Remove timer outputs as they mess with type inference

Author: Avik Pal

Manifest.toml

@@ -9,12 +9,6 @@ git-tree-sha1 = "055eb2690182ebc31087859c3dd8598371d3ef9e"
 uuid = "b99e7846-7c00-51b0-8f62-c81ae34c0232"
 version = "0.5.3"
 
-[[Crayons]]
-deps = ["Test"]
-git-tree-sha1 = "f621b8ef51fd2004c7cf157ea47f027fdeac5523"
-uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
-version = "4.0.0"
-
 [[Dates]]
 deps = ["Printf"]
 uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
@@ -87,12 +81,6 @@ uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
-[[TimerOutputs]]
-deps = ["Crayons", "Printf", "Test", "Unicode"]
-git-tree-sha1 = "b80671c06f8f8bae08c55d67b5ce292c5ae2660c"
-uuid = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
-version = "0.5.0"
-
 [[UUIDs]]
 deps = ["Random", "SHA"]
 uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

Project.toml

@@ -8,7 +8,6 @@ Libdl = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-TimerOutputs = "a759f4b9-e2f1-59dc-863e-4aeb61b1ea8f"
 
 [extras]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

src/NNlib.jl

@@ -1,9 +1,6 @@
 module NNlib
 using Requires, TimerOutputs
 
-const to = TimerOutput()
-
-
 # Include APIs
 include("dim_helpers.jl")
 

src/conv.jl

@@ -45,7 +45,7 @@ for (front_name, backend) in (
     # We only define 3d conv primitives, we reshape lower down to get 1d and 2d convolution
     @eval begin
         # im2col-accelerated function forwarding definition
-        @timeit_debug to function $(Symbol("$(front_name)!"))(
+        function $(Symbol("$(front_name)!"))(
                         out::AbstractArray{T,5}, in1::AbstractArray{T,5},
                         in2::AbstractArray{T,5}, cdims::ConvDims; kwargs...) where {T <: $G}
             $(Symbol("$(front_name)_$(backend)!"))(out, in1, in2, cdims; kwargs...)
@@ -106,7 +106,7 @@ for backend in (Symbol(), :_direct, :_im2col)
     # First make auto-allocating versions of the conv()-like calls:
     for name in (:conv, :depthwiseconv)
         @eval begin
-            @timeit_debug to function $(Symbol("$(name)$(backend)"))(
+            function $(Symbol("$(name)$(backend)"))(
                             x::AbstractArray{xT,N}, w::AbstractArray{wT,N},
                             cdims::ConvDims; kwargs...) where {xT, wT, N}
                 y = similar(x, promote_type(xT, wT), output_size(cdims)...,
@@ -118,7 +118,7 @@ for backend in (Symbol(), :_direct, :_im2col)
 
     for name in (:∇conv_data, :∇depthwiseconv_data)
         @eval begin
-            @timeit_debug to function $(Symbol("$(name)$(backend)"))(
+            function $(Symbol("$(name)$(backend)"))(
                             dy::AbstractArray{yT,N}, w::AbstractArray{wT,N},
                             cdims::ConvDims; kwargs...) where {yT, wT, N}
                 dx = similar(dy, input_size(cdims)..., channels_in(cdims),
@@ -131,7 +131,7 @@ for backend in (Symbol(), :_direct, :_im2col)
     # We do the conv/depthwiseconv filter backprops separately, as the shape calculation
     # for `w` is slightly different for depthwise than for normal dense convolution.
     @eval begin
-        @timeit_debug to function $(Symbol("∇conv_filter$(backend)"))(
+        function $(Symbol("∇conv_filter$(backend)"))(
                         x::AbstractArray{xT,N}, dy::AbstractArray{yT,N},
                         cdims::ConvDims; kwargs...) where {xT, yT, N}
             dw = similar(dy, kernel_size(cdims)..., channels_in(cdims),
@@ -141,7 +141,7 @@ for backend in (Symbol(), :_direct, :_im2col)
     end
 
     @eval begin
-        @timeit_debug to function $(Symbol("∇depthwiseconv_filter$(backend)"))(
+        function $(Symbol("∇depthwiseconv_filter$(backend)"))(
                         x::AbstractArray{xT,N}, dy::AbstractArray{yT,N},
                         cdims::ConvDims; kwargs...) where {xT, yT, N}
             dw = similar(dy, kernel_size(cdims)..., channel_multiplier(cdims),

src/pooling.jl

@@ -107,7 +107,7 @@ for backend in (Symbol(), :_direct, :_im2col)
     # First make auto-allocating versions of the basic pooling calls:
     for name in (:maxpool, :meanpool)
         @eval begin
-            @timeit_debug to function $(Symbol("$(name)$(backend)"))(
+            function $(Symbol("$(name)$(backend)"))(
                             x::AbstractArray{xT,N},
                             pdims::PoolDims; kwargs...) where {xT, N}
                 y = similar(x, output_size(pdims)..., channels_out(pdims), size(x, N))
@@ -116,7 +116,7 @@ for backend in (Symbol(), :_direct, :_im2col)
             end
 
             # Backprops too
-            @timeit_debug to function $(Symbol("∇$(name)$(backend)"))(
+            function $(Symbol("∇$(name)$(backend)"))(
                             dy::AbstractArray{T,N}, y::AbstractArray{T,N},
                             x::AbstractArray{T,N}, pdims::PoolDims;
                             kwargs...) where {T, N}