More lazy strings

src/functor.jl

@@ -64,12 +64,12 @@ Possible values of  `inactive` are:
 """
 function testmode!(m, mode)
   inactive = if mode isa Symbol
-    mode === :auto || throw(ArgumentError("testmode! accepts only the symbol :auto, got :$mode"))
+    mode === :auto || throw(ArgumentError(lazy"testmode! accepts only the symbol :auto, got :$mode"))
     nothing
   elseif mode isa Union{Bool,Nothing}
     mode
   else
-    throw(ArgumentError("testmode! does not accept $(repr(mode)) as the 2nd argument"))
+    throw(ArgumentError(lazy"testmode! does not accept $(repr(mode)) as the 2nd argument"))
   end
   foreach(x -> testmode!(x, inactive), trainable(m))
   m

src/layers/basic.jl

@@ -75,7 +75,7 @@ function Base.show(io::IO, c::Chain)
 end
 
 _show_layers(io, layers::Tuple) = join(io, layers, ", ")
-_show_layers(io, layers::NamedTuple) = join(io, ["$k = $v" for (k, v) in pairs(layers)], ", ")
+_show_layers(io, layers::NamedTuple) = join(io, [lazy"$k = $v" for (k, v) in pairs(layers)], ", ")
 _show_layers(io, layers::AbstractVector) = (print(io, "["); join(io, layers, ", "); print(io, "]"))
 
 # This is a temporary and naive implementation
@@ -531,7 +531,7 @@ function _parallel_check(layers, xs)
   nl = length(layers)
   nx = length(xs) 
   if (nl != nx)
-    throw(ArgumentError("Parallel with $nl sub-layers can take one input or $nl inputs, but got $nx inputs"))
+    throw(ArgumentError(lazy"Parallel with $nl sub-layers can take one input or $nl inputs, but got $nx inputs"))
   end
 end
 ChainRulesCore.@non_differentiable _parallel_check(nl, nx)
@@ -616,7 +616,7 @@ function _pairwise_check(x, layers, T)
   lx = length(x)
   N = length(layers)
   if T <: Tuple && lx != N
-    throw(ArgumentError("PairwiseFusion with $N sub-layers can take one input or $N inputs, but got $lx inputs"))
+    throw(ArgumentError(lazy"PairwiseFusion with $N sub-layers can take one input or $N inputs, but got $lx inputs"))
   end
 end
 ChainRulesCore.@non_differentiable _pairwise_check(lx, N, T)

src/layers/macro.jl

@@ -70,7 +70,7 @@ macro layer(exs...)
 
   for j in 1:length(rest)
     ex = rest[j]
-    Meta.isexpr(ex, :(=)) || error("The macro `@layer` expects here `keyword = (fields...,)`, got $ex")
+    Meta.isexpr(ex, :(=)) || error(lazy"The macro `@layer` expects here `keyword = (fields...,)`, got $ex")
 
     name = if ex.args[1] == :trainable
       :(Optimisers.trainable)
@@ -153,4 +153,4 @@ _macro_trainable(type, fun, field::Union{Symbol,QuoteNode}) = _macro_trainable(t
 
 _noquotenode(s::Symbol) = s
 _noquotenode(q::QuoteNode) = q.value  # lets you write trainable=(:x,:y) instead of (x,y)
-_noquotenode(ex) = error("expected a symbol here, as a field name, but got $ex")
+_noquotenode(ex) = error("expected a symbol here, as a field name, but got ", ex)

src/layers/normalise.jl

@@ -4,7 +4,7 @@ _isactive(m, x) = isnothing(m.active) ? NNlib.within_gradient(x) : m.active
 # Internal function, used only in this file.
 _tidy_active(mode::Bool) = mode
 _tidy_active(::Nothing) = nothing
-_tidy_active(mode) = mode === :auto ? nothing : throw(ArgumentError("active = $(repr(mode)) is not accepted, must be true/false/nothing or :auto"))
+_tidy_active(mode) = mode === :auto ? nothing : throw(ArgumentError(lazy"active = $(repr(mode)) is not accepted, must be true/false/nothing or :auto"))
 
 """
     Dropout(p; [dims, rng, active])
@@ -74,7 +74,7 @@ end
 Dropout(p::Real, dims, active) = Dropout(p, dims, active, default_rng())
 
 function Dropout(p::Real; dims=:, active::Union{Bool,Nothing} = nothing, rng = default_rng())
-  0 ≤ p ≤ 1 || throw(ArgumentError("Dropout expects 0 ≤ p ≤ 1, got p = $p"))
+  0 ≤ p ≤ 1 || throw(ArgumentError(lazy"Dropout expects 0 ≤ p ≤ 1, got p = $p"))
   Dropout(p, dims, active, rng)
 end
 
@@ -126,7 +126,7 @@ end
 
 AlphaDropout(p, active) = AlphaDropout(p, active, default_rng())
 function AlphaDropout(p; rng = default_rng(), active::Union{Bool,Nothing} = nothing)
-  0 ≤ p ≤ 1 || throw(ArgumentError("AlphaDropout expects 0 ≤ p ≤ 1, got p = $p"))
+  0 ≤ p ≤ 1 || throw(ArgumentError(lazy"AlphaDropout expects 0 ≤ p ≤ 1, got p = $p"))
   AlphaDropout(p, active, rng)
 end
 

src/optimise/train.jl

@@ -92,7 +92,7 @@ function train!(loss, ps::Params, data, opt::AbstractOptimiser; cb = () -> ())
       loss(batchmemaybe(d)...)
     end
     if !isfinite(l)
-      throw(DomainError("Loss is $l on data item $i, stopping training"))
+      throw(DomainError(lazy"Loss is $l on data item $i, stopping training"))
     end
     update!(opt, ps, gs)
     cb()

src/outputsize.jl

@@ -230,7 +230,7 @@ end
 function _makelazy(ex::Expr)
   n = _underscoredepth(ex)
   n == 0 && return ex
-  n == 1 && error("@autosize doesn't expect an underscore here: $ex")
+  n == 1 && error("@autosize doesn't expect an underscore here: ", ex)
   n == 2 && return :($LazyLayer($(string(ex)), $(_makefun(ex)), nothing))
   n > 2 && return Expr(ex.head, map(_makelazy, ex.args)...)
 end

src/train.jl

@@ -106,7 +106,7 @@ function train!(loss, model, data, opt; cb = nothing)
     d_splat = d isa Tuple ? d : (d,)
     l, gs = Zygote.withgradient(m -> loss(m, d_splat...), model)
     if !isfinite(l)
-      throw(DomainError("Loss is $l on data item $i, stopping training"))
+      throw(DomainError(lazy"Loss is $l on data item $i, stopping training"))
     end
     opt, model = Optimisers.update!(opt, model, gs[1])
     @logprogress Base.haslength(data) ? i/length(data) : nothing