added ChainRules examples with forward and backward#91
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+ Coverage 81.50% 81.82% +0.32%
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this is great. i've been stuck at finding the difference b/t frules and rrules in implementation. now its much clear |
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@oxinabox if you want to take a look |
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Looks good. |
raphaelsaavedra
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raphaelsaavedra
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Thanks for the work, looks really nice. Left some minor comments and a question.
examples/chainrules.jl
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| model = Model(() -> diff_optimizer(Clp.Optimizer)) | ||
| pv = unit_commitment(load1_demand, load2_demand, gen_costs, noload_costs, model=model) | ||
| energy_balance_cons = model[:energy_balance_cons] | ||
| MOI.set.(model, DiffOpt.ForwardIn{DiffOpt.ConstraintConstant}(), energy_balance_cons, [d1 + d2 for (d1, d2) in zip(Δload1_demand, Δload1_demand)]) |
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This might be a dumb question because I'm not familiar with DiffOpt and AD in general, but what are we doing here exactly and why is it specifically for the energy balance constraint?
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would it be too much to put a comment before each of these about what it does?
Not something that one would do in normal code, but since this is a tutorial it might be suitable.
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we are setting the perturbation for the right-hand-side of the energy balance constraints, which is the sum of perturbations of each of the quantities in the RHS (in that case sum of load demands)
Co-authored-by: Raphael Saavedra <raphael.saavedra93@gmail.com>
| - `noload_costs` is the vector of fixed activation costs of the generators, | ||
| and returning the optimal output power `p`. | ||
| """ | ||
| function unit_commitment(load1_demand, load2_demand, gen_costs, noload_costs; model = Model(() -> diff_optimizer(Clp.Optimizer))) |
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I think we can just do
| function unit_commitment(load1_demand, load2_demand, gen_costs, noload_costs; model = Model(() -> diff_optimizer(Clp.Optimizer))) | |
| function unit_commitment(load1_demand, load2_demand, gen_costs, noload_costs; model = Model(Clp.Optimizer)) |
| function ChainRulesCore.frule((_, Δload1_demand, Δload2_demand, Δgen_costs, Δnoload_costs), ::typeof(unit_commitment), load1_demand, load2_demand, gen_costs, noload_costs) | ||
| # creating the UC model with a DiffOpt optimizer wrapper around Clp | ||
| model = Model(() -> diff_optimizer(Clp.Optimizer)) |
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We need to also pass the kwargs in: so probably can do:
| function ChainRulesCore.frule((_, Δload1_demand, Δload2_demand, Δgen_costs, Δnoload_costs), ::typeof(unit_commitment), load1_demand, load2_demand, gen_costs, noload_costs) | |
| # creating the UC model with a DiffOpt optimizer wrapper around Clp | |
| model = Model(() -> diff_optimizer(Clp.Optimizer)) | |
| function ChainRulesCore.frule((_, Δload1_demand, Δload2_demand, Δgen_costs, Δnoload_costs), ::typeof(unit_commitment), load1_demand, load2_demand, gen_costs, noload_costs; model=Model(Clp.Optimizer)) | |
| # creating the UC model with a DiffOpt optimizer wrapper around Clp | |
| model = Model(() -> diff_optimizer(optimizer(model))) |
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one issue here is that backend(.) only gives you the top-most backend layer, so you add two layers of CachingOptimizer
| # Reverse-mode differentiation of the solution map | ||
| # The computed pullback takes a seed for the optimal solution `̄p` and returns | ||
| # derivatives wrt each input parameter. | ||
| function ChainRulesCore.rrule(::typeof(unit_commitment), load1_demand, load2_demand, gen_costs, noload_costs; model = Model(() -> diff_optimizer(Clp.Optimizer))) |
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we should write this assuming the Model they use isn't a diff_optimizer model.
And we should unwrap and rewrap the model
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unwrapping and rewrapping the model results in the same issue as above
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Copying the unit commitment example with ChainRules.
We depend on ChainRulesCore through SetDistances, so I added it as a dependency