Benchmark produce

perf/benchmark.jl

@@ -5,8 +5,10 @@ using BenchmarkTools
 ####################################################################
 
 function benchmark_driver!(f, x...; f_displayname=string(f))
+    x = (x..., nothing)
+
     println("benchmarking $(f_displayname)...")
-    tf = Libtask.TapedFunction(f, x)
+    tf = Libtask.TapedFunction(f, x...);
 
     print("  Run Original Function:")
     @btime $f($(x)...)
@@ -20,30 +22,48 @@ function benchmark_driver!(f, x...; f_displayname=string(f))
     print("  Run TapedFunction (compiled):")
     @btime $ctf($(x)...)
     GC.gc()
+
+    print("  Run TapedTask: ")
+    x = (x[1:end-1]..., produce);
+    # show the number of produce calls inside `f`
+    f_task = (tf, x; verbose=false) -> begin 
+        tt = TapedTask(tf, x...); 
+        c = 0
+        while consume(tt)!==nothing
+            c+=1
+        end
+        verbose && print("#produce=", c, "; "); 
+    end
+    f_task(tf, x; verbose=true) # print #produce calls.
+    @btime $f_task($tf, $x)
+    GC.gc()
 end
 
 ####################################################################
 
 
-function rosenbrock(x)
+function rosenbrock(x, callback=nothing)
     i = x[2:end]
     j = x[1:end-1]
-    return sum((1 .- j).^2 + 100*(i - j.^2).^2)
+    ret = sum((1 .- j).^2 + 100*(i - j.^2).^2)
+    callback !== nothing && callback(ret)
+    return ret
 end
 
 x = rand(100000)
 benchmark_driver!(rosenbrock, x)
 
 ####################################################################
 
-function ackley(x::AbstractVector)
+function ackley(x::AbstractVector, callback=nothing)
     a, b, c = 20.0, -0.2, 2.0*π
     len_recip = inv(length(x))
     sum_sqrs = zero(eltype(x))
     sum_cos = sum_sqrs
     for i in x
         sum_cos += cos(c*i)
         sum_sqrs += i^2
+        callback !== nothing && callback(sum_sqrs)
     end
     return (-a * exp(b * sqrt(len_recip*sum_sqrs)) -
             exp(len_recip*sum_cos) + a + MathConstants.e)
@@ -54,11 +74,13 @@ benchmark_driver!(ackley, x)
 
 ####################################################################
 function generate_matrix_test(n)
-    return x -> begin
+    return (x, callback=nothing) -> begin
         # @assert length(x) == 2n^2 + n
         a = reshape(x[1:n^2], n, n)
         b = reshape(x[n^2 + 1:2n^2], n, n)
-        return log.((a * b) + a - b)
+        ret = log.((a * b) + a - b)
+        callback !== nothing && callback(ret)
+        return ret
     end
 end
 
@@ -71,10 +93,12 @@ benchmark_driver!(matrix_test, x; f_displayname="matrix_test")
 relu(x) = log.(1.0 .+ exp.(x))
 sigmoid(n) = 1. / (1. + exp(-n))
 
-function neural_net(w1, w2, w3, x1)
+function neural_net(w1, w2, w3, x1, callback=nothing)
     x2 = relu(w1 * x1)
     x3 = relu(w2 * x2)
-    return sigmoid(LinearAlgebra.dot(w3, x3))
+    ret = sigmoid(LinearAlgebra.dot(w3, x3))
+    callback !== nothing && callback(ret)
+    return ret
 end
 
 xs = (randn(10,10), randn(10,10), randn(10), rand(10))