Cleanup for v0.14 release

docs/src/gpu.md

@@ -1,11 +1,23 @@
 # GPU Support
 
-NVIDIA GPU support should work out of the box on systems with CUDA and CUDNN installed. For more details see the [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) readme.
+Flux doesn't force a specific GPU backend and the corresponding package dependencies on the users. 
+Thanks to the [package extension mechanism](
+https://pkgdocs.julialang.org/v1/creating-packages/#Conditional-loading-of-code-in-packages-(Extensions)),
+Flux conditionally load GPU specific code once a GPU package is made available (e.g. through `using CUDA`).
+
+NVIDIA GPU support requires the packages `CUDA.jl` and `cuDNN.jl` to be installed in the environment. In the julia REPL, type `] add CUDA, cuDNN` to install them. For more details see the [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) readme.
 
 AMD GPU support is available since Julia 1.9 on systems with ROCm and MIOpen installed. For more details refer to the [AMDGPU.jl](https://github.com/JuliaGPU/AMDGPU.jl) repository.
 
 Metal GPU acceleration is available on Apple Silicon hardware. For more details refer to the [Metal.jl](https://github.com/JuliaGPU/Metal.jl) repository. Metal support in Flux is experimental and many features are not yet available.
 
+In order to trigger GPU support in Flux, you need to call `using CUDA`, `using AMDGPU` or `using Metal`
+in your code. Notice that for CUDA, explicitely loading also `cuDNN` is not required, but the package has to be installed in the environment. 
+
+
+!!! compat "Flux ≤ 0.13"
+    Old versions of Flux automatically installed CUDA.jl to provide GPU support. Starting from Flux v0.14, CUDA.jl is not a dependency anymore and has to be installed manually.
+
 ## Checking GPU Availability
 
 By default, Flux will run the checks on your system to see if it can support GPU functionality. You can check if Flux identified a valid GPU setup by typing the following:

docs/src/index.md

@@ -8,7 +8,8 @@ Flux is a library for machine learning. It comes "batteries-included" with many
 
 ### Installation
 
-Download [Julia 1.9](https://julialang.org/downloads/) or later, preferably the current stable release. You can add Flux using Julia's package manager, by typing `] add Flux` in the Julia prompt. This will automatically install several other packages, including [CUDA.jl](https://github.com/JuliaGPU/CUDA.jl) for Nvidia GPU support.
+Download [Julia 1.9](https://julialang.org/downloads/) or later, preferably the current stable release. You can add Flux using Julia's package manager, by typing `] add Flux` in the Julia prompt. 
+For Nvidia GPU support, you will also need to install the `CUDA` and the `cuDNN` packages. For AMD GPU support, install the `AMDGPU` package. For acceleration on Apple Silicon, install the `Metal` package.
 
 ### Learning Flux
 

src/deprecations.jl

@@ -1,19 +1,3 @@
-# v0.12 deprecations
-
-function ones(dims...)
-  Base.depwarn("Flux.ones(size...) is deprecated, please use Flux.ones32(size...) or Base.ones(Float32, size...)", :ones, force=true)
-  Base.ones(Float32, dims...)
-end
-ones(T::Type, dims...) = Base.ones(T, dims...)
-
-function zeros(dims...)
-  Base.depwarn("Flux.zeros(size...) is deprecated, please use Flux.zeros32(size...) or Base.zeros(Float32, size...)", :zeros, force=true)
-  Base.zeros(Float32, dims...)
-end
-zeros(T::Type, dims...) = Base.zeros(T, dims...)
-
-ones32(::Type, dims...) = throw(ArgumentError("Flux.ones32 is always Float32, use Base.ones to specify the element type"))
-zeros32(::Type, dims...) = throw(ArgumentError("Flux.zeros32 is always Float32, use Base.zeros to specify the element type"))
 
 # v0.13 deprecations
 
@@ -59,7 +43,7 @@ function loadparams!(m, xs)
 end
 
 # Channel notation: Changed to match Conv, but very softly deprecated!
-# Perhaps change to @deprecate for v0.14, but there is no plan to remove these.
+# Perhaps change to @deprecate for v0.15, but there is no plan to remove these.
 Dense(in::Integer, out::Integer, σ = identity; kw...) =
   Dense(in => out, σ; kw...)
 Bilinear(in1::Integer, in2::Integer, out::Integer, σ = identity; kw...) =
@@ -217,7 +201,7 @@ ChainRulesCore.@non_differentiable _greek_ascii_depwarn(::Any...)
 
 # v0.14 deprecations
 
-# Enable these when 0.14 is released, and delete const ClipGrad = Optimise.ClipValue etc: 
+# Enable these when 0.15 is released, and delete const ClipGrad = Optimise.ClipValue etc: 
 # Base.@deprecate_binding Optimiser OptimiserChain
 # Base.@deprecate_binding ClipValue ClipGrad
 

src/utils.jl

@@ -47,11 +47,7 @@ rng_from_array(::AbstractArray) = default_rng_value()
 
 @non_differentiable rng_from_array(::Any)
 
-if VERSION >= v"1.7"
-  default_rng_value() = Random.default_rng()
-else
-  default_rng_value() = Random.GLOBAL_RNG
-end
+default_rng_value() = Random.GLOBAL_RNG
 
 """
     default_rng_value()

test/layers/conv.jl

@@ -299,7 +299,5 @@ end
 
   c3 = ConvTranspose((3,), 2=>4, relu)
   @test c3(x) isa Array{Float32, 3}
-  if VERSION >= v"1.8"
-    @test (@inferred c3(x); true)  # fails on 1.6
-  end
+  @test (@inferred c3(x); true)
 end

test/layers/normalisation.jl

@@ -69,11 +69,7 @@ evalwgrad(f, x...) = pullback(f, x...)[1]
 
     # CPU RNGs map onto CPU ok
     if isempty(rng_kwargs)
-      if VERSION >= v"1.7"
-        @test cpu(m).rng isa Random.TaskLocalRNG
-      else
-        @test cpu(m).rng isa Random._GLOBAL_RNG
-      end
+      @test cpu(m).rng isa Random.TaskLocalRNG
     else
       @test cpu(m).rng === only(values(rng_kwargs))
     end
@@ -118,11 +114,7 @@ end
 
     # CPU RNGs map onto CPU ok
     if isempty(rng_kwargs)
-      if VERSION >= v"1.7"
-        @test cpu(m).rng isa Random.TaskLocalRNG
-      else
-        @test cpu(m).rng isa Random._GLOBAL_RNG
-      end
+      @test cpu(m).rng isa Random.TaskLocalRNG
     else
       @test cpu(m).rng === only(values(rng_kwargs))
     end

test/layers/recurrent.jl

@@ -58,17 +58,10 @@ end
 
     @test primal[1] ≈ e
 
-    if VERSION < v"1.7"
-        @test ∇Wi ≈ grads[:Wi]
-        @test ∇Wh ≈ grads[:Wh]
-        @test ∇b ≈ grads[:b]
-        @test_broken ∇state0 ≈ grads[:state0]
-    else
-        @test_broken ∇Wi ≈ grads[:Wi]
-        @test_broken ∇Wh ≈ grads[:Wh]
-        @test_broken ∇b ≈ grads[:b]
-        @test_broken ∇state0 ≈ grads[:state0]
-    end
+    @test_broken ∇Wi ≈ grads[:Wi]
+    @test_broken ∇Wh ≈ grads[:Wh]
+    @test_broken ∇b ≈ grads[:b]
+    @test_broken ∇state0 ≈ grads[:state0]
 end
 
 # Ref FluxML/Flux.jl#1209 1D input