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ChangeLog
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ChangeLog
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2021-02-13 Deniz Yuret <dyuret@WS001>
* todo:
+ atype: find a solution to the atype mess.
2021-02-10 dyuret <dyuret@login03.kuacc.ku.edu.tr>
* cudnn-debug:
this is using CUDNN_LOGINFO_DBG=1 CUDNN_LOGDEST_DBG=stdout (normally JULIA_CUDA=CUDNN accomplishes the same, but there's a bug in CUDNN's logging)
2020-08-14 dyuret <dyuret@login03.kuacc.ku.edu.tr>
* todo:
+ finish train20
+ test/distributions and update
+ update readmes
+ util -> deprecate.jl: globals no longer needed
+ check import exports, match final Knet exports to 1.3.9
+ clean deprecated directories
+ update all examples and tutorials
++ tutorial
++ cifar10-cnn
++ dcgan-mnist
++ DeepLearningFrameworks
++ dynet-benchmark
++ fashion-mnist
++ housing-linreg
xx julia-tutorial
++ lenet
++ mnist-mlp
++ optimizers
xx README.md
++ reinforcement-learning
++ resnet
++ rnnlm
xx rnn-tutorial
++ synthetic-linreg
++ variational-autoencoder
++ vgg
+ update documentation
- push 1.4.0
- start ops21, layers21
= get rid of data, use MLDatasets and Artifacts
= check prof/ops20.jl, improve cuarrays
2020-08-11 dyuret <dyuret@login03.kuacc.ku.edu.tr>
* globals: need to decide where to put them. want modules independent of each other and of globals, eliminate cross-dependencies.
- training: both users and code
- atype: both users and code
- seed!: for users, not code
- dir: for users, not code
- cuallocator: can stay in KnetArrays, no need to export or publicise
- gpu: deprecate this
- libknet8, @knet8: only code, put in own module
2020-08-07 Deniz Yuret <deniz@razer>
* TODO: for the ops20 release:
+ ops20 is done
+ test with fast cuda.jl
- benchmark ops20 with knetarray and cuarray
- test ops20 with knetarray and cuarray
+ gcnode with cuarrays
- jld with cuarrays
- change all examples, tutorials to use explicit module imports (test with Knet that doesn't export anything).
- refactor ops20-ka and ops20-cu?
- integrate layers20 from KnetLayers
- redesign rnn and bn for ops21
- get rid of dependencies jld, datastructures, timeroutput etc
- get rid of data/*: just use MLDatasets when you can. move data.jl into train.
- gpu init
2020-01-04 dyuret <dyuret@login02.kuacc.ku.edu.tr>
* issue#513: downloading artifacts: DataDeps.jl vs Artifacts.toml.
Shortcomings of Artifacts.toml:
- Can specify urls but automatic download only possible for tar.gz.
No multiple files, no binary files (mnist, iris etc).
- The nice artifact"iris" string only available from within package.
DataDeps.jl:
- Extra dependency.
- Solves the two issues above.
- Can optionally unpack.
- No unique hash etc.
- Should use Knet prefix for names?
* data-wishlist:
- mnist
- cifar
- ...
* model-wishlist:
- yolov
- lenet
- resnet
- rnnlm
- ...
* interface:
- using Knet: LeNet
- using Knet.LeNet
- using KnetModels.LeNet
- using Knet.Models: LeNet
- should come with init that can download or randomly initialize for various model sizes.
- standard predict / loss interface.
- data read/write functions? training functions?
- (sub)package name vs struct/constructor name?
- if single symbol sufficient, can go with the first one:
-- LeNet() or ResNet(:small) => pretrained model
-- LeNet(784, 128, 256, 10) => randomly initialized model
-- (m::LeNet)(x,y) => loss
-- (m::LeNet)(x) => predict
-- (m::LeNet)(d) => average loss over dataset: need to specify type for x above, or do this with separate func
-- what about accuracy?
-- RNNLM has single input. Need different interface for prediction.
-- use pred/loss? need to revise training routines like adam.
- any standard interface for datasets?
2019-09-04 dyuret <dyuret@login02.kuacc.ku.edu.tr>
* Knet.jl: Calling CuArrays on __init__ helps with the stability of some devices.
2019-09-03 dyuret <dyuret@login02.kuacc.ku.edu.tr>
* cuarrays: failing on gitlab-ci sometimes, related to gpu type?
Tesla-K20m-sm_35.log: CUDAnative:pass CuArrays:fail(1)/pass(4451) Knet:pass
gebrd!: Test Failed at /dev/shm/dyuret/julia/packages/CuArrays/wXQp8/test/solver.jl:172
Tesla-K80-sm_37.log: CUDAnative:pass CuArrays:pass Knet:pass
Quadro-M2000-sm_52.log: CUDAnative:pass CuArrays:pass Knet:pass
GeForce-GTX-1080-Ti-sm_61.log: CUDAnative:pass CuArrays:fail(1)/pass(4451) Knet:fail(1)/pass
gebrd!: Test Failed at /dev/shm/dyuret/julia/packages/CuArrays/wXQp8/test/solver.jl:172
cpuconv: Test Failed at /dev/shm/dyuret/julia/dev/Knet/test/conv.jl:44
Tesla-P4-sm_61.log: CUDAnative:pass CuArrays:pass Knet:hang/hang(11:18)
Tesla-V100-PCIE-32GB-sm_70.log: CUDAnative:pass CuArrays:fail(4446/5) Knet:pass
Batch 2D (in 4D): Test Failed at /dev/shm/dyuret/julia/packages/CuArrays/wXQp8/test/fft.jl:62
2D: Test Failed at /dev/shm/dyuret/julia/packages/CuArrays/wXQp8/test/fft.jl:165 (Float32)
3D: Test Failed at /dev/shm/dyuret/julia/packages/CuArrays/wXQp8/test/fft.jl:165 (Float32)
2D: Test Failed at /dev/shm/dyuret/julia/packages/CuArrays/wXQp8/test/fft.jl:165 (Float64)
3D: Test Failed at /dev/shm/dyuret/julia/packages/CuArrays/wXQp8/test/fft.jl:165 (Float64)
GeForce-RTX-2080-Ti-sm_70 *gitlab-ci* CUDAnative:fail/pass CuArrays:pass(4446 pass,5 broken) Knet:hang(10:56)
https://gitlab.com/JuliaGPU/Knet.jl/pipelines/80040815
/builds/JuliaGPU/Knet.jl/.julia/packages/CUDAnative/LkH1v/test/device/execution.jl:545
https://gitlab.com/JuliaGPU/Knet.jl/pipelines/80044768, 80054621
CuArrays:5-broken, Knet:hangs
2018-09-18 Deniz Yuret <dyuret@ku.edu.tr>
* gc: The current gc has three problems:
1. Waiting until memory is out to call gc.
- try to GC at 1GB, if you don't get half the memory back, increase to 2GB etc.
- need to keep track of how much available.
2. Hanging on to arrays that do not get reused.
- need a way to measure last used age for each bucket.
3. Not being able to reuse arrays for slightly smaller arrays.
- exponential buckets waste too much memory.
- we may not need this if #2 resolved.
2018-09-05 dyuret <dyuret@login03.kuacc.ku.edu.tr>
* examples:
? Knet/tutorial
+ charlm: deprecate, in tutorial
+ cifar10-cnn
+ dcgan-mnist
+ DeepLearningFrameworks: Knet.CNN example has accuracy regression.
? dynet-benchmark
+ fashion-mnist
+ housing-linreg
+ julia-tutorial: check old commands, turn into notebook.
+ knet-tutorial: deprecate -> Knet/tutorial
+ lenet
+ mnist-mlp
+ optimizers
x overfitting: deprecate this, it is replicated in tutorial.
? reinforcement-learning
+ resnet
= rnnlm: update this with new interface, check for performance regression
+ rnn-tutorial: check for performance regression
+ synthetic-linreg
+ variational-autoencoder
+ vgg
2018-08-11 Deniz Yuret <dyuret@ku.edu.tr>
* julia7-compat-todo:
- fix JLD.
- unsafe_copy! is not in base any more? fix unsafe_copy!, unsafe_convert in karray looking in base
- unary_nd, indexed_function, isequivalent, _dbg, ssize not in AutoGrad any more.
- fix KnetDisplay (summary line should show KnetArray) and other display/show problems.
- separate and move KnetArray to KnetML.
- reorganize unary.jl and broadcast.jl
- check TODO.
+ seed! gc dir -- just use the same names but have Knet versions.
- data
+ deps
- docs
- examples
- prof
+ src: CPU:20/20
+ test: CPU:14/14
- limit max memory allocated by kptr
- AutoGrad only uses broadcasted now, compare performance with using broadcast
- try making karray <: AbstractArray and overriding get/setindex.
- search for TODOs.
- new AutoGrad interface.
- test on other AD and GPUarray pkgs.
- add using LinearAlgebra: lmul!, rmul! to test/linalg.jl
- use global keyword in the for loops in tests
- update travis.yml (and even better add gpu testing through #312)
- add Project.toml
- add Manifest.toml to .gitignore
- update readme badges
- eventually, slim down update! and rnn gpu tests
2018-08-09 Deniz Yuret <dyuret@ku.edu.tr>
* broadcast: we override broadcast and broadcasted for Rec and KnetArray.
- dot operations turn into broadcasted expressions.
- Rec overrides broadcasted to call broadcast_r directly.
- KnetArray should override broadcasted to call broadcast directly.
2017-09-06 EC2 Default User <ec2-user@ip-172-31-24-9.us-east-2.compute.internal>
* julia6-compat-todo:
+ branches for cuarrays and gpu arrays
+ checkout autograd master in travis
+ need to figure out how to handle cat in autograd.
+ notebooks, vgg. resnet, prof
+ test autocad examples
x test all examples on 4,5,6
+ update news: autograd done, knet done.
- go thru issues: autograd done, issues left.
- branch for reversediff?
- test on latest
- speed test
- examples/optimizers.jl too slow.
- examples/charlm.jl does not pass gradcheck.
- new autograd interface
- broadcast without broadcast_func symbols
2017-09-01 EC2 Default User <ec2-user@ip-172-31-10-154.us-east-2.compute.internal>
* julia6-compat-todo:
julia4 julia5 julia6
kptr 1 1 1
gpu 1 1 1
distributions 1 1 1
update 1 1 1
karray 1 1 1
linalg 1 1 1
conv 1 1 1
broadcast 1 1 1
unary 1 1 1
reduction 1 1 1
2017-07-29 dyuret <dyuret@cn6.kuacc.ku.edu.tr>
* julia6-compat-todo:
- fix precompile warnings: WARNING: deprecated syntax "Expr(:ccall)". Use "Expr(:call, :ccall)" instead.
- Pkg.test passes: kptr, gpu, distributions, conv
- fix Pkg.test warnings: linalg
- fix Pkg.test errors: update, karray, broadcast, reduction, unary
- fix Pkg.build errors: Pkg.build does not work.
2017-05-17 Deniz Yuret <dyuret@ku.edu.tr>
* TODO-KUparser:
Speed issues with KUparser.
First epoch slower: do we need to pre-allocate and not use cudaMalloc?
Slow-down of long runs: is it gpu copy or parsing algorithm? (dynamic-oracle gives clues)
update! slows down in runtests if at the end!
Fix dynet benchmarks, incorporate more (lstm, logp) from cudnn.
Check out GPUArray and ReverseDiff.
Start testing with Julia6 maybe better with speed.
Need to figure out the new Julia6 broadcasting syntax.
Also try the recommendation to detect one-out-of-k argument signature.
2017-04-07 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
* TODO8:
# handle KnetFree==nothing in knetgc()
# gpu(false) does not clear out Knet memory structures?
# add mean/std for KnetArray.
# implement nce. debug nce8 branch. try on large data/vocab. why doesn't it converge on ptb?
# broadcast kernels: debug 16/17, add tests, add benchmarks.
# check batch normalization in resnet, add it to src. -- waiting for ilker.
# document load/save via JLD.
# recover kuparser.
# navigation: implement world represenation.
# apply new rnn ideas to doc, readme, tutorial, slides, ijulia.
# fix reduction kernels for large matrices (10K-100K rows start giving bad answers): enis looking
# Latest master julia6 failing on nightly; AutoGrad also fails.
# try feeding rnnlm subset of embed matrix as weight.
# functional lstm interface defining multi-output grad2 function.
# Setup attobot for next release: https://github.com/attobot/attobot.
# prepare and submit julia4/5 benchmarks: find out why benchmarks are slower on julia5/6: https://github.com/JuliaLang/julia/issues/18135 ?
# Issue 89: reduced_dims -> reduced_indices in 0.5.1, stop using unexported functions from base. Other examples: to_indexes, dims2string, deepcopy_internal, LinearFast, show_backtrace, decolon (in AutoGrad).
# AutoGrad docs convert from comments to docstrings in core.jl and include in Knet manual. wip in newdocs branch.
# AutoGrad tests convert to the new test system.
# add some test for deconv vs conv, gclip.
# gpu switching back and forth does not seem to work. do we really need multiple handles for cublas, cudnn? do we need them for libknet8?
# AutoGrad: run all tests with KnetArrays
# AutoGrad: support for keys, values, next for dictionaries.
# add benchmarks with new dynamic frameworks: Yoav tweet, Volkan email. assigned to Ilker, Enis.
# docs todo: Julia tutorial. simplify examples. Baris Bozkurt's comments.
# docs todo: perceptron. kernel perceptron. svm. lenet and vgg in cnn section.
# mnist2d: implement/test sparse arrays
# time doing a single im2col instead of N for conv4
# replace T<: conditions in functions with generated code for each type
# 0.7: rename the 73 functions, cpu tests (add conv), v0.5 compat. check old todo list
# DL439: If one has access to numerical computation on complex numbers, then there isa very efficient way to numerically estimate the gradient by using complex numbersas input to the function (Squire and Trapp, 1998)
# optimization:
## make BLK,THR dependent on the input size? may improve final sum which is only 10x100 in this example.
## extend benchmark tests to cover all combinations of 10,100,1000 dimensions.
## optimize reduction and broadcasting kernels.
## optimize logp / softmax.
## optimize conv4 / matmul - arrayfire? cudnn conv instead of matmul? cudnn conv algorithms? fft paper from https://arxiv.org/abs/1601.06815.
## try fusion: we can do layers in one kernel call: relu(wx+b).
## try streams or multiple gpus.
## for general arrays: broadcast, get/setindex, h/v/cat. Enis working on this.
2017-03-28 Deniz Yuret <dyuret@ku.edu.tr>
* examples/rnnlm.jl: Trying to replicate one of:
http://www.fit.vutbr.cz/research/groups/speech/publi/2010/mikolov_interspeech2010_IS100722.pdf
http://www.fit.vutbr.cz/~imikolov/rnnlm/asru_large_v4.pdf
https://arxiv.org/abs/1312.6026
https://arxiv.org/abs/1409.2329
https://arxiv.org/abs/1508.06615
(:epoch,28,:perp,103.928986f0,173.2658f0,157.65414f0) RNNLM.main("--seed 1 --epochs 30 --dropout 0.5 --hidden 200 --embed 200 --batch 100 --best foo.jld")
(:epoch,14,:perp,52.10398f0,140.70792f0,130.77716f0) RNNLM.main("--seed 1 --epochs 30 --dropout 0.5 --hidden 650 --embed 650 --batch 100 --best foo650.jld")
(:epoch,27,:perp,75.37883f0,135.43819f0,127.377f0) RNNLM.main("--seed 1 --epochs 30 --dropout 0.5 --hidden 200 --embed 200 --batch 100 --optim Adagrad()")
(:epoch,5,:perp,69.288506f0,184.24977f0,171.84555f0) RNNLM.main("--seed 1 --epochs 30 --hidden 200 --embed 200 --batch 100 --optim Adagrad()")
(:epoch,30,:perp,113.68978f0,166.22272f0,154.77658f0) RNNLM.main("--seed 1 --epochs 30 --hidden 200 200 --embed 200 --batch 100 --dropout 0.5 --optim Adagrad()")
(:epoch,25,:perp,181.603f0,239.55917f0,225.10878f0) RNNLM.main("--seed 1 --epochs 30 --hidden 200 200 --embed 200 --batch 100 --dropout 0.5 --optim Sgd(lr=1,gclip=5)")
Paper claims perp < 100. Potential reasons we don't get this:
x Dropout not working. significant drop when turned off.
- Difft way to measure (exclude eos etc)
- Adam bad, sgd/gclip good. (adagrad works better). Paper uses SGD(lr=1,gclip=5) halving lr if devperp does not go down by 1.
- Difft lstm type?
- Initialization different. [-0.05,0.05]
- Batchsize different = 20.
- Highway network.
- BPTT 35 time steps: charlm style not sentence bound?
- Number of layers = 2! Table 2 gives 2x300 for char model. 2x200 for word model?
2017-03-19 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
2017-03-17 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# charlm: Implement rnns using separate embedding vectors and a @primitive concat operation: fix charlm demo, implement efficient s2s demo, s2s with attention. Need to first solve minibatching. Is v/hcat efficient? KnetArray implements only 2-arg version. AutoGrad calls cat, supports multiple args, grad uses uncat which uses getindex with an array of indices. KnetArray probably does not support an array of indices.
# charlm: try indexing op instead of sparse matmul.
# charlm: try lstm time concatenation.
# charlm: add adam: sgd(3.5) works faster. short bptt in initial epochs works faster.
# Add dropout as a primitive. Need kernel.
# charlm: add dropout
# s2s profile.
# check julia4 and julia6 compat, find out why julia pkgs marks knet as broken.
# fix intermittent test errors: https://github.com/JuliaLang/julia/pull/20736#issuecomment-283834724
2017-03-15 Deniz Yuret <dyuret@ku.edu.tr>
* profile: %41 forw (%20 logp), %44 back (%21 logp), %14 sum_outgrads
* flat: 2027 logp, 1761 sum, 1410 gemm, 1373 sum_outgrads
4115 ...et/.julia/v0.5/AutoGrad/src/core.jl:88; forward_pass(::Function, ::Tuple{Dict{Symbol,Any},Arr...
12 /Users/dyuret/knet/master/prof/s2s.jl:83; state = initstate(inputs[1], model[:state0]) # 14
92 /Users/dyuret/knet/master/prof/s2s.jl:86; input = lstm_input(model[:embed1], input) # 85
717 /Users/dyuret/knet/master/prof/s2s.jl:87; state = lstm(model[:encode], state, input) # 723
9 /Users/dyuret/knet/master/prof/s2s.jl:91; input = lstm_input(model[:embed2], EOS) # 3
692 /Users/dyuret/knet/master/prof/s2s.jl:95; state = lstm(model[:decode], state, input) # 702
80 /Users/dyuret/knet/master/prof/s2s.jl:100; input = lstm_input(model[:embed2],output) # 61
39 /Users/dyuret/knet/master/prof/s2s.jl:102; state = lstm(model[:decode], state, input) # 30
1 /Users/dyuret/knet/master/prof/s2s.jl:106; gold = vcat(outputs..., EOS) # 1
2473 /Users/dyuret/knet/master/prof/s2s.jl:107; sumlogp = lstm_output(model[:output], preds, gold) # 2441
43 /Users/dyuret/knet/master/prof/s2s.jl:114; pred1 = vcat(preds...) # 46
248 /Users/dyuret/knet/master/prof/s2s.jl:115; pred2 = pred1 * param[1] # 242
144 /Users/dyuret/knet/master/prof/s2s.jl:116; pred3 = pred2 .+ param[2] # 145
2037 /Users/dyuret/knet/master/prof/s2s.jl:117; sumlogp = logprob(gold, pred3) # 2006
2027 ...rs/dyuret/knet/master/prof/s2s.jl:135; o1 = logp(ypred,2) # 1999
833 ...ret/.julia/v0.5/Knet/src/unary.jl:176; x1 = maximum(x,d...)
120 ...ret/.julia/v0.5/Knet/src/unary.jl:177; x2 = x .- x1
122 ...ret/.julia/v0.5/Knet/src/unary.jl:178; x3 = exp(x2)
827 ...ret/.julia/v0.5/Knet/src/unary.jl:179; x4 = sum(x3,d...)
1 ...ret/.julia/v0.5/Knet/src/unary.jl:180; x5 = log(x4)
123 ...ret/.julia/v0.5/Knet/src/unary.jl:181; x6 = x2 .- x5
6 ...rs/dyuret/knet/master/prof/s2s.jl:136; o2 = o1[index] # 4
4 ...rs/dyuret/knet/master/prof/s2s.jl:137; o3 = sum(o2) # 2
4409 ...et/.julia/v0.5/AutoGrad/src/core.jl:231; backward_pass(::AutoGrad.Rec{Dict{Symbol,Any}}, ::Aut...
932 ./<missing>:0; *(::Type{AutoGrad.Grad{2}}, ::Knet.KnetArray{Float32...
1 ./<missing>:0; +(::Type{AutoGrad.Grad{1}}, ::Knet.KnetArray{Float32...
410 ./<missing>:0; .*(::Type{AutoGrad.Grad{2}}, ::Knet.KnetArray{Float3...
148 ./<missing>:0; .+(::Type{AutoGrad.Grad{2}}, ::Knet.KnetArray{Float3...
21 ./<missing>:0; getindex(::Type{AutoGrad.Grad{1}}, ::Array{Any,1}, :...
2142 ./<missing>:0; logp(::Type{AutoGrad.Grad{1}}, ::Knet.KnetArray{Floa...
818 ...uret/.julia/v0.5/Knet/src/unary.jl:195; dx1 = sum(dy,d...)
120 ...uret/.julia/v0.5/Knet/src/unary.jl:196; dx2 = exp(y)
1038 ...uret/.julia/v0.5/Knet/src/unary.jl:197; dx3 = dx2 .* dx1
166 ...uret/.julia/v0.5/Knet/src/unary.jl:198; dx4 = dy - dx3
245 ./<missing>:0; lstm_input(::Type{AutoGrad.Grad{1}}, ::Knet.KnetArra...
143 ./<missing>:0; sigm(::Type{AutoGrad.Grad{1}}, ::Knet.KnetArray{Floa...
6 ./<missing>:0; sum(::Type{AutoGrad.Grad{1}}, ::Float32, ::Float32, ...
106 ./<missing>:0; tanh(::Type{AutoGrad.Grad{1}}, ::Knet.KnetArray{Floa...
10 ./base.jl:151; vector_any()
2 ...AutoGrad/src/base/abstractarray.jl:0; cat(::Type{AutoGrad.Grad{17}}, ::Knet.KnetArray{Floa...
85 ...AutoGrad/src/base/abstractarray.jl:85; cat(::Type{AutoGrad.Grad{11}}, ::Knet.KnetArray{Floa...
1373 ...et/.julia/v0.5/AutoGrad/src/core.jl:233; backward_pass(::AutoGrad.Rec{Dict{Symbol,Any}}, ::Aut...
360 ...lia/v0.5/AutoGrad/src/interfaces.jl:71; sum_outgrads(::Void, ::AutoGrad.UngetIndex)
569 ...lia/v0.5/AutoGrad/src/interfaces.jl:87; sum_outgrads(::Dict{Symbol,Any}, ::AutoGrad.UngetIndex)
3 ...lia/v0.5/AutoGrad/src/interfaces.jl:92; sum_outgrads(::Array{Any,1}, ::AutoGrad.UngetIndex)
77 ...uret/.julia/v0.5/Knet/src/karray.jl:905; sum_outgrads{T}(a::KnetArray{T},b::KnetArray{T})=(a+b)
346 ...uret/.julia/v0.5/Knet/src/karray.jl:908; c = sum_outgrads_karray(a, b.value, b.index...)
2017-03-12 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# fix repeated index gradient bug.
# s2s: Add a generic s2s example to Knet/examples. Need to solve minibatching / concat first.
# docs: write rnn chapter. fix earlier chapters.
2017-03-11 Deniz Yuret <dyuret@ku.edu.tr>
* indexing: repeated indices are not handled properly by
ungetindex, they need to be summed in backward pass but setindex
just overwrites. Let's understand how Julia indexing works:
a[:,i]
522 ./abstractarray.jl:752; getindex(::Array{Float64,2}, ::Colon, ::Array{Int64,1})
? ./multidimensional.jl:270; _getindex(::Base.LinearFast, ::Array{Float64,2},...
522 ./multidimensional.jl:291; _unsafe_getindex(::Base.LinearFast, ::Array{Float64,2},...
149 ./multidimensional.jl:296; macro expansion
373 ./multidimensional.jl:298; macro expansion
373 ./multidimensional.jl:340; _unsafe_getindex!
373 ./multidimensional.jl:348; macro expansion
6 ./cartesian.jl:62; macro expansion
367 ./cartesian.jl:64; macro expansion
45 ./cartesian.jl:62; macro expansion
7 ./multidimensional.jl:349; macro expansion
315 ./multidimensional.jl:350; macro expansion
_unsafe_getindex is a @generated function which uses @nexprs, @ncall, to_index.
http://docs.julialang.org/en/latest/manual/metaprogramming.html#Generated-functions-1
@generated functions return quoted expressions that get compiled
at runtime. Only the types of expressions can be accessed in their
body.
@ncall 3 func a
==> func(a_1, a_2, a_3)
@ncall 2 func a b i->c[i]
==> func(a, b, c[1], c[2])
@nexprs 4 i -> y[i] = A[i+j]
==> y[1]=A[1+j]; y[2]=A[2+j]...
to_index defined in operators.jl, converts to Array,Colon,Int
Numbers -> Int
Colon is kept
BitArray -> find(I) -> Vector{Int} # although there is a special _unsafe_getindex which skips using find
Other arrays are kept (int or cartesianindex)
index_shape(A,I_1,I_2,...) gives the destination shape
indices(b) => (Base.OneTo(10000),Base.OneTo(10000))
eachindex(b) => Base.OneTo(100000000)
Finally we call _unsafe_getindex!(dest,A,I_1,I_2,...) at multidimensional.jl:340
J = decolon(src,I_1,I_2,...) converts colons to explicit indices.
@nloops N itersym rangeexpr bodyexpr
@nloops N itersym rangeexpr preexpr bodyexpr
@nloops N itersym rangeexpr preexpr postexpr bodyexpr
# this indexes dest with linear, src with cartesian indices
D = eachindex(dest)
Ds = start(D)
for j_2 in J_2
for j_1 in J_1
d,Ds = next(D,Ds)
dest[d] = getindex(src,j_1,j_2)
end
end
We need to write an accumulating version of setindex! (addindex!) for ungetindex.
_setindex! for N-D indices L364
_setindex! for 1-D indices L368 uses _maybe_reshape(A)
_unsafe_setindex! is called
_unsafe_batchsetindex! is called with (A,_iterable(x),to_indexes(J...)...) L420
Here is macroexpanded version for N=2:
X is iterated over. A is called with individual indices.
We just need to zero out A and add for AutoGrad.
We need to write a KnetArray specific version in Knet.
quote # none, line 2:
begin
I_1 = I[1]
I_2 = I[2]
end # none, line 3:
idxlens = index_lengths(A,I_1,I_2) # none, line 4:
setindex_shape_check(X,idxlens[1],idxlens[2]) # none, line 5:
J = decolon(A,I_1,I_2) # none, line 6:
begin
J_1 = J[1]
J_2 = J[2]
end # none, line 7:
Xs = start(X) # none, line 8:
begin
$(Expr(:inbounds, true))
begin # cartesian.jl, line 62:
for j_2 = J_2 # cartesian.jl, line 63:
nothing # cartesian.jl, line 64:
begin # cartesian.jl, line 62:
for j_1 = J_1 # cartesian.jl, line 63:
nothing # cartesian.jl, line 64:
begin # none, line 9:
(v,Xs) = next(X,Xs) # none, line 10:
setindex!(A,v,j_1,j_2)
end # cartesian.jl, line 65:
nothing
end
end # cartesian.jl, line 65:
nothing
end
end
$(Expr(:inbounds, :pop))
end # none, line 12:
A
end
2017-03-10 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# Added KnetArray indexing support for: Int, Colon, UnitRange, StepRange, CartesianIndex, Array{Int}, Array{Bool}, Array{CartesianIndex}. Multidimensional indexing incomplete.
2017-03-03 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# Implement a decent hill climbing algorithm for hyperopt: do not repeat, have acceleration, independent step sizes, guarantee local minima, one dimension at a time, pick dimension/move using bandits or a smart queue. Wikipedia has good pseudocode. Bandits may work better.
2017-03-01 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# Add gclip to update!.
2017-02-26 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# Issue 88: better handling of difft elt types on data vs weights in update!
2017-02-23 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# bug in AutoGrad: broadcast.jl: $f(x1::Rec,x2::AbstractArray)=$f(x1,x2.value)
# gradcheck fails on: bmax(x,y)=broadcast(max,x,y) or bmin(x,y)=((y.<x).*y+(x.<y).*x)
# grad of convert (AutoGrad issue), implemented in karray.jl, need to port.
2017-02-22 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# automatically generate README.md from tutorial.md.
# make examples easier to load (turn off Pkg install for vgg etc, docs take too long)
## no easy way to do it without effecting source links.
2017-02-19 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# use mocha for cpu conv/pool. wip.
# cpuconv todo:
# ok: need to get rid of mask in pool_back
# ok: reimplement conv4 in terms of im2col
# ok: need low level blas call with pointers
# ok: reimplement conv4x conv4w using col2im?
# ok: need separate cpu and gpu libraries: condition makefile on finding nvcc, also cond openmpi like mocha/dep
2017-02-18 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# add tests for new update interface.
2017-02-16 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# permutedims ambiguity in AutoGrad in Julia 0.4.
# docs imagesize: The ![]() syntax doesn't support image size. However, you should be able to use the @raw block to insert custom HTML with an <img> tag as follows:
```@raw html
<img src="..." height="..." width="...">
```
# add x=unpool(pool(x)) test,
# add davide fix to knet before release
# Contents does not show on README.md, @ref doesn't work etc. New README?
# fix unclear docs for optimization, tutorial example? better yet, impl improved update interface.
2017-02-15 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# KnetArray ==, ===, isapprox etc. missing.
# copy! for KnetArray missing. (we have fill!, rand! etc. is copy! supported by AutoGrad? should refuse for Rec)
# deepcopy does not work for KnetArray
# override KnetArray and Array instead of cpu2gpu etc.
# add KnetArray{Float32}(3) type init like Array and deprecate the other.
# Make BaseTestNext conditional on julia version. If not possible, create header.jl in test/ and check version/pkg.
2017-02-13 Deniz Yuret <dyuret@ai-test.hpc.ku.edu.tr>
* DONE:
# Latest master failing on Julia 0.4 (convtest) and nightly distros.
# TonyKelman on Compat: If you use Compat in your tests, it needs to be in test/REQUIRE or REQUIRE.
# TonyKelman on removing nightly from travis: you can make this an allowed failure so it'll run but won't make your status red
2017-02-12 Deniz Yuret <dyuret@ai-test.hpc.ku.edu.tr>
* unit-test-TODO:
+ unary.jl: cpu=25s gpu=41s
+ broadcast.jl: cpu=19s gpu=34s
+ reduction.jl: cpu=18s gpu=28s (6 fail)
+ karray.jl: cpu=(fail) gpu=12s
+ linalg.jl: cpu=(fail) gpu=15s
+ update.jl: cpu=6.6s gpu=19s
+ kptr.jl: cpu=2.5s gpu=2.5s
+ gpu.jl: cpu=3s gpu=2.4s
+ distributions.jl: cpu=3s gpu=3s
+ conv.jl: cpu=(fail) gpu=13s (10 broken)
+ runtests.jl: switch to new tests, figure out why so slow
+ karray,linalg: cpu tests failing
+ reduction: gpu tests failing
+ conv.jl: debug unpool, add cpuconv
2017-02-10 Deniz Yuret <dyuret@ai-test.hpc.ku.edu.tr>
* DONE:
# doc warnings about missing broadcast, reduction, restricted cat, indexing, no bool array etc.
# figure out rtfd doc setup or forwarding.
# figure out no KnetArray in cpu problem
# KnetArray does not work on cpu-only (should it? no. operators overloaded assuming gpu)
# add back cpu convolution
# gputests.jl and cputests.jl are broken.
# resolve issues
2017-02-09 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# missing docs for distributions
# the update methods not documented yet.
# 0.8.1: tag new version for the paper when all is done.
# add transpose to KnetArray.
# source links? (especially for examples)
# docs todo: warn against overwriting arrays.
# use original docstrings for examples readme, put examples readme in docs.
2017-02-08 Deniz Yuret <dyuret@ku.edu.tr>
* Documenter.jl: (TODO) Julia documentation moved back to MD
supported by Documenter.jl. Supports latex, doctest, xrefs,
search. Hosted on github pages built throgh Travis (no need for
Python). PDF output? PDF:
https://juliadocs.github.io/Documenter.jl/stable/lib/internals/writers.html
Automatic conversion from rst? Try Pandoc. Use markdown_github as
output.
Using sips command on osx to resize images.
- doctest, does it accept ... ?
- how do we generate docs from docstrings? ```@docs
- can use function f end to document a zero method function
- you can document fields of a type.
- how do you refer to julia function docs from knet docstrings?
- @doc "..." foo is used when foo is defined by generated code
- can we default link text to link content?
- $(EXPORTS) used in module doc to list exported symbols.
- using ```@contents, @index, @docs
- where are the source links?
2017-02-07 Deniz Yuret <dyuret@ai-test.hpc.ku.edu.tr>
* src/Makefile (CFLAGS): code refactoring:
Function lists (cuda??.jl) and cuda code generators
(cuda??_gen.jl) do not go well together. The reason is the same
function list (e.g. binary array ops) are used by more than one
type of cuda kernel (same size vs broadcasting kernels). It makes
more sense to collect function lists in semantically named files
(unary, broadcasting, reduce etc.).
cuda1: abs2,abs,acos,... => unary
cuda10: add,sub,mul,... => broadcast
cuda11: add,sub,mul,...
cuda12: add,sub,mul,...
cuda20: sum,prod,... => reduction
cuda21: sum,prod,...
2016-10-24 dyuret <dyuret@ku.edu.tr>
* julia.st: for pretty-printing Julia use:
enscript -Ejulia -M A4 -2rGC -o foo1.ps core.jl
ps2pdf foo1.ps
enscript does not come with a julia format. You can create one
using matlab.st (for keywords) and python.st (for strings,
comments) under /usr/share/enscript/hl/julia.st.
2016-10-21 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# implement adam.
# gpu(true) leaves memory footprint on each device.
# Paulito old cudnn interface support.
# update the rest of the documentation.
# vgg: change default padding for conv4 to be (n-1)/2
# Davide push/vcat bug.
2016-10-05 dyuret <dyuret@ku.edu.tr>
* DONE:
# write paper for nips
# document KnetArray in readme. finish the under the hood section.
# put function references in documentation.
# try to measure back functions one by one as well for gpu profiling.
# implement axpy! for KnetArray if it is worth it to get faster updates.
2016-10-03 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# delete knet.ji in build script: no need.
# use Knet.dir set in Knet.jl.
# check if benchmarks keep minibatches in gpu.
# implement a install_extras command: no need instead we do following:
# automatic loading of packages by demos (use Pkg.add or introduce installExtras)
# implement vggnet demo.
# housing.jl bias fails gradcheck (because of Float32)
# check warnings in cpu-only knet.
# change charlm default winit.
2016-09-30 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# use Float32 in housing by default.
# charlm can transfer all minibatches to gpu before timing (slows down due to gc)
# reimplement lenet using loop.
# charlm: profile speed, add dropout, nlayer.
2016-09-20 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# broadcasting KnetArray bug when array size 1x1.
# extend readme with examples. revise intro. publish intro on blog. use the presentation.
2016-09-18 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# new amazon aws image
# finish cudnn, curand etc. if possible eliminate dependence on them.
# logp should take a second argument like sum.
2016-09-16 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# lenet: move cudnn calls to cuda44.jl
# karray: checkbounds without abstractarray.
# document KnetArray inline.
# remove importall Base.
# implement more efficient lstm making sure getindex does a view (transpose?)
# citation
# write examples/README
# charlm fails on cpu. change its default data file to something smaller. small files fail gradcheck.
# test gpu/cpu, osx/linux, 0.4, 0.5, 0.6. (currently failing on Travis).
# clean up old tags and register Knet
* Release:
Eliminated old tags. Just tag one 0.7 and one 0.8 version.
For each tagged version:
- minimize REQUIRE
- test on v0.4 v0.5 v0.6
- test on cpu vs gpu
julia 0.4 knet 0.7 cpu: ok
julia 0.4 knet 0.7 gpu: fail: mnist4d,copyseq
julia 0.4 knet 0.8 cpu: ok
julia 0.4 knet 0.8 gpu: ok
julia 0.4 autograd cpu: ok
julia 0.4 autograd gpu: ok
julia 0.5 knet 0.7 cpu: error
julia 0.5 knet 0.7 gpu: error
julia 0.5 knet 0.8 cpu: ok
julia 0.5 knet 0.8 gpu: ok
julia 0.5 autograd cpu: ok
julia 0.5 autograd gpu: ok
julia 0.6 knet 0.7 cpu: error
julia 0.6 knet 0.7 gpu: error
julia 0.6 knet 0.8 cpu: ok
julia 0.6 knet 0.8 gpu: ok
julia 0.6 autograd cpu: error, wip compat0.6, 0-dim array problem
julia 0.6 autograd gpu: error, wip compat0.6
julia 0.6 knet 0.8 cpu:
using Knet
WARNING: log{T <: Number}(x::AbstractArray{T}) is deprecated, use log.(x) instead.
WARNING: cos{T <: Number}(x::AbstractArray{T}) is deprecated, use cos.(x) instead.
WARNING: abs2{T <: Number}(x::AbstractArray{T}) is deprecated, use abs2.(x) instead.
Tests pass.
julia 0.6 autograd master cpu:
using AutoGrad: ok
Pkg.test("AutoGrad")
WARNING: max{T1 <: Real}(x::Real,y::AbstractArray{T1}) is deprecated, use max.(x,y) instead.
WARNING: abs{T <: Number}(x::AbstractArray{T}) is deprecated, use abs.(x) instead.
This is a common error I need to fix for other Julia versions as well:
WARNING: (AutoGrad.ungetindex,0.18437601415789295,[0.228639,0.085112],(2,),"MethodError(convert,(Array{Float64,N},OH416_A660_2_(2,)_0.18437601415789295))")
ERROR: LoadError: MethodError: no method matching erfinv(::Array{Float64,1})
julia 0.4 knet 0.7 gpu: fails mnist4d and copyseq
julia 0.4 knet 0.7 cpu: should add cpu conv test but they pass.
julia 0.5 knet 0.7 cpu:
using Knet
WARNING: Method definition randn!(Base.Random.AbstractRNG, AbstractArray{#T<:Any, N<:Any}) in module Random at random.jl:1207 overwritten in module Knet at /mnt/ai/home/dyuret/.julia/v0.5/Knet/src/util/array.jl:36.
WARNING: could not import Base.lastidx into LegacyStrings
WARNING: Base.writemime is deprecated. likely near /mnt/ai/home/dyuret/.julia/v0.5/Knet/src/net.jl:186
WARNING: deprecated syntax "[a=>b for (a,b) in c]". Use "Dict(a=>b for (a,b) in c)" instead.
Pkg.test("Knet")
WARNING: symbol is deprecated, use Symbol instead./mnt/ai/home/dyuret/.julia/v0.5/Knet/examples/linreg.jl:25
WARNING: Knet.Kfun.(:wdot) is deprecated; use Knet.Kfun.:wdot or getfield(Knet.Kfun, :wdot) instead./mnt/ai/home/dyuret/.julia/v0.5/Knet/src/compiler.jl:33
ERROR: expecting assignment expression got # /mnt/ai/home/dyuret/.julia/v0.5/Knet/src/kfun.jl, line 43:
in _comp(::Expr, ::Dict{Symbol,Symbol}, ::Dict{Symbol,Any}, ::Expr) at /mnt/ai/home/dyuret/.julia/v0.5/Knet/src/compiler.jl:97
julia 0.6 knet 0.7 cpu: (similar to julia 0.5)
using Knet
WARNING: Method definition randn!(Base.Random.AbstractRNG, AbstractArray{#T<:Any, N<:Any}) in module Random at random.jl:1281 overwritten in module Knet at /mnt/ai/home/dyuret/.julia/v0.6/Knet/src/util/array.jl:36.
WARNING: could not import Base.lastidx into LegacyStrings
WARNING: Base.writemime is deprecated. likely near /mnt/ai/home/dyuret/.julia/v0.6/Knet/src/net.jl:186
WARNING: deprecated syntax "[a=>b for (a,b) in c]".Use "Dict(a=>b for (a,b) in c)" instead.
Pkg.test("Knet")
WARNING: Method definition randn!(Base.Random.AbstractRNG, AbstractArray{#T<:Any, N<:Any}) in module Random at random.jl:1281 overwritten in module Knet at /mnt/ai/home/dyuret/.julia/v0.6/Knet/src/util/array.jl:36.
WARNING: symbol is deprecated, use Symbol instead.
WARNING: Knet.Kfun.(:wdot) is deprecated; use Knet.Kfun.:wdot or getfield(Knet.Kfun, :wdot) instead.
ERROR: expecting assignment expression got # /mnt/ai/home/dyuret/.julia/v0.6/Knet/src/kfun.jl, line 43:
in _comp(::Expr, ::Dict{Symbol,Symbol}, ::Dict{Symbol,Any}, ::Expr) at /mnt/ai/home/dyuret/.julia/v0.6/Knet/src/compiler.jl:97
julia 0.5 knet 0.7 gpu:
WARNING: Base.SparseMatrix is deprecated. (in CUSPARSE)
WARNING: Method definition (::Type{Knet._CudaArray})(CUDArt.CudaArray{#T<:Any, #N<:Any}) in module Knet at /state/partition1/dyuret/knet/publish/v0.5/Knet/src/util/cudart.jl:127 overwritten at /state/partition1/dyuret/knet/publish/v0.5/Knet/src/util/cudart.jl:128.
WARNING: Base.writemime is deprecated. likely near /state/partition1/dyuret/knet/publish/v0.5/Knet/src/util/cudart.jl:133
ERROR: LoadError: LoadError: LoadError: UndefVarError: TopNode not defined /state/partition1/dyuret/knet/publish/v0.5/Knet/src/util/deepcopy.jl, in expression starting on line 21
WARNING: Method definition randn!(Base.Random.AbstractRNG, AbstractArray{#T<:Any, N<:Any}) in module Random at random.jl:1207 overwritten in module Knet at /state/partition1/dyuret/knet/publish/v0.5/Knet/src/util/array.jl:36.
WARNING: deprecated syntax "[a=>b for (a,b) in c]". Use "Dict(a=>b for (a,b) in c)" instead.
WARNING: could not import Test.default_handler into Main
WARNING: could not import Test.Success into Main
WARNING: could not import Test.Failure into Main
ERROR: LoadError: LoadError: UndefVarError: Success not defined
WARNING: symbol is deprecated, use Symbol instead. /state/partition1/dyuret/knet/publish/v0.5/Knet/examples/linreg.jl:25
WARNING: Knet.Kfun.(:wdot) is deprecated; use Knet.Kfun.:wdot or getfield(Knet.Kfun, :wdot) instead. /state/partition1/dyuret/knet/publish/v0.5/Knet/src/compiler.jl:33
catastrophic failure of tests.
2016-09-14 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# lenet broken: chased bug down to KnetArray <: AbstractArray, what do we inherit?
# charlm: slowed down
# charlm: experiment with other forms of lstm
# try cpu sum and vcat see if better.
# check multi-gpu support on KnetArrays: can we copy, free, etc with a non-active device?
# charlm: optimize params
# 0.7: upper limit julia 0.5, remove downloading mnist from runtests.
# 0.7 has tests failing on Julia 0.4 and errors on Julia 0.5.
2016-09-13 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
## support cat and sub with KnetArray.
2016-09-12 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# mnist2d: sampling gradcheck
## need a good gradcheck to make sure, other TODO items in charlm.
# mnist2d: implement efficient softmax -- seems ok for now.
# load/save using JLD: need to write KnetArray handlers.
## turn gpu on by default if exists.
# eliminate dependence on CUDArt.
## support multiple gpus with KnetArrays
# need an efficient softmax, logsumexp.
## test on mnist --fast:
## cpu before: 2.75 gpu before: 2.25
## cpu after : 2.75 gpu after : 2.10
## test on lenet --fast:
## gpu before: 10.15 gpu after: 9.84 (mostly because of relu)
## after using 10^8 for knetgc limit: 9.30
## test on charlm with 10k lines of shakespeare (one epoch time) (10,2.419187890389808):
## compare with 10.6 secs/epoch for train in Knet7:
## gpu before: test: 2.32 train: 6.58
## gpu after : test: 2.33 train: 6.30
## bitarrays : test: 2.15 train: 6.20
## fixes : test: 2.16 train: 6.14
## charlm:
# loss results do not match Knet7 because of epsbump
# cpu=gpu tested but knet7=knet8 only forward up to epsbump, knet8 dont have keepstate or gclip
## implemented maximum but AutoGrad.ungetindex failing tests
2016-09-10 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# charlm keepstate problem: state has Values so gc does not work unless we getval or reinit state between iterations!
# charlm loss problem: knet7!=knet8 because of epsbump.
2016-09-06 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# mnist2d: cpu support, in general for all of Knet; test with v0.4 and v0.5
# mnist2d: cpu/gpu, Float16,32,64 options
# mnist2d: update documentation
# put info about additional packages for examples and gpu in README
# mnist4d
# gc problem.
* gc-problem: mnist4d explodes memory.
2016-09-04 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# mnist2d: add hidden option
# mnist2d: comparison with knet7
2016-08-29 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# profile AutoGrad more, figure out memory problem, good look at unbroadcast. memory use with/without forw. closures?
# make AutoGrad completely generic?
# tmpfree is dangerous, user visible variables from a=b*c may be overwritten!
# figure out why forw adds 0.38
# determine and minimize autograd overhead.
2016-08-28 Deniz Yuret <dyuret@ku.edu.tr>
* DONE:
# move cuda -> src, src -> src7
# should we introduce our own CudaArray type?
# define KnetArray. (KA) use instead of CudaArray.
# test cuda version compare with af.
# we could just support 0,1,2 dims or 0,1,N dims for map and reduce.
# finish cuda2arg, cublas
# need to write cuda21 vector reductions before full AutoGrad test.
* ArrayFire: gave up on it, my code is faster:
# can we get AF kernels to dump out?
# try c++ ArrayFire mnist example?
# will arrayfire memory management still work with rnns?
# eventually look at arrayfire convolution.
# thrust and tensorflow also opensource.
* JIT: could look at this if libknet8 gets too big:
# JIT compile kernels as needed: https://blog.maleadt.net/2015/01/15/julia-cuda/
## http://docs.nvidia.com/cuda/nvrtc/index.html#basic-usage
## http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#mathematical-functions-appendix
2016-08-27 Deniz Yuret <dyuret@ku.edu.tr>
* cuda: timing tests on 100K repetitions on 1000x100 output array with simple ops:
BLK=256,THR=256 for all (except reductions cuda20,cuda21 use 128,128)
F32 F64 where
0.73 1.40 cuda1 (unary)
0.73 1.40 cuda01 (scalar,array)
0.80 2.06 cuda11 (same size array)
0.86 2.08 cuda12 (same size array, broadcasting)
broadcasting (cuda12):
F32 F64
mat+mat 0.86 mat+mat 2.08