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Allow derivimplicit to use finite differences. #1444

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Allow derivimplicit to use finite differences. #1444

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29e6b6c
Allow `derivimplicit` to use finite differences.
1uc Sep 17, 2024
7b9225c
Add test.
1uc Sep 20, 2024
5576cc9
Fixups.
1uc Sep 20, 2024
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68 changes: 60 additions & 8 deletions python/nmodl/ode.py
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Original file line number Diff line number Diff line change
Expand Up @@ -344,6 +344,55 @@ def solve_lin_system(
return code, new_local_vars


def finite_difference_step_variable(sym):
return f"{sym}_delta_"


def discretize_derivative(expr):
if isinstance(expr, sp.Derivative):
x = expr.args[1][0]
dx = sp.symbols(finite_difference_step_variable(x))
return expr.as_finite_difference(dx)
else:
return expr


def transform_expression(expr, transform):
if expr.args is tuple():
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return expr

args = list(transform_expression(transform(arg), transform) for arg in expr.args)
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return expr.func(*args)


def transform_matrix_elements(mat, transform):
return sp.Matrix(
[
[transform_expression(mat[i, j], transform) for j in range(mat.rows)]
for i in range(mat.cols)
]
)


def finite_difference_variables(mat):
vars = []

def recurse(expr):
for arg in expr.args:
if isinstance(arg, sp.Derivative):
var = arg.args[1][0]
vars.append((var, finite_difference_step_variable(var)))

for expr in mat:
recurse(expr)

return vars
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def needs_finite_differences(mat):
return any(isinstance(expr, sp.Derivative) for expr in sp.preorder_traversal(mat))


def solve_non_lin_system(eq_strings, vars, constants, function_calls):
"""Solve non-linear system of equations, return solution as C code.

Expand All @@ -369,28 +418,31 @@ def solve_non_lin_system(eq_strings, vars, constants, function_calls):
custom_fcts = _get_custom_functions(function_calls)

jacobian = sp.Matrix(eqs).jacobian(state_vars)
if needs_finite_differences(jacobian):
jacobian = transform_matrix_elements(jacobian, discretize_derivative)

X_vec_map = {x: sp.symbols(f"X[{i}]") for i, x in enumerate(state_vars)}
dX_vec_map = {
finite_difference_step_variable(x): sp.symbols(f"dX_[{i}]")
for i, x in enumerate(state_vars)
}

vecFcode = []
for i, eq in enumerate(eqs):
vecFcode.append(
f"F[{i}] = {sp.ccode(eq.simplify().subs(X_vec_map).evalf(), user_functions=custom_fcts)}"
)
expr = eq.simplify().subs(X_vec_map).evalf()
rhs = sp.ccode(expr, user_functions=custom_fcts)
vecFcode.append(f"F[{i}] = {rhs}")

vecJcode = []
for i, j in itertools.product(range(jacobian.rows), range(jacobian.cols)):
flat_index = i + jacobian.rows * j

rhs = sp.ccode(
jacobian[i, j].simplify().subs(X_vec_map).evalf(),
user_functions=custom_fcts,
)
Jij = jacobian[i, j].simplify().subs({**X_vec_map, **dX_vec_map}).evalf()
rhs = sp.ccode(Jij, user_functions=custom_fcts)
vecJcode.append(f"J[{flat_index}] = {rhs}")

# interweave
code = _interweave_eqs(vecFcode, vecJcode)

code = search_and_replace_protected_identifiers_from_sympy(code, function_calls)

return code
Expand Down
8 changes: 8 additions & 0 deletions src/codegen/codegen_cpp_visitor.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -708,15 +708,23 @@

printer->fmt_text(
"void operator()(const Eigen::Matrix<{0}, {1}, 1>& nmodl_eigen_xm, Eigen::Matrix<{0}, {1}, "
"1>& nmodl_eigen_dxm, Eigen::Matrix<{0}, {1}, "
"1>& nmodl_eigen_fm, "
"Eigen::Matrix<{0}, {1}, {1}>& nmodl_eigen_jm) {2}",
float_type,
N,
is_functor_const(variable_block, functor_block) ? "const " : "");
printer->push_block();
printer->fmt_line("const {}* nmodl_eigen_x = nmodl_eigen_xm.data();", float_type);
printer->fmt_line("{}* nmodl_eigen_dx = nmodl_eigen_dxm.data();", float_type);
printer->fmt_line("{}* nmodl_eigen_j = nmodl_eigen_jm.data();", float_type);
printer->fmt_line("{}* nmodl_eigen_f = nmodl_eigen_fm.data();", float_type);

for (size_t i = 0; i < N; ++i) {
printer->fmt_line(
"nmodl_eigen_dx[{0}] = std::max(1e-6, 0.02*std::fabs(nmodl_eigen_x[{0}]));", i);
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}

print_statement_block(functor_block, false, false);
printer->pop_block();
printer->add_newline();
Expand Down Expand Up @@ -1665,7 +1673,7 @@
auto rhs = get_variable_name(state_name + "0");

if (state->is_array()) {
auto size = state->get_length();

Check warning on line 1676 in src/codegen/codegen_cpp_visitor.cpp

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GitHub Actions / { "flag_warnings": "ON", "glibc_asserts": "ON", "os": "ubuntu-22.04" } 

unused variable ‘size’ [-Wunused-variable]

Check warning on line 1676 in src/codegen/codegen_cpp_visitor.cpp

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GitHub Actions / { "flag_warnings": "ON", "os": "ubuntu-22.04", "sanitizer": "undefined" } 

unused variable 'size' [-Wunused-variable]
for (int i = 0; i < state->get_length(); ++i) {
printer->fmt_line("{}[{}] = {};", lhs, i, rhs);
}
Expand Down
7 changes: 3 additions & 4 deletions src/pybind/wrapper.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -82,14 +82,13 @@ std::tuple<std::vector<std::string>, std::string> call_solve_nonlinear_system(
exception_message = ""
try:
solutions = solve_non_lin_system(equation_strings,
state_vars,
vars,
function_calls)
state_vars,
vars,
function_calls)
except Exception as e:
# if we fail, fail silently and return empty string
import traceback
solutions = [""]
new_local_vars = [""]
exception_message = traceback.format_exc()
)";

Expand Down
7 changes: 5 additions & 2 deletions src/solver/newton/newton.hpp
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Expand Up @@ -81,6 +81,8 @@ EIGEN_DEVICE_FUNC int newton_solver(Eigen::Matrix<double, N, 1>& X,
FUNC functor,
double eps = EPS,
int max_iter = MAX_ITER) {
// If finite differences are needed, this is stores the stepwidth.
Eigen::Matrix<double, N, 1> dX;
// Vector to store result of function F(X):
Eigen::Matrix<double, N, 1> F;
// Matrix to store Jacobian of F(X):
Expand All @@ -89,7 +91,7 @@ EIGEN_DEVICE_FUNC int newton_solver(Eigen::Matrix<double, N, 1>& X,
int iter = -1;
while (++iter < max_iter) {
// calculate F, J from X using user-supplied functor
functor(X, F, J);
functor(X, dX, F, J);
if (is_converged(X, J, F, eps)) {
return iter;
}
Expand Down Expand Up @@ -127,10 +129,11 @@ EIGEN_DEVICE_FUNC int newton_solver_small_N(Eigen::Matrix<double, N, 1>& X,
int max_iter) {
bool invertible;
Eigen::Matrix<double, N, 1> F;
Eigen::Matrix<double, N, 1> dX;
Eigen::Matrix<double, N, N> J, J_inv;
int iter = -1;
while (++iter < max_iter) {
functor(X, F, J);
functor(X, dX, F, J);
if (is_converged(X, J, F, eps)) {
return iter;
}
Expand Down
19 changes: 11 additions & 8 deletions src/visitors/sympy_solver_visitor.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -179,6 +179,7 @@ void SympySolverVisitor::construct_eigen_solver_block(
const std::vector<std::string>& solutions,
bool linear) {
auto solutions_filtered = filter_string_vector(solutions, "X[", "nmodl_eigen_x[");
solutions_filtered = filter_string_vector(solutions_filtered, "dX_[", "nmodl_eigen_dx[");
solutions_filtered = filter_string_vector(solutions_filtered, "J[", "nmodl_eigen_j[");
solutions_filtered = filter_string_vector(solutions_filtered, "Jm[", "nmodl_eigen_jm[");
solutions_filtered = filter_string_vector(solutions_filtered, "F[", "nmodl_eigen_f[");
Expand All @@ -187,16 +188,19 @@ void SympySolverVisitor::construct_eigen_solver_block(
logger->debug("SympySolverVisitor :: -> adding statement: {}", sol);
}

std::vector<std::string> pre_solve_statements_and_setup_x_eqs(pre_solve_statements);
std::vector<std::string> pre_solve_statements_and_setup_x_eqs = pre_solve_statements;
std::vector<std::string> update_statements;

for (int i = 0; i < state_vars.size(); i++) {
auto update_state = state_vars[i] + " = nmodl_eigen_x[" + std::to_string(i) + "]";
auto setup_x = "nmodl_eigen_x[" + std::to_string(i) + "] = " + state_vars[i];
auto eigen_name = fmt::format("nmodl_eigen_x[{}]", i);

pre_solve_statements_and_setup_x_eqs.push_back(setup_x);
auto update_state = fmt::format("{} = {}", state_vars[i], eigen_name);
update_statements.push_back(update_state);
logger->debug("SympySolverVisitor :: setup_x_eigen: {}", setup_x);
logger->debug("SympySolverVisitor :: update_state: {}", update_state);

auto setup_x = fmt::format("{} = {}", eigen_name, state_vars[i]);
pre_solve_statements_and_setup_x_eqs.push_back(setup_x);
logger->debug("SympySolverVisitor :: setup_x_eigen: {}", setup_x);
}

visitor::SympyReplaceSolutionsVisitor solution_replacer(
Expand Down Expand Up @@ -304,9 +308,7 @@ void SympySolverVisitor::construct_eigen_solver_block(


void SympySolverVisitor::solve_linear_system(const ast::Node& node,
const std::vector<std::string>& pre_solve_statements

) {
const std::vector<std::string>& pre_solve_statements) {
// construct ordered vector of state vars used in linear system
init_state_vars_vector(&node);
// call sympy linear solver
Expand Down Expand Up @@ -373,6 +375,7 @@ void SympySolverVisitor::solve_non_linear_system(
return;
}
logger->debug("SympySolverVisitor :: Constructing eigen newton solve block");

construct_eigen_solver_block(pre_solve_statements, solutions, false);
}

Expand Down
30 changes: 22 additions & 8 deletions test/unit/newton/newton.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,7 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
GIVEN("1 linear eq") {
struct functor {
void operator()(const Eigen::Matrix<double, 1, 1>& X,
Eigen::Matrix<double, 1, 1>& /* dX */,
Eigen::Matrix<double, 1, 1>& F,
Eigen::Matrix<double, 1, 1>& J) const {
// Function F(X) to find F(X)=0 solution
Expand All @@ -30,15 +31,16 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
}
};
Eigen::Matrix<double, 1, 1> X{22.2536};
Eigen::Matrix<double, 1, 1> dX;
Eigen::Matrix<double, 1, 1> F;
Eigen::Matrix<double, 1, 1> J;
functor fn;
int iter_newton = newton::newton_solver(X, fn);
fn(X, F, J);
fn(X, dX, F, J);
THEN("find the solution") {
CAPTURE(iter_newton);
CAPTURE(X);
REQUIRE(iter_newton > 0);
REQUIRE(iter_newton == 1);
REQUIRE_THAT(X[0], Catch::Matchers::WithinRel(1.0, 0.01));
REQUIRE(F.norm() < max_error_norm);
}
Expand All @@ -47,18 +49,20 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
GIVEN("1 non-linear eq") {
struct functor {
void operator()(const Eigen::Matrix<double, 1, 1>& X,
Eigen::Matrix<double, 1, 1>& /* dX */,
Eigen::Matrix<double, 1, 1>& F,
Eigen::Matrix<double, 1, 1>& J) const {
F[0] = -3.0 * X[0] + std::sin(X[0]) + std::log(X[0] * X[0] + 11.435243) + 3.0;
J(0, 0) = -3.0 + std::cos(X[0]) + 2.0 * X[0] / (X[0] * X[0] + 11.435243);
}
};
Eigen::Matrix<double, 1, 1> X{-0.21421};
Eigen::Matrix<double, 1, 1> dX;
Eigen::Matrix<double, 1, 1> F;
Eigen::Matrix<double, 1, 1> J;
functor fn;
int iter_newton = newton::newton_solver(X, fn);
fn(X, F, J);
fn(X, dX, F, J);
THEN("find the solution") {
CAPTURE(iter_newton);
CAPTURE(X);
Expand All @@ -71,6 +75,7 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
GIVEN("system of 2 non-linear eqs") {
struct functor {
void operator()(const Eigen::Matrix<double, 2, 1>& X,
Eigen::Matrix<double, 2, 1>& /* dX */,
Eigen::Matrix<double, 2, 1>& F,
Eigen::Matrix<double, 2, 2>& J) const {
F[0] = -3.0 * X[0] * X[1] + X[0] + 2.0 * X[1] - 1.0;
Expand All @@ -82,11 +87,12 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
}
};
Eigen::Matrix<double, 2, 1> X{0.2, 0.4};
Eigen::Matrix<double, 2, 1> dX;
Eigen::Matrix<double, 2, 1> F;
Eigen::Matrix<double, 2, 2> J;
functor fn;
int iter_newton = newton::newton_solver(X, fn);
fn(X, F, J);
fn(X, dX, F, J);
THEN("find a solution") {
CAPTURE(iter_newton);
CAPTURE(X);
Expand All @@ -107,6 +113,7 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
double e = 0.01;
double z = 0.99;
void operator()(const Eigen::Matrix<double, 3, 1>& X,
Eigen::Matrix<double, 3, 1>& /* dX */,
Eigen::Matrix<double, 3, 1>& F,
Eigen::Matrix<double, 3, 3>& J) const {
F(0) = -(-_x_old - dt * (a * std::pow(X[0], 2) + X[1]) + X[0]);
Expand All @@ -124,11 +131,12 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
}
};
Eigen::Matrix<double, 3, 1> X{0.21231, 0.4435, -0.11537};
Eigen::Matrix<double, 3, 1> dX;
Eigen::Matrix<double, 3, 1> F;
Eigen::Matrix<double, 3, 3> J;
functor fn;
int iter_newton = newton::newton_solver(X, fn);
fn(X, F, J);
fn(X, dX, F, J);
THEN("find a solution") {
CAPTURE(iter_newton);
CAPTURE(X);
Expand All @@ -145,6 +153,7 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
double X3_old = 1.2345;
double dt = 0.2;
void operator()(const Eigen::Matrix<double, 4, 1>& X,
Eigen::Matrix<double, 4, 1>& /* dX */,
Eigen::Matrix<double, 4, 1>& F,
Eigen::Matrix<double, 4, 4>& J) const {
F[0] = -(-3.0 * X[0] * X[2] * dt + X[0] - X0_old + 2.0 * dt / X[1]);
Expand All @@ -170,11 +179,12 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
}
};
Eigen::Matrix<double, 4, 1> X{0.21231, 0.4435, -0.11537, -0.8124312};
Eigen::Matrix<double, 4, 1> dX;
Eigen::Matrix<double, 4, 1> F;
Eigen::Matrix<double, 4, 4> J;
functor fn;
int iter_newton = newton::newton_solver(X, fn);
fn(X, F, J);
fn(X, dX, F, J);
THEN("find a solution") {
CAPTURE(iter_newton);
CAPTURE(X);
Expand All @@ -186,6 +196,7 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
GIVEN("system of 5 non-linear eqs") {
struct functor {
void operator()(const Eigen::Matrix<double, 5, 1>& X,
Eigen::Matrix<double, 5, 1>& /* dX */,
Eigen::Matrix<double, 5, 1>& F,
Eigen::Matrix<double, 5, 5>& J) const {
F[0] = -3.0 * X[0] * X[2] + X[0] + 2.0 / X[1];
Expand Down Expand Up @@ -224,11 +235,12 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
};
Eigen::Matrix<double, 5, 1> X;
X << 8.234, -245.46, 123.123, 0.8343, 5.555;
Eigen::Matrix<double, 5, 1> dX;
Eigen::Matrix<double, 5, 1> F;
Eigen::Matrix<double, 5, 5> J;
functor fn;
int iter_newton = newton::newton_solver<5, functor>(X, fn);
fn(X, F, J);
fn(X, dX, F, J);
THEN("find a solution") {
CAPTURE(iter_newton);
CAPTURE(X);
Expand All @@ -240,6 +252,7 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")
GIVEN("system of 10 non-linear eqs") {
struct functor {
void operator()(const Eigen::Matrix<double, 10, 1>& X,
Eigen::Matrix<double, 10, 1>& /* dX */,
Eigen::Matrix<double, 10, 1>& F,
Eigen::Matrix<double, 10, 10>& J) const {
F[0] = -3.0 * X[0] * X[1] + X[0] + 2.0 * X[1];
Expand Down Expand Up @@ -360,11 +373,12 @@ SCENARIO("Non-linear system to solve with Newton Solver", "[analytic][solver]")

Eigen::Matrix<double, 10, 1> X;
X << 8.234, -5.46, 1.123, 0.8343, 5.555, 18.234, -2.46, 0.123, 10.8343, -4.685;
Eigen::Matrix<double, 10, 1> dX;
Eigen::Matrix<double, 10, 1> F;
Eigen::Matrix<double, 10, 10> J;
functor fn;
int iter_newton = newton::newton_solver<10, functor>(X, fn);
fn(X, F, J);
fn(X, dX, F, J);
THEN("find a solution") {
CAPTURE(iter_newton);
CAPTURE(X);
Expand Down
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