Layer instantiator
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Nobody has claimed this yet.
Assessment
- Difficulty
- 5/5
- Estimated time
- Over a week
- Newbie friendliness
- 35/100
- Issue type
- Feature
- Clarity
- Needs clarification
- Activity status
- Quiet
- Tech stack
- julia
- Domain
- backend-api-design
Research direction
Start by reviewing the existing CachingOptimizer, AbstractBridgeOptimizer, and MOI.ModelLike interfaces, then compare them with the layer cases in the table, including Dualization, ParametricOptInterface, DiffOpt, and MultiObjectiveAlgorithms. The issue is done when the layer API design and support rules are agreed, including incremental interfaces, index sharing, negative indices, and automatic caching behavior.
Written by the indexing model from the issue text.
Description
It would be useful to have some kind of layer API so that the user could just do:
model = Model(MOI.layers(POI.Optimizer, HiGHS.Optimizer)
The challenge is the need to automatically add CachingOptimizer when needed. We could also automatically detected the needed coefficient type but I think for now, we can require the user to explicitly use {Float32} at each layer if he chooses to not use Float64.
It is challenging to find the right interface for this because it's quite complicated. But for the same reason, combining the layers is very difficult for our users and the errors are quite cryptic. So I think we should make the effort to find something that works and make the layers plug&play
Current issues
Bridge layers
Bridge layers don't create index maps for efficiency reason. What they do is use negative indices for the constraint that are bridged. This means that you cannot stack two bridge layers without a CachingOptimizer in between. I remember @chriscoey and @lkapelevich being hit by this bug when stacking SingleBridgeOptimizer layers on top of Hypatia. @GiovanniKarra was also hit by this issue with SingleBridgeOptimizer. I also got hit by this recently in https://github.com/blegat/ComplementOpt.jl/pull/29 because ComplementOpt.Optimizer is a subtype of AbstractBridgeOptimizer and MOI.instantiate(() -> ComplementOpt.Optimizer(MOI.instantiate(Ipopt.Optimizer)) was creating a ComplementOpt.Optimizer layer with a LazyBridgeOptimizer layer directly following it so I had to explicitly create a cache in between like so: https://github.com/blegat/ComplementOpt.jl/blob/c852c0fd7016528e352a3a0b7158d611b51f0aad/test/runtests.jl#L322-L328
Need for incremental interface
POI needs an incremental interface and bridge layers do too. So a caching optimizer should automatically be added if needed.
Solution
The implementation would be something like
# This works for all layers of the table below
MOI.requires_incremental_interface(::Type{<:MOI.ModelLike}) = true
MOI.layers(optimizer_constructor; kws...) = MOI.instantiate(optimizer_constructor; kws...)
function MOI.layers(layer_type::Type{<:MOI.ModelLike}, args...; kws...)
model = MOI.layers(args...; kws...)
if (MOI.requires_incremental_interface(layer_type) && !MOI.supports_incremental_interface(model)) ||
(layer_type <: MOI.AbstractBridgeOptimizer && model isa MOI.AbstractBridgeOptimizer)
model = MOI.CachingOptimizer(# add a caching optimizer on top of `model`
end
return layer_type(model)
end
This does not completely resolves the issue with bridges. You could also have that model is not a bridge layer but its inner layer is a bridge optimizer and model does not map indices.
For a complete solution, we could have something like this that would be useful for https://github.com/jump-dev/JuMP.jl/issues/4014
abstract type Layer <: MOI.AbstractOptimizer end # Make `CachingOptimizer` and `AbstractBridgeOptimizer` be subtype of that
MOI.inner_optimizer(model::MOI.Layer) = model.inner # Name inner by convention ?
MOI.inner_optimizer(model::MOI.Bridges.AbstractBridgeOptimizer) = model.model
MOI.inner_optimizer(model::MOI.Utilities.CachingOptimizer) = model.Optimizer
Then, we add the following that should be implemented for any subtype of MOI.Layer (in addition to MOI.requires_incremental_interface)
MOI.share_indices_with_inner_optimizer(model::MOI.Layer) = MOI.supports_incremental_interface(model)
# Only exception to the above default implemention according to table below
MOI.share_indices_with_inner_optimizer(model::MOI.Utilities.CachingOptimizer) = false
and then the following one that shouldn't be implemented for layers, it correspond to the "index map" column in the above table
MOI.may_have_negative_indices(model::MOI.ModelLike) = false
MOI.may_have_negative_indices(model::MOI.AbstractBridgeOptimizer) = true
MOI.may_have_negative_indices(model::MOI.Layer) = MOI.share_indices_with_inner_optimizer(model) && MOI.may_have_negative_indices(MOI.inner_optimizer(model))
Then, we can do
function MOI.layers(layer_type::Type{<:MOI.Layer}, args...; kws...)
model = MOI.layers(args...; kws...)
if (MOI.requires_incremental_interface(layer_type) && !MOI.supports_incremental_interface(model)) ||
(layer_type <: MOI.AbstractBridgeOptimizer && MOI.may_have_negative_indices(model))
model = MOI.CachingOptimizer(# add a caching optimizer on top of `model`
end
return layer_type(model)
end
Table
Let's use this issue to collect the list of layers we want to support and their particularities before we commit with a specific design.
| Layer | supports_incremental_interface |
requires_incremental_interface |
share_indices_with_inner_optimizer |
|---|---|---|---|
CachingOptimizer |
✓ | ✓ | ✘ |
AbstractBridgeOptimizer |
✓ | ✓ | ✓ |
| Dualization | ✘ | ✓ | ✘ |
| ParametricOptInterface | ✓ | ✓ | ✓ |
| DiffOpt | ✓ | ✓ | ✓ |
| MultiObjectiveAlgorithms | ✓ | ✓ | ✓ |
Given that all layers require the incremental interface anyway, maybe we can remove requires_incremental_interface and only add it once we have a solver that needs it so the implementation is simply
function MOI.layers(layer_type::Type{<:MOI.Layer}, args...; kws...)
model = MOI.layers(args...; kws...)
if !MOI.supports_incremental_interface(model) ||
(layer_type <: MOI.AbstractBridgeOptimizer && MOI.may_have_negative_indices(model))
model = MOI.CachingOptimizer(# add a caching optimizer on top of `model`
end
return layer_type(model)
end
- Dominant language
- Julia
- Stars
- 506
- Forks
- 103
- Avg merge
- 1d 1h
- Merged PRs (30d)
- 9
Getting set up
This project ships no dev container, Dockerfile or contributing guide, so setting up is up to you: start from its README, and see our first-contribution guide for the general steps.
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
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