isa syntax

src/backend/mxnet/graph.jl

@@ -106,7 +106,7 @@ macro mxerr(stk, ex)
   @q try
     $(esc(ex))
   catch e
-    (isa(e, mx.MXError) && (node = errnode(e)) != nothing) || rethrow()
+    (e isa mx.MXError && (node = errnode(e)) != nothing) || rethrow()
     stk = $(esc(stk))
     haskey(stk, node) || rethrow()
     throw(Exception(striptrace(e), totrace(stk[node])))

src/backend/tensorflow/graph.jl

@@ -39,7 +39,7 @@ graph(op::Op, xs...) = op.f(xs...)
 
 function graph(ctx::Context, model, args...)
   node = graph(model, interpv(ctx, args)...)
-  isa(node, Tensor) && (ctx[:stacks][node.op.name] = stack(ctx))
+  node isa Tensor && (ctx[:stacks][node.op.name] = stack(ctx))
   return node
 end
 

src/compiler/code.jl

@@ -13,12 +13,12 @@ end
 function makegraph(graph, args)
   @assert length(args) == 1
   graph = prewalk(graph) do v
-    isa(value(v), Constant) && value(v).value == args[1] ?
+    value(v) isa Constant && value(v).value == args[1] ?
       inputnode(1) :
       v
   end
   graph = map(graph) do x
-    isa(x, Offset) ?
+    x isa Offset ?
       :(Flux.Offset($(Expr(:quote, x.name)), $(x.n), self.$(x.name))) :
       x
   end
@@ -44,7 +44,7 @@ runmodel(f, xs...) = f(xs...)
 
 function deref_params(v)
   v = map(v) do x
-    isa(x, Constant) && @capture(x.value, self.p_) ? Constant(:(Flux.state(self.$p))) : x
+    x isa Constant && @capture(x.value, self.p_) ? Constant(:(Flux.state(self.$p))) : x
   end
   prewalk(v) do v
     @capture(value(v), self.p_) ? vertex(:(Flux.runmodel), constant(:(self.$p)), inputs(v)...) : v
@@ -58,7 +58,7 @@ end
 
 import Lazy: groupby
 
-reifyparams(v::IVertex) = mapconst(x -> isa(x, Param) ? x.x : x, v)
+reifyparams(v::IVertex) = mapconst(x -> x isa Param ? x.x : x, v)
 
 # TODO: type hints for parameters
 

src/compiler/interp.jl

@@ -1,8 +1,8 @@
 using DataFlow.Interpreter
 
 function astuple(xs::Vertex)
-  isconstant(xs) && isa(value(xs).value, Tuple) ? value(xs).value :
-  isa(xs, Vertex) && value(xs) == tuple ? inputs(xs) :
+  isconstant(xs) && value(xs).value isa Tuple ? value(xs).value :
+  xs isa Vertex && value(xs) == tuple ? inputs(xs) :
   nothing
 end
 

src/compiler/loops.jl

@@ -45,7 +45,7 @@ function collect_state(v::IVertex)
   offset = Int[]
   default = Param[]
   prewalk!(v) do v
-    isa(value(v), Offset) || return v
+    value(v) isa Offset || return v
     if (i = findfirst(state, v[1])) == 0
       push!(state, v[1])
       push!(offset, max(0, -value(v).n))
@@ -91,7 +91,7 @@ function unrollgraph(v::IVertex, n; seq = true, stateful = true)
   for i = 1:n
     vars = inputs(steps[i][1])
     postwalk!(steps[i]) do v
-      isa(value(v), Offset) || return v
+      value(v) isa Offset || return v
       varid = findfirst(vars,v[1])
       getvar(varid, value(v).n + i, steps, offset, default, stateful = stateful)
     end
@@ -131,4 +131,4 @@ function unroll1(model)
   Unrolled(model, graph, state, false, 1)
 end
 
-flip(model) = Capacitor(map(x -> isa(x, Offset) ? -x : x, atomise(model)))
+flip(model) = Capacitor(map(x -> x isa Offset ? -x : x, atomise(model)))

src/compiler/shape.jl

@@ -9,8 +9,8 @@ end
 DataFlow.tocall(h::Hint, x) = :($x::$(h.typ))
 
 function gethint(v::IVertex)
-  while isa(value(v), Union{Line,Frame}) v = v[1] end
-  isa(value(v), Hint) && return value(v).typ
+  while value(v) isa Union{Line,Frame} v = v[1] end
+  value(v) isa Hint && return value(v).typ
   return
 end
 

src/dims/batching.jl

@@ -25,7 +25,7 @@ function rebatch(xs)
 end
 
 convertel(T::Type, xs::Batch) =
-  isa(eltype(eltype(xs)), T) ? xs :
+  eltype(eltype(xs)) isa T ? xs :
     Batch(map(x->convertel(T, x), xs))
 
 # Add batching semantics to functions operating on raw arrays