#![cfg(test)]
use crate::symbolic::CodegenIR::LinearBlock;
use crate::symbolic::codegen_adapters::{
ivp_dense_ir_blocks, ivp_dense_runtime_plan, ivp_residual_ir_blocks, ivp_residual_runtime_plan,
};
use crate::symbolic::codegen_orchestrator::ParallelResidualExecutor;
use crate::symbolic::codegen_orchestrator::{
DenseJacobianChunkBinding, ParallelDenseJacobianExecutor, ParallelExecutorConfig,
ParallelFallbackPolicy, ResidualChunkBinding, SequentialDenseJacobianExecutor,
SequentialResidualExecutor,
};
use crate::symbolic::codegen_runtime_api::{
DenseJacobianChunkingStrategy, ResidualChunkingStrategy,
};
use crate::symbolic::codegen_tasks::{IvpJacobianTask, IvpResidualTask};
use crate::symbolic::codegen_test_support::{
benchmark_parallel_config, median_duration, per_iter_ns,
};
use crate::symbolic::symbolic_engine::Expr;
use crate::symbolic::symbolic_functions::Jacobian;
use crate::symbolic::test_codegen_generated_ivp_fixtures::generated_ivp_fixture;
use nalgebra::DVector;
use std::time::Instant;
fn build_ivp_case() -> Jacobian {
let mut jacobian = Jacobian::new();
let eq_system = vec![
Expr::parse_expression("t + y"),
Expr::parse_expression("t*y - z"),
];
let variables = vec!["y".to_string(), "z".to_string()];
let arg = "t".to_string();
jacobian.generate_IVP_ODEsolver(eq_system, variables, arg);
jacobian
}
fn eval_ir_blocks_sequential(
args: &[f64],
blocks: &[(usize, LinearBlock)],
output_len: usize,
) -> Vec<f64> {
let mut out = vec![0.0; output_len];
for (offset, block) in blocks {
let len = block.outputs.len();
block.eval_into(args, &mut out[*offset..(*offset + len)]);
}
out
}
fn bind_ivp_residual_fixture_chunks<'a>(
plan: &'a crate::symbolic::codegen_runtime_api::ResidualRuntimePlan<'a>,
) -> Vec<ResidualChunkBinding<'a>> {
let chunk_fns: Vec<crate::symbolic::codegen_orchestrator::GeneratedChunkFn> =
if plan.chunks.len() == 1 {
vec![generated_ivp_fixture::fixture_ivp_residual_chunk_0]
} else {
vec![
generated_ivp_fixture::fixture_ivp_residual_chunk_split_0,
generated_ivp_fixture::fixture_ivp_residual_chunk_split_1,
]
};
plan.chunks
.iter()
.zip(chunk_fns.into_iter())
.map(|(chunk, eval)| ResidualChunkBinding {
fn_name: chunk.fn_name.as_str(),
output_offset: chunk.output_offset,
output_len: chunk.residuals.len(),
eval,
})
.collect()
}
fn bind_ivp_jacobian_fixture_chunks<'a>(
plan: &'a crate::symbolic::codegen_runtime_api::DenseJacobianRuntimePlan<'a>,
) -> Vec<DenseJacobianChunkBinding<'a>> {
plan.chunks
.iter()
.map(|chunk| DenseJacobianChunkBinding {
fn_name: chunk.fn_name.as_str(),
value_offset: chunk.value_offset,
value_len: chunk.value_range().len(),
eval: generated_ivp_fixture::fixture_ivp_jacobian_chunk_0,
})
.collect()
}
#[test]
fn ivp_residual_aot_ir_matches_existing_lambdify() {
let jacobian = build_ivp_case();
let task = IvpResidualTask {
fn_name: "ivp_residual_eval",
time_arg: "t",
residuals: &jacobian.vector_of_functions,
variables: &["y", "z"],
params: None,
};
let runtime_plan = ivp_residual_runtime_plan(&task, ResidualChunkingStrategy::Whole);
let blocks = ivp_residual_ir_blocks(&runtime_plan);
let time = 1.5;
let state = DVector::from_vec(vec![2.0, 4.0]);
let mut args = vec![time];
args.extend(state.iter().copied());
let expected = (jacobian.lambdified_functions_IVP_DVector)(time, &state);
let actual = DVector::from_vec(eval_ir_blocks_sequential(
args.as_slice(),
blocks.as_slice(),
runtime_plan.output_len,
));
assert_eq!(actual, expected);
}
#[test]
fn ivp_jacobian_aot_ir_matches_existing_lambdify() {
let jacobian = build_ivp_case();
let task = IvpJacobianTask {
fn_name: "ivp_jacobian_eval",
time_arg: "t",
jacobian: &jacobian.symbolic_jacobian,
variables: &["y", "z"],
params: None,
};
let runtime_plan = ivp_dense_runtime_plan(&task, DenseJacobianChunkingStrategy::Whole);
let blocks = ivp_dense_ir_blocks(&runtime_plan);
let time = 1.5;
let state = DVector::from_vec(vec![2.0, 4.0]);
let mut args = vec![time];
args.extend(state.iter().copied());
let expected = (jacobian.function_jacobian_IVP_DMatrix)(time, &state);
let actual = runtime_plan.assemble_dense_matrix(
eval_ir_blocks_sequential(args.as_slice(), blocks.as_slice(), runtime_plan.len())
.as_slice(),
);
assert_eq!(actual, expected);
}
#[test]
fn compiled_generated_ivp_fixture_matches_existing_lambdify() {
let jacobian = build_ivp_case();
let residual_task = IvpResidualTask {
fn_name: "fixture_ivp_residual",
time_arg: "t",
residuals: &jacobian.vector_of_functions,
variables: &["y", "z"],
params: None,
};
let residual_plan = ivp_residual_runtime_plan(&residual_task, ResidualChunkingStrategy::Whole);
let residual_executor = SequentialResidualExecutor::new(
&residual_plan,
bind_ivp_residual_fixture_chunks(&residual_plan),
);
let jacobian_task = IvpJacobianTask {
fn_name: "fixture_ivp_jacobian",
time_arg: "t",
jacobian: &jacobian.symbolic_jacobian,
variables: &["y", "z"],
params: None,
};
let jacobian_plan =
ivp_dense_runtime_plan(&jacobian_task, DenseJacobianChunkingStrategy::Whole);
let dense_bindings = bind_ivp_jacobian_fixture_chunks(&jacobian_plan);
let sequential_dense =
SequentialDenseJacobianExecutor::new(&jacobian_plan, dense_bindings.clone());
let parallel_dense = ParallelDenseJacobianExecutor::with_config(
&jacobian_plan,
dense_bindings,
ParallelExecutorConfig {
jobs_per_worker: 1,
max_residual_jobs: None,
max_sparse_jobs: Some(2),
fallback_policy: ParallelFallbackPolicy::Never,
},
);
let time = 1.5;
let state = DVector::from_vec(vec![2.0, 4.0]);
let mut args = vec![time];
args.extend(state.iter().copied());
let expected_residual = (jacobian.lambdified_functions_IVP_DVector)(time, &state);
let actual_residual = DVector::from_vec(residual_executor.eval(args.as_slice()));
assert_eq!(actual_residual, expected_residual);
let expected_jacobian = (jacobian.function_jacobian_IVP_DMatrix)(time, &state);
let actual_jacobian_seq = sequential_dense.eval_dense_matrix(args.as_slice());
let actual_jacobian_par = parallel_dense.eval_dense_matrix(args.as_slice());
assert_eq!(actual_jacobian_seq, expected_jacobian);
assert_eq!(actual_jacobian_par, expected_jacobian);
}
#[test]
fn ivp_runtime_plan_preserves_time_first_argument_order() {
let task = IvpResidualTask {
fn_name: "ivp_residual_eval",
time_arg: "t",
residuals: &[
Expr::parse_expression("t + y"),
Expr::parse_expression("t*y - z"),
],
variables: &["y", "z"],
params: None,
};
let runtime_plan = ivp_residual_runtime_plan(&task, ResidualChunkingStrategy::Whole);
assert_eq!(runtime_plan.input_names, vec!["t", "y", "z"]);
}
#[test]
fn benchmark_compiled_ivp_aot_runtime_paths() {
let jacobian = build_ivp_case();
let residual_task = IvpResidualTask {
fn_name: "fixture_ivp_residual",
time_arg: "t",
residuals: &jacobian.vector_of_functions,
variables: &["y", "z"],
params: None,
};
let residual_plan = ivp_residual_runtime_plan(
&residual_task,
ResidualChunkingStrategy::ByOutputCount {
max_outputs_per_chunk: 1,
},
);
let residual_bindings = bind_ivp_residual_fixture_chunks(&residual_plan);
let residual_seq = SequentialResidualExecutor::new(&residual_plan, residual_bindings.clone());
let residual_par = ParallelResidualExecutor::with_config(
&residual_plan,
residual_bindings,
benchmark_parallel_config(),
);
let jacobian_task = IvpJacobianTask {
fn_name: "fixture_ivp_jacobian",
time_arg: "t",
jacobian: &jacobian.symbolic_jacobian,
variables: &["y", "z"],
params: None,
};
let jacobian_plan =
ivp_dense_runtime_plan(&jacobian_task, DenseJacobianChunkingStrategy::Whole);
let dense_bindings = bind_ivp_jacobian_fixture_chunks(&jacobian_plan);
let dense_seq = SequentialDenseJacobianExecutor::new(&jacobian_plan, dense_bindings.clone());
let dense_par = ParallelDenseJacobianExecutor::with_config(
&jacobian_plan,
dense_bindings,
ParallelExecutorConfig {
jobs_per_worker: 1,
max_residual_jobs: None,
max_sparse_jobs: Some(2),
fallback_policy: ParallelFallbackPolicy::Never,
},
);
let time = 1.5;
let state = DVector::from_vec(vec![2.0, 4.0]);
let mut args = vec![time];
args.extend(state.iter().copied());
let expected_residual = (jacobian.lambdified_functions_IVP_DVector)(time, &state);
let expected_jacobian = (jacobian.function_jacobian_IVP_DMatrix)(time, &state);
assert_eq!(
DVector::from_vec(residual_seq.eval(args.as_slice())),
expected_residual
);
assert_eq!(
dense_seq.eval_dense_matrix(args.as_slice()),
expected_jacobian
);
assert_eq!(
DVector::from_vec(residual_par.eval(args.as_slice())),
expected_residual
);
assert_eq!(
dense_par.eval_dense_matrix(args.as_slice()),
expected_jacobian
);
const SAMPLES: usize = 7;
const RESIDUAL_ITERS: usize = 2000;
const JACOBIAN_ITERS: usize = 2000;
let mut residual_seq_samples = Vec::with_capacity(SAMPLES);
let mut residual_par_samples = Vec::with_capacity(SAMPLES);
let mut jacobian_seq_samples = Vec::with_capacity(SAMPLES);
let mut jacobian_par_samples = Vec::with_capacity(SAMPLES);
for _ in 0..SAMPLES {
let start = Instant::now();
for _ in 0..RESIDUAL_ITERS {
let _ = residual_seq.eval(args.as_slice());
}
residual_seq_samples.push(start.elapsed());
let start = Instant::now();
for _ in 0..RESIDUAL_ITERS {
let _ = residual_par.eval(args.as_slice());
}
residual_par_samples.push(start.elapsed());
let start = Instant::now();
for _ in 0..JACOBIAN_ITERS {
let _ = dense_seq.eval_dense_matrix(args.as_slice());
}
jacobian_seq_samples.push(start.elapsed());
let start = Instant::now();
for _ in 0..JACOBIAN_ITERS {
let _ = dense_par.eval_dense_matrix(args.as_slice());
}
jacobian_par_samples.push(start.elapsed());
}
let residual_seq_ns = per_iter_ns(median_duration(residual_seq_samples), RESIDUAL_ITERS);
let residual_par_ns = per_iter_ns(median_duration(residual_par_samples), RESIDUAL_ITERS);
let jacobian_seq_ns = per_iter_ns(median_duration(jacobian_seq_samples), JACOBIAN_ITERS);
let jacobian_par_ns = per_iter_ns(median_duration(jacobian_par_samples), JACOBIAN_ITERS);
println!("=== Compiled IVP AOT runtime ===");
println!("sequential AOT residual: {:.2} ns/call", residual_seq_ns);
println!("parallel AOT residual: {:.2} ns/call", residual_par_ns);
println!(
"sequential AOT dense Jacobian: {:.2} ns/call",
jacobian_seq_ns
);
println!(
"parallel AOT dense Jacobian: {:.2} ns/call",
jacobian_par_ns
);
println!(
"ivp plans: residual_chunks={}, jacobian_chunks={}, jobs_residual={}, jobs_dense={}",
residual_plan.chunks.len(),
jacobian_plan.chunks.len(),
residual_par.job_count(),
dense_par.job_count()
);
}