 2, corrected=false) .* (3 / 2).+ .9 .* [1., 1.] - testmode!(m) - @test !m.active - - x′ = m(x).data + x′ = m(x) @test isapprox(x′[1], (1 .- 0.3) / sqrt(1.3), atol = 1.0e-5) end # with activation function - let m = BatchNorm(2, sigmoid), x = param([1 3 5; - 2 4 6]) - @test m.active - m(x) - - testmode!(m) - @test !m.active - - y = m(x).data - @test isapprox(y, data(sigmoid.((x .- m.μ) ./ sqrt.(m.σ² .+ m.ϵ))), atol = 1.0e-7) + let m = BatchNorm(2, sigmoid), x = [1.0 3.0 5.0; + 2.0 4.0 6.0] + y = m(x) + @test isapprox(y, sigmoid.((x .- m.μ) ./ sqrt.(m.σ² .+ m.ϵ)), atol = 1.0e-7) end - let m = BatchNorm(2), x = param(reshape(1:6, 3, 2, 1)) + let m = trainmode(BatchNorm(2)), x = reshape(Float32.(1:6), 3, 2, 1) y = reshape(permutedims(x, [2, 1, 3]), 2, :) y = permutedims(reshape(m(y), 2, 3, 1), [2, 1, 3]) @test m(x) == y end - let m = BatchNorm(2), x = param(reshape(1:12, 2, 3, 2, 1)) + let m = trainmode(BatchNorm(2)), x = reshape(Float32.(1:12), 2, 3, 2, 1) y = reshape(permutedims(x, [3, 1, 2, 4]), 2, :) y = permutedims(reshape(m(y), 2, 2, 3, 1), [2, 3, 1, 4]) @test m(x) == y end - let m = BatchNorm(2), x = param(reshape(1:24, 2, 2, 3, 2, 1)) + let m = trainmode(BatchNorm(2)), x = reshape(Float32.(1:24), 2, 2, 3, 2, 1) y = reshape(permutedims(x, [4, 1, 2, 3, 5]), 2, :) y = permutedims(reshape(m(y), 2, 2, 2, 3, 1), [2, 3, 4, 1, 5]) @test m(x) == y @@ -115,20 +104,16 @@ end end end - @testset "InstanceNorm" begin # helper functions expand_inst = (x, as) -> reshape(repeat(x, outer=[1, as[length(as)]]), as...) # begin tests let m = InstanceNorm(2), sizes = (3, 2, 2), - x = param(reshape(collect(1:prod(sizes)), sizes)) - - @test m.β.data == [0, 0] # initβ(2) - @test m.γ.data == [1, 1] # initγ(2) - - @test m.active - - m(x) + x = reshape(collect(1:prod(sizes)), sizes) + x = Float64.(x) + @test m.β == [0, 0] # initβ(2) + @test m.γ == [1, 1] # initγ(2) + y = trainmode(m, x) #julia> x #[:, :, 1] = @@ -153,37 +138,28 @@ end # (1. - .1) * 0 + .1 * (5. + 11.) / 2 = .8 @test m.μ ≈ [0.5, 0.8] # momentum * var * num_items / (num_items - 1) + (1 - momentum) * sigma_sq - # julia> reshape(mean(.1 .* var(x.data, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1. + # julia> reshape(mean(.1 .* var(x, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1. # 2-element Array{Float64,1}: # 1. # 1. - @test m.σ² ≈ reshape(mean(.1 .* var(x.data, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1. + @test m.σ² ≈ reshape(mean(.1 .* var(x, dims = 1, corrected=false) .* (3 / 2), dims=3), :) .+ .9 .* 1. - testmode!(m) - @test !m.active - - x′ = m(x).data + x′ = m(x) @test isapprox(x′[1], (1 - 0.5) / sqrt(1. + 1f-5), atol = 1.0e-5) end # with activation function let m = InstanceNorm(2, sigmoid), sizes = (3, 2, 2), - x = param(reshape(collect(1:prod(sizes)), sizes)) - + x = reshape(collect(1:prod(sizes)), sizes) + x = Float64.(x) affine_shape = collect(sizes) affine_shape[1] = 1 - @test m.active - m(x) - - testmode!(m) - @test !m.active - - y = m(x).data - @test isapprox(y, data(sigmoid.((x .- expand_inst(m.μ, affine_shape)) ./ sqrt.(expand_inst(m.σ², affine_shape) .+ m.ϵ))), atol = 1.0e-7) + y = m(x) + @test isapprox(y, sigmoid.((x .- expand_inst(m.μ, affine_shape)) ./ sqrt.(expand_inst(m.σ², affine_shape) .+ m.ϵ)), atol = 1.0e-7) end - let m = InstanceNorm(2), sizes = (2, 4, 1, 2, 3), - x = param(reshape(collect(1:prod(sizes)), sizes)) + let m = trainmode(InstanceNorm(2)), sizes = (2, 4, 1, 2, 3), + x = Float32.(reshape(collect(1:prod(sizes)), sizes)) y = reshape(permutedims(x, [3, 1, 2, 4, 5]), :, 2, 3) y = reshape(m(y), sizes...) @test m(x) == y @@ -191,16 +167,16 @@ end # check that μ, σ², and the output are the correct size for higher rank tensors let m = InstanceNorm(2), sizes = (5, 5, 3, 4, 2, 6), - x = param(reshape(collect(1:prod(sizes)), sizes)) - y = m(x) + x = reshape(Float32.(collect(1:prod(sizes))), sizes) + y = trainmode(m, x) @test size(m.μ) == (sizes[end - 1], ) @test size(m.σ²) == (sizes[end - 1], ) @test size(y) == sizes end # show that instance norm is equal to batch norm when channel and batch dims are squashed - let m_inorm = InstanceNorm(2), m_bnorm = BatchNorm(12), sizes = (5, 5, 3, 4, 2, 6), - x = param(reshape(collect(1:prod(sizes)), sizes)) + let m_inorm = trainmode(InstanceNorm(2)), m_bnorm = trainmode(BatchNorm(12)), sizes = (5, 5, 3, 4, 2, 6), + x = reshape(Float32.(collect(1:prod(sizes))), sizes) @test m_inorm(x) == reshape(m_bnorm(reshape(x, (sizes[1:end - 2]..., :, 1))), sizes) end @@ -216,14 +192,12 @@ end squeeze(x) = dropdims(x, dims = tuple(findall(size(x) .== 1)...)) # To remove all singular dimensions let m = GroupNorm(4,2), sizes = (3,4,2), - x = param(reshape(collect(1:prod(sizes)), sizes)) + x = reshape(collect(1:prod(sizes)), sizes) + x = Float64.(x) + @test m.β == [0, 0, 0, 0] # initβ(32) + @test m.γ == [1, 1, 1, 1] # initγ(32) - @test m.β.data == [0, 0, 0, 0] # initβ(32) - @test m.γ.data == [1, 1, 1, 1] # initγ(32) - - @test m.active - - m(x) + y = trainmode(m, x) #julia> x #[:, :, 1] = @@ -243,7 +217,7 @@ end # (13. + 14. + 15. + 16. + 17. + 18.) / 6 = 15.5 # (19. + 20. + 21. + 22. + 23. + 24.) / 6 = 21.5 # - # μ = + # μ = # 3.5 15.5 # 9.5 21.5 # @@ -253,46 +227,37 @@ end @test m.μ ≈ [0.95, 1.55] # julia> mean(var(reshape(x,3,2,2,2),dims=(1,2)).* .1,dims=2) .+ .9*1. - # 2-element Array{Tracker.TrackedReal{Float64},1}: + # 2-element Array{Float64,1}: # 1.25 # 1.25 @test m.σ² ≈ mean(squeeze(var(reshape(x,3,2,2,2),dims=(1,2))).*.1,dims=2) .+ .9*1. - testmode!(m) - @test !m.active - - x′ = m(x).data + x′ = m(x) @test isapprox(x′[1], (1 - 0.95) / sqrt(1.25 + 1f-5), atol = 1.0e-5) end # with activation function let m = GroupNorm(4,2, sigmoid), sizes = (3, 4, 2), - x = param(reshape(collect(1:prod(sizes)), sizes)) - + x = reshape(collect(1:prod(sizes)), sizes) + x = Float64.(x) μ_affine_shape = ones(Int,length(sizes) + 1) μ_affine_shape[end-1] = 2 # Number of groups affine_shape = ones(Int,length(sizes) + 1) - affine_shape[end-2] = 2 # Channels per group + affine_shape[end-2] = 2 # Channels per group affine_shape[end-1] = 2 # Number of groups affine_shape[1] = sizes[1] affine_shape[end] = sizes[end] og_shape = size(x) - @test m.active - m(x) - - testmode!(m) - @test !m.active - y = m(x) x_ = reshape(x,affine_shape...) - out = reshape(data(sigmoid.((x_ .- reshape(m.μ,μ_affine_shape...)) ./ sqrt.(reshape(m.σ²,μ_affine_shape...) .+ m.ϵ))),og_shape) + out = reshape(sigmoid.((x_ .- reshape(m.μ,μ_affine_shape...)) ./ sqrt.(reshape(m.σ²,μ_affine_shape...) .+ m.ϵ)),og_shape) @test isapprox(y, out, atol = 1.0e-7) end - let m = GroupNorm(2,2), sizes = (2, 4, 1, 2, 3), - x = param(reshape(collect(1:prod(sizes)), sizes)) + let m = trainmode(GroupNorm(2,2)), sizes = (2, 4, 1, 2, 3), + x = Float32.(reshape(collect(1:prod(sizes)), sizes)) y = reshape(permutedims(x, [3, 1, 2, 4, 5]), :, 2, 3) y = reshape(m(y), sizes...) @test m(x) == y @@ -300,22 +265,22 @@ end # check that μ, σ², and the output are the correct size for higher rank tensors let m = GroupNorm(4,2), sizes = (5, 5, 3, 4, 4, 6), - x = param(reshape(collect(1:prod(sizes)), sizes)) - y = m(x) + x = Float32.(reshape(collect(1:prod(sizes)), sizes)) + y = trainmode(m, x) @test size(m.μ) == (m.G,1) @test size(m.σ²) == (m.G,1) @test size(y) == sizes end # show that group norm is the same as instance norm when the group size is the same as the number of channels - let IN = InstanceNorm(4), GN = GroupNorm(4,4), sizes = (2,2,3,4,5), - x = param(reshape(collect(1:prod(sizes)), sizes)) + let IN = trainmode(InstanceNorm(4)), GN = trainmode(GroupNorm(4,4)), sizes = (2,2,3,4,5), + x = Float32.(reshape(collect(1:prod(sizes)), sizes)) @test IN(x) ≈ GN(x) end # show that group norm is the same as batch norm for a group of size 1 and batch of size 1 - let BN = BatchNorm(4), GN = GroupNorm(4,4), sizes = (2,2,3,4,1), - x = param(reshape(collect(1:prod(sizes)), sizes)) + let BN = trainmode(BatchNorm(4)), GN = trainmode(GroupNorm(4,4)), sizes = (2,2,3,4,1), + x = Float32.(reshape(collect(1:prod(sizes)), sizes)) @test BN(x) ≈ GN(x) end diff --git a/test/layers/stateless.jl b/test/layers/stateless.jl index 34abb8cb..b853fc19 100644 --- a/test/layers/stateless.jl +++ b/test/layers/stateless.jl @@ -51,13 +51,13 @@ const ϵ = 1e-7 end @testset "no spurious promotions" begin - for T in (Float16, Float32, Float64) + for T in (Float32, Float64) y = rand(T, 2) ŷ = rand(T, 2) for f in (mse, crossentropy, logitcrossentropy) - fwd, back = Flux.Tracker.forward(mse, ŷ, y) - @test typeof(fwd) == Flux.Tracker.TrackedReal{T} - @test eltype(back(one(T))[1]) == Flux.Tracker.TrackedReal{T} + fwd, back = Flux.forward(f, ŷ, y) + @test fwd isa T + @test eltype(back(one(T))[1]) == T end end end diff --git a/test/optimise.jl b/test/optimise.jl index 784d3f9d..ac131b96 100644 --- a/test/optimise.jl +++ b/test/optimise.jl @@ -1,42 +1,44 @@ using Flux.Optimise using Flux.Optimise: runall -using Flux.Tracker +using Flux: Params, gradient using Test + @testset "Optimise" begin w = randn(10, 10) @testset for opt in [ADAMW(), ADAGrad(0.1), AdaMax(), ADADelta(0.9), AMSGrad(), NADAM(), RADAM(), Descent(0.1), ADAM(), Nesterov(), RMSProp(), Momentum()] - w′ = param(randn(10, 10)) + w′ = randn(10, 10) loss(x) = Flux.mse(w*x, w′*x) for t = 1: 10^5 θ = Params([w′]) - θ̄ = gradient(() -> loss(rand(10)), θ) + x = rand(10) + θ̄ = gradient(() -> loss(x), θ) Optimise.update!(opt, θ, θ̄) end - @test Flux.mse(w, w′) < 0.01 + @test loss(rand(10, 10)) < 0.01 end end @testset "Optimiser" begin w = randn(10, 10) @testset for Opt in [InvDecay, WeightDecay, ExpDecay] - w′ = param(randn(10, 10)) + w′ = randn(10, 10) loss(x) = Flux.mse(w*x, w′*x) opt = Optimiser(Opt(), ADAM(0.001)) for t = 1:10^5 - l = loss(rand(10)) - back!(l) - delta = Optimise.apply!(opt, w′.data, w′.grad) - w′.data .-= delta + θ = Params([w′]) + x = rand(10) + θ̄ = gradient(() -> loss(x), θ) + Optimise.update!(opt, θ, θ̄) end - @test Flux.mse(w, w′) < 0.01 + @test loss(rand(10, 10)) < 0.01 end end @testset "Training Loop" begin i = 0 - l = param(1) + l = 1 Flux.train!(() -> (sleep(0.1); i += 1; l), (), @@ -57,17 +59,18 @@ end @testset "ExpDecay" begin w = randn(10, 10) o = ExpDecay(0.1, 0.1, 1000, 1e-4) - w1 = param(randn(10,10)) + w1 = randn(10,10) loss(x) = Flux.mse(w*x, w1*x) flag = 1 decay_steps = [] for t = 1:10^5 - l = loss(rand(10)) - back!(l) prev_eta = o.eta - prev_grad = collect(w1.grad) - delta = Optimise.apply!(o, w1.data, w1.grad) - w1.data .-= delta + θ = Params([w1]) + x = rand(10) + θ̄ = gradient(() -> loss(x), θ) + prev_grad = collect(θ̄[w1]) + delta = Optimise.apply!(o, w1, θ̄[w1]) + w1 .-= delta new_eta = o.eta if new_eta != prev_eta push!(decay_steps, t) diff --git a/test/runtests.jl b/test/runtests.jl index ef39268a..61def2b1 100644 --- a/test/runtests.jl +++ b/test/runtests.jl @@ -1,11 +1,8 @@ -using Flux, Test, Random, Statistics +using Flux, Test, Random, Statistics, Documenter using Random Random.seed!(0) -# So we can use the system CuArrays -insert!(LOAD_PATH, 2, "@v#.#") - @testset "Flux" begin @info "Testing Basics" @@ -22,14 +19,14 @@ include("layers/normalisation.jl") include("layers/stateless.jl") include("layers/conv.jl") -@info "Running Gradient Checks" - -include("tracker.jl") - if isdefined(Flux, :CUDA) include("cuda/cuda.jl") else @warn "CUDA unavailable, not testing GPU support" end +if VERSION >= v"1.2" + doctest(Flux) +end + end diff --git a/test/tracker.jl b/test/tracker.jl deleted file mode 100644 index 5f3a291f..00000000 --- a/test/tracker.jl +++ /dev/null @@ -1,15 +0,0 @@ -using Flux, Test -using Tracker: gradcheck - -gradtest(f, xs::AbstractArray...) = gradcheck((xs...) -> sum(sin.(f(xs...))), xs...) -gradtest(f, dims...) = gradtest(f, rand.(Float64, dims)...) - -@testset "Tracker" begin - -@test gradtest(Flux.mse, rand(5,5), rand(5, 5)) -@test gradtest(Flux.crossentropy, rand(5,5), rand(5, 5)) - -@test gradtest(x -> Flux.normalise(x), rand(4,3)) -@test gradtest(x -> Flux.normalise(x, dims = 2), rand(3,4)) - -end diff --git a/test/utils.jl b/test/utils.jl index 366f02b0..3a840261 100644 --- a/test/utils.jl +++ b/test/utils.jl @@ -1,5 +1,5 @@ using Flux -using Flux: throttle, jacobian, glorot_uniform, glorot_normal, stack, unstack +using Flux: throttle, glorot_uniform, glorot_normal, stack, unstack using StatsBase: std using Random using Test @@ -52,15 +52,6 @@ using Test end end -@testset "Jacobian" begin - A = param(randn(2,2)) - x = randn(2) - m(x) = A*x - y = m(x) - J = jacobian(m,x) - @test J ≈ A.data -end - @testset "Initialization" begin # Set random seed so that these tests don't fail randomly Random.seed!(0) @@ -106,12 +97,11 @@ end @testset "Precision" begin m = Chain(Dense(10, 5, relu), Dense(5, 2)) x = rand(10) - @test eltype(m[1].W.data) == Float32 - @test eltype(m(x).data) == Float32 - @test eltype(f64(m)(x).data) == Float64 - @test eltype(f64(m)[1].W.data) == Float64 - @test eltype(f32(f64(m))[1].W.data) == Float32 - @test Tracker.isleaf(f32(f64(m))[1].W) + @test eltype(m[1].W) == Float32 + @test eltype(m(x)) == Float32 + @test eltype(f64(m)(x)) == Float64 + @test eltype(f64(m)[1].W) == Float64 + @test eltype(f32(f64(m))[1].W) == Float32 end @testset "Stacking" begin