Fix panic in case of a missing model

frame/benchmarking/src/analysis.rs

@@ -18,9 +18,11 @@
 //! Tools for analyzing the benchmark results.
 
 use std::collections::BTreeMap;
-use linregress::{FormulaRegressionBuilder, RegressionDataBuilder, RegressionModel};
+use linregress::{FormulaRegressionBuilder, RegressionDataBuilder};
 use crate::BenchmarkResults;
 
+pub use linregress::RegressionModel;
+
 pub struct Analysis {
 	pub base: u128,
 	pub slopes: Vec<u128>,

frame/benchmarking/src/lib.rs

@@ -26,7 +26,7 @@ mod analysis;
 
 pub use utils::*;
 #[cfg(feature = "std")]
-pub use analysis::{Analysis, BenchmarkSelector};
+pub use analysis::{Analysis, BenchmarkSelector, RegressionModel};
 #[doc(hidden)]
 pub use sp_io::storage::root as storage_root;
 pub use sp_runtime::traits::Zero;

utils/frame/benchmarking-cli/src/writer.rs

@@ -24,7 +24,7 @@ use std::path::PathBuf;
 use serde::Serialize;
 
 use crate::BenchmarkCmd;
-use frame_benchmarking::{BenchmarkBatch, BenchmarkSelector, Analysis};
+use frame_benchmarking::{BenchmarkBatch, BenchmarkSelector, Analysis, RegressionModel};
 use sp_runtime::traits::Zero;
 
 const VERSION: &'static str = env!("CARGO_PKG_VERSION");
@@ -134,6 +134,17 @@ fn map_results(batches: &[BenchmarkBatch]) -> Result<HashMap<String, Vec<Benchma
 	Ok(all_benchmarks)
 }
 
+// Get an iterator of errors from a model. If the model is `None` all errors are zero.
+fn extract_errors(model: &Option<RegressionModel>) -> impl Iterator<Item=u128> + '_ {
+	let mut errors = model.as_ref().map(|m| m.se.regressor_values.iter());
+	std::iter::from_fn(move || {
+		match &mut errors {
+			Some(model) => model.next().map(|val| *val as u128),
+			_ => Some(0),
+		}
+	})
+}
+
 // Analyze and return the relevant results for a given benchmark.
 fn get_benchmark_data(batch: &BenchmarkBatch) -> BenchmarkData {
 	// Analyze benchmarks to get the linear regression.
@@ -149,40 +160,40 @@ fn get_benchmark_data(batch: &BenchmarkBatch) -> BenchmarkData {
 
 	extrinsic_time.slopes.into_iter()
 		.zip(extrinsic_time.names.iter())
-		.zip(extrinsic_time.model.unwrap().se.regressor_values.iter())
+		.zip(extract_errors(&extrinsic_time.model))
 		.for_each(|((slope, name), error)| {
 			if !slope.is_zero() {
 				if !used_components.contains(&name) { used_components.push(name); }
 				used_extrinsic_time.push(ComponentSlope {
 					name: name.clone(),
 					slope: slope.saturating_mul(1000),
-					error: (*error as u128).saturating_mul(1000),
+					error: error.saturating_mul(1000),
 				});
 			}
 		});
 	reads.slopes.into_iter()
 		.zip(reads.names.iter())
-		.zip(reads.model.unwrap().se.regressor_values.iter())
+		.zip(extract_errors(&reads.model))
 		.for_each(|((slope, name), error)| {
 			if !slope.is_zero() {
 				if !used_components.contains(&name) { used_components.push(name); }
 				used_reads.push(ComponentSlope {
 					name: name.clone(),
 					slope,
-					error: *error as u128,
+					error,
 				});
 			}
 		});
 	writes.slopes.into_iter()
 		.zip(writes.names.iter())
-		.zip(writes.model.unwrap().se.regressor_values.iter())
+		.zip(extract_errors(&writes.model))
 		.for_each(|((slope, name), error)| {
 			if !slope.is_zero() {
 				if !used_components.contains(&name) { used_components.push(name); }
 				used_writes.push(ComponentSlope {
 					name: name.clone(),
 					slope,
-					error: *error as u128,
+					error,
 				});
 			}
 		});