Clear progress bar if single-chain sampling errors

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 = "5.7.1"
+version = "5.7.2"
 
 [deps]
 BangBang = "198e06fe-97b7-11e9-32a5-e1d131e6ad66"

src/sample.jl

@@ -150,118 +150,123 @@ function mcmcsample(
 
     @maybewithricherlogger begin
         init_progress!(progress)
-        # Determine threshold values for progress logging (by default, one
-        # update per 0.5% of progress, unless this has been passed in
-        # explicitly)
-        n_updates = get_n_updates(progress)
-        threshold = Ntotal / n_updates
-        next_update = threshold
 
-        # Obtain the initial sample and state.
-        sample, state = if num_warmup > 0
-            if initial_state === nothing
-                step_warmup(rng, model, sampler; kwargs...)
-            else
-                step_warmup(rng, model, sampler, initial_state; kwargs...)
-            end
-        else
-            if initial_state === nothing
-                step(rng, model, sampler; kwargs...)
-            else
-                step(rng, model, sampler, initial_state; kwargs...)
-            end
-        end
-
-        # Start the progress bar.
-        itotal = 1
-        if itotal >= next_update
-            update_progress!(progress, itotal / Ntotal)
-            next_update += threshold
-        end
-
-        # Discard initial samples.
-        for j in 1:discard_initial
-            # Obtain the next sample and state.
-            sample, state = if j ≤ num_warmup
-                step_warmup(rng, model, sampler, state; kwargs...)
+        try
+            # Determine threshold values for progress logging (by default, one
+            # update per 0.5% of progress, unless this has been passed in
+            # explicitly)
+            n_updates = get_n_updates(progress)
+            threshold = Ntotal / n_updates
+            next_update = threshold
+
+            # Obtain the initial sample and state.
+            sample, state = if num_warmup > 0
+                if initial_state === nothing
+                    step_warmup(rng, model, sampler; kwargs...)
+                else
+                    step_warmup(rng, model, sampler, initial_state; kwargs...)
+                end
             else
-                step(rng, model, sampler, state; kwargs...)
+                if initial_state === nothing
+                    step(rng, model, sampler; kwargs...)
+                else
+                    step(rng, model, sampler, initial_state; kwargs...)
+                end
             end
 
-            # Update the progress bar.
-            itotal += 1
+            # Start the progress bar.
+            itotal = 1
             if itotal >= next_update
                 update_progress!(progress, itotal / Ntotal)
                 next_update += threshold
             end
-        end
 
-        # Run callback.
-        callback === nothing || callback(rng, model, sampler, sample, state, 1; kwargs...)
-
-        # Save the sample.
-        samples = AbstractMCMC.samples(sample, model, sampler, N; kwargs...)
-        samples = save!!(samples, sample, 1, model, sampler, N; kwargs...)
-
-        # Step through the sampler.
-        for i in 2:N
-            # Discard thinned samples.
-            for _ in 1:(thinning - 1)
+            # Discard initial samples.
+            for j in 1:discard_initial
                 # Obtain the next sample and state.
-                sample, state = if i ≤ keep_from_warmup
+                sample, state = if j ≤ num_warmup
                     step_warmup(rng, model, sampler, state; kwargs...)
                 else
                     step(rng, model, sampler, state; kwargs...)
                 end
 
-                # Update progress bar.
+                # Update the progress bar.
                 itotal += 1
                 if itotal >= next_update
                     update_progress!(progress, itotal / Ntotal)
                     next_update += threshold
                 end
             end
 
-            # Obtain the next sample and state.
-            sample, state = if i ≤ keep_from_warmup
-                step_warmup(rng, model, sampler, state; kwargs...)
-            else
-                step(rng, model, sampler, state; kwargs...)
-            end
-
             # Run callback.
             callback === nothing ||
-                callback(rng, model, sampler, sample, state, i; kwargs...)
+                callback(rng, model, sampler, sample, state, 1; kwargs...)
 
             # Save the sample.
-            samples = save!!(samples, sample, i, model, sampler, N; kwargs...)
+            samples = AbstractMCMC.samples(sample, model, sampler, N; kwargs...)
+            samples = save!!(samples, sample, 1, model, sampler, N; kwargs...)
+
+            # Step through the sampler.
+            for i in 2:N
+                # Discard thinned samples.
+                for _ in 1:(thinning - 1)
+                    # Obtain the next sample and state.
+                    sample, state = if i ≤ keep_from_warmup
+                        step_warmup(rng, model, sampler, state; kwargs...)
+                    else
+                        step(rng, model, sampler, state; kwargs...)
+                    end
 
-            # Update the progress bar.
-            itotal += 1
-            if itotal >= next_update
-                update_progress!(progress, itotal / Ntotal)
-                next_update += threshold
+                    # Update progress bar.
+                    itotal += 1
+                    if itotal >= next_update
+                        update_progress!(progress, itotal / Ntotal)
+                        next_update += threshold
+                    end
+                end
+
+                # Obtain the next sample and state.
+                sample, state = if i ≤ keep_from_warmup
+                    step_warmup(rng, model, sampler, state; kwargs...)
+                else
+                    step(rng, model, sampler, state; kwargs...)
+                end
+
+                # Run callback.
+                callback === nothing ||
+                    callback(rng, model, sampler, sample, state, i; kwargs...)
+
+                # Save the sample.
+                samples = save!!(samples, sample, i, model, sampler, N; kwargs...)
+
+                # Update the progress bar.
+                itotal += 1
+                if itotal >= next_update
+                    update_progress!(progress, itotal / Ntotal)
+                    next_update += threshold
+                end
             end
+
+            # Get the sample stop time.
+            stop = time()
+            duration = stop - start
+            stats = SamplingStats(start, stop, duration)
+
+            return bundle_samples(
+                samples,
+                model,
+                sampler,
+                state,
+                chain_type;
+                stats=stats,
+                discard_initial=discard_initial,
+                thinning=thinning,
+                kwargs...,
+            )
+        finally
+            finish_progress!(progress)
         end
-        finish_progress!(progress)
     end
-
-    # Get the sample stop time.
-    stop = time()
-    duration = stop - start
-    stats = SamplingStats(start, stop, duration)
-
-    return bundle_samples(
-        samples,
-        model,
-        sampler,
-        state,
-        chain_type;
-        stats=stats,
-        discard_initial=discard_initial,
-        thinning=thinning,
-        kwargs...,
-    )
 end
 
 function mcmcsample(