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[
"MIT"
] | 1.0.0 | 0fd73bf40485c791e6c33672c643bf1303045e9a | src/BatchIterators.jl | code | 3037 | module BatchIterators
using Statistics
export BatchIterator
export choose_batchsize
export centered_batch_iterator
"""
BatchIterator(X; batchsize = nothing, limit=size(X,2))
Wrapper allowing to iterate over batches of `batchsize` columns of `X`. `X` can be of any type supporting `size` and 2d indexing. When `limit` is provided, iteration is restricted to the columns of `X[:, 1:limit]`.
"""
struct BatchIterator{T}
X::T
length::Int # Number of batches
bsz::Int # Batch size
limit::Int
function BatchIterator(X; batchsize=nothing, limit=size(X,2))
@assert limit > 0 && limit ≤ size(X,2)
bsz = (batchsize == nothing) ? choose_batchsize(size(X,1), limit) : batchsize
nb = ceil(Int, limit/bsz)
new{typeof(X)}(X, nb, bsz, limit)
end
end
view_compatible(::Any) = false
view_compatible(::Array) = true
view_compatible(bi::BatchIterator) = view_compatible(bi.X)
#######################################################################
# Iteration #
#######################################################################
function Base.getindex(it::BatchIterator, i)
d = i - it.length # > 0 means overflow, == 0 means last batch
cbsz = (d == 0) ? mod(it.limit - 1, it.bsz) + 1 : it.bsz # Size of current batch
if (i<1 || d > 0)
@error "Out of bounds."
else
# TODO using views here might impact type stability.
view_compatible(it) ? (@view it.X[:, (i-1)*it.bsz+1:(i-1)*it.bsz+cbsz]) : it.X[:, (i-1)*it.bsz+1:(i-1)*it.bsz+cbsz]
end
end
Base.length(it::BatchIterator) = it.length
function Base.iterate(it::BatchIterator{T}, st = 0) where T
st = st + 1 # new state
d = st - it.length # > 0 means overflow, == 0 means last batch
(d > 0) ? nothing : (it[st], st)
end
"""
centered_batch_iterator(X; kwargs...)
Similar to BatchIterator, but performs first one pass over the data to compute the mean, and centers the batches.
"""
function centered_batch_iterator(X; kwargs...)
bi = BatchIterator(X; kwargs...)
μ = vec(mean(mean(b, dims=2) for b in BatchIterator(X)))
(b .- μ for b in bi)
end
#######################################################################
# Utilities #
#######################################################################
"""
choose_batchsize(d, n; maxmemGB = 1.0, maxbatchsize = 2^14, sizeoneB = d*sizeof(Float64))
Computes the size (nb. of columns) of a batch, so that each column of the batch can be converted to a vector of size `sizeoneB` (in bytes) with a total memory constrained by `maxmemGB` (gigabytes).
"""
function choose_batchsize(d, n;
maxmemGB = 1.0,
maxbatchsize = 2^14,
sizeoneB = d*sizeof(Float64),
forcepow2 = true)
fullsizeGB = n * sizeoneB/1024^3 # Size of the sketches of all samples
batchsize = (fullsizeGB > maxmemGB) ? ceil(Int, n/ceil(Int, fullsizeGB/maxmemGB)) : n
batchsize = min(batchsize, maxbatchsize)
(forcepow2 && batchsize != n) ? prevpow(2, batchsize) : batchsize
end
end # module
| BatchIterators | https://github.com/Djoop/BatchIterators.jl.git |
[
"MIT"
] | 1.0.0 | 0fd73bf40485c791e6c33672c643bf1303045e9a | README.md | docs | 497 | # Summary
Licence: MIT.
A very small package providing the constructor `BatchIterator(X; batchsize=…, limit=…)` and the function `centered_batch_iterator(X; kwargs…)`, which allow iteration over blocks of columns of `X`, for any object `X` supporting 2d indexing and for which the function `size` is defined.
The function `choose_batchsize` helps finding a good batch size while controlling memory usage.
The package was originally designed to iterate over samples of an out-of-core dataset.
| BatchIterators | https://github.com/Djoop/BatchIterators.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/make.jl | code | 1151 | using PythonCallHelpers
using Documenter
DocMeta.setdocmeta!(PythonCallHelpers, :DocTestSetup, :(using PythonCallHelpers); recursive=true)
makedocs(;
modules=[PythonCallHelpers],
authors="singularitti <singularitti@outlook.com> and contributors",
repo="https://github.com/singularitti/PythonCallHelpers.jl/blob/{commit}{path}#{line}",
sitename="PythonCallHelpers.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://singularitti.github.io/PythonCallHelpers.jl",
edit_link="main",
assets=String[],
),
pages=[
"Home" => "index.md",
"Manual" => [
"Installation guide" => "installation.md",
],
"Public API" => "public.md",
"Developer Docs" => [
"Contributing" => "developers/contributing.md",
"Style Guide" => "developers/style-guide.md",
"Design Principles" => "developers/design-principles.md",
],
"Troubleshooting" => "troubleshooting.md",
],
)
deploydocs(;
repo="github.com/singularitti/PythonCallHelpers.jl",
devbranch="main",
)
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | src/PythonCallHelpers.jl | code | 3656 | module PythonCallHelpers
using PythonCall: Py, pygetattr, pyhasattr, pyconvert
export @pyimmutable, @pymutable, @pycallable
# Code from https://github.com/stevengj/PythonPlot.jl/blob/d17c1d5/src/PythonPlot.jl#L26-L52
struct LazyHelp
obj::Py
keys::Tuple{Vararg{String}}
LazyHelp(obj) = new(obj, ())
LazyHelp(obj, key::AbstractString) = new(obj, (key,))
LazyHelp(obj, key1::AbstractString, key2::AbstractString) = new(obj, (key1, key2))
LazyHelp(obj, keys::AbstractString...) = new(obj, keys)
end
function Base.show(io::IO, ::MIME"text/plain", help::LazyHelp)
obj = help.obj
for key in help.keys
obj = pygetattr(obj, key)
end
if pyhasattr(obj, "__doc__")
print(io, pyconvert(String, obj.__doc__))
else
print(io, "no Python docstring found for ", obj)
end
end
Base.show(io::IO, help::LazyHelp) = show(io, "text/plain", help)
function Base.Docs.catdoc(helps::LazyHelp...)
Base.Docs.Text() do io
for help in helps
show(io, "text/plain", help)
end
end
end
# See https://github.com/rafaqz/DimensionalData.jl/blob/4814246/src/Dimensions/dimension.jl#L382-L398
function pybasic(type, field)
return quote
using PythonCall: pyhasattr
import PythonCall: Py, pyconvert
# Code from https://github.com/stevengj/PythonPlot.jl/blob/d58f6c4/src/PythonPlot.jl#L65-L72
Py(x::$type) = getfield(x, $(QuoteNode(Symbol(field))))
pyconvert(::Type{$type}, py::Py) = $type(py)
Base.:(==)(x::$type, y::$type) = pyconvert(Bool, Py(x) == Py(y))
Base.isequal(x::$type, y::$type) = isequal(Py(x), Py(y))
Base.hash(x::$type, h::UInt) = hash(Py(x), h)
Base.Docs.doc(x::$type) = Text(pyconvert(String, Py(x).__doc__))
# Code from https://github.com/stevengj/PythonPlot.jl/blob/d58f6c4/src/PythonPlot.jl#L75-L80
Base.getproperty(x::$type, s::Symbol) = getproperty(Py(x), s)
Base.getproperty(x::$type, s::AbstractString) = getproperty(Py(x), Symbol(s))
Base.hasproperty(x::$type, s::Symbol) = pyhasattr(Py(x), s)
Base.propertynames(x::$type) = propertynames(Py(x))
end
end
"""
@pyimmutable type [supertype] [field]
Construct an immutable wrapper for a Python object, with a supertype and a default fieldname.
"""
macro pyimmutable(type, supertype=Any, field=:py)
return esc(
quote
using PythonCall: Py
struct $type <: $supertype
$field::Py
end
$(pybasic(type, field))
end,
)
end
"""
@pymutable type [supertype] [field]
Construct an mutable wrapper for a Python object, with a supertype and a default fieldname.
"""
macro pymutable(type, supertype=Any, field=:py)
return esc(
quote
using PythonCall: Py
mutable struct $type <: $supertype
$field::Py
end
$(pybasic(type, field))
# Code from https://github.com/stevengj/PythonPlot.jl/blob/d58f6c4/src/PythonPlot.jl#L77-L78
Base.setproperty!(x::$type, s::Symbol, v) = setproperty!(Py(x), s, v)
Base.setproperty!(x::$type, s::AbstractString, v) =
setproperty!(Py(x), Symbol(s), v)
end,
)
end
# See https://github.com/stevengj/PythonPlot.jl/issues/19
"""
@pycallable type
Make an existing type callable.
"""
macro pycallable(type)
return quote
using PythonCall: Py
import PythonCall: pycall
pycall(x::$type, args...; kws...) = pycall(Py(x), args...; kws...)
(x::$type)(args...; kws...) = pycall(Py(x), args...; kws...)
end
end
end
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | test/runtests.jl | code | 440 | using PythonCallHelpers
using Test
@testset "PythonCallHelpers.jl" begin
@testset "Test subtyping from `Any`" begin
@pymutable T Any o
@test supertype(T) == Any
@test fieldnames(T) == (:o,)
end
@testset "Test subtyping from an abstract type" begin
abstract type MyType end
@pyimmutable My MyType o
@test supertype(My) == MyType
@test fieldnames(My) == (:o,)
end
end
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | README.md | docs | 4084 | # PythonCallHelpers
| **Documentation** | **Build Status** | **Others** |
| :--------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------: |
| [![Stable][docs-stable-img]][docs-stable-url] [![Dev][docs-dev-img]][docs-dev-url] | [![Build Status][gha-img]][gha-url] [![Build Status][appveyor-img]][appveyor-url] [![Build Status][cirrus-img]][cirrus-url] [![pipeline status][gitlab-img]][gitlab-url] [![Coverage][codecov-img]][codecov-url] | [![GitHub license][license-img]][license-url] [![Code Style: Blue][style-img]][style-url] |
[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-stable-url]: https://singularitti.github.io/PythonCallHelpers.jl/stable
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
[docs-dev-url]: https://singularitti.github.io/PythonCallHelpers.jl/dev
[gha-img]: https://github.com/singularitti/PythonCallHelpers.jl/workflows/CI/badge.svg
[gha-url]: https://github.com/singularitti/PythonCallHelpers.jl/actions
[appveyor-img]: https://ci.appveyor.com/api/projects/status/github/singularitti/PythonCallHelpers.jl?svg=true
[appveyor-url]: https://ci.appveyor.com/project/singularitti/PythonCallHelpers-jl
[cirrus-img]: https://api.cirrus-ci.com/github/singularitti/PythonCallHelpers.jl.svg
[cirrus-url]: https://cirrus-ci.com/github/singularitti/PythonCallHelpers.jl
[gitlab-img]: https://gitlab.com/singularitti/PythonCallHelpers.jl/badges/main/pipeline.svg
[gitlab-url]: https://gitlab.com/singularitti/PythonCallHelpers.jl/-/pipelines
[codecov-img]: https://codecov.io/gh/singularitti/PythonCallHelpers.jl/branch/main/graph/badge.svg
[codecov-url]: https://codecov.io/gh/singularitti/PythonCallHelpers.jl
[license-img]: https://img.shields.io/github/license/singularitti/PythonCallHelpers.jl
[license-url]: https://github.com/singularitti/PythonCallHelpers.jl/blob/main/LICENSE
[style-img]: https://img.shields.io/badge/code%20style-blue-4495d1.svg
[style-url]: https://github.com/invenia/BlueStyle
The code is [hosted on GitHub](https://github.com/singularitti/PythonCallHelpers.jl),
with some continuous integration services to test its validity.
This repository is created and maintained by [@singularitti](https://github.com/singularitti).
You are very welcome to contribute.
## Installation
The package can be installed with the Julia package manager.
From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
```
pkg> add PythonCallHelpers
```
Or, equivalently, via the [`Pkg` API](https://pkgdocs.julialang.org/v1/getting-started/):
```julia
julia> import Pkg; Pkg.add("PythonCallHelpers")
```
## Documentation
- [**STABLE**][docs-stable-url] — **documentation of the most recently tagged version.**
- [**DEV**][docs-dev-url] — _documentation of the in-development version._
## Project status
The package is tested against, and being developed for, Julia `1.6` and above on Linux,
macOS, and Windows.
## Questions and contributions
You are welcome to post usage questions on [our discussion page][discussions-url].
Contributions are very welcome, as are feature requests and suggestions. Please open an
[issue][issues-url] if you encounter any problems. The [Contributing](@ref) page has
guidelines that should be followed when opening pull requests and contributing code.
[discussions-url]: https://github.com/singularitti/PythonCallHelpers.jl/discussions
[issues-url]: https://github.com/singularitti/PythonCallHelpers.jl/issues
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/src/index.md | docs | 2021 | ```@meta
CurrentModule = PythonCallHelpers
```
# PythonCallHelpers
Documentation for [PythonCallHelpers](https://github.com/singularitti/PythonCallHelpers.jl).
See the [Index](@ref main-index) for the complete list of documented functions
and types.
The code is [hosted on GitHub](https://github.com/singularitti/PythonCallHelpers.jl),
with some continuous integration services to test its validity.
This repository is created and maintained by [@singularitti](https://github.com/singularitti).
You are very welcome to contribute.
## Installation
The package can be installed with the Julia package manager.
From the Julia REPL, type `]` to enter the Pkg REPL mode and run:
```julia
pkg> add PythonCallHelpers
```
Or, equivalently, via the `Pkg` API:
```@repl
import Pkg; Pkg.add("PythonCallHelpers")
```
## Documentation
- [**STABLE**](https://singularitti.github.io/PythonCallHelpers.jl/stable) — **documentation of the most recently tagged version.**
- [**DEV**](https://singularitti.github.io/PythonCallHelpers.jl/dev) — _documentation of the in-development version._
## Project status
The package is tested against, and being developed for, Julia `1.6` and above on Linux,
macOS, and Windows.
## Questions and contributions
Usage questions can be posted on
[our discussion page](https://github.com/singularitti/PythonCallHelpers.jl/discussions).
Contributions are very welcome, as are feature requests and suggestions. Please open an
[issue](https://github.com/singularitti/PythonCallHelpers.jl/issues)
if you encounter any problems. The [Contributing](@ref) page has
a few guidelines that should be followed when opening pull requests and contributing code.
## Manual outline
```@contents
Pages = [
"installation.md",
"developers/contributing.md",
"developers/style-guide.md",
"developers/design-principles.md",
"troubleshooting.md",
]
Depth = 3
```
## Library outline
```@contents
Pages = ["public.md"]
```
### [Index](@id main-index)
```@index
Pages = ["public.md"]
```
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/src/installation.md | docs | 5269 | # [Installation Guide](@id installation)
Here are the installation instructions for package
[PythonCallHelpers](https://github.com/singularitti/PythonCallHelpers.jl).
If you have trouble installing it, please refer to our [Troubleshooting](@ref) page
for more information.
## Install Julia
First, you should install [Julia](https://julialang.org/). We recommend downloading it from
[its official website](https://julialang.org/downloads/). Please follow the detailed
instructions on its website if you have to
[build Julia from source](https://docs.julialang.org/en/v1/devdocs/build/build/).
Some computing centers provide preinstalled Julia. Please contact your administrator for
more information in that case.
Here's some additional information on
[how to set up Julia on HPC systems](https://github.com/hlrs-tasc/julia-on-hpc-systems).
If you have [Homebrew](https://brew.sh) installed,
[open `Terminal.app`](https://support.apple.com/guide/terminal/open-or-quit-terminal-apd5265185d-f365-44cb-8b09-71a064a42125/mac)
and type
```shell
brew install julia
```
to install it as a [formula](https://docs.brew.sh/Formula-Cookbook).
If you are also using macOS and want to install it as a prebuilt binary app, type
```shell
brew install --cask julia
```
instead.
If you want to install multiple Julia versions in the same operating system,
a recommended way is to use a version manager such as
[`juliaup`](https://github.com/JuliaLang/juliaup).
First, [install `juliaup`](https://github.com/JuliaLang/juliaup#installation).
Then, run
```shell
juliaup add release
juliaup default release
```
to configure the `julia` command to start the latest stable version of
Julia (this is also the default value).
There is a [short video introduction to `juliaup`](https://youtu.be/14zfdbzq5BM)
made by its authors.
### Which version should I pick?
You can install the "Current stable release" or the "Long-term support (LTS)
release".
- The "Current stable release" is the latest release of Julia. It has access to
newer features, and is likely faster.
- The "Long-term support release" is an older version of Julia that has
continued to receive bug and security fixes. However, it may not have the
latest features or performance improvements.
For most users, you should install the "Current stable release", and whenever
Julia releases a new version of the current stable release, you should update
your version of Julia. Note that any code you write on one version of the
current stable release will continue to work on all subsequent releases.
For users in restricted software environments (e.g., your enterprise IT controls
what software you can install), you may be better off installing the long-term
support release because you will not have to update Julia as frequently.
Versions higher than `v1.3`,
especially `v1.6`, are strongly recommended. This package may not work on `v1.0` and below.
Since the Julia team has set `v1.6` as the LTS release,
we will gradually drop support for versions below `v1.6`.
Julia and Julia packages support multiple operating systems and CPU architectures; check
[this table](https://julialang.org/downloads/#supported_platforms) to see if it can be
installed on your machine. For Mac computers with M-series processors, this package and its
dependencies may not work. Please install the Intel-compatible version of Julia (for macOS
x86-64) if any platform-related error occurs.
## Install PythonCallHelpers
Now I am using [macOS](https://en.wikipedia.org/wiki/MacOS) as a standard
platform to explain the following steps:
1. Open `Terminal.app`, and type `julia` to start an interactive session (known as the
[REPL](https://docs.julialang.org/en/v1/stdlib/REPL/)).
2. Run the following commands and wait for them to finish:
```julia-repl
julia> using Pkg
julia> Pkg.update()
julia> Pkg.add("PythonCallHelpers")
```
3. Run
```julia-repl
julia> using PythonCallHelpers
```
and have fun!
4. While using, please keep this Julia session alive. Restarting might cost some time.
If you want to install the latest in-development (probably buggy)
version of PythonCallHelpers, type
```@repl
using Pkg
Pkg.update()
pkg"add https://github.com/singularitti/PythonCallHelpers.jl"
```
in the second step above.
## Update PythonCallHelpers
Please [watch](https://docs.github.com/en/account-and-profile/managing-subscriptions-and-notifications-on-github/setting-up-notifications/configuring-notifications#configuring-your-watch-settings-for-an-individual-repository)
our [GitHub repository](https://github.com/singularitti/PythonCallHelpers.jl)
for new releases.
Once we release a new version, you can update PythonCallHelpers by typing
```@repl
using Pkg
Pkg.update("PythonCallHelpers")
Pkg.gc()
```
in the Julia REPL.
## Uninstall and reinstall PythonCallHelpers
Sometimes errors may occur if the package is not properly installed.
In this case, you may want to uninstall and reinstall the package. Here is how to do that:
1. To uninstall, in a Julia session, run
```julia-repl
julia> using Pkg
julia> Pkg.rm("PythonCallHelpers")
julia> Pkg.gc()
```
2. Press `ctrl+d` to quit the current session. Start a new Julia session and
reinstall PythonCallHelpers.
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/src/public.md | docs | 164 | ```@meta
CurrentModule = PythonCallHelpers
```
# API Reference
```@contents
Pages = ["public.md"]
Depth = 3
```
```@docs
@pyimmutable
@pymutable
@pycallable
```
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/src/troubleshooting.md | docs | 2158 | # Troubleshooting
```@contents
Pages = ["troubleshooting.md"]
Depth = 3
```
This page collects some possible errors you may encounter and trick how to fix them.
If you have some questions about how to use this code, you are welcome to
[discuss with us](https://github.com/singularitti/PythonCallHelpers.jl/discussions).
If you have additional tips, please either
[report an issue](https://github.com/singularitti/PythonCallHelpers.jl/issues/new) or
[submit a PR](https://github.com/singularitti/PythonCallHelpers.jl/compare) with suggestions.
## Installation problems
### Cannot find the `julia` executable
Make sure you have Julia installed in your environment. Please download the latest
[stable version](https://julialang.org/downloads/#current_stable_release) for your platform.
If you are using a *nix system, the recommended way is to use
[Juliaup](https://github.com/JuliaLang/juliaup). If you do not want to install Juliaup
or you are using other platforms that Julia supports, download the corresponding binaries.
Then, create a symbolic link to the Julia executable. If the path is not in your `$PATH`
environment variable, export it to your `$PATH`.
Some clusters, like
[Habanero](https://confluence.columbia.edu/confluence/display/rcs/Habanero+HPC+Cluster+User+Documentation),
[Comet](https://www.sdsc.edu/support/user_guides/comet.html),
or [Expanse](https://www.sdsc.edu/services/hpc/expanse/index.html),
already have Julia installed as a module, you may
just `module load julia` to use it. If not, either install by yourself or contact your
administrator.
## Loading PythonCallHelpers
### Julia compiles/loads slow
First, we recommend you download the latest version of Julia. Usually, the newest version
has the best performance.
If you just want Julia to do a simple task and only once, you could start the Julia REPL with
```bash
julia --compile=min
```
to minimize compilation or
```bash
julia --optimize=0
```
to minimize optimizations, or just use both. Or you could make a system image
and run with
```bash
julia --sysimage custom-image.so
```
See [Fredrik Ekre's talk](https://youtu.be/IuwxE3m0_QQ?t=313) for details.
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/src/developers/contributing.md | docs | 8613 | # Contributing
```@contents
Pages = ["contributing.md"]
Depth = 3
```
Welcome! This document explains some ways you can contribute to PythonCallHelpers.
## Code of conduct
This project and everyone participating in it is governed by the
["Contributor Covenant Code of Conduct"](https://github.com/MineralsCloud/.github/blob/main/CODE_OF_CONDUCT.md).
By participating, you are expected to uphold this code.
## Join the community forum
First up, join the [community forum](https://github.com/singularitti/PythonCallHelpers.jl/discussions).
The forum is a good place to ask questions about how to use PythonCallHelpers. You can also
use the forum to discuss possible feature requests and bugs before raising a
GitHub issue (more on this below).
Aside from asking questions, the easiest way you can contribute to PythonCallHelpers is to
help answer questions on the forum!
## Improve the documentation
Chances are, if you asked (or answered) a question on the community forum, then
it is a sign that the [documentation](https://singularitti.github.io/PythonCallHelpers.jl/dev/) could be
improved. Moreover, since it is your question, you are probably the best-placed
person to improve it!
The docs are written in Markdown and are built using
[Documenter.jl](https://github.com/JuliaDocs/Documenter.jl).
You can find the source of all the docs
[here](https://github.com/singularitti/PythonCallHelpers.jl/tree/main/docs).
If your change is small (like fixing typos, or one or two sentence corrections),
the easiest way to do this is via GitHub's online editor. (GitHub has
[help](https://help.github.com/articles/editing-files-in-another-user-s-repository/)
on how to do this.)
If your change is larger, or touches multiple files, you will need to make the
change locally and then use Git to submit a
[pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/about-pull-requests).
(See [Contribute code to PythonCallHelpers](@ref) below for more on this.)
## File a bug report
Another way to contribute to PythonCallHelpers is to file
[bug reports](https://github.com/singularitti/PythonCallHelpers.jl/issues/new?template=bug_report.md).
Make sure you read the info in the box where you write the body of the issue
before posting. You can also find a copy of that info
[here](https://github.com/singularitti/PythonCallHelpers.jl/blob/main/.github/ISSUE_TEMPLATE/bug_report.md).
!!! tip
If you're unsure whether you have a real bug, post on the
[community forum](https://github.com/singularitti/PythonCallHelpers.jl/discussions)
first. Someone will either help you fix the problem, or let you know the
most appropriate place to open a bug report.
## Contribute code to PythonCallHelpers
Finally, you can also contribute code to PythonCallHelpers!
!!! warning
If you do not have experience with Git, GitHub, and Julia development, the
first steps can be a little daunting. However, there are lots of tutorials
available online, including:
- [GitHub](https://guides.github.com/activities/hello-world/)
- [Git and GitHub](https://try.github.io/)
- [Git](https://git-scm.com/book/en/v2)
- [Julia package development](https://docs.julialang.org/en/v1/stdlib/Pkg/#Developing-packages-1)
Once you are familiar with Git and GitHub, the workflow for contributing code to
PythonCallHelpers is similar to the following:
### Step 1: decide what to work on
The first step is to find an [open issue](https://github.com/singularitti/PythonCallHelpers.jl/issues)
(or open a new one) for the problem you want to solve. Then, _before_ spending
too much time on it, discuss what you are planning to do in the issue to see if
other contributors are fine with your proposed changes. Getting feedback early can
improve code quality, and avoid time spent writing code that does not get merged into
PythonCallHelpers.
!!! tip
At this point, remember to be patient and polite; you may get a _lot_ of
comments on your issue! However, do not be afraid! Comments mean that people are
willing to help you improve the code that you are contributing to PythonCallHelpers.
### Step 2: fork PythonCallHelpers
Go to [https://github.com/singularitti/PythonCallHelpers.jl](https://github.com/singularitti/PythonCallHelpers.jl)
and click the "Fork" button in the top-right corner. This will create a copy of
PythonCallHelpers under your GitHub account.
### Step 3: install PythonCallHelpers locally
Similar to [Installation](@ref), open the Julia REPL and run:
```@repl
using Pkg
Pkg.update()
Pkg.develop("PythonCallHelpers")
```
Then the package will be cloned to your local machine. On *nix systems, the default path is
`~/.julia/dev/PythonCallHelpers` unless you modify the
[`JULIA_DEPOT_PATH`](http://docs.julialang.org/en/v1/manual/environment-variables/#JULIA_DEPOT_PATH-1)
environment variable. If you're on
Windows, this will be `C:\\Users\\<my_name>\\.julia\\dev\\PythonCallHelpers`.
In the following text, we will call it `PKGROOT`.
Go to `PKGROOT`, start a new Julia session and run
```@repl
using Pkg
Pkg.instantiate()
```
to instantiate the project.
### Step 4: checkout a new branch
!!! note
In the following, replace any instance of `GITHUB_ACCOUNT` with your GitHub
username.
The next step is to checkout a development branch. In a terminal (or command
prompt on Windows), run:
```shell
cd ~/.julia/dev/PythonCallHelpers
git remote add GITHUB_ACCOUNT https://github.com/GITHUB_ACCOUNT/PythonCallHelpers.jl.git
git checkout main
git pull
git checkout -b my_new_branch
```
### Step 5: make changes
Now make any changes to the source code inside the `~/.julia/dev/PythonCallHelpers`
directory.
Make sure you:
- Follow our [Style Guide](@ref style) and [run `JuliaFormatter.jl`](@ref formatter)
- Add tests and documentation for any changes or new features
!!! tip
When you change the source code, you'll need to restart Julia for the
changes to take effect. This is a pain, so install
[Revise.jl](https://github.com/timholy/Revise.jl).
### Step 6a: test your code changes
To test that your changes work, run the PythonCallHelpers test-suite by opening Julia and
running:
```@repl
cd("~/.julia/dev/PythonCallHelpers")
using Pkg
Pkg.activate(".")
Pkg.test()
```
!!! warning
Running the tests might take a long time.
!!! tip
If you're using `Revise.jl`, you can also run the tests by calling `include`:
```julia
include("test/runtests.jl")
```
This can be faster if you want to re-run the tests multiple times.
### Step 6b: test your documentation changes
Open Julia, then run:
```@repl
cd("~/.julia/dev/PythonCallHelpers/docs")
using Pkg
Pkg.activate(".")
include("src/make.jl")
```
After a while, a folder `PKGROOT/docs/build` will appear. Open
`PKGROOT/docs/build/index.html` with your favorite browser, and have fun!
!!! warning
Building the documentation might take a long time.
!!! tip
If there's a problem with the tests that you don't know how to fix, don't
worry. Continue to step 5, and one of the PythonCallHelpers contributors will comment
on your pull request telling you how to fix things.
### Step 7: make a pull request
Once you've made changes, you're ready to push the changes to GitHub. Run:
```shell
cd ~/.julia/dev/PythonCallHelpers
git add .
git commit -m "A descriptive message of the changes"
git push -u GITHUB_ACCOUNT my_new_branch
```
Then go to [https://github.com/singularitti/PythonCallHelpers.jl/pulls](https://github.com/singularitti/PythonCallHelpers.jl/pulls)
and follow the instructions that pop up to open a pull request.
### Step 8: respond to comments
At this point, remember to be patient and polite; you may get a _lot_ of
comments on your pull request! However, do not be afraid! A lot of comments
means that people are willing to help you improve the code that you are
contributing to PythonCallHelpers.
To respond to the comments, go back to step 5, make any changes, test the
changes in step 6, and then make a new commit in step 7. Your PR will
automatically update.
### Step 9: cleaning up
Once the PR is merged, clean-up your Git repository ready for the
next contribution!
```shell
cd ~/.julia/dev/PythonCallHelpers
git checkout main
git pull
```
!!! note
If you have suggestions to improve this guide, please make a pull request!
It's particularly helpful if you do this after your first pull request
because you'll know all the parts that could be explained better.
Thanks for contributing to PythonCallHelpers!
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/src/developers/design-principles.md | docs | 15492 | # Design Principles
```@contents
Pages = ["design-principles.md"]
Depth = 3
```
We adopt some [`SciML`](https://sciml.ai/) design [guidelines](https://github.com/SciML/SciMLStyle)
here. Please read it before contributing!
## Consistency vs adherence
According to PEP8:
> A style guide is about consistency. Consistency with this style guide is important.
> Consistency within a project is more important. Consistency within one module or function is the most important.
>
> However, know when to be inconsistent—sometimes style guide recommendations just aren't
> applicable. When in doubt, use your best judgment. Look at other examples and decide what
> looks best. And don’t hesitate to ask!
## Community contribution guidelines
For a comprehensive set of community contribution guidelines, refer to [ColPrac](https://github.com/SciML/ColPrac).
A relevant point to highlight PRs should do one thing. In the context of style, this means that PRs which update
the style of a package's code should not be mixed with fundamental code contributions. This separation makes it
easier to ensure that large style improvement are isolated from substantive (and potentially breaking) code changes.
## Open source contributions are allowed to start small and grow over time
If the standard for code contributions is that every PR needs to support every possible input type that anyone can
think of, the barrier would be too high for newcomers. Instead, the principle is to be as correct as possible to
begin with, and grow the generic support over time. All recommended functionality should be tested, any known
generality issues should be documented in an issue (and with a `@test_broken` test when possible). However, a
function which is known to not be GPU-compatible is not grounds to block merging, rather it is an encouragement for a
follow-up PR to improve the general type support!
## Generic code is preferred unless code is known to be specific
For example, the code:
```@repl
function f(A, B)
for i in 1:length(A)
A[i] = A[i] + B[i]
end
end
```
would not be preferred for two reasons. One is that it assumes `A` uses one-based indexing, which would fail in cases
like [OffsetArrays](https://github.com/JuliaArrays/OffsetArrays.jl) and [FFTViews](https://github.com/JuliaArrays/FFTViews.jl).
Another issue is that it requires indexing, while not all array types support indexing (for example,
[CuArrays](https://github.com/JuliaGPU/CuArrays.jl)). A more generic compatible implementation of this function would be
to use broadcast, for example:
```@repl
function f(A, B)
@. A = A + B
end
```
which would allow support for a wider variety of array types.
## Internal types should match the types used by users when possible
If `f(A)` takes the input of some collections and computes an output from those collections, then it should be
expected that if the user gives `A` as an `Array`, the computation should be done via `Array`s. If `A` was a
`CuArray`, then it should be expected that the computation should be internally done using a `CuArray` (or appropriately
error if not supported). For these reasons, constructing arrays via generic methods, like `similar(A)`, is preferred when
writing `f` instead of using non-generic constructors like `Array(undef,size(A))` unless the function is documented as
being non-generic.
## Trait definition and adherence to generic interface is preferred when possible
Julia provides many interfaces, for example:
- [Iteration](https://docs.julialang.org/en/v1/manual/interfaces/#man-interface-iteration)
- [Indexing](https://docs.julialang.org/en/v1/manual/interfaces/#Indexing)
- [Broadcast](https://docs.julialang.org/en/v1/manual/interfaces/#man-interfaces-broadcasting)
Those interfaces should be followed when possible. For example, when defining broadcast overloads,
one should implement a `BroadcastStyle` as suggested by the documentation instead of simply attempting
to bypass the broadcast system via `copyto!` overloads.
When interface functions are missing, these should be added to Base Julia or an interface package,
like [ArrayInterface.jl](https://github.com/JuliaArrays/ArrayInterface.jl). Such traits should be
declared and used when appropriate. For example, if a line of code requires mutation, the trait
`ArrayInterface.ismutable(A)` should be checked before attempting to mutate, and informative error
messages should be written to capture the immutable case (or, an alternative code which does not
mutate should be given).
One example of this principle is demonstrated in the generation of Jacobian matrices. In many scientific
applications, one may wish to generate a Jacobian cache from the user's input `u0`. A naive way to generate
this Jacobian is `J = similar(u0,length(u0),length(u0))`. However, this will generate a Jacobian `J` such
that `J isa Matrix`.
## Macros should be limited and only be used for syntactic sugar
Macros define new syntax, and for this reason they tend to be less composable than other coding styles
and require prior familiarity to be easily understood. One principle to keep in mind is, "can the person
reading the code easily picture what code is being generated?". For example, a user of Soss.jl may not know
what code is being generated by:
```julia
@model (x, α) begin
σ ~ Exponential()
β ~ Normal()
y ~ For(x) do xj
Normal(α + β * xj, σ)
end
return y
end
```
and thus using such a macro as the interface is not preferred when possible. However, a macro like
[`@muladd`](https://github.com/SciML/MuladdMacro.jl) is trivial to picture on a code (it recursively
transforms `a*b + c` to `muladd(a,b,c)` for more
[accuracy and efficiency](https://en.wikipedia.org/wiki/Multiply%E2%80%93accumulate_operation)), so using
such a macro for example:
```julia
julia> @macroexpand(@muladd k3 = f(t + c3 * dt, @. uprev + dt * (a031 * k1 + a032 * k2)))
:(k3 = f((muladd)(c3, dt, t), (muladd).(dt, (muladd).(a032, k2, (*).(a031, k1)), uprev)))
```
is recommended. Some macros in this category are:
- `@inbounds`
- [`@muladd`](https://github.com/SciML/MuladdMacro.jl)
- `@view`
- [`@named`](https://github.com/SciML/ModelingToolkit.jl)
- `@.`
- [`@..`](https://github.com/YingboMa/FastBroadcast.jl)
Some performance macros, like `@simd`, `@threads`, or
[`@turbo` from LoopVectorization.jl](https://github.com/JuliaSIMD/LoopVectorization.jl),
make an exception in that their generated code may be foreign to many users. However, they still are
classified as appropriate uses as they are syntactic sugar since they do (or should) not change the behavior
of the program in measurable ways other than performance.
## Errors should be caught as high as possible, and error messages should be contextualized for newcomers
Whenever possible, defensive programming should be used to check for potential errors before they are encountered
deeper within a package. For example, if one knows that `f(u0,p)` will error unless `u0` is the size of `p`, this
should be caught at the start of the function to throw a domain specific error, for example "parameters and initial
condition should be the same size".
## Subpackaging and interface packages is preferred over conditional modules via Requires.jl
Requires.jl should be avoided at all costs. If an interface package exists, such as
[ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl) for defining automatic differentiation
rules without requiring a dependency on the whole ChainRules.jl system, or
[RecipesBase.jl](https://github.com/JuliaPlots/RecipesBase.jl) which allows for defining Plots.jl
plot recipes without a dependency on Plots.jl, a direct dependency on these interface packages is
preferred.
Otherwise, instead of resorting to a conditional dependency using Requires.jl, it is
preferred one creates subpackages, i.e. smaller independent packages kept within the same Github repository
with independent versioning and package management. An example of this is seen in
[Optimization.jl](https://github.com/SciML/Optimization.jl) which has subpackages like
[OptimizationBBO.jl](https://github.com/SciML/Optimization.jl/tree/master/lib/OptimizationBBO) for
BlackBoxOptim.jl support.
Some important interface packages to know about are:
- [ChainRulesCore.jl](https://github.com/JuliaDiff/ChainRulesCore.jl)
- [RecipesBase.jl](https://github.com/JuliaPlots/RecipesBase.jl)
- [ArrayInterface.jl](https://github.com/JuliaArrays/ArrayInterface.jl)
- [CommonSolve.jl](https://github.com/SciML/CommonSolve.jl)
- [SciMLBase.jl](https://github.com/SciML/SciMLBase.jl)
## Functions should either attempt to be non-allocating and reuse caches, or treat inputs as immutable
Mutating codes and non-mutating codes fall into different worlds. When a code is fully immutable,
the compiler can better reason about dependencies, optimize the code, and check for correctness.
However, many times a code making the fullest use of mutation can outperform even what the best compilers
of today can generate. That said, the worst of all worlds is when code mixes mutation with non-mutating
code. Not only is this a mishmash of coding styles, it has the potential non-locality and compiler
proof issues of mutating code while not fully benefiting from the mutation.
## Out-Of-Place and Immutability is preferred when sufficient performant
Mutation is used to get more performance by decreasing the amount of heap allocations. However,
if it's not helpful for heap allocations in a given spot, do not use mutation. Mutation is scary
and should be avoided unless it gives an immediate benefit. For example, if
matrices are sufficiently large, then `A*B` is as fast as `mul!(C,A,B)`, and thus writing
`A*B` is preferred (unless the rest of the function is being careful about being fully non-allocating,
in which case this should be `mul!` for consistency).
Similarly, when defining types, using `struct` is preferred to `mutable struct` unless mutating
the struct is a common occurrence. Even if mutating the struct is a common occurrence, see whether
using [Setfield.jl](https://github.com/jw3126/Setfield.jl) is sufficient. The compiler will optimize
the construction of immutable structs, and thus this can be more efficient if it's not too much of a
code hassle.
## Tests should attempt to cover a wide gamut of input types
Code coverage numbers are meaningless if one does not consider the input types. For example, one can
hit all the code with `Array`, but that does not test whether `CuArray` is compatible! Thus, it's
always good to think of coverage not in terms of lines of code but in terms of type coverage. A good
list of number types to think about are:
- `Float64`
- `Float32`
- `Complex`
- [`Dual`](https://github.com/JuliaDiff/ForwardDiff.jl)
- `BigFloat`
Array types to think about testing are:
- `Array`
- [`OffsetArray`](https://github.com/JuliaArrays/OffsetArrays.jl)
- [`CuArray`](https://github.com/JuliaGPU/CUDA.jl)
## When in doubt, a submodule should become a subpackage or separate package
Keep packages to one core idea. If there's something separate enough to be a submodule, could it
instead be a separate well-tested and documented package to be used by other packages? Most likely
yes.
## Globals should be avoided whenever possible
Global variables should be avoided whenever possible. When required, global variables should be
constants and have an all uppercase name separated with underscores (e.g. `MY_CONSTANT`). They should be
defined at the top of the file, immediately after imports and exports but before an `__init__` function.
If you truly want mutable global style behavior you may want to look into mutable containers.
## Type-stable and Type-grounded code is preferred wherever possible
Type-stable and type-grounded code helps the compiler create not only more optimized code, but also
faster to compile code. Always keep containers well-typed, functions specializing on the appropriate
arguments, and types concrete.
## Closures should be avoided whenever possible
Closures can cause accidental type instabilities that are difficult to track down and debug; in the
long run it saves time to always program defensively and avoid writing closures in the first place,
even when a particular closure would not have been problematic. A similar argument applies to reading
code with closures; if someone is looking for type instabilities, this is faster to do when code does
not contain closures.
Furthermore, if you want to update variables in an outer scope, do so explicitly with `Ref`s or self
defined structs.
For example,
```julia
map(Base.Fix2(getindex, i), vector_of_vectors)
```
is preferred over
```julia
map(v -> v[i], vector_of_vectors)
```
or
```julia
[v[i] for v in vector_of_vectors]
```
## Numerical functionality should use the appropriate generic numerical interfaces
While you can use `A\b` to do a linear solve inside a package, that does not mean that you should.
This interface is only sufficient for performing factorizations, and so that limits the scaling
choices, the types of `A` that can be supported, etc. Instead, linear solves within packages should
use LinearSolve.jl. Similarly, nonlinear solves should use NonlinearSolve.jl. Optimization should use
Optimization.jl. Etc. This allows the full generic choice to be given to the user without depending
on every solver package (effectively recreating the generic interfaces within each package).
## Functions should capture one underlying principle
Functions mean one thing. Every dispatch of `+` should be "the meaning of addition on these types".
While in theory you could add dispatches to `+` that mean something different, that will fail in
generic code for which `+` means addition. Thus, for generic code to work, code needs to adhere to
one meaning for each function. Every dispatch should be an instantiation of that meaning.
## Internal choices should be exposed as options whenever possible
Whenever possible, numerical values and choices within scripts should be exposed as options
to the user. This promotes code reusability beyond the few cases the author may have expected.
## Prefer code reuse over rewrites whenever possible
If a package has a function you need, use the package. Add a dependency if you need to. If the
function is missing a feature, prefer to add that feature to said package and then add it as a
dependency. If the dependency is potentially troublesome, for example because it has a high
load time, prefer to spend time helping said package fix these issues and add the dependency.
Only when it does not seem possible to make the package "good enough" should using the package
be abandoned. If it is abandoned, consider building a new package for this functionality as you
need it, and then make it a dependency.
## Prefer to not shadow functions
Two functions can have the same name in Julia by having different namespaces. For example,
`X.f` and `Y.f` can be two different functions, with different dispatches, but the same name.
This should be avoided whenever possible. Instead of creating `MyPackage.sort`, consider
adding dispatches to `Base.sort` for your types if these new dispatches match the underlying
principle of the function. If it doesn't, prefer to use a different name. While using `MyPackage.sort`
is not conflicting, it is going to be confusing for most people unfamiliar with your code,
so `MyPackage.special_sort` would be more helpful to newcomers reading the code.
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.1.0 | 6536386eaeea150a0b83074ea454fd70399ea9ae | docs/src/developers/style-guide.md | docs | 2308 | # [Style Guide](@id style)
```@contents
Pages = ["style.md"]
Depth = 3
```
This section describes the coding style rules that apply to our code and that
we recommend you to use it also.
In some cases, our style guide diverges from Julia's official
[Style Guide](https://docs.julialang.org/en/v1/manual/style-guide/) (Please read it!).
All such cases will be explicitly noted and justified.
Our style guide adopts many recommendations from the
[BlueStyle](https://github.com/invenia/BlueStyle).
Please read the [BlueStyle](https://github.com/invenia/BlueStyle)
before contributing to this package.
If not following, your pull requests may not be accepted.
!!! info
The style guide is always a work in progress, and not all PythonCallHelpers code
follows the rules. When modifying PythonCallHelpers, please fix the style violations
of the surrounding code (i.e., leave the code tidier than when you
started). If large changes are needed, consider separating them into
another pull request.
## Formatting
### [Run JuliaFormatter](@id formatter)
PythonCallHelpers uses [JuliaFormatter](https://github.com/domluna/JuliaFormatter.jl) as
an auto-formatting tool.
We use the options contained in [`.JuliaFormatter.toml`](https://github.com/singularitti/PythonCallHelpers.jl/blob/main/.JuliaFormatter.toml).
To format your code, `cd` to the PythonCallHelpers directory, then run:
```@repl
using Pkg
Pkg.add("JuliaFormatter")
using JuliaFormatter: format
format("docs");
format("src");
format("test");
```
!!! info
A continuous integration check verifies that all PRs made to PythonCallHelpers have
passed the formatter.
The following sections outline extra style guide points that are not fixed
automatically by JuliaFormatter.
### Use the Julia extension for Visual Studio Code
Please use [VS Code](https://code.visualstudio.com/) with the
[Julia extension](https://marketplace.visualstudio.com/items?itemName=julialang.language-julia)
to edit, format, and test your code.
We do not recommend using other editors to edit your code for the time being.
This extension already has [JuliaFormatter](https://github.com/domluna/JuliaFormatter.jl)
integrated. So to format your code, follow the steps listed
[here](https://www.julia-vscode.org/docs/stable/userguide/formatter/).
| PythonCallHelpers | https://github.com/singularitti/PythonCallHelpers.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | src/MatrixMarket.jl | code | 208 | module MatrixMarket
using SparseArrays
using LinearAlgebra
using TranscodingStreams, CodecZlib
export mmread, mmwrite, mminfo
include("mminfo.jl")
include("mmread.jl")
include("mmwrite.jl")
end # module
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | src/mminfo.jl | code | 1825 | """
mminfo(file)
Read header information on the size and structure from file. The actual data matrix is not
parsed.
# Arguments
- `file`: The filename or io stream.
"""
function mminfo(filename::String)
stream = open(filename, "r")
if endswith(filename, ".gz")
stream = TranscodingStream(GzipDecompressor(), stream)
end
info = mminfo(stream)
close(stream)
return info
end
function mminfo(stream::IO)
firstline = chomp(readline(stream))
if !startswith(firstline, "%%MatrixMarket")
throw(FileFormatException("Expected start of header `%%MatrixMarket`"))
end
tokens = split(firstline)
if length(tokens) != 5
throw(FileFormatException("Not enough words on first line, got $(length(tokens)) words"))
end
(head1, rep, field, symm) = map(lowercase, tokens[2:5])
if head1 != "matrix"
throw(FileFormatException("Unknown MatrixMarket data type: $head1 (only `matrix` is supported)"))
end
dimline = readline(stream)
# Skip all comments and empty lines
while length(chomp(dimline)) == 0 || (length(dimline) > 0 && dimline[1] == '%')
dimline = readline(stream)
end
rows, cols, entries = parse_dimension(dimline, rep)
return rows, cols, entries, rep, field, symm
end
struct FileFormatException <: Exception
msg::String
end
Base.showerror(io::IO, e::FileFormatException) = print(io, e.msg)
function parse_dimension(line::String, rep::String)
dims = map(x -> parse(Int, x), split(line))
if length(dims) < (rep == "coordinate" ? 3 : 2)
throw(FileFormatException(string("Could not read in matrix dimensions from line: ", line)))
end
if rep == "coordinate"
return dims[1], dims[2], dims[3]
else
return dims[1], dims[2], (dims[1] * dims[2])
end
end
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | src/mmread.jl | code | 3866 | """
mmread(filename, infoonly=false, retcoord=false)
Read the contents of the Matrix Market file `filename` into a matrix, which will be either
sparse or dense, depending on the Matrix Market format indicated by `coordinate` (coordinate
sparse storage), or `array` (dense array storage).
# Arguments
- `filename::String`: The file to read.
- `infoonly::Bool=false`: Only information on the size and structure is returned from
reading the header. The actual data for the matrix elements are not parsed.
- `retcoord::Bool`: If it is `true`, the rows, column and value vectors are returned along
with the header information.
"""
function mmread(filename::String, infoonly::Bool=false, retcoord::Bool=false)
stream = open(filename, "r")
if endswith(filename, ".gz")
stream = TranscodingStream(GzipDecompressor(), stream)
end
result = infoonly ? mminfo(stream) : mmread(stream, retcoord)
close(stream)
return result
end
function mmread(stream::IO, infoonly::Bool=false, retcoord::Bool=false)
rows, cols, entries, rep, field, symm = mminfo(stream)
infoonly && return rows, cols, entries, rep, field, symm
T = parse_eltype(field)
symfunc = parse_symmetric(symm)
if rep == "coordinate"
rn = Vector{Int}(undef, entries)
cn = Vector{Int}(undef, entries)
vals = Vector{T}(undef, entries)
for i in 1:entries
line = readline(stream)
splits = find_splits(line, num_splits(T))
rn[i] = parse_row(line, splits)
cn[i] = parse_col(line, splits, T)
vals[i] = parse_val(line, splits, T)
end
result = retcoord ? (rn, cn, vals, rows, cols, entries, rep, field, symm) :
symfunc(sparse(rn, cn, vals, rows, cols))
else
vals = [parse(Float64, readline(stream)) for _ in 1:entries]
A = reshape(vals, rows, cols)
result = symfunc(A)
end
return result
end
function parse_eltype(field::String)
if field == "real"
return Float64
elseif field == "complex"
return ComplexF64
elseif field == "integer"
return Int64
elseif field == "pattern"
return Bool
else
throw(FileFormatException("Unsupported field $field."))
end
end
function parse_symmetric(symm::String)
if symm == "general"
return identity
elseif symm == "symmetric" || symm == "hermitian"
return hermitianize!
elseif symm == "skew-symmetric"
return skewsymmetrize!
else
throw(FileFormatException("Unknown matrix symmetry: $symm."))
end
end
function hermitianize!(M::AbstractMatrix)
M .+= tril(M, -1)'
return M
end
function skewsymmetrize!(M::AbstractMatrix)
M .-= tril(M, -1)'
return M
end
parse_row(line, splits) = parse(Int, line[1:splits[1]])
parse_col(line, splits, ::Type{Bool}) = parse(Int, line[splits[1]:end])
parse_col(line, splits, eltype) = parse(Int, line[splits[1]:splits[2]])
function parse_val(line, splits, ::Type{ComplexF64})
real = parse(Float64, line[splits[2]:splits[3]])
imag = parse(Float64, line[splits[3]:length(line)])
return ComplexF64(real, imag)
end
parse_val(line, splits, ::Type{Bool}) = true
parse_val(line, splits, ::Type{T}) where {T} = parse(T, line[splits[2]:length(line)])
num_splits(::Type{ComplexF64}) = 3
num_splits(::Type{Bool}) = 1
num_splits(elty) = 2
function find_splits(s::String, num)
splits = Vector{Int}(undef, num)
cur = 1
in_space = s[1] == '\t' || s[1] == ' '
@inbounds for i in 1:length(s)
if s[i] == '\t' || s[i] == ' '
if !in_space
in_space = true
splits[cur] = i
cur += 1
cur > num && break
end
else
in_space = false
end
end
splits
end
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | src/mmwrite.jl | code | 2067 | """
mmwrite(filename, matrix)
Write a sparse matrix to .mtx file format.
# Arguments
- `filename::String`: The file to write.
- `matrix::SparseMatrixCSC`: The sparse matrix to write.
"""
function mmwrite(filename::String, matrix::SparseMatrixCSC)
stream = open(filename, "w")
if endswith(filename, ".gz")
stream = TranscodingStream(GzipCompressor(), stream)
end
mmwrite(stream, matrix)
close(stream)
end
function mmwrite(stream::IO, matrix::SparseMatrixCSC)
nl = get_newline()
elem = generate_eltype(eltype(matrix))
sym = generate_symmetric(matrix)
# write header
write(stream, "%%MatrixMarket matrix coordinate $elem $sym$nl")
# only use lower triangular part of symmetric and Hermitian matrices
if issymmetric(matrix) || ishermitian(matrix)
matrix = tril(matrix)
end
# write matrix size and number of nonzeros
write(stream, "$(size(matrix, 1)) $(size(matrix, 2)) $(nnz(matrix))$nl")
rows = rowvals(matrix)
vals = nonzeros(matrix)
for i in 1:size(matrix, 2)
for j in nzrange(matrix, i)
entity = generate_entity(i, j, rows, vals, elem)
write(stream, entity)
end
end
end
generate_eltype(::Type{<:Bool}) = "pattern"
generate_eltype(::Type{<:Integer}) = "integer"
generate_eltype(::Type{<:AbstractFloat}) = "real"
generate_eltype(::Type{<:Complex}) = "complex"
generate_eltype(elty) = error("Invalid matrix type")
function generate_symmetric(m::AbstractMatrix)
if issymmetric(m)
return "symmetric"
elseif ishermitian(m)
return "hermitian"
else
return "general"
end
end
function generate_entity(i, j, rows, vals, kind::String)
nl = get_newline()
if kind == "pattern"
return "$(rows[j]) $i$nl"
elseif kind == "complex"
return "$(rows[j]) $i $(real(vals[j])) $(imag(vals[j]))$nl"
else
return "$(rows[j]) $i $(vals[j])$nl"
end
end
function get_newline()
if Sys.iswindows()
return "\r\n"
else
return "\n"
end
end
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | test/mtx.jl | code | 3243 | @testset "mtx" begin
mtx_filename = joinpath(TEST_PATH, "data", "test.mtx")
res = sparse(
[5, 4, 1, 2, 6],
[1, 5, 1, 4, 7],
[1, 1, 1, 1, 1],
11, 12
)
testmatrices = download_unzip_nist_files()
@testset "read/write mtx" begin
rows, cols, entries, rep, field, symm = mminfo(mtx_filename)
@test rows == 11
@test cols == 12
@test entries == 5
@test rep == "coordinate"
@test field == "integer"
@test symm == "general"
A = mmread(mtx_filename)
@test A isa SparseMatrixCSC
@test A == res
newfilename = replace(mtx_filename, "test.mtx" => "test_write.mtx")
mmwrite(newfilename, res)
f = open(mtx_filename)
sha_test = bytes2hex(sha256(read(f, String)))
close(f)
f = open(newfilename)
sha_new = bytes2hex(sha256(read(f, String)))
close(f)
@test sha_test == sha_new
rm(newfilename)
end
@testset "read/write mtx.gz" begin
gz_filename = mtx_filename * ".gz"
rows, cols, entries, rep, field, symm = mminfo(gz_filename)
@test rows == 11
@test cols == 12
@test entries == 5
@test rep == "coordinate"
@test field == "integer"
@test symm == "general"
A = mmread(gz_filename)
@test A isa SparseMatrixCSC
@test A == res
newfilename = replace(gz_filename, "test.mtx.gz" => "test_write.mtx.gz")
mmwrite(newfilename, res)
stream = GzipDecompressorStream(open(gz_filename))
adjusted_content = replace(read(stream, String), "\n" => get_newline())
sha_test = bytes2hex(sha256(adjusted_content))
close(stream)
stream = GzipDecompressorStream(open(newfilename))
sha_new = bytes2hex(sha256(read(stream, String)))
close(stream)
@test sha_test == sha_new
rm(newfilename)
end
@testset "read/write NIST mtx files" begin
# verify mmread(mmwrite(A)) == A
for filename in filter(t -> endswith(t, ".mtx"), readdir())
new_filename = replace(filename, ".mtx" => "_.mtx")
A = MatrixMarket.mmread(filename)
MatrixMarket.mmwrite(new_filename, A)
new_A = MatrixMarket.mmread(new_filename)
@test new_A == A
rm(new_filename)
end
end
@testset "read/write NIST mtx.gz files" begin
for gz_filename in filter(t -> endswith(t, ".mtx.gz"), readdir())
mtx_filename = replace(gz_filename, ".mtx.gz" => ".mtx")
# reading from .mtx and .mtx.gz must be identical
A_gz = MatrixMarket.mmread(gz_filename)
A = MatrixMarket.mmread(mtx_filename)
@test A_gz == A
# writing to .mtx and .mtx.gz must be identical
new_filename = replace(gz_filename, ".mtx.gz" => "_.mtx.gz")
mmwrite(new_filename, A)
new_A = MatrixMarket.mmread(new_filename)
@test new_A == A
rm(new_filename)
end
end
# clean up
for filename in filter(t -> endswith(t, ".mtx"), readdir())
rm(filename)
rm(filename * ".gz")
end
end
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | test/runtests.jl | code | 319 | using MatrixMarket
using CodecZlib
using Downloads
using GZip
using SparseArrays
using SHA
using Test
include("test_utils.jl")
const TEST_PATH = @__DIR__
const NIST_FILELIST = download_nist_filelist()
tests = [
"mtx",
]
@testset "MatrixMarket.jl" begin
for t in tests
include("$(t).jl")
end
end
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | test/test_utils.jl | code | 1905 | function get_newline()
if Sys.iswindows()
return "\r\n"
else
return "\n"
end
end
function gunzip(fname)
destname, ext = splitext(fname)
if ext != ".gz"
error("gunzip: $fname: unknown suffix -- ignored")
end
open(destname, "w") do f
GZip.open(fname) do g
write(f, read(g, String))
end
end
destname
end
function download_nist_filelist()
isfile("matrices.html") ||
Downloads.download("math.nist.gov/MatrixMarket/matrices.html", "matrices.html")
matrixmarketdata = Any[]
open("matrices.html") do f
for line in readlines(f)
if occursin("""<A HREF="/MatrixMarket/data/""", line)
collectionname, setname, matrixname = split(split(line, '"')[2], '/')[4:6]
matrixname = split(matrixname, '.')[1]
push!(matrixmarketdata, (collectionname, setname, matrixname))
end
end
end
rm("matrices.html")
return matrixmarketdata
end
function download_unzip_nist_files()
# Download one matrix at random plus some specifically chosen ones.
n = rand(1:length(NIST_FILELIST))
testmatrices = [
("NEP", "mhd", "mhd1280b"),
("Harwell-Boeing", "acoust", "young4c"),
("Harwell-Boeing", "platz", "plsk1919"),
NIST_FILELIST[n]
]
for (collectionname, setname, matrixname) in testmatrices
fn = string(collectionname, '_', setname, '_', matrixname)
mtxfname = string(fn, ".mtx")
if !isfile(mtxfname)
url = "https://math.nist.gov/pub/MatrixMarket2/$collectionname/$setname/$matrixname.mtx.gz"
gzfname = string(fn, ".mtx.gz")
try
Downloads.download(url, gzfname)
catch
continue
end
gunzip(gzfname)
end
end
return testmatrices
end
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"MIT"
] | 0.5.0 | 903fd51345b94a3aa271118ca189d0c4a2cae2e3 | README.md | docs | 1344 | # MatrixMarket
[](https://travis-ci.org/JuliaSparse/MatrixMarket.jl)
Package to read/write matrices from/to files in the [Matrix Market native exchange
format](http://math.nist.gov/MatrixMarket/formats.html#MMformat).
The [Matrix Market](http://math.nist.gov/MatrixMarket/) is a NIST repository of
"test data for use in comparative studies of algorithms for numerical linear
algebra, featuring nearly 500 sparse matrices from a variety of applications,
as well as matrix generation tools and services." Over time, the [Matrix Market's
native exchange format](http://math.nist.gov/MatrixMarket/formats.html#MMformat)
has become one of the _de facto_ standard file formats for exchanging matrix
data.
## Usage
### Read
using MatrixMarket
M = MatrixMarket.mmread("myfile.mtx")
`M` will be a sparse or dense matrix depending on whether the file contains a matrix
in coordinate format or array format. The specific type of `M` may be `Symmetric` or
`Hermitian` depending on the symmetry information contained in the file header.
MatrixMarket.mmread("myfile.mtx", true)
Returns raw data from the file header. Does not read in the actual matrix elements.
### Write
MatrixMarket.mmwrite("myfile.mtx", M)
`M` has to be a sparse matrix.
| MatrixMarket | https://github.com/JuliaSparse/MatrixMarket.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | benchmark/stack.jl | code | 1709 | using NiLangCore, BenchmarkTools
bg = BenchmarkGroup()
# pop!/push!
bg["NiLang"] = @benchmarkable begin
@instr PUSH!(x)
@instr POP!(x)
end seconds=1 setup=(x=3.0)
# @invcheckoff pop!/push!
bg["NiLang-@invcheckoff"] = @benchmarkable begin
@instr @invcheckoff PUSH!(x)
@instr @invcheckoff POP!(x)
end seconds=1 setup=(x=3.0)
# @invcheckoff pop!/push!
bg["NiLang-@invcheckoff-@inbounds"] = @benchmarkable begin
@instr @invcheckoff @inbounds PUSH!(x)
@instr @invcheckoff @inbounds POP!(x)
end seconds=1 setup=(x=3.0)
# Julia pop!/push!
bg["Julia"] = @benchmarkable begin
push!(stack, x)
x = pop!(stack)
end seconds=1 setup=(x=3.0; stack=Float64[])
# FastStack-inbounds-Any
bg["FastStack-inbounds-Any"] = @benchmarkable begin
@inbounds push!(stack, x)
@inbounds pop!(stack)
end seconds=1 setup=(x=3.0; stack=FastStack(10))
# Julia pop!/push!
bg["Julia-Any"] = @benchmarkable begin
push!(stack, x)
x = pop!(stack)
end seconds=1 setup=(x=3.0; stack=Any[])
# setindex
bg["setindex"] = @benchmarkable begin
stack[2] = x
x = 0.0
x = stack[2]
end seconds=1 setup=(x=3.0; stack=Float64[1.0, 2.0])
# setindex-inbounds
bg["setindex-inbounds"] = @benchmarkable begin
stack[2] = x
x = 0.0
x = stack[2]
end seconds=1 setup=(x=3.0; stack=Float64[1.0, 2.0])
# FastStack
bg["FastStack"] = @benchmarkable begin
push!(stack, x)
x = 0.0
x = pop!(stack)
end seconds=1 setup=(x=3.0; stack=FastStack{Float64}(10))
# FastStack-inbounds
bg["FastStack-inbounds"] = @benchmarkable begin
@inbounds push!(stack, x)
x = 0.0
@inbounds x = pop!(stack)
end seconds=1 setup=(x=3.0; stack=FastStack{Float64}(10))
tune!(bg)
run(bg) | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | benchmark/try.jl | code | 2297 | using Zygote
f(x, y) = (x+exp(y), y)
invf(x, y) = (x-exp(y), y)
# ∂L/∂x2 = ∂L/∂x1*∂x1/∂x2 + ∂L/∂y1*∂y1/∂y2 = ∂L/∂x1*invf'(x2) + ∂L/∂y1*invf'(y2)
x1, y1 = 1.4, 4.4
x2, y2 = f(x,y)
function gf(x, y, gx, gy)
x2, y2 = f(x, y)
invJ1 = gradient((x2, y2)->invf(x2, y2)[1], x2, y2)
invJ2 = gradient((x2, y2)->invf(x2, y2)[2], x2, y2)
return (x2, y2, gx, gy)
end
gradient((x, y)->invf(x, y)[1], x, y)
mutable struct A{T}
x::T
end
Base.:*(x1::A, x2::A) = A(x1.x*x2.x)
Base.:+(x1::A, x2::A) = A(x1.x+x2.x)
Base.zero(::A{T}) where T = A(T(0))
struct A2{T}
x::T
end
Base.:*(x1::A2, x2::A2) = A2(x1.x*x2.x)
Base.:+(x1::A2, x2::A2) = A2(x1.x+x2.x)
Base.zero(::A2{T}) where T = A2(T(0))
struct BG{T}
x::T
g::B{T}
BG(x::T) where T = new{T}(x)
end
struct BG{T}
x::T
g::BG{T}
BG(x::T) where T = new{T}(x)
end
mutable struct AG{T}
x::T
g::AG{T}
AG(x::T) where T = new{T}(x)
AG(x::T, g::TG) where {T,TG} = new{T}(x, T(g))
end
Base.:*(x1::AG, x2::AG) = AG(x1.x*x2.x)
Base.:+(x1::AG, x2::AG) = AG(x1.x+x2.x)
Base.zero(::AG{T}) where T = AG(T(0))
init(ag::AG{T}) where T = (ag.g = AG(T(0)))
using BenchmarkTools
ma = fill(A(1.0), 100,100)
ma2 = fill(A2(1.0), 100,100)
function f(ma, mb)
M, N, K = size(ma, 1), size(mb, 2), size(ma, 2)
res = fill(zero(ma[1]), M, N)
for i=1:M
for j=1:N
for k=1:K
@inbounds res[i,j] += ma[i,k]*mb[k,j]
end
end
end
return res
end
@benchmark f(ma, ma)
@benchmark f(ma2, ma2)
ma = fill(AG(1.0), 100,100)
@benchmark ma*ma
a = A(0.4)
ag = AG(0.4)
using NiLangCore
@benchmark isdefined($ag, :g)
@benchmark $ag + $ag
ag.g = AG(0.0)
@benchmark $a + $a
struct SG{T}
x::T
g::Ref{T}
SG(x::T) where T = new{T}(x)
end
Base.:*(x1::SG, x2::SG) = SG(x1.x*x2.x)
Base.:+(x1::SG, x2::SG) = SG(x1.x+x2.x)
Base.zero(::SG{T}) where T = SG(T(0))
init(ag::AG{T}) where T = (ag.g = AG(T(0)))
using BenchmarkTools
ma = fill(SG(1.0), 100,100)
@benchmark ma*ma
a = A(0.4)
ag = AG(0.4)
using NiLangCore
@benchmark isdefined($ag, :g)
@benchmark $ag + $ag
ag.g = AG(0.0)
@benchmark $a + $a
using NiLang, NiLang.AD
@i function test(x, one, N::Int)
for i = 1:N
x += one
end
end
invcheckon(true)
@benchmark test'(Loss(0.0), 1.0, 1000000)
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | docs/make.jl | code | 382 | using Documenter, NiLangCore
makedocs(;
modules=[NiLangCore],
format=Documenter.HTML(),
pages=[
"Home" => "index.md",
],
repo="https://github.com/GiggleLiu/NiLangCore.jl/blob/{commit}{path}#L{line}",
sitename="NiLangCore.jl",
authors="JinGuo Liu, thautwarm",
assets=String[],
)
deploydocs(;
repo="github.com/GiggleLiu/NiLangCore.jl",
)
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/Core.jl | code | 6182 | ############# function properties #############
export isreversible, isreflexive, isprimitive
export protectf
"""
isreversible(f, ARGT)
Return `true` if a function is reversible.
"""
isreversible(f, ::Type{ARGT}) where ARGT = hasmethod(~f, ARGT)
"""
isreflexive(f)
Return `true` if a function is self-inverse.
"""
isreflexive(f) = (~f) === f
"""
isprimitive(f)
Return `true` if `f` is an `instruction` that can not be decomposed anymore.
"""
isprimitive(f) = false
############# ancillas ################
export InvertibilityError, @invcheck
"""
deanc(a, b)
Deallocate varialbe `a` with value `b`. It will throw an error if
* `a` and `b` are objects with different types,
* `a` is not equal to `b` (for floating point numbers, an error within `NiLangCore.GLOBAL_ATOL[]` is allowed),
"""
function deanc end
function deanc(a::T, b::T) where T <: AbstractFloat
if a !== b && abs(b - a) > GLOBAL_ATOL[]
throw(InvertibilityError("deallocate fail (floating point numbers): $a ≂̸ $b"))
end
end
deanc(x::T, val::T) where T<:Tuple = deanc.(x, val)
deanc(x::T, val::T) where T<:AbstractArray = x === val || deanc.(x, val)
deanc(a::T, b::T) where T<:AbstractString = a === b || throw(InvertibilityError("deallocate fail (string): $a ≂̸ $b"))
function deanc(x::T, val::T) where T<:Dict
if x !== val
if length(x) != length(val)
throw(InvertibilityError("deallocate fail (dict): length of dict not the same, got $(length(x)) and $(length(val))!"))
else
for (k, v) in x
if haskey(val, k)
deanc(x[k], val[k])
else
throw(InvertibilityError("deallocate fail (dict): key $k of dict does not exist!"))
end
end
end
end
end
deanc(a, b) = throw(InvertibilityError("deallocate fail (type mismatch): `$(typeof(a))` and `$(typeof(b))`"))
@generated function deanc(a::T, b::T) where T
nf = fieldcount(a)
if isprimitivetype(T)
:(a === b || throw(InvertibilityError("deallocate fail (primitive): $a ≂̸ $b")))
else
Expr(:block, [:($deanc(a.$NAME, b.$NAME)) for NAME in fieldnames(T)]...)
end
end
"""
InvertibilityError <: Exception
InvertibilityError(ex)
The error for irreversible statements.
"""
struct InvertibilityError <: Exception
ex
end
"""
@invcheck x val
The macro version `NiLangCore.deanc`, with more informative error.
"""
macro invcheck(x, val)
esc(_invcheck(x, val))
end
# the expression for reversibility checking
function _invcheck(x, val)
Expr(:try, Expr(:block, :($deanc($x, $val))), :e, Expr(:block,
:(println("deallocate fail `$($(QuoteNode(x))) → $($(QuoteNode(val)))`")),
:(throw(e)))
)
end
_invcheck(docheck::Bool, arg, res) = docheck ? _invcheck(arg, res) : nothing
"""
chfield(x, field, val)
Change a `field` of an object `x`.
The `field` can be a `Val` type
```jldoctest; setup=:(using NiLangCore)
julia> chfield(1+2im, Val(:im), 5)
1 + 5im
```
or a function
```jldoctest; setup=:(using NiLangCore)
julia> using NiLangCore
julia> struct GVar{T, GT}
x::T
g::GT
end
julia> @fieldview xx(x::GVar) = x.x
julia> chfield(GVar(1.0, 0.0), xx, 2.0)
GVar{Float64, Float64}(2.0, 0.0)
```
"""
function chfield end
########### Inv ##########
export Inv, invtype
"""
Inv{FT} <: Function
Inv(f)
The inverse of a function.
"""
struct Inv{FT} <: Function
f::FT
end
Inv(f::Inv) = f.f
@static if VERSION >= v"1.6"
Base.:~(f::Base.ComposedFunction) = (~(f.inner)) ∘ (~(f.outer))
end
Base.:~(f::Function) = Inv(f)
Base.:~(::Type{Inv{T}}) where T = T # for type, it is a destructor
Base.:~(::Type{T}) where T = Inv{T} # for type, it is a destructor
Base.show(io::IO, b::Inv) = print(io, "~$(b.f)")
Base.display(bf::Inv) where f = print(bf)
"""
protectf(f)
Protect a function from being inverted, useful when using an callable object.
"""
protectf(x) = x
protectf(x::Inv) = x.f
invtype(::Type{T}) where T = Inv{<:T}
######### Infer
export PlusEq, MinusEq, XorEq, MulEq, DivEq
"""
PlusEq{FT} <: Function
PlusEq(f)
Called when executing `out += f(args...)` instruction. The following two statements are same
```jldoctest; setup=:(using NiLangCore)
julia> x, y, z = 0.0, 2.0, 3.0
(0.0, 2.0, 3.0)
julia> x, y, z = PlusEq(*)(x, y, z)
(6.0, 2.0, 3.0)
julia> x, y, z = 0.0, 2.0, 3.0
(0.0, 2.0, 3.0)
julia> @instr x += y*z
julia> x, y, z
(6.0, 2.0, 3.0)
```
"""
struct PlusEq{FT} <: Function
f::FT
end
"""
MinusEq{FT} <: Function
MinusEq(f)
Called when executing `out -= f(args...)` instruction. See `PlusEq` for detail.
"""
struct MinusEq{FT} <: Function
f::FT
end
"""
MulEq{FT} <: Function
MulEq(f)
Called when executing `out *= f(args...)` instruction. See `PlusEq` for detail.
"""
struct MulEq{FT} <: Function
f::FT
end
"""
DivEq{FT} <: Function
DivEq(f)
Called when executing `out /= f(args...)` instruction. See `PlusEq` for detail.
"""
struct DivEq{FT} <: Function
f::FT
end
"""
XorEq{FT} <: Function
XorEq(f)
Called when executing `out ⊻= f(args...)` instruction. See `PlusEq` for detail.
"""
struct XorEq{FT} <: Function
f::FT
end
const OPMX{FT} = Union{PlusEq{FT}, MinusEq{FT}, XorEq{FT}, MulEq{FT}, DivEq{FT}}
for (TP, OP) in [(:PlusEq, :+), (:MinusEq, :-), (:XorEq, :⊻)]
@eval (inf::$TP)(out!, args...; kwargs...) = $OP(out!, inf.f(args...; kwargs...)), args...
@eval (inf::$TP)(out!::Tuple, args...; kwargs...) = $OP.(out!, inf.f(args...; kwargs...)), args... # e.g. allow `(x, y) += sincos(a)`
end
Base.:~(op::PlusEq) = MinusEq(op.f)
Base.:~(om::MinusEq) = PlusEq(om.f)
Base.:~(op::MulEq) = DivEq(op.f)
Base.:~(om::DivEq) = MulEq(om.f)
Base.:~(om::XorEq) = om
for (T, S) in [(:PlusEq, "+="), (:MinusEq, "-="), (:MulEq, "*="), (:DivEq, "/="), (:XorEq, "⊻=")]
@eval Base.display(o::$T) = print($S, "(", o.f, ")")
@eval Base.display(o::Type{$T}) = print($S)
@eval Base.show_function(io::IO, o::$T, compact::Bool) = print(io, "$($S)($(o.f))")
@eval Base.show_function(io::IO, ::MIME"plain/text", o::$T, compact::Bool) = Base.show(io, o)
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/NiLangCore.jl | code | 414 | module NiLangCore
using MLStyle
using TupleTools
include("lens.jl")
include("utils.jl")
include("symboltable.jl")
include("stack.jl")
include("Core.jl")
include("vars.jl")
include("instr.jl")
include("dualcode.jl")
include("preprocess.jl")
include("variable_analysis.jl")
include("compiler.jl")
include("checks.jl")
if Base.VERSION >= v"1.4.2"
include("precompile.jl")
_precompile_()
end
end # module
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/checks.jl | code | 1912 | export check_inv, world_similar, almost_same
@nospecialize
"""
check_inv(f, args; atol::Real=1e-8, verbose::Bool=false, kwargs...)
Return true if `f(args..., kwargs...)` is reversible.
"""
function check_inv(f, args; atol::Real=1e-8, verbose::Bool=false, kwargs...)
args0 = deepcopy(args)
args_ = f(args...; kwargs...)
args = length(args) == 1 ? (args_,) : args_
args_ = (~f)(args...; kwargs...)
args = length(args) == 1 ? (args_,) : args_
world_similar(args0, args, atol=atol, verbose=verbose)
end
function world_similar(a, b; atol::Real=1e-8, verbose::Bool=false)
for (xa, xb) in zip(a, b)
if !almost_same(xa, xb; atol=atol)
verbose && println("$xa does not match $xb")
return false
end
end
return true
end
@specialize
"""
almost_same(a, b; atol=GLOBAL_ATOL[], kwargs...) -> Bool
Return true if `a` and `b` are almost same w.r.t. `atol`.
"""
function almost_same(a::T, b::T; atol=GLOBAL_ATOL[], kwargs...) where T <: AbstractFloat
a === b || abs(b - a) < atol
end
function almost_same(a::TA, b::TB; kwargs...) where {TA, TB}
false
end
function almost_same(a::T, b::T; kwargs...) where {T<:Dict}
length(a) != length(b) && return false
for (k, v) in a
haskey(b, k) && almost_same(v, b[k]; kwargs...) || return false
end
return true
end
@generated function almost_same(a::T, b::T; kwargs...) where T
nf = fieldcount(a)
if isprimitivetype(T)
:(a === b)
else
quote
res = true
@nexprs $nf i-> res = res && almost_same(getfield(a, i), getfield(b, i); kwargs...)
res
end
end
end
almost_same(x::T, y::T; kwargs...) where T<:AbstractArray = all(almost_same.(x, y; kwargs...))
almost_same(x::FastStack, y::FastStack; kwargs...) where T<:AbstractArray = all(almost_same.(x.data[1:x.top[]], y.data[1:y.top[]]; kwargs...))
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/compiler.jl | code | 15496 | struct CompileInfo
invcheckon::Ref{Bool}
end
CompileInfo() = CompileInfo(Ref(true))
function compile_body(m::Module, body::AbstractVector, info)
out = []
for ex in body
ex_ = compile_ex(m, ex, info)
ex_ !== nothing && push!(out, ex_)
end
return out
end
deleteindex!(d::AbstractDict, index) = delete!(d, index)
@inline function map_func(x::Symbol)
if x == :+=
PlusEq, false
elseif x == :.+=
PlusEq, true
elseif x == :-=
MinusEq, false
elseif x == :.-=
MinusEq, true
elseif x == :*=
MulEq, false
elseif x == :.*=
MulEq, true
elseif x == :/=
DivEq, false
elseif x == :./=
DivEq, true
elseif x == :⊻=
XorEq, false
elseif x == :.⊻=
XorEq, true
else
error("`$x` can not be mapped to a reversible function.")
end
end
# e.g. map `x += sin(z)` => `PlusEq(sin)(x, z)`.
function to_standard_format(ex::Expr)
head::Symbol = ex.head
F, isbcast = map_func(ex.head)
a, b = ex.args
if !isbcast
@match b begin
:($f($(args...); $(kwargs...))) => :($F($f)($a, $(args...); $(kwargs...)))
:($f($(args...))) => :($F($f)($a, $(args...)))
:($x || $y) => :($F($logical_or)($a, $x, $y))
:($x && $y) => :($F($logical_and)($a, $x, $y))
_ => :($F(identity)($a, $b))
end
else
@match b begin
:($f.($(args...); $(kwargs...))) => :($F($f).($a, $(args...); $(kwargs...)))
:($f.($(args...))) => :($F($f).($a, $(args...)))
:($f($(args...); $(kwargs...))) => :($F($(removedot(f))).($a, $(args...); $(kwargs...)))
:($f($(args...))) => :($F($(removedot(f))).($a, $(args...)))
_ => :($F(identity).($a, $b))
end
end
end
logical_or(a, b) = a || b
logical_and(a, b) = a && b
"""
compile_ex(m::Module, ex, info)
Compile a NiLang statement to a regular julia statement.
"""
function compile_ex(m::Module, ex, info)
@match ex begin
:($a += $b) || :($a .+= $b) ||
:($a -= $b) || :($a .-= $b) ||
:($a *= $b) || :($a .*= $b) ||
:($a /= $b) || :($a ./= $b) ||
:($a ⊻= $b) || :($a .⊻= $b) => compile_ex(m, to_standard_format(ex), info)
:(($t1=>$t2)($x)) => assign_ex(x, :(convert($t2, $x)), info.invcheckon[])
:(($t1=>$t2).($x)) => assign_ex(x, :(convert.($t2, $x)), info.invcheckon[])
# multi args expanded in preprocessing
# general
:($x ↔ $y) => begin
e1 = isemptyvar(x)
e2 = isemptyvar(y)
if e1 && e2
nothing
elseif e1 && !e2
_push_value(x, _pop_value(y), info.invcheckon[])
elseif !e1 && e2
_push_value(y, _pop_value(x), info.invcheckon[])
else
tmp = gensym("temp")
Expr(:block, :($tmp = $y), assign_ex(y, x, info.invcheckon[]), assign_ex(x, tmp, info.invcheckon[]))
end
end
# stack
:($s[end] → $x) => begin
if info.invcheckon[]
y = gensym("result")
Expr(:block, :($y=$loaddata($x, $pop!($s))), _invcheck(y, x), assign_ex(x, y, info.invcheckon[]))
else
y = gensym("result")
Expr(:block, :($y=$loaddata($x, $pop!($s))), assign_ex(x, y, info.invcheckon[]))
end
end
:($s[end+1] ← $x) => :($push!($s, $_copy($x)))
# dict
:($x[$index] ← $tp) => begin
assign_expr = :($x[$index] = $tp)
if info.invcheckon[]
Expr(:block, _assert_nokey(x, index), assign_expr)
else
assign_expr
end
end
:($x[$index] → $tp) => begin
delete_expr = :($(deleteindex!)($x, $index))
if info.invcheckon[]
Expr(:block, _invcheck(:($x[$index]), tp), delete_expr)
else
delete_expr
end
end
# general
:($x ← $tp) => :($x = $tp)
:($x → $tp) => begin
if info.invcheckon[]
_invcheck(x, tp)
end
end
:($f($(args...))) => begin
assignback_ex(ex, info.invcheckon[])
end
:($f.($(allargs...))) => begin
args, kwargs = seperate_kwargs(allargs)
symres = gensym("results")
ex = :($symres = $unzipped_broadcast($kwargs, $f, $(args...)))
Expr(:block, ex, assign_vars(args, symres, info.invcheckon[]).args...)
end
Expr(:if, _...) => compile_if(m, copy(ex), info)
:(while ($pre, $post); $(body...); end) => begin
whilestatement(pre, post, compile_body(m, body, info), info)
end
:(for $i=$range; $(body...); end) => begin
forstatement(i, range, compile_body(m, body, info), info, nothing)
end
:(@simd $line for $i=$range; $(body...); end) => begin
forstatement(i, range, compile_body(m, body, info), info, Symbol("@simd")=>line)
end
:(@threads $line for $i=$range; $(body...); end) => begin
forstatement(i, range, compile_body(m, body, info), info, Symbol("@threads")=>line)
end
:(@avx $line for $i=$range; $(body...); end) => begin
forstatement(i, range, compile_body(m, body, info), info, Symbol("@avx")=>line)
end
:(begin $(body...) end) => begin
Expr(:block, compile_body(m, body, info)...)
end
:(@safe $line $subex) => subex
:(@inbounds $line $subex) => Expr(:macrocall, Symbol("@inbounds"), line, compile_ex(m, subex, info))
:(@invcheckoff $line $subex) => begin
state = info.invcheckon[]
info.invcheckon[] = false
ex = compile_ex(m, subex, info)
info.invcheckon[] = state
ex
end
:(@cuda $line $(args...)) => begin
fcall = @match args[end] begin
:($f($(args...))) => Expr(:call,
Expr(:->,
:(args...),
Expr(:block,
:($f(args...)),
nothing
)
),
args...
)
_ => error("expect a function after @cuda, got $(args[end])")
end
Expr(:macrocall, Symbol("@cuda"), line, args[1:end-1]..., fcall)
end
:(@launchkernel $line $device $thread $ndrange $f($(args...))) => begin
res = gensym("results")
Expr(:block,
:($res = $f($device, $thread)($(args...); ndrange=$ndrange)),
:(wait($res))
)
end
:(nothing) => ex
::Nothing => ex
::LineNumberNode => ex
_ => error("statement not supported: `$ex`")
end
end
function compile_if(m::Module, ex, info)
pres = []
posts = []
ex = analyse_if(m, ex, info, pres, posts)
Expr(:block, pres..., ex, posts...)
end
function analyse_if(m::Module, ex, info, pres, posts)
var = gensym("branch")
if ex.head == :if
pre, post = ex.args[1].args
ex.args[1] = var
elseif ex.head == :elseif
pre, post = ex.args[1].args[2].args
ex.args[1].args[2] = var
end
push!(pres, :($var = $pre))
if info.invcheckon[]
push!(posts, _invcheck(var, post))
end
ex.args[2] = Expr(:block, compile_body(m, ex.args[2].args, info)...)
if length(ex.args) == 3
if ex.args[3].head == :elseif
ex.args[3] = analyse_if(m, ex.args[3], info, pres, posts)
elseif ex.args[3].head == :block
ex.args[3] = Expr(:block, compile_body(m, ex.args[3].args, info)...)
end
end
ex
end
function whilestatement(precond, postcond, body, info)
ex = Expr(:block,
Expr(:while,
precond,
Expr(:block, body...),
),
)
if info.invcheckon[]
pushfirst!(ex.args, _invcheck(postcond, false))
push!(ex.args[end].args[end].args,
_invcheck(postcond, true)
)
end
ex
end
function forstatement(i, range, body, info, mcr)
assigns, checkers = compile_range(range)
exf = Expr(:for, :($i=$range), Expr(:block, body...))
if !(mcr isa Nothing)
exf = Expr(:macrocall, mcr.first, mcr.second, exf)
end
if info.invcheckon[]
Expr(:block, assigns..., exf, checkers...)
else
exf
end
end
_pop_value(x) = @match x begin
:($s[end]) => :($pop!($s))
:($s[$ind]) => :($pop!($s, $ind)) # dict (notice pop over vector elements is not allowed.)
:($x::$T) => :($(_pop_value(x))::$T)
:(($(args...)),) => Expr(:tuple, _pop_value.(args)...)
_ => x
end
_push_value(x, val, invcheck) = @match x begin
:($s[end+1]) => :($push!($s, $val))
:($s[$arg]::∅) => begin
ex = :($s[$arg] = $val)
if invcheck
Expr(:block, _assert_nokey(s, arg), ex)
else
ex
end
end
_ => assign_ex(x, val, invcheck)
end
function _assert_nokey(x, index)
str = "dictionary `$x` already has key `$index`"
Expr(:if, :(haskey($x, $index)), :(throw(InvertibilityError($str))))
end
_copy(x) = copy(x)
_copy(x::Tuple) = copy.(x)
export @code_julia
"""
@code_julia ex
Get the interpreted expression of `ex`.
```julia
julia> @code_julia x += exp(3.0)
quote
var"##results#267" = ((PlusEq)(exp))(x, 3.0)
x = var"##results#267"[1]
try
(NiLangCore.deanc)(3.0, var"##results#267"[2])
catch e
@warn "deallocate fail: `3.0 → var\"##results#267\"[2]`"
throw(e)
end
end
julia> @code_julia @invcheckoff x += exp(3.0)
quote
var"##results#257" = ((PlusEq)(exp))(x, 3.0)
x = var"##results#257"[1]
end
```
"""
macro code_julia(ex)
QuoteNode(compile_ex(__module__, ex, CompileInfo()))
end
compile_ex(m::Module, ex) = compile_ex(m, ex, CompileInfo())
export @i
"""
@i function fname(args..., kwargs...) ... end
@i struct sname ... end
Define a reversible function/type.
```jldoctest; setup=:(using NiLangCore)
julia> @i function test(out!, x)
out! += identity(x)
end
julia> test(0.2, 0.8)
(1.0, 0.8)
```
See `test/compiler.jl` for more examples.
"""
macro i(ex)
ex = gen_ifunc(__module__, ex)
ex.args[1] = :(Base.@__doc__ $(ex.args[1]))
esc(ex)
end
# generate the reversed function
function gen_ifunc(m::Module, ex)
mc, fname, args, ts, body = precom(m, ex)
fname = _replace_opmx(fname)
# implementations
ftype = get_ftype(fname)
head = :($fname($(args...)) where {$(ts...)})
dfname = dual_fname(fname)
dftype = get_ftype(dfname)
fdef1 = Expr(:function, head, Expr(:block, compile_body(m, body, CompileInfo())..., functionfoot(args)))
dualhead = :($dfname($(args...)) where {$(ts...)})
fdef2 = Expr(:function, dualhead, Expr(:block, compile_body(m, dual_body(m, body), CompileInfo())..., functionfoot(args)))
if mc !== nothing
fdef1 = Expr(:macrocall, mc[1], mc[2], fdef1)
fdef2 = Expr(:macrocall, mc[1], mc[2], fdef2)
end
#ex = :(Base.@__doc__ $fdef1; if $ftype != $dftype; $fdef2; end)
ex = Expr(:block, fdef1,
Expr(:if, :($ftype != $dftype), fdef2),
)
end
export nilang_ir
"""
nilang_ir(ex; reversed::Bool=false)
Get the NiLang reversible IR from the function expression `ex`,
return the reversed function if `reversed` is `true`.
This IR is not directly executable on Julia, please use
`macroexpand(Main, :(@i function .... end))` to get the julia expression of a reversible function.
```jldoctest; setup=:(using NiLangCore)
julia> ex = :(@inline function f(x!::T, y) where T
@routine begin
anc ← zero(T)
anc += identity(x!)
end
x! += y * anc
~@routine
end);
julia> NiLangCore.nilang_ir(Main, ex) |> NiLangCore.rmlines
:(@inline function f(x!::T, y) where T
begin
anc ← zero(T)
anc += identity(x!)
end
x! += y * anc
begin
anc -= identity(x!)
anc → zero(T)
end
end)
julia> NiLangCore.nilang_ir(Main, ex; reversed=true) |> NiLangCore.rmlines
:(@inline function (~f)(x!::T, y) where T
begin
anc ← zero(T)
anc += identity(x!)
end
x! -= y * anc
begin
anc -= identity(x!)
anc → zero(T)
end
end)
```
"""
function nilang_ir(m::Module, ex; reversed::Bool=false)
mc, fname, args, ts, body = precom(m, ex)
fname = _replace_opmx(fname)
# implementations
if reversed
dfname = :(~$fname) # use fake head for readability
head = :($dfname($(args...)) where {$(ts...)})
body = dual_body(m, body)
else
head = :($fname($(args...)) where {$(ts...)})
end
fdef = Expr(:function, head, Expr(:block, body...))
if mc !== nothing
fdef = Expr(:macrocall, mc[1], mc[2], fdef)
end
fdef
end
# seperate and return `args` and `kwargs`
@inline function seperate_kwargs(args)
if length(args) > 0 && args[1] isa Expr && args[1].head == :parameters
args = args[2:end], args[1]
else
args, Expr(:parameters)
end
end
# add a `return` statement to the end of the function body.
function functionfoot(args)
args = get_argname.(seperate_kwargs(args)[1])
if length(args) == 1
if args[1] isa Expr && args[1].head == :(...)
args[1].args[1]
else
args[1]
end
else
:(($(args...),))
end
end
# to provide the eye candy for defining `x += f(args...)` like functions
_replace_opmx(ex) = @match ex begin
:(:+=($f)) => :($(gensym())::PlusEq{typeof($f)})
:(:-=($f)) => :($(gensym())::MinusEq{typeof($f)})
:(:*=($f)) => :($(gensym())::MulEq{typeof($f)})
:(:/=($f)) => :($(gensym())::DivEq{typeof($f)})
:(:⊻=($f)) => :($(gensym())::XorEq{typeof($f)})
_ => ex
end
export @instr
"""
@instr ex
Execute a reversible instruction.
"""
macro instr(ex)
ex = precom_ex(__module__, ex, NiLangCore.PreInfo())
#variable_analysis_ex(ex, SymbolTable())
esc(Expr(:block, NiLangCore.compile_ex(__module__, ex, CompileInfo()), nothing))
end
# the range of for statement
compile_range(range) = @match range begin
:($start:$step:$stop) => begin
start_, step_, stop_ = gensym("start"), gensym("step"), gensym("stop")
Any[:($start_ = $start),
:($step_ = $step),
:($stop_ = $stop)],
Any[_invcheck(start_, start),
_invcheck(step_, step),
_invcheck(stop_, stop)]
end
:($start:$stop) => begin
start_, stop_ = gensym("start"), gensym("stop")
Any[:($start_ = $start),
:($stop_ = $stop)],
Any[_invcheck(start_, start),
_invcheck(stop_, stop)]
end
:($list) => begin
list_ = gensym("iterable")
Any[:($list_ = deepcopy($list))],
Any[_invcheck(list_, list)]
end
end
"""
get_ftype(fname)
Return the function type, e.g.
* `obj::ABC` => `ABC`
* `f` => `typeof(f)`
"""
function get_ftype(fname)
@match fname begin
:($x::$tp) => tp
_ => :($NiLangCore._typeof($fname))
end
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/dualcode.jl | code | 5191 | # get the expression of the inverse function
function dual_func(m::Module, fname, args, ts, body)
:(function $(:(~$fname))($(args...)) where {$(ts...)};
$(dual_body(m, body)...);
end)
end
# get the function name of the inverse function
function dual_fname(op)
@match op begin
:($x::$tp) => :($x::$invtype($tp))
:(~$x) => x
_ => :($(gensym("~$op"))::$_typeof(~$op))
end
end
_typeof(x) = typeof(x)
_typeof(x::Type{T}) where T = Type{T}
"""
dual_ex(m::Module, ex)
Get the dual expression of `ex`.
"""
function dual_ex(m::Module, ex)
@match ex begin
:(($t1=>$t2)($x)) => :(($t2=>$t1)($x))
:(($t1=>$t2).($x)) => :(($t2=>$t1).($x))
:($x ↔ $y) => dual_swap(x, y)
:($s[end+1] ← $x) => :($s[end] → $x)
:($s[end] → $x) => :($s[end+1] ← $x)
:($x → $val) => :($x ← $val)
:($x ← $val) => :($x → $val)
:($f($(args...))) => startwithdot(f) ? :($(getdual(removedot(sym))).($(args...))) : :($(getdual(f))($(args...)))
:($f.($(args...))) => :($(getdual(f)).($(args...)))
:($a += $b) => :($a -= $b)
:($a .+= $b) => :($a .-= $b)
:($a -= $b) => :($a += $b)
:($a .-= $b) => :($a .+= $b)
:($a *= $b) => :($a /= $b)
:($a .*= $b) => :($a ./= $b)
:($a /= $b) => :($a *= $b)
:($a ./= $b) => :($a .*= $b)
:($a ⊻= $b) => :($a ⊻= $b)
:($a .⊻= $b) => :($a .⊻= $b)
Expr(:if, _...) => dual_if(m, copy(ex))
:(while ($pre, $post); $(body...); end) => begin
Expr(:while, :(($post, $pre)), Expr(:block, dual_body(m, body)...))
end
:(for $i=$start:$step:$stop; $(body...); end) => begin
Expr(:for, :($i=$stop:(-$step):$start), Expr(:block, dual_body(m, body)...))
end
:(for $i=$start:$stop; $(body...); end) => begin
j = gensym("j")
Expr(:for, :($j=$start:$stop), Expr(:block, :($i ← $stop-$j+$start), dual_body(m, body)..., :($i → $stop-$j+$start)))
end
:(for $i=$itr; $(body...); end) => begin
Expr(:for, :($i=Base.Iterators.reverse($itr)), Expr(:block, dual_body(m, body)...))
end
:(@safe $line $subex) => Expr(:macrocall, Symbol("@safe"), line, subex)
:(@cuda $line $(args...)) => Expr(:macrocall, Symbol("@cuda"), line, args[1:end-1]..., dual_ex(m, args[end]))
:(@launchkernel $line $(args...)) => Expr(:macrocall, Symbol("@launchkernel"), line, args[1:end-1]..., dual_ex(m, args[end]))
:(@inbounds $line $subex) => Expr(:macrocall, Symbol("@inbounds"), line, dual_ex(m, subex))
:(@simd $line $subex) => Expr(:macrocall, Symbol("@simd"), line, dual_ex(m, subex))
:(@threads $line $subex) => Expr(:macrocall, Symbol("@threads"), line, dual_ex(m, subex))
:(@avx $line $subex) => Expr(:macrocall, Symbol("@avx"), line, dual_ex(m, subex))
:(@invcheckoff $line $subex) => Expr(:macrocall, Symbol("@invcheckoff"), line, dual_ex(m, subex))
:(begin $(body...) end) => Expr(:block, dual_body(m, body)...)
:(nothing) => ex
::LineNumberNode => ex
::Nothing => ex
:() => ex
_ => error("can not invert target expression $ex")
end
end
function dual_if(m::Module, ex)
_dual_cond(cond) = @match cond begin
:(($pre, $post)) => :(($post, $pre))
end
if ex.head == :if
ex.args[1] = _dual_cond(ex.args[1])
elseif ex.head == :elseif
ex.args[1].args[2] = _dual_cond(ex.args[1].args[2])
end
ex.args[2] = Expr(:block, dual_body(m, ex.args[2].args)...)
if length(ex.args) == 3
if ex.args[3].head == :elseif
ex.args[3] = dual_if(m, ex.args[3])
elseif ex.args[3].head == :block
ex.args[3] = Expr(:block, dual_body(m, ex.args[3].args)...)
end
end
ex
end
function dual_swap(x, y)
e1 = isemptyvar(x)
e2 = isemptyvar(y)
if e1 && !e2 || !e1 && e2
:($(_dual_swap_var(x)) ↔ $(_dual_swap_var(y)))
else
:($y ↔ $x)
end
end
_dual_swap_var(x) = @match x begin
:($s[end+1]) => :($s[end])
:($x::∅) => :($x)
:($s[end]) => :($s[end+1])
_ => :($x::∅)
end
export @code_reverse
"""
@code_reverse ex
Get the reversed expression of `ex`.
```jldoctest; setup=:(using NiLangCore)
julia> @code_reverse x += exp(3.0)
:(x -= exp(3.0))
```
"""
macro code_reverse(ex)
QuoteNode(dual_ex(__module__, ex))
end
getdual(f) = @match f begin
:(~$f) => f
_ => :(~$f)
end
function dual_body(m::Module, body)
out = []
# fix function LineNumberNode
if length(body) > 1 && body[1] isa LineNumberNode && body[2] isa LineNumberNode
push!(out, body[1])
start = 2
else
start = 1
end
ptr = length(body)
# reverse the statements
len = 0
while ptr >= start
if ptr-len==0 || body[ptr-len] isa LineNumberNode
ptr-len != 0 && push!(out, body[ptr-len])
for j=ptr:-1:ptr-len+1
push!(out, dual_ex(m, body[j]))
end
ptr -= len+1
len = 0
else
len += 1
end
end
return out
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/instr.jl | code | 5453 | export @dual, @selfdual, @dualtype
"""
@dual f invf
Define `f` and `invf` as a pair of dual instructions, i.e. reverse to each other.
"""
macro dual(f, invf)
esc(quote
if !$NiLangCore.isprimitive($f)
$NiLangCore.isprimitive(::typeof($f)) = true
end
if !$NiLangCore.isprimitive($invf)
$NiLangCore.isprimitive(::typeof($invf)) = true
end
if Base.:~($f) !== $invf
Base.:~(::typeof($f)) = $invf;
end
if Base.:~($invf) !== $f
Base.:~(::typeof($invf)) = $f;
end
end)
end
macro dualtype(t, invt)
esc(quote
$invtype($t) === $invt || begin
$NiLangCore.invtype(::Type{$t}) = $invt
$NiLangCore.invtype(::Type{T}) where T<:$t = $invt{T.parameters...}
end
$invtype($invt) === $t || begin
$NiLangCore.invtype(::Type{$invt}) = $t
$NiLangCore.invtype(::Type{T}) where T<:$invt = $t{T.parameters...}
end
end)
end
@dualtype PlusEq MinusEq
@dualtype DivEq MulEq
@dualtype XorEq XorEq
"""
@selfdual f
Define `f` as a self-dual instructions.
"""
macro selfdual(f)
esc(:(@dual $f $f))
end
export @const
@eval macro $(:const)(ex)
esc(ex)
end
export @skip!
macro skip!(ex)
esc(ex)
end
export @assignback
# TODO: include control flows.
"""
@assignback f(args...) [invcheck]
Assign input variables with output values: `args... = f(args...)`, turn off invertibility error check if the second argument is false.
"""
macro assignback(ex, invcheck=true)
ex = precom_ex(__module__, ex, PreInfo())
esc(assignback_ex(ex, invcheck))
end
function assignback_ex(ex::Expr, invcheck::Bool)
@match ex begin
:($f($(args...))) => begin
symres = gensym("results")
ex = :($symres = $f($(args...)))
res = assign_vars(seperate_kwargs(args)[1], symres, invcheck)
pushfirst!(res.args, ex)
return res
end
_ => error("assign back fail, got $ex")
end
end
"""
assign_vars(args, symres, invcheck)
Get the expression of assigning `symres` to `args`.
"""
function assign_vars(args, symres, invcheck)
exprs = []
for (i,arg) in enumerate(args)
exi = @match arg begin
:($ag...) => begin
i!=length(args) && error("`args...` like arguments should only appear as the last argument!")
ex = :(ntuple(j->$symres[j+$(i-1)], length($ag)))
assign_ex(ag, i==1 ? :(length($ag) == 1 ? ($symres,) : $ex) : ex, invcheck)
end
_ => if length(args) == 1
assign_ex(arg, symres, invcheck)
else
assign_ex(arg, :($symres[$i]), invcheck)
end
end
exi !== nothing && push!(exprs, exi)
end
Expr(:block, exprs...)
end
error_message_fcall(arg) = """
function arguments should not contain function calls on variables, got `$arg`, try to decompose it into elementary statements, e.g. statement `z += f(g(x))` should be written as
y += g(x)
z += y
If `g` is a dataview (a function map an object to its field or a bijective function), one can also use the pipline like
z += f(x |> g)
"""
assign_ex(arg, res, invcheck) = @match arg begin
::Number || ::String => _invcheck(invcheck, arg, res)
::Symbol || ::GlobalRef => _isconst(arg) ? _invcheck(invcheck, arg, res) : :($arg = $res)
:(@skip! $line $x) => nothing
:(@fields $line $x) => assign_ex(x, Expr(:call, default_constructor, :(typeof($x)), Expr(:..., res)), invcheck)
:($x::∅) => assign_ex(x, res, invcheck)
:($x::$T) => assign_ex(x, :($loaddata($T, $res)), invcheck)
:($x.$k) => _isconst(x) ? _invcheck(invcheck, arg, res) : assign_ex(x, :(chfield($x, $(Val(k)), $res)), invcheck)
# tuples must be index through (x |> 1)
:($a |> tget($x)) => assign_ex(a, :($(TupleTools.insertat)($a, $x, ($res,))), invcheck)
:($a |> subarray($(ranges...))) => :(($res===view($a, $(ranges...))) || (view($a, $(ranges...)) .= $res))
:($x |> $f) => _isconst(x) ? _invcheck(invcheck, arg,res) : assign_ex(x, :(chfield($x, $f, $res)), invcheck)
:($x .|> $f) => _isconst(x) ? _invcheck(invcheck, arg,res) : assign_ex(x, :(chfield.($x, Ref($f), $res)), invcheck)
:($x') => _isconst(x) ? _invcheck(invcheck, arg, res) : assign_ex(x, :(chfield($x, adjoint, $res)), invcheck)
:(-$x) => _isconst(x) ? _invcheck(invcheck, arg,res) : assign_ex(x, :(chfield($x, -, $res)), invcheck)
:($t{$(p...)}($(args...))) => begin
if length(args) == 1
assign_ex(args[1], :($getfield($res, 1)), invcheck)
else
assign_vars(args, :($type2tuple($res)), invcheck)
end
end
:($f($(args...))) => all(_isconst, args) || error(error_message_fcall(arg))
:($f.($(args...))) => all(_isconst, args) || error(error_message_fcall(arg))
:($a[$(x...)]) => begin
:($a[$(x...)] = $res)
end
:(($(args...),)) => begin
# TODO: avoid possible repeated evaluation (not here, in swap)
Expr(:block, [assign_ex(args[i], :($res[$i]), invcheck) for i=1:length(args)]...)
end
_ => _invcheck(invcheck, arg, res)
end
export @assign
"""
@assign a b [invcheck]
Perform the assign `a = b` in a reversible program.
Turn off invertibility check if the `invcheck` is false.
"""
macro assign(a, b, invcheck=true)
esc(assign_ex(a, b, invcheck))
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/lens.jl | code | 3188 | export _zero, @fields
# update a field of a struct.
@inline @generated function field_update(main :: T, field::Val{Field}, value) where {T, Field}
fields = fieldnames(T)
Expr(:new, T, Any[field !== Field ? :(main.$field) : :value for field in fields]...)
end
# the default constructor of a struct
@inline @generated function default_constructor(::Type{T}, fields::Vararg{Any,N}) where {T,N}
Expr(:new, T, Any[:(fields[$i]) for i=1:N]...)
end
"""
_zero(T)
_zero(x::T)
Create a `zero` of type `T` by recursively applying `zero` to its fields.
"""
@inline @generated function _zero(::Type{T}) where {T}
Expr(:new, T, Any[:(_zero($field)) for field in T.types]...)
end
@inline @generated function _zero(x::T) where {T}
Expr(:new, T, Any[:(_zero(x.$field)) for field in fieldnames(T)]...)
end
function lens_compile(ex, cache, value)
@match ex begin
:($a.$b.$c = $d) => begin
updated =
Expr(:let,
Expr(:block, :($cache = $cache.$b), :($value = $d)),
:($field_update($cache, $(Val(c)), $value)))
lens_compile(:($a.$b = $updated), cache, value)
end
:($a.$b = $c) => begin
Expr(:let,
Expr(:block, :($cache = $a), :($value=$c)),
:($field_update($cache, $(Val(b)), $value)))
end
_ => error("Malformed update notation $ex, expect the form like 'a.b = c'.")
end
end
function with(ex)
cache = gensym("cache")
value = gensym("value")
lens_compile(ex, cache, value)
end
"""
e.g. `@with x.y = val` will return a new object similar to `x`, with the `y` field changed to `val`.
"""
macro with(ex)
with(ex) |> esc
end
@inline @generated function _zero(::Type{T}) where {T<:Tuple}
Expr(:tuple, Any[:(_zero($field)) for field in T.types]...)
end
_zero(::Type{T}) where T<:Real = zero(T)
_zero(::Type{String}) = ""
_zero(::Type{Symbol}) = Symbol("")
_zero(::Type{Char}) = '\0'
_zero(::Type{T}) where {ET,N,T<:AbstractArray{ET,N}} = reshape(ET[], ntuple(x->0, N))
_zero(::Type{T}) where {A,B,T<:Dict{A,B}} = Dict{A,B}()
#_zero(x::T) where T = _zero(T) # not adding this line!
_zero(x::T) where T<:Real = zero(x)
_zero(::String) = ""
_zero(::Symbol) = Symbol("")
_zero(::Char) = '\0'
_zero(x::T) where T<:AbstractArray = zero(x)
function _zero(d::T) where {A,B,T<:Dict{A,B}}
Dict{A,B}([x=>_zero(y) for (x,y) in d])
end
@static if VERSION > v"1.6.100"
@generated function chfield(x, ::Val{FIELD}, xval) where FIELD
if ismutabletype(x)
Expr(:block, :(x.$FIELD = xval), :x)
else
:(@with x.$FIELD = xval)
end
end
else
@generated function chfield(x, ::Val{FIELD}, xval) where FIELD
if x.mutable
Expr(:block, :(x.$FIELD = xval), :x)
else
:(@with x.$FIELD = xval)
end
end
end
@generated function chfield(x, f::Function, xval)
Expr(:block, _invcheck(:(f(x)), :xval), :x)
end
# convert field of an object to a tuple
@generated function type2tuple(x::T) where T
Expr(:tuple, [:(x.$v) for v in fieldnames(T)]...)
end
macro fields(ex)
esc(:($type2tuple($ex)))
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/precompile.jl | code | 6932 | function _precompile_()
ccall(:jl_generating_output, Cint, ()) == 1 || return nothing
Base.precompile(Tuple{typeof(gen_ifunc),Module,Expr}) # time: 1.9265794
Base.precompile(Tuple{typeof(render_arg),Expr}) # time: 0.045112614
Base.precompile(Tuple{typeof(dual_ex),Module,Expr}) # time: 0.04482814
Base.precompile(Tuple{typeof(memkernel),Expr}) # time: 0.042466346
Base.precompile(Tuple{typeof(functionfoot),Vector{Any}}) # time: 0.028968
Base.precompile(Tuple{PlusEq{typeof(cos)},Float64,Float64}) # time: 0.024845816
Base.precompile(Tuple{typeof(assign_ex),Expr,Expr,Bool}) # time: 0.02307638
Base.precompile(Tuple{typeof(pushvar!),Vector{Symbol},Expr}) # time: 0.008682777
Base.precompile(Tuple{typeof(precom_opm),Symbol,Symbol,Expr}) # time: 0.024082141
Base.precompile(Tuple{typeof(precom_opm),Symbol,Expr,Expr}) # time: 0.014433064
Base.precompile(Tuple{PlusEq{typeof(sin)},Float64,Float64}) # time: 0.004275546
Base.precompile(Tuple{typeof(get_argname),Expr}) # time: 0.00546523
Base.precompile(Tuple{typeof(forstatement),Symbol,Expr,Vector{Any},CompileInfo,Nothing}) # time: 0.003776189
Base.precompile(Tuple{typeof(julia_usevar!),SymbolTable,Expr}) # time: 0.049144164
Base.precompile(Tuple{typeof(julia_usevar!),SymbolTable,Symbol}) # time: 0.003254819
Base.precompile(Tuple{typeof(precom_ex),Module,LineNumberNode,PreInfo}) # time: 0.001450645
Base.precompile(Tuple{typeof(precom_body),Module,SubArray{Any, 1, Vector{Any}, Tuple{UnitRange{Int64}}, true},PreInfo}) # time: 0.001423381
Base.precompile(Tuple{typeof(precom_body),Module,Vector{Any},PreInfo}) # time: 0.001126582
Base.precompile(Tuple{typeof(variable_analysis_ex),Expr,SymbolTable}) # time: 0.31368348
Base.precompile(Tuple{typeof(precom_ex),Module,Expr,PreInfo}) # time: 0.20325074
Base.precompile(Tuple{typeof(compile_ex),Module,Expr,CompileInfo}) # time: 0.19640462
Base.precompile(Tuple{Core.kwftype(typeof(almost_same)),NamedTuple{(:atol,), Tuple{Float64}},typeof(almost_same),Vector{Int64},Vector{Int64}}) # time: 0.080980584
Base.precompile(Tuple{typeof(unzipped_broadcast),PlusEq{typeof(*)},Tuple{Float64, Float64},Tuple{Float64, Float64},Tuple{Float64, Float64}}) # time: 0.07962415
Base.precompile(Tuple{typeof(unzipped_broadcast),PlusEq{typeof(identity)},Vector{Float64},Vector{Float64}}) # time: 0.060837016
Base.precompile(Tuple{typeof(almost_same),Vector{Float64},Vector{Float64}}) # time: 0.043818954
Base.precompile(Tuple{typeof(functionfoot),Vector{Expr}}) # time: 0.03988443
Base.precompile(Tuple{typeof(assign_ex),Expr,Symbol,Bool}) # time: 0.03273888
Base.precompile(Tuple{typeof(assign_ex),Expr,Float64,Bool}) # time: 0.031806484
Base.precompile(Tuple{typeof(Base.show_function),IOContext{IOBuffer},PlusEq{typeof(abs2)},Bool}) # time: 0.025549678
Base.precompile(Tuple{typeof(precom_opm),Symbol,Symbol,Symbol}) # time: 0.023482375
Base.precompile(Tuple{PlusEq{typeof(^)},ComplexF64,ComplexF64,Vararg{ComplexF64, N} where N}) # time: 0.018600011
Base.precompile(Tuple{typeof(functionfoot),Vector{Symbol}}) # time: 0.018092046
Base.precompile(Tuple{PlusEq{typeof(tan)},Float64,Float64}) # time: 0.015275044
Base.precompile(Tuple{typeof(precom_if),Module,Expr,PreInfo}) # time: 0.010871046
Base.precompile(Tuple{MinusEq{typeof(^)},Union{Float64, ComplexF64},Union{Float64, ComplexF64},Union{Float64, ComplexF64}}) # time: 0.010170748
Base.precompile(Tuple{PlusEq{typeof(*)},Float64,Float64,Vararg{Float64, N} where N}) # time: 0.007921303
Base.precompile(Tuple{PlusEq{typeof(+)},Float64,Float64,Vararg{Float64, N} where N}) # time: 0.00782437
Base.precompile(Tuple{typeof(_isconst),Expr}) # time: 0.007764913
Base.precompile(Tuple{typeof(swapvars!),SymbolTable,Expr,Expr}) # time: 0.007498742
Base.precompile(Tuple{typeof(Base.show_function),IOContext{IOBuffer},PlusEq{typeof(angle)},Bool}) # time: 0.007020253
Base.precompile(Tuple{typeof(precom_opm),Symbol,Expr,Int64}) # time: 0.006372358
Base.precompile(Tuple{typeof(precom_opm),Symbol,Symbol,Int64}) # time: 0.006353995
Base.precompile(Tuple{typeof(Base.show_function),IOContext{IOBuffer},PlusEq{typeof(abs)},Bool}) # time: 0.005321797
Base.precompile(Tuple{typeof(unzipped_broadcast),PlusEq{typeof(*)},Vector{Float64},Vector{Float64},Vector{Float64}}) # time: 0.005210431
Base.precompile(Tuple{typeof(isreversible),Function,Type{Tuple{Number, Any, Any}}}) # time: 0.003735743
Base.precompile(Tuple{typeof(removedot),Symbol}) # time: 0.003666485
Base.precompile(Tuple{PlusEq{typeof(*)},Float64,Int64,Vararg{Any, N} where N}) # time: 0.003475684
Base.precompile(Tuple{MinusEq{typeof(*)},Float64,Int64,Vararg{Any, N} where N}) # time: 0.003429493
Base.precompile(Tuple{PlusEq{typeof(*)},Float64,Float64,Vararg{Any, N} where N}) # time: 0.002768054
Base.precompile(Tuple{MinusEq{typeof(*)},Float64,Float64,Vararg{Any, N} where N}) # time: 0.002728645
Base.precompile(Tuple{typeof(assign_vars),SubArray{Any, 1, Vector{Any}, Tuple{UnitRange{Int64}}, true},Expr,Bool}) # time: 0.00237789
Base.precompile(Tuple{typeof(unzipped_broadcast),MinusEq{typeof(*)},Vector{Float64},Vector{Float64},Vector{Float64}}) # time: 0.002377491
Base.precompile(Tuple{typeof(almost_same),Tuple{Float64, Float64, Vector{Float64}, Vector{Float64}},Tuple{Float64, Float64, Vector{Float64}, Vector{Float64}}}) # time: 0.002303422
Base.precompile(Tuple{typeof(unzipped_broadcast),MinusEq{typeof(identity)},Vector{Float64},Vector{Float64}}) # time: 0.00225225
Base.precompile(Tuple{typeof(dual_swap),Expr,Expr}) # time: 0.002167955
Base.precompile(Tuple{PlusEq{typeof(sin)},ComplexF64,ComplexF64}) # time: 0.002135965
Base.precompile(Tuple{typeof(whilestatement),Expr,Expr,Vector{Any},CompileInfo}) # time: 0.001927632
Base.precompile(Tuple{typeof(variable_analysis_ex),LineNumberNode,SymbolTable}) # time: 0.001729012
Base.precompile(Tuple{typeof(compile_ex),Module,LineNumberNode,CompileInfo}) # time: 0.001608265
Base.precompile(Tuple{typeof(assign_ex),Int64,Expr,Bool}) # time: 0.001299157
Base.precompile(Tuple{typeof(_pop_value),Expr}) # time: 0.001298544
Base.precompile(Tuple{typeof(assign_ex),Symbol,Symbol,Bool}) # time: 0.001287453
Base.precompile(Tuple{typeof(assign_ex),Float64,Expr,Bool}) # time: 0.001275525
Base.precompile(Tuple{MinusEq{typeof(/)},Float64,Float64,Vararg{Any, N} where N}) # time: 0.001080575
Base.precompile(Tuple{typeof(_push_value),Expr,Expr,Bool}) # time: 0.00106306
Base.precompile(Tuple{MinusEq{typeof(*)},Float64,Float64,Vararg{Float64, N} where N}) # time: 0.001049093
Base.precompile(Tuple{PlusEq{typeof(/)},Float64,Float64,Vararg{Any, N} where N}) # time: 0.00102371
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/preprocess.jl | code | 7899 | export precom
# precompiling information
struct PreInfo
routines::Vector{Any}
end
PreInfo() = PreInfo([])
"""
precom(module, ex)
Precompile a function, returns a tuple of (macros, function name, arguments, type parameters, function body).
"""
function precom(m::Module, ex)
mc, fname, args, ts, body = match_function(ex)
vars = Symbol[]
newargs = map(args) do arg
@match arg begin
:(::$tp)=>Expr(:(::), gensym(), tp)
_ => arg
end
end
for arg in newargs
pushvar!(vars, arg)
end
info = PreInfo()
body_out = precom_body(m, body, info)
if !isempty(info.routines)
error("`@routine` and `~@routine` must appear in pairs, mising `~@routine`!")
end
st = SymbolTable(vars, Symbol[], Symbol[])
st_after = copy(st)
variable_analysis_ex.(body_out, Ref(st_after))
checksyms(st_after, st)
mc, fname, newargs, ts, body_out
end
function precom_body(m::Module, body::AbstractVector, info)
Any[precom_ex(m, ex, info) for ex in body]
end
# precompile `+=`, `-=`, `*=` and `/=`
function precom_opm(f, out, arg2)
if f in [:(+=), :(-=), :(*=), :(/=)]
@match arg2 begin
:($x |> $view) => Expr(f, out, :(identity($arg2)))
:($subf($(subargs...))) => Expr(f, out, arg2)
_ => Expr(f, out, :(identity($arg2)))
end
elseif f in [:(.+=), :(.-=), :(.*=), :(./=)]
@match arg2 begin
:($x |> $view) || :($x .|> $view) => Expr(f, out, :(identity.($arg2)))
:($subf.($(subargs...))) => Expr(f, out, arg2)
:($subf($(subargs...))) => Expr(f, out, arg2)
_ => Expr(f, out, :(identity.($arg2)))
end
end
end
# precompile `⊻=`
function precom_ox(f, out, arg2)
if f == :(⊻=)
@match arg2 begin
:($x |> $view) => Expr(f, out, :(identity($arg2)))
:($subf($(subargs...))) ||
:($a || $b) || :($a && $b) => Expr(f, out, arg2)
_ => Expr(f, out, :(identity($arg2)))
end
elseif f == :(.⊻=)
@match arg2 begin
:($x |> $view) || :($x .|> $view) => Expr(f, out, :(identity.($arg2)))
:($subf.($(subargs...))) => Expr(f, out, arg2)
:($subf($(subargs...))) => Expr(f, out, arg2)
_ => Expr(f, out, :(identity.($arg2)))
end
end
end
"""
precom_ex(module, ex, info)
Precompile a single statement `ex`, where `info` is a `PreInfo` instance.
"""
function precom_ex(m::Module, ex, info)
@match ex begin
:($x ← $val) || :($x → $val) => ex
:($x ↔ $y) => ex
:($(xs...), $y ← $val) => precom_ex(m, :(($(xs...), $y) ← $val), info)
:($(xs...), $y → $val) => precom_ex(m, :(($(xs...), $y) → $val), info)
:($a += $b) => precom_opm(:+=, a, b)
:($a -= $b) => precom_opm(:-=, a, b)
:($a *= $b) => precom_opm(:*=, a, b)
:($a /= $b) => precom_opm(:/=, a, b)
:($a ⊻= $b) => precom_ox(:⊻=, a, b)
:($a .+= $b) => precom_opm(:.+=, a, b)
:($a .-= $b) => precom_opm(:.-=, a, b)
:($a .*= $b) => precom_opm(:.*=, a, b)
:($a ./= $b) => precom_opm(:./=, a, b)
:($a .⊻= $b) => precom_ox(:.⊻=, a, b)
Expr(:if, _...) => precom_if(m, copy(ex), info)
:(while ($pre, $post); $(body...); end) => begin
post = post == :~ ? pre : post
info = PreInfo()
Expr(:while, :(($pre, $post)), Expr(:block, precom_body(m, body, info)...))
end
:(@from $line $post while $pre; $(body...); end) => precom_ex(m, Expr(:while, :(($pre, !$post)), ex.args[4].args[2]), info)
:(begin $(body...) end) => begin
Expr(:block, precom_body(m, body, info)...)
end
# TODO: allow ommit step.
:(for $i=$range; $(body...); end) ||
:(for $i in $range; $(body...); end) => begin
info = PreInfo()
Expr(:for, :($i=$(precom_range(range))), Expr(:block, precom_body(m, body, info)...))
end
:(@safe $line $subex) => ex
:(@cuda $line $(args...)) => ex
:(@launchkernel $line $(args...)) => ex
:(@inbounds $line $subex) => Expr(:macrocall, Symbol("@inbounds"), line, precom_ex(m, subex, info))
:(@simd $line $subex) => Expr(:macrocall, Symbol("@simd"), line, precom_ex(m, subex, info))
:(@threads $line $subex) => Expr(:macrocall, Symbol("@threads"), line, precom_ex(m, subex, info))
:(@avx $line $subex) => Expr(:macrocall, Symbol("@avx"), line, precom_ex(m, subex, info))
:(@invcheckoff $line $subex) => Expr(:macrocall, Symbol("@invcheckoff"), line, precom_ex(m, subex, info))
:(@routine $line $expr) => begin
precode = precom_ex(m, expr, info)
push!(info.routines, precode)
precode
end
:(~(@routine $line)) => begin
if isempty(info.routines)
error("`@routine` and `~@routine` must appear in pairs, mising `@routine`!")
end
precom_ex(m, dual_ex(m, pop!(info.routines)), info)
end
# 1. precompile to expand macros
# 2. get dual expression
# 3. precompile to analyze vaiables
:(~$expr) => precom_ex(m, dual_ex(m, precom_ex(m, expr, PreInfo())), info)
:($f($(args...))) => :($f($(args...)))
:($f.($(args...))) => :($f.($(args...)))
:(nothing) => ex
Expr(:macrocall, _...) => precom_ex(m, macroexpand(m, ex), info)
::LineNumberNode => ex
::Nothing => ex
_ => error("unsupported statement: $ex")
end
end
precom_range(range) = @match range begin
_ => range
end
function precom_if(m, ex, exinfo)
_expand_cond(cond) = @match cond begin
:(($pre, ~)) => :(($pre, $pre))
:(($pre, $post)) => :(($pre, $post))
:($pre) => :(($pre, $pre))
end
if ex.head == :if
ex.args[1] = _expand_cond(ex.args[1])
elseif ex.head == :elseif
ex.args[1].args[2] = _expand_cond(ex.args[1].args[2])
end
info = PreInfo()
ex.args[2] = Expr(:block, precom_body(m, ex.args[2].args, info)...)
if length(ex.args) == 3
if ex.args[3].head == :elseif
ex.args[3] = precom_if(m, ex.args[3], exinfo)
elseif ex.args[3].head == :block
info = PreInfo()
ex.args[3] = Expr(:block, precom_body(m, ex.args[3].args, info)...)
else
error("unknown statement following `if` $ex.")
end
end
ex
end
export @code_preprocess
"""
@code_preprocess ex
Preprocess `ex` and return the symmetric reversible IR.
```jldoctest; setup=:(using NiLangCore)
julia> NiLangCore.rmlines(@code_preprocess if (x < 3, ~) x += exp(3.0) end)
:(if (x < 3, x < 3)
x += exp(3.0)
end)
```
"""
macro code_preprocess(ex)
QuoteNode(precom_ex(__module__, ex, PreInfo()))
end
precom_ex(m::Module, ex) = precom_ex(m, ex, PreInfo())
# push a new variable to variable set `x`, for allocating `target`
function pushvar!(x::Vector{Symbol}, target)
@match target begin
::Symbol => begin
if target in x
throw(InvertibilityError("Symbol `$target` should not be used as the allocation target, it is an existing variable in the current scope."))
else
push!(x, target)
end
end
:(($(tar...),)) => begin
for t in tar
pushvar!(x, t)
end
end
:($tar = _) => pushvar!(x, tar)
:($tar...) => pushvar!(x, tar)
:($tar::$tp) => pushvar!(x, tar)
Expr(:parameters, targets...) => begin
for tar in targets
pushvar!(x, tar)
end
end
Expr(:kw, tar, val) => begin
pushvar!(x, tar)
end
_ => error("unknown variable expression $(target)")
end
nothing
end | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/stack.jl | code | 1837 | export FastStack, GLOBAL_STACK, FLOAT64_STACK, FLOAT32_STACK, COMPLEXF64_STACK, COMPLEXF32_STACK, BOOL_STACK, INT64_STACK, INT32_STACK
const GLOBAL_STACK = []
struct FastStack{T}
data::Vector{T}
top::Base.RefValue{Int}
end
function FastStack{T}(n::Int) where T
FastStack{T}(Vector{T}(undef, n), Ref(0))
end
function FastStack(n::Int) where T
FastStack{Any}(Vector{Any}(undef, n), Ref(0))
end
Base.show(io::IO, x::FastStack{T}) where T = print(io, "FastStack{$T}($(x.top[])/$(length(x.data)))")
Base.show(io::IO, ::MIME"text/plain", x::FastStack{T}) where T = show(io, x)
Base.length(stack::FastStack) = stack.top[]
Base.empty!(stack::FastStack) = (stack.top[] = 0; stack)
@inline function Base.push!(stack::FastStack, val)
stack.top[] += 1
@boundscheck stack.top[] <= length(stack.data) || throw(BoundsError(stack, stack.top[]))
stack.data[stack.top[]] = val
return stack
end
@inline function Base.pop!(stack::FastStack)
@boundscheck stack.top[] > 0 || throw(BoundsError(stack, stack.top[]))
val = stack.data[stack.top[]]
stack.top[] -= 1
return val
end
# default stack size is 10^6 (~8M for Float64)
let
empty_exprs = Expr[:($empty!($GLOBAL_STACK))]
for DT in [:Float64, :Float32, :ComplexF64, :ComplexF32, :Int64, :Int32, :Bool]
STACK = Symbol(uppercase(String(DT)), :_STACK)
@eval const $STACK = FastStack{$DT}(1000000)
# allow in-stack and out-stack different, to support loading data to GVar.
push!(empty_exprs, Expr(:call, empty!, STACK))
end
@eval function empty_global_stacks!()
$(empty_exprs...)
end
end
"""
loaddata(t, x)
load data `x`, matching type `t`.
"""
loaddata(::Type{T}, x::T) where T = x
loaddata(::Type{T1}, x::T) where {T1,T} = convert(T1,x)
loaddata(::T1, x::T) where {T1,T} = loaddata(T1, x) | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/symboltable.jl | code | 4127 | # * existing: the ancillas and input arguments in the local scope.
# They should be protected to avoid duplicated allocation.
# * deallocated: the ancillas removed.
# They should be recorded to avoid using after deallocation.
# * unclassified: the variables from global scope.
# They can not be allocation target.
struct SymbolTable
existing::Vector{Symbol}
deallocated::Vector{Symbol}
unclassified::Vector{Symbol}
end
function SymbolTable()
SymbolTable(Symbol[], Symbol[], Symbol[])
end
Base.copy(st::SymbolTable) = SymbolTable(copy(st.existing), copy(st.deallocated), copy(st.unclassified))
# remove a variable from a list
function removevar!(lst::AbstractVector, var)
index = findfirst(==(var), lst)
deleteat!(lst, index)
end
# replace a variable in a list with target variable
function replacevar!(lst::AbstractVector, var, var2)
index = findfirst(==(var), lst)
lst[index] = var2
end
# allocate a new variable
function allocate!(st::SymbolTable, var::Symbol)
if var ∈ st.existing
throw(InvertibilityError("Repeated allocation of variable `$(var)`"))
elseif var ∈ st.deallocated
removevar!(st.deallocated, var)
push!(st.existing, var)
elseif var ∈ st.unclassified
throw(InvertibilityError("Variable `$(var)` used before allocation."))
else
push!(st.existing, var)
end
nothing
end
# find the list containing var
function findlist(st::SymbolTable, var::Symbol)
if var ∈ st.existing
return st.existing
elseif var ∈ st.unclassified
return st.unclassified
elseif var in st.deallocated
return st.deallocated
else
return nothing
end
end
# using a variable
function operate!(st::SymbolTable, var::Symbol)
if var ∈ st.existing || var ∈ st.unclassified
elseif var ∈ st.deallocated
throw(InvertibilityError("Operating on deallocate variable `$(var)`"))
else
push!(st.unclassified, var::Symbol)
end
nothing
end
# deallocate a variable
function deallocate!(st::SymbolTable, var::Symbol)
if var ∈ st.deallocated
throw(InvertibilityError("Repeated deallocation of variable `$(var)`"))
elseif var ∈ st.existing
removevar!(st.existing, var)
push!(st.deallocated, var)
elseif var ∈ st.unclassified
throw(InvertibilityError("Deallocating an external variable `$(var)`"))
else
throw(InvertibilityError("Deallocating an external variable `$(var)`"))
end
nothing
end
# check symbol table to make sure there is symbols introduced in the local scope that has not yet deallocated.
# `a` is the symbol table after running local scope, `b` is the symbol table before running the local scope.
function checksyms(a::SymbolTable, b::SymbolTable=SymbolTable())
diff = setdiff(a.existing, b.existing)
if !isempty(diff)
error("Some variables not deallocated correctly: $diff")
end
end
function swapsyms!(st::SymbolTable, var1::Symbol, var2::Symbol)
lst1 = findlist(st, var1)
lst2 = findlist(st, var2)
if lst1 !== nothing && lst2 !== nothing
# exchange variables
i1 = findfirst(==(var1), lst1)
i2 = findfirst(==(var2), lst2)
lst2[i2], lst1[i1] = lst1[i1], lst2[i2]
elseif lst1 !== nothing
replacevar!(lst1, var1, var2)
operate!(st, var1)
elseif lst2 !== nothing
replacevar!(lst2, var2, var1)
operate!(st, var2)
else
operate!(st, var1)
operate!(st, var2)
end
end
function swapsyms_asymetric!(st::SymbolTable, var1s::Vector, var2::Symbol)
length(var1s) == 0 && return
lst1 = findlist(st, var1s[1])
for k=2:length(var1s)
if findlist(st, var1s[k]) !== lst1
error("variable status not aligned: $var1s")
end
end
lst2 = findlist(st, var2)
if lst1 !== nothing
removevar!.(Ref(lst1), var1s)
push!(lst1, var2)
else
operate!(st, var2)
end
if lst2 !== nothing
removevar!(lst2, var2)
push!.(Ref(lst2),var1s)
else
operate!.(Ref(st), var1s)
end
end | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/utils.jl | code | 4048 | const GLOBAL_ATOL = Ref(1e-8)
########### macro tools #############
startwithdot(sym::Symbol) = string(sym)[1] == '.'
startwithdot(sym::Expr) = false
startwithdot(sym) = false
function removedot(f)
string(f)[1] == '.' || error("$f is not a broadcasting.")
Symbol(string(f)[2:end])
end
"""
get_argname(ex)
Return the argument name of a function argument expression, e.g. `x::Float64 = 4` gives `x`.
"""
function get_argname(fname)
@match fname begin
::Symbol => fname
:($x::$t) => x
:($x::$t=$y) => x
:($x=$y) => x
:($x...) => :($x...)
:($x::$t...) => :($x...)
Expr(:parameters, args...) => fname
_ => error("can not get the function name of expression $fname.")
end
end
"""
match_function(ex)
Analyze a function expression, returns a tuple of `(macros, function name, arguments, type parameters (in where {...}), statements in the body)`
"""
function match_function(ex)
@match ex begin
:(function $(fname)($(args...)) $(body...) end) ||
:($fname($(args...)) = $(body...)) => (nothing, fname, args, [], body)
Expr(:function, :($fname($(args...)) where {$(ts...)}), xbody) => (nothing, fname, args, ts, xbody.args)
Expr(:macrocall, mcname, line, fdef) => ([mcname, line], match_function(fdef)[2:end]...)
_ => error("must input a function, got $ex")
end
end
"""
rmlines(ex::Expr)
Remove line number nodes for pretty printing.
"""
rmlines(ex::Expr) = begin
hd = ex.head
if hd == :macrocall
Expr(:macrocall, ex.args[1], nothing, rmlines.(ex.args[3:end])...)
else
tl = Any[rmlines(ex) for ex in ex.args if !(ex isa LineNumberNode)]
Expr(hd, tl...)
end
end
rmlines(@nospecialize(a)) = a
########### ordered dict ###############
struct MyOrderedDict{TK,TV}
keys::Vector{TK}
vals::Vector{TV}
end
function MyOrderedDict{K,V}() where {K,V}
MyOrderedDict(K[], V[])
end
function Base.setindex!(d::MyOrderedDict, val, key)
ind = findfirst(x->x===key, d.keys)
if ind isa Nothing
push!(d.keys, key)
push!(d.vals, val)
else
@inbounds d.vals[ind] = val
end
return d
end
function Base.getindex(d::MyOrderedDict, key)
ind = findfirst(x->x===key, d.keys)
if ind isa Nothing
throw(KeyError(ind))
else
return d.vals[ind]
end
end
function Base.delete!(d::MyOrderedDict, key)
ind = findfirst(x->x==key, d.keys)
if ind isa Nothing
throw(KeyError(ind))
else
deleteat!(d.vals, ind)
deleteat!(d.keys, ind)
end
end
Base.length(d::MyOrderedDict) = length(d.keys)
function Base.pop!(d::MyOrderedDict)
k = pop!(d.keys)
v = pop!(d.vals)
k, v
end
Base.isempty(d::MyOrderedDict) = length(d.keys) == 0
########### broadcasting ###############
"""
unzipped_broadcast(f, args...)
unzipped broadcast for arrays and tuples, e.g. `SWAP.([1,2,3], [4,5,6])` will do inplace element-wise swap, and return `[4,5,6], [1,2,3]`.
"""
unzipped_broadcast(f) = error("must provide at least one argument in broadcasting!")
function unzipped_broadcast(f, arg::AbstractArray; kwargs...)
arg .= f.(arg)
end
function unzipped_broadcast(f, arg::Tuple; kwargs...)
f.(arg)
end
@generated function unzipped_broadcast(f, args::Vararg{<:AbstractArray,N}; kwargs...) where N
argi = [:(args[$k][i]) for k=1:N]
quote
for i = 1:same_length(args)
($(argi...),) = f($(argi...); kwargs...)
end
return args
end
end
@generated function unzipped_broadcast(f, args::Vararg{<:Tuple,N}; kwargs...) where N
quote
same_length(args)
res = map(f, args...)
($([:($getindex.(res, $i)) for i=1:N]...),)
end
end
function same_length(args)
length(args) == 0 && error("can not broadcast over an empty set of arguments.")
l = length(args[1])
for j=2:length(args)
@assert l == length(args[j]) "length of arguments should be the same `$(length(args[j])) != $l`"
end
return l
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/variable_analysis.jl | code | 10585 | function variable_analysis_ex(ex, syms::SymbolTable)
use!(x) = usevar!(syms, x)
allocate!(x) = allocatevar!(syms, x)
deallocate!(x) = deallocatevar!(syms, x)
@match ex begin
:($x[$key] ← $val) || :($x[$key] → $val) => (use!(x); use!(key); use!(val))
:($x ← $val) => allocate!(x)
:($x → $val) => deallocate!(x)
:($x ↔ $y) => swapvars!(syms, x, y)
:($a += $f($(b...))) || :($a -= $f($(b...))) ||
:($a *= $f($(b...))) || :($a /= $f($(b...))) ||
:($a .+= $f($(b...))) || :($a .-= $f($(b...))) ||
:($a .*= $f($(b...))) || :($a ./= $f($(b...))) ||
:($a .+= $f.($(b...))) || :($a .-= $f.($(b...))) ||
:($a .*= $f.($(b...))) || :($a ./= $f.($(b...))) => begin
ex.args[1] = render_arg(a)
b .= render_arg.(b)
use!(a)
use!.(b)
check_args(Any[a, b...])
end
:($a ⊻= $f($(b...))) || :($a .⊻= $f($(b...))) || :($a .⊻= $f.($(b...))) => begin
ex.args[1] = render_arg(a)
b .= render_arg.(b)
use!(a)
use!(b)
end
:($a ⊻= $b || $c) || :($a ⊻= $b && $c) => begin
ex.args[1] = render_arg(a)
ex.args[2].args .= render_arg.(ex.args[2].args)
use!(a)
use!(b)
end
Expr(:if, _...) => variable_analysis_if(ex, syms)
:(while $condition; $(body...); end) => begin
julia_usevar!(syms, condition)
localsyms = SymbolTable(Symbol[], copy(syms.deallocated), Symbol[])
variable_analysis_ex.(body, Ref(localsyms))
checksyms(localsyms)
end
:(begin $(body...) end) => begin
variable_analysis_ex.(body, Ref(syms))
end
# TODO: allow ommit step.
:(for $i=$range; $(body...); end) => begin
julia_usevar!(syms, range)
localsyms = SymbolTable(Symbol[], copy(syms.deallocated), Symbol[])
variable_analysis_ex.(body, Ref(localsyms))
checksyms(localsyms)
ex
end
:(@safe $line $subex) => julia_usevar!(syms, subex)
:(@cuda $line $(args...)) => variable_analysis_ex(args[end], syms)
:(@launchkernel $line $(args...)) => variable_analysis_ex(args[end], syms)
:(@inbounds $line $subex) => variable_analysis_ex(subex, syms)
:(@simd $line $subex) => variable_analysis_ex(subex, syms)
:(@threads $line $subex) => variable_analysis_ex(subex, syms)
:(@avx $line $subex) => variable_analysis_ex(subex, syms)
:(@invcheckoff $line $subex) => variable_analysis_ex(subex, syms)
# 1. precompile to expand macros
# 2. get dual expression
# 3. precompile to analyze vaiables
:($f($(args...))) => begin
args .= render_arg.(args)
check_args(args)
use!.(args)
end
:($f.($(args...))) => begin
args .= render_arg.(args)
check_args(args)
use!.(args)
end
:(nothing) => nothing
::LineNumberNode => nothing
::Nothing => nothing
_ => error("unsupported statement: $ex")
end
end
function variable_analysis_if(ex, exsyms)
syms = copy(exsyms)
julia_usevar!(exsyms, ex.args[1])
variable_analysis_ex.(ex.args[2].args, Ref(exsyms))
checksyms(exsyms, syms)
if length(ex.args) == 3
if ex.args[3].head == :elseif
variable_analysis_if(ex.args[3], exsyms)
elseif ex.args[3].head == :block
syms = copy(exsyms)
variable_analysis_ex.(ex.args[3].args, Ref(exsyms))
checksyms(exsyms, syms)
else
error("unknown statement following `if` $ex.")
end
end
end
usevar!(syms::SymbolTable, arg) = @match arg begin
::Number || ::String => nothing
::Symbol => _isconst(arg) || operate!(syms, arg)
:(@skip! $line $x) => julia_usevar!(syms, x)
:($x.$k) => usevar!(syms, x)
:($a |> subarray($(ranges...))) => (usevar!(syms, a); julia_usevar!.(Ref(syms), ranges))
:($x |> tget($f)) || :($x |> $f) || :($x .|> $f) || :($x::$f) => (usevar!(syms, x); julia_usevar!(syms, f))
:($x') || :(-$x) => usevar!(syms, x)
:($t{$(p...)}($(args...))) => begin
usevar!(syms, t)
usevar!.(Ref(syms), p)
usevar!.(Ref(syms), args)
end
:($a[$(x...)]) => begin
usevar!(syms, a)
usevar!.(Ref(syms), x)
end
:(($(args...),)) => usevar!.(Ref(syms), args)
_ => julia_usevar!(syms, arg)
end
julia_usevar!(syms::SymbolTable, ex) = @match ex begin
::Symbol => _isconst(ex) || operate!(syms, ex)
:($a:$b:$c) => julia_usevar!.(Ref(syms), [a, b, c])
:($a:$c) => julia_usevar!.(Ref(syms), [a, c])
:($a && $b) || :($a || $b) || :($a[$b]) => julia_usevar!.(Ref(syms), [a, b])
:($a.$b) => julia_usevar!(syms, a)
:(($(v...),)) || :(begin $(v...) end) => julia_usevar!.(Ref(syms), v)
:($f($(v...))) || :($f[$(v...)]) => begin
julia_usevar!(syms, f)
julia_usevar!.(Ref(syms), v)
end
:($args...) => julia_usevar!(syms, args)
Expr(:parameters, targets...) => julia_usevar!.(Ref(syms), targets)
Expr(:kw, tar, val) => julia_usevar!(syms, val)
::LineNumberNode => nothing
_ => nothing
end
# push a new variable to variable set `x`, for allocating `target`
allocatevar!(st::SymbolTable, target) = @match target begin
::Symbol => allocate!(st, target)
:(($(tar...),)) => begin
for t in tar
allocatevar!(st, t)
end
end
:($tar = $y) => allocatevar!(st, y)
:($tar...) => allocatevar!(st, tar)
:($tar::$tp) => allocatevar!(st, tar)
Expr(:parameters, targets...) => begin
for tar in targets
allocatevar!(st, tar)
end
end
Expr(:kw, tar, val) => begin
allocatevar!(st, tar)
end
_ => _isconst(target) || error("unknown variable expression $(target)")
end
# pop a variable from variable set `x`, for deallocating `target`
deallocatevar!(st::SymbolTable, target) = @match target begin
::Symbol => deallocate!(st, target)
:(($(tar...),)) => begin
for t in tar
deallocatevar!(st, t)
end
end
_ => error("unknow variable expression $(target)")
end
function swapvars!(st::SymbolTable, x, y)
e1 = isemptyvar(x)
e2 = isemptyvar(y)
# check assersion
for (e, v) in ((e1, x), (e2, y))
e && dosymbol(v) do sv
if sv ∈ st.existing || sv ∈ st.unclassified
throw(InvertibilityError("can not assert variable to empty: $v"))
end
end
end
if e1 && e2
elseif e1 && !e2
dosymbol(sx -> allocate!(st, sx), x)
dosymbol(sy -> deallocate!(st, sy), y)
usevar!(st, x)
elseif !e1 && e2
dosymbol(sx -> deallocate!(st, sx), x)
dosymbol(sy -> allocate!(st, sy), y)
usevar!(st, y)
else # both are nonempty
sx = dosymbol(identity, x)
sy = dosymbol(identity, y)
if sx === nothing || sy === nothing # e.g. x.y ↔ k.c
usevar!(st, x)
usevar!(st, y)
elseif sx isa Symbol && sy isa Symbol # e.g. x ↔ y
swapsyms!(st, sx, sy)
elseif sx isa Vector && sy isa Vector # e.g. (x, y) ↔ (a, b)
@assert length(sx) == length(sy)
swapsyms!.(Ref(st), sx, sy)
elseif sx isa Vector && sy isa Symbol # e.g. (x, y) ↔ args
swapsyms_asymetric!(st, sx, sy)
elseif sx isa Symbol && sy isa Vector # e.g. args ↔ (x, y)
swapsyms_asymetric!(st, sy, sx)
end
end
end
isemptyvar(ex) = @match ex begin
:($x[end+1]) => true
:($x::∅) => true
_ => false
end
dosymbol(f, ex) = @match ex begin
x::Symbol => f(x)
:(@fields $line $sym) => dosymbol(f, sym)
:($x::$T) => dosymbol(f, x)
:(($(args...),)) => dosymbol.(Ref(f), args)
_ => nothing
end
_isconst(x) = @match x begin
::Symbol => x ∈ Symbol[:im, :π, :Float64, :Float32, :Int, :Int64, :Int32, :Bool, :UInt8, :String, :Char, :ComplexF64, :ComplexF32, :(:), :end, :nothing]
::QuoteNode || ::Bool || ::Char || ::Number || ::String => true
:($f($(args...))) => all(_isconst, args)
:(@const $line $ex) => true
_ => false
end
# avoid share read/write
function check_args(args)
args_kernel = []
for i=1:length(args)
out = memkernel(args[i])
if out isa Vector
for o in out
if o !== nothing
push!(args_kernel, o)
end
end
elseif out !== nothing
push!(args_kernel, out)
end
end
# error on shared read or shared write.
for i=1:length(args_kernel)
for j in i+1:length(args_kernel)
if args_kernel[i] == args_kernel[j]
throw(InvertibilityError("$i-th argument and $j-th argument shares the same memory $(args_kernel[i]), shared read and shared write are not allowed!"))
end
end
end
end
# Returns the memory `identifier`, it is used to avoid shared read/write.
memkernel(ex) = @match ex begin
::Symbol => ex
:(@const $line $x) => memkernel(x)
:($a |> subarray($(inds...))) || :($a[$(inds...)]) => :($(memkernel(a))[$(inds...)])
:($x.$y) => :($(memkernel(x)).$y)
:($a |> tget($x)) => :($(memkernel(a))[$x])
:($x |> $f) || :($x .|> $f) || :($x') || :(-$x) || :($x...) => memkernel(x)
:($t{$(p...)}($(args...))) || :(($(args...),)) => memkernel.(args)
_ => nothing # Julia scope, including `@skip!`, `f(x)` et. al.
end
# Modify the argument, e.g. `x.[1,3:5]` is rendered as `x |> subarray(1,3:5)`.
render_arg(ex) = @match ex begin
::Symbol => ex
:(@skip! $line $x) => ex
:(@const $line $x) => Expr(:macrocall, Symbol("@const"), line, render_arg(x))
:($a.[$(inds...)]) => :($(render_arg(a)) |> subarray($(inds...)))
:($a |> subarray($(inds...))) => :($(render_arg(a)) |> subarray($(inds...)))
:($a[$(inds...)]) => :($(render_arg(a))[$(inds...)])
:($x.$y) => :($(render_arg(x)).$y)
:($a |> tget($x)) => :($(render_arg(a)) |> tget($x))
:($x |> $f) => :($(render_arg(x)) |> $f)
:($x .|> $f) => :($(render_arg(x)) .|> $f)
:($x') => :($(render_arg(x))')
:(-$x) => :(-$(render_arg(x)))
:($ag...) => :($(render_arg(ag))...)
:($t{$(p...)}($(args...))) => :($t{($p...)}($(render_arg.(args)...)))
:(($(args...),)) => :(($(render_arg.(args)...),))
_ => ex # Julia scope, including `@skip!`, `f(x)` et. al.
end | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | src/vars.jl | code | 1440 | using Base.Cartesian
export chfield
############# ancillas ################
export @fieldview
"""
@fieldview fname(x::TYPE) = x.fieldname
@fieldview fname(x::TYPE) = x[i]
...
Create a function fieldview that can be accessed by a reversible program
```jldoctest; setup=:(using NiLangCore)
julia> struct GVar{T, GT}
x::T
g::GT
end
julia> @fieldview xx(x::GVar) = x.x
julia> chfield(GVar(1.0, 0.0), xx, 2.0)
GVar{Float64, Float64}(2.0, 0.0)
```
"""
macro fieldview(ex)
@match ex begin
:($f($obj::$tp) = begin $line; $ex end) => begin
xval = gensym("value")
esc(Expr(:block,
:(Base.@__doc__ $f($obj::$tp) = begin $line; $ex end),
:($NiLangCore.chfield($obj::$tp, ::typeof($f), $xval) = $(Expr(:block, assign_ex(ex, xval, false), obj)))
))
end
_ => error("expect expression `f(obj::type) = obj.prop`, got $ex")
end
end
chfield(a, b, c) = error("chfield($a, $b, $c) not defined!")
chfield(x, ::typeof(identity), xval) = xval
chfield(x::T, ::typeof(-), y::T) where T = -y
chfield(x::T, ::typeof(adjoint), y) where T = adjoint(y)
############ dataview patches ############
export tget, subarray
"""
tget(i::Int)
Get the i-th entry of a tuple.
"""
tget(i::Int) = x::Tuple -> x[i]
"""
subarray(ranges...)
Get a subarray, same as `view` in Base.
"""
subarray(args...) = x -> view(x, args...) | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/Core.jl | code | 621 | using Test, NiLangCore
@testset "basic" begin
@test ~(~sin) === sin
@test ~(~typeof(sin)) === typeof(sin)
@test isreflexive(XorEq(NiLangCore.logical_or))
println(XorEq(*))
println(PlusEq(+))
println(MinusEq(-))
println(MulEq(*))
println(DivEq(/))
end
@static if VERSION > v"1.5.100"
@testset "composite function" begin
@i function f1(x)
x.:1 += x.:2
end
@i function f2(x)
x.:2 += cos(x.:1)
end
@i function f3(x)
x.:1 ↔ x.:2
end
x = (2.0, 3.0)
y = (f3∘f2∘f1)(x)
z = (~(f3∘f2∘f1))(y)
@show x, z
@test all(x .≈ z)
end
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/compiler.jl | code | 19887 | using NiLangCore
using Test
using Base.Threads
@testset "to_standard_format" begin
for (OP, FUNC) in [(:+=, PlusEq), (:-=, MinusEq), (:*=, MulEq), (:/=, DivEq), (:⊻=, XorEq)]
@test NiLangCore.to_standard_format(Expr(OP, :x, :y)) == :($FUNC(identity)(x, y))
@test NiLangCore.to_standard_format(Expr(OP, :x, :(sin(y; z=3)))) == :($FUNC(sin)(x, y; z=3))
OPD = Symbol(:., OP)
@test NiLangCore.to_standard_format(Expr(OPD, :x, :y)) == :($FUNC(identity).(x, y))
@test NiLangCore.to_standard_format(Expr(OPD, :x, :(sin.(y)))) == :($FUNC(sin).(x, y))
@test NiLangCore.to_standard_format(Expr(OPD, :x, :(y .* z))) == :($FUNC(*).(x, y, z))
end
@test NiLangCore.to_standard_format(Expr(:⊻=, :x, :(y && z))) == :($XorEq($(NiLangCore.logical_and))(x, y, z))
@test NiLangCore.to_standard_format(Expr(:⊻=, :x, :(y || z))) == :($XorEq($(NiLangCore.logical_or))(x, y, z))
end
@testset "i" begin
@i function test1(a::T, b, out) where T<:Number
add(a, b)
out += a * b
end
@i function tt(a, b)
out ← 0.0
test1(a, b, out)
(~test1)(a, b, out)
a += b
out → 0.0
end
# compute (a+b)*b -> out
x = 3.0
y = 4.0
out = 0.0
@test isreversible(test1, Tuple{Number, Any, Any})
@test check_inv(test1, (x, y, out))
@test check_inv(tt, (x, y))
@test check_inv(tt, (x, y))
end
@testset "if statement 1" begin
# compute (a+b)*b -> out
@i function test1(a, b, out)
add(a, b)
if (a > 8, a > 8)
out += a*b
else
end
end
x = 3
y = 4
out = 0
@instr test1(x, y, out)
@test out==0
@test x==7
@instr (~test1)(x, y, out)
@test out==0
@test x==3
end
@testset "if statement error" begin
x = 3
y = 4
out = 0
# compute (a+b)*b -> out
@i function test1(a, b, out)
add(a, b)
if (out < 4, out < 4)
out += a*b
else
end
end
@test_throws InvertibilityError test1(x, y, out)
end
@testset "if statement 3" begin
x = 3
y = 4
out = 0
@i @inline function test1(a, b, out)
add(a, b)
if (a > 2, a > 2)
out += a*b
else
end
end
x = 3
y = 4
out = 0
@instr test1(x, y, out)
@test out==28
@instr (~test1)(x, y, out)
@test out==0
end
@testset "if statement 4" begin
@i function test1(a, b, out)
add(a, b)
if a > 8.0
out += a*b
end
end
@test test1(1.0, 8.0, 0.0)[3] == 72.0
@i function test2(a, b)
add(a, b)
if a > 8.0
a -= b^2
end
end
@test_throws InvertibilityError test2(1.0, 8.0)
@test_throws LoadError macroexpand(Main, :(@i function test3(a, b)
add(a, b)
if a > 8.0
a -= b*b
end
end))
end
@testset "for" begin
@i function looper(x, y, k)
for i=1:1:k
x += y
end
end
x = 0.0
y = 1.0
k = 3
@instr looper(x, y, k)
@test x == 3
@instr (~looper)(x, y, k)
@test x == 0.0
shiba = 18
@i function looper2(x, y, k)
for i=1:1:k
k += shiba
x += y
end
end
@test_throws InvertibilityError looper2(x, y, k)
end
@testset "while" begin
@i function looper(x, y)
while (x<100, x>0)
x += y
end
end
x = 0.0
y = 9
@instr looper(x, y)
@test x == 108
@instr (~looper)(x, y)
@test x == 0.0
@i function looper2(x, y)
while (x<100, x>-10)
x += y
end
end
@test_throws InvertibilityError looper2(x, y)
@test_throws LoadError macroexpand(@__MODULE__, :(@i function looper3(x, y)
while (x<100, x>0)
z ← 0
x += y
z += 1
end
end))
end
@testset "ancilla" begin
one, ten = 1, 10
@i function looper(x, y)
z ← 0
x += y
z += one
z -= one
z → 0
end
x = 0.0
y = 9
@instr looper(x, y)
@test x[] == 9
@instr (~looper)(x, y)
@test x[] == 0.0
@i function looper2(x, y)
z ← 0
x += y
z += one
z -= ten
z → 0
end
x = 0.0
y = 9
@test_throws InvertibilityError looper2(x, y)
end
@testset "broadcast" begin
# compute (a+b)*b -> out
@i function test1(a, b)
a .+= b
end
x = [3, 1.0]
y = [4, 2.0]
@instr test1(x, y)
@test x == [7, 3.0]
@instr (~test1)(x, y)
@test x == [3, 1.0]
@i function test2(a, b, out)
a .+= identity.(b)
out .+= (a .* b)
end
x = Array([3, 1.0])
y = [4, 2.0]
out = Array([0.0, 1.0])
@instr test2(x, y, out)
@test out==[28, 7]
@test check_inv(test2, (x, y, out))
end
@testset "broadcast arr" begin
@i function f5(x, y, z, a, b)
x += y + z
b += a + x
end
@i function f4(x, y, z, a)
x += y + z
a += y + x
end
@i function f3(x, y, z)
y += x + z
end
@i function f2(x, y)
y += x
end
@i function f1(x)
l ← zero(x)
l += x
x -= 2 * l
l += x
l → zero(x)
end
a = randn(10)
b = randn(10)
c = randn(10)
d = randn(10)
e = randn(10)
aa = copy(a)
@instr f1.(aa)
@test aa ≈ -a
aa = copy(a)
bb = copy(b)
@instr f2.(aa, bb)
@test aa ≈ a
@test bb ≈ b + a
aa = copy(a)
bb = copy(b)
cc = copy(c)
@instr f3.(aa, bb, cc)
@test aa ≈ a
@test bb ≈ b + a + c
@test cc ≈ c
aa = copy(a)
bb = copy(b)
cc = copy(c)
dd = copy(d)
@instr f4.(aa, bb, cc, dd)
@test aa ≈ a + b + c
@test bb ≈ b
@test cc ≈ c
@test dd ≈ a + 2b + c + d
aa = copy(a)
bb = copy(b)
cc = copy(c)
dd = copy(d)
ee = copy(e)
@instr f5.(aa, bb, cc, dd, ee)
@test aa ≈ a + b + c
@test bb ≈ b
@test cc ≈ c
@test dd ≈ d
@test ee ≈ a + b + c + d + e
x = randn(5)
@test_throws AssertionError @instr x .+= c
end
@testset "broadcast tuple" begin
@i function f5(x, y, z, a, b)
x += y + z
b += a + x
end
@i function f4(x, y, z, a)
x += y + z
a += y + x
end
@i function f3(x, y, z)
y += x + z
end
@i function f2(x, y)
y += x
end
@i function f1(x)
l ← zero(x)
l += x
x -= 2 * l
l += x
l → zero(x)
end
a = (1,2)
b = (3,1)
c = (6,7)
d = (1,11)
e = (4,1)
aa = a
@instr f1.(aa)
@test aa == -1 .* a
aa = a
bb = b
@instr f2.(aa, bb)
@test aa == a
@test bb == b .+ a
aa = a
bb = b
cc = c
@instr f3.(aa, bb, cc)
@test aa == a
@test bb == b .+ a .+ c
@test cc == c
aa = a
bb = b
cc = c
dd = d
@instr f4.(aa, bb, cc, dd)
@test aa == a .+ b .+ c
@test bb == b
@test cc == c
@test dd == a .+ 2 .* b .+ c .+ d
aa = a
bb = b
cc = c
dd = d
ee = e
@instr f5.(aa, bb, cc, dd, ee)
@test aa == a .+ b .+ c
@test bb == b
@test cc == c
@test dd == d
@test ee == a .+ b .+ c .+ d .+ e
x = (2,1,5)
@test_throws AssertionError @instr x .+= c
end
@testset "broadcast 2" begin
# compute (a+b)*b -> out
@i function test1(a, b)
a += b
end
x = [3, 1.0]
y = [4, 2.0]
@instr test1.(x, y)
@test x == [7, 3.0]
@instr (~test1).(x, y)
@test x == [3, 1.0]
@i function test2(a, b, out)
add(a, b)
out += (a * b)
end
x = [3, 1.0]
y = [4, 2.0]
out = [0.0, 1.0]
@instr test2.(x, y, out)
@test out==[28, 7]
@instr (~test2).(x, y, out)
@test out==[0, 1.0]
args = (x, y, out)
@instr test2.(args...)
@test args[3]==[28, 7]
end
@testset "neg sign" begin
@i function test(out, x, y)
out += x * (-y)
end
@test check_inv(test, (0.1, 2.0, -2.5); verbose=true)
end
@testset "@ibounds" begin
@i function test(x, y)
for i=1:length(x)
@inbounds x[i] += y[i]
end
end
@test test([1,2], [2,3]) == ([3,5], [2,3])
end
@testset "kwargs" begin
@i function test(out, x; y)
out += x * (-y)
end
@test check_inv(test, (0.1, 2.0); y=0.5, verbose=true)
end
@testset "routines" begin
@i function test(out, x)
@routine begin
out += x
end
~@routine
end
out, x = 0.0, 1.0
@instr test(out, x)
@test out == 0.0
end
@testset "inverse a prog" begin
@i function test(out, x)
~(out += x;
out += x)
~for i=1:3
out += x
end
end
out, x = 0.0, 1.0
@test check_inv(test, (out, x))
@instr test(out, x)
@test out == -5.0
end
@testset "invcheck" begin
@i function test(out, x)
anc ← 0
@invcheckoff for i=1:x[]
x[] -= 1
end
@invcheckoff while (anc<3, anc<3)
anc += 1
end
out += anc
@invcheckoff anc → 0
end
res = test(0, Ref(7))
@test res[1] == 3
@test res[2][] == 0
end
@testset "nilang ir" begin
ex = :(
@inline function f(x!::T, y) where T
anc ← zero(T)
@routine anc += x!
x! += y * anc
~@routine
anc → zero(T)
end
)
ex2 = :(
@inline function f(x!::T, y) where T
anc ← zero(T)
anc += identity(x!)
x! += y * anc
anc -= identity(x!)
anc → zero(T)
end)
ex3 = :(
@inline function (~f)(x!::T, y) where T
anc ← zero(T)
anc += identity(x!)
x! -= y * anc
anc -= identity(x!)
anc → zero(T)
end)
@test nilang_ir(@__MODULE__, ex) |> NiLangCore.rmlines == ex2 |> NiLangCore.rmlines
@test nilang_ir(@__MODULE__, ex; reversed=true) |> NiLangCore.rmlines == ex3 |> NiLangCore.rmlines
end
@testset "protectf" begin
struct C<:Function end
# protected
@i function (a::C)(x)
@safe @show a
if (protectf(a) isa Inv, ~)
add(x, 1.0)
else
sub(x, 1.0)
end
end
a = C()
@test (~a)(a(1.0)) == 1.0
# not protected
@i function (a::C)(x)
@safe @show a
if (a isa Inv, ~)
add(x, 1.0)
else
sub(x, 1.0)
end
end
@test (~a)(a(1.0)) == -1.0
end
@testset "ifelse statement" begin
@i function f(x, y)
if (x > 0, ~)
y += 1
elseif (x < 0, ~)
y += 2
else
y += 3
end
end
@test f(1, 0) == (1, 1)
@test f(-2, 0) == (-2, 2)
@test f(0, 0) == (0, 3)
@i function f2(x, y)
if (x > 0, x < 0)
y += 1
elseif (x < 0, x < 0)
y += 2
else
y += 3
end
end
@test_throws InvertibilityError f2(-1, 0)
end
@testset "skip!" begin
x = 0.4
@instr (@skip! 3) += x
@test x == 0.4
y = 0.3
@instr x += @const y
@test x == 0.7
@test y == 0.3
end
@testset "for x in range" begin
@i function f(x, y)
for item in y
x += item
end
end
@test check_inv(f, (0.0, [1,2,5]))
end
@testset "@simd and @threads" begin
@i function f(x)
@threads for i=1:length(x)
x[i] += 1
end
end
x = [1,2,3]
@test f(x) == [2,3,4]
@i function f2(x)
@simd for i=1:length(x)
x[i] += 1
end
end
x = [1,2,3]
@test f2(x) == [2,3,4]
end
@testset "xor over ||" begin
x = false
@instr x ⊻= true || false
@test x
@instr x ⊻= true && false
@test x
end
macro zeros(T, x, y)
esc(:($x ← zero($T); $y ← zero($T)))
end
@testset "macro" begin
@i function f(x)
@zeros Float64 a b
x += a * b
~@zeros Float64 a b
end
@test f(3.0) == 3.0
end
@testset "allow nothing pass" begin
@i function f(x)
nothing
end
@test f(2) == 2
end
@testset "ancilla check" begin
ex1 = :(@i function f(x)
x ← 0
end)
@test_throws LoadError macroexpand(Main, ex1)
ex2 = :(@i function f(x)
y ← 0
y ← 0
end)
@test_throws LoadError macroexpand(Main, ex2)
ex3 = :(@i function f(x)
y ← 0
y → 0
end)
@test macroexpand(Main, ex3) isa Expr
ex4 = :(@i function f(x; y=5)
y ← 0
end)
@test_throws LoadError macroexpand(Main, ex4)
ex5 = :(@i function f(x)
y → 0
end)
@test_throws LoadError macroexpand(Main, ex5)
ex6 = :(@i function f(x::Int)
y ← 0
y → 0
end)
@test macroexpand(Main, ex6) isa Expr
ex7 = :(@i function f(x::Int)
if x>3
y ← 0
y → 0
elseif x<-3
y ← 0
y → 0
else
y ← 0
y → 0
end
end)
@test macroexpand(Main, ex7) isa Expr
ex8 = :(@i function f(x; y=5)
z ← 0
z → 0
end)
@test macroexpand(Main, ex8) isa Expr
ex9 = :(@i function f(x; y)
z ← 0
z → 0
end)
@test macroexpand(Main, ex9) isa Expr
ex10 = :(@i function f(x; y)
begin
z ← 0
end
~begin
z ← 0
end
end)
@test macroexpand(Main, ex10) isa Expr
end
@testset "dict access" begin
d = Dict(3=>4)
@instr d[3] → 4
@instr d[4] ← 3
@test d == Dict(4=>3)
@test_throws InvertibilityError @instr d[4] → 5
@test (@instr @invcheckoff d[8] → 5; true)
@test_throws InvertibilityError @instr d[4] ← 5
@test (@instr @invcheckoff d[4] ← 5; true)
end
@testset "@routine,~@routine" begin
@test_throws LoadError macroexpand(Main, :(@i function f(x)
@routine begin
end
end))
@test_throws LoadError macroexpand(Main, :(@i function f(x)
~@routine
end))
@test macroexpand(Main, :(@i function f(x)
@routine begin end
~@routine
end)) !== nothing
end
@testset "@from post while pre" begin
@i function f()
x ← 5
z ← 0
@from z==0 while x > 0
x -= 1
z += 1
end
z → 5
x → 0
end
@test f() == ()
@test (~f)() == ()
end
@testset "argument with function call" begin
@test_throws LoadError @macroexpand @i function f(x, y)
x += sin(exp(y))
end
@i function f(x, y)
x += sin(exp(0.4)) + y
end
end
@testset "allocation multiple vars" begin
info = NiLangCore.PreInfo()
@test NiLangCore.precom_ex(NiLangCore, :(x,y ← var), info) == :((x, y) ← var)
@test NiLangCore.precom_ex(NiLangCore, :(x,y → var), info) == :((x, y) → var)
@test NiLangCore.precom_ex(NiLangCore, :((x,y) ↔ (a, b)), info) == :((x,y) ↔ (a,b))
@test (@code_reverse (x,y) ← var) == :((x, y) → var)
@test (@code_reverse (x,y) → var) == :((x, y) ← var)
@test (@code_julia (x,y) ← var) == :((x, y) = var)
@test (@code_julia (x,y) → var) == :(try
$(NiLangCore.deanc)((x, y), var)
catch e
$(:(println("deallocate fail `$($(QuoteNode(:((x, y))))) → $(:var)`")) |> NiLangCore.rmlines)
throw(e)
end)
x = randn(2,4)
@i function f(y, x)
m, n ← size(x)
(l, k) ← size(x)
y += m*n
y += l*k
(l, k) → size(x)
m, n → size(x)
end
twosize = f(0, x)[1]
@test twosize == 16
@test (~f)(twosize, x)[1] == 0
@i function g(x)
(m, n) ← size(x)
(m, n) → (7, 5)
end
@test_throws InvertibilityError g(x)
@test_throws InvertibilityError (~g)(x)
end
@testset "argument without argname" begin
@i function f(::Complex)
end
@test f(1+2im) == 1+2im
end
@testset "tuple input" begin
@i function f(x::Tuple{<:Tuple, <:Real})
f(x.:1)
(x.:1).:1 += x.:2
end
@i function f(x::Tuple{<:Real, <:Real})
x.:1 += x.:2
end
@i function g(data)
f(((data.:1, data.:2), data.:3))
end
@test g((1,2,3)) == (6,2,3)
end
@testset "single/zero argument" begin
@i function f(x)
neg(x)
end
@i function g(x::Vector)
neg.(x)
end
@test f(3) == -3
@test g([3, 2]) == [-3, -2]
x = (3,)
@instr f(x...)
@test x == (-3,)
x = ([3, 4],)
@instr f.(x...)
@test x == ([-3, -4],)
@i function f()
end
x = ()
@instr f(x...)
@test x == ()
end
@testset "type constructor" begin
@i function f(x, y, a, b)
add(Complex{}(x, y), Complex{}(a, b))
end
@test f(1,2, 3, 4) == (4, 6, 3, 4)
@test_throws LoadError macroexpand(NiLangCore, :(@i function f(x, y, a, b)
add(Complex(x, y), Complex{}(a, b))
end))
@i function g(x::Inv, y::Inv)
add(x.f, y.f)
end
@i function g(x, y)
g(Inv{}(x), Inv{}(y))
end
@test g(2, 3) == (5, 3)
end
@testset "variable_analysis" begin
# kwargs should not be assigned
@test_throws LoadError macroexpand(@__MODULE__, :(@i function f1(x; y=4)
y ← 5
y → 5
end))
# deallocated variables should not be used
@test_throws LoadError macroexpand(@__MODULE__, :(@i function f1(x; y=4)
z ← 5
z → 5
x += 2 * z
end))
# deallocated variables should not be used in local scope
@test_throws LoadError macroexpand(@__MODULE__, :(@i function f1(x; y=4)
z ← 5
z → 5
for i=1:10
x += 2 * z
end
end))
end
@testset "boolean" begin
@i function f1(x, y, z)
x ⊻= true
y .⊻= z
end
@test f1(false, [true, false], [true, false]) == (true, [false, false], [true, false])
@i function f2(x, y, z)
z[2] ⊻= true && y[1]
z[1] ⊻= z[2] || x
end
@test f2(false, [true, false], [true, false]) == (false, [true, false], [false, true])
end
@testset "swap ↔" begin
@i function f1(x, y)
j::∅ ↔ k::∅ # dummy swap
a::∅ ↔ x
a ↔ y
a ↔ x::∅ # ↔ is symmetric
end
@test f1(2, 3) == (3, 2)
@test check_inv(f1, (2, 3))
# stack
@i function f2(x, y)
x[end+1] ↔ y
y ← 2
end
@test f2([1,2,3], 4) == ([1,2,3,4], 2)
@test check_inv(f2, ([1,2,3], 3))
@i function f4(x, y)
y ↔ x[end+1]
y ← 2
end
@test f4([1,2,3], 4) == ([1,2,3,4], 2)
@test check_inv(f4, ([1,2,3], 3))
@i function f3(x, y::TY, s) where TY
y → _zero(TY)
x[end] ↔ (y::TY)::∅
@safe @show x[2], s
x[2] ↔ s
end
@test f3(Float32[1,2,3], 0.0, 4f0) == (Float32[1,4], 3.0, 2f0)
@test check_inv(f3, (Float32[1,2,3], 0.0, 4f0))
end
@testset "feed tuple and types" begin
@i function f3(a, d::Complex)
a.:1 += d.re
d.re ↔ d.im
end
@i function f4(a, b, c, d, e)
f3((a, b, c), Complex{}(d, e))
end
@test f4(1,2,3,4,5) == (5,2,3,5,4)
@test check_inv(f4, (1,2,3,4,5))
end
@testset "exchange tuple and fields" begin
@i function f1(x, y, z)
(x, y) ↔ @fields z
end
@test f1(1,2, 3+4im) == (3,4,1+2im)
@i function f2(re, x)
r, i ← @fields x
re += r
r, i → @fields x
end
@test f2(0.0, 3.0+2im) == (3.0, 3.0 + 2.0im)
@i function f3(x, y, z)
(@fields z) ↔ (x, y)
end
@test f3(1,2, 3+4im) == (3,4,1+2im)
@test_throws LoadError macroexpand(@__MODULE__, :(@i function f3(x, y, z)
(x, y) ↔ (z, j)
end))
@i function f4(x, y, z, j)
(x, y) ↔ (z, j)
end
@test f4(1,2, 3, 4) == (3,4,1,2)
@i function swap_fields(obj::Complex)
(x, y)::∅ ↔ @fields obj
x += y
(x, y) ↔ (@fields obj)::∅
end
@test swap_fields(1+2im) == (3+2im)
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/instr.jl | code | 4877 | using NiLangCore
using NiLangCore: compile_ex, dual_ex, precom_ex, memkernel, render_arg, check_args
using Test
import Base: +, -
value(x) = x
NiLangCore.chfield(x::T, ::typeof(value), y::T) where T = y
function add(a!::Number, b::Number)
a!+b, b
end
function neg(b::Number)
-b
end
@selfdual neg
@i function add(a!, b)
add(a! |> value, b |> value)
end
function sub(a!::Number, b::Number)
a!-b, b
end
@i function sub(a!, b)
sub(a! |> value, b |> value)
end
@dual add sub
function XOR(a!::Integer, b::Integer)
xor(a!, b), b
end
@selfdual XOR
#@nograd XOR
@testset "boolean" begin
x = false
@instr x ⊻= true
@test x
@instr x ⊻= true || false
@test !x
@instr x ⊻= true && false
@instr x ⊻= !false
@test x
end
@testset "@dual" begin
@test isreversible(add, Tuple{Any,Any})
@test isreversible(sub, Tuple{Any,Any})
@test !isreflexive(add)
@test ~(add) == sub
a=2.0
b=1.0
@instr add(a, b)
@test a == 3.0
args = (1,2)
@instr add(args...)
@test args == (3,2)
@instr sub(a, b)
@test a == 2.0
@test check_inv(add, (a, b))
@test isprimitive(add)
@test isprimitive(sub)
end
@testset "@selfdual" begin
@test !isreversible(XOR, Tuple{Any, Any})
@test !isreversible(~XOR, Tuple{Any, Any})
@test isreversible(~XOR, Tuple{Integer, Integer})
@test isreversible(XOR, Tuple{Integer, Integer})
@test isreflexive(XOR)
@test isprimitive(XOR)
@test ~(XOR) == XOR
a=2
b=1
@instr XOR(a, b)
@test a == 3
@instr XOR(a, b)
@test a == 2
end
@testset "+=, -=" begin
x = 1.0
y = 1.0
@instr PlusEq(exp)(y, x)
@test x ≈ 1
@test y ≈ 1+exp(1.0)
@instr (~PlusEq(exp))(y, x)
@test x ≈ 1
@test y ≈ 1
end
@testset "+= and const" begin
x = 0.5
@instr x += π
@test x == 0.5+π
@instr x += log(π)
@test x == 0.5 + π + log(π)
@instr x += log(π)/2
@test x == 0.5 + π + 3*log(π)/2
@instr x += log(2*π)/2
@test x == 0.5 + π + 3*log(π)/2 + log(2π)/2
end
@testset "+= keyword functions" begin
g(x; y=2) = x^y
z = 0.0
x = 2.0
@instr z += g(x; y=4)
@test z == 16.0
end
@testset "constant value" begin
@test @const 2 == 2
@test NiLangCore._isconst(:(@const grad(x)))
end
@testset "+=, -=, *=, /=" begin
@test compile_ex(@__MODULE__, :(x += y * z), NiLangCore.CompileInfo()).args[1].args[2] == :($PlusEq(*)(x, y, z))
@test compile_ex(@__MODULE__, dual_ex(@__MODULE__, :(x -= y * z)), NiLangCore.CompileInfo()).args[1].args[2] == :($PlusEq(*)(x, y, z))
@test compile_ex(@__MODULE__, :(x /= y * z), NiLangCore.CompileInfo()).args[1].args[2] == :($DivEq(*)(x, y, z))
@test compile_ex(@__MODULE__, dual_ex(@__MODULE__, :(x *= y * z)), NiLangCore.CompileInfo()).args[1].args[2] == :($DivEq(*)(x, y, z))
@test ~MulEq(*) == DivEq(*)
@test ~DivEq(*) == MulEq(*)
function (g::MulEq)(y, a, b)
y * g.f(a, b), a, b
end
function (g::DivEq)(y, a, b)
y / g.f(a, b), a, b
end
a, b, c = 1.0, 2.0, 3.0
@instr a *= b + c
@test a == 5.0
@instr a /= b + c
@test a == 1.0
end
@testset "shared read write check" begin
for (x, y) in [
(:((-x[3].g' |> NEG).k[5]) , :((x[3]).g.k[5]))
(:((-(x |> subarray(3)).g' |> NEG).k[5]) , :((x[3]).g.k[5]))
(:(@skip! x.g) , nothing)
(:(@const x .|> g) , :x)
(:(cos.(x[2])) , nothing)
(:(cos(x[2])) , nothing)
(:((x |> g)...) , :x)
(:((x |> g, y.:1)) , [:x, :(y.:1)])
(:((x |> g, y |> tget(1))) , [:x, :(y[1])])]
@test memkernel(deepcopy(x)) == y
@test render_arg(deepcopy(x)) == x
end
@test render_arg(:(x.y.[2:3])) == :(x.y |> subarray(2:3))
@test memkernel(:(x.y |> subarray(2:3))) == (:(x.y[2:3]))
@test render_arg(:(x.y.[2:3] |> value)) == :(x.y |> subarray(2:3) |> value)
@test memkernel(:(x.y |> subarray(2:3) |> value)) == :(x.y[2:3])
@test_throws InvertibilityError check_args([:a, :(a |> grad)])
@test check_args([:(a.x), :(a.g |> grad)]) isa Nothing
@test_throws InvertibilityError check_args([:(a.x), :(b[3]), :(b[3])])
@test_throws InvertibilityError check_args([:(a.x), :((b, a.x))]) isa Nothing
# TODO: check variable on the same tree, like `a.b` and `a`
end
@testset "dual type" begin
struct AddX{T}
x::T
end
struct SubX{T}
x::T
end
@dualtype AddX SubX
@dualtype AddX SubX
@i function (f::AddX)(x::Real) end
@test hasmethod(AddX(3), Tuple{Real})
@test hasmethod(SubX(3), Tuple{Real})
for (TA, TB) in [(AddX, SubX), (MulEq, DivEq), (XorEq, XorEq), (PlusEq, MinusEq)]
@test invtype(TA) == TB
@test invtype(TA{typeof(*)}) == TB{typeof(*)}
@test invtype(TB) == TA
@test invtype(TB{typeof(*)}) == TA{typeof(*)}
end
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/invtype.jl | code | 1268 | using NiLangCore
using NiLangCore: type2tuple
using Test
struct NiTypeTest{T} <: IWrapper{T}
x::T
g::T
end
NiTypeTest(x) = NiTypeTest(x, zero(x))
@fieldview value(invtype::NiTypeTest) = invtype.x
@fieldview gg(invtype::NiTypeTest) = invtype.g
@testset "inv type" begin
it = NiTypeTest(0.5)
@test eps(typeof(it)) === eps(Float64)
@test value(it) == 0.5
@test it ≈ NiTypeTest(0.5)
@test it > 0.4
@test it < NiTypeTest(0.6)
@test it < 7
@test 0.4 < it
@test 7 > it
@test chfield(it, value, 0.3) == NiTypeTest(0.3)
it = chfield(it, Val(:g), 0.2)
@test almost_same(NiTypeTest(0.5+1e-15), NiTypeTest(0.5))
@test !almost_same(NiTypeTest(1.0), NiTypeTest(1))
it = NiTypeTest(0.5)
@test chfield(it, gg, 0.3) == NiTypeTest(0.5, 0.3)
end
@testset "mutable struct set field" begin
mutable struct MS{T}
x::T
y::T
z::T
end
ms = MS(0.5, 0.6, 0.7)
@i function f(ms)
ms.x += 1
ms.y += 1
ms.z -= ms.x ^ 2
end
ms2 = f(ms)
@test (ms2.x, ms2.y, ms2.z) == (1.5, 1.6, -1.55)
struct IMS{T}
x::T
y::T
z::T
end
ms = IMS(0.5, 0.6, 0.7)
ms2 = f(ms)
@test (ms2.x, ms2.y, ms2.z) == (1.5, 1.6, -1.55)
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/lens.jl | code | 1130 | using NiLangCore, Test
@testset "update field" begin
@test NiLangCore.field_update(1+2im, Val(:im), 4) == 1+4im
struct TestUpdateField1{A, B}
a::A
end
@test NiLangCore.field_update(TestUpdateField1{Int,Float64}(1), Val(:a), 4) == TestUpdateField1{Int,Float64}(4)
struct TestUpdateField2{A}
a::A
function TestUpdateField2(a::T) where T
new{T}(a)
end
end
@test NiLangCore.field_update(TestUpdateField2(1), Val(:a), 4) == TestUpdateField2(4)
@test NiLangCore.default_constructor(ComplexF64, 1.0, 2.0) == 1+2im
end
@testset "_zero" begin
@test _zero(Tuple{Float64, Float32,String,Matrix{Float64},Char,Dict{Int,Int}}) == (0.0, 0f0, "", zeros(0,0), '\0', Dict{Int,Int}())
@test _zero(ComplexF64) == 0.0 + 0.0im
@test _zero((1,2.0,"adsf",randn(2,2),'d',Dict(2=>5))) == (0, 0.0,"",zeros(2,2),'\0',Dict(2=>0))
@test _zero(1+2.0im) == 0.0 + 0.0im
@test _zero(()) == ()
@test _zero((1,2)) == (0, 0)
@test _zero(Symbol) == Symbol("")
@test _zero(:x) == Symbol("")
end
@testset "fields" begin
@test (@fields 1+3im) == (1,3)
end | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/runtests.jl | code | 480 | using NiLangCore
using Test
@testset "Core.jl" begin
include("Core.jl")
end
@testset "stack.jl" begin
include("stack.jl")
end
@testset "lens.jl" begin
include("lens.jl")
end
@testset "utils.jl" begin
include("utils.jl")
end
@testset "symboltable.jl" begin
include("symboltable.jl")
end
@testset "instr.jl" begin
include("instr.jl")
end
@testset "vars.jl" begin
include("vars.jl")
end
@testset "compiler.jl" begin
include("compiler.jl")
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/stack.jl | code | 4790 | using NiLangCore, Test
@testset "stack" begin
for (stack, x) in [
(FLOAT64_STACK, 0.3), (FLOAT32_STACK, 0f4),
(INT64_STACK, 3), (INT32_STACK, Int32(3)),
(COMPLEXF64_STACK, 4.0+0.3im), (COMPLEXF32_STACK, 4f0+0f3im),
(BOOL_STACK, true),
]
println(stack)
push!(stack, x)
@test pop!(stack) === x
end
end
@testset "stack operations" begin
z = 1.0
NiLangCore.empty_global_stacks!()
@test_throws ArgumentError (@instr GLOBAL_STACK[end] ↔ y::∅)
y = 4.0
@test_throws ArgumentError (@instr GLOBAL_STACK[end] → y)
@test_throws BoundsError (@instr @invcheckoff GLOBAL_STACK[end] ↔ y)
@test_throws ArgumentError (@instr @invcheckoff GLOBAL_STACK[end] → y)
x = 0.3
NiLangCore.empty_global_stacks!()
@instr GLOBAL_STACK[end+1] ↔ x
@instr GLOBAL_STACK[end] ↔ x::∅
@test x === 0.3
@instr @invcheckoff GLOBAL_STACK[end+1] ↔ x
y = 0.5
@instr GLOBAL_STACK[end+1] ↔ y
@instr @invcheckoff GLOBAL_STACK[end] ↔ x::∅
@test x == 0.5
x =0.3
st = Float64[]
@instr st[end+1] ↔ x
@test length(st) == 1
@instr st[end] ↔ x::∅
@test length(st) == 0
@test x === 0.3
@instr st[end+1] ↔ x
@test length(st) == 1
y = 0.5
@instr st[end+1] ↔ y
@instr @invcheckoff st[end] ↔ x::∅
@test x == 0.5
@i function test(x)
x2 ← zero(x)
x2 += x^2
GLOBAL_STACK[end+1] ↔ x
x::∅ ↔ x2
end
@test test(3.0) == 9.0
l = length(NiLangCore.GLOBAL_STACK)
@test check_inv(test, (3.0,))
@test length(NiLangCore.GLOBAL_STACK) == l
@i function test2(x)
x2 ← zero(x)
x2 += x^2
@invcheckoff GLOBAL_STACK[end+1] ↔ x
x::∅ ↔ x2
end
@test test2(3.0) == 9.0
l = length(NiLangCore.GLOBAL_STACK)
@test check_inv(test2, (3.0,))
@test length(NiLangCore.GLOBAL_STACK) == l
x = 3.0
@instr GLOBAL_STACK[end+1] ↔ x
NiLangCore.empty_global_stacks!()
l = length(NiLangCore.GLOBAL_STACK)
@test l == 0
end
@testset "copied push/pop stack operations" begin
NiLangCore.empty_global_stacks!()
x =0.3
@instr GLOBAL_STACK[end+1] ← x
@test x === 0.3
@instr GLOBAL_STACK[end] → x
@test x === 0.3
@instr GLOBAL_STACK[end+1] ← x
x = 0.4
@test_throws InvertibilityError @instr GLOBAL_STACK[end] → x
y = 0.5
@instr GLOBAL_STACK[end+1] ← y
@instr @invcheckoff GLOBAL_STACK[end] → x
@test x == 0.5
st = []
x = [0.3]
@instr st[end+1] ← x
@test st[1] !== [0.3]
@test st[1] ≈ [0.3]
x =0.3
st = Float64[]
@instr ~(st[end] → x)
@test x === 0.3
@test length(st) == 1
@instr ~(st[end+1] ← x)
@test length(st) == 0
@test x === 0.3
@instr @invcheckoff st[end+1] ← x
@test length(st) == 1
x = 0.4
@test_throws InvertibilityError @instr st[end] → x
@test length(st) == 0
y = 0.5
@instr st[end+1] ← y
@instr @invcheckoff st[end] → x
@test x == 0.5
@i function test(x, x2)
x2 += x^2
GLOBAL_STACK[end+1] ← x
x ↔ x2
end
@test test(3.0, 0.0) == (9.0, 3.0)
l = length(NiLangCore.GLOBAL_STACK)
@test check_inv(test, (3.0, 0.0))
@test length(NiLangCore.GLOBAL_STACK) == l
end
@testset "dictionary & vector" begin
# allocate and deallocate
@i function f1(d, y)
d["y"] ← y
end
d = Dict("x" => 34)
@test f1(d, 3) == (Dict("x"=>34, "y"=>3), 3)
@test_throws InvertibilityError f1(d, 3)
d = Dict("x" => 34)
@test check_inv(f1, (d, 3))
# not available on vectors
@i function f2(d, y)
d[2] ← y
end
@test_throws MethodError f2([1,2,3], 3)
# swap
@i function f3(d, y)
d["y"] ↔ y
end
d = Dict("y" => 34)
@test f3(d, 3) == (Dict("y"=>3), 34)
d = Dict("z" => 34)
@test_throws KeyError f3(d, 3)
d = Dict("y" => 34)
@test check_inv(f3, (d, 3))
# swap on vector
@i function f4(d, y, x)
d[2] ↔ y
d[end] ↔ x
end
d = [11,12,13]
@test f4(d, 1,2) == ([11,1,2],12,13)
d = [11,12,13]
@test check_inv(f4, (d, 1,2))
# swap to empty
@i function f5(d, x::T) where T
d["x"]::∅ ↔ x # swap in
d["y"] ↔ x::∅ # swap out
end
d = Dict("y" => 34)
@test f5(d, 3) == (Dict("x"=>3), 34)
d = Dict("y" => 34)
@test check_inv(f5, (d, 3))
d = Dict("x" => 34)
@test_throws InvertibilityError f5(d, 3)
# not available on vectors
@i function f6(d, y)
d[2]::∅ ↔ y
end
@test_throws MethodError f6([1,2,3], 3)
end
@testset "inverse stack" begin
@i function f(x)
x[end+1] ← 1
end
x = FastStack{Int}(3)
@test check_inv(f, (x,))
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/symboltable.jl | code | 2654 | using Test, NiLangCore
using NiLangCore: SymbolTable, allocate!, deallocate!, operate!, swapvars!, variable_analysis_ex
@testset "variable analysis" begin
st = SymbolTable()
# allocate! : not exist
allocate!(st, :x)
allocate!(st, :y)
@test st.existing == [:x, :y]
# allocate! : existing
@test_throws InvertibilityError allocate!(st, :x)
@test st.existing == [:x, :y]
# deallocate! : not exist
@test_throws InvertibilityError deallocate!(st, :z)
# deallocate! : existing
deallocate!(st, :y)
@test st.existing == [:x]
@test st.deallocated == [:y]
# deallocate! : deallocated
@test_throws InvertibilityError deallocate!(st, :y)
# operate! : deallocated
@test_throws InvertibilityError operate!(st, :y)
# allocate! : deallocated
allocate!(st, :y)
@test st.existing == [:x, :y]
@test st.deallocated == []
# operate! : not exist
operate!(st, :j)
@test st.unclassified == [:j]
# operate! : existing
operate!(st, :y)
@test st.unclassified == [:j]
# allocate! unclassified
@test_throws InvertibilityError allocate!(st, :j)
# operate! : unclassified
operate!(st, :j)
@test st.unclassified == [:j]
# deallocate! : unclassified
@test_throws InvertibilityError deallocate!(st, :j)
# swap both existing
swapvars!(st, :j, :x)
@test st.unclassified == [:x]
@test st.existing == [:j, :y]
# swap existing - nonexisting
swapvars!(st, :j, :k)
@test st.unclassified == [:x, :j]
@test st.existing == [:k, :y]
# swap nonexisting - existing
swapvars!(st, :o, :x)
@test st.unclassified == [:o, :j, :x]
@test st.existing == [:k, :y]
# swap both not existing
swapvars!(st, :m, :n)
@test st.unclassified == [:o, :j, :x, :m, :n]
# push and pop variables
end
@testset "variable analysis" begin
st = SymbolTable([:x, :y], [], [])
ex = :((x,y) ↔ (a, b))
variable_analysis_ex(ex, st)
@test st.existing == [:a, :b]
@test st.unclassified == [:x, :y]
st = SymbolTable([:x, :y], [], [])
ex = :((x,y) ↔ b)
variable_analysis_ex(ex, st)
@test st.existing == [:b]
@test st.unclassified == [:x, :y]
ex = :(b ↔ (x,y))
variable_analysis_ex(ex, st)
@test st.existing == [:x, :y]
@test st.unclassified == [:b]
st = SymbolTable([:x, :y], [], [])
ex = :(b ↔ x)
variable_analysis_ex(ex, st)
@test st.existing == [:b, :y]
@test st.unclassified == [:x]
st = SymbolTable([], [], [])
ex = :(b ↔ (x, y))
variable_analysis_ex(ex, st)
@test st.existing == []
@test st.unclassified == [:b, :x, :y]
end | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/utils.jl | code | 1670 | using Test, NiLangCore
using NiLangCore: get_argname, get_ftype, match_function, MyOrderedDict
@testset "match function" begin
ex = match_function(:(function f(x) x end))
@test ex[1] == nothing
@test ex[2] == :f
@test ex[3] == [:x]
@test ex[4] == []
@test length(filter(x->!(x isa LineNumberNode), ex[5])) == 1
ex = match_function(:(@inline function f(x; y) x end))
@test ex[1][1] == Symbol("@inline")
@test ex[1][2] isa LineNumberNode
@test ex[2] == :f
@test ex[3] == [Expr(:parameters, :y), :x]
@test length(filter(x->!(x isa LineNumberNode), ex[5])) == 1
@test ex[4] == []
ex = match_function(:(function f(x::T) where T x end))
@test ex[2] == :f
@test ex[3] == [:(x::T)]
@test length(filter(x->!(x isa LineNumberNode), ex[5])) == 1
@test ex[4] == [:T]
ex = match_function(:(f(x)=x))
@test ex[2] == :f
@test ex[3] == [:x]
@test length(ex[5]) == 2
@test ex[4] == []
end
@testset "argname and type" begin
@test get_argname(:(y=3)) == :y
@test get_argname(:(y::Int)) == :y
@test get_argname(:(y::Int=3)) == :y
@test get_argname(:(f(; k::Int=4)).args[2]) == :(f(; k::Int=4)).args[2]
end
@testset "my ordered dict" begin
od = MyOrderedDict{Any, Any}()
od[:a] = 2
od[:b] = 4
od[:c] = 7
@test length(od) == 3
@test od[:b] == 4
od[:b] = 1
@test od[:b] == 1
delete!(od, :b)
@test_throws KeyError od[:b]
@test pop!(od) == (:c, 7)
@test length(od) == 1
end
@testset "unzipped broadcast" begin
x = [1, 2, 3.0]
res = NiLangCore.unzipped_broadcast(exp, x)
@test res === x
@test res ≈ exp.([1, 2, 3.0])
end
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | test/vars.jl | code | 3544 | using Test, NiLangCore
using NiLangCore: type2tuple
@testset "dataview" begin
x = 1.0
@test_throws ErrorException chfield(x, "asdf", 3.0)
@test chfield(x, identity, 2.0) === 2.0
@assign -x 0.1
@test x == -0.1
x = 1+2.0im
@assign x' 0.1+1im
@test x == 0.1-1im
x = (3, 4)
@instr (x.:1) += 3
@test x == (6, 4)
x = 3
y = (4,)
@instr x += y.:1
@test x == 7
x = [3, 4]
y = ([4, 4],)
@instr x .+= y.:1
@test x == [7.0, 8.0]
x = true
y = (true,)
@instr x ⊻= y.:1
@test x == false
x = [true, false]
y = ([true, true],)
@instr x .⊻= (y |> tget(1))
@test x == [false, true]
x = ones(4)
y = ones(2)
@instr (x |> subarray(1:2)) += y
@test x == [2,2,1,1]
@instr (x |> subarray(1)) += (y |> subarray(1))
@test x == [3,2,1,1]
end
@testset "anc, deanc" begin
@i function f(y)
x ← y
x → 1.0
end
f(1.0)
@test_throws InvertibilityError f(1.1)
@i function f2(y)
x ← y
x → (1.0, 2.0)
end
f2((1.0, 2.0))
@test_throws InvertibilityError f2((1.1, 2.0))
@i function f3(y)
x ← y
x → [1.0, 2.0]
end
f3([1.0, 2.0])
@test_throws InvertibilityError f3([1.1, 2.0])
struct B
a
b
end
@i function f4(y)
x ← y
x → B(1.0, 2.0)
end
f4(B(1.0, 2.0))
@test_throws InvertibilityError f4(B(1.0, 1.1))
@i function f5(y)
x ← y
x → ""
end
f5("")
@test_throws InvertibilityError f5("a")
end
@testset "inv and tuple output" begin
a, b = false, false
@instr ~(a ⊻= true)
@test a == true
@instr ~((a, b) ⊻= (true, true))
@test a == false
@test b == true
y = 1.0
x = 1.0
@instr ~(~(y += 1.0))
@test y == 2.0
@instr ~(~((x, y) += (1.0, 1.0)))
@test y == 3.0
@test x == 2.0
@instr ~((x, y) += (1.0, 1.0))
@test y == 2.0
@test x == 1.0
@instr ~(y += 1.0)
@test y == 1.0
z = [1.0, 2.0]
@instr ~(~(z .+= [1.0, 2.0]))
@test z ≈ [2.0, 4.0]
end
@testset "chfield" begin
x = [1,2,3]
@test chfield(x, length, 3) == x
@test_throws InvertibilityError chfield(x, length, 2)
end
@testset "invcheck" begin
@test (@invcheck 0.3 0.3) isa Any
@test_throws InvertibilityError (@invcheck 0.3 0.4)
@test_throws InvertibilityError (@invcheck 3 3.0)
end
@testset "dict" begin
@i function f1()
d ← Dict(1=>1, 2=>2)
d → Dict(2=>2)
end
@i function f2()
d ← Dict(1=>1)
d → Dict(2=>1)
end
@i function f3()
d ← Dict(1=>1)
d → Dict(1=>2)
end
@i function f4()
d ← Dict(1=>1)
d → Dict(1=>1)
end
@test_throws InvertibilityError f1()
@test_throws InvertibilityError f2()
@test_throws InvertibilityError f3()
@test f4() == ()
end
@testset "fieldview" begin
@fieldview first_real(x::Vector{ComplexF64}) = x[1].re
x = [1.0im, 2+3im]
@instr (x |> first_real) += 3
@test x == [3+1.0im, 2+3.0im]
end
@testset "mutable struct set field" begin
mutable struct MS{T}
x::T
y::T
z::T
end
ms = MS(0.5, 0.6, 0.7)
@i function f(ms)
ms.x += 1
ms.y += 1
ms.z -= ms.x ^ 2
end
ms2 = f(ms)
@test (ms2.x, ms2.y, ms2.z) == (1.5, 1.6, -1.55)
struct IMS{T}
x::T
y::T
z::T
end
ms = IMS(0.5, 0.6, 0.7)
ms2 = f(ms)
@test (ms2.x, ms2.y, ms2.z) == (1.5, 1.6, -1.55)
end | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | README.md | docs | 2844 | # NiLangCore
The core package for reversible eDSL NiLang.
[](https://codecov.io/gh/GiggleLiu/NiLangCore.jl)
**Warning**
Requires Julia version >= 1.3.
## Examples
1. Define a pair of dual instructions
```julia
julia> using NiLangCore
julia> function ADD(a!::Number, b::Number)
a! + b, b
end
ADD (generic function with 3 methods)
julia> function SUB(a!::Number, b::Number)
a! - b, b
end
SUB (generic function with 3 methods)
julia> @dual ADD SUB
```
2. Define a reversible function
```julia
julia> @i function test(a, b)
SUB(a, b)
end
```
## Reversible IR
```julia
julia> using NiLangCore
julia> @code_reverse x += f(y)
:(x -= f(y))
julia> @code_reverse x .+= f.(y)
:(x .-= f.(y))
julia> @code_reverse x ⊻= f(y)
:(x ⊻= f(y))
julia> @code_reverse x ← zero(T)
:(x → zero(T))
julia> @code_reverse begin y += f(x) end
quote
#= /home/leo/.julia/dev/NiLangCore/src/dualcode.jl:82 =#
y -= f(x)
#= REPL[52]:1 =#
end
julia> julia> @code_reverse if (precond, postcond) y += f(x) else y += g(x) end
:(if (postcond, precond)
#= /home/leo/.julia/dev/NiLangCore/src/dualcode.jl:69 =#
y -= f(x)
#= REPL[48]:1 =#
else
#= /home/leo/.julia/dev/NiLangCore/src/dualcode.jl:69 =#
y -= g(x)
#= REPL[48]:1 =#
end)
julia> @code_reverse while (precond, postcond) y += f(x) end
:(@from !postcond while precond
#= /home/leo/.julia/dev/NiLangCore/src/dualcode.jl:72 =#
y -= f(x)
#= REPL[49]:1 =#
end)
julia> @code_reverse for i=start:step:stop y += f(x) end
:(for i = stop:-step:start
#= /home/leo/.julia/dev/NiLangCore/src/dualcode.jl:76 =#
y -= f(x)
#= REPL[50]:1 =#
end)
julia> @code_reverse @safe println(x)
:(#= /home/leo/.julia/dev/NiLangCore/src/dualcode.jl:81 =# @safe println(x))
```
## A note on symbols
The `←` (\leftarrow + TAB) operation copies B to A, its inverse is `→` (\rightarrow + TAB)
* push into a stack, `A[end+1] ← B` => `[A..., B], B`
* add a key-value pair into a dict, `A[i] ← B` => `{A..., i=>B}, B`
* allocate a new ancilla, `(A = ∅) ← B` => `(A = B), B`
The `↔` (\leftrightarrow + TAB) operation swaps B and A, it is self reversible
* swap two variables, `A ↔ B` => `B, A`
* transfer into a stack, `A[end+1] ↔ B` => `[A..., B], ∅`
* transfer a key-value pair into a dict, `A[i] ↔ B` => `haskey ? {(A\A[i])..., i=>B}, A[i] : {A..., i=>B}, ∅`
* transfer the value of two variables, `(A = ∅) ↔ B` => `(A = B), ∅`
One can use `var::∅` to annotate `var` as a fresh new variable (only new variables can be allocated), use `var[end+1]` to represent stack top for push and `var[end]` for stack top for pop. | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | benchmark/README.md | docs | 351 | # Benchmark Report
### May 8th, 2021
```
"FastStack-inbounds" => Trial(3.136 ns)
"NiLang-@invcheckoff-@inbounds" => Trial(2.096 ns)
"NiLang-@invcheckoff" => Trial(5.341 ns)
"FastStack" => Trial(6.775 ns)
"NiLang" => Trial(22.935 ns)
"Julia" => Trial(12.062 ns)
"setindex-inbounds" => Trial(2.362 ns)
"setindex" => Trial(2.321 ns)
``` | NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"Apache-2.0"
] | 0.10.6 | d3bfb7acf19fca70751bb70b014c6d57e4dd9b18 | docs/src/index.md | docs | 72 | # NiLangCore.jl
```@index
```
```@autodocs
Modules = [NiLangCore]
```
| NiLangCore | https://github.com/GiggleLiu/NiLangCore.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | docs/make.jl | code | 403 | using AbstractMCMC
using Documenter
using Random
DocMeta.setdocmeta!(AbstractMCMC, :DocTestSetup, :(using AbstractMCMC); recursive=true)
makedocs(;
sitename="AbstractMCMC",
format=Documenter.HTML(),
modules=[AbstractMCMC],
pages=["Home" => "index.md", "api.md", "design.md"],
checkdocs=:exports,
)
deploydocs(; repo="github.com/TuringLang/AbstractMCMC.jl.git", push_preview=true)
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/AbstractMCMC.jl | code | 2207 | module AbstractMCMC
using BangBang: BangBang
using ConsoleProgressMonitor: ConsoleProgressMonitor
using LogDensityProblems: LogDensityProblems
using LoggingExtras: LoggingExtras
using ProgressLogging: ProgressLogging
using StatsBase: StatsBase
using TerminalLoggers: TerminalLoggers
using Transducers: Transducers
using FillArrays: FillArrays
using Distributed: Distributed
using Logging: Logging
using Random: Random
# Reexport sample
using StatsBase: sample
export sample
# Parallel sampling types
export MCMCThreads, MCMCDistributed, MCMCSerial
"""
AbstractChains
`AbstractChains` is an abstract type for an object that stores
parameter samples generated through a MCMC process.
"""
abstract type AbstractChains end
"""
AbstractSampler
The `AbstractSampler` type is intended to be inherited from when
implementing a custom sampler. Any persistent state information should be
saved in a subtype of `AbstractSampler`.
When defining a new sampler, you should also overload the function
`transition_type`, which tells the `sample` function what type of parameter
it should expect to receive.
"""
abstract type AbstractSampler end
"""
AbstractModel
An `AbstractModel` represents a generic model type that can be used to perform inference.
"""
abstract type AbstractModel end
"""
AbstractMCMCEnsemble
An `AbstractMCMCEnsemble` algorithm represents a specific algorithm for sampling MCMC chains
in parallel.
"""
abstract type AbstractMCMCEnsemble end
"""
MCMCThreads
The `MCMCThreads` algorithm allows users to sample MCMC chains in parallel using multiple
threads.
"""
struct MCMCThreads <: AbstractMCMCEnsemble end
"""
MCMCDistributed
The `MCMCDistributed` algorithm allows users to sample MCMC chains in parallel using multiple
processes.
"""
struct MCMCDistributed <: AbstractMCMCEnsemble end
"""
MCMCSerial
The `MCMCSerial` algorithm allows users to sample serially, with no thread or process parallelism.
"""
struct MCMCSerial <: AbstractMCMCEnsemble end
include("samplingstats.jl")
include("logging.jl")
include("interface.jl")
include("sample.jl")
include("stepper.jl")
include("transducer.jl")
include("logdensityproblems.jl")
end # module AbstractMCMC
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/interface.jl | code | 3810 | """
chainscat(c::AbstractChains...)
Concatenate multiple chains.
By default, the chains are concatenated along the third dimension by calling
`cat(c...; dims=3)`.
"""
chainscat(c::AbstractChains...) = cat(c...; dims=3)
"""
chainsstack(c::AbstractVector)
Stack chains in `c`.
By default, the vector of chains is returned unmodified. If `eltype(c) <: AbstractChains`,
then `reduce(chainscat, c)` is called.
"""
chainsstack(c) = c
chainsstack(c::AbstractVector{<:AbstractChains}) = reduce(chainscat, c)
"""
bundle_samples(samples, model, sampler, state, chain_type[; kwargs...])
Bundle all `samples` that were sampled from the `model` with the given `sampler` in a chain.
The final `state` of the `sampler` can be included in the chain. The type of the chain can
be specified with the `chain_type` argument.
By default, this method returns `samples`.
"""
function bundle_samples(
samples, model::AbstractModel, sampler::AbstractSampler, state, ::Type{T}; kwargs...
) where {T}
# dispatch to internal method for default implementations to fix
# method ambiguity issues (see #120)
return _bundle_samples(samples, model, sampler, state, T; kwargs...)
end
function _bundle_samples(
samples,
@nospecialize(::AbstractModel),
@nospecialize(::AbstractSampler),
@nospecialize(::Any),
::Type;
kwargs...,
)
return samples
end
function _bundle_samples(
samples::Vector,
@nospecialize(::AbstractModel),
@nospecialize(::AbstractSampler),
@nospecialize(::Any),
::Type{Vector{T}};
kwargs...,
) where {T}
return map(samples) do sample
convert(T, sample)
end
end
"""
step(rng, model, sampler[, state; kwargs...])
Return a 2-tuple of the next sample and the next state of the MCMC `sampler` for `model`.
Samples describe the results of a single step of the `sampler`. As an example, a sample
might include a vector of parameters sampled from a prior distribution.
When sampling using [`sample`](@ref), every `step` call after the first has access to the
current `state` of the sampler.
"""
function step end
"""
samples(sample, model, sampler[, N; kwargs...])
Generate a container for the samples of the MCMC `sampler` for the `model`, whose first
sample is `sample`.
The method can be called with and without a predefined number `N` of samples.
"""
function samples(sample, ::AbstractModel, ::AbstractSampler, N::Integer; kwargs...)
ts = Vector{typeof(sample)}(undef, 0)
sizehint!(ts, N)
return ts
end
function samples(sample, ::AbstractModel, ::AbstractSampler; kwargs...)
return Vector{typeof(sample)}(undef, 0)
end
"""
save!!(samples, sample, iteration, model, sampler[, N; kwargs...])
Save the `sample` of the MCMC `sampler` at the current `iteration` in the container of
`samples`.
The function can be called with and without a predefined number `N` of samples. By default,
AbstractMCMC uses `push!!` from the Julia package
[BangBang](https://github.com/tkf/BangBang.jl) to append to the container, and widen its
type if needed.
"""
function save!!(
samples::Vector,
sample,
iteration::Integer,
::AbstractModel,
::AbstractSampler,
N::Integer;
kwargs...,
)
s = BangBang.push!!(samples, sample)
s !== samples && sizehint!(s, N)
return s
end
function save!!(
samples, sample, iteration::Integer, ::AbstractModel, ::AbstractSampler; kwargs...
)
return BangBang.push!!(samples, sample)
end
# Deprecations
Base.@deprecate transitions(
transition, model::AbstractModel, sampler::AbstractSampler, N::Integer; kwargs...
) samples(transition, model, sampler, N; kwargs...) false
Base.@deprecate transitions(
transition, model::AbstractModel, sampler::AbstractSampler; kwargs...
) samples(transition, model, sampler; kwargs...) false
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/logdensityproblems.jl | code | 4003 | """
LogDensityModel <: AbstractMCMC.AbstractModel
Wrapper around something that implements the LogDensityProblem.jl interface.
Note that this does _not_ implement the LogDensityProblems.jl interface itself,
but it simply useful for indicating to the `sample` and other `AbstractMCMC` methods
that the wrapped object implements the LogDensityProblems.jl interface.
# Fields
- `logdensity`: The object that implements the LogDensityProblems.jl interface.
"""
struct LogDensityModel{L} <: AbstractModel
logdensity::L
function LogDensityModel{L}(logdensity::L) where {L}
if LogDensityProblems.capabilities(logdensity) === nothing
throw(
ArgumentError(
"The log density function does not support the LogDensityProblems.jl interface",
),
)
end
return new{L}(logdensity)
end
end
LogDensityModel(logdensity::L) where {L} = LogDensityModel{L}(logdensity)
# Fallbacks: Wrap log density function in a model
"""
sample(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler,
N_or_isdone;
kwargs...,
)
Wrap the `logdensity` function in a [`LogDensityModel`](@ref), and call `sample` with the resulting model instead of `logdensity`.
The `logdensity` function has to support the [LogDensityProblems.jl](https://github.com/tpapp/LogDensityProblems.jl) interface.
"""
function StatsBase.sample(
rng::Random.AbstractRNG, logdensity, sampler::AbstractSampler, N_or_isdone; kwargs...
)
return StatsBase.sample(rng, _model(logdensity), sampler, N_or_isdone; kwargs...)
end
"""
sample(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler,
parallel::AbstractMCMCEnsemble,
N::Integer,
nchains::Integer;
kwargs...,
)
Wrap the `logdensity` function in a [`LogDensityModel`](@ref), and call `sample` with the resulting model instead of `logdensity`.
The `logdensity` function has to support the [LogDensityProblems.jl](https://github.com/tpapp/LogDensityProblems.jl) interface.
"""
function StatsBase.sample(
rng::Random.AbstractRNG,
logdensity,
sampler::AbstractSampler,
parallel::AbstractMCMCEnsemble,
N::Integer,
nchains::Integer;
kwargs...,
)
return StatsBase.sample(
rng, _model(logdensity), sampler, parallel, N, nchains; kwargs...
)
end
"""
steps(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler;
kwargs...,
)
Wrap the `logdensity` function in a [`LogDensityModel`](@ref), and call `steps` with the resulting model instead of `logdensity`.
The `logdensity` function has to support the [LogDensityProblems.jl](https://github.com/tpapp/LogDensityProblems.jl) interface.
"""
function steps(rng::Random.AbstractRNG, logdensity, sampler::AbstractSampler; kwargs...)
return steps(rng, _model(logdensity), sampler; kwargs...)
end
"""
Sample(
rng::Random.AbstractRNG=Random.default_rng(),
logdensity,
sampler::AbstractSampler;
kwargs...,
)
Wrap the `logdensity` function in a [`LogDensityModel`](@ref), and call `Sample` with the resulting model instead of `logdensity`.
The `logdensity` function has to support the [LogDensityProblems.jl](https://github.com/tpapp/LogDensityProblems.jl) interface.
"""
function Sample(rng::Random.AbstractRNG, logdensity, sampler::AbstractSampler; kwargs...)
return Sample(rng, _model(logdensity), sampler; kwargs...)
end
function _model(logdensity)
if LogDensityProblems.capabilities(logdensity) === nothing
throw(
ArgumentError(
"the log density function does not support the LogDensityProblems.jl interface. Please implement the interface or provide a model of type `AbstractMCMC.AbstractModel`",
),
)
end
return LogDensityModel(logdensity)
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/logging.jl | code | 1785 | # avoid creating a progress bar with @withprogress if progress logging is disabled
# and add a custom progress logger if the current logger does not seem to be able to handle
# progress logs
macro ifwithprogresslogger(progress, exprs...)
return esc(
quote
if $progress
if $hasprogresslevel($Logging.current_logger())
$ProgressLogging.@withprogress $(exprs...)
else
$with_progresslogger($Base.@__MODULE__, $Logging.current_logger()) do
$ProgressLogging.@withprogress $(exprs...)
end
end
else
$(exprs[end])
end
end,
)
end
# improved checks?
function hasprogresslevel(logger)
return Logging.min_enabled_level(logger) ≤ ProgressLogging.ProgressLevel
end
# filter better, e.g., according to group?
function with_progresslogger(f, _module, logger)
logger1 = LoggingExtras.EarlyFilteredLogger(progresslogger()) do log
log._module === _module && log.level == ProgressLogging.ProgressLevel
end
logger2 = LoggingExtras.EarlyFilteredLogger(logger) do log
log._module !== _module || log.level != ProgressLogging.ProgressLevel
end
return Logging.with_logger(f, LoggingExtras.TeeLogger(logger1, logger2))
end
function progresslogger()
# detect if code is running under IJulia since TerminalLogger does not work with IJulia
# https://github.com/JuliaLang/IJulia.jl#detecting-that-code-is-running-under-ijulia
if (Sys.iswindows() && VERSION < v"1.5.3") ||
(isdefined(Main, :IJulia) && Main.IJulia.inited)
return ConsoleProgressMonitor.ProgressLogger()
else
return TerminalLoggers.TerminalLogger()
end
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/sample.jl | code | 19542 | # Default implementations of `sample`.
const PROGRESS = Ref(true)
"""
setprogress!(progress::Bool; silent::Bool=false)
Enable progress logging globally if `progress` is `true`, and disable it otherwise.
Optionally disable informational message if `silent` is `true`.
"""
function setprogress!(progress::Bool; silent::Bool=false)
if !silent
@info "progress logging is $(progress ? "enabled" : "disabled") globally"
end
PROGRESS[] = progress
return progress
end
function StatsBase.sample(
model_or_logdensity, sampler::AbstractSampler, N_or_isdone; kwargs...
)
return StatsBase.sample(
Random.default_rng(), model_or_logdensity, sampler, N_or_isdone; kwargs...
)
end
"""
sample(
rng::Random.AbatractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler,
N_or_isdone;
kwargs...,
)
Sample from the `model` with the Markov chain Monte Carlo `sampler` and return the samples.
If `N_or_isdone` is an `Integer`, exactly `N_or_isdone` samples are returned.
Otherwise, sampling is performed until a convergence criterion `N_or_isdone` returns `true`.
The convergence criterion has to be a function with the signature
```julia
isdone(rng, model, sampler, samples, state, iteration; kwargs...)
```
where `state` and `iteration` are the current state and iteration of the sampler, respectively.
It should return `true` when sampling should end, and `false` otherwise.
"""
function StatsBase.sample(
rng::Random.AbstractRNG,
model::AbstractModel,
sampler::AbstractSampler,
N_or_isdone;
kwargs...,
)
return mcmcsample(rng, model, sampler, N_or_isdone; kwargs...)
end
function StatsBase.sample(
model_or_logdensity,
sampler::AbstractSampler,
parallel::AbstractMCMCEnsemble,
N::Integer,
nchains::Integer;
kwargs...,
)
return StatsBase.sample(
Random.default_rng(), model_or_logdensity, sampler, parallel, N, nchains; kwargs...
)
end
"""
sample(
rng::Random.AbstractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler,
parallel::AbstractMCMCEnsemble,
N::Integer,
nchains::Integer;
kwargs...,
)
Sample `nchains` Monte Carlo Markov chains from the `model` with the `sampler` in parallel
using the `parallel` algorithm, and combine them into a single chain.
"""
function StatsBase.sample(
rng::Random.AbstractRNG,
model::AbstractModel,
sampler::AbstractSampler,
parallel::AbstractMCMCEnsemble,
N::Integer,
nchains::Integer;
kwargs...,
)
return mcmcsample(rng, model, sampler, parallel, N, nchains; kwargs...)
end
# Default implementations of regular and parallel sampling.
function mcmcsample(
rng::Random.AbstractRNG,
model::AbstractModel,
sampler::AbstractSampler,
N::Integer;
progress=PROGRESS[],
progressname="Sampling",
callback=nothing,
discard_initial=0,
thinning=1,
chain_type::Type=Any,
initial_state=nothing,
kwargs...,
)
# Check the number of requested samples.
N > 0 || error("the number of samples must be ≥ 1")
Ntotal = thinning * (N - 1) + discard_initial + 1
# Start the timer
start = time()
local state
@ifwithprogresslogger progress name = progressname begin
# Determine threshold values for progress logging
# (one update per 0.5% of progress)
if progress
threshold = Ntotal ÷ 200
next_update = threshold
end
# Obtain the initial sample and state.
sample, state = if initial_state === nothing
step(rng, model, sampler; kwargs...)
else
step(rng, model, sampler, initial_state; kwargs...)
end
# Discard initial samples.
for i in 1:discard_initial
# Update the progress bar.
if progress && i >= next_update
ProgressLogging.@logprogress i / Ntotal
next_update = i + threshold
end
# Obtain the next sample and state.
sample, state = step(rng, model, sampler, state; kwargs...)
end
# Run callback.
callback === nothing || callback(rng, model, sampler, sample, state, 1; kwargs...)
# Save the sample.
samples = AbstractMCMC.samples(sample, model, sampler, N; kwargs...)
samples = save!!(samples, sample, 1, model, sampler, N; kwargs...)
# Update the progress bar.
itotal = 1 + discard_initial
if progress && itotal >= next_update
ProgressLogging.@logprogress itotal / Ntotal
next_update = itotal + threshold
end
# Step through the sampler.
for i in 2:N
# Discard thinned samples.
for _ in 1:(thinning - 1)
# Obtain the next sample and state.
sample, state = step(rng, model, sampler, state; kwargs...)
# Update progress bar.
if progress && (itotal += 1) >= next_update
ProgressLogging.@logprogress itotal / Ntotal
next_update = itotal + threshold
end
end
# Obtain the next sample and state.
sample, state = step(rng, model, sampler, state; kwargs...)
# Run callback.
callback === nothing ||
callback(rng, model, sampler, sample, state, i; kwargs...)
# Save the sample.
samples = save!!(samples, sample, i, model, sampler, N; kwargs...)
# Update the progress bar.
if progress && (itotal += 1) >= next_update
ProgressLogging.@logprogress itotal / Ntotal
next_update = itotal + threshold
end
end
end
# Get the sample stop time.
stop = time()
duration = stop - start
stats = SamplingStats(start, stop, duration)
return bundle_samples(
samples,
model,
sampler,
state,
chain_type;
stats=stats,
discard_initial=discard_initial,
thinning=thinning,
kwargs...,
)
end
function mcmcsample(
rng::Random.AbstractRNG,
model::AbstractModel,
sampler::AbstractSampler,
isdone;
chain_type::Type=Any,
progress=PROGRESS[],
progressname="Convergence sampling",
callback=nothing,
discard_initial=0,
thinning=1,
initial_state=nothing,
kwargs...,
)
# Start the timer
start = time()
local state
@ifwithprogresslogger progress name = progressname begin
# Obtain the initial sample and state.
sample, state = if initial_state === nothing
step(rng, model, sampler; kwargs...)
else
step(rng, model, sampler, state; kwargs...)
end
# Discard initial samples.
for _ in 1:discard_initial
# Obtain the next sample and state.
sample, state = step(rng, model, sampler, state; kwargs...)
end
# Run callback.
callback === nothing || callback(rng, model, sampler, sample, state, 1; kwargs...)
# Save the sample.
samples = AbstractMCMC.samples(sample, model, sampler; kwargs...)
samples = save!!(samples, sample, 1, model, sampler; kwargs...)
# Step through the sampler until stopping.
i = 2
while !isdone(rng, model, sampler, samples, state, i; progress=progress, kwargs...)
# Discard thinned samples.
for _ in 1:(thinning - 1)
# Obtain the next sample and state.
sample, state = step(rng, model, sampler, state; kwargs...)
end
# Obtain the next sample and state.
sample, state = step(rng, model, sampler, state; kwargs...)
# Run callback.
callback === nothing ||
callback(rng, model, sampler, sample, state, i; kwargs...)
# Save the sample.
samples = save!!(samples, sample, i, model, sampler; kwargs...)
# Increment iteration counter.
i += 1
end
end
# Get the sample stop time.
stop = time()
duration = stop - start
stats = SamplingStats(start, stop, duration)
# Wrap the samples up.
return bundle_samples(
samples,
model,
sampler,
state,
chain_type;
stats=stats,
discard_initial=discard_initial,
thinning=thinning,
kwargs...,
)
end
function mcmcsample(
rng::Random.AbstractRNG,
model::AbstractModel,
sampler::AbstractSampler,
::MCMCThreads,
N::Integer,
nchains::Integer;
progress=PROGRESS[],
progressname="Sampling ($(min(nchains, Threads.nthreads())) threads)",
initial_params=nothing,
initial_state=nothing,
kwargs...,
)
# Check if actually multiple threads are used.
if Threads.nthreads() == 1
@warn "Only a single thread available: MCMC chains are not sampled in parallel"
end
# Check if the number of chains is larger than the number of samples
if nchains > N
@warn "Number of chains ($nchains) is greater than number of samples per chain ($N)"
end
# Copy the random number generator, model, and sample for each thread
nchunks = min(nchains, Threads.nthreads())
chunksize = cld(nchains, nchunks)
interval = 1:nchunks
rngs = [deepcopy(rng) for _ in interval]
models = [deepcopy(model) for _ in interval]
samplers = [deepcopy(sampler) for _ in interval]
# Create a seed for each chain using the provided random number generator.
seeds = rand(rng, UInt, nchains)
# Ensure that initial parameters and states are `nothing` or of the correct length
check_initial_params(initial_params, nchains)
check_initial_state(initial_state, nchains)
# Set up a chains vector.
chains = Vector{Any}(undef, nchains)
@ifwithprogresslogger progress name = progressname begin
# Create a channel for progress logging.
if progress
channel = Channel{Bool}(length(interval))
end
Distributed.@sync begin
if progress
# Update the progress bar.
Distributed.@async begin
# Determine threshold values for progress logging
# (one update per 0.5% of progress)
threshold = nchains ÷ 200
nextprogresschains = threshold
progresschains = 0
while take!(channel)
progresschains += 1
if progresschains >= nextprogresschains
ProgressLogging.@logprogress progresschains / nchains
nextprogresschains = progresschains + threshold
end
end
end
end
Distributed.@async begin
try
Distributed.@sync for (i, _rng, _model, _sampler) in
zip(1:nchunks, rngs, models, samplers)
chainidxs = if i == nchunks
((i - 1) * chunksize + 1):nchains
else
((i - 1) * chunksize + 1):(i * chunksize)
end
Threads.@spawn for chainidx in chainidxs
# Seed the chunk-specific random number generator with the pre-made seed.
Random.seed!(_rng, seeds[chainidx])
# Sample a chain and save it to the vector.
chains[chainidx] = StatsBase.sample(
_rng,
_model,
_sampler,
N;
progress=false,
initial_params=if initial_params === nothing
nothing
else
initial_params[chainidx]
end,
initial_state=if initial_state === nothing
nothing
else
initial_state[chainidx]
end,
kwargs...,
)
# Update the progress bar.
progress && put!(channel, true)
end
end
finally
# Stop updating the progress bar.
progress && put!(channel, false)
end
end
end
end
# Concatenate the chains together.
return chainsstack(tighten_eltype(chains))
end
function mcmcsample(
rng::Random.AbstractRNG,
model::AbstractModel,
sampler::AbstractSampler,
::MCMCDistributed,
N::Integer,
nchains::Integer;
progress=PROGRESS[],
progressname="Sampling ($(Distributed.nworkers()) processes)",
initial_params=nothing,
initial_state=nothing,
kwargs...,
)
# Check if actually multiple processes are used.
if Distributed.nworkers() == 1
@warn "Only a single process available: MCMC chains are not sampled in parallel"
end
# Check if the number of chains is larger than the number of samples
if nchains > N
@warn "Number of chains ($nchains) is greater than number of samples per chain ($N)"
end
# Ensure that initial parameters and states are `nothing` or of the correct length
check_initial_params(initial_params, nchains)
check_initial_state(initial_state, nchains)
_initial_params =
initial_params === nothing ? FillArrays.Fill(nothing, nchains) : initial_params
_initial_state =
initial_state === nothing ? FillArrays.Fill(nothing, nchains) : initial_state
# Create a seed for each chain using the provided random number generator.
seeds = rand(rng, UInt, nchains)
# Set up worker pool.
pool = Distributed.CachingPool(Distributed.workers())
local chains
@ifwithprogresslogger progress name = progressname begin
# Create a channel for progress logging.
if progress
channel = Distributed.RemoteChannel(() -> Channel{Bool}(Distributed.nworkers()))
end
Distributed.@sync begin
if progress
# Update the progress bar.
Distributed.@async begin
# Determine threshold values for progress logging
# (one update per 0.5% of progress)
threshold = nchains ÷ 200
nextprogresschains = threshold
progresschains = 0
while take!(channel)
progresschains += 1
if progresschains >= nextprogresschains
ProgressLogging.@logprogress progresschains / nchains
nextprogresschains = progresschains + threshold
end
end
end
end
Distributed.@async begin
try
function sample_chain(seed, initial_params, initial_state)
# Seed a new random number generator with the pre-made seed.
Random.seed!(rng, seed)
# Sample a chain.
chain = StatsBase.sample(
rng,
model,
sampler,
N;
progress=false,
initial_params=initial_params,
initial_state=initial_state,
kwargs...,
)
# Update the progress bar.
progress && put!(channel, true)
# Return the new chain.
return chain
end
chains = Distributed.pmap(
sample_chain, pool, seeds, _initial_params, _initial_state
)
finally
# Stop updating the progress bar.
progress && put!(channel, false)
end
end
end
end
# Concatenate the chains together.
return chainsstack(tighten_eltype(chains))
end
function mcmcsample(
rng::Random.AbstractRNG,
model::AbstractModel,
sampler::AbstractSampler,
::MCMCSerial,
N::Integer,
nchains::Integer;
progressname="Sampling",
initial_params=nothing,
initial_state=nothing,
kwargs...,
)
# Check if the number of chains is larger than the number of samples
if nchains > N
@warn "Number of chains ($nchains) is greater than number of samples per chain ($N)"
end
# Ensure that initial parameters and states are `nothing` or of the correct length
check_initial_params(initial_params, nchains)
check_initial_state(initial_state, nchains)
_initial_params =
initial_params === nothing ? FillArrays.Fill(nothing, nchains) : initial_params
_initial_state =
initial_state === nothing ? FillArrays.Fill(nothing, nchains) : initial_state
# Create a seed for each chain using the provided random number generator.
seeds = rand(rng, UInt, nchains)
# Sample the chains.
function sample_chain(i, seed, initial_params, initial_state)
# Seed a new random number generator with the pre-made seed.
Random.seed!(rng, seed)
# Sample a chain.
return StatsBase.sample(
rng,
model,
sampler,
N;
progressname=string(progressname, " (Chain ", i, " of ", nchains, ")"),
initial_params=initial_params,
initial_state=initial_state,
kwargs...,
)
end
chains = map(sample_chain, 1:nchains, seeds, _initial_params, _initial_state)
# Concatenate the chains together.
return chainsstack(tighten_eltype(chains))
end
tighten_eltype(x) = x
tighten_eltype(x::Vector{Any}) = map(identity, x)
@nospecialize check_initial_params(x, n) = throw(
ArgumentError(
"initial parameters must be specified as a vector of length equal to the number of chains or `nothing`",
),
)
check_initial_params(::Nothing, n) = nothing
function check_initial_params(x::AbstractArray, n)
if length(x) != n
throw(
ArgumentError(
"incorrect number of initial parameters (expected $n, received $(length(x))"
),
)
end
return nothing
end
@nospecialize check_initial_state(x, n) = throw(
ArgumentError(
"initial states must be specified as a vector of length equal to the number of chains or `nothing`",
),
)
check_initial_state(::Nothing, n) = nothing
function check_initial_state(x::AbstractArray, n)
if length(x) != n
throw(
ArgumentError(
"incorrect number of initial states (expected $n, received $(length(x))"
),
)
end
return nothing
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/samplingstats.jl | code | 404 | """
SamplingStats
A struct that tracks sampling information.
The fields available are:
- `start`: A `Float64` Unix timestamp indicating the start time of sampling.
- `stop`: A `Float64` Unix timestamp indicating the stop time of sampling.
- `duration`: The sampling time duration, defined as `stop - start`.
"""
struct SamplingStats
start::Float64
stop::Float64
duration::Float64
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/stepper.jl | code | 2246 | struct Stepper{A<:Random.AbstractRNG,M<:AbstractModel,S<:AbstractSampler,K}
rng::A
model::M
sampler::S
kwargs::K
end
# Initial sample.
function Base.iterate(stp::Stepper)
# Unpack iterator.
rng = stp.rng
model = stp.model
sampler = stp.sampler
kwargs = stp.kwargs
discard_initial = get(kwargs, :discard_initial, 0)::Int
# Start sampling algorithm and discard initial samples if desired.
sample, state = step(rng, model, sampler; kwargs...)
for _ in 1:discard_initial
sample, state = step(rng, model, sampler, state; kwargs...)
end
return sample, state
end
# Subsequent samples.
function Base.iterate(stp::Stepper, state)
# Unpack iterator.
rng = stp.rng
model = stp.model
sampler = stp.sampler
kwargs = stp.kwargs
thinning = get(kwargs, :thinning, 1)::Int
# Return next sample, possibly after thinning the chain if desired.
for _ in 1:(thinning - 1)
_, state = step(rng, model, sampler, state; kwargs...)
end
return step(rng, model, sampler, state; kwargs...)
end
Base.IteratorSize(::Type{<:Stepper}) = Base.IsInfinite()
Base.IteratorEltype(::Type{<:Stepper}) = Base.EltypeUnknown()
function steps(model_or_logdensity, sampler::AbstractSampler; kwargs...)
return steps(Random.default_rng(), model_or_logdensity, sampler; kwargs...)
end
"""
steps(
rng::Random.AbstractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler;
kwargs...,
)
Create an iterator that returns samples from the `model` with the Markov chain Monte Carlo
`sampler`.
# Examples
```jldoctest; setup=:(using AbstractMCMC: steps)
julia> struct MyModel <: AbstractMCMC.AbstractModel end
julia> struct MySampler <: AbstractMCMC.AbstractSampler end
julia> function AbstractMCMC.step(rng, ::MyModel, ::MySampler, state=nothing; kwargs...)
# all samples are zero
return 0.0, state
end
julia> iterator = steps(MyModel(), MySampler());
julia> collect(Iterators.take(iterator, 10)) == zeros(10)
true
```
"""
function steps(
rng::Random.AbstractRNG, model::AbstractModel, sampler::AbstractSampler; kwargs...
)
return Stepper(rng, model, sampler, kwargs)
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | src/transducer.jl | code | 2887 | struct Sample{A<:Random.AbstractRNG,M<:AbstractModel,S<:AbstractSampler,K} <:
Transducers.Transducer
rng::A
model::M
sampler::S
kwargs::K
end
function Sample(model_or_logdensity, sampler::AbstractSampler; kwargs...)
return Sample(Random.default_rng(), model_or_logdensity, sampler; kwargs...)
end
"""
Sample(
rng::Random.AbstractRNG=Random.default_rng(),
model::AbstractModel,
sampler::AbstractSampler;
kwargs...,
)
Create a transducer that returns samples from the `model` with the Markov chain Monte Carlo
`sampler`.
# Examples
```jldoctest; setup=:(using AbstractMCMC: Sample)
julia> struct MyModel <: AbstractMCMC.AbstractModel end
julia> struct MySampler <: AbstractMCMC.AbstractSampler end
julia> function AbstractMCMC.step(rng, ::MyModel, ::MySampler, state=nothing; kwargs...)
# all samples are zero
return 0.0, state
end
julia> transducer = Sample(MyModel(), MySampler());
julia> collect(transducer(1:10)) == zeros(10)
true
```
"""
function Sample(
rng::Random.AbstractRNG, model::AbstractModel, sampler::AbstractSampler; kwargs...
)
return Sample(rng, model, sampler, kwargs)
end
# Initial sample.
function Transducers.start(rf::Transducers.R_{<:Sample}, result)
# Unpack transducer.
td = Transducers.xform(rf)
rng = td.rng
model = td.model
sampler = td.sampler
kwargs = td.kwargs
discard_initial = get(kwargs, :discard_initial, 0)::Int
# Start sampling algorithm and discard initial samples if desired.
sample, state = step(rng, model, sampler; kwargs...)
for _ in 1:discard_initial
sample, state = step(rng, model, sampler, state; kwargs...)
end
return Transducers.wrap(
rf, (sample, state), Transducers.start(Transducers.inner(rf), result)
)
end
# Subsequent samples.
function Transducers.next(rf::Transducers.R_{<:Sample}, result, input)
# Unpack transducer.
td = Transducers.xform(rf)
rng = td.rng
model = td.model
sampler = td.sampler
kwargs = td.kwargs
thinning = get(kwargs, :thinning, 1)::Int
let rng = rng,
model = model,
sampler = sampler,
kwargs = kwargs,
thinning = thinning,
inner_rf = Transducers.inner(rf)
Transducers.wrapping(rf, result) do (sample, state), iresult
iresult2 = Transducers.next(inner_rf, iresult, sample)
# Perform thinning if desired.
for _ in 1:(thinning - 1)
_, state = step(rng, model, sampler, state; kwargs...)
end
return step(rng, model, sampler, state; kwargs...), iresult2
end
end
end
function Transducers.complete(rf::Transducers.R_{Sample}, result)
_, inner_result = Transducers.unwrap(rf, result)
return Transducers.complete(Transducers.inner(rf), inner_result)
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | test/logdensityproblems.jl | code | 3845 | @testset "logdensityproblems.jl" begin
# Add worker processes.
# Memory requirements on Windows are ~4x larger than on Linux, hence number of processes is reduced
# See, e.g., https://github.com/JuliaLang/julia/issues/40766 and https://github.com/JuliaLang/Pkg.jl/pull/2366
pids = addprocs(Sys.iswindows() ? div(Sys.CPU_THREADS::Int, 2) : Sys.CPU_THREADS::Int)
# Load all required packages (`utils.jl` needs LogDensityProblems, Logging, and Random).
@everywhere begin
using AbstractMCMC
using AbstractMCMC: sample
using LogDensityProblems
using Logging
using Random
include("utils.jl")
end
@testset "LogDensityModel" begin
ℓ = MyLogDensity(10)
model = @inferred AbstractMCMC.LogDensityModel(ℓ)
@test model isa AbstractMCMC.LogDensityModel{MyLogDensity}
@test model.logdensity === ℓ
@test_throws ArgumentError AbstractMCMC.LogDensityModel(mylogdensity)
end
@testset "fallback for log densities" begin
# Sample with log density
dim = 10
ℓ = MyLogDensity(dim)
Random.seed!(1234)
N = 1_000
samples = sample(ℓ, MySampler(), N)
# Samples are of the correct dimension and log density values are correct
@test length(samples) == N
@test all(length(x.a) == dim for x in samples)
@test all(x.b ≈ LogDensityProblems.logdensity(ℓ, x.a) for x in samples)
# Same chain as if LogDensityModel is used explicitly
Random.seed!(1234)
samples2 = sample(AbstractMCMC.LogDensityModel(ℓ), MySampler(), N)
@test length(samples2) == N
@test all(x.a == y.a && x.b == y.b for (x, y) in zip(samples, samples2))
# Same chain if sampling is performed with convergence criterion
Random.seed!(1234)
isdone(rng, model, sampler, state, samples, iteration; kwargs...) = iteration > N
samples3 = sample(ℓ, MySampler(), isdone)
@test length(samples3) == N
@test all(x.a == y.a && x.b == y.b for (x, y) in zip(samples, samples3))
# Same chain if sampling is performed with iterator
Random.seed!(1234)
samples4 = collect(Iterators.take(AbstractMCMC.steps(ℓ, MySampler()), N))
@test length(samples4) == N
@test all(x.a == y.a && x.b == y.b for (x, y) in zip(samples, samples4))
# Same chain if sampling is performed with transducer
Random.seed!(1234)
xf = AbstractMCMC.Sample(ℓ, MySampler())
samples5 = collect(xf(1:N))
@test length(samples5) == N
@test all(x.a == y.a && x.b == y.b for (x, y) in zip(samples, samples5))
# Parallel sampling
for alg in (MCMCSerial(), MCMCDistributed(), MCMCThreads())
chains = sample(ℓ, MySampler(), alg, N, 2)
@test length(chains) == 2
samples = vcat(chains[1], chains[2])
@test length(samples) == 2 * N
@test all(length(x.a) == dim for x in samples)
@test all(x.b ≈ LogDensityProblems.logdensity(ℓ, x.a) for x in samples)
end
# Log density has to satisfy the LogDensityProblems interface
@test_throws ArgumentError sample(mylogdensity, MySampler(), N)
@test_throws ArgumentError sample(mylogdensity, MySampler(), isdone)
@test_throws ArgumentError sample(mylogdensity, MySampler(), MCMCSerial(), N, 2)
@test_throws ArgumentError sample(mylogdensity, MySampler(), MCMCThreads(), N, 2)
@test_throws ArgumentError sample(
mylogdensity, MySampler(), MCMCDistributed(), N, 2
)
@test_throws ArgumentError AbstractMCMC.steps(mylogdensity, MySampler())
@test_throws ArgumentError AbstractMCMC.Sample(mylogdensity, MySampler())
end
# Remove workers
rmprocs(pids...)
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | test/runtests.jl | code | 596 | using AbstractMCMC
using ConsoleProgressMonitor: ProgressLogger
using IJulia
using LogDensityProblems
using LoggingExtras: TeeLogger, EarlyFilteredLogger
using TerminalLoggers: TerminalLogger
using FillArrays: FillArrays
using Transducers
using Distributed
using Logging: Logging
using Random
using Statistics
using Test
using Test: collect_test_logs
const LOGGERS = Set()
const CURRENT_LOGGER = Logging.current_logger()
include("utils.jl")
@testset "AbstractMCMC" begin
include("sample.jl")
include("stepper.jl")
include("transducer.jl")
include("logdensityproblems.jl")
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | test/sample.jl | code | 25262 | @testset "sample.jl" begin
@testset "Basic sampling" begin
@testset "REPL" begin
empty!(LOGGERS)
Random.seed!(1234)
N = 1_000
chain = sample(MyModel(), MySampler(), N; loggers=true)
@test length(LOGGERS) == 1
logger = first(LOGGERS)
@test logger isa TeeLogger
@test logger.loggers[1].logger isa
(Sys.iswindows() && VERSION < v"1.5.3" ? ProgressLogger : TerminalLogger)
@test logger.loggers[2].logger === CURRENT_LOGGER
@test Logging.current_logger() === CURRENT_LOGGER
# test output type and size
@test chain isa Vector{<:MySample}
@test length(chain) == N
# test some statistical properties
tail_chain = @view chain[2:end]
@test mean(x.a for x in tail_chain) ≈ 0.5 atol = 6e-2
@test var(x.a for x in tail_chain) ≈ 1 / 12 atol = 5e-3
@test mean(x.b for x in tail_chain) ≈ 0.0 atol = 5e-2
@test var(x.b for x in tail_chain) ≈ 1 atol = 6e-2
# initial parameters
chain = sample(
MyModel(), MySampler(), 3; progress=false, initial_params=(b=3.2, a=-1.8)
)
@test chain[1].a == -1.8
@test chain[1].b == 3.2
end
@testset "IJulia" begin
# emulate running IJulia kernel
@eval IJulia begin
inited = true
end
empty!(LOGGERS)
Random.seed!(1234)
N = 10
sample(MyModel(), MySampler(), N; loggers=true)
@test length(LOGGERS) == 1
logger = first(LOGGERS)
@test logger isa TeeLogger
@test logger.loggers[1].logger isa ProgressLogger
@test logger.loggers[2].logger === CURRENT_LOGGER
@test Logging.current_logger() === CURRENT_LOGGER
@eval IJulia begin
inited = false
end
end
@testset "Custom logger" begin
empty!(LOGGERS)
Random.seed!(1234)
N = 10
logger = Logging.ConsoleLogger(stderr, Logging.LogLevel(-1))
Logging.with_logger(logger) do
sample(MyModel(), MySampler(), N; loggers=true)
end
@test length(LOGGERS) == 1
@test first(LOGGERS) === logger
@test Logging.current_logger() === CURRENT_LOGGER
end
@testset "Suppress output" begin
logs, _ = collect_test_logs(; min_level=Logging.LogLevel(-1)) do
sample(MyModel(), MySampler(), 100; progress=false)
end
@test all(l.level > Logging.LogLevel(-1) for l in logs)
# disable progress logging globally
@test !(@test_logs (:info, "progress logging is disabled globally") AbstractMCMC.setprogress!(
false
))
@test !AbstractMCMC.PROGRESS[]
logs, _ = collect_test_logs(; min_level=Logging.LogLevel(-1)) do
sample(MyModel(), MySampler(), 100)
end
@test all(l.level > Logging.LogLevel(-1) for l in logs)
# enable progress logging globally
@test (@test_logs (:info, "progress logging is enabled globally") AbstractMCMC.setprogress!(
true
))
@test AbstractMCMC.PROGRESS[]
end
end
@testset "Multithreaded sampling" begin
if Threads.nthreads() == 1
warnregex = r"^Only a single thread available"
@test_logs (:warn, warnregex) sample(
MyModel(), MySampler(), MCMCThreads(), 10, 10
)
end
# No dedicated chains type
N = 10_000
chains = sample(MyModel(), MySampler(), MCMCThreads(), N, 1000)
@test chains isa Vector{<:Vector{<:MySample}}
@test length(chains) == 1000
@test all(length(x) == N for x in chains)
Random.seed!(1234)
chains = sample(MyModel(), MySampler(), MCMCThreads(), N, 1000; chain_type=MyChain)
# test output type and size
@test chains isa Vector{<:MyChain}
@test length(chains) == 1000
@test all(x -> length(x.as) == length(x.bs) == N, chains)
@test all(ismissing(x.as[1]) for x in chains)
# test some statistical properties
@test all(x -> isapprox(mean(@view x.as[2:end]), 0.5; atol=5e-2), chains)
@test all(x -> isapprox(var(@view x.as[2:end]), 1 / 12; atol=5e-3), chains)
@test all(x -> isapprox(mean(@view x.bs[2:end]), 0; atol=5e-2), chains)
@test all(x -> isapprox(var(@view x.bs[2:end]), 1; atol=1e-1), chains)
# test reproducibility
Random.seed!(1234)
chains2 = sample(MyModel(), MySampler(), MCMCThreads(), N, 1000; chain_type=MyChain)
@test all(ismissing(x.as[1]) for x in chains2)
@test all(c1.as[i] == c2.as[i] for (c1, c2) in zip(chains, chains2), i in 2:N)
@test all(c1.bs[i] == c2.bs[i] for (c1, c2) in zip(chains, chains2), i in 1:N)
# Unexpected order of arguments.
str = "Number of chains (10) is greater than number of samples per chain (5)"
@test_logs (:warn, str) match_mode = :any sample(
MyModel(), MySampler(), MCMCThreads(), 5, 10; chain_type=MyChain
)
# Suppress output.
logs, _ = collect_test_logs(; min_level=Logging.LogLevel(-1)) do
sample(
MyModel(),
MySampler(),
MCMCThreads(),
10_000,
1000;
progress=false,
chain_type=MyChain,
)
end
@test all(l.level > Logging.LogLevel(-1) for l in logs)
# Smoke test for nchains < nthreads
if Threads.nthreads() == 2
sample(MyModel(), MySampler(), MCMCThreads(), N, 1)
end
# initial parameters
nchains = 100
initial_params = [(b=randn(), a=rand()) for _ in 1:nchains]
chains = sample(
MyModel(),
MySampler(),
MCMCThreads(),
3,
nchains;
progress=false,
initial_params=initial_params,
)
@test length(chains) == nchains
@test all(
chain[1].a == params.a && chain[1].b == params.b for
(chain, params) in zip(chains, initial_params)
)
initial_params = (a=randn(), b=rand())
chains = sample(
MyModel(),
MySampler(),
MCMCThreads(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains),
)
@test length(chains) == nchains
@test all(
chain[1].a == initial_params.a && chain[1].b == initial_params.b for
chain in chains
)
# Too many `initial_params`
@test_throws ArgumentError sample(
MyModel(),
MySampler(),
MCMCThreads(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains + 1),
)
# Too few `initial_params`
@test_throws ArgumentError sample(
MyModel(),
MySampler(),
MCMCThreads(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains - 1),
)
end
@testset "Multicore sampling" begin
if nworkers() == 1
warnregex = r"^Only a single process available"
@test_logs (:warn, warnregex) sample(
MyModel(), MySampler(), MCMCDistributed(), 10, 10; chain_type=MyChain
)
end
# Add worker processes.
# Memory requirements on Windows are ~4x larger than on Linux, hence number of processes is reduced
# See, e.g., https://github.com/JuliaLang/julia/issues/40766 and https://github.com/JuliaLang/Pkg.jl/pull/2366
pids = addprocs(
Sys.iswindows() ? div(Sys.CPU_THREADS::Int, 2) : Sys.CPU_THREADS::Int
)
# Load all required packages (`utils.jl` needs LogDensityProblems, Logging, and Random).
@everywhere begin
using AbstractMCMC
using AbstractMCMC: sample
using LogDensityProblems
using Logging
using Random
include("utils.jl")
end
# No dedicated chains type
N = 10_000
chains = sample(MyModel(), MySampler(), MCMCThreads(), N, 1000)
@test chains isa Vector{<:Vector{<:MySample}}
@test length(chains) == 1000
@test all(length(x) == N for x in chains)
Random.seed!(1234)
chains = sample(
MyModel(), MySampler(), MCMCDistributed(), N, 1000; chain_type=MyChain
)
# Test output type and size.
@test chains isa Vector{<:MyChain}
@test all(ismissing(c.as[1]) for c in chains)
@test length(chains) == 1000
@test all(x -> length(x.as) == length(x.bs) == N, chains)
# Test some statistical properties.
@test all(x -> isapprox(mean(@view x.as[2:end]), 0.5; atol=5e-2), chains)
@test all(x -> isapprox(var(@view x.as[2:end]), 1 / 12; atol=5e-3), chains)
@test all(x -> isapprox(mean(@view x.bs[2:end]), 0; atol=5e-2), chains)
@test all(x -> isapprox(var(@view x.bs[2:end]), 1; atol=1e-1), chains)
# Test reproducibility.
Random.seed!(1234)
chains2 = sample(
MyModel(), MySampler(), MCMCDistributed(), N, 1000; chain_type=MyChain
)
@test all(ismissing(c.as[1]) for c in chains2)
@test all(c1.as[i] == c2.as[i] for (c1, c2) in zip(chains, chains2), i in 2:N)
@test all(c1.bs[i] == c2.bs[i] for (c1, c2) in zip(chains, chains2), i in 1:N)
# Unexpected order of arguments.
str = "Number of chains (10) is greater than number of samples per chain (5)"
@test_logs (:warn, str) match_mode = :any sample(
MyModel(), MySampler(), MCMCDistributed(), 5, 10; chain_type=MyChain
)
# Suppress output.
logs, _ = collect_test_logs(; min_level=Logging.LogLevel(-1)) do
sample(
MyModel(),
MySampler(),
MCMCDistributed(),
10_000,
100;
progress=false,
chain_type=MyChain,
)
end
@test all(l.level > Logging.LogLevel(-1) for l in logs)
# initial parameters
nchains = 100
initial_params = [(a=randn(), b=rand()) for _ in 1:nchains]
chains = sample(
MyModel(),
MySampler(),
MCMCDistributed(),
3,
nchains;
progress=false,
initial_params=initial_params,
)
@test length(chains) == nchains
@test all(
chain[1].a == params.a && chain[1].b == params.b for
(chain, params) in zip(chains, initial_params)
)
initial_params = (b=randn(), a=rand())
chains = sample(
MyModel(),
MySampler(),
MCMCDistributed(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains),
)
@test length(chains) == nchains
@test all(
chain[1].a == initial_params.a && chain[1].b == initial_params.b for
chain in chains
)
# Too many `initial_params`
@test_throws ArgumentError sample(
MyModel(),
MySampler(),
MCMCDistributed(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains + 1),
)
# Too few `initial_params`
@test_throws ArgumentError sample(
MyModel(),
MySampler(),
MCMCDistributed(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains - 1),
)
# Remove workers
rmprocs(pids...)
end
@testset "Serial sampling" begin
# No dedicated chains type
N = 10_000
chains = sample(MyModel(), MySampler(), MCMCSerial(), N, 1000; progress=false)
@test chains isa Vector{<:Vector{<:MySample}}
@test length(chains) == 1000
@test all(length(x) == N for x in chains)
Random.seed!(1234)
chains = sample(
MyModel(),
MySampler(),
MCMCSerial(),
N,
1000;
chain_type=MyChain,
progress=false,
)
# Test output type and size.
@test chains isa Vector{<:MyChain}
@test all(ismissing(c.as[1]) for c in chains)
@test length(chains) == 1000
@test all(x -> length(x.as) == length(x.bs) == N, chains)
# Test some statistical properties.
@test all(x -> isapprox(mean(@view x.as[2:end]), 0.5; atol=5e-2), chains)
@test all(x -> isapprox(var(@view x.as[2:end]), 1 / 12; atol=5e-3), chains)
@test all(x -> isapprox(mean(@view x.bs[2:end]), 0; atol=5e-2), chains)
@test all(x -> isapprox(var(@view x.bs[2:end]), 1; atol=1e-1), chains)
# Test reproducibility.
Random.seed!(1234)
chains2 = sample(
MyModel(),
MySampler(),
MCMCSerial(),
N,
1000;
chain_type=MyChain,
progress=false,
)
@test all(ismissing(c.as[1]) for c in chains2)
@test all(c1.as[i] == c2.as[i] for (c1, c2) in zip(chains, chains2), i in 2:N)
@test all(c1.bs[i] == c2.bs[i] for (c1, c2) in zip(chains, chains2), i in 1:N)
# Unexpected order of arguments.
str = "Number of chains (10) is greater than number of samples per chain (5)"
@test_logs (:warn, str) match_mode = :any sample(
MyModel(), MySampler(), MCMCSerial(), 5, 10; chain_type=MyChain
)
# Suppress output.
logs, _ = collect_test_logs(; min_level=Logging.LogLevel(-1)) do
sample(
MyModel(),
MySampler(),
MCMCSerial(),
10_000,
100;
progress=false,
chain_type=MyChain,
)
end
@test all(l.level > Logging.LogLevel(-1) for l in logs)
# initial parameters
nchains = 100
initial_params = [(a=rand(), b=randn()) for _ in 1:nchains]
chains = sample(
MyModel(),
MySampler(),
MCMCSerial(),
3,
nchains;
progress=false,
initial_params=initial_params,
)
@test length(chains) == nchains
@test all(
chain[1].a == params.a && chain[1].b == params.b for
(chain, params) in zip(chains, initial_params)
)
initial_params = (b=rand(), a=randn())
chains = sample(
MyModel(),
MySampler(),
MCMCSerial(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains),
)
@test length(chains) == nchains
@test all(
chain[1].a == initial_params.a && chain[1].b == initial_params.b for
chain in chains
)
# Too many `initial_params`
@test_throws ArgumentError sample(
MyModel(),
MySampler(),
MCMCSerial(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains + 1),
)
# Too few `initial_params`
@test_throws ArgumentError sample(
MyModel(),
MySampler(),
MCMCSerial(),
3,
nchains;
progress=false,
initial_params=FillArrays.Fill(initial_params, nchains - 1),
)
end
@testset "Ensemble sampling: Reproducibility" begin
N = 1_000
nchains = 10
# Serial sampling
Random.seed!(1234)
chains_serial = sample(
MyModel(),
MySampler(),
MCMCSerial(),
N,
nchains;
progress=false,
chain_type=MyChain,
)
@test all(ismissing(c.as[1]) for c in chains_serial)
# Multi-threaded sampling
Random.seed!(1234)
chains_threads = sample(
MyModel(),
MySampler(),
MCMCThreads(),
N,
nchains;
progress=false,
chain_type=MyChain,
)
@test all(ismissing(c.as[1]) for c in chains_threads)
@test all(
c1.as[i] == c2.as[i] for (c1, c2) in zip(chains_serial, chains_threads),
i in 2:N
)
@test all(
c1.bs[i] == c2.bs[i] for (c1, c2) in zip(chains_serial, chains_threads),
i in 1:N
)
# Multi-core sampling
Random.seed!(1234)
chains_distributed = sample(
MyModel(),
MySampler(),
MCMCDistributed(),
N,
nchains;
progress=false,
chain_type=MyChain,
)
@test all(ismissing(c.as[1]) for c in chains_distributed)
@test all(
c1.as[i] == c2.as[i] for (c1, c2) in zip(chains_serial, chains_distributed),
i in 2:N
)
@test all(
c1.bs[i] == c2.bs[i] for (c1, c2) in zip(chains_serial, chains_distributed),
i in 1:N
)
end
@testset "Chain constructors" begin
chain1 = sample(MyModel(), MySampler(), 100)
chain2 = sample(MyModel(), MySampler(), 100; chain_type=MyChain)
@test chain1 isa Vector{<:MySample}
@test chain2 isa MyChain
end
@testset "Sample stats" begin
chain = sample(MyModel(), MySampler(), 1000; chain_type=MyChain)
@test chain.stats.stop >= chain.stats.start
@test chain.stats.duration == chain.stats.stop - chain.stats.start
end
@testset "Discard initial samples" begin
# Create a chain and discard initial samples.
Random.seed!(1234)
N = 100
discard_initial = 50
chain = sample(MyModel(), MySampler(), N; discard_initial=discard_initial)
@test length(chain) == N
@test !ismissing(chain[1].a)
# Repeat sampling without discarding initial samples.
# On Julia < 1.6 progress logging changes the global RNG and hence is enabled here.
# https://github.com/TuringLang/AbstractMCMC.jl/pull/102#issuecomment-1142253258
Random.seed!(1234)
ref_chain = sample(
MyModel(), MySampler(), N + discard_initial; progress=VERSION < v"1.6"
)
@test all(chain[i].a == ref_chain[i + discard_initial].a for i in 1:N)
@test all(chain[i].b == ref_chain[i + discard_initial].b for i in 1:N)
end
@testset "Thin chain by a factor of `thinning`" begin
# Run a thinned chain with `N` samples thinned by factor of `thinning`.
Random.seed!(100)
N = 100
thinning = 3
chain = sample(MyModel(), MySampler(), N; thinning=thinning)
@test length(chain) == N
@test ismissing(chain[1].a)
# Repeat sampling without thinning.
# On Julia < 1.6 progress logging changes the global RNG and hence is enabled here.
# https://github.com/TuringLang/AbstractMCMC.jl/pull/102#issuecomment-1142253258
Random.seed!(100)
ref_chain = sample(MyModel(), MySampler(), N * thinning; progress=VERSION < v"1.6")
@test all(chain[i].a == ref_chain[(i - 1) * thinning + 1].a for i in 2:N)
@test all(chain[i].b == ref_chain[(i - 1) * thinning + 1].b for i in 1:N)
end
@testset "Sample without predetermined N" begin
Random.seed!(1234)
chain = sample(MyModel(), MySampler())
bmean = mean(x.b for x in chain)
@test ismissing(chain[1].a)
@test abs(bmean) <= 0.001 || length(chain) == 10_000
# Discard initial samples.
Random.seed!(1234)
discard_initial = 50
chain = sample(MyModel(), MySampler(); discard_initial=discard_initial)
bmean = mean(x.b for x in chain)
@test !ismissing(chain[1].a)
@test abs(bmean) <= 0.001 || length(chain) == 10_000
# On Julia < 1.6 progress logging changes the global RNG and hence is enabled here.
# https://github.com/TuringLang/AbstractMCMC.jl/pull/102#issuecomment-1142253258
Random.seed!(1234)
N = length(chain)
ref_chain = sample(
MyModel(),
MySampler(),
N;
discard_initial=discard_initial,
progress=VERSION < v"1.6",
)
@test all(chain[i].a == ref_chain[i].a for i in 1:N)
@test all(chain[i].b == ref_chain[i].b for i in 1:N)
# Thin chain by a factor of `thinning`.
Random.seed!(1234)
thinning = 3
chain = sample(MyModel(), MySampler(); thinning=thinning)
bmean = mean(x.b for x in chain)
@test ismissing(chain[1].a)
@test abs(bmean) <= 0.001 || length(chain) == 10_000
# On Julia < 1.6 progress logging changes the global RNG and hence is enabled here.
# https://github.com/TuringLang/AbstractMCMC.jl/pull/102#issuecomment-1142253258
Random.seed!(1234)
N = length(chain)
ref_chain = sample(
MyModel(), MySampler(), N; thinning=thinning, progress=VERSION < v"1.6"
)
@test all(chain[i].a == ref_chain[i].a for i in 2:N)
@test all(chain[i].b == ref_chain[i].b for i in 1:N)
end
@testset "Sample vector of `NamedTuple`s" begin
chain = sample(MyModel(), MySampler(), 1_000; chain_type=Vector{NamedTuple})
# Check output type
@test chain isa Vector{<:NamedTuple}
@test length(chain) == 1_000
@test all(keys(x) == (:a, :b) for x in chain)
# Check some statistical properties
@test ismissing(chain[1].a)
@test mean(x.a for x in view(chain, 2:1_000)) ≈ 0.5 atol = 6e-2
@test var(x.a for x in view(chain, 2:1_000)) ≈ 1 / 12 atol = 1e-2
@test mean(x.b for x in chain) ≈ 0 atol = 0.11
@test var(x.b for x in chain) ≈ 1 atol = 0.15
end
@testset "Testing callbacks" begin
function count_iterations(
rng, model, sampler, sample, state, i; iter_array, kwargs...
)
return push!(iter_array, i)
end
N = 100
it_array = Float64[]
sample(MyModel(), MySampler(), N; callback=count_iterations, iter_array=it_array)
@test it_array == collect(1:N)
# sampling without predetermined N
it_array = Float64[]
chain = sample(
MyModel(), MySampler(); callback=count_iterations, iter_array=it_array
)
@test it_array == collect(1:size(chain, 1))
end
@testset "Providing initial state" begin
function record_state(
rng, model, sampler, sample, state, i; states_channel, kwargs...
)
return put!(states_channel, state)
end
initial_state = 10
@testset "sample" begin
n = 10
states_channel = Channel{Int}(n)
chain = sample(
MyModel(),
MySampler(),
n;
initial_state=initial_state,
callback=record_state,
states_channel=states_channel,
)
# Extract the states.
states = [take!(states_channel) for _ in 1:n]
@test length(states) == n
for i in 1:n
@test states[i] == initial_state + i
end
end
@testset "sample with $mode" for mode in
[MCMCSerial(), MCMCThreads(), MCMCDistributed()]
nchains = 4
initial_state = 10
states_channel = if mode === MCMCDistributed()
# Need to use `RemoteChannel` for this.
RemoteChannel(() -> Channel{Int}(nchains))
else
Channel{Int}(nchains)
end
chain = sample(
MyModel(),
MySampler(),
mode,
1,
nchains;
initial_state=FillArrays.Fill(initial_state, nchains),
callback=record_state,
states_channel=states_channel,
)
# Extract the states.
states = [take!(states_channel) for _ in 1:nchains]
@test length(states) == nchains
for i in 1:nchains
@test states[i] == initial_state + 1
end
end
end
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | test/stepper.jl | code | 2333 | @testset "stepper.jl" begin
@testset "Iterator sampling" begin
Random.seed!(1234)
as = []
bs = []
iter = AbstractMCMC.steps(MyModel(), MySampler())
iter = AbstractMCMC.steps(MyModel(), MySampler(); a=1.0) # `a` shouldn't do anything
for (count, t) in enumerate(iter)
if count >= 1000
break
end
# don't save missing values
t.a === missing && continue
push!(as, t.a)
push!(bs, t.b)
end
@test length(as) == length(bs) == 998
@test mean(as) ≈ 0.5 atol = 2e-2
@test var(as) ≈ 1 / 12 atol = 5e-3
@test mean(bs) ≈ 0.0 atol = 5e-2
@test var(bs) ≈ 1 atol = 5e-2
@test Base.IteratorSize(iter) == Base.IsInfinite()
@test Base.IteratorEltype(iter) == Base.EltypeUnknown()
end
@testset "Discard initial samples" begin
# Create a chain of `N` samples after discarding some initial samples.
Random.seed!(1234)
N = 50
discard_initial = 10
iter = AbstractMCMC.steps(MyModel(), MySampler(); discard_initial=discard_initial)
as = []
bs = []
for t in Iterators.take(iter, N)
push!(as, t.a)
push!(bs, t.b)
end
# Repeat sampling with `sample`.
Random.seed!(1234)
chain = sample(
MyModel(), MySampler(), N; discard_initial=discard_initial, progress=false
)
@test all(as[i] == chain[i].a for i in 1:N)
@test all(bs[i] == chain[i].b for i in 1:N)
end
@testset "Thin chain by a factor of `thinning`" begin
# Create a thinned chain with a thinning factor of `thinning`.
Random.seed!(1234)
N = 50
thinning = 3
iter = AbstractMCMC.steps(MyModel(), MySampler(); thinning=thinning)
as = []
bs = []
for t in Iterators.take(iter, N)
push!(as, t.a)
push!(bs, t.b)
end
# Repeat sampling with `sample`.
Random.seed!(1234)
chain = sample(MyModel(), MySampler(), N; thinning=thinning, progress=false)
@test as[1] === chain[1].a === missing
@test all(as[i] == chain[i].a for i in 2:N)
@test all(bs[i] == chain[i].b for i in 1:N)
end
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | test/transducer.jl | code | 3308 | @testset "transducer.jl" begin
Random.seed!(1234)
@testset "Basic sampling" begin
N = 1_000
local chain
Logging.with_logger(TerminalLogger()) do
xf = AbstractMCMC.Sample(MyModel(), MySampler(); sleepy=true, logger=true)
chain = collect(xf(withprogress(1:N; interval=1e-3)))
end
# test output type and size
@test chain isa Vector{<:MySample}
@test length(chain) == N
# test some statistical properties
tail_chain = @view chain[2:end]
@test mean(x.a for x in tail_chain) ≈ 0.5 atol = 6e-2
@test var(x.a for x in tail_chain) ≈ 1 / 12 atol = 5e-3
@test mean(x.b for x in tail_chain) ≈ 0.0 atol = 5e-2
@test var(x.b for x in tail_chain) ≈ 1 atol = 6e-2
end
@testset "drop" begin
xf = AbstractMCMC.Sample(MyModel(), MySampler())
chain = collect(Drop(1)(xf(1:10)))
@test chain isa Vector{MySample{Float64,Float64}}
@test length(chain) == 9
end
# Reproduce iterator example
@testset "iterator example" begin
# filter missing values and split transitions
xf = opcompose(
AbstractMCMC.Sample(MyModel(), MySampler()),
OfType(MySample{Float64,Float64}),
Map(x -> (x.a, x.b)),
)
as, bs = foldl(xf, 1:999; init=(Float64[], Float64[])) do (as, bs), (a, b)
push!(as, a)
push!(bs, b)
as, bs
end
@test length(as) == length(bs) == 998
@test mean(as) ≈ 0.5 atol = 2e-2
@test var(as) ≈ 1 / 12 atol = 5e-3
@test mean(bs) ≈ 0.0 atol = 5e-2
@test var(bs) ≈ 1 atol = 5e-2
end
@testset "Discard initial samples" begin
# Create a chain of `N` samples after discarding some initial samples.
Random.seed!(1234)
N = 50
discard_initial = 10
xf = opcompose(
AbstractMCMC.Sample(MyModel(), MySampler(); discard_initial=discard_initial),
Map(x -> (x.a, x.b)),
)
as, bs = foldl(xf, 1:N; init=([], [])) do (as, bs), (a, b)
push!(as, a)
push!(bs, b)
as, bs
end
# Repeat sampling with `sample`.
Random.seed!(1234)
chain = sample(
MyModel(), MySampler(), N; discard_initial=discard_initial, progress=false
)
@test all(as[i] == chain[i].a for i in 1:N)
@test all(bs[i] == chain[i].b for i in 1:N)
end
@testset "Thin chain by a factor of `thinning`" begin
# Create a thinned chain with a thinning factor of `thinning`.
Random.seed!(1234)
N = 50
thinning = 3
xf = opcompose(
AbstractMCMC.Sample(MyModel(), MySampler(); thinning=thinning),
Map(x -> (x.a, x.b)),
)
as, bs = foldl(xf, 1:N; init=([], [])) do (as, bs), (a, b)
push!(as, a)
push!(bs, b)
as, bs
end
# Repeat sampling with `sample`.
Random.seed!(1234)
chain = sample(MyModel(), MySampler(), N; thinning=thinning, progress=false)
@test as[1] === chain[1].a === missing
@test all(as[i] == chain[i].a for i in 2:N)
@test all(bs[i] == chain[i].b for i in 1:N)
end
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | test/utils.jl | code | 2891 | struct MyModel <: AbstractMCMC.AbstractModel end
struct MySample{A,B}
a::A
b::B
end
struct MySampler <: AbstractMCMC.AbstractSampler end
struct AnotherSampler <: AbstractMCMC.AbstractSampler end
struct MyChain{A,B,S} <: AbstractMCMC.AbstractChains
as::Vector{A}
bs::Vector{B}
stats::S
end
MyChain(a, b) = MyChain(a, b, NamedTuple())
function AbstractMCMC.step(
rng::AbstractRNG,
model::MyModel,
sampler::MySampler,
state::Union{Nothing,Integer}=nothing;
loggers=false,
initial_params=nothing,
kwargs...,
)
# sample `a` is missing in the first step if not provided
a, b = if state === nothing && initial_params !== nothing
initial_params.a, initial_params.b
else
(state === nothing ? missing : rand(rng)), randn(rng)
end
loggers && push!(LOGGERS, Logging.current_logger())
_state = state === nothing ? 1 : state + 1
return MySample(a, b), _state
end
function AbstractMCMC.bundle_samples(
samples::Vector{<:MySample},
model::MyModel,
sampler::MySampler,
::Any,
::Type{MyChain};
stats=nothing,
kwargs...,
)
as = [t.a for t in samples]
bs = [t.b for t in samples]
return MyChain(as, bs, stats)
end
function isdone(
rng::AbstractRNG,
model::MyModel,
s::MySampler,
samples,
state,
iteration::Int;
kwargs...,
)
# Calculate the mean of x.b.
bmean = mean(x.b for x in samples)
return abs(bmean) <= 0.001 || iteration > 10_000
end
# Set a default convergence function.
function AbstractMCMC.sample(model, sampler::MySampler; kwargs...)
return sample(Random.default_rng(), model, sampler, isdone; kwargs...)
end
function AbstractMCMC.chainscat(
chain::Union{MyChain,Vector{<:MyChain}}, chains::Union{MyChain,Vector{<:MyChain}}...
)
return vcat(chain, chains...)
end
# Conversion to NamedTuple
Base.convert(::Type{NamedTuple}, x::MySample) = (a=x.a, b=x.b)
# Gaussian log density (without additive constants)
# Without LogDensityProblems.jl interface
mylogdensity(x) = -sum(abs2, x) / 2
# With LogDensityProblems.jl interface
struct MyLogDensity
dim::Int
end
LogDensityProblems.logdensity(::MyLogDensity, x) = mylogdensity(x)
LogDensityProblems.dimension(m::MyLogDensity) = m.dim
function LogDensityProblems.capabilities(::Type{MyLogDensity})
return LogDensityProblems.LogDensityOrder{0}()
end
# Define "sampling"
function AbstractMCMC.step(
rng::AbstractRNG,
model::AbstractMCMC.LogDensityModel{MyLogDensity},
::MySampler,
state::Union{Nothing,Integer}=nothing;
kwargs...,
)
# Sample from multivariate normal distribution
ℓ = model.logdensity
dim = LogDensityProblems.dimension(ℓ)
θ = randn(rng, dim)
logdensity_θ = LogDensityProblems.logdensity(ℓ, θ)
_state = state === nothing ? 1 : state + 1
return MySample(θ, logdensity_θ), _state
end
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | README.md | docs | 1149 | # AbstractMCMC.jl
Abstract types and interfaces for Markov chain Monte Carlo methods.
[](https://turinglang.github.io/AbstractMCMC.jl/stable)
[](https://turinglang.github.io/AbstractMCMC.jl/dev)
[](https://github.com/TuringLang/AbstractMCMC.jl/actions?query=workflow%3ACI+branch%3Amaster)
[](https://github.com/TuringLang/AbstractMCMC.jl/actions?query=workflow%3AIntegrationTest+branch%3Amaster)
[](https://codecov.io/gh/TuringLang/AbstractMCMC.jl)
[](https://coveralls.io/github/TuringLang/AbstractMCMC.jl?branch=master)
[](https://github.com/invenia/BlueStyle)
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | docs/src/api.md | docs | 4119 | # API
AbstractMCMC defines an interface for sampling Markov chains.
## Model
```@docs
AbstractMCMC.AbstractModel
AbstractMCMC.LogDensityModel
```
## Sampler
```@docs
AbstractMCMC.AbstractSampler
```
## Sampling a single chain
```@docs
AbstractMCMC.sample(::AbstractRNG, ::AbstractMCMC.AbstractModel, ::AbstractMCMC.AbstractSampler, ::Any)
AbstractMCMC.sample(::AbstractRNG, ::Any, ::AbstractMCMC.AbstractSampler, ::Any)
```
### Iterator
```@docs
AbstractMCMC.steps(::AbstractRNG, ::AbstractMCMC.AbstractModel, ::AbstractMCMC.AbstractSampler)
AbstractMCMC.steps(::AbstractRNG, ::Any, ::AbstractMCMC.AbstractSampler)
```
### Transducer
```@docs
AbstractMCMC.Sample(::AbstractRNG, ::AbstractMCMC.AbstractModel, ::AbstractMCMC.AbstractSampler)
AbstractMCMC.Sample(::AbstractRNG, ::Any, ::AbstractMCMC.AbstractSampler)
```
## Sampling multiple chains in parallel
```@docs
AbstractMCMC.sample(
::AbstractRNG,
::AbstractMCMC.AbstractModel,
::AbstractMCMC.AbstractSampler,
::AbstractMCMC.AbstractMCMCEnsemble,
::Integer,
::Integer,
)
AbstractMCMC.sample(
::AbstractRNG,
::Any,
::AbstractMCMC.AbstractSampler,
::AbstractMCMC.AbstractMCMCEnsemble,
::Integer,
::Integer,
)
```
Two algorithms are provided for parallel sampling with multiple threads and multiple processes, and one allows for the user to sample multiple chains in serial (no parallelization):
```@docs
AbstractMCMC.MCMCThreads
AbstractMCMC.MCMCDistributed
AbstractMCMC.MCMCSerial
```
## Common keyword arguments
Common keyword arguments for regular and parallel sampling are:
- `progress` (default: `AbstractMCMC.PROGRESS[]` which is `true` initially): toggles progress logging
- `chain_type` (default: `Any`): determines the type of the returned chain
- `callback` (default: `nothing`): if `callback !== nothing`, then
`callback(rng, model, sampler, sample, state, iteration)` is called after every sampling step,
where `sample` is the most recent sample of the Markov chain and `state` and `iteration` are the current state and iteration of the sampler
- `discard_initial` (default: `0`): number of initial samples that are discarded
- `thinning` (default: `1`): factor by which to thin samples.
- `initial_state` (default: `nothing`): if `initial_state !== nothing`, the first call to [`AbstractMCMC.step`](@ref)
is passed `initial_state` as the `state` argument.
!!! info
The common keyword arguments `progress`, `chain_type`, and `callback` are not supported by the iterator [`AbstractMCMC.steps`](@ref) and the transducer [`AbstractMCMC.Sample`](@ref).
There is no "official" way for providing initial parameter values yet.
However, multiple packages such as [EllipticalSliceSampling.jl](https://github.com/TuringLang/EllipticalSliceSampling.jl) and [AdvancedMH.jl](https://github.com/TuringLang/AdvancedMH.jl) support an `initial_params` keyword argument for setting the initial values when sampling a single chain.
To ensure that sampling multiple chains "just works" when sampling of a single chain is implemented, [we decided to support `initial_params` in the default implementations of the ensemble methods](https://github.com/TuringLang/AbstractMCMC.jl/pull/94):
- `initial_params` (default: `nothing`): if `initial_params isa AbstractArray`, then the `i`th element of `initial_params` is used as initial parameters of the `i`th chain. If one wants to use the same initial parameters `x` for every chain, one can specify e.g. `initial_params = FillArrays.Fill(x, N)`.
Progress logging can be enabled and disabled globally with `AbstractMCMC.setprogress!(progress)`.
```@docs
AbstractMCMC.setprogress!
```
## Chains
The `chain_type` keyword argument allows to set the type of the returned chain. A common
choice is to return chains of type `Chains` from [MCMCChains.jl](https://github.com/TuringLang/MCMCChains.jl).
AbstractMCMC defines the abstract type `AbstractChains` for Markov chains.
```@docs
AbstractMCMC.AbstractChains
```
For chains of this type, AbstractMCMC defines the following two methods.
```@docs
AbstractMCMC.chainscat
AbstractMCMC.chainsstack
```
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | docs/src/design.md | docs | 4016 | # Design
This page explains the default implementations and design choices of AbstractMCMC.
It is not intended for users but for developers that want to implement the AbstractMCMC
interface for Markov chain Monte Carlo sampling. The user-facing API is explained in
[API](@ref).
## Overview
AbstractMCMC provides a default implementation of the user-facing interface described
in [API](@ref). You can completely neglect these and define your own implementation of the
interface. However, as described below, in most use cases the default implementation
allows you to obtain support of parallel sampling, progress logging, callbacks, iterators,
and transducers for free by just defining the sampling step of your inference algorithm,
drastically reducing the amount of code you have to write. In general, the docstrings
of the functions described below might be helpful if you intend to make use of the default
implementations.
## Basic structure
The simplified structure for regular sampling (the actual implementation contains
some additional error checks and support for progress logging and callbacks) is
```julia
StatsBase.sample(
rng::Random.AbstractRNG,
model::AbstractMCMC.AbstractModel,
sampler::AbstractMCMC.AbstractSampler,
nsamples::Integer;
chain_type = ::Type{Any},
kwargs...
)
# Obtain the initial sample and state.
sample, state = AbstractMCMC.step(rng, model, sampler; kwargs...)
# Save the sample.
samples = AbstractMCMC.samples(sample, model, sampler, N; kwargs...)
samples = AbstractMCMC.save!!(samples, sample, 1, model, sampler, N; kwargs...)
# Step through the sampler.
for i in 2:N
# Obtain the next sample and state.
sample, state = AbstractMCMC.step(rng, model, sampler, state; kwargs...)
# Save the sample.
samples = AbstractMCMC.save!!(samples, sample, i, model, sampler, N; kwargs...)
end
return AbstractMCMC.bundle_samples(samples, model, sampler, state, chain_type; kwargs...)
end
```
All other default implementations make use of the same structure and in particular
call the same methods.
## Sampling step
The only method for which no default implementation is provided (and hence which
downstream packages *have* to implement) is [`AbstractMCMC.step`](@ref). It defines
the sampling step of the inference method.
```@docs
AbstractMCMC.step
```
## Collecting samples
!!! note
This section does not apply to the iterator and transducer interface.
After the initial sample is obtained, the default implementations for regular and parallel sampling
(not for the iterator and the transducer since it is not needed there) create a container for all
samples (the initial one and all subsequent samples) using `AbstractMCMC.samples`.
```@docs
AbstractMCMC.samples
```
In each step, the sample is saved in the container by `AbstractMCMC.save!!`. The notation `!!`
follows the convention of the package [BangBang.jl](https://github.com/JuliaFolds/BangBang.jl)
which is used in the default implementation of `AbstractMCMC.save!!`. It indicates that the
sample is pushed to the container but a "widening" fallback is used if the container type
does not allow to save the sample. Therefore `AbstractMCMC.save!!` *always has* to return the container.
```@docs
AbstractMCMC.save!!
```
For most use cases the default implementation of `AbstractMCMC.samples` and `AbstractMCMC.save!!`
should work out of the box and hence need not to be overloaded in downstream code.
## Creating chains
!!! note
This section does not apply to the iterator and transducer interface.
At the end of the sampling procedure for regular and paralle sampling we transform
the collection of samples to the desired output type by calling `AbstractMCMC.bundle_samples`.
```@docs
AbstractMCMC.bundle_samples
```
The default implementation should be fine in most use cases, but downstream packages
could, e.g., save the final state of the sampler as well if they overload
`AbstractMCMC.bundle_samples`.
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 5.2.0 | b0489adc45a7c8cf0d8e2ddf764f89c1c3decebd | docs/src/index.md | docs | 604 | # AbstractMCMC.jl
*Abstract types and interfaces for Markov chain Monte Carlo methods.*
AbstractMCMC defines an interface for sampling and combining Markov chains.
It comes with a default sampling algorithm that provides support of progress
bars, parallel sampling (multithreaded and multicore), and user-provided callbacks
out of the box. Typically developers only have to define the sampling step
of their inference method in an iterator-like fashion to make use of this
functionality. Additionally, the package defines an iterator and a transducer
for sampling Markov chains based on the interface.
| AbstractMCMC | https://github.com/TuringLang/AbstractMCMC.jl.git |
[
"MIT"
] | 0.1.3 | d95b8e6e93a173b71406d24fce591fa44a8cf3f9 | docs/make.jl | code | 519 | using StableMap
using Documenter
DocMeta.setdocmeta!(StableMap, :DocTestSetup, :(using StableMap); recursive=true)
makedocs(;
modules=[StableMap],
authors="Chris Elrod <elrodc@gmail.com> and contributors",
repo="https://github.com/chriselrod/StableMap.jl/blob/{commit}{path}#{line}",
sitename="StableMap.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
edit_link="main",
assets=String[],
),
pages=[
"Home" => "index.md",
],
)
| StableMap | https://github.com/chriselrod/StableMap.jl.git |
[
"MIT"
] | 0.1.3 | d95b8e6e93a173b71406d24fce591fa44a8cf3f9 | src/StableMap.jl | code | 3215 | module StableMap
using ArrayInterface
using LinearAlgebra
export stable_map, stable_map!
function stable_map!(f, dst::AbstractArray, arg0)
N = length(dst)
eachindex(arg0) == Base.oneto(N) ||
throw(ArgumentError("All args must have same axes."))
@inbounds for i = 1:N
dst[i] = f(arg0[i])
end
return dst
end
function stable_map!(
f,
dst::AbstractArray{T},
arg0,
args::Vararg{Any,K}
) where {K,T}
N = length(dst)
all(==(Base.oneto(N)), map(eachindex, (arg0, args...))) ||
throw(ArgumentError("All args must have same axes."))
@inbounds for i = 1:N
# fᵢ = f(map(Base.Fix2(Base.unsafe_getindex, i), args)...)
# dst[i] = convert(T, fᵢ)::T
dst[i] = f(arg0[i], map(Base.Fix2(Base.unsafe_getindex, i), args)...)
end
return dst
end
function stable_map!(f, dst::AbstractArray)
N = length(dst)
@inbounds for i = 1:N
# fᵢ = f(map(Base.Fix2(Base.unsafe_getindex, i), args)...)
# dst[i] = convert(T, fᵢ)::T
dst[i] = f()
end
return dst
end
function narrowing_map!(
f,
dst::AbstractArray{T},
start::Int,
args::Vararg{Any,K}
) where {K,T}
N = length(dst)
all(==(Base.oneto(N)), map(eachindex, args)) ||
throw(ArgumentError("All args must have same axes."))
@inbounds for i = start:N
xi = f(map(Base.Fix2(Base.unsafe_getindex, i), args)...)
if xi isa T
dst[i] = xi
else
Ti = typeof(xi)
PT = promote_type(Ti, T)
if PT === T
dst[i] = convert(T, xi)
elseif Base.isconcretetype(PT)
dst_promote = Array{PT}(undef, size(dst))
copyto!(
view(dst_promote, Base.OneTo(i - 1)),
view(dst, Base.OneTo(i - 1))
)
dst_promote[i] = convert(PT, xi)::PT
return narrowing_map!(f, dst_promote, i + 1, args...)
else
dst_union = Array{Union{T,Ti}}(undef, size(dst))
copyto!(
view(dst_union, Base.OneTo(i - 1)),
view(dst, Base.OneTo(i - 1))
)
dst_union[i] = xi
return narrowing_map!(f, dst_union, i + 1, args...)
end
end
end
return dst
end
isconcreteunion(TU) =
if TU isa Union
isconcretetype(TU.a) && isconcreteunion(TU.b)
else
isconcretetype(TU)
end
function promote_return(f::F, args...) where {F}
T = Base.promote_op(f, map(eltype, args)...)
Base.isconcretetype(T) && return T
T isa Union || return nothing
TU = Base.promote_union(T)
Base.isconcretetype(TU) && return TU
isconcreteunion(TU) && return TU
nothing
end
function stable_map(f::F, args::Vararg{AbstractArray,K}) where {K,F}
# assume specialized implementation
all(ArrayInterface.ismutable, args) || return map(f, args...)
T = promote_return(f, args...)
first_arg = first(args)
T === nothing ||
return stable_map!(f, Array{T}(undef, size(first_arg)), args...)
x = f(map(first, args)...)
dst = similar(first_arg, typeof(x))
@inbounds dst[1] = x
narrowing_map!(f, dst, 2, args...)
end
function stable_map(f, A::Diagonal{T}) where {T}
B = Matrix{promote_type(T, Float32)}(undef, size(A))
@inbounds for i in eachindex(A)
B[i] = f(A[i])
end
return B
end
@inline stable_map(f::F, arg1::A, args::Vararg{A,K}) where {F,K,A} =
map(f, arg1, args...)
end
| StableMap | https://github.com/chriselrod/StableMap.jl.git |
[
"MIT"
] | 0.1.3 | d95b8e6e93a173b71406d24fce591fa44a8cf3f9 | test/runtests.jl | code | 507 | using StableMap
using Test
using ForwardDiff
@testset "StableMap.jl" begin
x = rand(10);
@test stable_map(exp, x) ≈ map(exp, x)
unstablemax(x,y) = Base.inferencebarrier(x > y ? x : y)
y = rand(-10:10, 10);
res = stable_map(unstablemax, x, y)
@test res isa Vector{Float64}
@test res == map(unstablemax, x, y)
f(x) = Base.inferencebarrier(x > 1 ? x : 1.0)
@test stable_map(f, [ForwardDiff.Dual(0f0,1f0), ForwardDiff.Dual(2f0,1f0)]) isa Vector{ForwardDiff.Dual{Nothing,Float64,1}}
end
| StableMap | https://github.com/chriselrod/StableMap.jl.git |
[
"MIT"
] | 0.1.3 | d95b8e6e93a173b71406d24fce591fa44a8cf3f9 | README.md | docs | 4344 | # StableMap
[](https://github.com/chriselrod/StableMap.jl/actions/workflows/CI.yml?query=branch%3Amain)
[](https://codecov.io/gh/chriselrod/StableMap.jl)
The map that preserves the relative order of inputs mapped to outputs.
So do other maps, of course.
StableMap tries to return vectors that are as concretely typed as possible.
For example:
```julia
julia> using StableMap, ForwardDiff, BenchmarkTools
[ Info: Precompiling StableMap [626594ce-0aac-4e81-a7f6-bc4bb5ff97e9]
julia> f(x) = x > 1 ? x : 1.0
f (generic function with 1 method)
julia> g(x) = Base.inferencebarrier(x > 1 ? x : 1.0)
g (generic function with 1 method)
julia> h(x) = Base.inferencebarrier(x)
h (generic function with 1 method)
julia> x = [ForwardDiff.Dual(0f0,1f0), ForwardDiff.Dual(2f0,1f0)];
julia> y = [ForwardDiff.Dual(2f0,1f0), ForwardDiff.Dual(0f0,1f0)];
julia> @btime map(f, $x)
208.010 ns (4 allocations: 176 bytes)
2-element Vector{Real}:
1.0
Dual{Nothing}(2.0,1.0)
julia> @btime stable_map(f, $x)
93.329 ns (1 allocation: 96 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float64, 1}}:
Dual{Nothing}(1.0,0.0)
Dual{Nothing}(2.0,1.0)
julia> @btime map(f, $y)
210.378 ns (4 allocations: 176 bytes)
2-element Vector{Real}:
Dual{Nothing}(2.0,1.0)
1.0
julia> @btime stable_map(f, $y)
94.547 ns (1 allocation: 96 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float64, 1}}:
Dual{Nothing}(2.0,1.0)
Dual{Nothing}(1.0,0.0)
julia> @btime map(g, $x)
890.247 ns (10 allocations: 272 bytes)
2-element Vector{Real}:
1.0
Dual{Nothing}(2.0,1.0)
julia> @btime stable_map(g, $x)
3.221 μs (18 allocations: 800 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float64, 1}}:
Dual{Nothing}(1.0,0.0)
Dual{Nothing}(2.0,1.0)
julia> @btime map(g, $y)
866.372 ns (10 allocations: 272 bytes)
2-element Vector{Real}:
Dual{Nothing}(2.0,1.0)
1.0
julia> @btime stable_map(g, $y)
3.357 μs (18 allocations: 800 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float64, 1}}:
Dual{Nothing}(2.0,1.0)
Dual{Nothing}(1.0,0.0)
julia> @btime map(h, $x)
531.503 ns (5 allocations: 144 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float32, 1}}:
Dual{Nothing}(0.0,1.0)
Dual{Nothing}(2.0,1.0)
julia> @btime stable_map(h, $x)
810.656 ns (4 allocations: 128 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float32, 1}}:
Dual{Nothing}(0.0,1.0)
Dual{Nothing}(2.0,1.0)
julia> @btime map(h, $y)
535.145 ns (5 allocations: 144 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float32, 1}}:
Dual{Nothing}(2.0,1.0)
Dual{Nothing}(0.0,1.0)
julia> @btime stable_map(h, $y)
816.471 ns (4 allocations: 128 bytes)
2-element Vector{ForwardDiff.Dual{Nothing, Float32, 1}}:
Dual{Nothing}(2.0,1.0)
Dual{Nothing}(0.0,1.0)
```
It can be faster at handling small unions than `Base.map`, but is currently slower for functions than return `Any`. However, in both cases, it has the benefit of returning as concretely-typed arrays as possible.
It will try to promote returned objects to the same type, and if this is not possible, it will return a small union.
```julia
julia> m(x) = x > 1.0 ? x : [x]
m (generic function with 1 method)
julia> @btime map(m, $x)
257.890 ns (4 allocations: 208 bytes)
2-element Vector{Any}:
ForwardDiff.Dual{Nothing, Float32, 1}[Dual{Nothing}(0.0,1.0)]
Dual{Nothing}(2.0,1.0)
julia> @btime stable_map(m, $x)
194.158 ns (3 allocations: 144 bytes)
2-element Vector{Union{ForwardDiff.Dual{Nothing, Float32, 1}, Vector{ForwardDiff.Dual{Nothing, Float32, 1}}}}:
ForwardDiff.Dual{Nothing, Float32, 1}[Dual{Nothing}(0.0,1.0)]
Dual{Nothing}(2.0,1.0)
julia> @btime map(m, $y)
260.979 ns (4 allocations: 224 bytes)
2-element Vector{Any}:
Dual{Nothing}(2.0,1.0)
ForwardDiff.Dual{Nothing, Float32, 1}[Dual{Nothing}(0.0,1.0)]
julia> @btime stable_map(m, $y)
190.128 ns (3 allocations: 144 bytes)
2-element Vector{Union{ForwardDiff.Dual{Nothing, Float32, 1}, Vector{ForwardDiff.Dual{Nothing, Float32, 1}}}}:
Dual{Nothing}(2.0,1.0)
ForwardDiff.Dual{Nothing, Float32, 1}[Dual{Nothing}(0.0,1.0)]
```
| StableMap | https://github.com/chriselrod/StableMap.jl.git |
[
"MIT"
] | 0.1.3 | d95b8e6e93a173b71406d24fce591fa44a8cf3f9 | docs/src/index.md | docs | 183 | ```@meta
CurrentModule = StableMap
```
# StableMap
Documentation for [StableMap](https://github.com/chriselrod/StableMap.jl).
```@index
```
```@autodocs
Modules = [StableMap]
```
| StableMap | https://github.com/chriselrod/StableMap.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | docs/make.jl | code | 614 | using Documenter, KrylovPreconditioners
makedocs(
modules = [KrylovPreconditioners],
doctest = true,
linkcheck = true,
format = Documenter.HTML(assets = ["assets/style.css"],
ansicolor = true,
prettyurls = get(ENV, "CI", nothing) == "true",
collapselevel = 1),
sitename = "KrylovPreconditioners.jl",
pages = ["Home" => "index.md",
"Reference" => "reference.md"
]
)
deploydocs(
repo = "github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git",
push_preview = true,
devbranch = "main",
)
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/KrylovPreconditionersAMDGPUExt.jl | code | 510 | module KrylovPreconditionersAMDGPUExt
using LinearAlgebra
using SparseArrays
using AMDGPU
using AMDGPU.rocSPARSE
using LinearAlgebra: checksquare, BlasReal, BlasFloat
import LinearAlgebra: ldiv!, mul!
import Base: size, eltype, unsafe_convert
using KrylovPreconditioners
const KP = KrylovPreconditioners
using KernelAbstractions
const KA = KernelAbstractions
include("AMDGPU/ic0.jl")
include("AMDGPU/ilu0.jl")
include("AMDGPU/blockjacobi.jl")
include("AMDGPU/operators.jl")
include("AMDGPU/scaling.jl")
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/KrylovPreconditionersCUDAExt.jl | code | 493 | module KrylovPreconditionersCUDAExt
using LinearAlgebra
using SparseArrays
using CUDA
using CUDA.CUSPARSE
using LinearAlgebra: checksquare, BlasReal, BlasFloat
import LinearAlgebra: ldiv!, mul!
import Base: size, eltype, unsafe_convert
using KrylovPreconditioners
const KP = KrylovPreconditioners
using KernelAbstractions
const KA = KernelAbstractions
include("CUDA/ic0.jl")
include("CUDA/ilu0.jl")
include("CUDA/blockjacobi.jl")
include("CUDA/operators.jl")
include("CUDA/scaling.jl")
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/AMDGPU/blockjacobi.jl | code | 1687 | KP.BlockJacobiPreconditioner(J::rocSPARSE.ROCSparseMatrixCSR; options...) = BlockJacobiPreconditioner(SparseMatrixCSC(J); options...)
function KP.create_blocklist(cublocks::ROCArray, npart)
blocklist = Array{ROCArray{Float64,2}}(undef, npart)
for b in 1:npart
blocklist[b] = ROCMatrix{Float64}(undef, size(cublocks,1), size(cublocks,2))
end
return blocklist
end
function _update_gpu(p, j_rowptr, j_colval, j_nzval, device::ROCBackend)
nblocks = p.nblocks
blocksize = p.blocksize
fillblock_gpu_kernel! = KP._fillblock_gpu!(device)
# Fill Block Jacobi" begin
fillblock_gpu_kernel!(
p.cublocks, size(p.id,1),
p.cupartitions, p.cumap,
j_rowptr, j_colval, j_nzval,
p.cupart, p.culpartitions, p.id,
ndrange=(nblocks, blocksize),
)
KA.synchronize(device)
# Invert blocks begin
for b in 1:nblocks
p.blocklist[b] .= p.cublocks[:,:,b]
end
AMDGPU.@sync pivot, info = AMDGPU.rocSOLVER.getrf_batched!(p.blocklist)
AMDGPU.@sync pivot, info, p.blocklist = AMDGPU.rocSOLVER.getri_batched!(p.blocklist, pivot)
for b in 1:nblocks
p.cublocks[:,:,b] .= p.blocklist[b]
end
return
end
"""
function update!(J::ROCSparseMatrixCSR, p)
Update the preconditioner `p` from the sparse Jacobian `J` in CSR format for ROCm
1) The dense blocks `cuJs` are filled from the sparse Jacobian `J`
2) To a batch inversion of the dense blocks using CUBLAS
3) Extract the preconditioner matrix `p.P` from the dense blocks `cuJs`
"""
function KP.update!(p::BlockJacobiPreconditioner, J::rocSPARSE.ROCSparseMatrixCSR)
_update_gpu(p, J.rowPtr, J.colVal, J.nzVal, p.device)
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/AMDGPU/ic0.jl | code | 4395 | mutable struct AMD_IC0{SM} <: AbstractKrylovPreconditioner
n::Int
desc::rocSPARSE.ROCMatrixDescriptor
buffer::ROCVector{UInt8}
info::rocSPARSE.MatInfo
timer_update::Float64
P::SM
end
for (bname, aname, sname, T) in ((:rocsparse_scsric0_buffer_size, :rocsparse_scsric0_analysis, :rocsparse_scsric0, :Float32),
(:rocsparse_dcsric0_buffer_size, :rocsparse_dcsric0_analysis, :rocsparse_dcsric0, :Float64),
(:rocsparse_ccsric0_buffer_size, :rocsparse_ccsric0_analysis, :rocsparse_ccsric0, :ComplexF32),
(:rocsparse_zcsric0_buffer_size, :rocsparse_zcsric0_analysis, :rocsparse_zcsric0, :ComplexF64))
@eval begin
function KP.kp_ic0(A::ROCSparseMatrixCSR{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = rocSPARSE.ROCMatrixDescriptor('G', 'L', 'N', 'O')
info = rocSPARSE.MatInfo()
buffer_size = Ref{Csize_t}()
rocSPARSE.$bname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, buffer_size)
buffer = ROCVector{UInt8}(undef, buffer_size[])
rocSPARSE.$aname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info,
rocSPARSE.rocsparse_analysis_policy_force, rocSPARSE.rocsparse_solve_policy_auto, buffer)
posit = Ref{Cint}(1)
rocSPARSE.rocsparse_csric0_zero_pivot(rocSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
rocSPARSE.$sname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, rocSPARSE.rocsparse_solve_policy_auto, buffer)
return AMD_IC0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::AMD_IC0{ROCSparseMatrixCSR{$T,Cint}}, A::ROCSparseMatrixCSR{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
rocSPARSE.$sname(rocSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.rowPtr, p.P.colVal, p.info, rocSPARSE.rocsparse_solve_policy_auto, p.buffer)
return p
end
function KP.kp_ic0(A::ROCSparseMatrixCSC{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = rocSPARSE.ROCMatrixDescriptor('G', 'L', 'N', 'O')
info = rocSPARSE.MatInfo()
buffer_size = Ref{Csize_t}()
rocSPARSE.$bname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, buffer_size)
buffer = ROCVector{UInt8}(undef, buffer_size[])
rocSPARSE.$aname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info,
rocSPARSE.rocsparse_analysis_policy_force, rocSPARSE.rocsparse_solve_policy_auto, buffer)
posit = Ref{Cint}(1)
rocSPARSE.rocsparse_csric0_zero_pivot(rocSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
rocSPARSE.$sname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, rocSPARSE.rocsparse_solve_policy_auto, buffer)
return AMD_IC0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::AMD_IC0{ROCSparseMatrixCSC{$T,Cint}}, A::ROCSparseMatrixCSC{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
rocSPARSE.$sname(rocSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.colPtr, p.P.rowVal, p.info, rocSPARSE.rocsparse_solve_policy_auto, p.buffer)
return p
end
end
end
for ArrayType in (:(ROCVector{T}), :(ROCMatrix{T}))
@eval begin
function ldiv!(ic::AMD_IC0{ROCSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
ldiv!(LowerTriangular(ic.P), x) # Forward substitution with L
ldiv!(LowerTriangular(ic.P)', x) # Backward substitution with Lᴴ
return x
end
function ldiv!(y::$ArrayType, ic::AMD_IC0{ROCSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
ic.timer_update += @elapsed begin
copyto!(y, x)
ldiv!(ic, y)
end
return y
end
function ldiv!(ic::AMD_IC0{ROCSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasReal
ldiv!(UpperTriangular(ic.P)', x) # Forward substitution with L
ldiv!(UpperTriangular(ic.P), x) # Backward substitution with Lᴴ
return x
end
function ldiv!(y::$ArrayType, ic::AMD_IC0{ROCSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasReal
ic.timer_update += @elapsed begin
copyto!(y, x)
ldiv!(ic, y)
end
return y
end
end
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/AMDGPU/ilu0.jl | code | 4396 | mutable struct AMD_ILU0{SM} <: AbstractKrylovPreconditioner
n::Int
desc::rocSPARSE.ROCMatrixDescriptor
buffer::ROCVector{UInt8}
info::rocSPARSE.MatInfo
timer_update::Float64
P::SM
end
for (bname, aname, sname, T) in ((:rocsparse_scsrilu0_buffer_size, :rocsparse_scsrilu0_analysis, :rocsparse_scsrilu0, :Float32),
(:rocsparse_dcsrilu0_buffer_size, :rocsparse_dcsrilu0_analysis, :rocsparse_dcsrilu0, :Float64),
(:rocsparse_ccsrilu0_buffer_size, :rocsparse_ccsrilu0_analysis, :rocsparse_ccsrilu0, :ComplexF32),
(:rocsparse_zcsrilu0_buffer_size, :rocsparse_zcsrilu0_analysis, :rocsparse_zcsrilu0, :ComplexF64))
@eval begin
function KP.kp_ilu0(A::ROCSparseMatrixCSR{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = rocSPARSE.ROCMatrixDescriptor('G', 'L', 'N', 'O')
info = rocSPARSE.MatInfo()
buffer_size = Ref{Csize_t}()
rocSPARSE.$bname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, buffer_size)
buffer = ROCVector{UInt8}(undef, buffer_size[])
rocSPARSE.$aname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, rocSPARSE.rocsparse_analysis_policy_force, rocSPARSE.rocsparse_solve_policy_auto, buffer)
posit = Ref{Cint}(1)
rocSPARSE.rocsparse_csrilu0_zero_pivot(rocSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
rocSPARSE.$sname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, rocSPARSE.rocsparse_solve_policy_auto, buffer)
return AMD_ILU0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::AMD_ILU0{ROCSparseMatrixCSR{$T,Cint}}, A::ROCSparseMatrixCSR{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
rocSPARSE.$sname(rocSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.rowPtr, p.P.colVal, p.info, rocSPARSE.rocsparse_solve_policy_auto, p.buffer)
return p
end
function KP.kp_ilu0(A::ROCSparseMatrixCSC{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = rocSPARSE.ROCMatrixDescriptor('G', 'L', 'N', 'O')
info = rocSPARSE.MatInfo()
buffer_size = Ref{Csize_t}()
rocSPARSE.$bname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, buffer_size)
buffer = ROCVector{UInt8}(undef, buffer_size[])
rocSPARSE.$aname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, rocSPARSE.rocsparse_analysis_policy_force, rocSPARSE.rocsparse_solve_policy_auto, buffer)
posit = Ref{Cint}(1)
rocSPARSE.rocsparse_csrilu0_zero_pivot(rocSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
rocSPARSE.$sname(rocSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, rocSPARSE.rocsparse_solve_policy_auto, buffer)
return AMD_ILU0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::AMD_ILU0{ROCSparseMatrixCSC{$T,Cint}}, A::ROCSparseMatrixCSC{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
rocSPARSE.$sname(rocSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.colPtr, p.P.rowVal, p.info, rocSPARSE.rocsparse_solve_policy_auto, p.buffer)
return p
end
end
end
for ArrayType in (:(ROCVector{T}), :(ROCMatrix{T}))
@eval begin
function ldiv!(ilu::AMD_ILU0{ROCSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
ldiv!(UnitLowerTriangular(ilu.P), x) # Forward substitution with L
ldiv!(UpperTriangular(ilu.P), x) # Backward substitution with U
return x
end
function ldiv!(y::$ArrayType, ilu::AMD_ILU0{ROCSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
copyto!(y, x)
ilu.timer_update += @elapsed begin
ldiv!(ilu, y)
end
return y
end
function ldiv!(ilu::AMD_ILU0{ROCSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasReal
ldiv!(LowerTriangular(ilu.P), x) # Forward substitution with L
ldiv!(UnitUpperTriangular(ilu.P), x) # Backward substitution with U
return x
end
function ldiv!(y::$ArrayType, ilu::AMD_ILU0{ROCSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasReal
copyto!(y, x)
ilu.timer_update += @elapsed begin
ldiv!(ilu, y)
end
return y
end
end
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/AMDGPU/operators.jl | code | 9830 | using AMDGPU.HIP
mutable struct AMD_KrylovOperator{T} <: AbstractKrylovOperator{T}
type::Type{T}
m::Int
n::Int
nrhs::Int
transa::Char
descA::rocSPARSE.ROCSparseMatrixDescriptor
buffer_size::Ref{Csize_t}
buffer::ROCVector{UInt8}
end
eltype(A::AMD_KrylovOperator{T}) where T = T
size(A::AMD_KrylovOperator) = (A.m, A.n)
for (SparseMatrixType, BlasType) in ((:(ROCSparseMatrixCSR{T}), :BlasFloat),
(:(ROCSparseMatrixCSC{T}), :BlasFloat),
(:(ROCSparseMatrixCOO{T}), :BlasFloat))
@eval begin
function KP.KrylovOperator(A::$SparseMatrixType; nrhs::Int=1, transa::Char='N') where T <: $BlasType
m,n = size(A)
alpha = Ref{T}(one(T))
beta = Ref{T}(zero(T))
descA = rocSPARSE.ROCSparseMatrixDescriptor(A, 'O')
if nrhs == 1
descX = rocSPARSE.ROCDenseVectorDescriptor(T, n)
descY = rocSPARSE.ROCDenseVectorDescriptor(T, m)
algo = rocSPARSE.rocSPARSE.rocsparse_spmv_alg_default
buffer_size = Ref{Csize_t}()
if HIP.runtime_version() ≥ v"6-"
rocSPARSE.rocsparse_spmv(rocSPARSE.handle(), transa, alpha, descA, descX,
beta, descY, T, algo, rocSPARSE.rocsparse_spmv_stage_buffer_size,
buffer_size, C_NULL)
else
rocSPARSE.rocsparse_spmv(rocSPARSE.handle(), transa, alpha, descA, descX,
beta, descY, T, algo, buffer_size, C_NULL)
end
buffer = ROCVector{UInt8}(undef, buffer_size[])
if HIP.runtime_version() ≥ v"6-"
rocSPARSE.rocsparse_spmv(rocSPARSE.handle(), transa, alpha, descA, descX,
beta, descY, T, algo, rocSPARSE.rocsparse_spmv_stage_preprocess,
buffer_size, buffer)
end
return AMD_KrylovOperator{T}(T, m, n, nrhs, transa, descA, buffer_size, buffer)
else
descX = rocSPARSE.ROCDenseMatrixDescriptor(T, n, nrhs)
descY = rocSPARSE.ROCDenseMatrixDescriptor(T, m, nrhs)
algo = rocSPARSE.rocsparse_spmm_alg_default
buffer_size = Ref{Csize_t}()
transb = 'N'
rocSPARSE.rocsparse_spmm(rocSPARSE.handle(), transa, 'N', alpha, descA, descX, beta, descY, T,
algo, rocSPARSE.rocsparse_spmm_stage_buffer_size, buffer_size, C_NULL)
buffer = ROCVector{UInt8}(undef, buffer_size[])
rocSPARSE.rocsparse_spmm(rocSPARSE.handle(), transa, 'N', alpha, descA, descX, beta, descY, T,
algo, rocSPARSE.rocsparse_spmm_stage_preprocess, buffer_size, buffer)
return AMD_KrylovOperator{T}(T, m, n, nrhs, transa, descA, buffer_size, buffer)
end
end
function KP.update!(A::AMD_KrylovOperator{T}, B::$SparseMatrixType) where T <: $BlasFloat
descB = rocSPARSE.ROCSparseMatrixDescriptor(B, 'O')
A.descA = descB
return A
end
end
end
function LinearAlgebra.mul!(y::ROCVector{T}, A::AMD_KrylovOperator{T}, x::ROCVector{T}) where T <: BlasFloat
(length(y) != A.m) && throw(DimensionMismatch("length(y) != A.m"))
(length(x) != A.n) && throw(DimensionMismatch("length(x) != A.n"))
(A.nrhs == 1) || throw(DimensionMismatch("A.nrhs != 1"))
descY = rocSPARSE.ROCDenseVectorDescriptor(y)
descX = rocSPARSE.ROCDenseVectorDescriptor(x)
algo = rocSPARSE.rocsparse_spmv_alg_default
alpha = Ref{T}(one(T))
beta = Ref{T}(zero(T))
if HIP.runtime_version() ≥ v"6-"
rocSPARSE.rocsparse_spmv(rocSPARSE.handle(), A.transa, alpha, A.descA, descX,
beta, descY, T, algo, rocSPARSE.rocsparse_spmv_stage_compute,
A.buffer_size, A.buffer)
else
rocSPARSE.rocsparse_spmv(rocSPARSE.handle(), A.transa, alpha, A.descA, descX,
beta, descY, T, algo, A.buffer_size, A.buffer)
end
end
function LinearAlgebra.mul!(Y::ROCMatrix{T}, A::AMD_KrylovOperator{T}, X::ROCMatrix{T}) where T <: BlasFloat
mY, nY = size(Y)
mX, nX = size(X)
(mY != A.m) && throw(DimensionMismatch("mY != A.m"))
(mX != A.n) && throw(DimensionMismatch("mX != A.n"))
(nY == nX == A.nrhs) || throw(DimensionMismatch("nY != A.nrhs or nX != A.nrhs"))
descY = rocSPARSE.ROCDenseMatrixDescriptor(Y)
descX = rocSPARSE.ROCDenseMatrixDescriptor(X)
algo = rocSPARSE.rocsparse_spmm_alg_default
alpha = Ref{T}(one(T))
beta = Ref{T}(zero(T))
rocSPARSE.rocsparse_spmm(rocSPARSE.handle(), A.transa, 'N', alpha, A.descA, descX,
beta, descY, T, algo, rocSPARSE.rocsparse_spmm_stage_compute, A.buffer_size, A.buffer)
end
mutable struct AMD_TriangularOperator{T} <: AbstractTriangularOperator{T}
type::Type{T}
m::Int
n::Int
nrhs::Int
transa::Char
descA::rocSPARSE.ROCSparseMatrixDescriptor
buffer_size::Ref{Csize_t}
buffer::ROCVector{UInt8}
end
eltype(A::AMD_TriangularOperator{T}) where T = T
size(A::AMD_TriangularOperator) = (A.m, A.n)
for (SparseMatrixType, BlasType) in ((:(ROCSparseMatrixCSR{T}), :BlasFloat),
(:(ROCSparseMatrixCOO{T}), :BlasFloat))
@eval begin
function KP.TriangularOperator(A::$SparseMatrixType, uplo::Char, diag::Char; nrhs::Int=1, transa::Char='N') where T <: $BlasType
m,n = size(A)
alpha = Ref{T}(one(T))
descA = rocSPARSE.ROCSparseMatrixDescriptor(A, 'O')
rocsparse_uplo = Ref{rocSPARSE.rocsparse_fill_mode}(uplo)
rocsparse_diag = Ref{rocSPARSE.rocsparse_diag_type}(diag)
rocSPARSE.rocsparse_spmat_set_attribute(descA, rocSPARSE.rocsparse_spmat_fill_mode, rocsparse_uplo, Csize_t(sizeof(rocsparse_uplo)))
rocSPARSE.rocsparse_spmat_set_attribute(descA, rocSPARSE.rocsparse_spmat_diag_type, rocsparse_diag, Csize_t(sizeof(rocsparse_diag)))
if nrhs == 1
descX = rocSPARSE.ROCDenseVectorDescriptor(T, n)
descY = rocSPARSE.ROCDenseVectorDescriptor(T, m)
algo = rocSPARSE.rocsparse_spsv_alg_default
buffer_size = Ref{Csize_t}()
rocSPARSE.rocsparse_spsv(rocSPARSE.handle(), transa, alpha, descA, descX, descY, T, algo,
rocSPARSE.rocsparse_spsv_stage_buffer_size, buffer_size, C_NULL)
buffer = ROCVector{UInt8}(undef, buffer_size[])
rocSPARSE.rocsparse_spsv(rocSPARSE.handle(), transa, alpha, descA, descX, descY, T, algo,
rocSPARSE.rocsparse_spsv_stage_preprocess, buffer_size, buffer)
return AMD_TriangularOperator{T}(T, m, n, nrhs, transa, descA, buffer_size, buffer)
else
descX = rocSPARSE.ROCDenseMatrixDescriptor(T, n, nrhs)
descY = rocSPARSE.ROCDenseMatrixDescriptor(T, m, nrhs)
algo = rocSPARSE.rocsparse_spsm_alg_default
buffer_size = Ref{Csize_t}()
rocSPARSE.rocsparse_spsm(rocSPARSE.handle(), transa, 'N', alpha, descA, descX, descY, T, algo,
rocSPARSE.rocsparse_spsm_stage_buffer_size, buffer_size, C_NULL)
buffer = ROCVector{UInt8}(undef, buffer_size[])
rocSPARSE.rocsparse_spsm(rocSPARSE.handle(), transa, 'N', alpha, descA, descX, descY, T, algo,
rocSPARSE.rocsparse_spsm_stage_preprocess, buffer_size, buffer)
return AMD_TriangularOperator{T}(T, m, n, nrhs, transa, descA, buffer_size, buffer)
end
end
function KP.update!(A::AMD_TriangularOperator{T}, B::$SparseMatrixType) where T <: $BlasFloat
(B isa ROCSparseMatrixCOO) && rocSPARSE.rocsparse_coo_set_pointers(A.descA, B.rowInd, B.colInd, B.nzVal)
(B isa ROCSparseMatrixCSR) && rocSPARSE.rocsparse_csr_set_pointers(A.descA, B.rowPtr, B.colVal, B.nzVal)
return A
end
end
end
function LinearAlgebra.ldiv!(y::ROCVector{T}, A::AMD_TriangularOperator{T}, x::ROCVector{T}) where T <: BlasFloat
(length(y) != A.m) && throw(DimensionMismatch("length(y) != A.m"))
(length(x) != A.n) && throw(DimensionMismatch("length(x) != A.n"))
(A.nrhs == 1) || throw(DimensionMismatch("A.nrhs != 1"))
descY = rocSPARSE.ROCDenseVectorDescriptor(y)
descX = rocSPARSE.ROCDenseVectorDescriptor(x)
algo = rocSPARSE.rocsparse_spsv_alg_default
alpha = Ref{T}(one(T))
rocSPARSE.rocsparse_spsv(rocSPARSE.handle(), A.transa, alpha, A.descA, descX, descY, T,
algo, rocSPARSE.rocsparse_spsv_stage_compute, A.buffer_size, A.buffer)
end
function LinearAlgebra.ldiv!(Y::ROCMatrix{T}, A::AMD_TriangularOperator{T}, X::ROCMatrix{T}) where T <: BlasFloat
mY, nY = size(Y)
mX, nX = size(X)
(mY != A.m) && throw(DimensionMismatch("mY != A.m"))
(mX != A.n) && throw(DimensionMismatch("mX != A.n"))
(nY == nX == A.nrhs) || throw(DimensionMismatch("nY != A.nrhs or nX != A.nrhs"))
descY = rocSPARSE.ROCDenseMatrixDescriptor(Y)
descX = rocSPARSE.ROCDenseMatrixDescriptor(X)
algo = rocSPARSE.rocsparse_spsm_alg_default
alpha = Ref{T}(one(T))
rocSPARSE.rocsparse_spsm(rocSPARSE.handle(), A.transa, 'N', alpha, A.descA, descX, descY, T,
algo, rocSPARSE.rocsparse_spsm_stage_compute, A.buffer_size, A.buffer)
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/AMDGPU/scaling.jl | code | 98 | KP.scaling_csr!(A::rocSPARSE.ROCSparseMatrixCSR, b::ROCVector) = scaling_csr!(A, b, ROCBackend())
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/CUDA/blockjacobi.jl | code | 1671 | KP.BlockJacobiPreconditioner(J::CUSPARSE.CuSparseMatrixCSR; options...) = BlockJacobiPreconditioner(SparseMatrixCSC(J); options...)
function KP.create_blocklist(cublocks::CuArray, npart)
blocklist = Array{CuArray{Float64,2}}(undef, npart)
for b in 1:npart
blocklist[b] = CuMatrix{Float64}(undef, size(cublocks,1), size(cublocks,2))
end
return blocklist
end
function _update_gpu(p, j_rowptr, j_colval, j_nzval, device::CUDABackend)
nblocks = p.nblocks
blocksize = p.blocksize
fillblock_gpu_kernel! = KP._fillblock_gpu!(device)
# Fill Block Jacobi" begin
fillblock_gpu_kernel!(
p.cublocks, size(p.id,1),
p.cupartitions, p.cumap,
j_rowptr, j_colval, j_nzval,
p.cupart, p.culpartitions, p.id,
ndrange=(nblocks, blocksize),
)
KA.synchronize(device)
# Invert blocks begin
for b in 1:nblocks
p.blocklist[b] .= p.cublocks[:,:,b]
end
CUDA.@sync pivot, info = CUDA.CUBLAS.getrf_batched!(p.blocklist, true)
CUDA.@sync pivot, info, p.blocklist = CUDA.CUBLAS.getri_batched(p.blocklist, pivot)
for b in 1:nblocks
p.cublocks[:,:,b] .= p.blocklist[b]
end
return
end
"""
function update!(J::CuSparseMatrixCSR, p)
Update the preconditioner `p` from the sparse Jacobian `J` in CSR format for CUDA
1) The dense blocks `cuJs` are filled from the sparse Jacobian `J`
2) To a batch inversion of the dense blocks using CUBLAS
3) Extract the preconditioner matrix `p.P` from the dense blocks `cuJs`
"""
function KP.update!(p::BlockJacobiPreconditioner, J::CUSPARSE.CuSparseMatrixCSR)
_update_gpu(p, J.rowPtr, J.colVal, J.nzVal, p.device)
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/CUDA/ic0.jl | code | 4586 | mutable struct IC0Info
info::CUSPARSE.csric02Info_t
function IC0Info()
info_ref = Ref{CUSPARSE.csric02Info_t}()
CUSPARSE.cusparseCreateCsric02Info(info_ref)
obj = new(info_ref[])
finalizer(CUSPARSE.cusparseDestroyCsric02Info, obj)
obj
end
end
unsafe_convert(::Type{CUSPARSE.csric02Info_t}, info::IC0Info) = info.info
mutable struct NVIDIA_IC0{SM} <: AbstractKrylovPreconditioner
n::Int
desc::CUSPARSE.CuMatrixDescriptor
buffer::CuVector{UInt8}
info::IC0Info
timer_update::Float64
P::SM
end
for (bname, aname, sname, T) in ((:cusparseScsric02_bufferSize, :cusparseScsric02_analysis, :cusparseScsric02, :Float32),
(:cusparseDcsric02_bufferSize, :cusparseDcsric02_analysis, :cusparseDcsric02, :Float64),
(:cusparseCcsric02_bufferSize, :cusparseCcsric02_analysis, :cusparseCcsric02, :ComplexF32),
(:cusparseZcsric02_bufferSize, :cusparseZcsric02_analysis, :cusparseZcsric02, :ComplexF64))
@eval begin
function KP.kp_ic0(A::CuSparseMatrixCSR{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = CUSPARSE.CuMatrixDescriptor('G', 'L', 'N', 'O')
info = IC0Info()
buffer_size = Ref{Cint}()
CUSPARSE.$bname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
CUSPARSE.$aname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
posit = Ref{Cint}(1)
CUSPARSE.cusparseXcsric02_zeroPivot(CUSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
CUSPARSE.$sname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
return NVIDIA_IC0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::NVIDIA_IC0{CuSparseMatrixCSR{$T,Cint}}, A::CuSparseMatrixCSR{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
CUSPARSE.$sname(CUSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.rowPtr, p.P.colVal, p.info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, p.buffer)
return p
end
function KP.kp_ic0(A::CuSparseMatrixCSC{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = CUSPARSE.CuMatrixDescriptor('G', 'L', 'N', 'O')
info = IC0Info()
buffer_size = Ref{Cint}()
CUSPARSE.$bname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
CUSPARSE.$aname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
posit = Ref{Cint}(1)
CUSPARSE.cusparseXcsric02_zeroPivot(CUSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
CUSPARSE.$sname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
return NVIDIA_IC0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::NVIDIA_IC0{CuSparseMatrixCSC{$T,Cint}}, A::CuSparseMatrixCSC{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
CUSPARSE.$sname(CUSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.colPtr, p.P.rowVal, p.info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, p.buffer)
return p
end
end
end
for ArrayType in (:(CuVector{T}), :(CuMatrix{T}))
@eval begin
function ldiv!(ic::NVIDIA_IC0{CuSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
ldiv!(LowerTriangular(ic.P), x) # Forward substitution with L
ldiv!(LowerTriangular(ic.P)', x) # Backward substitution with Lᴴ
return x
end
function ldiv!(y::$ArrayType, ic::NVIDIA_IC0{CuSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
copyto!(y, x)
ic.timer_update += @elapsed begin
ldiv!(ic, y)
end
return y
end
function ldiv!(ic::NVIDIA_IC0{CuSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasFloat
ldiv!(UpperTriangular(ic.P)', x) # Forward substitution with L
ldiv!(UpperTriangular(ic.P), x) # Backward substitution with Lᴴ
return x
end
function ldiv!(y::$ArrayType, ic::NVIDIA_IC0{CuSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasReal
copyto!(y, x)
ic.timer_update += @elapsed begin
ldiv!(ic, y)
end
return y
end
end
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/CUDA/ilu0.jl | code | 4643 | mutable struct ILU0Info
info::CUSPARSE.csrilu02Info_t
function ILU0Info()
info_ref = Ref{CUSPARSE.csrilu02Info_t}()
CUSPARSE.cusparseCreateCsrilu02Info(info_ref)
obj = new(info_ref[])
finalizer(CUSPARSE.cusparseDestroyCsrilu02Info, obj)
obj
end
end
unsafe_convert(::Type{CUSPARSE.csrilu02Info_t}, info::ILU0Info) = info.info
mutable struct NVIDIA_ILU0{SM} <: AbstractKrylovPreconditioner
n::Int
desc::CUSPARSE.CuMatrixDescriptor
buffer::CuVector{UInt8}
info::ILU0Info
timer_update::Float64
P::SM
end
for (bname, aname, sname, T) in ((:cusparseScsrilu02_bufferSize, :cusparseScsrilu02_analysis, :cusparseScsrilu02, :Float32),
(:cusparseDcsrilu02_bufferSize, :cusparseDcsrilu02_analysis, :cusparseDcsrilu02, :Float64),
(:cusparseCcsrilu02_bufferSize, :cusparseCcsrilu02_analysis, :cusparseCcsrilu02, :ComplexF32),
(:cusparseZcsrilu02_bufferSize, :cusparseZcsrilu02_analysis, :cusparseZcsrilu02, :ComplexF64))
@eval begin
function KP.kp_ilu0(A::CuSparseMatrixCSR{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = CUSPARSE.CuMatrixDescriptor('G', 'L', 'N', 'O')
info = ILU0Info()
buffer_size = Ref{Cint}()
CUSPARSE.$bname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
CUSPARSE.$aname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
posit = Ref{Cint}(1)
CUSPARSE.cusparseXcsrilu02_zeroPivot(CUSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
CUSPARSE.$sname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.rowPtr, P.colVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
return NVIDIA_ILU0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::NVIDIA_ILU0{CuSparseMatrixCSR{$T,Cint}}, A::CuSparseMatrixCSR{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
CUSPARSE.$sname(CUSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.rowPtr, p.P.colVal, p.info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, p.buffer)
return p
end
function KP.kp_ilu0(A::CuSparseMatrixCSC{$T,Cint})
P = copy(A)
n = checksquare(P)
desc = CUSPARSE.CuMatrixDescriptor('G', 'L', 'N', 'O')
info = ILU0Info()
buffer_size = Ref{Cint}()
CUSPARSE.$bname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
CUSPARSE.$aname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
posit = Ref{Cint}(1)
CUSPARSE.cusparseXcsrilu02_zeroPivot(CUSPARSE.handle(), info, posit)
(posit[] ≥ 0) && error("Structural/numerical zero in A at ($(posit[]),$(posit[])))")
CUSPARSE.$sname(CUSPARSE.handle(), n, nnz(P), desc, P.nzVal, P.colPtr, P.rowVal, info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, buffer)
return NVIDIA_ILU0(n, desc, buffer, info, 0.0, P)
end
function KP.update!(p::NVIDIA_ILU0{CuSparseMatrixCSC{$T,Cint}}, A::CuSparseMatrixCSC{$T,Cint})
copyto!(p.P.nzVal, A.nzVal)
CUSPARSE.$sname(CUSPARSE.handle(), p.n, nnz(p.P), p.desc, p.P.nzVal, p.P.colPtr, p.P.rowVal, p.info, CUSPARSE.CUSPARSE_SOLVE_POLICY_USE_LEVEL, p.buffer)
return p
end
end
end
for ArrayType in (:(CuVector{T}), :(CuMatrix{T}))
@eval begin
function ldiv!(ilu::NVIDIA_ILU0{CuSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
ldiv!(UnitLowerTriangular(ilu.P), x) # Forward substitution with L
ldiv!(UpperTriangular(ilu.P), x) # Backward substitution with U
return x
end
function ldiv!(y::$ArrayType, ilu::NVIDIA_ILU0{CuSparseMatrixCSR{T,Cint}}, x::$ArrayType) where T <: BlasFloat
copyto!(y, x)
ilu.timer_update += @elapsed begin
ldiv!(ilu, y)
end
return y
end
function ldiv!(ilu::NVIDIA_ILU0{CuSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasReal
ldiv!(LowerTriangular(ilu.P), x) # Forward substitution with L
ldiv!(UnitUpperTriangular(ilu.P), x) # Backward substitution with U
return x
end
function ldiv!(y::$ArrayType, ilu::NVIDIA_ILU0{CuSparseMatrixCSC{T,Cint}}, x::$ArrayType) where T <: BlasReal
copyto!(y, x)
ilu.timer_update += @elapsed begin
ldiv!(ilu, y)
end
return y
end
end
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/CUDA/operators.jl | code | 8276 | mutable struct NVIDIA_KrylovOperator{T} <: AbstractKrylovOperator{T}
type::Type{T}
m::Int
n::Int
nrhs::Int
transa::Char
descA::CUSPARSE.CuSparseMatrixDescriptor
buffer::CuVector{UInt8}
end
eltype(A::NVIDIA_KrylovOperator{T}) where T = T
size(A::NVIDIA_KrylovOperator) = (A.m, A.n)
for (SparseMatrixType, BlasType) in ((:(CuSparseMatrixCSR{T}), :BlasFloat),
(:(CuSparseMatrixCSC{T}), :BlasFloat),
(:(CuSparseMatrixCOO{T}), :BlasFloat))
@eval begin
function KP.KrylovOperator(A::$SparseMatrixType; nrhs::Int=1, transa::Char='N') where T <: $BlasType
m,n = size(A)
alpha = Ref{T}(one(T))
beta = Ref{T}(zero(T))
descA = CUSPARSE.CuSparseMatrixDescriptor(A, 'O')
if nrhs == 1
descX = CUSPARSE.CuDenseVectorDescriptor(T, n)
descY = CUSPARSE.CuDenseVectorDescriptor(T, m)
algo = CUSPARSE.CUSPARSE_SPMV_ALG_DEFAULT
buffer_size = Ref{Csize_t}()
CUSPARSE.cusparseSpMV_bufferSize(CUSPARSE.handle(), transa, alpha, descA, descX, beta, descY, T, algo, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
return NVIDIA_KrylovOperator{T}(T, m, n, nrhs, transa, descA, buffer)
else
descX = CUSPARSE.CuDenseMatrixDescriptor(T, n, nrhs)
descY = CUSPARSE.CuDenseMatrixDescriptor(T, m, nrhs)
algo = CUSPARSE.CUSPARSE_SPMM_ALG_DEFAULT
buffer_size = Ref{Csize_t}()
CUSPARSE.cusparseSpMM_bufferSize(CUSPARSE.handle(), transa, 'N', alpha, descA, descX, beta, descY, T, algo, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
if !(A isa CuSparseMatrixCOO)
CUSPARSE.cusparseSpMM_preprocess(CUSPARSE.handle(), transa, 'N', alpha, descA, descX, beta, descY, T, algo, buffer)
end
return NVIDIA_KrylovOperator{T}(T, m, n, nrhs, transa, descA, buffer)
end
end
function KP.update!(A::NVIDIA_KrylovOperator{T}, B::$SparseMatrixType) where T <: $BlasFloat
descB = CUSPARSE.CuSparseMatrixDescriptor(B, 'O')
A.descA = descB
return A
end
end
end
function LinearAlgebra.mul!(y::CuVector{T}, A::NVIDIA_KrylovOperator{T}, x::CuVector{T}) where T <: BlasFloat
(length(y) != A.m) && throw(DimensionMismatch("length(y) != A.m"))
(length(x) != A.n) && throw(DimensionMismatch("length(x) != A.n"))
(A.nrhs == 1) || throw(DimensionMismatch("A.nrhs != 1"))
descY = CUSPARSE.CuDenseVectorDescriptor(y)
descX = CUSPARSE.CuDenseVectorDescriptor(x)
algo = CUSPARSE.CUSPARSE_SPMV_ALG_DEFAULT
alpha = Ref{T}(one(T))
beta = Ref{T}(zero(T))
CUSPARSE.cusparseSpMV(CUSPARSE.handle(), A.transa, alpha, A.descA, descX, beta, descY, T, algo, A.buffer)
end
function LinearAlgebra.mul!(Y::CuMatrix{T}, A::NVIDIA_KrylovOperator{T}, X::CuMatrix{T}) where T <: BlasFloat
mY, nY = size(Y)
mX, nX = size(X)
(mY != A.m) && throw(DimensionMismatch("mY != A.m"))
(mX != A.n) && throw(DimensionMismatch("mX != A.n"))
(nY == nX == A.nrhs) || throw(DimensionMismatch("nY != A.nrhs or nX != A.nrhs"))
descY = CUSPARSE.CuDenseMatrixDescriptor(Y)
descX = CUSPARSE.CuDenseMatrixDescriptor(X)
algo = CUSPARSE.CUSPARSE_SPMM_ALG_DEFAULT
alpha = Ref{T}(one(T))
beta = Ref{T}(zero(T))
CUSPARSE.cusparseSpMM(CUSPARSE.handle(), A.transa, 'N', alpha, A.descA, descX, beta, descY, T, algo, A.buffer)
end
mutable struct NVIDIA_TriangularOperator{T,S} <: AbstractTriangularOperator{T}
type::Type{T}
m::Int
n::Int
nrhs::Int
transa::Char
descA::CUSPARSE.CuSparseMatrixDescriptor
descT::S
buffer::CuVector{UInt8}
end
eltype(A::NVIDIA_TriangularOperator{T}) where T = T
size(A::NVIDIA_TriangularOperator) = (A.m, A.n)
for (SparseMatrixType, BlasType) in ((:(CuSparseMatrixCSR{T}), :BlasFloat),
(:(CuSparseMatrixCOO{T}), :BlasFloat))
@eval begin
function KP.TriangularOperator(A::$SparseMatrixType, uplo::Char, diag::Char; nrhs::Int=1, transa::Char='N') where T <: $BlasType
m,n = size(A)
alpha = Ref{T}(one(T))
descA = CUSPARSE.CuSparseMatrixDescriptor(A, 'O')
cusparse_uplo = Ref{CUSPARSE.cusparseFillMode_t}(uplo)
cusparse_diag = Ref{CUSPARSE.cusparseDiagType_t}(diag)
CUSPARSE.cusparseSpMatSetAttribute(descA, 'F', cusparse_uplo, Csize_t(sizeof(cusparse_uplo)))
CUSPARSE.cusparseSpMatSetAttribute(descA, 'D', cusparse_diag, Csize_t(sizeof(cusparse_diag)))
if nrhs == 1
descT = CUSPARSE.CuSparseSpSVDescriptor()
descX = CUSPARSE.CuDenseVectorDescriptor(T, n)
descY = CUSPARSE.CuDenseVectorDescriptor(T, m)
algo = CUSPARSE.CUSPARSE_SPSV_ALG_DEFAULT
buffer_size = Ref{Csize_t}()
CUSPARSE.cusparseSpSV_bufferSize(CUSPARSE.handle(), transa, alpha, descA, descX, descY, T, algo, descT, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
CUSPARSE.cusparseSpSV_analysis(CUSPARSE.handle(), transa, alpha, descA, descX, descY, T, algo, descT, buffer)
return NVIDIA_TriangularOperator{T,CUSPARSE.CuSparseSpSVDescriptor}(T, m, n, nrhs, transa, descA, descT, buffer)
else
descT = CUSPARSE.CuSparseSpSMDescriptor()
descX = CUSPARSE.CuDenseMatrixDescriptor(T, n, nrhs)
descY = CUSPARSE.CuDenseMatrixDescriptor(T, m, nrhs)
algo = CUSPARSE.CUSPARSE_SPSM_ALG_DEFAULT
buffer_size = Ref{Csize_t}()
CUSPARSE.cusparseSpSM_bufferSize(CUSPARSE.handle(), transa, 'N', alpha, descA, descX, descY, T, algo, descT, buffer_size)
buffer = CuVector{UInt8}(undef, buffer_size[])
CUSPARSE.cusparseSpSM_analysis(CUSPARSE.handle(), transa, 'N', alpha, descA, descX, descY, T, algo, descT, buffer)
return NVIDIA_TriangularOperator{T,CUSPARSE.CuSparseSpSMDescriptor}(T, m, n, nrhs, transa, descA, descT, buffer)
end
end
function KP.update!(A::NVIDIA_TriangularOperator{T,CUSPARSE.CuSparseSpSVDescriptor}, B::$SparseMatrixType) where T <: $BlasFloat
CUSPARSE.version() ≥ v"12.2" || error("This operation is only support by CUDA ≥ v12.3")
descB = CUSPARSE.CuSparseMatrixDescriptor(B, 'O')
A.descA = descB
CUSPARSE.cusparseSpSV_updateMatrix(CUSPARSE.handle(), A.descT, B.nzVal, 'G')
return A
end
function KP.update!(A::NVIDIA_TriangularOperator{T,CUSPARSE.CuSparseSpSMDescriptor}, B::$SparseMatrixType) where T <: $BlasFloat
return error("This operation will be supported in CUDA v12.4")
end
end
end
function LinearAlgebra.ldiv!(y::CuVector{T}, A::NVIDIA_TriangularOperator{T}, x::CuVector{T}) where T <: BlasFloat
(length(y) != A.m) && throw(DimensionMismatch("length(y) != A.m"))
(length(x) != A.n) && throw(DimensionMismatch("length(x) != A.n"))
(A.nrhs == 1) || throw(DimensionMismatch("A.nrhs != 1"))
descY = CUSPARSE.CuDenseVectorDescriptor(y)
descX = CUSPARSE.CuDenseVectorDescriptor(x)
algo = CUSPARSE.CUSPARSE_SPSV_ALG_DEFAULT
alpha = Ref{T}(one(T))
CUSPARSE.cusparseSpSV_solve(CUSPARSE.handle(), A.transa, alpha, A.descA, descX, descY, T, algo, A.descT)
end
function LinearAlgebra.ldiv!(Y::CuMatrix{T}, A::NVIDIA_TriangularOperator{T}, X::CuMatrix{T}) where T <: BlasFloat
mY, nY = size(Y)
mX, nX = size(X)
(mY != A.m) && throw(DimensionMismatch("mY != A.m"))
(mX != A.n) && throw(DimensionMismatch("mX != A.n"))
(nY == nX == A.nrhs) || throw(DimensionMismatch("nY != A.nrhs or nX != A.nrhs"))
descY = CUSPARSE.CuDenseMatrixDescriptor(Y)
descX = CUSPARSE.CuDenseMatrixDescriptor(X)
algo = CUSPARSE.CUSPARSE_SPSM_ALG_DEFAULT
alpha = Ref{T}(one(T))
CUSPARSE.cusparseSpSM_solve(CUSPARSE.handle(), A.transa, 'N', alpha, A.descA, descX, descY, T, algo, A.descT)
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | ext/CUDA/scaling.jl | code | 96 | KP.scaling_csr!(A::CUSPARSE.CuSparseMatrixCSR, b::CuVector) = scaling_csr!(A, b, CUDABackend())
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | src/KrylovPreconditioners.jl | code | 1413 | module KrylovPreconditioners
using LinearAlgebra, SparseArrays
using Adapt
using KernelAbstractions
const KA = KernelAbstractions
using LinearAlgebra: checksquare, BlasReal, BlasFloat
import LinearAlgebra: ldiv!
abstract type AbstractKrylovPreconditioner end
export AbstractKrylovPreconditioner
abstract type AbstractKrylovOperator{T} end
export AbstractKrylovOperator
abstract type AbstractTriangularOperator{T} end
export AbstractTriangularOperator
update!(p::AbstractKrylovPreconditioner, A::SparseMatrixCSC) = error("update!() for $(typeof(p)) is not implemented.")
update!(p::AbstractKrylovPreconditioner, A) = error("update!() for $(typeof(p)) is not implemented.")
update!(p::AbstractKrylovOperator, A::SparseMatrixCSC) = error("update!() for $(typeof(p)) is not implemented.")
update!(p::AbstractKrylovOperator, A) = error("update!() for $(typeof(p)) is not implemented.")
export update!, get_timer, reset_timer!
function get_timer(p::AbstractKrylovPreconditioner)
return p.timer_update
end
function reset_timer!(p::AbstractKrylovPreconditioner)
p.timer_update = 0.0
end
function KrylovOperator end
export KrylovOperator
function TriangularOperator end
export TriangularOperator
# Preconditioners
include("ic0.jl")
include("ilu0.jl")
include("blockjacobi.jl")
# Scaling
include("scaling.jl")
export scaling_csr!
# Ordering
# include(ordering.jl)
end # module KrylovPreconditioners
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | src/blockjacobi.jl | code | 9993 | export BlockJacobiPreconditioner
using LightGraphs, Metis
"""
overlap(Graph, subset, level)
Given subset embedded within Graph, compute subset2 such that
subset2 contains subset and all of its adjacent vertices.
"""
function overlap(Graph, subset; level=1)
@assert level > 0
subset2 = [LightGraphs.neighbors(Graph, v) for v in subset]
subset2 = reduce(vcat, subset2)
subset2 = unique(vcat(subset, subset2))
level -= 1
if level == 0
return subset2
else
return overlap(Graph, subset2, level=level)
end
end
"""
BlockJacobiPreconditioner
Overlapping-Schwarz preconditioner.
### Attributes
* `nblocks::Int64`: Number of partitions or blocks.
* `blocksize::Int64`: Size of each block.
* `partitions::Vector{Vector{Int64}}``: `npart` partitions stored as lists
* `cupartitions`: `partitions` transfered to the GPU
* `lpartitions::Vector{Int64}``: Length of each partitions.
* `culpartitions::Vector{Int64}``: Length of each partitions, on the GPU.
* `blocks`: Dense blocks of the block-Jacobi
* `cublocks`: `Js` transfered to the GPU
* `map`: The partitions as a mapping to construct views
* `cumap`: `cumap` transferred to the GPU`
* `part`: Partitioning as output by Metis
* `cupart`: `part` transferred to the GPU
"""
mutable struct BlockJacobiPreconditioner{AT,GAT,VI,GVI,GMT,MI,GMI} <: AbstractKrylovPreconditioner
nblocks::Int64
blocksize::Int64
partitions::MI
cupartitions::GMI
lpartitions::VI
culpartitions::GVI
rest_size::VI
curest_size::GVI
blocks::AT
cublocks::GAT
map::VI
cumap::GVI
part::VI
cupart::GVI
id::GMT
blocklist::Vector{GMT}
timer_update::Float64
device::KA.Backend
end
function create_blocklist(blocks::Array, npart)
blocklist = Array{Array{Float64,2}}(undef, npart)
for b in 1:npart
blocklist[b] = Matrix{Float64}(undef, size(blocks,1), size(blocks,2))
end
return blocklist
end
function BlockJacobiPreconditioner(J, npart::Int64, device=CPU(), olevel=0)
if npart < 2
error("Number of partitions `npart` should be at" *
"least 2 for partitioning in Metis")
end
adj = build_adjmatrix(SparseMatrixCSC(J))
g = LightGraphs.Graph(adj)
part = Metis.partition(g, npart)
partitions = Vector{Vector{Int64}}()
for i in 1:npart
push!(partitions, [])
end
for (i,v) in enumerate(part)
push!(partitions[v], i)
end
# We keep track of the partition size pre-overlap.
# This will allow us to implement the RAS update.
rest_size = length.(partitions)
# overlap
if olevel > 0
for i in 1:npart
partitions[i] = overlap(g, partitions[i], level=olevel)
end
end
lpartitions = length.(partitions)
blocksize = maximum(length.(partitions))
blocks = zeros(Float64, blocksize, blocksize, npart)
# Get partitions into bit typed structure
bpartitions = zeros(Int64, blocksize, npart)
bpartitions .= 0.0
for i in 1:npart
bpartitions[1:length(partitions[i]),i] .= Vector{Int64}(partitions[i])
end
id = Matrix{Float64}(I, blocksize, blocksize)
for i in 1:npart
blocks[:,:,i] .= id
end
nmap = 0
for b in partitions
nmap += length(b)
end
map = zeros(Int64, nmap)
part = zeros(Int64, nmap)
for b in 1:npart
for (i,el) in enumerate(partitions[b])
map[el] = i
part[el] = b
end
end
id = adapt(device, id)
cubpartitions = adapt(device, bpartitions)
culpartitions = adapt(device, lpartitions)
curest_size = adapt(device, rest_size)
cublocks = adapt(device, blocks)
cumap = adapt(device, map)
cupart = adapt(device, part)
blocklist = create_blocklist(cublocks, npart)
return BlockJacobiPreconditioner(
npart, blocksize, bpartitions,
cubpartitions, lpartitions,
culpartitions, rest_size,
curest_size, blocks,
cublocks, map,
cumap, part,
cupart, id, blocklist, 0.0,
device
)
end
function BlockJacobiPreconditioner(J::SparseMatrixCSC; nblocks=-1, device=CPU(), noverlaps=0)
n = size(J, 1)
npartitions = if nblocks > 0
nblocks
else
div(n, 32)
end
return BlockJacobiPreconditioner(J, npartitions, device, noverlaps)
end
Base.eltype(::BlockJacobiPreconditioner) = Float64
# NOTE: Custom kernel to implement blocks - vector multiplication.
# The blocks have very unbalanced sizes, leading to imbalances
# between the different threads.
# CUBLAS.gemm_strided_batched has been tested has well, but is
# overall 3x slower than this custom kernel : due to the various sizes
# of the blocks, gemm_strided is performing too many unecessary operations,
# impairing its performance.
@kernel function mblock_b_kernel!(y, b, p_len, rp_len, part, blocks)
j, i = @index(Global, NTuple)
@inbounds len = p_len[i]
@inbounds rlen = rp_len[i]
if j <= rlen
accum = 0.0
idxA = @inbounds part[j, i]
for k=1:len
idxB = @inbounds part[k, i]
@inbounds accum = accum + blocks[j, k, i]*b[idxB]
end
@inbounds y[idxA] = accum
end
end
@kernel function mblock_B_kernel!(y, b, p_len, rp_len, part, blocks)
p = size(b, 2)
i, j = @index(Global, NTuple)
len = p_len[i]
rlen = rp_len[i]
if j <= rlen
for ℓ=1:p
accum = 0.0
idxA = @inbounds part[j, i]
for k=1:len
idxB = @inbounds part[k, i]
@inbounds accum = accum + blocks[j, k, i]*b[idxB,ℓ]
end
@inbounds y[idxA,ℓ] = accum
end
end
end
function LinearAlgebra.mul!(y, C::BlockJacobiPreconditioner, b::Vector{T}) where T
n = size(b, 1)
fill!(y, zero(T))
for i=1:C.nblocks
rlen = C.lpartitions[i]
part = C.partitions[1:rlen, i]
blck = C.blocks[1:rlen, 1:rlen, i]
for j=1:C.rest_size[i]
idx = part[j]
y[idx] += dot(blck[j, :], b[part])
end
end
end
function LinearAlgebra.mul!(Y, C::BlockJacobiPreconditioner, B::Matrix{T}) where T
n, p = size(B)
fill!(Y, zero(T))
for i=1:C.nblocks
rlen = C.lpartitions[i]
part = C.partitions[1:rlen, i]
blck = C.blocks[1:rlen, 1:rlen, i]
for rhs=1:p
for j=1:C.rest_size[i]
idx = part[j]
Y[idx,rhs] += dot(blck[j, :], B[part,rhs])
end
end
end
end
function LinearAlgebra.mul!(y, C::BlockJacobiPreconditioner, b::AbstractVector{T}) where T
device = KA.get_backend(b)
n = size(b, 1)
fill!(y, zero(T))
max_rlen = maximum(C.rest_size)
ndrange = (max_rlen, C.nblocks)
C.timer_update += @elapsed begin mblock_b_kernel!(device)(
y, b, C.culpartitions, C.curest_size,
C.cupartitions, C.cublocks,
ndrange=ndrange,
)
KA.synchronize(device)
end
end
function LinearAlgebra.mul!(Y, C::BlockJacobiPreconditioner, B::AbstractMatrix{T}) where T
device = KA.get_backend(B)
n, p = size(B)
fill!(Y, zero(T))
max_rlen = maximum(C.rest_size)
ndrange = (C.nblocks, max_rlen)
C.timer_update += @elapsed begin mblock_B_kernel!(device)(
Y, B, C.culpartitions, C.curest_size,
C.cupartitions, C.cublocks,
ndrange=ndrange,
)
KA.synchronize(device)
end
end
"""
build_adjmatrix
Build the adjacency matrix of a matrix A corresponding to the undirected graph
"""
function build_adjmatrix(A)
rows = Int64[]
cols = Int64[]
vals = Float64[]
rowsA = rowvals(A)
m, n = size(A)
for i = 1:n
for j in nzrange(A, i)
push!(rows, rowsA[j])
push!(cols, i)
push!(vals, 1.0)
push!(rows, i)
push!(cols, rowsA[j])
push!(vals, 1.0)
end
end
return sparse(rows,cols,vals,size(A,1),size(A,2))
end
"""
_fillblock_gpu
Fill the dense blocks of the preconditioner from the sparse CSR matrix arrays
"""
@kernel function _fillblock_gpu!(blocks, blocksize, partition, map, rowPtr, colVal, nzVal, part, lpartitions, id)
b,k = @index(Global, NTuple)
for i in 1:blocksize
blocks[k,i,b] = id[k,i]
end
@synchronize
@inbounds if k <= lpartitions[b]
# select row
i = partition[k, b]
# iterate matrix
for row_ptr in rowPtr[i]:(rowPtr[i + 1] - 1)
# retrieve column value
col = colVal[row_ptr]
# iterate partition list and see if pertains to it
for j in 1:lpartitions[b]
if col == partition[j, b]
@inbounds blocks[k, j, b] = nzVal[row_ptr]
end
end
end
end
end
"""
function update!(p, J::SparseMatrixCSC)
Update the preconditioner `p` from the sparse Jacobian `J` in CSC format for the CPU
Note that this implements the same algorithm as for the GPU and becomes very slow on CPU with growing number of blocks.
"""
function update!(p::BlockJacobiPreconditioner, J::SparseMatrixCSC)
# TODO: Enabling threading leads to a crash here
for b in 1:p.nblocks
p.blocks[:,:,b] = p.id[:,:]
for k in 1:p.lpartitions[b]
i = p.partitions[k,b]
for j in J.colptr[i]:J.colptr[i+1]-1
if b == p.part[J.rowval[j]]
p.blocks[p.map[J.rowval[j]], p.map[i], b] = J.nzval[j]
end
end
end
end
for b in 1:p.nblocks
# Invert blocks
p.blocks[:,:,b] .= inv(p.blocks[:,:,b])
end
end
function Base.show(precond::BlockJacobiPreconditioner)
npartitions = precond.npart
nblock = precond.nblocks
println("#partitions: $npartitions, Blocksize: n = ", nblock,
" Mbytes = ", (nblock*nblock*npartitions*8.0)/(1024.0*1024.0))
println("Block Jacobi block size: $(precond.nJs)")
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | src/ic0.jl | code | 79 | export kp_ic0
kp_ic0(A) = error("Not implemented for this type $(typeof(A))")
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | src/ilu0.jl | code | 81 | export kp_ilu0
kp_ilu0(A) = error("Not implemented for this type $(typeof(A))")
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | src/scaling.jl | code | 605 | @kernel function scaling_csr_kernel!(rowPtr, nzVal, b)
m = @index(Global, Linear)
max = 0.0
@inbounds for i = rowPtr[m]:(rowPtr[m + 1] - 1)
absnzVal = abs(nzVal[i])
# This works somehow better in ExaPF. Was initially a bug I thought
# absnzVal = nzVal[i]
if absnzVal > max
max = absnzVal
end
end
if max < 1.0
b[m] /= max
@inbounds for i = rowPtr[m]:(rowPtr[m + 1] - 1)
nzVal[i] /= max
end
end
end
function scaling_csr!(A, b, backend::KA.Backend)
scaling_csr_kernel!(backend)(A.rowPtr, A.nzVal, b; ndrange=length(b))
synchronize(backend)
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | test/runtests.jl | code | 539 | using AMDGPU
using CUDA
# using oneAPI
using Test
@testset "KrylovPreconditioners" begin
if AMDGPU.functional()
@info "Testing AMDGPU backend"
@testset "Testing AMDGPU backend" begin
include("gpu/amd.jl")
end
end
if CUDA.functional()
@info "Testing CUDA backend"
@testset "Testing CUDA backend" begin
include("gpu/nvidia.jl")
end
end
# if oneAPI.functional()
# @info "Testing oneAPI backend"
# @testset "Testing oneAPI backend" begin
# include("gpu/intel.jl")
# end
# end
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | test/gpu/amd.jl | code | 1926 | using AMDGPU, AMDGPU.rocSPARSE, AMDGPU.rocSOLVER
_get_type(J::ROCSparseMatrixCSR) = ROCArray{Float64, 1, AMDGPU.Mem.HIPBuffer}
_is_csr(J::ROCSparseMatrixCSR) = true
_is_csc(J::ROCSparseMatrixCSR) = false
include("gpu.jl")
@testset "AMD -- AMDGPU.jl" begin
@test AMDGPU.functional()
AMDGPU.allowscalar(false)
@testset "IC(0)" begin
@testset "ROCSparseMatrixCSC -- $FC" for FC in (Float64,)
test_ic0(FC, ROCVector{FC}, ROCSparseMatrixCSC{FC})
end
@testset "ROCSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_ic0(FC, ROCVector{FC}, ROCSparseMatrixCSR{FC})
end
end
@testset "ILU(0)" begin
@testset "ROCSparseMatrixCSC -- $FC" for FC in (Float64,)
test_ilu0(FC, ROCVector{FC}, ROCSparseMatrixCSC{FC})
end
@testset "ROCSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_ilu0(FC, ROCVector{FC}, ROCSparseMatrixCSR{FC})
end
end
@testset "KrylovOperator" begin
@testset "ROCSparseMatrixCOO -- $FC" for FC in (Float64, ComplexF64)
test_operator(FC, ROCVector{FC}, ROCMatrix{FC}, ROCSparseMatrixCOO{FC})
end
@testset "ROCSparseMatrixCSC -- $FC" for FC in (Float64, ComplexF64)
test_operator(FC, ROCVector{FC}, ROCMatrix{FC}, ROCSparseMatrixCSC{FC})
end
@testset "ROCSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_operator(FC, ROCVector{FC}, ROCMatrix{FC}, ROCSparseMatrixCSR{FC})
end
end
@testset "TriangularOperator" begin
@testset "ROCSparseMatrixCOO -- $FC" for FC in (Float64, ComplexF64)
test_triangular(FC, ROCVector{FC}, ROCMatrix{FC}, ROCSparseMatrixCOO{FC})
end
@testset "ROCSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_triangular(FC, ROCVector{FC}, ROCMatrix{FC}, ROCSparseMatrixCSR{FC})
end
end
@testset "Block Jacobi preconditioner" begin
test_block_jacobi(ROCBackend(), ROCArray, ROCSparseMatrixCSR)
end
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | test/gpu/gpu.jl | code | 5907 | using SparseArrays, Random, Test
using LinearAlgebra, Krylov, KrylovPreconditioners
Random.seed!(666)
function test_ic0(FC, V, M)
n = 100
R = real(FC)
A_cpu = rand(FC, n, n)
A_cpu = A_cpu * A_cpu'
A_cpu = sparse(A_cpu)
b_cpu = rand(FC, n)
A_gpu = M(A_cpu)
b_gpu = V(b_cpu)
P = kp_ic0(A_gpu)
x_gpu, stats = cg(A_gpu, b_gpu, M=P, ldiv=true)
r_gpu = b_gpu - A_gpu * x_gpu
@test stats.niter ≤ 5
if (FC <: ComplexF64) && V.body.name.name == :ROCArray
@test_broken norm(r_gpu) ≤ 1e-6
else
@test norm(r_gpu) ≤ 1e-8
end
A_gpu = M(A_cpu + 200*I)
update!(P, A_gpu)
x_gpu, stats = cg(A_gpu, b_gpu, M=P, ldiv=true)
r_gpu = b_gpu - A_gpu * x_gpu
@test stats.niter ≤ 5
if (FC <: ComplexF64) && V.body.name.name == :ROCArray
@test_broken norm(r_gpu) ≤ 1e-6
else
@test norm(r_gpu) ≤ 1e-8
end
end
function test_ilu0(FC, V, M)
n = 100
R = real(FC)
A_cpu = rand(FC, n, n)
A_cpu = sparse(A_cpu)
b_cpu = rand(FC, n)
A_gpu = M(A_cpu)
b_gpu = V(b_cpu)
P = kp_ilu0(A_gpu)
x_gpu, stats = gmres(A_gpu, b_gpu, N=P, ldiv=true)
r_gpu = b_gpu - A_gpu * x_gpu
@test stats.niter ≤ 5
@test norm(r_gpu) ≤ 1e-8
A_gpu = M(A_cpu + 200*I)
update!(P, A_gpu)
x_gpu, stats = gmres(A_gpu, b_gpu, N=P, ldiv=true)
r_gpu = b_gpu - A_gpu * x_gpu
@test stats.niter ≤ 5
@test norm(r_gpu) ≤ 1e-8
end
function test_operator(FC, V, DM, SM)
m = 200
n = 100
A_cpu = rand(FC, n, n)
A_cpu = sparse(A_cpu)
b_cpu = rand(FC, n)
A_gpu = SM(A_cpu)
b_gpu = V(b_cpu)
opA_gpu = KrylovOperator(A_gpu)
x_gpu, stats = gmres(opA_gpu, b_gpu)
r_gpu = b_gpu - A_gpu * x_gpu
@test stats.solved
@test norm(r_gpu) ≤ 1e-8
A_cpu = rand(FC, m, n)
A_cpu = sparse(A_cpu)
A_gpu = SM(A_cpu)
opA_gpu = KrylovOperator(A_gpu)
for i = 1:5
y_cpu = rand(FC, m)
x_cpu = rand(FC, n)
mul!(y_cpu, A_cpu, x_cpu)
y_gpu = V(y_cpu)
x_gpu = V(x_cpu)
mul!(y_gpu, opA_gpu, x_gpu)
@test collect(y_gpu) ≈ y_cpu
end
for j = 1:5
y_cpu = rand(FC, m)
x_cpu = rand(FC, n)
A_cpu2 = A_cpu + j*I
mul!(y_cpu, A_cpu2, x_cpu)
y_gpu = V(y_cpu)
x_gpu = V(x_cpu)
A_gpu2 = SM(A_cpu2)
update!(opA_gpu, A_gpu2)
mul!(y_gpu, opA_gpu, x_gpu)
@test collect(y_gpu) ≈ y_cpu
end
nrhs = 3
opA_gpu = KrylovOperator(A_gpu; nrhs)
for i = 1:5
Y_cpu = rand(FC, m, nrhs)
X_cpu = rand(FC, n, nrhs)
mul!(Y_cpu, A_cpu, X_cpu)
Y_gpu = DM(Y_cpu)
X_gpu = DM(X_cpu)
mul!(Y_gpu, opA_gpu, X_gpu)
@test collect(Y_gpu) ≈ Y_cpu
end
for j = 1:5
Y_cpu = rand(FC, m, nrhs)
X_cpu = rand(FC, n, nrhs)
A_cpu2 = A_cpu + j*I
mul!(Y_cpu, A_cpu2, X_cpu)
Y_gpu = DM(Y_cpu)
X_gpu = DM(X_cpu)
A_gpu2 = SM(A_cpu2)
update!(opA_gpu, A_gpu2)
mul!(Y_gpu, opA_gpu, X_gpu)
@test collect(Y_gpu) ≈ Y_cpu
end
end
function test_triangular(FC, V, DM, SM)
n = 100
for (uplo, diag, triangle) in [('L', 'U', UnitLowerTriangular),
('L', 'N', LowerTriangular ),
('U', 'U', UnitUpperTriangular),
('U', 'N', UpperTriangular )]
A_cpu = rand(FC, n, n)
A_cpu = uplo == 'L' ? tril(A_cpu) : triu(A_cpu)
A_cpu = diag == 'U' ? A_cpu - Diagonal(A_cpu) + I : A_cpu
A_cpu = sparse(A_cpu)
b_cpu = rand(FC, n)
A_gpu = SM(A_cpu)
b_gpu = V(b_cpu)
opA_gpu = TriangularOperator(A_gpu, uplo, diag)
for i = 1:5
y_cpu = rand(FC, n)
x_cpu = rand(FC, n)
ldiv!(y_cpu, triangle(A_cpu), x_cpu)
y_gpu = V(y_cpu)
x_gpu = V(x_cpu)
ldiv!(y_gpu, opA_gpu, x_gpu)
@test collect(y_gpu) ≈ y_cpu
end
for j = 1:5
y_cpu = rand(FC, n)
x_cpu = rand(FC, n)
A_cpu2 = A_cpu + j*tril(A_cpu,-1) + j*triu(A_cpu,1)
ldiv!(y_cpu, triangle(A_cpu2), x_cpu)
y_gpu = V(y_cpu)
x_gpu = V(x_cpu)
A_gpu2 = SM(A_cpu2)
update!(opA_gpu, A_gpu2)
ldiv!(y_gpu, opA_gpu, x_gpu)
@test collect(y_gpu) ≈ y_cpu
end
nrhs = 3
opA_gpu = TriangularOperator(A_gpu, uplo, diag; nrhs)
for i = 1:5
Y_cpu = rand(FC, n, nrhs)
X_cpu = rand(FC, n, nrhs)
ldiv!(Y_cpu, triangle(A_cpu), X_cpu)
Y_gpu = DM(Y_cpu)
X_gpu = DM(X_cpu)
ldiv!(Y_gpu, opA_gpu, X_gpu)
@test collect(Y_gpu) ≈ Y_cpu
end
if V.body.name.name != :CuArray
for j = 1:5
Y_cpu = rand(FC, n, nrhs)
X_cpu = rand(FC, n, nrhs)
A_cpu2 = A_cpu + j*tril(A_cpu,-1) + j*triu(A_cpu,1)
ldiv!(Y_cpu, triangle(A_cpu2), X_cpu)
Y_gpu = DM(Y_cpu)
X_gpu = DM(X_cpu)
A_gpu2 = SM(A_cpu2)
update!(opA_gpu, A_gpu2)
ldiv!(Y_gpu, opA_gpu, X_gpu)
@test collect(Y_gpu) ≈ Y_cpu
end
end
end
end
_get_type(J::SparseMatrixCSC) = Vector{Float64}
function generate_random_system(n::Int, m::Int)
# Add a diagonal term for conditionning
A = randn(n, m) + 15I
x♯ = randn(m)
b = A * x♯
# Be careful: all algorithms work with sparse matrix
spA = sparse(A)
return spA, b, x♯
end
function test_block_jacobi(device, AT, SMT)
n, m = 100, 100
A, b, x♯ = generate_random_system(n, m)
# Transfer data to device
A = A |> SMT
b = b |> AT
x♯ = x♯ |> AT
x = similar(b); r = similar(b)
nblocks = 2
if _is_csr(A)
scaling_csr!(A, b, device)
end
precond = BlockJacobiPreconditioner(A, nblocks, device)
update!(precond, A)
S = _get_type(A)
linear_solver = Krylov.BicgstabSolver(n, m, S)
Krylov.bicgstab!(
linear_solver, A, b;
N=precond,
atol=1e-10,
rtol=1e-10,
verbose=0,
history=true,
)
n_iters = linear_solver.stats.niter
copyto!(x, linear_solver.x)
r = b - A * x
resid = norm(r) / norm(b)
@test(resid ≤ 1e-6)
@test x ≈ x♯
@test n_iters ≤ n
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | test/gpu/intel.jl | code | 130 | using oneAPI
include("gpu.jl")
@testset "Intel -- oneAPI.jl" begin
@test oneAPI.functional()
oneAPI.allowscalar(false)
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | test/gpu/nvidia.jl | code | 1879 | using CUDA, CUDA.CUSPARSE, CUDA.CUSOLVER
_get_type(J::CuSparseMatrixCSR) = CuArray{Float64, 1, CUDA.Mem.DeviceBuffer}
_is_csr(J::CuSparseMatrixCSR) = true
_is_csc(J::CuSparseMatrixCSR) = false
include("gpu.jl")
@testset "Nvidia -- CUDA.jl" begin
@test CUDA.functional()
CUDA.allowscalar(false)
@testset "IC(0)" begin
@testset "CuSparseMatrixCSC -- $FC" for FC in (Float64,)
test_ic0(FC, CuVector{FC}, CuSparseMatrixCSC{FC})
end
@testset "CuSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_ic0(FC, CuVector{FC}, CuSparseMatrixCSR{FC})
end
end
@testset "ILU(0)" begin
@testset "CuSparseMatrixCSC -- $FC" for FC in (Float64,)
test_ilu0(FC, CuVector{FC}, CuSparseMatrixCSC{FC})
end
@testset "CuSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_ilu0(FC, CuVector{FC}, CuSparseMatrixCSR{FC})
end
end
@testset "KrylovOperator" begin
@testset "CuSparseMatrixCOO -- $FC" for FC in (Float64, ComplexF64)
test_operator(FC, CuVector{FC}, CuMatrix{FC}, CuSparseMatrixCOO{FC})
end
@testset "CuSparseMatrixCSC -- $FC" for FC in (Float64, ComplexF64)
test_operator(FC, CuVector{FC}, CuMatrix{FC}, CuSparseMatrixCSC{FC})
end
@testset "CuSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_operator(FC, CuVector{FC}, CuMatrix{FC}, CuSparseMatrixCSR{FC})
end
end
@testset "TriangularOperator" begin
@testset "CuSparseMatrixCOO -- $FC" for FC in (Float64, ComplexF64)
test_triangular(FC, CuVector{FC}, CuMatrix{FC}, CuSparseMatrixCOO{FC})
end
@testset "CuSparseMatrixCSR -- $FC" for FC in (Float64, ComplexF64)
test_triangular(FC, CuVector{FC}, CuMatrix{FC}, CuSparseMatrixCSR{FC})
end
end
@testset "Block Jacobi preconditioner" begin
test_block_jacobi(CUDABackend(), CuArray, CuSparseMatrixCSR)
end
end
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | README.md | docs | 1668 | # KrylovPreconditioners.jl
| **Documentation** | **CI** | **Coverage** | **Downloads** |
|:-----------------:|:------:|:------------:|:-------------:|
| [![docs-stable][docs-stable-img]][docs-stable-url] [![docs-dev][docs-dev-img]][docs-dev-url] | [![build-gh][build-gh-img]][build-gh-url] [![build-cirrus][build-cirrus-img]][build-cirrus-url] | [![codecov][codecov-img]][codecov-url] | [![downloads][downloads-img]][downloads-url] |
[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
[docs-stable-url]: https://JuliaSmoothOptimizers.github.io/KrylovPreconditioners.jl/stable
[docs-dev-img]: https://img.shields.io/badge/docs-dev-purple.svg
[docs-dev-url]: https://JuliaSmoothOptimizers.github.io/KrylovPreconditioners.jl/dev
[build-gh-img]: https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl/workflows/CI/badge.svg?branch=main
[build-gh-url]: https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl/actions
[build-cirrus-img]: https://img.shields.io/cirrus/github/JuliaSmoothOptimizers/KrylovPreconditioners.jl?logo=Cirrus%20CI
[build-cirrus-url]: https://cirrus-ci.com/github/JuliaSmoothOptimizers/KrylovPreconditioners.jl
[codecov-img]: https://codecov.io/gh/JuliaSmoothOptimizers/KrylovPreconditioners.jl/branch/main/graph/badge.svg
[codecov-url]: https://app.codecov.io/gh/JuliaSmoothOptimizers/KrylovPreconditioners.jl
[downloads-img]: https://shields.io/endpoint?url=https://pkgs.genieframework.com/api/v1/badge/KrylovPreconditioners
[downloads-url]: https://pkgs.genieframework.com?packages=KrylovPreconditioners
The best sidekick of [Krylov.jl](https://github.com/JuliaSmoothOptimizers/Krylov.jl) └(^o^ )Ｘ( ^o^)┘
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |
[
"MPL-2.0"
] | 0.2.2 | 3372f4dfa2499b0aa0b478a5082aff34915532e8 | docs/src/index.md | docs | 982 | # [KrylovPreconditioners.jl documentation](@id Home)
This package provides a collection of preconditioners.
## How to Install
KrylovPreconditioners.jl can be installed and tested through the Julia package manager:
```julia
julia> ]
pkg> add KrylovPreconditioners
pkg> test KrylovPreconditioners
```
# Bug reports and discussions
If you think you found a bug, feel free to open an [issue](https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl/issues).
Focused suggestions and requests can also be opened as issues. Before opening a pull request, start an issue or a discussion on the topic, please.
If you want to ask a question not suited for a bug report, feel free to start a discussion [here](https://github.com/JuliaSmoothOptimizers/Organization/discussions). This forum is for general discussion about this repository and the [JuliaSmoothOptimizers](https://github.com/JuliaSmoothOptimizers) organization, so questions about any of our packages are welcome.
| KrylovPreconditioners | https://github.com/JuliaSmoothOptimizers/KrylovPreconditioners.jl.git |