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fix(liquid): add PartialEq derives and fix Cuda device fallback semantics

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Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-02-22 22:46:48 +01:00
parent 65a938bac8
commit c4514d7a4f
1 changed files with 7 additions and 4 deletions
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11
ml/src/liquid/candle_cfc.rs
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@@ -10,7 +10,7 @@ use serde::{Deserialize, Serialize};
use crate::MLError;
/// Device configuration for CfC training
#[derive(Debug, Clone, Serialize, Deserialize)]
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub enum DeviceConfig {
Cpu,
Cuda(usize),
@@ -21,8 +21,11 @@ impl DeviceConfig {
pub fn resolve(&self) -> Result<Device, MLError> {
match self {
DeviceConfig::Cpu => Ok(Device::Cpu),
DeviceConfig::Cuda(id) => Device::cuda_if_available(*id).map_err(|e| {
MLError::ConfigurationError(format!("CUDA device {} error: {}", id, e))
DeviceConfig::Cuda(id) => Device::new_cuda(*id).map_err(|e| {
MLError::ConfigurationError(format!(
"CUDA device {} required but unavailable: {}",
id, e
))
}),
DeviceConfig::Auto => Device::cuda_if_available(0)
.map_err(|e| MLError::ConfigurationError(format!("Auto device error: {}", e))),
@@ -35,7 +38,7 @@ impl DeviceConfig {
/// This is distinct from `cells::CfCConfig` which configures the FixedPoint inference path.
/// `CfCTrainConfig` configures the Candle-based differentiable training path with
/// gradient descent, dropout, and market regime adaptation.
#[derive(Debug, Clone, Serialize, Deserialize)]
#[derive(Debug, Clone, PartialEq, Serialize, Deserialize)]
pub struct CfCTrainConfig {
pub input_size: usize,
pub hidden_size: usize,