Set-up JuliaFormatter (#67)

* Set-up JuliaFormatter

* Implemented formatting

.JuliaFormatter.toml

@@ -0,0 +1,6 @@
+style = "blue"
+
+ignore = ["src/Wrapper.jl"]
+pipe_to_function_call = false
+whitespace_in_kwargs = true
+whitespace_typedefs = true

.github/workflows/Format.yml

@@ -0,0 +1,8 @@
+name: Format suggestions
+on:
+  pull_request:
+jobs:
+  code-style:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: julia-actions/julia-format@v3

README.md

@@ -1,5 +1,8 @@
 # Torch.jl
 
+[![Build Status](https://github.com/FluxML/Torch.jl/actions/workflows/CI.yaml/badge.svg?branch=master)](https://github.com/FluxML/Torch.jl/actions/workflows/CI.yaml?query=branch%3Amaster)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/JuliaDiff/BlueStyle)
+
 Sensible extensions for exposing torch in Julia.
 
 This package is aimed at providing the `Tensor` type, which offloads all computations over to [ATen](https://pytorch.org/cppdocs/), the foundational tensor library for PyTorch, written in C++.

deps/julia_wrapper_generator/generator.jl

@@ -22,11 +22,11 @@ function rewrite!(e::Expr)
 end
 
 function rewrite!(e::Expr, ::Val{:function})
-    rewrite!(e.args[2], Val(e.args[2].head))
+    return rewrite!(e.args[2], Val(e.args[2].head))
 end
 
 function rewrite!(e::Expr, ::Val{:block})
-    e.args[1] = Expr(:macrocall, Symbol("@runtime_error_check"), nothing, e.args[1])
+    return e.args[1] = Expr(:macrocall, Symbol("@runtime_error_check"), nothing, e.args[1])
 end
 
 function rewrite!(dag::ExprDAG)

deps/julia_wrapper_generator/generator.toml

@@ -1,6 +1,6 @@
 [general]
 library_name = "libtorch_c_api"
-output_file_path = "../../src/wrapper.jl"
+output_file_path = "../../src/Wrapper.jl"
 prologue_file_path = "./prologue.jl"
 module_name = "Wrapper"
 jll_pkg_name = "TorchCAPI_jll"

deps/julia_wrapper_generator/prologue.jl

@@ -1,15 +1,15 @@
 function get_error()
-  err = cglobal((:myerr, libtorch_c_api), Cstring) |> unsafe_load
-  unsafe_string(err)
+    err = cglobal((:myerr, libtorch_c_api), Cstring) |> unsafe_load
+    return unsafe_string(err)
 end
 
 macro runtime_error_check(ex)
-  quote
-    x = $ex
-    if x == 1
-      cs = get_error()
-      flush_error()
-      throw(cs)
-    end
-  end |> esc
+    return quote
+        x = $ex
+        if x == 1
+            cs = get_error()
+            flush_error()
+            throw(cs)
+        end
+    end |> esc
 end

src/Torch.jl

@@ -13,7 +13,7 @@ using FillArrays
 
 TURN_ON_LOGGING = false
 
-include("wrapper.jl")
+include("Wrapper.jl")
 
 using .Wrapper
 
@@ -32,23 +32,25 @@ include("statistics.jl")
 include("grads.jl")
 include("utils.jl")
 
-@init @require Flux="587475ba-b771-5e3f-ad9e-33799f191a9c" begin
-  using .Flux
+@init @require Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c" begin
+    using .Flux
 
-  function (tbn::Flux.BatchNorm)(x::Tensor)
-    tbn.λ.(Torch.batchnorm(x, tbn.γ,  tbn.β,  tbn.μ, tbn.σ², 0, tbn.momentum, tbn.ϵ, 1))
-  end
+    function (tbn::Flux.BatchNorm)(x::Tensor)
+        return tbn.λ.(
+            Torch.batchnorm(x, tbn.γ, tbn.β, tbn.μ, tbn.σ², 0, tbn.momentum, tbn.ϵ, 1)
+        )
+    end
 
-  function Flux.Zygote.accum(t1::Tensor, t2::Tensor{T,N}) where {T,N}
-    ptr = Ref(Ptr{Cvoid}())
+    function Flux.Zygote.accum(t1::Tensor, t2::Tensor{T, N}) where {T, N}
+        ptr = Ref(Ptr{Cvoid}())
 
-    Torch.Wrapper.atg_add_(ptr, t1.ptr, t2.ptr)
-    Tensor{T,N}(ptr[], Torch.on(t1))
-  end
+        Torch.Wrapper.atg_add_(ptr, t1.ptr, t2.ptr)
+        return Tensor{T, N}(ptr[], Torch.on(t1))
+    end
 
-  eval(:(Flux.Zygote.@nograd Torch.Wrapper.at_copy_data))
-  eval(:(Flux.Zygote.@nograd Torch.Wrapper.at_dim))
-  torch(x) = Flux.fmap(to_tensor, x)
+    eval(:(Flux.Zygote.@nograd Torch.Wrapper.at_copy_data))
+    eval(:(Flux.Zygote.@nograd Torch.Wrapper.at_dim))
+    torch(x) = Flux.fmap(to_tensor, x)
 end
 
 end # module

src/broadcast.jl

@@ -9,23 +9,23 @@ using Base.Broadcast: broadcast_shape
 # Base.BroadcastStyle(::Type{Tensor}) = TensorStyle()
 
 for op in (:+, :-, :/)
-  @eval function broadcasted(::typeof($op), t1::Tensor, t2::Tensor)
-    $op(t1, t2)
-  end
+    @eval function broadcasted(::typeof($op), t1::Tensor, t2::Tensor)
+        return $op(t1, t2)
+    end
 end
 
 for op in (:+, :-)
-  @eval function broadcasted(::typeof($op), t1::Tensor, t2::TensorVector)
-    t_ = reshape(t2, -1, 1)
-    $op(t1, t_)
-  end
+    @eval function broadcasted(::typeof($op), t1::Tensor, t2::TensorVector)
+        t_ = reshape(t2, -1, 1)
+        return $op(t1, t_)
+    end
 end
 
-function broadcasted(::typeof(*), t1::Tensor{T,N}, t2::Tensor{T,M}) where {T,N,M}
-  ptr = Ref(Ptr{Cvoid}())
+function broadcasted(::typeof(*), t1::Tensor{T, N}, t2::Tensor{T, M}) where {T, N, M}
+    ptr = Ref(Ptr{Cvoid}())
 
-  atg_mul(ptr, t1.ptr, t2.ptr)
-  Tensor{T,max(N,M)}(ptr[], on(t1))
+    atg_mul(ptr, t1.ptr, t2.ptr)
+    return Tensor{T, max(N, M)}(ptr[], on(t1))
 end
 
 broadcasted(::typeof(NNlib.relu), t::Tensor) = NNlib.relu(t)
@@ -34,22 +34,21 @@ broadcasted(::typeof(identity), t::Tensor) = identity(t)
 broadcasted(::typeof(NNlib.sigmoid), t::Tensor) = NNlib.sigmoid(t)
 
 for op in (:+, :-, :*, :/)
-  @eval function broadcasted(::typeof($op), t::Tensor, args...)
-    $op(t, args...)
-  end
+    @eval function broadcasted(::typeof($op), t::Tensor, args...)
+        return $op(t, args...)
+    end
 end
 
 broadcasted(::typeof(sqrt), t::Tensor) = sqrt(t)
 
-function broadcasted(::typeof(copy), t::Tensor{T,N}) where {T,N}
-  t
+function broadcasted(::typeof(copy), t::Tensor{T, N}) where {T, N}
+    return t
 end
 
 @adjoint function broadcast(::typeof(NNlib.sigmoid), t::Tensor)
-
-  NNlib.sigmoid(t), Δ -> (∇sigmoid(Δ, t),)
+    return NNlib.sigmoid(t), Δ -> (∇sigmoid(Δ, t),)
 end
 
-@adjoint function broadcasted(::typeof(NNlib.relu), t::Tensor{T}) where T
-  relu(t), Δ -> (nothing, ∇leaky_relu(Δ, t, zero(T)),)
+@adjoint function broadcasted(::typeof(NNlib.relu), t::Tensor{T}) where {T}
+    return relu(t), Δ -> (nothing, ∇leaky_relu(Δ, t, zero(T)))
 end