Fix tanh_fast for large Float64 (#408)

* fix tanh_fast for large Float64

* bump version

Author: mcabbott

Project.toml

@@ -1,6 +1,6 @@
 name = "NNlib"
 uuid = "872c559c-99b0-510c-b3b7-b6c96a88d5cd"
-version = "0.8.4"
+version = "0.8.5"
 
 [deps]
 Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"

src/activations.jl

@@ -780,7 +780,7 @@ end
     # Instead, we switch to a polynomial, which is very accurate within its range:
     x2 = x * x
     ypoly = x * evalpoly(x2, (1.0, -0.33333333333324583, 0.13333333325511604, -0.05396823125794372, 0.02186660872609521, -0.008697141630499953))
-    ifelse(x2 > 900.0, sign(y), ifelse(x2 < 0.017, oftype(y, ypoly), y))
+    ifelse(x2 > 900.0, sign(x), ifelse(x2 < 0.017, ypoly, y))
 end
 
 # These approximations are very badly behaved for Float16; none are fast.

test/activations.jl

@@ -227,7 +227,7 @@ end
 @testset "tanh_fast & sigmoid_fast: Float64" begin
 
     x64 = 1e-6:1e-4:5
-    xbig = 6:3:200.0
+    xbig = vcat(6:3:200.0, 1000, 10^6, typemax(Float64))
 
     @testset "tanh" begin
         mean_eps(tanh, tanh, x64)  # 0.06582
@@ -263,7 +263,7 @@ end
 @testset "tanh_fast & sigmoid_fast: Float32" begin
 
     x32 = 1f-6:1f-4:5
-    xbig32 = 6:3:200f0
+    xbig32 = vcat(6:3:200f0, 1000, typemax(Float32))
 
     @testset "tanh" begin
         mean_eps(tanh, tanh, x32)  # 0.065