Performance and Benchmarks

.github/workflows/BenchmarksAndMicroIntegration.yml

@@ -0,0 +1,40 @@
+name: Benchmarks and MicroIntegration
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+
+jobs:
+  test:
+    name: Benchmarks and MicroIntegration
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@v1
+        with:
+          version: 1
+          arch: x64
+      - uses: julia-actions/julia-buildpkg@latest
+      - name: setup enviroment
+        shell: julia --color=yes --project=perf {0}
+        run: |
+          using Pkg
+          try
+            # force it to use this PR's version of the package
+            pkg"add Turing#hg/new-libtask2" # TODO: remove this when Turing is updated
+            Pkg.develop(PackageSpec(path="."))  # resolver may fail with main deps
+            Pkg.update()
+          catch err
+            err isa Pkg.Resolve.ResolverError || rethrow()
+            # If we can't resolve that means this is incompatible by SemVer and this is fine
+            # It means we marked this as a breaking change, so we don't need to worry about
+            # Mistakenly introducing a breaking change, as we have intentionally made one
+            @info "Not compatible with this release. No problem." exception=err
+            exit(0)  # Exit immediately, as a success
+          end
+      - name: run
+        run: julia --color=yes --project=perf perf/runtests.jl

Project.toml

@@ -3,10 +3,11 @@ uuid = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
 license = "MIT"
 desc = "Tape based task copying in Turing"
 repo = "https://github.com/TuringLang/Libtask.jl.git"
-version = "0.6.2"
+version = "0.6.3"
 
 [deps]
 IRTools = "7869d1d1-7146-5819-86e3-90919afe41df"
+LRUCache = "8ac3fa9e-de4c-5943-b1dc-09c6b5f20637"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MacroTools = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"

perf/Project.toml

@@ -0,0 +1,14 @@
+[deps]
+AbstractMCMC = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
+AdvancedPS = "576499cb-2369-40b2-a588-c64705576edc"
+BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
+DynamicPPL = "366bfd00-2699-11ea-058f-f148b4cae6d8"
+Libtask = "6f1fad26-d15e-5dc8-ae53-837a1d7b8c9f"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"
+
+[compat]
+julia = "1.3"
+
+[targets]
+test = ["Test", "BenchmarkTools"]

perf/p0.jl

@@ -0,0 +1,47 @@
+# ]add  Turing#hg/new-libtask2
+
+using Libtask
+using Turing, DynamicPPL, AdvancedPS
+using BenchmarkTools
+
+@model gdemo(x, y) = begin
+    # Assumptions
+    σ ~ InverseGamma(2,3)
+    μ ~ Normal(0,sqrt(σ))
+    # Observations
+    x ~ Normal(μ, sqrt(σ))
+    y ~ Normal(μ, sqrt(σ))
+end
+
+
+# Case 1: Sample from the prior.
+
+m = Turing.Core.TracedModel(gdemo(1.5, 2.), SampleFromPrior(), VarInfo())
+
+f = m.evaluator[1];
+
+args = m.evaluator[2:end];
+
+@show "Directly call..."
+@btime f(args...)
+# (2.0, VarInfo (2 variables (μ, σ), dimension 2; logp: -6.162))
+
+@show "CTask construction..."
+t = @btime  Libtask.CTask(f, args...)
+# schedule(t.task) # work fine!
+# @show Libtask.result(t.tf.tape)
+@show "Step in a tape..."
+@btime Libtask.step_in(t.tf.tape, args)
+
+# Case 2: SMC sampler
+
+m = Turing.Core.TracedModel(gdemo(1.5, 2.), Sampler(SMC(50)), VarInfo());
+@show "Directly call..."
+@btime m.evaluator[1](m.evaluator[2:end]...)
+
+@show "CTask construction..."
+t = @btime Libtask.CTask(m.evaluator[1], m.evaluator[2:end]...);
+# schedule(t.task)
+# @show Libtask.result(t.tf.tape)
+@show "Step in a tape..."
+@btime Libtask.step_in(t.tf.tape, m.evaluator[2:end])

perf/p1.jl

@@ -0,0 +1,39 @@
+using Turing, Test, AbstractMCMC, DynamicPPL, Random
+
+import AbstractMCMC.AbstractSampler
+
+function check_numerical(chain,
+                         symbols::Vector,
+                         exact_vals::Vector;
+                         atol=0.2,
+                         rtol=0.0)
+    for (sym, val) in zip(symbols, exact_vals)
+        E = val isa Real ?
+            mean(chain[sym]) :
+            vec(mean(chain[sym], dims=1))
+        @info (symbol=sym, exact=val, evaluated=E)
+        @test E ≈ val atol=atol rtol=rtol
+    end
+end
+
+function check_MoGtest_default(chain; atol=0.2, rtol=0.0)
+    check_numerical(chain,
+                    [:z1, :z2, :z3, :z4, :mu1, :mu2],
+                    [1.0, 1.0, 2.0, 2.0, 1.0, 4.0],
+                    atol=atol, rtol=rtol)
+end
+
+@model gdemo_d(x, y) = begin
+    s ~ InverseGamma(2, 3)
+    m ~ Normal(0, sqrt(s))
+    x ~ Normal(m, sqrt(s))
+    y ~ Normal(m, sqrt(s))
+    return s, m
+end
+
+alg = CSMC(15)
+chain = sample(gdemo_d(1.5, 2.0), alg, 5_000)
+
+@show chain
+
+check_numerical(chain, [:s, :m], [49/24, 7/6], atol=0.1)

perf/p2.jl

@@ -0,0 +1,63 @@
+using Turing, Test, AbstractMCMC, DynamicPPL, Random, Turing.RandomMeasures, Libtask
+
+@model infiniteGMM(x) = begin
+    # Hyper-parameters, i.e. concentration parameter and parameters of H.
+    α = 1.0
+    μ0 = 0.0
+    σ0 = 1.0
+
+    # Define random measure, e.g. Dirichlet process.
+    rpm = DirichletProcess(α)
+
+    # Define the base distribution, i.e. expected value of the Dirichlet process.
+    H = Normal(μ0, σ0)
+
+    # Latent assignment.
+    z = tzeros(Int, length(x))
+
+    # Locations of the infinitely many clusters.
+    μ = tzeros(Float64, 0)
+
+    for i in 1:length(x)
+
+        # Number of clusters.
+        K = maximum(z)
+        nk = Vector{Int}(map(k -> sum(z .== k), 1:K))
+
+        # Draw the latent assignment.
+        z[i] ~ ChineseRestaurantProcess(rpm, nk)
+
+        # Create a new cluster?
+        if z[i] > K
+            push!(μ, 0.0)
+
+            # Draw location of new cluster.
+            μ[z[i]] ~ H
+        end
+
+        # Draw observation.
+        x[i] ~ Normal(μ[z[i]], 1.0)
+    end
+end
+
+# Generate some test data.
+Random.seed!(1)
+
+data = vcat(randn(10), randn(10) .- 5, randn(10) .+ 10)
+data .-= mean(data)
+data /= std(data)
+
+# MCMC sampling
+Random.seed!(2)
+iterations = 500
+model_fun = infiniteGMM(data)
+
+m = Turing.Core.TracedModel(model_fun, Sampler(SMC(50)), VarInfo())
+f = m.evaluator[1]
+args = m.evaluator[2:end]
+
+t = Libtask.CTask(f, args...)
+
+Libtask.step_in(t.tf.tape, args)
+
+@show Libtask.result(t.tf.tape)

perf/runtests.jl

@@ -0,0 +1,3 @@
+include("p0.jl")
+include("p1.jl")
+include("p2.jl")

src/Libtask.jl

@@ -3,6 +3,8 @@ module Libtask
 using IRTools
 using MacroTools
 
+using LRUCache
+
 export CTask, consume, produce
 export TArray, tzeros, tfill, TRef
 

src/tapedfunction.jl

@@ -6,6 +6,11 @@ mutable struct Tape
     owner
 end
 
+"""
+    Instruction
+
+An `Instruction` stands for a function call
+"""
 mutable struct Instruction{F} <: AbstractInstruction
     fun::F
     input::Tuple
@@ -46,13 +51,20 @@ function Base.show(io::IO, box::Box)
     println(io, "Box($(box.val))")
 end
 
+function Base.show(io::IO, instruction::AbstractInstruction)
+    println(io, "A $(typeof(instruction))")
+end
+
 function Base.show(io::IO, instruction::Instruction)
     fun = instruction.fun
     tape = instruction.tape
     println(io, "Instruction($(fun)$(map(val, instruction.input)), tape=$(objectid(tape)))")
 end
 
 function Base.show(io::IO, tp::Tape)
+    # we use an extra IOBuffer to collect all the data and then
+    # output it once to avoid output interrupt during task context
+    # switching
     buf = IOBuffer()
     print(buf, "$(length(tp))-element Tape")
     isempty(tp) || println(buf, ":")
@@ -66,10 +78,30 @@ function Base.show(io::IO, tp::Tape)
 end
 
 function (instr::Instruction{F})() where F
-    output = instr.fun(map(val, instr.input)...)
-    instr.output.val = output
+    # catch run-time exceptions / errors.
+    try
+        output = instr.fun(map(val, instr.input)...)
+        instr.output.val = output
+    catch e
+        println(e, catch_backtrace());
+        rethrow(e);
+    end
 end
 
+function _new end
+function (instr::Instruction{typeof(_new)})()
+    # catch run-time exceptions / errors.
+    try
+        expr = Expr(:new, map(val, instr.input)...)
+        output = eval(expr)
+        instr.output.val = output
+    catch e
+        println(e, catch_backtrace());
+        rethrow(e);
+    end
+end
+
+
 function increase_counter!(t::Tape)
     t.counter > length(t) && return
     # instr = t[t.counter]
@@ -101,6 +133,19 @@ function run_and_record!(tape::Tape, f, args...)
     return output
 end
 
+function run_and_record!(tape::Tape, ::typeof(_new), args...)
+    output = try
+        expr = Expr(:new, map(val, args)...)
+        box(eval(expr))
+    catch e
+        @warn e
+        Box{Any}(nothing)
+    end
+    ins = Instruction(_new, args, output, tape)
+    push!(tape, ins)
+    return output
+end
+
 function unbox_condition(ir)
     for blk in IRTools.blocks(ir)
         vars = keys(blk)
@@ -169,9 +214,15 @@ function intercept(ir; recorder=:run_and_record!)
 
     for (x, st) in ir
         x == tape && continue
-        Meta.isexpr(st.expr, :call) || continue
-        new_args = (x == args_var) ? st.expr.args : _replace_args(st.expr.args, arg_pairs)
-        ir[x] = IRTools.xcall(@__MODULE__, recorder, tape, new_args...)
+        if Meta.isexpr(st.expr, :call)
+            new_args = (x == args_var) ? st.expr.args : _replace_args(st.expr.args, arg_pairs)
+            ir[x] = IRTools.xcall(@__MODULE__, recorder, tape, new_args...)
+        elseif Meta.isexpr(st.expr, :new)
+            args = st.expr.args
+            ir[x] = IRTools.xcall(@__MODULE__, recorder, tape, _new, args...)
+        else
+            @warn "Unknown IR code: " st
+        end
     end
     # the real return value will be in the last instruction on the tape
     IRTools.return!(ir, tape)
@@ -190,6 +241,13 @@ mutable struct TapedFunction
     end
 end
 
+function reset!(tf::TapedFunction, ir::IRTools.IR, tape::Tape)
+    tf.ir = ir
+    tf.tape = tape
+    setowner!(tape, tf)
+    return tf
+end
+
 function (tf::TapedFunction)(args...)
     if isempty(tf.tape)
         ir = IRTools.@code_ir tf.func(args...)

src/tapedtask.jl

@@ -16,9 +16,22 @@ struct TapedTask
     end
 end
 
+const TRCache = LRU{Any, Any}(maxsize=10)
+
 function TapedTask(tf::TapedFunction, args...)
-    tf.owner != nothing && error("TapedFunction is owned to another task.")
-    isempty(tf.tape) && tf(args...)
+    tf.owner !== nothing && error("TapedFunction is owned by another task.")
+    if isempty(tf.tape)
+        cache_key = (tf.func, typeof.(args)...)
+        if haskey(TRCache, cache_key)
+            ir, tape = TRCache[cache_key]
+            # Here we don't need change the initial arguments of the tape,
+            # it will be set when we `step_in` to the tape.
+            reset!(tf, ir, copy(tape, Dict{UInt64, Any}(); partial=false))
+        else
+            tf(args...)
+            TRCache[cache_key] = (tf.ir, tf.tape)
+        end
+    end
     produce_ch = Channel()
     consume_ch = Channel{Int}()
     task = @task try
@@ -199,14 +212,16 @@ function Base.copy(x::Instruction, on_tape::Tape, roster::Dict{UInt64, Any})
     Instruction(x.fun, input, output, on_tape)
 end
 
-function Base.copy(t::Tape, roster::Dict{UInt64, Any})
+function Base.copy(t::Tape, roster::Dict{UInt64, Any}; partial=true)
     old_data = t.tape
-    new_data = Vector{AbstractInstruction}()
-    new_tape = Tape(new_data, t.counter, t.owner)
+    len = partial ? length(old_data) - t.counter + 1 : length(old_data)
+    start = partial ? t.counter : 1
+    new_data = Vector{AbstractInstruction}(undef, len)
+    new_tape = Tape(new_data, 1, t.owner)
 
-    for x in old_data
+    for (i, x) in enumerate(old_data[start:end])
         new_ins = copy(x, new_tape, roster)
-        push!(new_data, new_ins)
+        new_data[i] = new_ins
     end
 
     return new_tape