Release 0.35: Remove Selectors, reimplementing .~, dropping LogDensityProblemsAD, and a few other changes
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| # DynamicPPL Changelog | ||
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| ## 0.35.0 | ||
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| **Breaking changes** | ||
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| ### `.~` right hand side must be a univariate distribution | ||
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| Previously we allowed statements like | ||
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| ```julia | ||
| x .~ [Normal(), Gamma()] | ||
| ``` | ||
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| where the right hand side of a `.~` was an array of distributions, and ones like | ||
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| ```julia | ||
| x .~ MvNormal(fill(0.0, 2), I) | ||
| ``` | ||
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| where the right hand side was a multivariate distribution. | ||
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| These are no longer allowed. The only things allowed on the right hand side of a `.~` statement are univariate distributions, such as | ||
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| ```julia | ||
| x = Array{Float64,3}(undef, 2, 3, 4) | ||
| x .~ Normal() | ||
| ``` | ||
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| The reasons for this are internal code simplification and the fact that broadcasting where both sides are multidimensional but of different dimensions is typically confusing to read. | ||
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| If the right hand side and the left hand side have the same dimension, one can simply use `~`. Arrays of distributions can be replaced with `product_distribution`. So instead of | ||
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| ```julia | ||
| x .~ [Normal(), Gamma()] | ||
| x .~ Normal.(y) | ||
| x .~ MvNormal(fill(0.0, 2), I) | ||
| ``` | ||
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| do | ||
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| ```julia | ||
| x ~ product_distribution([Normal(), Gamma()]) | ||
| x ~ product_distribution(Normal.(y)) | ||
| x ~ MvNormal(fill(0.0, 2), I) | ||
| ``` | ||
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| This is often more performant as well. Note that using `~` rather than `.~` does change the internal storage format a bit: With `.~` `x[i]` are stored as separate variables, with `~` as a single multivariate variable `x`. In most cases this does not change anything for the user, but if it does cause issues, e.g. if you are dealing with `VarInfo` objects directly and need to keep the old behavior, you can always expand into a loop, such as | ||
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| ```julia | ||
| dists = Normal.(y) | ||
| for i in 1:length(dists) | ||
| x[i] ~ dists[i] | ||
| end | ||
| ``` | ||
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| Cases where the right hand side is of a different dimension than the left hand side, and neither is a scalar, must be replaced with a loop. For example, | ||
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| ```julia | ||
| x = Array{Float64,3}(undef, 2, 3, 4) | ||
| x .~ MvNormal(fill(0, 2), I) | ||
| ``` | ||
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| should be replaced with something like | ||
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| ```julia | ||
| x = Array{Float64,3}(2, 3, 4) | ||
| for i in 1:3, j in 1:4 | ||
| x[:, i, j] ~ MvNormal(fill(0, 2), I) | ||
| end | ||
| ``` | ||
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| This release also completely rewrites the internal implementation of `.~`, where from now on all `.~` statements are turned into loops over `~` statements at macro time. However, the only breaking aspect of this change is the above change to what's allowed on the right hand side. | ||
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| ### Remove indexing by samplers | ||
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| This release removes the feature of `VarInfo` where it kept track of which variable was associated with which sampler. This means removing all user-facing methods where `VarInfo`s where being indexed with samplers. In particular, | ||
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| - `link` and `invlink`, and their `!!` versions, no longer accept a sampler as an argument to specify which variables to (inv)link. The `link(varinfo, model)` methods remain in place, and as a new addition one can give a `Tuple` of `VarName`s to (inv)link only select variables, as in `link(varinfo, varname_tuple, model)`. | ||
| - `set_retained_vns_del_by_spl!` has been replaced by `set_retained_vns_del!` which applies to all variables. | ||
| - `getindex`, `setindex!`, and `setindex!!` no longer accept samplers as arguments | ||
| - `unflatten` no longer accepts a sampler as an argument | ||
| - `eltype(::VarInfo)` no longer accepts a sampler as an argument | ||
| - `keys(::VarInfo)` no longer accepts a sampler as an argument | ||
| - `VarInfo(::VarInfo, ::Sampler, ::AbstractVector)` no longer accepts the sampler argument. | ||
| - `push!!` and `push!` no longer accept samplers or `Selector`s as arguments | ||
| - `getgid`, `setgid!`, `updategid!`, `getspace`, and `inspace` no longer exist | ||
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| ### Reverse prefixing order | ||
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| - For submodels constructed using `to_submodel`, the order in which nested prefixes are applied has been changed. | ||
| Previously, the order was that outer prefixes were applied first, then inner ones. | ||
| This version reverses that. | ||
| To illustrate: | ||
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| ```julia | ||
| using DynamicPPL, Distributions | ||
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| @model function subsubmodel() | ||
| return x ~ Normal() | ||
| end | ||
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| @model function submodel() | ||
| x ~ to_submodel(prefix(subsubmodel(), :c), false) | ||
| return x | ||
| end | ||
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| @model function parentmodel() | ||
| x1 ~ to_submodel(prefix(submodel(), :a), false) | ||
| return x2 ~ to_submodel(prefix(submodel(), :b), false) | ||
| end | ||
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| keys(VarInfo(parentmodel())) | ||
| ``` | ||
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| Previously, the final line would return the variable names `c.a.x` and `c.b.x`. | ||
| With this version, it will return `a.c.x` and `b.c.x`, which is more intuitive. | ||
| (Note that this change brings `to_submodel`'s behaviour in line with the now-deprecated `@submodel` macro.) | ||
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| This change also affects sampling in Turing.jl. | ||
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| ### `LogDensityFunction` argument order | ||
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| - The method `LogDensityFunction(varinfo, model, context)` has been removed. | ||
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| The only accepted order is `LogDensityFunction(model, varinfo, context; adtype)`. | ||
| (For an explanation of `adtype`, see below.) | ||
| The varinfo and context arguments are both still optional. | ||
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| **Other changes** | ||
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| ### `LogDensityProblems` interface | ||
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| LogDensityProblemsAD is now removed as a dependency. | ||
| Instead of constructing a `LogDensityProblemAD.ADgradient` object, we now directly use `DifferentiationInterface` to calculate the gradient of the log density with respect to model parameters. | ||
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| Note that if you wish, you can still construct an `ADgradient` out of a `LogDensityFunction` object (there is nothing preventing this). | ||
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| However, in this version, `LogDensityFunction` now takes an extra AD type argument. | ||
| If this argument is not provided, the behaviour is exactly the same as before, i.e. you can calculate `logdensity` but not its gradient. | ||
| However, if you do pass an AD type, that will allow you to calculate the gradient as well. | ||
| You may thus find that it is easier to instead do this: | ||
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| ```julia | ||
| @model f() = ... | ||
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| ldf = LogDensityFunction(f(); adtype=AutoForwardDiff()) | ||
| ``` | ||
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| This will return an object which satisfies the `LogDensityProblems` interface to first-order, i.e. you can now directly call both | ||
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| ``` | ||
| LogDensityProblems.logdensity(ldf, params) | ||
| LogDensityProblems.logdensity_and_gradient(ldf, params) | ||
| ``` | ||
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| without having to construct a separate `ADgradient` object. | ||
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| If you prefer, you can also construct a new `LogDensityFunction` with a new AD type afterwards. | ||
| The model, varinfo, and context will be taken from the original `LogDensityFunction`: | ||
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| ```julia | ||
| @model f() = ... | ||
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| ldf = LogDensityFunction(f()) # by default, no adtype set | ||
| ldf_with_ad = LogDensityFunction(ldf, AutoForwardDiff()) | ||
| ``` | ||
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| ## 0.34.2 | ||
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| - Fixed bugs in ValuesAsInModelContext as well as DebugContext where underlying PrefixContexts were not being applied. | ||
| From a user-facing perspective, this means that for models which use manually prefixed submodels, e.g. | ||
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| ```julia | ||
| using DynamicPPL, Distributions | ||
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| @model inner() = x ~ Normal() | ||
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| @model function outer() | ||
| x1 ~ to_submodel(prefix(inner(), :a), false) | ||
| return x2 ~ to_submodel(prefix(inner(), :b), false) | ||
| end | ||
| ``` | ||
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| will: (1) no longer error when sampling due to `check_model_and_trace`; and (2) contain both submodel's variables in the resulting chain (the behaviour before this patch was that the second `x` would override the first `x`). | ||
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| - More broadly, implemented a general `prefix(ctx::AbstractContext, ::VarName)` which traverses the context tree in `ctx` to apply all necessary prefixes. This was a necessary step in fixing the above issues, but it also means that `prefix` is now capable of handling context trees with e.g. multiple prefixes at different levels of nesting. | ||
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| ## 0.34.1 | ||
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| - Fix an issue that prevented merging two VarInfos if they had different dimensions for a variable. | ||
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| - Upper bound the compat version of KernelAbstractions to work around an issue in determining the right VarInfo type to use. | ||
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| ## 0.34.0 | ||
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| **Breaking** | ||
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| - `rng` argument removed from `values_as_in_model`, and `varinfo` made non-optional. This means that the only signatures allowed are | ||
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| ``` | ||
| values_as_in_model(::Model, ::Bool, ::AbstractVarInfo) | ||
| values_as_in_model(::Model, ::Bool, ::AbstractVarInfo, ::AbstractContext) | ||
| ``` | ||
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| If you aren't using this function (it's probably only used in Turing.jl) then this won't affect you. | ||
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| ## 0.33.1 | ||
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| Reworked internals of `condition` and `decondition`. | ||
| There are no changes to the public-facing API, but internally you can no longer use `condition` and `decondition` on an `AbstractContext`, you can only use it on a `DynamicPPL.Model`. If you want to modify a context, use `ConditionContext` and `decondition_context`. | ||
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| ## 0.33.0 | ||
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| **Breaking** | ||
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| - `values_as_in_model()` now requires an extra boolean parameter, specifying whether variables on the lhs of `:=` statements are to be included in the resulting `OrderedDict` of values. | ||
| The type signature is now `values_as_in_model([rng,] model, include_colon_eq::Bool [, varinfo, context])` | ||
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| **Other** | ||
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| - Moved the implementation of `predict` from Turing.jl to DynamicPPL.jl; the user-facing behaviour is otherwise the same | ||
| - Improved error message when a user tries to initialise a model with parameters that don't correspond strictly to the underlying VarInfo used | ||
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