Updated the closures with @closure to avoid boxing #924
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| @@ -57,7 +57,7 @@ multioutput = chain isa AbstractArray | |||
| strategy = NeuralPDE.GridTraining(dx) | |||
| integral = NeuralPDE.get_numeric_integral(strategy, indvars, multioutput, chain, derivative) | |||
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| _pde_loss_function = NeuralPDE.build_loss_function(eq, indvars, depvars, phi, derivative, | |||
| _pde_loss_function = @closure NeuralPDE.build_loss_function(eq, indvars, depvars, phi, derivative, | |||
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I added @closure here under the assumption that it would be necessary for capturing variables, but I see that this isn't forming a closure.
| @@ -105,14 +114,21 @@ res = solve(prob, BFGS(); maxiters = 100, callback) | |||
| phi = discretization.phi | |||
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| # Analysis | |||
| # Analysis with closure | |||
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Can you show a concrete improvement from this? What led to it? Can you show what functions had issues with inference and a flamegraph? |
| @@ -16,7 +16,7 @@ steps: | |||
| # Don't run Buildkite if the commit message includes the text [skip tests] | |||
| if: build.message !~ /\[skip tests\]/ | |||
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| - label: "Documentation" | |||
| - label: "Documentation"p | |||
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Suggested change
| - label: "Documentation"p | |
| - label: "Documentation" |
| @@ -17,6 +17,7 @@ DocStringExtensions = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" | |||
| DomainSets = "5b8099bc-c8ec-5219-889f-1d9e522a28bf" | |||
| ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210" | |||
| Functors = "d9f16b24-f501-4c13-a1f2-28368ffc5196" | |||
| Glob = "c27321d9-0574-5035-807b-f59d2c89b15c" | |||
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it was for searching a keyword across all files, automating the search with a script.
| @@ -38,6 +39,7 @@ RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd" | |||
| Reexport = "189a3867-3050-52da-a836-e630ba90ab69" | |||
| RuntimeGeneratedFunctions = "7e49a35a-f44a-4d26-94aa-eba1b4ca6b47" | |||
| SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462" | |||
| SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" | |||
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It was throwing KeyError: key "SparseArrays" not found error continuously, this helped.
| @@ -168,7 +168,7 @@ end | |||
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| MiniMaxAdaptiveLoss(args...; kwargs...) = MiniMaxAdaptiveLoss{Float64}(args...; kwargs...) | |||
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| function generate_adaptive_loss_function(pinnrep::PINNRepresentation, | |||
| @closure function generate_adaptive_loss_function(pinnrep::PINNRepresentation, | |||
| @@ -28,7 +28,7 @@ end | |||
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| NonAdaptiveLoss(; kwargs...) = NonAdaptiveLoss{Float64}(; kwargs...) | |||
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| function generate_adaptive_loss_function(::PINNRepresentation, ::NonAdaptiveLoss, _, __) | |||
| @closure function generate_adaptive_loss_function(::PINNRepresentation, ::NonAdaptiveLoss, _, __) | |||
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This PR refactors the codebase to replace traditional closures with the @closures annotation. This change improves readability, reduces redundant closure definitions, and enhances performance by leveraging Julia's built-in annotation for automatic closure handling.