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6 Commits

Author SHA1 Message Date
jgrusewski
cf442ff317 fix: restore explicit buffer_size in GPU profiles (auto-sizing removed) 
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-04 01:24:19 +02:00
jgrusewski
f5b21406a8 fix: restore batch_size to GPU profiles — profile defaults, not auto-scaling 
Auto-scaling returned 8192 on 4GB RTX 3050 (should be 64), causing
17s/epoch instead of 0.28s. The VRAM math didn't account for IQN (1.1GB),
attention, IQL, replay buffer (70% VRAM).

Profile-tested values:
- RTX 3050: 64 (4GB, minimal)
- A100: 2048 (40-80GB)
- H100: 8192 (80GB, production)
- Default: 256 (conservative)

Auto-scaling remains as fallback for batch_size=0 (unknown GPU).

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-02 21:26:24 +02:00
jgrusewski
5546a45bc3 refactor: remove gpu_n_episodes override — auto-scale from VRAM everywhere 
gpu_n_episodes was manually overridden in GPU profiles, training configs,
test files, and hyperopt — all set to 0 or small fixed values that
bypassed the auto-scaling logic, causing a div-by-zero crash in
train_baseline_rl.

Now: single auto-scaling path via optimal_n_episodes() from VRAM/SM
count. No manual override field. Cap at 16384 (consistent with
AutoBatchSizer's 8192 cap pattern). Floor at 32 for small GPUs.

Removed gpu_n_episodes from:
- DQNHyperparameters, PpoHyperparameters structs
- All 4 GPU profiles (rtx3050, h100, a100, default)
- Training profiles (smoketest, localdev)
- ExperienceProfile struct + serde
- Hyperopt adapter
- All test overrides

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-02 14:21:45 +02:00
jgrusewski
12fdd18223 refactor: remove entire CPU training path — 5,307 lines of dead code 
Deleted:
- DQN::compute_loss_internal (280 lines) — old Candle forward+loss
- DQN::train_step (55 lines) — old Candle training step
- DQN::compute_gradients (47 lines) — old gradient accumulation
- ComputeLossResult struct — only used by deleted functions
- RegimeConditionalDQN::train_step (65 lines) — old dispatch
- RegimeConditionalDQN::train_step_gpu_regime (100 lines) — old GPU path
- RegimeConditionalDQN::compute_gradients_gpu (130 lines) — old regime gradients
- RegimeConditionalDQN::compute_gradients (92 lines) — old dispatch
- DQNAgentType::train_step dispatch — dead
- DQNAgentType::compute_gradients dispatch — dead
- GpuDqnTrainer::upload_batch (71 lines) — old CPU→GPU upload
- train_step.rs (500 lines) — entire module including ensure_fused_ctx
- dqn_benchmark.rs — used old train_step
- examples.rs — used old train_step
- validation/adapters.rs (289 lines) — used old train_step
- dqn/trainable_adapter.rs — used old train_step
- gpu_smoketest.rs — tested old train_step
- Gradient accumulation path in training_loop.rs (144 lines)
- IQN d_h_s2().clone() → raw pointer (zero alloc)
- Causal intervention format! string alloc removed
- Dead HER relabel functions (320 lines)

Kept:
- ensure_fused_ctx logic inlined into training_loop.rs
- set_noise_sigma_scale re-added to RegimeConditionalDQN

Fixed:
- GpuReplayBuffer max_batch_size wired from batch_size parameter
  (was hardcoded 1024, blocking batch_size=8192)

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-04-02 09:06:23 +02:00
jgrusewski
e4b7d2ffb0 feat: GPU TOML profile system — remove ALL hardcoded VRAM if/else chains 
Created config/gpu/{default,rtx3050,h100,a100}.toml with all GPU-specific
parameters: batch_size, num_atoms, buffer_size, hidden_dim_base,
replay_buffer_vram_fraction, gpu_n_episodes, gpu_timesteps_per_episode,
cuda_stack_bytes.

GpuProfile::load() auto-detects GPU by device name, falls back to
embedded defaults (include_str!). Override via FOXHUNT_GPU_PROFILE env.

Removed dead code:
- detect_vram_mb(), vram_scaled_hidden_dims(), vram_scaled_base_dim(),
  resolve_hidden_dim_base() + 18 tests for these functions

All callers updated: train_baseline_rl, DQNTrainer constructor,
PPO trainer, smoke tests, pipeline tests.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-21 11:39:40 +01:00
jgrusewski
8a68bdf21d feat: GPU TOML profile system — replace hardcoded VRAM if/else chains 
Replace scattered VRAM-based if/else chains with a declarative TOML profile
system. GPU profiles (rtx3050, a100, h100, default) are selected by device
name and embedded at compile time via include_str! for zero-filesystem
fallback in CI/containers, with filesystem and env var overrides.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
 2026-03-21 11:21:55 +01:00