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update doc strings to reflect ADTypes
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@jClugstor
jClugstor committed Dec 9, 2024
1 parent 6a1dbf7 commit 8beaca3
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Showing 4 changed files with 35 additions and 41 deletions.
16 changes: 7 additions & 9 deletions lib/OrdinaryDiffEqBDF/src/algorithms.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -8,28 +8,26 @@ function BDF_docstring(description::String,
autodiff = AutoForwardDiff(),
standardtag = Val{true}(),
concrete_jac = nothing,
diff_type = Val{:forward}(),
linsolve = nothing,
precs = DEFAULT_PRECS,
""" * "\n" * extra_keyword_default

keyword_default_description = """
- `chunk_size`: The chunk size used with ForwardDiff.jl. Defaults to `Val{0}()`
and thus uses the internal ForwardDiff.jl algorithm for the choice.
- `autodiff`: Specifies whether to use automatic differentiation via
- `autodiff`: Uses [ADTypes.jl](https://sciml.github.io/ADTypes.jl/stable/)
to specify whether to use automatic differentiation via
[ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) or finite
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `Val{true}()` for automatic differentiation.
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `AutoForwardDiff()` for automatic differentiation, which by default uses
`chunksize = 0`, and thus uses the internal ForwardDiff.jl algorithm for the choice.
To use `FiniteDiff.jl`, the `AutoFiniteDiff()` ADType can be used, which has a keyword argument
`fdtype` with default value `Val{:forward}()`, and alternatives `Val{:central}()` and `Val{:complex}()`.
- `standardtag`: Specifies whether to use package-specific tags instead of the
ForwardDiff default function-specific tags. For more information, see
[this blog post](https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/).
Defaults to `Val{true}()`.
- `concrete_jac`: Specifies whether a Jacobian should be constructed. Defaults to
`nothing`, which means it will be chosen true/false depending on circumstances
of the solver, such as whether a Krylov subspace method is used for `linsolve`.
- `diff_type`: The type of differentiation used in FiniteDiff.jl if `autodiff=false`.
Defaults to `Val{:forward}`, with alternatives of `Val{:central}` and
`Val{:complex}`.
- `linsolve`: Any [LinearSolve.jl](https://github.com/SciML/LinearSolve.jl) compatible linear solver.
For example, to use [KLU.jl](https://github.com/JuliaSparse/KLU.jl), specify
`$name(linsolve = KLUFactorization()`).
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15 changes: 7 additions & 8 deletions lib/OrdinaryDiffEqCore/src/doc_utils.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -91,22 +91,21 @@ function differentiation_rk_docstring(description::String,
""" * extra_keyword_default

keyword_default_description = """
- `chunk_size`: The chunk size used with ForwardDiff.jl. Defaults to `Val{0}()`
and thus uses the internal ForwardDiff.jl algorithm for the choice.
- `autodiff`: Specifies whether to use automatic differentiation via
- `autodiff`: Uses [ADTypes.jl](https://sciml.github.io/ADTypes.jl/stable/)
to specify whether to use automatic differentiation via
[ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) or finite
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `Val{true}()` for automatic differentiation.
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `AutoForwardDiff()` for automatic differentiation, which by default uses
`chunksize = 0`, and thus uses the internal ForwardDiff.jl algorithm for the choice.
To use `FiniteDiff.jl`, the `AutoFiniteDiff()` ADType can be used, which has a keyword argument
`fdtype` with default value `Val{:forward}()`, and alternatives `Val{:central}()` and `Val{:complex}()`.
- `standardtag`: Specifies whether to use package-specific tags instead of the
ForwardDiff default function-specific tags. For more information, see
[this blog post](https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/).
Defaults to `Val{true}()`.
- `concrete_jac`: Specifies whether a Jacobian should be constructed. Defaults to
`nothing`, which means it will be chosen true/false depending on circumstances
of the solver, such as whether a Krylov subspace method is used for `linsolve`.
- `diff_type`: The type of differentiation used in FiniteDiff.jl if `autodiff=false`.
Defaults to `Val{:forward}`, with alternatives of `Val{:central}` and
`Val{:complex}`.
- `linsolve`: Any [LinearSolve.jl](https://github.com/SciML/LinearSolve.jl) compatible linear solver.
For example, to use [KLU.jl](https://github.com/JuliaSparse/KLU.jl), specify
`$name(linsolve = KLUFactorization()`).
Expand Down
30 changes: 14 additions & 16 deletions lib/OrdinaryDiffEqRosenbrock/src/OrdinaryDiffEqRosenbrock.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -57,22 +57,21 @@ function rosenbrock_wolfbrandt_docstring(description::String,
""" * extra_keyword_default

keyword_default_description = """
- `chunk_size`: The chunk size used with ForwardDiff.jl. Defaults to `Val{0}()`
and thus uses the internal ForwardDiff.jl algorithm for the choice.
- `standardtag`: Specifies whether to use package-specific tags instead of the
ForwardDiff default function-specific tags. For more information, see
[this blog post](https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/).
Defaults to `Val{true}()`.
- `autodiff`: Specifies whether to use automatic differentiation via
- `autodiff`: Uses [ADTypes.jl](https://sciml.github.io/ADTypes.jl/stable/)
to specify whether to use automatic differentiation via
[ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) or finite
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `Val{true}()` for automatic differentiation.
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `AutoForwardDiff()` for automatic differentiation, which by default uses
`chunksize = 0`, and thus uses the internal ForwardDiff.jl algorithm for the choice.
To use `FiniteDiff.jl`, the `AutoFiniteDiff()` ADType can be used, which has a keyword argument
`fdtype` with default value `Val{:forward}()`, and alternatives `Val{:central}()` and `Val{:complex}()`.
- `concrete_jac`: Specifies whether a Jacobian should be constructed. Defaults to
`nothing`, which means it will be chosen true/false depending on circumstances
of the solver, such as whether a Krylov subspace method is used for `linsolve`.
- `diff_type`: The type of differentiation used in FiniteDiff.jl if `autodiff=false`.
Defaults to `Val{:forward}`, with alternatives of `Val{:central}` and
`Val{:complex}`.
- `linsolve`: Any [LinearSolve.jl](https://github.com/SciML/LinearSolve.jl) compatible linear solver.
For example, to use [KLU.jl](https://github.com/JuliaSparse/KLU.jl), specify
`$name(linsolve = KLUFactorization()`).
Expand Down Expand Up @@ -131,22 +130,21 @@ function rosenbrock_docstring(description::String,
extra_keyword_default = "",
with_step_limiter = false)
keyword_default = """
- `chunk_size`: The chunk size used with ForwardDiff.jl. Defaults to `Val{0}()`
and thus uses the internal ForwardDiff.jl algorithm for the choice.
- `standardtag`: Specifies whether to use package-specific tags instead of the
ForwardDiff default function-specific tags. For more information, see
[this blog post](https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/).
Defaults to `Val{true}()`.
- `autodiff`: Specifies whether to use automatic differentiation via
- `autodiff`: Uses [ADTypes.jl](https://sciml.github.io/ADTypes.jl/stable/)
to specify whether to use automatic differentiation via
[ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) or finite
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `Val{true}()` for automatic differentiation.
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `AutoForwardDiff()` for automatic differentiation, which by default uses
`chunksize = 0`, and thus uses the internal ForwardDiff.jl algorithm for the choice.
To use `FiniteDiff.jl`, the `AutoFiniteDiff()` ADType can be used, which has a keyword argument
`fdtype` with default value `Val{:forward}()`, and alternatives `Val{:central}()` and `Val{:complex}()`.
- `concrete_jac`: Specifies whether a Jacobian should be constructed. Defaults to
`nothing`, which means it will be chosen true/false depending on circumstances
of the solver, such as whether a Krylov subspace method is used for `linsolve`.
- `diff_type`: The type of differentiation used in FiniteDiff.jl if `autodiff=false`.
Defaults to `Val{:forward}`, with alternatives of `Val{:central}` and
`Val{:complex}`.
- `linsolve`: Any [LinearSolve.jl](https://github.com/SciML/LinearSolve.jl) compatible linear solver.
For example, to use [KLU.jl](https://github.com/JuliaSparse/KLU.jl), specify
`$name(linsolve = KLUFactorization()`).
Expand Down
15 changes: 7 additions & 8 deletions lib/OrdinaryDiffEqSDIRK/src/algorithms.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -15,22 +15,21 @@ function SDIRK_docstring(description::String,
""" * extra_keyword_default

keyword_default_description = """
- `chunk_size`: The chunk size used with ForwardDiff.jl. Defaults to `Val{0}()`
and thus uses the internal ForwardDiff.jl algorithm for the choice.
- `autodiff`: Specifies whether to use automatic differentiation via
- `autodiff`: Uses [ADTypes.jl](https://sciml.github.io/ADTypes.jl/stable/)
to specify whether to use automatic differentiation via
[ForwardDiff.jl](https://github.com/JuliaDiff/ForwardDiff.jl) or finite
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `Val{true}()` for automatic differentiation.
differencing via [FiniteDiff.jl](https://github.com/JuliaDiff/FiniteDiff.jl).
Defaults to `AutoForwardDiff()` for automatic differentiation, which by default uses
`chunksize = 0`, and thus uses the internal ForwardDiff.jl algorithm for the choice.
To use `FiniteDiff.jl`, the `AutoFiniteDiff()` ADType can be used, which has a keyword argument
`fdtype` with default value `Val{:forward}()`, and alternatives `Val{:central}()` and `Val{:complex}()`.
- `standardtag`: Specifies whether to use package-specific tags instead of the
ForwardDiff default function-specific tags. For more information, see
[this blog post](https://www.stochasticlifestyle.com/improved-forwarddiff-jl-stacktraces-with-package-tags/).
Defaults to `Val{true}()`.
- `concrete_jac`: Specifies whether a Jacobian should be constructed. Defaults to
`nothing`, which means it will be chosen true/false depending on circumstances
of the solver, such as whether a Krylov subspace method is used for `linsolve`.
- `diff_type`: The type of differentiation used in FiniteDiff.jl if `autodiff=false`.
Defaults to `Val{:forward}`, with alternatives of `Val{:central}` and
`Val{:complex}`.
- `linsolve`: Any [LinearSolve.jl](https://github.com/SciML/LinearSolve.jl) compatible linear solver.
For example, to use [KLU.jl](https://github.com/JuliaSparse/KLU.jl), specify
`$name(linsolve = KLUFactorization()`).
Expand Down

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