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fix: parameterize IQN_SHARED_H1 + IQN_HIDDEN in all 9 IQN kernels

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Build-time #define was 256 but runtime config can be 128 (hidden_dim_base).
Caused CUDA_ERROR_ILLEGAL_ADDRESS in IQN trunk gradient. Now passed as
kernel params matching the STATE_DIM parameterization pattern.

Also: warn→error for training failures in optimizer, eprintln for debugging.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
2026-03-26 01:55:52 +01:00
parent 232a1f0301
commit 4ab8ca0162
19 changed files with 633 additions and 6 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
crates/ml-hyperopt/src/optimizer.rs
View File

@@ -491,13 +491,15 @@ impl ArgminOptimizer {
let obj = if raw_objective.is_finite() {
raw_objective
} else {
warn!("Trial {} produced non-finite objective ({:?}), using penalty 1e6", trial_num, raw_objective);
tracing::error!("Trial {} produced NON-FINITE objective ({:?}), using penalty 1e6", trial_num, raw_objective);
1e6
};
(obj, M::extract_metrics(&metrics))
}
Err(e) => {
warn!("Trial {} training failed (scored as penalty): {:#}", trial_num, e);
tracing::error!("Trial {} training FAILED: {:#}", trial_num, e);
eprintln!("!!! TRIAL {} TRAINING FAILED: {:#}", trial_num, e);
eprintln!("!!! FULL ERROR CHAIN: {:?}", e);
(1e6, None)
}
};
@@ -717,7 +719,7 @@ impl ArgminOptimizer {
});
for err in lhs_errors.into_iter().flatten() {
warn!("LHS evaluation error (continuing): {}", err);
tracing::error!("LHS evaluation FAILED (continuing): {:#}", err);
}
// OOM Layer 2: Ramp up after first chunk succeeds
@@ -1117,7 +1119,7 @@ where
let metrics = match model.train_with_params(params.clone()) {
Ok(m) => m,
Err(e) => {
warn!("Training failed for trial {}: {}", trial_num, e);
tracing::error!("Trial {} training FAILED: {:#}", trial_num, e);
return Ok(1e6); // Penalty for training failure
},
};
@@ -1258,7 +1260,7 @@ where
let metrics = match model.train_with_params(params.clone()) {
Ok(m) => m,
Err(e) => {
warn!("Training failed for trial {}: {}", trial_num, e);
tracing::error!("Trial {} training FAILED: {:#}", trial_num, e);
return Ok(1e6);
},
};

71
crates/ml/ml/hyperopt_results/dqn/20260326_002727/best_params.json Normal file
View File

@@ -0,0 +1,71 @@
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"volatility_window": 23,
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"use_spectral_norm": true,
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"iqn_lambda": 0.8479734309322158,
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"hidden_dim_base": 128,
"noisy_epsilon_floor": 0.1,
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"cql_alpha": 0.5059776371295834,
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"dsr_eta": 0.003947815597664894,
"branch_hidden_dim": 64,
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"w_dsr": 1.0,
"w_pnl": 0.3,
"w_dd": 1.0,
"w_idle": 0.01,
"dd_threshold": 0.01,
"loss_aversion": 1.5,
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"q_gap_threshold": 0.1,
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"her_ratio": 0.48591734800375336,
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"use_cvar_action_selection": 0.028233223186665324,
"cvar_alpha": 0.03929750758139713,
"min_hold_bars": 9
}

7
crates/ml/ml/hyperopt_results/dqn/20260326_002727/campaign_summary.json Normal file
View File

@@ -0,0 +1,7 @@
{
"trials_completed": 1,
"best_loss": 1000000.0,
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"best_checkpoint_path": null,
"total_time_seconds": 9.581508487
}

71
crates/ml/ml/hyperopt_results/dqn/20260326_003121/best_params.json Normal file
View File

@@ -0,0 +1,71 @@
{
"learning_rate": 0.00005994999354431849,
"batch_size": 307,
"gamma": 0.9572818589229506,
"buffer_size": 66246,
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"huber_delta": 17.775790174607682,
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"per_beta_start": 0.20130083854118397,
"dueling_hidden_dim": 128,
"n_steps": 4,
"tau": 0.008986246204051671,
"num_atoms": 101,
"v_min": -280.91110000212706,
"v_max": 280.91110000212706,
"noisy_sigma_init": 0.24583814763225417,
"minimum_profit_factor": 1.5028035348681215,
"weight_decay": 0.00010597074820791314,
"kelly_fractional": 0.5096243658522589,
"kelly_max_fraction": 0.12010947414322902,
"kelly_min_trades": 20,
"volatility_window": 23,
"use_ensemble_uncertainty": false,
"ensemble_size": 5.0,
"beta_variance": 0.5,
"beta_disagreement": 0.5,
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"curiosity_weight": 0.0,
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"lr_decay_type": 0.0,
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"gae_lambda": 0.95,
"noisy_sigma_initial": 0.5,
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"use_spectral_norm": true,
"use_attention": true,
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"iqn_lambda": 0.8479734309322158,
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"cvar_alpha": 0.03929750758139713,
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}

7
crates/ml/ml/hyperopt_results/dqn/20260326_003121/campaign_summary.json Normal file
View File

@@ -0,0 +1,7 @@
{
"trials_completed": 1,
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71
crates/ml/ml/hyperopt_results/dqn/20260326_003657/best_params.json Normal file
View File

@@ -0,0 +1,71 @@
{
"learning_rate": 0.00005994999354431849,
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"gamma": 0.9572818589229506,
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7
crates/ml/ml/hyperopt_results/dqn/20260326_003657/campaign_summary.json Normal file
View File

@@ -0,0 +1,7 @@
{
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71
crates/ml/ml/hyperopt_results/dqn/20260326_003717/best_params.json Normal file
View File

@@ -0,0 +1,71 @@
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"iqn_lambda": 0.8479734309322158,
"spectral_norm_sigma_max": 4.8543579475657985,
"hidden_dim_base": 128,
"noisy_epsilon_floor": 0.1,
"count_bonus_coefficient": 0.23831546372779777,
"cql_alpha": 0.5059776371295834,
"eval_softmax_temp": 1.0,
"dsr_eta": 0.003947815597664894,
"branch_hidden_dim": 64,
"gradient_accumulation_steps": 1,
"w_dsr": 1.0,
"w_pnl": 0.3,
"w_dd": 1.0,
"w_idle": 0.01,
"dd_threshold": 0.01,
"loss_aversion": 1.5,
"time_decay_rate": 0.0005,
"q_gap_threshold": 0.1,
"c51_warmup_epochs": 10,
"dt_pretrain_epochs": 0,
"her_ratio": 0.48591734800375336,
"curiosity_weight_tunable": 0.09052455736721342,
"use_cvar_action_selection": 0.028233223186665324,
"cvar_alpha": 0.03929750758139713,
"min_hold_bars": 9
}

7
crates/ml/ml/hyperopt_results/dqn/20260326_004146/campaign_summary.json Normal file
View File

@@ -0,0 +1,7 @@
{
"trials_completed": 1,
"best_loss": 1000000.0,
"best_params_json": "{\n \"learning_rate\": 0.00005994999354431849,\n \"batch_size\": 307,\n \"gamma\": 0.9572818589229506,\n \"buffer_size\": 66246,\n \"max_position_absolute\": 1.1030284538648683,\n \"huber_delta\": 17.775790174607682,\n \"entropy_coefficient\": 0.38184099247597697,\n \"transaction_cost_multiplier\": 1.7738774066741243,\n \"per_alpha\": 0.45251155566697393,\n \"per_beta_start\": 0.20130083854118397,\n \"dueling_hidden_dim\": 128,\n \"n_steps\": 4,\n \"tau\": 0.008986246204051671,\n \"num_atoms\": 101,\n \"v_min\": -280.91110000212706,\n \"v_max\": 280.91110000212706,\n \"noisy_sigma_init\": 0.24583814763225417,\n \"minimum_profit_factor\": 1.5028035348681215,\n \"weight_decay\": 0.00010597074820791314,\n \"kelly_fractional\": 0.5096243658522589,\n \"kelly_max_fraction\": 0.12010947414322902,\n \"kelly_min_trades\": 20,\n \"volatility_window\": 23,\n \"use_ensemble_uncertainty\": false,\n \"ensemble_size\": 5.0,\n \"beta_variance\": 0.5,\n \"beta_disagreement\": 0.5,\n \"beta_entropy\": 0.2,\n \"warmup_ratio\": 0.0,\n \"curiosity_weight\": 0.0,\n \"td_error_clamp_max\": 10.0,\n \"batch_diversity_cooldown\": 50.0,\n \"lr_decay_type\": 0.0,\n \"sharpe_weight\": 0.10176748614555509,\n \"gae_lambda\": 0.95,\n \"noisy_sigma_initial\": 0.5,\n \"noisy_sigma_final\": 0.3,\n \"use_spectral_norm\": true,\n \"use_attention\": true,\n \"use_residual\": true,\n \"norm_type\": 1.0,\n \"activation_type\": 1.0,\n \"num_quantiles\": 32,\n \"qr_kappa\": 1.0,\n \"iqn_lambda\": 0.8479734309322158,\n \"spectral_norm_sigma_max\": 4.8543579475657985,\n \"hidden_dim_base\": 128,\n \"noisy_epsilon_floor\": 0.1,\n \"count_bonus_coefficient\": 0.23831546372779777,\n \"cql_alpha\": 0.5059776371295834,\n \"eval_softmax_temp\": 1.0,\n \"dsr_eta\": 0.003947815597664894,\n \"branch_hidden_dim\": 64,\n \"gradient_accumulation_steps\": 1,\n \"w_dsr\": 1.0,\n \"w_pnl\": 0.3,\n \"w_dd\": 1.0,\n \"w_idle\": 0.01,\n \"dd_threshold\": 0.01,\n \"loss_aversion\": 1.5,\n \"time_decay_rate\": 0.0005,\n \"q_gap_threshold\": 0.1,\n \"c51_warmup_epochs\": 10,\n \"dt_pretrain_epochs\": 0,\n \"her_ratio\": 0.48591734800375336,\n \"curiosity_weight_tunable\": 0.09052455736721342,\n \"use_cvar_action_selection\": 0.028233223186665324,\n \"cvar_alpha\": 0.03929750758139713,\n \"min_hold_bars\": 9\n}",
"best_checkpoint_path": null,
"total_time_seconds": 3.006564608
}

2
crates/ml/src/hyperopt/campaign.rs
View File

@@ -332,7 +332,7 @@ mod tests {
data_dir: effective_dir,
max_batch_size: 64, // RTX 3050 4GB
early_stopping_eta: 3,
max_epochs_per_trial: 10, // Quick validation
max_epochs_per_trial: 50, // Local validation with hold enforcement
results_base_dir: PathBuf::from("ml/hyperopt_results"),
mode: CampaignMode::Full,
mbp10_data_dir: None,

1
crates/ml/src/trainers/dqn/trainer/training_loop.rs
View File

@@ -1244,6 +1244,7 @@ impl DQNTrainer {
anyhow::anyhow!("No batch data for fused training step")
})?;
fused.run_full_step(batch_data, &mut *agent, &self.device)
.map_err(|e| { eprintln!("!!! FUSED STEP ERROR: {:#}", e); e })
.context("Fused CUDA training step failed")?
} else {
agent.train_step(explicit_batch)