Add chain_number keyword argument when performing multi-chain sampling

Project.toml

@@ -3,7 +3,7 @@ uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001"
 keywords = ["markov chain monte carlo", "probabilistic programming"]
 license = "MIT"
 desc = "A lightweight interface for common MCMC methods."
-version = "5.7.2"
+version = "5.8.0"
 
 [deps]
 BangBang = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"

docs/src/api.md

@@ -71,8 +71,9 @@ Common keyword arguments for regular and parallel sampling are:
 - `progress` (default: `AbstractMCMC.PROGRESS[]` which is `true` initially): toggles progress logging. See the section on [Progress logging](#progress-logging) below for more details.
 - `chain_type` (default: `Any`): determines the type of the returned chain
 - `callback` (default: `nothing`): if `callback !== nothing`, then
-  `callback(rng, model, sampler, sample, iteration)` is called after every sampling step,
+  `callback(rng, model, sampler, sample, iteration; kwargs...)` is called after every sampling step,
   where `sample` is the most recent sample of the Markov chain and `iteration` is the current iteration
+   - Keyword arguments `kwargs...` are passed down from the call to `sample(...)`. If you are performing multiple-chain sampling, then `kwargs` _additionally_ contains a `chain_number` keyword argument, which runs from 1 to the number of chains. This is not present when performing single-chain sampling.
 - `num_warmup` (default: `0`): number of "warm-up" steps to take before the first "regular" step, 
    i.e. number of times to call [`AbstractMCMC.step_warmup`](@ref) before the first call to 
    [`AbstractMCMC.step`](@ref).

src/sample.jl

@@ -549,6 +549,7 @@ function mcmcsample(
                                 else
                                     initial_state[chainidx]
                                 end,
+                                chain_number=chainidx,
                                 kwargs...,
                             )
                         end
@@ -669,7 +670,7 @@ function mcmcsample(
             Distributed.@async begin
                 try
                     function sample_chain(
-                        seed, initial_params, initial_state, child_progress
+                        seed, initial_params, initial_state, child_progress, chainidx
                     )
                         # Seed a new random number generator with the pre-made seed.
                         Random.seed!(rng, seed)
@@ -683,6 +684,7 @@ function mcmcsample(
                             progress=child_progress,
                             initial_params=initial_params,
                             initial_state=initial_state,
+                            chain_number=chainidx,
                             kwargs...,
                         )
 
@@ -696,6 +698,7 @@ function mcmcsample(
                         _initial_params,
                         _initial_state,
                         child_progresses,
+                        1:nchains;
                     )
                 finally
                     if progress == :overall
@@ -755,6 +758,7 @@ function mcmcsample(
             progressname=string(progressname, " (Chain ", i, " of ", nchains, ")"),
             initial_params=initial_params,
             initial_state=initial_state,
+            chain_number=i,
             kwargs...,
         )
     end

test/sample.jl

@@ -679,6 +679,22 @@
         @test all(chain[i].b == ref_chain[i].b for i in 1:N)
     end
 
+    @testset "chain_number keyword argument" begin
+        @testset for m in [MCMCSerial(), MCMCThreads(), MCMCDistributed()]
+            niters = 10
+            channel = Channel{Int}() do chn
+                # check that the `chain_number` keyword argument is passed to the callback
+                function callback(args...; kwargs...)
+                    @test haskey(kwargs, :chain_number)
+                    return put!(chn, kwargs[:chain_number])
+                end
+                chain = sample(MyModel(), MySampler(), m, niters, 4; callback=callback)
+            end
+            chain_numbers = collect(channel)
+            @test sort(chain_numbers) == repeat(1:4; inner=niters)
+        end
+    end
+
     @testset "Sample vector of `NamedTuple`s" begin
         chain = sample(MyModel(), MySampler(), 1_000; chain_type=Vector{NamedTuple})
         # Check output type