Merge pull request #42 from TuringLang/logger-type

Loosen the logger type in TensorBoardLogger

Project.toml

@@ -1,7 +1,7 @@
 name = "TuringCallbacks"
 uuid = "ea0860ee-d0ef-45ef-82e6-cc37d6be2f9c"
 authors = ["Tor Erlend Fjelde <[email protected]> and contributors"]
-version = "0.3.0"
+version = "0.3.1"
 
 [deps]
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"

docs/src/index.md

@@ -22,7 +22,7 @@ pkg> add TuringCallbacks.jl
 ```
 
 ## Visualizing sampling on-the-fly
-`TensorBoardCallback` is a wrapper around `TensorBoardLogger.TBLogger` which can be used to create a `callback` compatible with `Turing.sample`.
+`TensorBoardCallback` is a wrapper around `Base.CoreLogging.AbstractLogger` which can be used to create a `callback` compatible with `Turing.sample`.
 
 To actually visualize the results of the logging, you need to have installed `tensorboard` in Python. If you do not have `tensorboard` installed,
 it should hopefully be sufficient to just run
@@ -35,7 +35,7 @@ python3 -m tensorboard.main --logdir tensorboard_logs/run
 ```
 Now we're ready to actually write some Julia code.
 
-The following snippet demonstrates the usage of `TensorBoardCallback` on a simple model. 
+The following snippet demonstrates the usage of `TensorBoardCallback` on a simple model.
 This will write a set of statistics at each iteration to an event-file compatible with Tensorboard:
 
 ```julia

src/callbacks/tensorboard.jl

@@ -3,18 +3,20 @@ using Dates
 """
     $(TYPEDEF)
 
-Wraps a `TensorBoardLogger.TBLogger` to construct a callback to be passed to `AbstractMCMC.step`.
+Wraps a `CoreLogging.AbstractLogger` to construct a callback to be
+passed to `AbstractMCMC.step`.
 
 # Usage
 
     TensorBoardCallback(; kwargs...)
     TensorBoardCallback(directory::string[, stats]; kwargs...)
-    TensorBoardCallback(lg::TBLogger[, stats]; kwargs...)
+    TensorBoardCallback(lg::AbstractLogger[, stats]; kwargs...)
 
-Constructs an instance of a `TensorBoardCallback`, creating a `TBLogger` if `directory` is 
+Constructs an instance of a `TensorBoardCallback`, creating a `TBLogger` if `directory` is
 provided instead of `lg`.
 
 ## Arguments
+- `lg`: an instance of an `AbstractLogger` which implements `TuringCallbacks.increment_step!`.
 - `stats = nothing`: `OnlineStat` or lookup for variable name to statistic estimator.
   If `stats isa OnlineStat`, we will create a `DefaultDict` which copies `stats` for unseen
   variable names.
@@ -24,9 +26,9 @@ provided instead of `lg`.
 
 ## Keyword arguments
 - `num_bins::Int = 100`: Number of bins to use in the histograms.
-- `filter = nothing`: Filter determining whether or not we should log stats for a 
+- `filter = nothing`: Filter determining whether or not we should log stats for a
   particular variable and value; expected signature is `filter(varname, value)`.
-  If `isnothing` a default-filter constructed from `exclude` and 
+  If `isnothing` a default-filter constructed from `exclude` and
   `include` will be used.
 - `exclude = nothing`: If non-empty, these variables will not be logged.
 - `include = nothing`: If non-empty, only these variables will be logged.
@@ -41,7 +43,7 @@ $(TYPEDFIELDS)
 """
 struct TensorBoardCallback{L,F,VI,VE}
     "Underlying logger."
-    logger::TBLogger
+    logger::AbstractLogger
     "Lookup for variable name to statistic estimate."
     stats::L
     "Filter determining whether or not we should log stats for a particular variable."
@@ -68,15 +70,15 @@ function TensorBoardCallback(args...; comment = "", directory = nothing, kwargs.
     else
         directory
     end
-    
+
     # Set up the logger
     lg = TBLogger(log_dir, min_level=Logging.Info; step_increment=0)
 
     return TensorBoardCallback(lg, args...; kwargs...)
 end
 
 function TensorBoardCallback(
-    lg::TBLogger,
+    lg::AbstractLogger,
     stats = nothing;
     num_bins::Int = 100,
     exclude = nothing,
@@ -162,6 +164,9 @@ extras(transition; kwargs...) = ()
 extras(transition, state; kwargs...) = extras(transition; kwargs...)
 extras(model, sampler, transition, state; kwargs...) = extras(transition, state; kwargs...)
 
+increment_step!(lg::TensorBoardLogger.TBLogger, Δ_Step) =
+    TensorBoardLogger.increment_step!(lg, Δ_Step)
+
 function (cb::TensorBoardCallback)(rng, model, sampler, transition, state, iteration; kwargs...)
     stats = cb.stats
     lg = cb.logger
@@ -189,6 +194,6 @@ function (cb::TensorBoardCallback)(rng, model, sampler, transition, state, itera
             end
         end
         # Increment the step for the logger.
-        TensorBoardLogger.increment_step!(lg, 1)
+        increment_step!(lg, 1)
     end
 end

src/tensorboardlogger.jl

@@ -68,7 +68,7 @@ end
 
 # Unlike the `preprocess` overload, this allows us to specify if we want to normalize
 function TBL.log_histogram(
-    logger::TBLogger, name::AbstractString, hist::OnlineStats.HistogramStat;
+    logger::AbstractLogger, name::AbstractString, hist::OnlineStats.HistogramStat;
     step=nothing, normalize=false
 )
     edges = edges(hist)