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update formatting rules #639

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c1145b9
update formatting rules
superwhiskers Nov 2, 2026
2810da6
Apply pre-commit fixes
superwhiskers Nov 3, 2026
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5 changes: 3 additions & 2 deletions .clang-format
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@ BasedOnStyle: Microsoft

# Indentation
IndentWidth: 2 # 2 spaces per indent
ContinuationIndentWidth: 2
AccessModifierOffset: -2
IndentAccessModifiers: false # Align access modifiers to braces
NamespaceIndentation: All # Indent namspace contents
Expand All @@ -12,7 +13,6 @@ ConstructorInitializerIndentWidth: 2
# Comments which match this regex will be unformatted (and therefore can be longer or have more whitespace than other comments)
CommentPragmas: '^\*\*'


# Alignment
AlignConsecutiveAssignments:
Enabled: true
Expand All @@ -37,7 +37,8 @@ AlignConsecutiveMacros:
AcrossComments: true

# Newlines
ColumnLimit: 0
AlignAfterOpenBracket: BlockIndent # this will need to be replaced w/ clang-format 22
ColumnLimit: 80
BreakBeforeBraces: Allman # Braces on their own lines
SeparateDefinitionBlocks: Always # Separate definitions (functions etc.) with an empty line
AlwaysBreakTemplateDeclarations: true # Put template on their own lines
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12 changes: 3 additions & 9 deletions GridKit/AutomaticDifferentiation/DependencyTracking/Variable.hpp
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Expand Up @@ -36,19 +36,15 @@ namespace GridKit
@brief Default constructor.
*/
Variable()
: value_(0.0),
variable_number_(INVALID_VAR_NUMBER),
is_fixed_(false)
: value_(0.0), variable_number_(INVALID_VAR_NUMBER), is_fixed_(false)
{
}

/**
@brief Constructor which initializes the value.
*/
explicit Variable(double value)
: value_(value),
variable_number_(INVALID_VAR_NUMBER),
is_fixed_(false)
: value_(value), variable_number_(INVALID_VAR_NUMBER), is_fixed_(false)
{
}

Expand All @@ -58,9 +54,7 @@ namespace GridKit

*/
Variable(double value, size_t variable_number)
: value_(value),
variable_number_(variable_number),
is_fixed_(false)
: value_(value), variable_number_(variable_number), is_fixed_(false)
{
dependencies_[variable_number_] = 1.0;
}
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64 changes: 38 additions & 26 deletions GridKit/AutomaticDifferentiation/DependencyTracking/VariableOperators.hpp
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Expand Up @@ -367,33 +367,41 @@ namespace GridKit
namespace std
{

#define IMPL_FUN_1(FUN, DER) \
inline GridKit::DependencyTracking::Variable FUN(const GridKit::DependencyTracking::Variable& x) \
{ \
double val = FUN(x()); \
double der = DER(x()); \
GridKit::DependencyTracking::Variable res(x); /* copy derivatives of x*/ \
res.setValue(val); /* set function value f(x) */ \
res.scaleDependencies(der); /* compute derivatives of f(x) */ \
return res; \
#define IMPL_FUN_1(FUN, DER) \
inline GridKit::DependencyTracking::Variable FUN( \
const GridKit::DependencyTracking::Variable& x \
) \
{ \
double val = FUN(x()); \
double der = DER(x()); \
GridKit::DependencyTracking::Variable res(x); /* copy derivatives of x*/ \
res.setValue(val); /* set function value f(x) */ \
res.scaleDependencies(der); /* compute derivatives of f(x) */ \
return res; \
}

#define IMPL_FUN_2(FUN, DER1, DER2) \
inline GridKit::DependencyTracking::Variable FUN(const GridKit::DependencyTracking::Variable& x, \
const GridKit::DependencyTracking::Variable& y) \
{ \
double val = FUN(x(), y()); \
double der1 = DER1(x(), y()); \
double der2 = DER2(x(), y()); \
GridKit::DependencyTracking::Variable res1(x); /* copy derivatives of x*/ \
GridKit::DependencyTracking::Variable res2(y); /* copy derivatives of y*/ \
res1.setValue(val); /* set function value f(x, y) */ \
res2.setValue(val); /* set function value f(x, y) */ \
res1.scaleDependencies(der1); /* compute derivatives of f(x, y) with respect to x */ \
res2.scaleDependencies(der2); /* compute derivatives of f(x, y) with respect to y */ \
GridKit::DependencyTracking::Variable res(std::move(res1)); /* relocate derivatives w.r.t. x into return */ \
res.addDependencies(res2); /* add derivatives with respect to y to return */ \
return res; \
#define IMPL_FUN_2(FUN, DER1, DER2) \
inline GridKit::DependencyTracking::Variable FUN( \
const GridKit::DependencyTracking::Variable& x, \
const GridKit::DependencyTracking::Variable& y \
) \
{ \
double val = FUN(x(), y()); \
double der1 = DER1(x(), y()); \
double der2 = DER2(x(), y()); \
GridKit::DependencyTracking::Variable res1(x); /* copy derivatives of x*/ \
GridKit::DependencyTracking::Variable res2(y); /* copy derivatives of y*/ \
res1.setValue(val); /* set function value f(x, y) */ \
res2.setValue(val); /* set function value f(x, y) */ \
res1.scaleDependencies(der1 \
); /* compute derivatives of f(x, y) with respect to x */ \
res2.scaleDependencies(der2 \
); /* compute derivatives of f(x, y) with respect to y */ \
GridKit::DependencyTracking::Variable res(std::move(res1) \
); /* relocate derivatives w.r.t. x into return */ \
res.addDependencies(res2 \
); /* add derivatives with respect to y to return */ \
return res; \
}

IMPL_FUN_1(sin, GridKit::DependencyTracking::sin_derivative)
Expand All @@ -412,7 +420,11 @@ namespace std
IMPL_FUN_1(sqrt, GridKit::DependencyTracking::sqrt_derivative)
IMPL_FUN_1(abs, GridKit::DependencyTracking::abs_derivative)

IMPL_FUN_2(atan2, GridKit::DependencyTracking::atan2_derivative1, GridKit::DependencyTracking::atan2_derivative2)
IMPL_FUN_2(
atan2,
GridKit::DependencyTracking::atan2_derivative1,
GridKit::DependencyTracking::atan2_derivative2
)

#undef IMPL_FUN_1
#undef IMPL_FUN_2
Expand Down
132 changes: 74 additions & 58 deletions GridKit/AutomaticDifferentiation/Enzyme/D2LDx2.hpp
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Expand Up @@ -19,8 +19,9 @@ namespace GridKit
namespace Sparse
{
/**
* @brief Enzyme sparse Hessian of \f$L = \sigma f + \lambda^T g\f$, by one
* forward sweep per local variable over the reverse gradient of \f$L\f$
* @brief Enzyme sparse Hessian of \f$L = \sigma f + \lambda^T g\f$, by
* one forward sweep per local variable over the reverse gradient of
* \f$L\f$
*
* @tparam ModelT - optimization component model
*/
Expand All @@ -40,48 +41,56 @@ namespace GridKit
* @param[out] nnz - Number of nonzeros
*/
template <typename ScalarT, typename IdxT, typename RealT>
static void eval(const ModelT* model,
const size_t n_var,
const IdxT* variable_indices,
const ScalarT* x,
const RealT sigma,
const RealT* lambda,
IdxT* rows,
IdxT* cols,
ScalarT* vals,
IdxT& nnz)
static void eval(
const ModelT* model,
const size_t n_var,
const IdxT* variable_indices,
const ScalarT* x,
const RealT sigma,
const RealT* lambda,
IdxT* rows,
IdxT* cols,
ScalarT* vals,
IdxT& nnz
)
{
std::array<ScalarT, ObjectiveT::NUM_INPUTS> gradient{};
for (size_t var_i = 0; var_i < n_var; ++var_i)
{
// Sparse storage. @see LowerSparseStorage.hpp
ScalarT* seed = __enzyme_todense<ScalarT*>((void*) ident_load<ScalarT, IdxT>,
(void*) ident_store<ScalarT, IdxT>,
var_i);
ScalarT* dgradient = __enzyme_todense<ScalarT*>((void*) sparse_load<ScalarT, IdxT>,
(void*) sparse_store<ScalarT, IdxT>,
var_i,
1.0, // value scaling
variable_indices,
variable_indices,
rows,
cols,
vals,
&nnz);
ScalarT* seed = __enzyme_todense<ScalarT*>(
(void*) ident_load<ScalarT, IdxT>,
(void*) ident_store<ScalarT, IdxT>,
var_i
);
ScalarT* dgradient = __enzyme_todense<ScalarT*>(
(void*) sparse_load<ScalarT, IdxT>,
(void*) sparse_store<ScalarT, IdxT>,
var_i,
1.0, // value scaling
variable_indices,
variable_indices,
rows,
cols,
vals,
&nnz
);

__enzyme_fwddiff<void>((void*) lagrangianGradient<ScalarT, RealT>,
enzyme_const,
model,
enzyme_dup,
x,
seed,
enzyme_dupnoneed,
gradient.data(),
dgradient,
enzyme_const,
sigma,
enzyme_const,
lambda);
__enzyme_fwddiff<void>(
(void*) lagrangianGradient<ScalarT, RealT>,
enzyme_const,
model,
enzyme_dup,
x,
seed,
enzyme_dupnoneed,
gradient.data(),
dgradient,
enzyme_const,
sigma,
enzyme_const,
lambda
);
}
}

Expand All @@ -90,10 +99,12 @@ namespace GridKit
using ConstraintsT = ModelWrapper<ModelT, MemberFunctions::Constraints>;

template <typename ScalarT, typename RealT>
[[gnu::always_inline]] inline static ScalarT lagrangian(const ModelT* model,
const ScalarT* x,
const RealT sigma,
const RealT* lambda)
[[gnu::always_inline]] inline static ScalarT lagrangian(
const ModelT* model,
const ScalarT* x,
const RealT sigma,
const RealT* lambda
)
{
std::array<ScalarT, ConstraintsT::NUM_OUTPUTS> g{};
ConstraintsT::eval(model, x, g.data());
Expand All @@ -106,24 +117,29 @@ namespace GridKit
return value;
}

/// Add the Lagrangian gradient to `gradient`, whose tangent alone is used
/// Add the Lagrangian gradient to `gradient`, whose tangent alone is
/// used
template <typename ScalarT, typename RealT>
[[gnu::always_inline]] inline static void lagrangianGradient(const ModelT* model,
const ScalarT* x,
ScalarT* gradient,
const RealT sigma,
const RealT* lambda)
[[gnu::always_inline]] inline static void lagrangianGradient(
const ModelT* model,
const ScalarT* x,
ScalarT* gradient,
const RealT sigma,
const RealT* lambda
)
{
__enzyme_autodiff<void>((void*) lagrangian<ScalarT, RealT>,
enzyme_const,
model,
enzyme_dup,
x,
gradient,
enzyme_const,
sigma,
enzyme_const,
lambda);
__enzyme_autodiff<void>(
(void*) lagrangian<ScalarT, RealT>,
enzyme_const,
model,
enzyme_dup,
x,
gradient,
enzyme_const,
sigma,
enzyme_const,
lambda
);
}
};
} // namespace Sparse
Expand Down
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