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test/test_utils.jl

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@@ -142,7 +142,6 @@ function test_approx_structs(l, l̄, l̄2; atol=1e-5, rtol=1e-5,
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end
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"""
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to32(m)
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@@ -166,57 +165,3 @@ function to64(m)
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f(x) = adapt(Float64, x)
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return fmap(f, m)
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end
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# function gpu_gradtest(l, x_cpu = nothing, args...; test_cpu = true)
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# isnothing(x_cpu) && error("Missing input to test the layers against.")
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# @testset "$name GPU grad tests" begin
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# for layer in layers
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# @testset "$layer Layer GPU grad test" begin
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# # compute output and grad of parameters
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# l_cpu = layer(args...)
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# ps_cpu = Flux.params(l_cpu)
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# y_cpu, back_cpu = pullback(() -> sum(l_cpu(x_cpu)), ps_cpu)
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# gs_cpu = back_cpu(1f0)
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# x_gpu = gpu(x_cpu)
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# l_gpu = l_cpu |> gpu
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# ps_gpu = Flux.params(l_gpu)
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# if typeof(l_gpu) <: BROKEN_LAYERS
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# @test_broken gradient(() -> sum(l_gpu(x_gpu)), ps_gpu) isa Flux.Zygote.Grads
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# else
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# y_gpu, back_gpu = pullback(() -> sum(l_gpu(x_gpu)), ps_gpu)
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# gs_gpu = back_gpu(1f0) # TODO many layers error out when backprop int 1, should fix
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# # compute grad of input
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# xg_cpu = gradient(x -> sum(l_cpu(x)), x_cpu)[1]
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# xg_gpu = gradient(x -> sum(l_gpu(x)), x_gpu)[1]
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# # test
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# if test_cpu
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# @test y_gpu ≈ y_cpu rtol=1f-3 atol=1f-3
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# if isnothing(xg_cpu)
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# @test isnothing(xg_gpu)
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# else
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# @test Array(xg_gpu) ≈ xg_cpu rtol=1f-3 atol=1f-3
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# end
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# end
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# @test gs_gpu isa Flux.Zygote.Grads
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# for (p_cpu, p_gpu) in zip(ps_cpu, ps_gpu)
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# if isnothing(gs_cpu[p_cpu])
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# @test isnothing(gs_gpu[p_gpu])
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# else
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# @test gs_gpu[p_gpu] isa Flux.CUDA.CuArray
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# if test_cpu
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# @test Array(gs_gpu[p_gpu]) ≈ gs_cpu[p_cpu] rtol=1f-3 atol=1f-3
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# end
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# end
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# end
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# end
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# end
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# end
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# end
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# end

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