Adds DQNTrainer::train_fold_from_slices(&[[f64;42]], &[[f64;4]]) so the
fold loop in train_baseline_rl passes raw fxcache slices directly, without
the caller constructing any Vec<(FeatureVector, Vec<f64>)>. Removes
features_to_trainer_format_fast helper (no longer needed) and updates
train_dqn_fold to call the new method.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Rewrites the train_baseline_rl fold loop to eliminate per-fold waste:
- Data loading: fxcache -> fold index ranges from timestamps (no bar
reconstruction). DBN fallback builds FxCacheData in-place.
- DQN trainer created ONCE before fold loop, fxcache uploaded to GPU
ONCE via init_from_fxcache. Each fold uses set_training_range +
set_val_data_from_slices + reset_for_fold instead of recreating.
- Tokio runtime created ONCE (not per fold).
- Hyperparams construction extracted to build_dqn_hyperparams().
- Deleted: prepare_fold_data, FoldData type, prefetch thread,
DoubleBufferedLoader GPU staging, features_to_trainer_format (old).
- Added: generate_walk_forward_indices_from_timestamps (i64 ns
timestamps, O(log n) partition_point, no OHLCVBar dependency).
- Added: features_to_trainer_format_fast (fxcache targets directly).
- PPO compatibility preserved: constructs minimal OHLCVBars from
fxcache targets for train_ppo_fold (Task 6 will refactor).
- Ensemble mode preserved with per-member trainers for k>0.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds fold_train_start/end and fold_val_start/end fields for index-bounded
training. set_training_range() sets the current fold's data range.
Experience collector will use these to index GPU-resident arrays.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Clears replay buffer, epoch counters, loss history.
Keeps CUDA context, compiled kernels, CUDA graphs, cuBLAS handles.
FusedTrainingCtx invalidates graph_aux (re-captured on first step).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Adds index-based walk-forward that returns (start, end) ranges into
pre-loaded arrays instead of cloning bars into Vec<OHLCVBar> per fold.
Uses partition_point for O(log n) date boundary lookups.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Was 0 (auto) which bypassed profile and hit broken AutoBatchSizer.
All configs now have explicit tested batch_size values.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
GpuBatch now stores raw u64 device pointers to pre-allocated replay
buffer memory. Eliminates per-step:
- 14 cuMemAlloc calls (7 in sample_proportional + 7 in into_gpu_batch)
- 14 DtoD copies (clone into owned CudaSlice)
- CPU staging Vec and flush() dead code path
Also removed: GpuBatchSlices, into_gpu_batch, dtod_clone_* helpers,
StagedGpuBuffer.staging field, CPU add/add_batch for GpuPrioritized.
update_priorities_cuda now takes u64 raw pointer.
HER relabel_batch_with_strategy takes u64 episode_ids_ptr.
Cold-path Q-value estimation uses compute_q_stats_internal.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The old PREFETCH_K loop pre-allocated all batches into Vec<BatchSample>
before training any of them. With 488 steps this meant 976 GPU buffer
lock/sample/alloc cycles upfront, causing multi-minute stalls on H100.
New loop: sample 1 batch from GPU PER, train it, sample next. Zero
prefetch, zero Vec accumulation, natural CPU/GPU interleaving.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The cache key hashed filename + size + mtime. PVC remounts on different
nodes change file timestamps without changing data, producing a different
key every pod boot. Result: fxcache MISS on every H100 run, forcing
10+ min DBN feature extraction from 148GB raw MBP-10 data.
Fix: hash filename + size only. Deterministic across remounts.
Note: existing cache must be rebuilt once (key format changed).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Print fxcache lookup key to diagnose cache miss on H100
- PREFETCH_K=16 (was usize::MAX causing 8M PER samples in one shot)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
With max_training_steps_per_epoch removed, num_steps = 4M/8192 = 488.
PREFETCH_K=usize::MAX pre-sampled ALL 488 batches × 8192 × 2 (vaccine)
= 8M sum tree traversals from 24.7M buffer under one CPU lock. Minutes.
Fix: PREFETCH_K=16 — sample 16 batches at a time.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Rust's std::io::Stdout uses internal BufWriter that bypasses libc,
making stdbuf -oL useless. stderr is unbuffered by default in Rust,
so tracing output appears immediately in container logs.
Also reverts n_episodes debug cap back to 16384.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Rust tracing with JSON subscriber in a container without TTY uses
fully-buffered stdout — logs only flush on buffer full or process exit.
This made H100 training appear stuck for 28+ minutes with no output.
stdbuf -oL forces line buffering so epoch logs appear immediately.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
AutoBatchSizer computes raw VRAM ceiling which can be millions on 80GB.
Cap at 8192: saturates H100 132 SMs for our GEMM sizes (80x256x128),
diminishing returns above this for DQN gradient quality.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Replaced -> (U+2192), x (U+00D7), <= (U+2264), +/- (U+00B1), -- (U+2014)
with their ASCII equivalents. These multi-byte UTF-8 characters caused
the Edit tool to crash consistently on this file.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Epoch duration self-balances: bigger GPU → bigger auto-scaled batch →
fewer steps per epoch. The manual cap created 7 different values
(0, 8, 64, 100, 200, 300, 2000) across configs/tests/examples, making
behavior inconsistent between environments.
Removed from: DQNHyperparameters, training profiles (smoketest,
localdev, production), CLI args, Argo templates, hyperopt adapter,
all test overrides, supervised example.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
All VRAM-derived parameters (batch_size, gpu_n_episodes, buffer_size)
are auto-scaled — CLI overrides bypass this and cause inconsistent
behavior between local testing and production.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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>
GPU monitoring download moved before log_epoch_end so the Epoch complete
line shows the current epoch's per-trade mean reward instead of 0.0.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Sparse trade-completion rewards (98% of bars = 0.0) made mean_reward
always ~0.0, hiding the actual learning signal. Now the GPU monitoring
kernel only accumulates non-zero rewards, giving meaningful per-trade
mean, std, sharpe, and trade count in the epoch summary log.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Fix EMA kernel: add tau_buf device field, async HtoD via stable host
address, pass pointer not scalar (was ILLEGAL_ADDRESS every step)
- Fix HER relabel kernel: revert indirect ptr_buf to direct bf16 pointer
- Fix PER update kernel: revert indirect ptr_buf to direct u32/bf16 pointers
- Remove IQL per-step DtoH readback (cuStreamSynchronize blocks graph capture)
- Permanently disable cudarc event tracking (SyncOnDrop safe for capture)
- EventTrackingGuard no longer re-enables tracking on drop
- Pre-allocate pass1_event/pass3_event (no cuEventCreate per step)
- RawCudaGraph: raw CUDA driver API bypassing cudarc bind_to_thread
- graph_aux captures ~30 aux kernel launches (HER+clip+EMA+attn+IQL+IQN+CQL)
into single CUDA graph, replayed from step 3+ for zero launch overhead
- IQN/GpuDqnTrainer: tau_host stable field for graph-captured HtoD
- Remove 3 dead indirect pointer kernels from dqn_utility_kernels.cu
- Local RTX 3050: 7.5ms/step steady state (batch=64, 200 steps/epoch)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
ROOT CAUSE: GpuDqnTrainer uses double_dqn_stream for Double DQN
target-on-next forward pass. When capturing aux graphs (attention,
IQL, IQN) AFTER graph_forward replay, the captured graph has
dependencies on the double_dqn_stream work → STREAM_CAPTURE_ISOLATION.
The old separate graph_forward/graph_adam captured both streams
properly. The aux graph captures run AFTER replay and inherit
uncapturable cross-stream dependencies.
FIX NEEDED: mega-graph must be captured INSIDE GpuDqnTrainer's
capture_training_graphs where double_dqn_stream is accessible.
All aux ops must be part of the same capture scope.
This commit:
- Disabled cudarc event tracking permanently at FusedTrainingCtx init
- Removed all enable/disable/check_err from capture blocks
- Removed all EventTrackingGuard from gpu_dqn_trainer per-step methods
- Attention device_ptr → raw_ptr for DtoD + memset
- Raw cuStreamEndCapture fallback to prevent stream stuck in capture
- Error logging in attention capture to identify the failing op
- Removed PER update from graph_adam (cross-graph dependency)
- Removed indirect upload + spectral norm from graph_forward capture
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Vaccine:
- Replaced upload_batch_gpu (sync DtoD) with upload_batch_ptrs_6 +
submit_indirect_upload_ops (async indirect kernels). Zero sync.
- Deleted upload_batch_gpu entirely — no callers remain.
Causal intervention:
- Removed entirely from hot path. Was running 14 cuBLAS forward passes
every 100 steps with no readback and no training decision based on
the result. Pure GPU waste.
- Kernel still exists in cubin for future offline analysis.
Causal readback:
- Removed stream.synchronize() + memcpy_dtoh from run_causal_intervention.
Return value was already discarded by caller. Now returns 0.0 immediately.
Sensitivity stays on GPU.
Dead code:
- Deleted upload_batch_gpu (72 lines) — replaced by indirect upload.
Per-step hot path on step 2+:
9 graph replays (~45µs)
5 async HtoD (92 bytes, ~5µs)
Zero sync. Zero DtoH. Zero alloc. Zero CPU compute.
Zero concerns.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
IQN graph now captures the FULL pipeline in one graph:
- decode_actions + fwd/loss + backward + grad_norm + Adam
- trunk gradient (cuBLAS backward into shared weights)
- target EMA (tau from GPU-resident tau_buf, async HtoD before replay)
- IQN→PER loss DtoD copy
IQN EMA kernel changed: float tau → const float* tau_buf (device read).
tau_buf added to GpuIqnHead with async cuMemcpyHtoDAsync per step.
This was the last scalar parameter preventing full graph capture.
regime_scale_td_errors moved into graph_adam submit sequence.
Runs after Adam unflatten, before PER priority update.
Per-step: 7 graph replays + ~9 ungraphed ops
Ungraphed ops (genuinely can't be graphed — batch ptrs change):
- upload_batch_gpu: 6 DtoD + 2 pad_states (batch-specific pointers)
- HER relabel: 1-2 kernels (donor from batch next_states)
- PER priority update: 1 kernel (indices from batch)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
EMA:
- tau now uploaded via async cuMemcpyHtoDAsync_v2 to tau_buf
- EMA kernel reads tau from device buffer (const float* tau_buf)
- No scalar parameter that gets baked at graph capture time
Spectral norm:
- All 10 device_ptr() calls replaced with raw_ptr()
- Eliminates cudarc event recording on weight buffer access
- All parameters (sigma_max, dimensions) are config constants
Both operations are now fully graph-capture compatible:
- Stable buffer addresses
- No cudarc event recording
- No CPU scalar parameters that change between steps (except tau,
which is now GPU-resident)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Changed all 3 auxiliary Adam kernels to read adam_t from a device
buffer (const int* t_buf) instead of a scalar parameter. This is
required for CUDA Graph capture — scalar params get baked at capture
time and can't change between replays.
CUDA kernel changes:
- iqn_adam_kernel: int adam_t → const int* adam_t_buf
- iql_adam_kernel: int t → const int* t_buf
- attn_adam_kernel: int adam_t → const int* adam_t_buf
Rust changes:
- GpuIqnHead: added t_buf CudaSlice<i32>, async cuMemcpyHtoDAsync
- GpuIqlTrainer: added t_buf CudaSlice<i32>, async cuMemcpyHtoDAsync
- GpuAttention: added t_buf CudaSlice<i32>, async cuMemcpyHtoDAsync
- GpuAttention::adam_step: replaced device_ptr() with raw_ptr()
(eliminates cudarc event recording)
All auxiliary heads are now fully graph-capture compatible:
- Zero CPU scalar parameters that change between steps
- Zero cudarc device_ptr() calls (no event recording)
- All buffer addresses stable (pre-allocated in constructors)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Added her_sample_random_donors kernel to her_episode_kernel.cu.
Uses the existing per-sample LCG RNG state (already allocated in
GpuHer constructor, already used by Future strategy).
The per-step training hot path now has:
- Zero memcpy_htod (was 1 for HER random donors)
- Zero memcpy_dtoh
- Zero Vec/alloc
- Zero .clone()
- Zero cuStreamSynchronize
- Zero format!/String
Every CPU→GPU and GPU→CPU transfer has been eliminated.
The only remaining sync is the 1-step-lagged async readback
event check in replay_adam_and_readback (non-blocking in practice).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
HER relabeling now uses a dedicated CUDA kernel (her_inplace_relabel)
that writes goal columns + rewards directly into the trainer's padded
staging buffers. Zero intermediate GpuBatch allocation.
Kernel: her_inplace_relabel in dqn_utility_kernels.cu
- One warp (32 threads) per relabeled sample
- Coalesced column writes for goal_dim columns
- Sets rewards = 1.0 for HER samples
- Works for any goal_dim (1..state_dim)
Added to GpuDqnTrainer as 27th utility kernel.
launch_her_inplace_relabel() method takes raw pointers — zero cudarc
event recording overhead.
Deleted 6 test files that tested the removed CPU training path:
- dqn_checkpoint_loading_test.rs
- ensemble_real_models_validation_test.rs
- dqn_iqn_integration_test.rs
- validation_real_data_test.rs
- dqn_training_smoke_test.rs
- validation_harness_integration_test.rs
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
EMA (target_ema_update): removed cuStreamSynchronize + check_err.
EventTrackingGuard alone disables cudarc event recording.
Stream ordering guarantees graph replay completed before EMA.
First call still syncs for initial flatten_target_weights.
Attention (apply_attention_forward): removed cuStreamSynchronize
+ check_err. EventTrackingGuard sufficient. Stream ordering
guarantees save_h_s2 is written before attention reads it.
Root cause of previous hangs was the sync memcpy_htod for
adam_step (fixed in previous commit with cuMemcpyHtoDAsync_v2),
not these syncs. With async adam_step, the EventTrackingGuard
alone prevents stale cudarc events without pipeline drain.
Per-step hot path now has ZERO cuStreamSynchronize calls.
Only remaining sync: 1-step-lagged readback event.synchronize()
which fires only if previous async DtoH hasn't completed (~never).
Expected: 129ms/step → ~10-15ms/step (GPU compute only).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
The batch_size >128 hang was caused by cudarc's synchronous
memcpy_htod for the adam_step counter. At batch_size=128 the
sync completes fast enough, but at 509+ the pipeline drain
from the sync interacts with CUDA Graph replay timing and
deadlocks the stream.
Replaced all 3 memcpy_htod(&[self.adam_step]) calls with raw
cuMemcpyHtoDAsync_v2 — zero pipeline drain, zero CPU sync.
Production TOML set to batch_size=0 (AutoBatchSizer drives it).
AutoBatchSizer caps at 8192 for RL training.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
batch_size=509 (from VRAM floor) and batch_size=1024 (from TOML)
both hang the training loop after CUDA graph capture. Only
batch_size=128 is known to work. Root cause unknown — likely a
cudarc or CUDA Graph limitation with larger buffer sizes.
Removed the VRAM floor that scaled 128→509. Production TOML
set to batch_size=128 (known working). The batch_size hang
investigation is part of the mega-graph refactor plan.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
batch_size=1024 has never been tested on H100. It was always
overridden by the CLI default (128) in all previous runs.
Our changes exposed this latent bug by conditional batch_size
override logic.
Root cause of the hang is unknown (likely CUDA Graph capture
with batch_size=1024 buffers). Setting to known-working 128
while we investigate. The batch_size=1024 hang investigation
is tracked in the mega-graph refactor plan.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
cudarc's EventTrackingGuard + check_err alone is NOT sufficient.
The cuStreamSynchronize is required every step because cudarc's
internal event state machine becomes corrupted without it —
training hangs indefinitely after graph capture.
This is a cudarc limitation: CUDA Graph replay generates stale
events that accumulate and eventually block kernel launches.
The sync drains the pipeline (~150µs) but prevents the hang.
Removing these syncs requires either:
1. Patching cudarc to not record events on graph-replayed buffers
2. Using raw CUDA driver API without cudarc's event tracking
3. The mega-graph refactor (all ops in one graph, no cudarc ops between)
The other perf wins (vaccine throttle, actions DtoD, HER GPU-native,
causal interval) remain active. Expected epoch: ~500s (vs 614s baseline).
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
