Added new documentation folders for v0.0.4
This commit is contained in:
Eduardo Cueto Mendoza
parent
278af852f4
commit
6021828772
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@ -2,3 +2,4 @@
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*.jl.cov
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*.jl.mem
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/docs/build/
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.vscode/*
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410
Manifest.toml
410
Manifest.toml
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@ -1,2 +1,412 @@
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# This file is machine-generated - editing it directly is not advised
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[[AbstractFFTs]]
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|
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||||
|
||||
[[Adapt]]
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||||
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||||
|
||||
[[ArrayLayouts]]
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[[Base64]]
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[[CUDAnative]]
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||||
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[[ChainRules]]
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[[CodeTracking]]
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[[CodecZlib]]
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[[ColorTypes]]
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[[Colors]]
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[[CommonSubexpressions]]
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||||
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[[Cthulhu]]
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||||
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||||
|
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[[DataAPI]]
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||||
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||||
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||||
|
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[[DataStructures]]
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[[Dates]]
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[[DiffResults]]
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||||
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||||
|
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[[ExprTools]]
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||||
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||||
|
||||
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|
||||
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||||
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||||
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||||
[[Flux]]
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||||
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||||
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||||
|
||||
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||||
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|
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||||
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||||
|
||||
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|
||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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|
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
|
||||
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|
||||
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|
||||
git-tree-sha1 = "d8d8b8a9f4119829410ecd706da4cc8594a1e020"
|
||||
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
|
||||
version = "0.10.3"
|
||||
|
||||
[[StaticArrays]]
|
||||
deps = ["LinearAlgebra", "Random", "Statistics"]
|
||||
git-tree-sha1 = "5c06c0aeb81bef54aed4b3f446847905eb6cbda0"
|
||||
uuid = "90137ffa-7385-5640-81b9-e52037218182"
|
||||
version = "0.12.3"
|
||||
|
||||
[[Statistics]]
|
||||
deps = ["LinearAlgebra", "SparseArrays"]
|
||||
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||
|
||||
[[StatsBase]]
|
||||
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics"]
|
||||
git-tree-sha1 = "a6102b1f364befdb05746f386b67c6b7e3262c45"
|
||||
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
|
||||
version = "0.33.0"
|
||||
|
||||
[[Test]]
|
||||
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
|
||||
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
|
||||
|
||||
[[TimerOutputs]]
|
||||
deps = ["Printf"]
|
||||
git-tree-sha1 = "f458ca23ff80e46a630922c555d838303e4b9603"
|
||||
uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
|
||||
version = "0.5.6"
|
||||
|
||||
[[TranscodingStreams]]
|
||||
deps = ["Random", "Test"]
|
||||
git-tree-sha1 = "7c53c35547de1c5b9d46a4797cf6d8253807108c"
|
||||
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
|
||||
version = "0.9.5"
|
||||
|
||||
[[UUIDs]]
|
||||
deps = ["Random", "SHA"]
|
||||
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
|
||||
|
||||
[[Unicode]]
|
||||
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
|
||||
|
||||
[[ZipFile]]
|
||||
deps = ["Libdl", "Printf", "Zlib_jll"]
|
||||
git-tree-sha1 = "254975fef2fc526583bb9b7c9420fe66ffe09f2f"
|
||||
uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
|
||||
version = "0.9.2"
|
||||
|
||||
[[Zlib_jll]]
|
||||
deps = ["Libdl", "Pkg"]
|
||||
git-tree-sha1 = "622d8b6dc0c7e8029f17127703de9819134d1b71"
|
||||
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
|
||||
version = "1.2.11+14"
|
||||
|
||||
[[Zygote]]
|
||||
deps = ["AbstractFFTs", "ArrayLayouts", "ChainRules", "FillArrays", "ForwardDiff", "Future", "IRTools", "InteractiveUtils", "LinearAlgebra", "MacroTools", "NNlib", "Random", "Requires", "Statistics", "ZygoteRules"]
|
||||
git-tree-sha1 = "2e2c82549fb0414df10469082fd001e2ede8547c"
|
||||
uuid = "e88e6eb3-aa80-5325-afca-941959d7151f"
|
||||
version = "0.4.22"
|
||||
|
||||
[[ZygoteRules]]
|
||||
deps = ["MacroTools"]
|
||||
git-tree-sha1 = "b3b4882cc9accf6731a08cc39543fbc6b669dca8"
|
||||
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
|
||||
version = "0.2.0"
|
||||
|
|
|
|||
|
|
@ -1,7 +1,12 @@
|
|||
name = "GreenFlux"
|
||||
uuid = "ccad5352-7643-4eb2-b711-e9c298e87bf0"
|
||||
authors = ["Eduardo Cueto Mendoza"]
|
||||
version = "0.1.0"
|
||||
version = "0.0.4"
|
||||
|
||||
[deps]
|
||||
CUDAapi = "3895d2a7-ec45-59b8-82bb-cfc6a382f9b3"
|
||||
Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
|
||||
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
|
||||
|
||||
[compat]
|
||||
julia = "1"
|
||||
|
|
|
|||
|
|
@ -1,5 +1,24 @@
|
|||
module GreenFlux
|
||||
|
||||
# Write your package code here.
|
||||
using Flux: Chain, Recur, Dense, Conv, MaxPool, GlobalMaxPool, MeanPool, GlobalMeanPool,
|
||||
DepthwiseConv, ConvTranspose, CrossCor, GRUCell, LSTMCell,RNNCell, Maxout
|
||||
using Flux: params, outdims, sigmoid, rrelu, elu, celu, softsign, softplus, tanh, gelu,
|
||||
hardsigmoid, logsigmoid,swish, selu, softmax, logsoftmax, hardtanh,
|
||||
leakyrelu, relu6, lisht, tanhshrink, logcosh, mish, relu, trelu,
|
||||
softshrink, identity
|
||||
using Statistics: mean
|
||||
using CUDAapi: has_cuda_gpu
|
||||
|
||||
export avgpowerdraw, modelflops
|
||||
|
||||
include("power/powerdraw.jl")
|
||||
include("neflops/measureutils.jl")
|
||||
include("neflops/layerflops.jl")
|
||||
include("neflops/gradientflops.jl")
|
||||
include("neflops/modelflops.jl")
|
||||
|
||||
function __init__()
|
||||
@info "Finished loading GreenFlux..."
|
||||
end
|
||||
|
||||
end # module
|
||||
|
|
|
|||
|
|
@ -0,0 +1,206 @@
|
|||
"""
|
||||
gradientflops(layer, input)::Float64
|
||||
|
||||
Calculates the number of non-embedding Floating Point Operations `neFLOPs` for most layers.
|
||||
`layer` should be any of the [Flux](https://github.com/FluxML/Flux.jl) model layers except
|
||||
`GlobalMaxPool` and `GlobalMeanPool`.
|
||||
|
||||
# Example
|
||||
```julia
|
||||
layer = Conv((2, 2), 1=>16, relu)
|
||||
input = (4,4)
|
||||
layerflops(Conv((2, 2), 1=>16, relu),(4,4))
|
||||
```
|
||||
"""
|
||||
function gradientflops(layer::Dense, input::Tuple)
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fn = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
bi = length(layer.b)
|
||||
noofoper = gradientoperations(layer)
|
||||
return convert(Float64, ((xo*yo+bi)*noofoper)*Fm), (xo,yo)
|
||||
end
|
||||
|
||||
function gradientflops(layer::Maxout,input::Tuple)
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fm = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
a,b = layer.over
|
||||
return convert(Float64,xo*yo*Fm), (x,1)
|
||||
end
|
||||
|
||||
|
||||
function gradientflops(layer::Conv, input::Tuple)
|
||||
_,_,_,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fm = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
noofoper = gradientoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,xo*yo*noofoper*Fm), (xo,yo,outputfeaturemaps)
|
||||
else
|
||||
return convert(Float64,((xo*yo+size(layer.bias))*noofoper)*Fm), (xo,yo,outputfeaturemaps)
|
||||
end
|
||||
end
|
||||
|
||||
function gradientflops(layer::MaxPool, input::Tuple)
|
||||
kernelsizex,kernelsizey = layer.k
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fm = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
return convert(Float64,outx*outy*Fm), (outx,outy)
|
||||
end
|
||||
|
||||
gradientflops(layer::GlobalMaxPool, input::Tuple) = error("Must be implemented in a future release")
|
||||
|
||||
function gradientflops(layer::MeanPool, input::Tuple)
|
||||
kernelsizex,kernelsizey = layer.k
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fm = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
return convert(Float64,outx*outy*Fm), (outx,outy)
|
||||
end
|
||||
|
||||
gradientflops(layer::GlobalMeanPool, input::Tuple) = error("Must be implemented in a future release")
|
||||
|
||||
function gradientflops(layer::DepthwiseConv, input::Tuple)
|
||||
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fm = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
noofoper = gradientoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
|
||||
else
|
||||
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
|
||||
end
|
||||
end
|
||||
|
||||
function gradientflops(layer::ConvTranspose, input::Tuple)
|
||||
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fm = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
noofoper = gradientoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
|
||||
else
|
||||
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
|
||||
end
|
||||
end
|
||||
|
||||
function gradientflops(layer::CrossCor, input::Tuple)
|
||||
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
x,y,Fm = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
elseif length(input) == 2
|
||||
x,y,_ = input
|
||||
xo, yo = outdims(layer,(x,y))
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
noofoper = gradientoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm), (outx,outy)
|
||||
else
|
||||
return convert(Float64,kernelsizex*kernelsizey*noofoper*Fm + (length(layer.bias)-1)), (outx,outy)
|
||||
end
|
||||
end
|
||||
|
||||
function gradientflops(layer::Recur{T}, input::Tuple) where {T <: RNNCell}
|
||||
inM,inN = input
|
||||
WhM,WhN = size(layer.cell.Wh)
|
||||
WiM,WiN = size(layer.cell.Wi)
|
||||
hM = length(layer.cell.h)
|
||||
noofoper = activationoperations(layer)
|
||||
if size(layer.cell.b) == ()
|
||||
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM)*noofoper)), (hM,1)
|
||||
else
|
||||
bM = length(layer.cell.b)
|
||||
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + bM)*noofoper)), (bM,1)
|
||||
end
|
||||
end
|
||||
|
||||
function gradientflops(layer::Recur{T}, input::Tuple) where {T <: LSTMCell}
|
||||
inM,inN = input
|
||||
WhM,WhN = size(layer.cell.Wh)
|
||||
WiM,WiN = size(layer.cell.Wi)
|
||||
hM = length(layer.cell.h)
|
||||
noofoper = 3
|
||||
if size(layer.cell.b) == ()
|
||||
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM)), (hM,1)
|
||||
else
|
||||
bM = length(layer.cell.b)
|
||||
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+(3*bM)+((bM*noofoper)+bM)), (bM,1)
|
||||
end
|
||||
end
|
||||
|
||||
function gradientflops(layer::Recur{T}, input::Tuple) where {T <: GRUCell}
|
||||
inM,inN = input
|
||||
WhM,WhN = size(layer.cell.Wh)
|
||||
WiM,WiN = size(layer.cell.Wi)
|
||||
hM = length(layer.cell.h)
|
||||
noofoper = 3
|
||||
if size(layer.cell.b) == ()
|
||||
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM)), (hM,1)
|
||||
else
|
||||
bM = length(layer.cell.b)
|
||||
return convert(Float64,((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*(WiN+hM)-inM + 2*bM)*noofoper)+(4bM)), (bM,1)
|
||||
end
|
||||
end
|
||||
|
|
@ -0,0 +1,223 @@
|
|||
"""
|
||||
layerflops(layer, input)::Float64
|
||||
|
||||
Calculates the number of non-embedding Floating Point Operations `neFLOPs` for most layers.
|
||||
`layer` should be any of the [Flux](https://github.com/FluxML/Flux.jl) model layers except
|
||||
`GlobalMaxPool` and `GlobalMeanPool`.
|
||||
|
||||
# Example
|
||||
```julia
|
||||
layer = Conv((2, 2), 1=>16, relu)
|
||||
input = (4,4)
|
||||
layerflops(Conv((2, 2), 1=>16, relu),(4,4))
|
||||
```
|
||||
|
||||
"""
|
||||
function layerflops(layer::Dense,input::Tuple)
|
||||
N,M = size(layer.W)
|
||||
Mi = 0; Ni = 0; Fm = 1; out = 0
|
||||
if length(input) == 3
|
||||
Mi,Ni,Fm = input
|
||||
out = outdims(layer,(Mi,Ni))
|
||||
elseif length(input) == 2
|
||||
Mi,Ni = input
|
||||
Fm = 1
|
||||
out = outdims(layer,input)
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
bi = length(layer.b)
|
||||
if length(out) < 2
|
||||
out = (out[1],1)
|
||||
end
|
||||
noofopers = activationoperations(layer)
|
||||
return convert(Float64,((2*Mi*N - M)+bi)*noofopers*Fm), out
|
||||
end
|
||||
|
||||
function layerflops(layer::Maxout,input::Tuple)
|
||||
i = 0; j = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
i,j,Fm = input
|
||||
elseif length(input) == 2
|
||||
i,j = input
|
||||
Fm = 1
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
a,b = layer.over
|
||||
return convert(Float64,a*b*j*i*Fm), (j,1)
|
||||
end
|
||||
|
||||
|
||||
function layerflops(layer::Conv, input::Tuple)
|
||||
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0
|
||||
if length(input) == 3
|
||||
x, y, _ = input
|
||||
elseif length(input) == 2
|
||||
x, y = input
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
noofoper = activationoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
else
|
||||
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
end
|
||||
end
|
||||
|
||||
function layerflops(layer::MaxPool, input::Tuple)
|
||||
Fm = 1
|
||||
if length(input) == 3
|
||||
_,_,Fm = input
|
||||
elseif length(input) == 2
|
||||
_,_ = input
|
||||
Fm = 1
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
kernelsizex,kernelsizey = layer.k
|
||||
outx, outy = outdims(layer,input)
|
||||
return convert(Float64,kernelsizex*kernelsizey*outx*outy*Fm), (outx,outy)
|
||||
end
|
||||
|
||||
layerflops(layer::GlobalMaxPool, input::Tuple) = error("Must be implemented in a future release")
|
||||
|
||||
function layerflops(layer::MeanPool, input::Tuple)
|
||||
Fm = 1
|
||||
if length(input) == 3
|
||||
_,_,Fm = input
|
||||
elseif length(input) == 2
|
||||
_,_ = input
|
||||
Fm = 1
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
kernelsizex,kernelsizey = layer.k
|
||||
outx, outy = outdims(layer,input)
|
||||
return convert(Float64,kernelsizex*kernelsizey*outx*outy*Fm), (outx,outy)
|
||||
end
|
||||
|
||||
layerflops(layer::GlobalMeanPool, input::Tuple) = error("Must be implemented in a future release")
|
||||
|
||||
function layerflops(layer::DepthwiseConv, input::Tuple)
|
||||
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0
|
||||
if length(input) == 3
|
||||
x, y, _ = input
|
||||
elseif length(input) == 2
|
||||
x, y = input
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
noofoper = activationoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
else
|
||||
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
end
|
||||
end
|
||||
|
||||
function layerflops(layer::ConvTranspose, input::Tuple)
|
||||
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0
|
||||
if length(input) == 3
|
||||
x, y, _ = input
|
||||
elseif length(input) == 2
|
||||
x, y = input
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
noofoper = activationoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
else
|
||||
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
end
|
||||
end
|
||||
|
||||
function layerflops(layer::CrossCor, input::Tuple)
|
||||
kernelsizex,kernelsizey,inputfeaturemaps,outputfeaturemaps = size(layer.weight)
|
||||
x = 0; y = 0
|
||||
if length(input) == 3
|
||||
x, y, _ = input
|
||||
elseif length(input) == 2
|
||||
x, y = input
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
outx, outy = outdims(layer,(x,y))
|
||||
noofoper = activationoperations(layer)
|
||||
if size(layer.bias) == ()
|
||||
return convert(Float64,inputfeaturemaps*(kernelsizex*kernelsizey+(kernelsizex*kernelsizey-1))*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
else
|
||||
return convert(Float64,(2*kernelsizex*kernelsizey)*inputfeaturemaps*outx*outy + outx*outy*noofoper*outputfeaturemaps), (outx,outy,outputfeaturemaps)
|
||||
end
|
||||
end
|
||||
|
||||
function layerflops(layer::Recur{T}, input::Tuple) where {T <: RNNCell}
|
||||
inM = 0; inN = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
inM,inN, Fm = input
|
||||
elseif length(input) == 2
|
||||
inM,inN = input
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
WhM,WhN = size(layer.cell.Wh)
|
||||
WiM,WiN = size(layer.cell.Wi)
|
||||
hM = length(layer.cell.h)
|
||||
noofoper = activationoperations(layer)
|
||||
if size(layer.cell.b) == ()
|
||||
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM)*noofoper))*Fm), (hM,1)
|
||||
else
|
||||
bM = length(layer.cell.b)
|
||||
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + bM)*noofoper))*Fm), (bM,1)
|
||||
end
|
||||
end
|
||||
|
||||
function layerflops(layer::Recur{T}, input::Tuple) where {T <: LSTMCell}
|
||||
inM = 0; inN = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
inM,inN, Fm = input
|
||||
elseif length(input) == 2
|
||||
inM,inN = input
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
WhM,WhN = size(layer.cell.Wh)
|
||||
WiM,WiN = size(layer.cell.Wi)
|
||||
hM = length(layer.cell.h)
|
||||
noofoper = 3
|
||||
if size(layer.cell.b) == ()
|
||||
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM))*Fm), (hM,1)
|
||||
else
|
||||
bM = length(layer.cell.b)
|
||||
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+(3*bM)+((bM*noofoper)+bM))*Fm), (bM,1)
|
||||
end
|
||||
end
|
||||
|
||||
function layerflops(layer::Recur{T}, input::Tuple) where {T <: GRUCell}
|
||||
inM = 0; inN = 0; Fm = 1
|
||||
if length(input) == 3
|
||||
inM,inN, Fm = input
|
||||
elseif length(input) == 2
|
||||
inM,inN = input
|
||||
else
|
||||
error("Not a valid Input size, expected (::Int,::Int) or (::Int,::Int,::Int)")
|
||||
end
|
||||
WhM,WhN = size(layer.cell.Wh)
|
||||
WiM,WiN = size(layer.cell.Wi)
|
||||
hM = length(layer.cell.h)
|
||||
noofoper = 3
|
||||
if size(layer.cell.b) == ()
|
||||
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM)*noofoper)+(3*hM)+((hM*noofoper)+hM))*Fm), (hM,1)
|
||||
else
|
||||
bM = length(layer.cell.b)
|
||||
return convert(Float64,(((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*WiN-inM + 2*bM)*noofoper)+((2*WhN*WhM-hM + 2*WiM*(WiN+hM)-inM + 2*bM)*noofoper)+(4bM))*Fm), (bM,1)
|
||||
end
|
||||
end
|
||||
|
|
@ -0,0 +1,149 @@
|
|||
"""
|
||||
activationoperations(layer)::Int
|
||||
|
||||
Outputs the approximate mumber of operations for a Flux activation function.
|
||||
|
||||
```julia
|
||||
layer = Conv(weight = weight,
|
||||
σ = sigmoid)
|
||||
|
||||
activationoperations(Conv(weight = weight,
|
||||
σ = sigmoid))
|
||||
```
|
||||
"""
|
||||
function activationoperations(layer::Recur)
|
||||
layer = recurrentunpack(layer)
|
||||
activation = layer.σ
|
||||
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||
activation == softsign || activation == softplus || activation == tanh
|
||||
noofoper = 3
|
||||
elseif activation == gelu
|
||||
noofoper = 8
|
||||
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||
activation == selu || activation == softmax || activation == logsoftmax
|
||||
noofoper = 4
|
||||
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||
activation == lisht || activation == tanhshrink
|
||||
noofoper = 2
|
||||
elseif activation == logcosh || activation == mish
|
||||
noofoper = 5
|
||||
elseif activation == relu || activation == trelu || activation == softshrink
|
||||
noofoper = 1
|
||||
elseif activation == identity
|
||||
noofoper = 0
|
||||
else
|
||||
@info "Unkown activation type defaulting to identity"
|
||||
return 0
|
||||
end
|
||||
return noofoper
|
||||
end
|
||||
|
||||
|
||||
function activationoperations(layer)
|
||||
activation = layer.σ
|
||||
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||
activation == softsign || activation == softplus || activation == tanh
|
||||
noofoper = 4
|
||||
elseif activation == gelu
|
||||
noofoper = 9
|
||||
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||
activation == selu || activation == softmax || activation == logsoftmax
|
||||
noofoper = 5
|
||||
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||
activation == lisht || activation == tanhshrink
|
||||
noofoper = 3
|
||||
elseif activation == logcosh || activation == mish
|
||||
noofoper = 6
|
||||
elseif activation == relu || activation == trelu || activation == softshrink
|
||||
noofoper = 2
|
||||
elseif activation == identity
|
||||
noofoper = 1
|
||||
else
|
||||
@info "Unkown activation type defaulting to identity"
|
||||
return 1
|
||||
end
|
||||
return noofoper
|
||||
end
|
||||
|
||||
"""
|
||||
gradientoperations(layer)::Int
|
||||
|
||||
Outputs the approximate mumber of operations for the gradient of a Flux activation
|
||||
function.
|
||||
|
||||
```julia
|
||||
layer = Conv(weight = weight,
|
||||
σ = sigmoid)
|
||||
|
||||
gradientoperations(Conv(weight = weight,
|
||||
σ = sigmoid))
|
||||
```
|
||||
"""
|
||||
function gradientoperations(layer::Recur)
|
||||
layer = recurrentunpack(layer)
|
||||
activation = layer.σ
|
||||
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||
activation == softsign || activation == softplus || activation == tanh
|
||||
noofoper = 4
|
||||
elseif activation == gelu
|
||||
noofoper = 8
|
||||
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||
activation == selu || activation == softmax || activation == logsoftmax
|
||||
noofoper = 4
|
||||
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||
activation == lisht || activation == tanhshrink
|
||||
noofoper = 2
|
||||
elseif activation == logcosh || activation == mish
|
||||
noofoper = 5
|
||||
elseif activation == relu || activation == trelu || activation == softshrink
|
||||
noofoper = 1
|
||||
elseif activation == identity
|
||||
noofoper = 0
|
||||
else
|
||||
@info "Unkown activation type defaulting to identity"
|
||||
return 1
|
||||
end
|
||||
return noofoper
|
||||
end
|
||||
|
||||
function gradientoperations(layer)
|
||||
activation = layer.σ
|
||||
if activation == sigmoid || activation == rrelu || activation == elu || activation == celu ||
|
||||
activation == softsign || activation == softplus || activation == tanh
|
||||
noofoper = 4
|
||||
elseif activation == gelu
|
||||
noofoper = 8
|
||||
elseif activation == hardsigmoid || activation == logsigmoid || activation == swish ||
|
||||
activation == selu || activation == softmax || activation == logsoftmax
|
||||
noofoper = 4
|
||||
elseif activation == hardtanh || activation == leakyrelu || activation == relu6 ||
|
||||
activation == lisht || activation == tanhshrink
|
||||
noofoper = 2
|
||||
elseif activation == logcosh || activation == mish
|
||||
noofoper = 5
|
||||
elseif activation == relu || activation == trelu || activation == softshrink
|
||||
noofoper = 1
|
||||
elseif activation == identity
|
||||
noofoper = 0
|
||||
else
|
||||
@info "Unkown activation type defaulting to identity"
|
||||
return 1
|
||||
end
|
||||
return noofoper
|
||||
end
|
||||
|
||||
function recurrentunpack(layer::Recur)
|
||||
return layer.cell
|
||||
end
|
||||
|
||||
islayer(::Any) = false
|
||||
islayer(::Recur) = true
|
||||
islayer(::Dense) = true
|
||||
islayer(::Conv) = true
|
||||
islayer(::MaxPool) = true
|
||||
islayer(::MeanPool) = true
|
||||
islayer(::DepthwiseConv) = true
|
||||
islayer(::ConvTranspose) = true
|
||||
islayer(::CrossCor) = true
|
||||
islayer(::Maxout) = true
|
||||
|
||||
|
|
@ -0,0 +1,44 @@
|
|||
"""
|
||||
modelflops(model::Chain)::Float64
|
||||
|
||||
Calculates the approximate number of Floating Point Operations that the model will require
|
||||
|
||||
|
||||
```julia
|
||||
weight = rand(Float64, 3, 3, 5)
|
||||
bias = zeros(Float64, 5)
|
||||
Conv(weight = weight,
|
||||
bias = bias,
|
||||
σ = sigmoid)
|
||||
```
|
||||
"""
|
||||
function modelflops(model::Chain,inputsize::Tuple,samplesize::Float64,batchsize::Float64)
|
||||
x = 0; y = 0; Fm = 1
|
||||
if length(inputsize) == 3
|
||||
x,y,Fm = inputsize
|
||||
elseif length(inputsize) == 2
|
||||
x,y = inputsize
|
||||
end
|
||||
modellayers = collect(model)
|
||||
nelayers = Array{Any,1}()
|
||||
layeroutput = Array{Tuple,1}()
|
||||
outsizes = Array{Tuple,1}()
|
||||
lossandgradient = 1
|
||||
for ml in modellayers
|
||||
if islayer(ml)
|
||||
push!(nelayers,ml)
|
||||
end
|
||||
end
|
||||
output = (0,0)
|
||||
for mli in 1:length(nelayers)
|
||||
if mli == 1
|
||||
noflops, output = layerflops(nelayers[mli],inputsize)
|
||||
layeroutput = vcat(layeroutput, noflops)
|
||||
else
|
||||
noflops, output = layerflops(nelayers[mli],output)
|
||||
layeroutput = vcat(layeroutput, noflops)
|
||||
end
|
||||
end
|
||||
numberoflayers = length(layeroutput)
|
||||
return sum(layeroutput) * samplesize + lossandgradient*batchsize
|
||||
end
|
||||
|
|
@ -0,0 +1,138 @@
|
|||
"""
|
||||
gpupowerdraw()::Float64
|
||||
|
||||
The function uses Linux `nvidia-smi` package to sample and get the average electricity
|
||||
draw of the GPUs.
|
||||
"""
|
||||
function gpupowerdraw()
|
||||
if has_cuda_gpu()
|
||||
gpucommand = `nvidia-smi`
|
||||
usage = Array{Any}(undef,60)
|
||||
cap = Array{Any}(undef,60)
|
||||
nogpus = 0
|
||||
|
||||
for count in 1:60
|
||||
smis = Array{Any}[]
|
||||
smiss = Array{Any}[]
|
||||
gpus = Array{Any}[]
|
||||
powerdraw = Array{Float64}[]
|
||||
powercap = Array{Float64}[]
|
||||
|
||||
smi = read(gpucommand, String);
|
||||
smi = split(smi, "\n")
|
||||
for s in smi
|
||||
push!(smis,split(s, " "))
|
||||
end
|
||||
for s in smis
|
||||
push!(smiss,filter(x->x≠"",s))
|
||||
end
|
||||
for strings in smiss
|
||||
if length(strings) > 5 && strings[6] == "/" && strings[10] == "/"
|
||||
push!(gpus,strings)
|
||||
end
|
||||
end
|
||||
|
||||
nogpus = length(gpus)
|
||||
|
||||
for g in gpus
|
||||
usagestr = ""
|
||||
capstr = ""
|
||||
if g[5] == "N/A"
|
||||
usagestr = "0.0"
|
||||
else
|
||||
usagestr = usagestr * g[5]
|
||||
end
|
||||
if g[7] == "N/A"
|
||||
capstr = "0.0"
|
||||
else
|
||||
capstr = capstr * g[7]
|
||||
end
|
||||
powerdraw = vcat(powerdraw, parse(Float64,usagestr))
|
||||
powercap = vcat(powercap, parse(Float64,capstr))
|
||||
end
|
||||
usage[count] = mean(powerdraw)
|
||||
cap[count] = mean(powercap)
|
||||
|
||||
sleep(1)
|
||||
end
|
||||
return nogpus, mean(usage), mean(cap)
|
||||
else
|
||||
@info "This computer does not have acces to a GPU passing to CPU and RAM computations"
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
"""
|
||||
cpupowerdraw()::Float64
|
||||
|
||||
This function uses the Linux `powerstat` utility to get the average CPU energy cost.
|
||||
"""
|
||||
function cpupowerdraw()
|
||||
cpucommand = `powerstat -R -n -d0`
|
||||
try
|
||||
cpu = read(cpucommand, String);
|
||||
cpu = split(cpu,"\n")
|
||||
cpu = cpu[66][60:64]
|
||||
|
||||
return parse(Float64,cpu)
|
||||
catch e
|
||||
@info "powerstat not installed in your computer"
|
||||
end
|
||||
end
|
||||
|
||||
|
||||
#TODO: further fine tune the model
|
||||
"""
|
||||
rampowerdraw()::Float64
|
||||
|
||||
[Approximate RAM Power Draw](https://www.jedec.org/) the values are provided by the JEDEC we just take the
|
||||
ratio of activated memory against the unactivated for the maximum power value and convert it
|
||||
to hours.
|
||||
"""
|
||||
function rampowerdraw()
|
||||
ramcommand = `free`
|
||||
powerused = Array{Float64}(undef,60)
|
||||
for count in 1:60
|
||||
ram = read(ramcommand, String);
|
||||
ram = split(ram,"\n")
|
||||
ram = split(ram[2]," ")
|
||||
filter!(x->x≠"",ram)
|
||||
usedram = parse(Float64,ram[3])
|
||||
totalram = parse(Float64,ram[2])
|
||||
powerused[count] = ((usedram*1.575)/totalram)*1.904
|
||||
sleep(1)
|
||||
end
|
||||
return mean(powerused)
|
||||
end
|
||||
|
||||
|
||||
#TODO: modify the code to work in Windows.
|
||||
"""
|
||||
avgpowerdraw()::Float64
|
||||
|
||||
[Average Power Draw](https://arxiv.org/abs/1906.02243) where `pc` is the average power
|
||||
draw (in watts) from all CPU sockets during training, let `pr` be the average power draw from all
|
||||
DRAM (main memory) sockets, let `pg` be the average power draw of the GPUs during training and `g`
|
||||
the number of available gpus.
|
||||
|
||||
`apd = 1.58*t*(pc + pr + g*pg)/1000`
|
||||
|
||||
returns the average power consumption in kWh.
|
||||
"""
|
||||
function avgpowerdraw()
|
||||
if has_cuda_gpu()
|
||||
starttime = time()
|
||||
g, pg, _ = gpupowerdraw()
|
||||
pc = cpupowerdraw()
|
||||
pr = rampowerdraw()
|
||||
endtime = time()
|
||||
elapsedtime = (endtime - starttime)/3600
|
||||
return 1.58*elapsedtime*(pc + pr + g*pg)/1000
|
||||
else
|
||||
pc = cpupowerdraw()
|
||||
pr = rampowerdraw()
|
||||
endtime = time()
|
||||
elapsedtime = (endtime - starttime)/3600
|
||||
return 1.58*elapsedtime*(pc + pr)/1000
|
||||
end
|
||||
end
|
||||
Loading…
Reference in New Issue