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style = "sciml" | ||
margin = 92 | ||
indent = 4 | ||
format_docstrings = true | ||
separate_kwargs_with_semicolon = true | ||
always_for_in = true | ||
annotate_untyped_fields_with_any = false |
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steps: | ||
- label: ":julia: Julia {{matrix.julia}}" | ||
plugins: | ||
- JuliaCI/julia#v1: | ||
version: "{{matrix.julia}}" | ||
- JuliaCI/julia-test#v1: | ||
test_args: "--quickfail" | ||
agents: | ||
os: "linux" | ||
queue: "juliaecosystem" | ||
arch: "x86_64" | ||
env: | ||
GROUP: "CPU" | ||
if: build.message !~ /\[skip tests\]/ | ||
timeout_in_minutes: 240 | ||
matrix: | ||
setup: | ||
julia: | ||
- "1" | ||
|
||
- label: ":julia: Julia {{matrix.julia}} + CUDA GPU" | ||
plugins: | ||
- JuliaCI/julia#v1: | ||
version: "{{matrix.julia}}" | ||
- JuliaCI/julia-test#v1: | ||
test_args: "--quickfail" | ||
agents: | ||
queue: "juliagpu" | ||
cuda: "*" | ||
env: | ||
GROUP: "CUDA" | ||
if: build.message !~ /\[skip tests\]/ | ||
timeout_in_minutes: 240 | ||
matrix: | ||
setup: | ||
julia: | ||
- "1" | ||
|
||
- label: ":julia: Julia: {{matrix.julia}} + AMD GPU" | ||
plugins: | ||
- JuliaCI/julia#v1: | ||
version: "{{matrix.julia}}" | ||
- JuliaCI/julia-test#v1: | ||
test_args: "--quickfail" | ||
env: | ||
JULIA_AMDGPU_CORE_MUST_LOAD: "1" | ||
JULIA_AMDGPU_HIP_MUST_LOAD: "1" | ||
JULIA_AMDGPU_DISABLE_ARTIFACTS: "1" | ||
GROUP: "AMDGPU" | ||
agents: | ||
queue: "juliagpu" | ||
rocm: "*" | ||
rocmgpu: "*" | ||
if: build.message !~ /\[skip tests\]/ | ||
timeout_in_minutes: 240 | ||
matrix: | ||
setup: | ||
julia: | ||
- "1" |
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name: CompatHelper | ||
on: | ||
schedule: | ||
- cron: 0 0 * * * | ||
workflow_dispatch: | ||
jobs: | ||
CompatHelper: | ||
runs-on: ubuntu-latest | ||
steps: | ||
- name: Pkg.add("CompatHelper") | ||
run: julia -e 'using Pkg; Pkg.add("CompatHelper")' | ||
- name: CompatHelper.main() | ||
env: | ||
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} | ||
COMPATHELPER_PRIV: ${{ secrets.DOCUMENTER_KEY }} | ||
run: julia -e 'using CompatHelper; CompatHelper.main()' |
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name: TagBot | ||
on: | ||
issue_comment: | ||
types: | ||
- created | ||
workflow_dispatch: | ||
inputs: | ||
lookback: | ||
default: 3 | ||
permissions: | ||
actions: read | ||
checks: read | ||
contents: write | ||
deployments: read | ||
issues: read | ||
discussions: read | ||
packages: read | ||
pages: read | ||
pull-requests: read | ||
repository-projects: read | ||
security-events: read | ||
statuses: read | ||
jobs: | ||
TagBot: | ||
if: github.event_name == 'workflow_dispatch' || github.actor == 'JuliaTagBot' | ||
runs-on: ubuntu-latest | ||
steps: | ||
- uses: JuliaRegistries/TagBot@v1 | ||
with: | ||
token: ${{ secrets.GITHUB_TOKEN }} | ||
ssh: ${{ secrets.DOCUMENTER_KEY }} |
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/Manifest.toml | ||
test/Manifest.toml | ||
.vscode | ||
wip | ||
test_ext | ||
ext_compat | ||
.CondaPkg |
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[deps] | ||
# Conda package names and versions | ||
python = ">=3.10,<3.12" | ||
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[pip.deps] | ||
flax = ">= 0.7" | ||
numpy = ">= 1" |
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MIT License | ||
|
||
Copyright (c) 2023 Avik Pal <[email protected]> and contributors | ||
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Permission is hereby granted, free of charge, to any person obtaining a copy | ||
of this software and associated documentation files (the "Software"), to deal | ||
in the Software without restriction, including without limitation the rights | ||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | ||
copies of the Software, and to permit persons to whom the Software is | ||
furnished to do so, subject to the following conditions: | ||
|
||
The above copyright notice and this permission notice shall be included in all | ||
copies or substantial portions of the Software. | ||
|
||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | ||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | ||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | ||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | ||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | ||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | ||
SOFTWARE. |
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name = "LuxJax" | ||
uuid = "0265fd5b-45c4-47b1-878d-f9552b087dff" | ||
authors = ["Avik Pal <[email protected]> and contributors"] | ||
version = "0.1.0" | ||
|
||
[deps] | ||
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" | ||
ConcreteStructs = "2569d6c7-a4a2-43d3-a901-331e8e4be471" | ||
CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" | ||
DLPack = "53c2dc0f-f7d5-43fd-8906-6c0220547083" | ||
Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196" | ||
LuxCore = "bb33d45b-7691-41d6-9220-0943567d0623" | ||
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" | ||
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" | ||
|
||
[compat] | ||
julia = "1.9" |
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# LuxJax | ||
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LuxJax allows you to use Neural Networks written in Jax with the Lux API, allowing seamless | ||
integration with the rest of the SciML ecosystem. | ||
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||
Lux.jl is great and is quite fast and useful if you are implementing custom operations. | ||
However, there are quite a few standard workloads where XLA can highly optimize the training | ||
and inference. This package bridges that gap, and allows you to use the fast Jax Neural | ||
Networks with the SciMLverse! | ||
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||
## Installation | ||
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The installation is currently a bit manual. First install this package. | ||
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```julia | ||
import Pkg | ||
Pkg.add("https://github.com/LuxDL/LuxJax.jl") | ||
``` | ||
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Then install the Jax dependencies. | ||
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```julia | ||
using LuxJax | ||
LuxJax.install("<setup>") | ||
``` | ||
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`install` will install the Jax dependencies based on the `setup` provided! | ||
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## Usage Example | ||
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```julia | ||
using LuxJax | ||
``` | ||
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## Tips | ||
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* When mixing jax and julia it's recommended to disable jax's preallocation with setting the | ||
environment variable `XLA_PYTHON_CLIENT_PREALLOCATE=false`. | ||
|
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## Roadmap | ||
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- [ ] Automatic Differentiation | ||
- [ ] Capture Chain Rules | ||
- [ ] Reverse Mode | ||
- [ ] Forward Mode (Very Low Priority) | ||
- [ ] Capture ForwardDiff Duals for Forward Mode | ||
- [ ] Automatically Map Lux Models to Flax Models similar to the Flux to Lux conversion | ||
- [ ] Handle Component Arrays | ||
- [ ] Demonstrate Training of Neural ODEs using Jax and SciMLSensitivity.jl | ||
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## Acknowledgements | ||
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||
This packages is a more opinionated take on | ||
[PyCallChainRules.jl](https://github.com/rejuvyesh/PyCallChainRules.jl) |
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module LuxJax | ||
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using CondaPkg, PythonCall # Interact with Jax | ||
using DLPack # Transfer Data between Julia and Jax | ||
using ChainRulesCore, Functors, Random | ||
using LuxCore | ||
import ConcreteStructs: @concrete | ||
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# Setup Jax | ||
const jax = PythonCall.pynew() | ||
const dlpack = PythonCall.pynew() | ||
const jnp = PythonCall.pynew() | ||
const numpy = PythonCall.pynew() | ||
const flax = PythonCall.pynew() | ||
const linen = PythonCall.pynew() | ||
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const is_jax_setup = Ref{Bool}(false) | ||
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const VALID_JAX_SETUPS = ("cpu", "cuda12_pip", "cuda11_pip", "cuda12_local", "cuda11_local", | ||
"tpu") | ||
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function __load_jax_dependencies(; force::Bool = true) | ||
try | ||
CondaPkg.withenv() do | ||
PythonCall.pycopy!(jax, pyimport("jax")) | ||
PythonCall.pycopy!(dlpack, pyimport("jax.dlpack")) | ||
PythonCall.pycopy!(jnp, pyimport("jax.numpy")) | ||
PythonCall.pycopy!(flax, pyimport("flax")) | ||
PythonCall.pycopy!(linen, pyimport("flax.linen")) | ||
end | ||
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is_jax_setup[] = true | ||
catch err | ||
is_jax_setup[] = false | ||
|
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if force | ||
rethrow(err) | ||
else | ||
@warn "Jax is not installed. Please install Jax first using `LuxJax.install(<setup>)`!" | ||
@debug err | ||
end | ||
end | ||
end | ||
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function __init__() | ||
CondaPkg.withenv() do | ||
PythonCall.pycopy!(numpy, pyimport("numpy")) | ||
end | ||
__load_jax_dependencies(; force = false) | ||
end | ||
|
||
""" | ||
install(setup::String = "cpu") | ||
Installs Jax into the correct environment. The `setup` argument can be one of the following: | ||
- `"cpu"`: Installs Jax with CPU support. | ||
- `"cuda12_pip"`: Installs Jax with CUDA 12 support using pip. | ||
- `"cuda11_pip"`: Installs Jax with CUDA 11 support using pip. | ||
- `"cuda12_local"`: Installs Jax with CUDA 12 support using a local CUDA installation. | ||
- `"cuda11_local"`: Installs Jax with CUDA 11 support using a local CUDA installation. | ||
- `"tpu"`: Installs Jax with TPU support. | ||
""" | ||
function install(setup::String = "cpu") | ||
@assert setup ∈ VALID_JAX_SETUPS "Invalid setup: $(setup)! Select one of $(VALID_JAX_SETUPS)!" | ||
CondaPkg.withenv() do | ||
python = CondaPkg.which("python") | ||
run(`$(python) --version`) | ||
run(`$(python) -m pip install --upgrade pip`) | ||
if occursin("cpu", setup) | ||
run(`$(python) -m pip install --upgrade jax\[cpu\]\>=0.4`) | ||
elseif occursin("cuda", setup) | ||
run(`$(python) -m pip install --upgrade jax\[$(setup)\]\>=0.4 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html`) | ||
else # TPU | ||
run(`$(python) -m pip install --upgrade jax\[$(setup)\]\>=0.4 -f https://storage.googleapis.com/jax-releases/libtpu_releases.html`) | ||
end | ||
end | ||
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__load_jax_dependencies() | ||
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return | ||
end | ||
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# FIXME: Wrap DLPack functions to allow them to work for tuple, namedtuple and functors | ||
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# Lux Interop | ||
__from_flax_params(ps) = (; params = Tuple(map(p -> DLPack.wrap(p, dlpack.to_dlpack), ps))) | ||
|
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function __to_flax_params(ps, tree) | ||
return jax.tree_util.tree_unflatten(tree, | ||
pylist(map(p -> DLPack.share(p, dlpack.from_dlpack), ps.params))) | ||
end | ||
|
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@concrete struct LuxFlaxWrapper <: LuxCore.AbstractExplicitLayer | ||
flaxmodel | ||
input_shape | ||
tree_structure | ||
end | ||
|
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function LuxFlaxWrapper(flaxmodel, input_shape) | ||
return LuxFlaxWrapper(flaxmodel, input_shape, PythonCall.pynew()) | ||
end | ||
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function LuxCore.initialparameters(rng::AbstractRNG, layer::LuxFlaxWrapper) | ||
seed = rand(rng, UInt32) | ||
params = layer.flaxmodel.init(jax.random.PRNGKey(seed), | ||
jnp.ones((1, reverse(layer.input_shape)...))) | ||
ps_flat, tree_structure = jax.tree_util.tree_flatten(params) | ||
PythonCall.pycopy!(layer.tree_structure, tree_structure) | ||
return __from_flax_params(ps_flat) | ||
end | ||
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(l::LuxFlaxWrapper)(x, ps, st) = LuxCore.apply(l, x, ps, st) | ||
|
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function LuxCore.apply(l::LuxFlaxWrapper, x, ps, st::NamedTuple) | ||
x_jax = DLPack.share(x, dlpack.from_dlpack) | ||
ps_jax = __to_flax_params(ps, l.tree_structure) | ||
y = l.flaxmodel.apply(ps_jax, x_jax) | ||
return DLPack.wrap(y, dlpack.to_dlpack), st | ||
end | ||
|
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function ChainRulesCore.rrule(::typeof(LuxCore.apply), l::LuxFlaxWrapper, x, ps, | ||
st::NamedTuple) | ||
projectₓ = ProjectTo(x) | ||
projectₚ = ProjectTo(ps) | ||
x_jax = DLPack.share(x, dlpack.from_dlpack) | ||
ps_jax = __to_flax_params(ps, l.tree_structure) | ||
y, jax_vjpfun = jax.vjp(l.flaxmodel.apply, ps_jax, x_jax) | ||
function ∇flax_apply(Δ) | ||
# FIXME: Fix dispatches so that we dont have to collect | ||
∂y = DLPack.share(collect(first(unthunk(Δ))), dlpack.from_dlpack) | ||
(∂ps_jax, ∂x_jax) = jax_vjpfun(∂y) | ||
∂x = projectₓ(DLPack.wrap(∂x_jax, dlpack.to_dlpack)) | ||
∂ps = projectₚ(__from_flax_params(jax.tree_util.tree_flatten(∂ps_jax)[0])) | ||
return (NoTangent(), NoTangent(), ∂x, ∂ps, NoTangent()) | ||
end | ||
return (DLPack.wrap(y, dlpack.to_dlpack), st), ∇flax_apply | ||
end | ||
|
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# exports | ||
export jax, jnp, flax, linen | ||
export LuxFlaxWrapper | ||
|
||
end |
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[deps] | ||
Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595" | ||
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" | ||
Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196" | ||
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" | ||
SafeTestsets = "1bc83da4-3b8d-516f-aca4-4fe02f6d838f" | ||
StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3" | ||
Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40" | ||
Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f" |
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using Aqua, LuxJax, SafeTestsets, Test | ||
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||
@testset "LuxJax.jl" begin | ||
@test 1 == 1 | ||
end |