Debugging

Debugging inference in RxInfer can be quite challenging, mostly due to the reactive nature of the inference, undefined order of computations, the use of observables, and generally hard-to-read stack traces in Julia. Below we discuss ways to help you find problems in your model that prevents you from getting the results you want.

Getting Help from the Community

When you encounter issues that are difficult to debug, the RxInfer community is here to help. To get the most effective support:

1. Share Session Data: For complex issues, you can share your session data to help us understand exactly what's happening in your model. See Session Sharing to learn how.

2. Join Community Meetings: We discuss common issues and solutions in our regular community meetings. See Getting Help with Issues for more information.

Requesting a trace of messages

RxInfer provides a way that allows to save the history of the computations leading up to the computed messages and marginals in the inference procedure. This history is added on top of messages and marginals and is referred to as a Memory Addon. Below is an example explaining how you can extract this history and use it to fix a bug.

We show the application of the Memory Addon on the coin toss example from earlier in the documentation. We model the binary outcome $x$ (heads or tails) using a Bernoulli distribution, with a parameter $\theta$ that represents the probability of landing on heads. We have a Beta prior distribution for the $\theta$ parameter, with a known shape $\alpha$ and rate $\beta$ parameter.

\[\theta \sim \mathrm{Beta}(a, b)\]

\[x_i \sim \mathrm{Bernoulli}(\theta)\]

where $x_i \in {0, 1}$ are the binary observations (heads = 1, tails = 0). This is the corresponding RxInfer model:

using RxInfer, Random, Plots

n = 4
θ_real = 0.3
dataset = float.(rand(Bernoulli(θ_real), n))

@model function coin_model(x)
    θ  ~ Beta(4, huge)
    x .~ Bernoulli(θ)
end

result = infer(
    model = coin_model(),
    data  = (x = dataset, ),
)

Inference results:
  Posteriors       | available for (θ)

The model will run without errors. But when we plot the posterior distribution for $\theta$, something's wrong. The posterior seems to be a flat distribution:

rθ = range(0, 1, length = 1000)

plot(rθ, (rvar) -> pdf(result.posteriors[:θ], rvar), label="Infered posterior")
vline!([θ_real], label="Real θ", title = "Inference results")

We can figure out what's wrong by tracing the computation of the posterior with the Memory Addon. To obtain the trace, we have to add addons = (AddonMemory(),) as an argument to the inference function. Note, that the argument to the addons keyword argument must be a tuple, because multiple addons can be activated at the same time. Here, we create a tuple with a single element however.

result = infer(
    model = coin_model(),
    data  = (x = dataset, ),
    addons = (AddonMemory(),)
)

Inference results:
  Posteriors       | available for (θ)

Now we have access to the messages that led to the marginal posterior:

RxInfer.ReactiveMP.getaddons(result.posteriors[:θ])

(AddonMemory(Product memory:
 Message mapping memory:
    At the node: Beta
    Towards interface: Val{:out}()
    With local constraint: Marginalisation()
    With addons: (AddonMemory(nothing),)
    With input marginals on Val{(:a, :b)}() edges: (PointMass{Int64}(4), PointMass{TinyHugeNumbers.HugeNumber}(huge))
    With the result: Beta{Float64}(α=4.0, β=1.0e12)
 Message mapping memory:
    At the node: Bernoulli
    Towards interface: Val{:p}()
    With local constraint: Marginalisation()
    With addons: (AddonMemory(nothing),)
    With input marginals on Val{(:out,)}() edges: (PointMass{Float64}(0.0),)
    With the result: Beta{Float64}(α=1.0, β=2.0)
 Message mapping memory:
    At the node: Bernoulli
    Towards interface: Val{:p}()
    With local constraint: Marginalisation()
    With addons: (AddonMemory(nothing),)
    With input marginals on Val{(:out,)}() edges: (PointMass{Float64}(0.0),)
    With the result: Beta{Float64}(α=1.0, β=2.0)
 Message mapping memory:
    At the node: Bernoulli
    Towards interface: Val{:p}()
    With local constraint: Marginalisation()
    With addons: (AddonMemory(nothing),)
    With input marginals on Val{(:out,)}() edges: (PointMass{Float64}(0.0),)
    With the result: Beta{Float64}(α=1.0, β=2.0)
 Message mapping memory:
    At the node: Bernoulli
    Towards interface: Val{:p}()
    With local constraint: Marginalisation()
    With addons: (AddonMemory(nothing),)
    With input marginals on Val{(:out,)}() edges: (PointMass{Float64}(1.0),)
    With the result: Beta{Float64}(α=2.0, β=1.0)
),)

The messages in the factor graph are marked in color. If you're interested in the mathematics behind these results, consider verifying them manually using the general equation for sum-product messages:

\[\underbrace{\overrightarrow{\mu}_{θ}(θ)}_{\substack{ \text{outgoing}\\ \text{message}}} = \sum_{x_1,\ldots,x_n} \underbrace{\overrightarrow{\mu}_{X_1}(x_1)\cdots \overrightarrow{\mu}_{X_n}(x_n)}_{\substack{\text{incoming} \\ \text{messages}}} \cdot \underbrace{f(θ,x_1,\ldots,x_n)}_{\substack{\text{node}\\ \text{function}}}\]

Note that the posterior (yellow) has a rate parameter on the order of 1e12. Our plot failed because a Beta distribution with such a rate parameter cannot be accurately depicted using the range of $\theta$ we used in the code block above. So why does the posterior have this rate parameter?

All the observations (purple, green, pink, blue) have much smaller rate parameters. It seems the prior distribution (red) has an unusual rate parameter, namely 1e12. If we look back at the model, the parameter was set to huge (which is a reserved keyword meaning 1e12). Reducing the prior rate parameter will ensure the posterior has a reasonable rate parameter as well.

@model function coin_model(x)
    θ  ~ Beta(4, 100)
    x .~ Bernoulli(θ)
end

result = infer(
    model = coin_model(),
    data  = (x = dataset, ),
)

Inference results:
  Posteriors       | available for (θ)

rθ = range(0, 1, length = 1000)

plot(rθ, (rvar) -> pdf(result.posteriors[:θ], rvar), fillalpha = 0.4, fill = 0, label="Infered posterior")
vline!([θ_real], label="Real θ", title = "Inference results")

Now the posterior has much more sensible shape thus confirming that we have identified the original issue correctly. We can run the model with more observations, to get an even better posterior:

result = infer(
    model = coin_model(),
    data  = (x = float.(rand(Bernoulli(θ_real), 1000)), ),
)

rθ = range(0, 1, length = 1000)
plot(rθ, (rvar) -> pdf(result.posteriors[:θ], rvar), fillalpha = 0.4, fill = 0, label="Infered posterior (1000 observations)")
vline!([θ_real], label="Real θ", title = "Inference results")

Using callbacks in the infer function

Another way to inspect the inference procedure is to use the callbacks or events from the infer function. Read more about callbacks in the documentation to the infer function. Here, we show a simple application of callbacks to a simple IID inference problem. We start with model specification:

using RxInfer

@model function iid_normal(y)
    μ  ~ Normal(mean = 0.0, variance = 100.0)
    γ  ~ Gamma(shape = 1.0, rate = 1.0)
    y .~ Normal(mean = μ, precision = γ)
end

Next, let us define a syntehtic dataset:

dataset = rand(NormalMeanPrecision(3.1415, 30.0), 100)

Now, we can use the callbacks argument of the infer function to track the order of posteriors computation and their intermediate values for each variational iteration:

# A callback that will be called every time before a variational iteration starts
function before_iteration_callback(model, iteration)
    println("Starting iteration ", iteration)
end

# A callback that will be called every time after a variational iteration finishes
function after_iteration_callback(model, iteration)
    println("Iteration ", iteration, " has been finished")
end

# A callback that will be called every time a posterior is updated
function on_marginal_update_callback(model, variable_name, posterior)
    println("Latent variable ", variable_name, " has been updated. Estimated mean is ", mean(posterior), " with standard deviation ", std(posterior))
end

on_marginal_update_callback (generic function with 1 method)

After we have defined all callbacks of interest, we can call the infer function passing them in the callback argument as a named tuple:

init = @initialization begin
    q(μ) = vague(NormalMeanVariance)
end

result = infer(
    model = iid_normal(),
    data  = (y = dataset, ),
    constraints = MeanField(),
    iterations = 5,
    initialization = init,
    returnvars = KeepLast(),
    callbacks = (
        on_marginal_update = on_marginal_update_callback,
        before_iteration   = before_iteration_callback,
        after_iteration    = after_iteration_callback
    )
)

Starting iteration 1
Latent variable γ has been updated. Estimated mean is 1.0199999999900741e-12 with standard deviation 1.4282856856946711e-13
Latent variable μ has been updated. Estimated mean is 3.173709285253581e-8 with standard deviation 9.999999948999998
Iteration 1 has been finished
Starting iteration 2
Latent variable γ has been updated. Estimated mean is 0.009295451774142401 with standard deviation 0.0013016235991374434
Latent variable μ has been updated. Estimated mean is 3.0783628513486154 with standard deviation 1.0316708653990776
Iteration 2 has been finished
Starting iteration 3
Latent variable γ has been updated. Estimated mean is 0.9146171605310012 with standard deviation 0.128072019440181
Latent variable μ has been updated. Estimated mean is 3.1111395656161647 with standard deviation 0.10455783610144849
Iteration 3 has been finished
Starting iteration 4
Latent variable γ has been updated. Estimated mean is 16.800724916764793 with standard deviation 2.3525720498178884
Latent variable μ has been updated. Estimated mean is 3.111461203389182 with standard deviation 0.024396902860405786
Iteration 4 has been finished
Starting iteration 5
Latent variable γ has been updated. Estimated mean is 20.24837272056352 with standard deviation 2.8353393054581146
Latent variable μ has been updated. Estimated mean is 3.11146435670152 with standard deviation 0.022223060283036392
Iteration 5 has been finished

We can see that the callback has been correctly executed for each intermediate variational iteration.

println("Estimated mean: ", mean(result.posteriors[:μ]))
println("Estimated precision: ", mean(result.posteriors[:γ]))

Estimated mean: 3.11146435670152
Estimated precision: 20.24837272056352

Using LoggerPipelineStage

ReactiveMP inference engine allows attaching extra computations to the default computational pipeline of message passing. Read more about pipelines in the corresponding section of ReactiveMP. Here we show how to use LoggerPipelineStage to trace the order of message passing updates for debugging purposes. We start with model specification:

using RxInfer

@model function iid_normal_with_pipeline(y)
    μ  ~ Normal(mean = 0.0, variance = 100.0)
    γ  ~ Gamma(shape = 1.0, rate = 1.0)
    y .~ Normal(mean = μ, precision = γ) where { pipeline = LoggerPipelineStage() }
end

Next, let us define a syntehtic dataset:

# We use less data points in the dataset to reduce the amount of text printed
# during the inference
dataset = rand(NormalMeanPrecision(3.1415, 30.0), 5)

Now, we can call the infer function. We combine the pipeline logger stage with the callbacks, which were introduced in the previous section:

result = infer(
    model = iid_normal_with_pipeline(),
    data  = (y = dataset, ),
    constraints = MeanField(),
    iterations = 5,
    initialization = init,
    returnvars = KeepLast(),
    callbacks = (
        on_marginal_update = on_marginal_update_callback,
        before_iteration   = before_iteration_callback,
        after_iteration    = after_iteration_callback
    )
)

Starting iteration 1
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable γ has been updated. Estimated mean is 1.399999999984956e-12 with standard deviation 7.48331477346747e-13
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable μ has been updated. Estimated mean is 2.2499518541229236e-9 with standard deviation 9.9999999965
Iteration 1 has been finished
Starting iteration 2
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable γ has been updated. Estimated mean is 0.01264157345164596 with standard deviation 0.0067572052408280655
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable μ has been updated. Estimated mean is 2.7751634501296594 with standard deviation 3.6959077490390193
Iteration 2 has been finished
Starting iteration 3
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable γ has been updated. Estimated mean is 0.09812841460406856 with standard deviation 0.05245184390796128
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable μ has been updated. Estimated mean is 3.1500150426190414 with standard deviation 1.4133064075956687
Iteration 3 has been finished
Starting iteration 4
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable γ has been updated. Estimated mean is 0.5794523824450117 with standard deviation 0.3097303267227338
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable μ has been updated. Estimated mean is 3.203161115035252 with standard deviation 0.5864863661373421
Iteration 4 has been finished
Starting iteration 5
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][τ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable γ has been updated. Estimated mean is 1.8454905536888426 with standard deviation 0.9864561946330556
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
[Log]: [NormalMeanPrecision][μ]: DeferredMessage([ use `as_message` to compute the message ])
Latent variable μ has been updated. Estimated mean is 3.210737387764812 with standard deviation 0.32902120196886464
Iteration 5 has been finished

We can see the order of message update events. Note that ReactiveMP may decide to compute messages lazily, in which case the actual computation of the value of a message will be deferred until later moment. In this case, LoggerPipelineStage will report DeferredMessage.

Using RxInferBenchmarkCallbacks for Performance Analysis

RxInfer provides a built-in benchmarking callback structure called RxInferBenchmarkCallbacks that helps collect timing information during the inference procedure. This structure aggregates timing information across multiple runs, allowing you to track performance statistics (min/max/average/etc.) of your model's creation and inference procedure.

Here's how to use it:

using RxInfer


# Create a benchmark callbacks instance to track performance
benchmark_callbacks = RxInferBenchmarkCallbacks()

# Run inference multiple times to gather statistics
for i in 1:3  # Usually you'd want more runs for better statistics
    infer(
        model = iid_normal(),
        data = (y = dataset, ),
        constraints = MeanField(),
        iterations = 5,
        initialization = init,
        callbacks = benchmark_callbacks
    )
end

In order to nicely display the statistics, you may want to install PrettyTables.jl package. It is not bundled with RxInfer by default, but, if installed manually, it makes the output more readable.

using PrettyTables

# Display the benchmark statistics in a nicely formatted table
PrettyTables.pretty_table(benchmark_callbacks)

RxInfer inference benchmark statistics: 3 evaluations
╭────────────────┬────────────┬────────────┬────────────┬────────────┬────────────╮
│      Operation │        Min │        Max │       Mean │     Median │        Std │
├────────────────┼────────────┼────────────┼────────────┼────────────┼────────────┤
│ Model creation │ 277.768 μs │ 473.332 μs │ 361.165 μs │ 332.394 μs │ 100.907 μs │
│      Inference │  43.853 μs │  88.298 μs │  60.375 μs │  48.974 μs │  24.317 μs │
│      Iteration │   3.438 μs │  35.444 μs │   7.644 μs │   4.196 μs │   8.407 μs │
╰────────────────┴────────────┴────────────┴────────────┴────────────┴────────────╯

The RxInferBenchmarkCallbacks structure collects timestamps at various stages of the inference process:

• Before and after model creation
• Before and after inference starts/ends
• Before and after each iteration
• Before and after autostart (for streaming inference)

RxInferBenchmarkCallbacks(; capacity = RxInfer.DEFAULT_BENCHMARK_CALLBACKS_BUFFER_CAPACITY)

# Create a callbacks instance to track performance
callbacks = RxInferBenchmarkCallbacks()

# Run inference multiple times to gather statistics
for _ in 1:10
    infer(
        model = my_model(),
        data = my_data,
        callbacks = callbacks
    )
end

# Display the timing statistics (you need to install `PrettyTables.jl` to use `pretty_table` function)
using PrettyTables

PrettyTables.pretty_table(callbacks)

get_benchmark_stats(callbacks::RxInferBenchmarkCallbacks)

DEFAULT_BENCHMARK_CALLBACKS_BUFFER_CAPACITY

This information can be used to:

• Track performance statistics (min/max/average) of your inference procedure
• Identify performance variability across runs
• Monitor the time spent in different stages of inference
• Establish performance baselines for your models
• Detect performance regressions

The timestamps are collected using time_ns() for high precision timing measurements and are automatically formatted into human-readable durations when displayed.