User directive 2026-05-17: borrow TFT GRN over the planned 2-layer MLP heads.
GRN structure: 2-layer GELU MLP body (eta_2 → eta_1) + GLU gate + main +
skip-projection from trunk → final sigmoid. Gives per-horizon "linear vs
deeper-transform" gating, matches the regime-conditional alpha pattern
(pearl_snapshot_alpha_is_regime_conditional). 5x parameter count vs the
2-layer MLP but the gated residual is exactly what TFT empirically wins on.
Phase 2C (TGN Δt Fourier features): 8 sin/cos features of Δt at log-spaced
periods [60s, 6s, 600ms, 60ms] appended to snap_features. Critical with
decision-stride>1 where Δt varies across positions. Bumps FEATURE_DIM 32→40.
Phase 2D (TFT VSN): per-feature softmax-normalised gates at the trunk entry,
replacing raw concat of snap_features. Learns to down-weight noisy
features per regime (canonical: trade-flow in low-volume, OFI in
spread-Q4). 2 new param tensors, 1 new cuda kernel (fwd+bwd).
Existing 2-layer MLP kernels from Tasks 1.3/1.4 stay in the cubin as
ablation baseline; wired path becomes GRN.
Phase 1.7 plan now spells out the full GRN forward + backward chain rule
(skip + sigmoid(gate) * main → outer sigmoid), kernel signatures,
parameter Xavier init, AdamW × 10 setup, and an extended numerical-grad
check covering all 10 new param tensors.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per user direction "no gating, this is the new default": the stacked
Mamba2 -> CfC -> heads design is THE production architecture. There's
no competing-baseline comparison to run. Validation reduces to normal
training metrics (per-horizon val AUC, train loss curve, sanity floor
of >0.5 AUC).
Deletions:
- crates/ml-alpha/src/gate/cfc_vs_mamba2.rs (gate verdict logic)
- crates/ml-alpha/src/gate/mod.rs
- crates/ml-alpha/examples/alpha_gate.rs (gate runner binary)
Renames:
- crates/ml-alpha/src/gate/auc.rs -> crates/ml-alpha/src/eval/auc.rs
- lib.rs: pub mod gate -> pub mod eval (gate implied comparison;
eval doesn't)
Spec amendments:
- Drop the "Gate baseline strategy" amendment (committed earlier
this session)
- Reframe the stacked-architecture amendment as a "decision" not a
"gate"; production path is unambiguous
- Reframe Section 4 "Validation gate: CfC must meet Mamba2" -> just
"Validation: per-horizon val AUC" with the >0.5 sanity floor
Doc cleanups: stale "Mamba2 gate baseline" mentions in build.rs and
pinned_mem.rs replaced with neutral wording. The Argo template
comment about "downstream gate consumption" becomes "for monitoring".
Test status: all 26+ ml-alpha tests pass. AUC tests (6/6) still pass
under the eval:: namespace.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Defines the Mamba2-only baseline for the stacked-vs-baseline gate
verdict as an ablation of the SAME PerceptionTrainer (a --bypass-cfc
flag), not a separate model. Apples-to-apples; same data window,
same hyperparameters, same code path. The only difference is whether
the CfC step is in the loop.
Three ablation options evaluated:
1. --bypass-cfc flag (recommended): Mamba2 -> heads directly
2. --mamba2-state-dim 2 (crippled Mamba2, CfC stays)
3. Frozen CfC initialized to identity (no code branch needed)
Option 1 wins on clarity: it answers "is CfC additive on top of
Mamba2" unambiguously, with the same Mamba2 capacity and same
training regime in both arms.
Concrete next-session work documented (1-2 hours):
- PerceptionTrainerConfig.bypass_cfc: bool + step() branch
- alpha_train --bypass-cfc CLI flag
- alpha-perception-template.yaml workflow parameter + bash branch
- submit both runs, fetch summaries, alpha_gate, commit verdict
gate_verdict logic unchanged — the cfc/mamba2 naming in the report
becomes stacked/bypass at the binding layer; the verdict math is
generic.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per-horizon P(up) at h ∈ {30, 100, 300, 1000, 6000} snapshots forward.
Single-block 5-thread kernel; each thread is its own 128-dim dot
product + sigmoid. No atomicAdd.
Tests (5/5 pass on sm_86) assert invariants only:
- sigmoid output ∈ [0, 1] for all heads
- zero weights + zero bias → 0.5 exactly
- bias = +20 → saturates near 1
- bias = -20 → saturates near 0
- per-head independence (mixed-bias configuration)
Addendum updated to explicitly state no-CPU-mirror discipline per
feedback_no_cpu_test_fallbacks.md.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Mid-execution architecture revision: Mamba2 stays as a sequence
encoder; CfC becomes the layer on top (replacing the Phase 1d.3 MLP
stacker). Gate becomes 'stacked AUC >= Mamba2-only stacker AUC at
every horizon' — proves the CfC layer is additive, rather than CfC
alone beating Mamba2 alone.
Plan 1 kernels (cfc_step, heads, projection, BCE, AdamW, Graph A)
are unchanged. Only CfcTrunk's forward path gains a Mamba2 prefix
that consumes the snapshot stream and emits a 128-dim h_mamba which
CfC reads. The Mamba2 kernel (mamba2_alpha_kernel.cubin) is already
in the build.
Option B (parallel + fused dual-stream) is documented as the Plan 2
fallback if the stacked gate fails.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Cargo.toml: drops gbdt; adds memmap2 + approx; keeps ml-core only
(cannot depend on ml: would cycle since ml depends on ml-alpha for
the Mamba2 gate baseline).
build.rs: compiles 7 cubins (mamba2_alpha + 6 new placeholders)
with -O3 --use_fast_math --ftz --fmad. Skips kernels whose source
isn't present yet so partial check-ins work. Every env::var paired
with rerun-if-env-changed per the canonical build pearl.
src/pinned_mem.rs: local copy of MappedF32Buffer (mirrors
ml::cuda_pipeline::mapped_pinned::MappedF32Buffer). Drives the only
permitted CPU<->GPU path per feedback_no_htod_htoh_only_mapped_pinned.
Eventually the move-to-ml-core refactor will deduplicate; out of
scope for the Phase A branch.
Addendum: updates the import path to ml_alpha::pinned_mem.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Plan 1 was written against an older cudarc device-centric API. cudarc 0.19
moved alloc/launch ownership to the stream (partly for CUDA Graph capture
hygiene). This addendum pins:
- MlDevice -> CudaContext -> CudaStream construction
- Cubin load + module + function caching
- MappedF32Buffer staging -> DtoD-async -> CudaSlice (canonical CPU->GPU)
- Slow-path readback via DtoD into a staging MappedF32Buffer
- launch_builder(&func).arg(...).launch(cfg) idiom
- IsvBus and MappedPinnedSnapshotSlot/FillSlot using the real
MappedF32Buffer API (host_slice_mut, read_all, dev_ptr field)
- CUDA Graph A capture via stream.begin_capture / end_capture / instantiate
Plan 1 kernel .cu source, CPU oracles, finite-diff thresholds, smoke
criteria, and gate logic are unchanged. Only Rust binding code uses
these patterns.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Cleanup of compiler warnings flagged by both local cargo check and the
cluster ensure-binary log. Per `feedback_no_hiding`, every site is
either deleted or wired up — no #[allow] suppressions.
Lib (5 sites):
- gpu_backtest_evaluator.rs:34 — drop unused DevicePtrMut.
- gpu_dqn_trainer.rs:49 — drop unused DevicePtrMut (8 device_ptr_mut
calls don't need the trait import in current cudarc). Line 19852:
drop unnecessary parens around `b * sh2`.
- training_loop.rs:20 — drop unused DevicePtrMut; unbrace single-
symbol use at 5766.
- state_reset_registry.rs:4 — delete the 10-symbol use-block of slot
constants. Names appear in description strings (documentation only),
symbols are never referenced.
Examples (3 sites):
- alpha_dqn_h600_smoke.rs:181, 186 — drop COL_RAW_CLOSE, FEAT_DIM,
FillCoeffs, FillModel imports.
- alpha_baseline.rs:79 — delete unused MappedI32::read. Batched path
uses read_all for N-element action readback; the single-element
method was leftover from the pre-batched legacy path.
Lib + examples now have zero removable warnings. The remaining
unsafe_block lints (each cudarc kernel launch needs unsafe) are
structural and not actionable under the project's -W unsafe-code
policy.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Closes the TrainingPersist loop for the regime defense per the
KELLY_F_SMOOTH precedent (pearl_kelly_cap_signal_driven_floors).
Three layers:
(1) Per-step: alpha_regime_vol_update_kernel tracks min(vol_obs > 1e-12)
in new slot 553 (REGIME_VOL_OBS_MIN_INDEX). Filtered against artifact-
zero observations from stationary snapshots where curr == prev.
(2) Per-cell: stacker_threshold_controller_update gains a fifth branch
that reads vol_ref (slot 550) at cell-end, computes
`target = floor_target_ratio × vol_ref`, slow-EMAs the floor anchor
(slot 552) toward the target with rate `floor_update_rate` (0.1 per
cell). Subfloor 1e-12 inside the kernel guards against the anchor
collapsing to zero.
(3) Per-invocation: alpha_baseline reads
`config/ml/alpha_baseline_state.json` at startup and seeds slot 552
from the `regime_vol_ref_floor` field. At end of main(), the learned
floor is written back via tmp+rename atomic write so concurrent
walk-forward invocations see a consistent file. Matches the
cross-fold-persistent shape of KELLY_F_SMOOTH.
Block extended to 15 slots (539..=553). Smoke + kernel unit test
pass -1/-1 for the new floor-controller indices (backward compat).
Walk-forward CV verdict (Q1 fxcache, 3 sequential folds):
iteration fold-A fold-B fold-C mean ± SD
pre-defense (no regime) +91.52 -21.44 +46.74 +38.94 ± 56.88
hardcoded 1e-9 floor -19.77 +65.04 +6.45 +17.24 ± 43.42
learned floor (0.5 × cell_min) +74.78 -12.72 +8.80 +23.62 ± 45.59
learned floor (0.1 × vol_ref) -19.53 -26.51 +15.46 -10.20 ± 22.49
Controller infrastructure is structurally correct (loop closes, floor
persists across invocations, kernel + disk + ISV all roundtrip). The
TUNING is data-dependent — single-quarter CV doesn't have enough
regime diversity to anchor the floor against. Multi-quarter fxcache
validation is the next step (built cluster-side on the 9-quarter
2024-Q1..2026-Q1 ES futures dataset, downloaded as a single artifact
for local CV).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two coupled fixes to the vol-EMA regime detector exposed by walk-forward
CV after all features were promoted to always-on:
(1) Bootstrap window for vol_ref (slot 551 = REGIME_VOL_REF_SAMPLES)
- Replace the Pearl-A "first observation replaces directly" bootstrap
with a running mean over the first N=100 vol_ema observations, then
switch to β-tracking. For IID observations the running-mean estimator
has variance σ²/N — a 100-sample mean is 10× less noisy than the
single-shot replace.
(2) Permanent floor on vol_ref (slot 552 = REGIME_VOL_REF_FLOOR)
- The bootstrap alone exposed the asymmetric deadband-deadlock: if
vol_ref converged to a tiny value during a calm initial stretch,
vol_ref / vol_ema fired the moment any realistic vol resumed and
Kelly stayed trapped at regime_scale_floor=0.25 forever. Floor
lives in ISV slot (TrainingPersist) with a hardcoded 1e-12 sub-floor
inside the kernel as numerical-underflow guard.
- Host seeds slot 552 with 1e-9. Future controller kernel will refine
this from observed cell-level vol minima with cross-fold persistence.
Walk-forward CV on Q1 fxcache (3 folds, window=700K, train_frac=0.6):
cost fold-A fold-B fold-C mean ± SD
0.00 -19.77 +65.04 (100%) +6.45 +17.24 ± 43.42
Fold B turnaround is the headline: -47.64 (bootstrap-only) → +65.04
(bootstrap + floor) confirms the floor is the load-bearing fix.
Cross-fold std-dev compressed 24% at cost=0; mean dropped from +38.94
(pre-defense) to +17.24 (with defense). Classic mean/variance trade.
Block extended to 14 slots (539..=552). Kernel sig: vol_ref_floor
moved from f32 scalar to vol_ref_floor_index i32, so the anchor is
named/addressable in ISV.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Adds two coupled interventions on the regime fragility exposed by
walk-forward CV (mean Sharpe +27 ± 56 at half-tick across 3 folds —
std-dev ≈ mean means the strategy is regime-dependent).
(1) Vol-EMA regime detector (new ISV slots 549/550)
- New alpha_regime_vol_update.cu kernel: per inference step, reads B
parallel-env mid prices, computes cross-env mean squared log-return,
and maintains ISV[549]=REGIME_VOL_EMA (Wiener-α with 0.4 floor +
Pearl A bootstrap) and ISV[550]=REGIME_VOL_REF (slow tracker β=0.005,
≈200-step horizon).
- Block-tree-reduce (no atomicAdd), guards against zero/non-finite mids.
(2) Pre-emptive Kelly attenuation (modified stacker controller)
- stacker_threshold_controller.cu takes 3 new args: regime_vol_ema_idx,
regime_vol_ref_idx, regime_scale_floor.
- Multiplies its reactive Sharpe-error Kelly output by
regime_scale = clamp(vol_ref / vol_ema, 0.25, 1.0)
- Disabled when indices = -1 (backward-compatible smoke + kernel test).
(3) Cost-aware training (--train-cost-hi)
- alpha_compose_backtest --train-cost-hi: when > --train-cost, each
training epoch samples cost ~ U[lo, hi] so the Q-network learns
cost-conservative behaviour across the realistic ES range.
(4) Wiring
- alpha_compose_backtest --regime-scale enables both per-step regime
kernel firing during eval AND the regime hookup in the per-episode
controller call. Mapped-pinned mids buffers, all compute device-side.
- ExecutionEnv exposes current_mid() so the host gather reads the
active snapshot mid per env without leaking the private cursor field.
Smoke + test sites pass -1/-1 for regime indices (backward compat).
Doc: docs/isv-slots.md ledger for slots 549/550.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
T10 wires the C51 → Mamba2 backward chain in both binaries:
- New alpha_train_window_store_batched_kernel captures per-step windows
for end-of-epoch re-forward (Mamba2 cache required for backward).
- Mamba2Block::backward_from_h_enriched lets the C51 grad-input feed
directly into Mamba2 backward, skipping the unused W_out projection.
- alpha_c51_grad_input → backward_from_h_enriched → Mamba2AdamW.step
closes the loop in alpha_dqn_h600_smoke and alpha_compose_backtest.
Smoke (--temporal --c51): all 4 KCs PASS. R_mean -6.3 → +4.2 vs
Phase E.3 close R_mean -4.7 (no-temporal). EARLY_Q_MOVEMENT
calibration (mamba2_snapshot + mamba2_weight_distance) lifts the
diagnostic from 0.0023 (head-only) to 0.0590 (head + encoder),
giving an honest learning signal when the encoder absorbs gradient.
Backtest (--c51 --temporal --window-k 16 --isv-continual):
cost=0.0000 best τ=0.250 Sharpe_ann=+34.56 (was +10.41 head-only,
-22.54 frozen-Mamba2)
cost=0.0625 best τ=0.250 Sharpe_ann=+33.22
cost=0.1250 best τ=0.250 Sharpe_ann=+30.85 (Phase 1d.4 baseline: -4.0)
cost=0.2500 best τ=0.250 Sharpe_ann=+27.73
cost=0.5000 best τ=0.250 Sharpe_ann=+15.68
Caveat: in-sample results; OOS gate next.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
T15: training rewrite mirroring T14 eval. N_par parallel envs
lockstep H steps per epoch; ONE batched C51 update at B = N_par * H.
Expected ~15× speedup vs sequential.
- NEW kernel alpha_h_enriched_store_batched_kernel for batched
h_enriched slot writes
- Training section greenfielded: legacy sequential loop deleted
- CLI flag --n-train-par (default 50)
- Terminal next-state slot zeroed; done=1 at horizon masks Q_next
contribution in Bellman projection — no terminal Mamba2 forward
- docs/isv-slots.md updated per kernel-audit-doc hook
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The Phase E.4.A T14 backtest at B=1 with per-step stream.synchronize()
was running ~150μs/step × 9M steps = ~22 min — dominated by sync
overhead, not GPU compute. RTX 3050 Ti to L40S swap wouldn't help
(launch overhead is the bottleneck, not FLOPS).
Solution: batched parallel envs. N=cli.n_eval_episodes environments
run in LOCKSTEP per cell — ONE sync per step (instead of N syncs).
Expected ~30× speedup at N=500.
Changes:
1. ExecutionEnv snapshots → Arc<Vec<SnapshotRow>>
- new() wraps Vec into Arc internally (backward compat)
- new_arc() takes pre-existing Arc (for parallel envs)
- snapshots_arc() accessor for snapshot sharing
- 50MB × N memory duplication avoided
2. alpha_window_push_batched_kernel (NEW CUDA)
- Same chronological shift+insert semantics as single-env kernel
- Grid (state_dim_blocks, B, 1): one thread per (batch, feature)
- launcher: launch_alpha_window_push_batched
3. MappedI32 (per-binary) gains len param + read_all()
- smoke & backtest pass len=1 for existing single-int use
- backtest passes len=N for batched action readback
4. backtest binary eval loop GREENFIELDED
- Legacy sequential 'for ep in 0..N { for step in ... }' loop
body deleted entirely
- New: 'for step in 0..horizon' outer, lockstep over N envs
- Build N envs sharing snapshots_arc at cell start
- Per step: gather N states (CPU loop, <100μs for N=500) →
write to mapped-pinned [N, STATE_DIM] → push kernel B=N →
Mamba2 batched forward → C51 batched forward → Thompson
batched → ONE sync → read N actions → step N envs on CPU
- ISV-continual moved from per-episode to per-cell (single fire
with aggregate stats)
5. docs/isv-slots.md updated per kernel-audit hook
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
The Phase E.4.A T8 wiring stored Mamba2's per-step cache.h_enriched
into h_enriched_buf_dev via a dtoh+htod sequence:
let h_host = stream.clone_dtoh(cache.h_enriched.cuda_data())?;
let mut buf_host = stream.clone_dtoh(&h_enriched_buf_dev)?; // <- whole buffer
for j in 0..hidden_dim { buf_host[slot_offset + j] = h_host[j]; }
stream.memcpy_htod(&buf_host, &mut h_enriched_buf_dev)?; // <- whole buffer
This violates feedback_cpu_is_read_only AND
feedback_no_htod_htoh_only_mapped_pinned. Worse, the buffer-wide
dtoh+htod every step is ~20K floats × 600 steps × 500 eps × 30 cells
= ~9M roundtrips totaling significant PCIe latency in the backtest.
Fix: new tiny CUDA kernel alpha_h_enriched_store_kernel in
alpha_window_push.cu (one thread per hidden-dim feature, writes
src[j] → buf[slot_offset + j]). Replaces the dtoh/htod sequence
in both smoke and backtest binaries.
Estimated speed-up at backtest scale: 3-6× on the temporal eval
path. Pure-GPU per-step inference restored — no synchronisation
points on the hot path.
docs/isv-slots.md updated per kernel-audit-doc hook.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Two documentation deliverables produced while T14 backtest runs:
1. Plan update (specs/2026-05-15-phase-e-4-a-temporal-foundation.md):
adds 'Execution Status' section reflecting actual T1-T14
progression. T5 deferred (real MBP-10 peek), T9 skipped (GRN
moved to E.4.B per integration notes), T10 partial (new C51
grad-input kernel landed but Mamba2 backward wiring deferred),
T14 in flight. Documents the 4 execution learnings:
research-first saved a week of duplicate kernel work; cheap
falsification experiments (Path 2, Path 3) avoided expensive
investments; C51 borrow was the largest single Sharpe-lift in
the session; GpuTensor/CudaSlice interop friction is the real
integration cost.
2. T10 patch sketch (specs/2026-05-15-t10-mamba2-backward-from-h-enriched.md):
ready-to-apply patch for ml-alpha::Mamba2Block adding a new
public method backward_from_h_enriched(cache, d_h_enriched).
Bypasses the W_out projection backward, accepts the
[B, hidden_dim] gradient from C51's grad-input kernel directly,
zero-initialises dw_out/db_out (AdamW step on zero grad is a
no-op with correct moment decay — effectively freezes W_out
params which is correct semantics since Phase E never uses
them). Includes the smoke binary wiring snippet that consumes
the new method via launch_alpha_c51_grad_input → Mamba2
backward → AdamW step. Application gated on T14 backtest
validation — if frozen Mamba2 already lifts Sharpe, T10
becomes optimisation rather than prerequisite.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Phase E.4.A Task 10 partial: adds the C51 gradient-w.r.t.-input
kernel needed to chain the C51 head's loss gradient back into the
Mamba2 temporal encoder.
Kernel signature: alpha_c51_grad_input_kernel reads probs[B, A, K],
m[B, K], actions[B], W[A*K, in_dim] and writes d_input[B, in_dim].
Math: dL/d_input[b,j] = Σ_k (p[b,a_taken,k] − m[b,k]) · W[a_taken*K+k, j]
(only the taken-action column of W contributes; restricted by the
sparse-over-actions C51 CE gradient structure).
Status: kernel + Rust launcher in place. Mamba2 backward NOT yet
wired in the smoke binary because ml_alpha::Mamba2Block::backward
takes d_logit [B, 1] (post-W_out scalar gradient), not
d_h_enriched [B, hidden_dim]. Two paths to complete:
1. Modify ml-alpha to expose backward_from_h_enriched(cache,
d_h_enriched) bypassing W_out.
2. Replicate the post-W_out backward sequence inline.
Both deferred pending backtest validation (T14): if frozen Mamba2
already lifts backtest Sharpe meaningfully, T10 becomes
optimisation rather than prerequisite. Smoke gate already passed
gate 1 (R_mean +3.9 vs C51-flat -1.1) with frozen weights.
docs/isv-slots.md updated.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Switch alpha_window_push from circular-buffer-with-head_idx layout
to shift+insert layout matching production mamba2_update_history.
Slot 0 = oldest, slot K-1 = newest after each push, matching
Mamba2Block's [B, K, in_dim] input contract directly (no reorder).
Cost: O(K-1) shifts per state_dim feature per push. For K=16,
state_dim=10: 10 threads × ~15 ops each = trivial.
Kernel signature: drops head_idx, adds K. Test updated to verify
chronological shift across 3 pushes into 4-slot buffer.
docs/isv-slots.md updated per kernel-audit-doc hook.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Phase E.4.A Task 6: tiny CUDA kernel that pushes a state[state_dim]
vector into slot `head_idx` of a circular window buffer
[K, state_dim]. Host tracks head_idx and zeros buffer on episode
reset. This is the GPU-side append primitive that the smoke
binary's per-step inference will call (T7) before Mamba2 over the
buffer (T8).
GPU smoke (alpha_window_push_circular_writes_to_indexed_slot):
writes state_a at slot 0, state_b at slot 2, verifies non-targeted
slots stay zero. PASS.
docs/isv-slots.md: documents kernel as slot-agnostic per
kernel-audit-doc hook requirement.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
C51 distributional Q-network with GPU Thompson selection borrowed
minimally from production (alpha_c51.cu: forward, project, grad,
expected_q, thompson_select kernels; ~260 lines). Uses Huber
negative-tail compression in projection per production
block_bellman_project_f. Action selection 100% GPU via mapped-pinned
i32 output + __threadfence_system + host volatile read (matches
gpu_training_guard MappedBuffer pattern).
Backtest result (2D sweep, 500 episodes per cell, 30 cells):
cost=0 C51 +10.41 vs linear-Q -15.72 (+26pt, BEATS Phase 1d.4
no-RL baseline +4.4 by 6pt)
cost=0.125 C51 -13.81 vs -29.17 (+15pt closes half-tick gap)
Win rate at cost=0 best τ: linear-Q 0.008 → C51 0.552.
Calibration hypothesis vindicated; documented in
memory/pearl_c51_thompson_closed_phase_e3_gap.md.
Also in this commit (Phase E.3 follow-up cleanup):
- --pruned-actions falsified (2.4× worse Sharpe). Documented in
memory/pearl_action_pruning_falsified.md.
- --real-spread falsified for ES futures (76% of bars at 1-tick floor).
- SnapshotRow bid_l/ask_l extended from [f32; 3] to [f32; 10].
L4-L10 synthesized in this commit; real MBP-10 peek lands in E.4.A T5.
- docs/isv-slots.md updated per kernel-audit-doc hook requirement.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Design doc (specs/): TFT-style architecture for Phase E execution
policy — sliding window → Mamba2 SSM → GRN trunk → MoE regime gate
→ C51 head → Thompson selector. Two core pillars added per user:
A) Full L1-L10 LOB depth input via hybrid MBP-10 peek
B) ISV-continual-learning: controllers fire at training AND
inference; Q-net weights frozen at inference but effective
policy adapts via ISV modulation
Plan doc (plans/): 14-task implementation plan for E.4.A foundation
(window buffer + L1-L10 depth + Mamba2 forward+backward + ISV-eval
controllers). Falsification gates: smoke R_mean improvement ≥ 50%,
backtest cost=0 Sharpe ≥ +8 (no regression vs C51-flat +10.41),
half-tick Sharpe ≥ -8 (closes 5pt+ of 10pt gap to Phase 1d.4
baseline -4.0).
TGGN (foxhunt Temporal Graph Gated Network) explicitly deferred to
Phase E.5+: existing CPU graph implementation + GPU adapter at
ml-supervised/src/tgnn/ — marginal benefit for single-instrument ES
futures vs the TFT-Mamba2 stack; revisit for multi-instrument
extension or production HFT inference layer.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Findings:
- Production Mamba2 (gpu_dqn_trainer) is coupled to SH2=256 trunk +
ofi_embed + ISV[8] temporal routing — not portable to Phase E.
- ml-alpha::mamba2_block::Mamba2Block is from-scratch, fully
configurable (in_dim/hidden_dim/state_dim/seq_len), GPU-pure with
forward_train/backward/AdamW. Used in Phase 1d.2 to lift AUC 0.50
to 0.66. ml-alpha is already a workspace dep of ml.
- GRN skipped for E.4.A — Mamba2 output goes straight to C51 head.
Reintroduce GRN in E.4.B if Sharpe gates don't pass.
- Controller-at-inference: kernel has no training-mode branches;
Wiener state preserved across episodes/cost cells for natural
live-deployment simulation.
Revises Tasks 8-10 of the plan: use Mamba2Block API instead of
writing custom kernels. Only new CUDA needed: alpha_c51_grad_input
(C51 gradient w.r.t. input features, for Mamba2 backward chain).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Phase E.2 Task 16. Engagement-rate self-correcting controller per
pearl_engagement_rate_self_correction. Single-block, single-thread
kernel; runs once per rollout-end boundary.
ISV slots driven:
543 STACKER_THRESHOLD_INDEX clamp [0, 0.5] P-controller on rate
545 TRADE_RATE_OBSERVED_EMA_INDEX Pearl A+D floored Wiener-α
546 STACKER_KELLY_ATTENUATION_INX clamp [0.1, 1.0] P-controller on Sharpe
Reads ISV[544] TRADE_RATE_TARGET_INDEX (TrainingPersist anchor, set once
at training start).
Control law:
observed = trade_count / max(decisions, 1)
ISV[545] ← Pearl_A+D_floored(observed, prev, x_lag)
[α* floor = 0.4 per pearl_wiener_alpha_floor_for_nonstationary;
controller co-adapts with policy → need responsive EMA]
err_rate = ISV[545] - ISV[544]
ISV[543] ← clamp(0, 0.5, ISV[543] + k_threshold · err_rate)
err_sharpe = rollout_sharpe - target_sharpe
ISV[546] ← clamp(0.1, 1.0, prev_atten + k_atten · err_sharpe)
where prev_atten = 1.0 if ISV[546] == 0.0 (sentinel-start)
else ISV[546]
Wiener-α is INLINE (not via canonical apply_pearls_ad_kernel chain)
because the EMA is part of the control loop, not a separate diagnostic
slot. Lower latency, fewer kernels per step.
Floor at 0.1 on Kelly attenuation per
pearl_blend_formulas_must_have_permanent_floor — can't be 0, would
zero out position sizing permanently.
Pub launcher `launch_stacker_threshold_controller` in alpha_kernels.rs
with full safety asserts. Slot indices passed as i32 args (decouple
slot numbering from kernel).
GPU smoke test `stacker_threshold_controller_smoke_matches_hand_
computation` verifies 2-iteration sequence:
Iter 1 (Pearl A): observed=0.30 → ISV[545]=0.30; ISV[543]: 0.05 → 0.052
Iter 2 (Pearl D): observed=0.05 → ISV[545]=0.175 (α* hit floor 0.5)
ISV[543]: 0.052 → 0.05275
Both within 1e-5 tolerance. Anchor slot 544 unchanged.
`cargo test -p ml --lib alpha_kernels`: 6 pass (compile witness + 5 GPU
smokes including this one) on RTX 3050 Ti in 2.18s.
Audit doc docs/isv-slots.md updated per Invariant 7.
Three stabilizers applied to the H=600 DQN smoke after initial run showed
unstable training (early_mvmt=2268× at lr=1e-6, NaN at lr=1e-4):
1. Reward normalization (--reward-scale, default 1000)
Rewards divided by scale BEFORE the Munchausen target. TD error
drops from ~1000 (raw reward magnitude at H=600) into O(1) target /
gradient / weight-update scale. Action selection + rollout-R
reporting use ORIGINAL rewards (so rvr math stays correct against
the Task 7c baseline).
2. Target network (--target-update-every, default 10 episodes)
Separate w_target_dev / b_target_dev buffers. Q_next(s') forward
uses target weights; SGD updates online only. Hard-update copies
online → target every K episodes. Breaks the V_soft(s') chase-its-
own-tail divergence of online-only Munchausen.
3. Gradient clipping (--grad-clip, default 1.0)
New `alpha_clip_inplace_kernel` in alpha_linear_q.cu (element-wise
clamp). Applied to dW and db after grad, before SGD. Safety net.
Diagnostic fix: weight_norm was direction-insensitive — orthogonal
rotations don't change ||W||_F, so early_mvmt read ≈0 even when training.
Switched to weight_distance_from_init = ||W_now − W_init||_F +
||b_now − b_init||_F (captures rotation). q_early = q_init + distance
so kernel's |q_early − q_init| / |q_init| ratio = distance / ||W_init||_F.
With lr bumped back up to 1e-4 (default for the stabilized config),
verified at horizon=100, n_episodes=200:
Q_SPREAD_EMA = 23.64 (≥ 0.05) PASS
ACTION_ENTROPY_EMA = 1.86 (≥ 1.0986) PASS
RETURN_VS_RANDOM_EMA = +0.586 (≥ 0.0) PASS
EARLY_Q_MOVEMENT_EMA = 0.0212 (≥ 0.01) PASS
Overall: PASS (H=6000 scale-up VIABLE)
early_mvmt grew monotonically (0.005 → 0.021) across the 200-episode
run — direction-sensitive diagnostic confirms genuine policy learning.
Audit doc docs/isv-slots.md updated per Invariant 7.
Next: H=600 / 1000-episode run on full data; if PASS holds, Task 13
(H=6000 scale-up) unlocks.
Phase E.1 Task 12a. Three new CUDA kernels for the H=600 DQN smoke
(Task 12 proper) that lands in a follow-up commit:
alpha_linear_q_forward_kernel Q = X · W^T + b
alpha_linear_q_grad_kernel dW, db sparse MSE-TD over taken actions
alpha_linear_q_sgd_step_kernel element-wise params -= lr · grad
Architecture: single linear layer, no hidden layer. The Phase E state
vector has meaningful direct features (alpha_logit, spread_bps, position,
ofi_sum_5, …) so linear Q can capture real relations like Q[Buy] ∝
alpha_logit. If linear can't pass the kill-criteria gate, no architecture
upgrade will save it — and the smoke proceeds with NoisyNet escalation
per the plan.
Sparse gradient: only the taken action contributes (standard DQN TD
loss). No atomicAdd needed — one thread per (i, j) loops over the batch
and adds only when actions[b] == i.
GPU contract:
- No host branches inside any kernel (graph-capture compatible)
- No atomicAdd (per feedback_no_atomicadd)
- All compute on GPU (forward, grad, weight update)
- Tiny launch overhead — fits per-step (batch=64 forward = 576 threads,
1 block; grad = 99 threads, 1 block)
Three pub(crate) Rust launchers in alpha_kernels.rs match the
launch_apply_pearls pattern. Cubin embedded via include_bytes!.
Smoke test `linear_q_forward_grad_sgd_round_trip_matches_hand_math`
exercises all three kernels end-to-end on a small (batch=2, state_dim=2,
n_actions=3) case with full hand-math:
Forward: Q = [[2.1, 3.2, 0.3], [4.1, 5.2, 0.3]] ✓
Grad: dW = [[-1.8, -2.7], [0.8, 1.0], [0, 0]]
db = [-0.9, 0.2, 0] ✓
SGD: W' = [[1.18, 0.27], [-0.08, 0.90], [0, 0]]
b' = [0.19, 0.18, 0.30] ✓
All within 1e-4 tolerance. `cargo test -p ml --lib alpha_kernels`:
5/5 pass on RTX 3050 Ti in 2.04s (compile witness + 4 GPU smokes).
Audit doc docs/isv-slots.md updated per Invariant 7.
End-to-end test for the kernel COMPOSITION that the H=600 DQN smoke
(Task 12 proper) will use at each rollout boundary:
t+0 alpha_kill_criteria_compute_kernel → scratch[0..4]
t+1 apply_pearls_ad_kernel(n_slots=4) → ISV[539..543]
The two prior alpha_kernels smokes (munchausen + kill_criteria) validated
kernels in isolation. This smoke validates the COMPOSITION on the same
stream — failures here are different (stream-ordering, Pearls index base,
Wiener offset base, scratch visibility) and would silently break Task 12.
Two iterations with stationary synthetic inputs:
Iter 1 (Pearl A bootstrap)
prev_x_mean=0 AND x_lag=0 → ISV[539..542] populated with raw scratch
observations = [0.2041, 0.8980, 1.0670, 0.1] within 0.01 tolerance.
Anchor slots 547/548 remain at Task 7c values (-5185, 4953).
Iter 2 (Pearl D stationary)
dx_mean = dx_step = 0 → α* = 0 → ISV unchanged from iter 1 within
1e-4. Stationary signal stays at the bootstrap value.
Test would catch:
- Producer's scratch write not visible to applicator (stream-ordering)
- Wrong Pearls isv_idx_base / wiener_offset_base
- Pearl A sentinel detection broken (formula yields 0 at t=0)
- Wiener state corruption (iter 2 drifts from iter 1)
Reuses launch_apply_pearls from sp4_wiener_ema.rs (pub(crate)). Helper
fn run_chained_iter factors the producer→applicator sequence so the two
iterations are byte-identical apart from the Wiener state's evolution.
`cargo test -p ml --lib alpha_kernels`: 4 pass (compile witness + 2 prior
GPU smokes + this chained smoke) on RTX 3050 Ti in 1.89s total. Audit doc
docs/isv-slots.md updated per Invariant 7.
Remaining Task 12 work (full H=600 DQN trainer integration with this
pipeline at rollout boundaries) is queued for a dedicated session.
Adds the second of the two Phase E.1 kernel smoke tests in
alpha_kernels.rs (companion to the munchausen_target smoke from
91d1a52b9). End-to-end exercises the alpha_kill_criteria_compute_kernel
launcher with synthetic inputs covering all 4 outputs:
q_values = [[1, 2, 3], [5, 5, 5]] → q_spread ≈ 0.2041
action_counts = [10, 30, 60] → action_entropy ≈ 0.8980
rollout_R=100, isv[547]=-5185,
isv[548]=4953 → return_vs_random ≈ 1.0670
q_init=50, q_early=55 → early_movement = 0.1000
ISV buffer is sized to 552 floats with slots 547/548 populated using the
committed Task 7c baseline values — this exercises the production
slot-indexing path through the kernel's
`isv[random_baseline_mean_slot]` / `isv[random_baseline_std_slot]` reads,
not just isolated kernel arithmetic.
Does NOT chain apply_pearls_ad_kernel afterward — the smoothing path is
canonical SP4 applicator territory already covered elsewhere. This test
isolates the kill-criteria producer arithmetic.
Tolerance 0.01 on all four observations; passes on RTX 3050 Ti in <2s
including kernel JIT.
`cargo test -p ml --lib alpha_kernels`: 3 pass (compile witness + both
GPU smokes). Audit doc docs/isv-slots.md updated per Invariant 7.
The kill-criteria producer, Munchausen target kernel, Rust launchers,
fit/baseline binaries, and their output JSON artifacts are *durable
infrastructure* of the alpha trading system (live across Phase E/F/G/...),
not milestone-scoped to Phase E specifically. Aligns with the earlier
`phase_e_isv_slots.rs` → `alpha_isv_slots.rs` rename rationale.
What was renamed:
Code files:
crates/ml/src/cuda_pipeline/phase_e_kill_criteria.cu → alpha_kill_criteria.cu
crates/ml/src/cuda_pipeline/phase_e_munchausen_target.cu → alpha_munchausen_target.cu
crates/ml/src/cuda_pipeline/phase_e_kernels.rs → alpha_kernels.rs
crates/ml/examples/phase_e_fit_fill_model.rs → alpha_fit_fill_model.rs
crates/ml/examples/phase_e_random_baseline.rs → alpha_random_baseline.rs
Artifacts:
config/ml/phase_e_fill_coeffs.json → alpha_fill_coeffs.json
config/ml/phase_e_random_baseline.json → alpha_random_baseline.json
Kernel function names:
phase_e_kill_criteria_compute_kernel → alpha_kill_criteria_compute_kernel
phase_e_munchausen_target_kernel → alpha_munchausen_target_kernel
Rust launcher names:
launch_phase_e_kill_criteria → launch_alpha_kill_criteria
launch_phase_e_munchausen_target → launch_alpha_munchausen_target
Static cubin names:
PHASE_E_MUNCHAUSEN_TARGET_CUBIN → ALPHA_MUNCHAUSEN_TARGET_CUBIN
Historical milestone tags in doc-comments ("Phase E.1 Task N (2026-05-15)")
are RETAINED — they record WHEN the work landed and what plan it
implemented, which doesn't change with the system-scoped rename.
Plus: ADDS the alpha_munchausen_target GPU smoke test in alpha_kernels.rs.
End-to-end validates the launcher + kernel against hand-computed expected
values: batch=2 with one terminal sample; expected targets [29.8, 1.1];
got match within 0.05 tolerance on RTX 3050 Ti. PROVES the Task 9/10
kernels actually run on GPU.
All affected references updated in:
- build.rs (kernel compile list)
- mod.rs (module registration)
- state_reset_registry.rs (4 RegistryEntry descriptions for slots 539-542)
- alpha_isv_slots.rs (slot table comment)
- docs/isv-slots.md (audit-doc cross-references)
Verified:
cargo test -p ml --lib alpha_kernels: 2/2 pass (including GPU smoke)
cargo test -p ml --lib state_reset_registry: 10/10 pass
cargo build -p ml --release --example alpha_fit_fill_model --example alpha_random_baseline: clean
Phase E.1 Task 11. Two pub(crate) launchers expose the kill-criteria
producer (Task 9) and Munchausen target augmentation (Task 10) for use
by future trainer integration:
launch_phase_e_kill_criteria(stream, kernel, q_values_dev, action_counts_dev,
scalar_inputs_dev, isv_dev, batch, n_actions,
scratch_out_dev)
→ kicks the kill_criteria producer; caller chains apply_pearls_ad_kernel
(n_slots=4, isv_idx_base=ALPHA_ISV_BLOCK_LO=539) to smooth into slots
539..542 via Pearl A bootstrap + Pearl D Wiener-α.
launch_phase_e_munchausen_target(stream, kernel, q_next_dev, q_current_dev,
actions_dev, rewards_dev, dones_dev,
gamma, alpha_m, tau, log_clip_min,
target_out_dev, batch, n_actions)
→ one-thread-per-sample target augmentation; α_m/τ/log_clip_min are
scalar args so a downstream ISV-driven controller can tune them.
Plan deviation: Task 11 plan-spec said "replace hardcoded n_step=32,
gamma=0.999 literals" but grep across gpu_dqn_trainer.rs found ZERO such
literals — the trainer already reads gamma via read_isv_signal_at(
GAMMA_DIR_EFF_INDEX) and epsilon via read_isv_signal_at(AUX_TRUNK_EPS_
INDEX). The actual E.1 deliverable was Rust launchers for the new
cubins, which this commit lands.
Both launchers follow the launch_apply_pearls pattern in
sp4_wiener_ema.rs — pre-loaded CudaFunction as parameter, u64 device
pointers, debug_assert! guards.
Audit doc docs/isv-slots.md updated per Invariant 7.
Tested via `cargo test -p ml --lib phase_e_kernels` — 1 compile-witness
passes. Real GPU integration test in Task 12.
Phase E.1 Task 10. Standalone target-augmentation kernel:
m = α_m · max(τ · log π(a|s), log_clip_min)
V_soft(s') = max(Q_next) + τ · log Σ exp((Q_next − max) / τ)
target = r + m + γ · V_soft(s') (terminal: r + m)
π(a|s) ∝ exp(Q_online(s, a) / τ) — softmax policy from the online net.
Munchausen bonus is implicit KL regularisation between successive policies;
soft-V replaces the hard max bootstrap with a τ-weighted softmax average.
Both softmaxes are computed via log-sum-exp with the max-trick. This is
essential at τ ≈ 0.03 where raw exp(Q/τ) would overflow f32 for any
Q-spread > 25 nats. The kernel is one-thread-per-batch-sample, no
atomicAdd, no host branches.
α_m, τ, log_clip_min are kernel args (not hard-coded), so a Phase E.2+
controller can ISV-drive them. Typical Vieillard values: α_m=0.9, τ=0.03,
log_clip_min=-1.0.
Does NOT touch any ISV slot — pure target augmentation.
Cubin: target/release/build/ml-*/out/phase_e_munchausen_target.cubin (12.8 KB).
Launcher integration is Task 11 (consumes target_out where the C51/MSE
loss kernels currently consume `r + γ · max_a' Q_target`). Audit doc
docs/isv-slots.md updated per Invariant 7.
Phase E.0 Task 9. Single-block, single-thread CUDA producer that writes 4
raw scalar observations into a contiguous scratch_out[0..4] block:
scratch_out[0] → ISV[539] Q_SPREAD_EMA (kill: ≥ 0.05)
scratch_out[1] → ISV[540] ACTION_ENTROPY_EMA (kill: ≥ 0.5·ln(9) ≈ 1.10)
scratch_out[2] → ISV[541] RETURN_VS_RANDOM_EMA (kill: ≥ 0, i.e. ≥ random)
scratch_out[3] → ISV[542] EARLY_Q_MOVEMENT_EMA (kill: ≥ 0.01, learned)
Downstream `apply_pearls_ad_kernel` (n_slots=4) chained on the same stream
applies Pearl A first-observation bootstrap + Pearl D Wiener-α smoothing —
composes with the canonical val_sharpe_delta_compute_kernel pattern rather
than reimplementing Wiener math (per feedback_no_cpu_compute_strict — one
Wiener implementation in the codebase, the GPU one).
Inputs:
q_values[batch, n_actions] most-recent Q forward output (device)
action_counts[n_actions] empirical action histogram (device)
scalar_inputs[3] [rollout_R_mean, q_init_norm, q_early_norm]
via mapped-pinned (host writes between rollouts)
isv reads slots 547 + 548 (random baseline
mean/std — populated once by Task 7c,
TrainingPersist)
No atomicAdd (per feedback_no_atomicadd), no host branches
(per pearl_no_host_branches_in_captured_graph), no CPU compute
(per feedback_cpu_is_read_only). __threadfence_system() before exit for
the chained Pearls applicator's visibility guarantee.
Cubin produced: target/release/build/ml-*/out/phase_e_kill_criteria.cubin
(16.8 KB).
Launcher integration is Task 11 (separate commit so this task can stand
alone for cubin validation). Audit doc docs/isv-slots.md updated per
Invariant 7.
Per feedback_registry_entries_need_dispatch_arms — both must land in the
same commit; the test `every_fold_and_soft_reset_entry_has_dispatch_arm`
walks training_loop.rs::reset_named_state source and asserts coverage.
- 10 RegistryEntry rows appended after SP22 block (full producer/consumer
rationale per existing dense-description pattern)
- 7 dispatch arms in reset_named_state (the 3 TrainingPersist anchors
— slots 544/547/548 — are not dispatched per the existing convention)
- All sentinels 0.0 per pearl_first_observation_bootstrap
All 10 registry tests pass. Audit doc docs/isv-slots.md updated per
Invariant-7.
12 contiguous slots reserved for durable alpha-system infrastructure:
- 539-542: diagnostics (Q-spread, action entropy, return-vs-random,
early-Q-movement EMAs) — read by Phase E kill criteria
- 543-546: stacker-threshold controller (threshold, target, observed-rate,
Kelly attenuation) — engagement-rate self-correction
- 547-548: random-uniform baseline (mean, std) — anchor for kill criterion 541
- 549-550: reserved spare (absorb growth without bumping ISV_TOTAL_DIM)
Named `alpha_isv_slots` rather than `phase_e_isv_slots` because these slots
are intended to outlive any individual Phase E/F/G milestone — the SP4..SP22
naming was iteration-scoped, but this block is system-scoped infrastructure.
ISV_TOTAL_DIM bumped to 551. All indices validated in 4 unit tests
(bounds, membership, uniqueness, total-dim coverage). Audit doc
docs/isv-slots.md updated per Invariant-7.
32 tasks across 5 milestones (E.0 foundation → E.4 shadow-mode), with
locked design decisions from three rounds of focused research memos:
- Q1 (fill sim): medium-tier Poisson regression from 5.2M trade tape
- Q2 (reward): terminal-only, n-step credit (consumes ISV slot 517)
- Q3 (alpha trust): implicit calibrated trust via state features
- Q4 (state window): current snapshot + 2 short-horizon scalars
- Q5 (sizing): hybrid decoupled fractional Kelly × Phase E attenuation
Trainer choice: DQN primary (Rainbow + Munchausen target), PPO control
on H=600 truncated only if kill criteria fire. Exploration: ε-greedy
with kill-criteria gate at end of week 2; NoisyNet escalation (4-6 days
due to dead scaffolding in our codebase) if criteria fail; RND beyond
that.
ISV consumption: 5 existing slots (n_step=517, γ=43-46, ε=41, Kelly=280,
reward_caps=452-453); new block 539..550 reserved for Phase E (10 in
active use, 2 spare). One new controller (stacker-threshold
engagement-rate-self-correction at slot 543).
Hardcoded by design: Kelly contract cap (Category-1 safety),
kill-criteria thresholds (circuit breakers). All other knobs are
ISV-driven per pearl_controller_anchors_isv_driven.
Decisive gates at week 1 (H=600 kill criteria), week 4 (composition
backtest Sharpe at half-tick > 0), and week 5 (shadow-vs-backtest
PnL within 30%).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Smoke train-k95mj epoch 0 + epoch 1 printed
`trade_outcome_ce=0.000e0` — K=3 CE EMA stuck at Pearl A sentinel
across all training steps.
Root cause chain:
1. insert_batch is called with bs = total = 4_096_000 (cf-mult-
expanded), replay cap = 300_000 (L40S GpuProfile). Tail-clip:
off = 3_796_000, slice(off..) takes the last 300K elements.
2. Prior fix (256a5fa5a) filled CF half [base_total, total) with
-1 mask via cuMemsetD32Async. Last 300K elements at
[3_796_000, 4_096_000) are entirely in this CF half → 100%
mask.
3. Replay ring fills with 300K mask labels. PER gather samples
batches → every batch has label == -1 for all samples →
aux_trade_outcome_loss_reduce returns
loss = 0 / fmaxf(valid=0, 1) = 0 every step.
4. aux_outcome_ce_ema_update bootstrap requires `loss > 0.0f`;
never fires → ISV[538] pinned at 0.0 sentinel.
Fix: CF half mirrors the on-policy half (matches K=2 sibling
aux_sign_labels which writes the same bar-resolved label to both
halves). Replace cuMemsetD32Async(-1) + single memcpy with TWO
memcpy_dtod calls (on-policy half + CF half), both sourced from
the same exp_aux_to_label_per_sample[base_total].
Semantic justification: the K=3 outcome label depends on the bar's
trade-close event, not the action. The CF action's hypothetical
trade outcome at the same bar approximates to the same label. The
B4b-1 kernel writes -1 to non-close slots (~97%) and 0/1/2 to
close slots (~3%); duplicating into CF preserves this sparsity →
ring tail retains ~7-9K real labels per 300K-element fill →
Pearl A bootstraps → K=3 head trains.
Lib suite 1016/0 green. Bug was runtime-only (small-batch lib
tests don't trigger the off > 0 tail-clip path). Audit doc
updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The F-3c commit (cdd3dc6ed) pushed `aux_trade_outcome_ce_ema` into
the regression-detection metrics vec but never wired a `tracing::
info!` console emit. Smoke `train-q5k5k` ran clean past the
rollout + post-rollout phases — but `argo logs train-q5k5k | grep
aux_trade_outcome` returned zero hits, so the operator had no way
to read the K=3 head's CE EMA trajectory.
Fix: extend the existing K=2 aux HEALTH_DIAG print
HEALTH_DIAG[N]: aux [next_bar_mse=… regime_ce=… w=…]
to include `trade_outcome_ce=…`:
HEALTH_DIAG[N]: aux [next_bar_mse=… regime_ce=…
trade_outcome_ce=… w=…]
Reads ISV[538] via the same `trainer.read_isv_signal_at(...)`
accessor the regression-detection vec uses — single source of
truth, no duplicated mirroring.
Expected operator-visible trajectory across smoke epochs:
Epoch 0 (cold-start, Pearl A sentinel): 0.000
Epoch 1+ (first non-zero bootstrap): ~1.099 (= ln(3))
Healthy learning: 0.5 - 0.7
Pinned at ln(3) for many epochs: head can't learn
cargo check -p ml clean. Audit doc updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Phase B4b-2 introduced `exp_aux_to_label_per_sample` sized
`[alloc_episodes × alloc_timesteps]` (no cf-mult expansion — the
B4b-1 per-step kernel only writes the on-policy half). The batch
finalisation cloned into the emitted `aux_outcome_labels` with size
`total = base_total × 2` (cf-mult expanded), so
`dtod_clone_i32(src, total, ...)` invoked `src.slice(..total)` on a
half-sized source → `CudaSlice::try_slice` returned `None` → the
internal `unwrap()` panicked at cudarc safe/core.rs:1648.
Repro: workflow train-xzv56 panicked after rollout completed
(timestep=999) on fold 0; rollout itself ran clean, the OOM from
the prior commit is gone.
Fix: replace the single `dtod_clone_i32` with a 3-step build:
1. alloc_zeros::<i32>(total)
2. cuMemsetD32Async(ptr, 0xFFFFFFFFu32, total, stream) — fills
all `total` slots with i32 mask sentinel -1 (byte pattern
0xFFFFFFFF reinterprets as i32(-1))
3. memcpy_dtod the first `base_total` real labels from
exp_aux_to_label_per_sample into the on-policy half
CF half remains at -1. The K=3 sparse-CE loss masks `label == -1`
out of the mean and B_valid count, so CF samples contribute zero
gradient — exactly the semantic we want (CF actions have no
observed trade-close outcome to predict against).
Lib suite: 1016/0 green. Audit doc updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
dqn-production.toml hard-coded three H100-tuned values that shadow
GpuProfile auto-detection: batch_size=16384, buffer_size=500K,
gpu_n_episodes=4096. After the L40S-default flip lands (prior commit
8b8bb1af7), workflow `train-ft8ph` deterministically OOMed at fold 0/1/2
with `build_next_states_f32` 4 GiB alloc — because the H100-sized
hyperparams.batch_size + buffer_size + gpu_n_episodes ate ~38 GB of the
L40S's 46 GB usable VRAM before the rollout step.
DqnTrainingProfile.apply_to() runs AFTER train_baseline_rl.rs populates
hyperparams from GpuProfile, so the production TOML always wins. All
three fields are `Option<...>` in the TOML schema — removing the lines
turns apply_to into a no-op for them, and the GpuProfile-detected values
flow through:
field | L40S | H100 | (was forced)
batch_size | 4096 | 8192 | 16384
buffer_size | 300K | 500K | 500K
gpu_n_episodes | 2048 | 4096 | 4096
Two pinned assertions in training_profile.rs::tests checked the old
contract `hp.batch_size == 16384`. Rewritten to assert
`hp.batch_size == baseline_batch_size` — locks the new contract that
VRAM-tuned values stay GpuProfile-sourced.
Lib suite 1016/0 green. Audit doc updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Two collector-side device buffers used by the K=3 trade-outcome head
were missing FoldReset registry coverage:
- prev_aux_outcome_probs [alloc_episodes × 3]
TRUE stale-read risk. Producer writes end-of-step; consumer
(experience_state_gather) reads start-of-next-step into
state[121..124). Without FoldReset the new fold's step-0 state
gather would inject the previous fold's last-step softmax probs
into the first batch's state slots.
- exp_aux_to_input_buf [alloc_episodes × 262]
Cleanliness-only. Concat kernel overwrites all 262 columns every
step before the K=3 forward reads them, so no steady-state stale-
read risk. Registered for parity with the rest of the K=3
pipeline + to satisfy feedback_registry_entries_need_dispatch_
arms (the pin test asserts every registry entry has a matching
dispatch arm in reset_named_state).
Both fields promoted to pub(crate) on GpuExperienceCollector so
reset_named_state can reach them. Matching dispatch arms added with
the standard memset_zeros pattern (is_win_per_env / hold_baseline_
buffer style).
Tests: All 10 state_reset_registry tests pass, including the critical
every_fold_and_soft_reset_entry_has_dispatch_arm pin test that walks
the dispatch body and validates parity with registry entries. Full
lib suite 1015/1 (the failing test is the pre-existing
test_dqn_checkpoint_round_trip NoisyLinear flake — pred1/pred2 sign
mismatch surfacing ~30-50% of full-suite runs, documented in
project_sp22_h6_vnext_resume memory as unrelated to this work).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Phase B5b's K=3 input concat passed plan_params=NULL because the
collector had no trade plan launch. Trainer-side K=3 forward trained
on real plan_params while the collector queried at plan_params=0 —
documented train/inference asymmetry on the plan-conditioning surface.
Phase B5b-2 mirrors the (now-corrected) trainer
`launch_trade_plan_forward` chain on the collector inside the rollout
step:
1. SGEMM: hidden[N, AH] = h_s2_q[N, SH2] @ W_fc[AH, SH2]^T
2. bias+relu in-place on hidden
3. SGEMM: pre_out[N, 6] = hidden[N, AH] @ W_out[6, AH]^T
4. trade_plan_activate → exp_plan_params[N, 6]
Weight resolution uses the same `aux_w_ptrs` array the K=3 forward
already consumes (`f32_weight_ptrs_from_base`); indices 91-94 match
the corrected trainer-side reads. The `trade_plan_activate` kernel is
loaded from `EXPERIENCE_KERNELS_CUBIN` (same cubin the rest of
`exp_module_extra` uses; the trainer loads it from there too).
The K=3 concat now takes `exp_plan_params.raw_ptr()` instead of NULL
— both sides see f(h_s2; W_plan_*_init), symmetry restored. Plan
tensors at [91..94] still have no backward (no Adam updates), so the
plan-head weights stay at Xavier cold-start forever. This commit
delivers the symmetry the K=3 head requires, not a learned plan
signal — adding a real plan-head backward is a follow-up project.
New struct fields on GpuExperienceCollector:
- exp_trade_plan_hidden_buf [alloc_episodes × adv_h]
- exp_trade_plan_pre_out_buf [alloc_episodes × 6]
- exp_plan_params [alloc_episodes × 6]
- exp_trade_plan_activate_kernel: CudaFunction
Lib test suite: 1016/0 green maintained. Audit doc updated.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
`launch_trade_plan_forward` read `w_fc/b_fc/w_out/b_out` from
`padded_byte_offset(¶m_sizes, [82..86))` — i.e., the ISV-conditioning
+ recursive-confidence tensors (`b_isv_gate`, `w_isv_gamma`,
`b_isv_gamma`, `w_conf_fc`). The actual plan tensors live at
`[91..95)` per `compute_param_sizes` and the matching Xavier
init block. No backward exists for the plan head, so the plan
tensors at `[91..94]` sat at cold-start Xavier values forever
while `plan_params` was being driven by whichever ISV/conf
weights happened to occupy the wrong offsets.
Every downstream consumer (`backtest_plan_kernel`, `plan_isv` slots
in `experience_kernels`, `compute_plan_params` in `q_value_provider`,
regime gating, and the SP22 H6 vNext B5b concat path) was therefore
conditioning on noise correlated with ISV optimisation, not on a
learned plan. Discovered while preparing Phase B5b-2 (collector
trade plan launch). Two-line index fix + diagnostic comment +
audit doc entry.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Completes the F-3 observability story. Smoke runs now print
aux_trade_outcome_ce_ema every epoch in the standard HEALTH_DIAG output
— no ISV inspector required.
Changes:
health_diag.rs:
- New field aux_trade_outcome_ce: f32 appended at END of
HealthDiagSnapshot per "Field order is stable; adding fields
appends to the end" doc rule. Existing fields' byte offsets
preserved → no kernel-side word offset re-validation.
- snapshot_size_is_stable pin: 149 × 4 → 150 × 4 = 600 bytes.
health_diag_kernel.cu:
- New WORD_AUX_TRADE_OUTCOME_CE = 149 (appended at end).
- WORD_TOTAL = 150 (was 149); static_assert bumped.
- New kernel arg aux_trade_outcome_ce_idx appended after
moe_lambda_eff_idx.
- New mirror write after the existing MoE mirrors. Stream-implicit
ordering: aux_outcome_ce_ema_update (F-3b) fires before
health_diag_isv_mirror, so the read picks up the just-updated EMA.
gpu_health_diag.rs:
- launch_isv_mirror gets new aux_trade_outcome_ce_idx: i32 arg.
gpu_dqn_trainer.rs:
- launch_health_diag_isv_mirror passes
AUX_TRADE_OUTCOME_CE_EMA_INDEX as the new arg.
training_loop.rs:
- Per-epoch metrics push appends ("aux_trade_outcome_ce_ema",
ISV[538]) to the standard out vec. Console / CSV automatically
includes the new column.
End-to-end F-3 chain now closed:
K=3 fwd → aux_to_loss_scalar_buf → aux_outcome_ce_ema_update
→ ISV[538] → health_diag_isv_mirror → snap.aux_trade_outcome_ce
→ training_loop metrics → console.
Operator sees CE every epoch:
- Cold-start: 0.000
- After bootstrap: ~1.098 (= ln(3))
- After training: ideally 0.5-0.7 (head learning)
Phase F end-to-end ready. The vNext stack has:
- Full GPU kernel chain (A2-A5 + D + plan-conditioning)
- Full Rust wireup (B0-B4, B4b-1/2, C-1/C-2, B5b)
- Real labels reaching trainer
- K=3 → policy via state slots + atom-shift
- End-to-end CE observability
Verification:
- cargo check -p ml clean.
- cargo test -p ml --lib → 1016/0 green (incl. bumped
snapshot_size_is_stable byte-size pin test).
Audit: docs/dqn-wire-up-audit.md Phase F-3c section.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Completes the Phase F-3 observability chain. Kernel + ISV slot landed
in F-3a; this commit wires the launcher into the trainer's per-step
training graph and registers the FoldReset entry.
Changes:
- gpu_dqn_trainer.rs:
- New pub(crate) static AUX_OUTCOME_CE_EMA_CUBIN embed
- New aux_outcome_ce_ema_kernel: CudaFunction field on GpuDqnTrainer
- Constructor loads kernel handle + struct-init
- New launch_aux_outcome_ce_ema() method (single-thread launch,
ISV slot 538, α=0.05)
- training_loop.rs:
- Launch call appended after launch_aux_heads_loss_ema()
- New dispatch arm "aux_trade_outcome_ce_ema" in reset_named_state
- state_reset_registry.rs:
- New RegistryEntry for "aux_trade_outcome_ce_ema" (FoldReset
sentinel 0.0)
End-to-end observability now live: K=3 head's batch-mean sparse-CE
flows into ISV[538] every step via Pearl A-bootstrapped EMA.
Smoke runs can read this slot to verify learning:
- Cold-start: 0.0
- After first step with B_valid > 0: bootstrap to first observation
(~1.098 = ln(3) for uniform K=3 prediction)
- Healthy learning: monotonic decrease toward 0.5-0.7 over epochs
- Falsification: pinned at ~1.098 for many epochs
Phase F prep complete. The trade-outcome aux head's:
- Forward chain (A2-A5 + B0-B4)
- Real labels reach trainer (B4b-1/2)
- K=3 → policy state (C-1/C-2)
- K=3 → Q-target atom-shift (D)
- Plan-conditioning (B5b)
- Smoke observability (F-3 + F-3b)
are all wired. Phase F deployment (argo-train.sh smoke) is next.
Verification:
- cargo check -p ml clean.
- cargo test -p ml --lib → 1016/0 green (incl. every_fold_and_soft_
reset_entry_has_dispatch_arm pin test).
Audit: docs/dqn-wire-up-audit.md Phase F-3b section.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
