From 5836280a34bdbe5f620f22582bf736417381f596 Mon Sep 17 00:00:00 2001
From: jgrusewski <jgrusewski@localhost>
Date: Fri, 17 Apr 2026 09:35:16 +0200
Subject: [PATCH] =?UTF-8?q?cleanup:=20remove=20AutoBatchSizer=20=E2=80=94?=
 =?UTF-8?q?=20batch=5Fsize=20from=20config=20is=20source=20of=20truth?=
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AutoBatchSizer didn't account for experience collector VRAM, causing
OOM on H100 with state_dim=96. batch_size is configured per GPU
profile (h100.toml=8192, rtx3050.toml=64). No auto-sizing needed.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
---
 .../src/trainers/dqn/trainer/constructor.rs   | 40 ++-----------------
 1 file changed, 4 insertions(+), 36 deletions(-)

diff --git a/crates/ml/src/trainers/dqn/trainer/constructor.rs b/crates/ml/src/trainers/dqn/trainer/constructor.rs
index c4e4abe87..1d75b1337 100644
--- a/crates/ml/src/trainers/dqn/trainer/constructor.rs
+++ b/crates/ml/src/trainers/dqn/trainer/constructor.rs
@@ -23,7 +23,6 @@ use crate::dqn::regime_conditional::RegimeConditionalDQN;
 use crate::dqn::reward::{RewardConfig, RewardFunction};
 use crate::features::microstructure_features::*;
 use crate::labeling::triple_barrier::TripleBarrierEngine;
-use crate::memory_optimization::auto_batch_size::{AutoBatchSizer, BatchSizeConfig};
 use crate::TrainingMetrics;
 use super::super::config::{DQNAgentType, DQNHyperparameters};
 use super::DQNTrainer;
@@ -77,43 +76,12 @@ impl DQNTrainer {
             full_dims, param_count, model_overhead_mb
         );
 
-        // Dynamic batch sizing: fully VRAM-derived, no hardcoded caps.
-        // AutoBatchSizer computes the safe maximum from free GPU memory.
-        let max_safe_batch = match AutoBatchSizer::new() {
-            Ok(sizer) => {
-                let config = BatchSizeConfig {
-                    model_memory_mb: model_overhead_mb,
-                    safety_margin: 0.15,
-                    ..BatchSizeConfig::default()
-                };
-                let safe = sizer.max_safe_batch_size(&config);
-                info!(
-                    "AutoBatchSizer: GPU VRAM ceiling = {} (configured: {})",
-                    safe, hyperparams.batch_size
-                );
-                safe
-            }
-            Err(e) => {
-                info!("AutoBatchSizer unavailable ({e}), using batch_size as-is");
-                hyperparams.batch_size.max(64)
-            }
-        };
-
-        // batch_size == 0 -> auto-compute from VRAM (no artificial cap)
+        // batch_size is configured per GPU profile (h100.toml, rtx3050.toml).
+        // No auto-sizing — the config is the source of truth.
         if hyperparams.batch_size == 0 {
-            hyperparams.batch_size = max_safe_batch.min(8192); // fallback only — profiles should set batch_size
-            info!("AutoBatchSizer: batch_size auto-computed to {}", hyperparams.batch_size);
-        }
-
-
-        // Cap to VRAM ceiling (never exceed what the GPU can handle)
-        if hyperparams.batch_size > max_safe_batch {
-            info!(
-                "DQN batch_size capped from {} -> {} (VRAM ceiling)",
-                hyperparams.batch_size, max_safe_batch
-            );
-            hyperparams.batch_size = max_safe_batch.min(8192); // fallback only — profiles should set batch_size
+            hyperparams.batch_size = 1024; // sensible default if somehow unset
         }
+        info!("DQN batch_size: {} (from config)", hyperparams.batch_size);
 
         // Use override device if provided (hyperopt shares one CUDA context),
         // otherwise require CUDA GPU  -- no CPU fallback in CUDA builds.