12151ccf6aaa1fa4d3e213917fbefc300beadfe6
5043 Commits
| Author | SHA1 | Message | Date | |
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jgrusewski
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12151ccf6a |
feat(alpha): fitted FillModel coefficients from 500K ES.FUT snapshots
Phase E.0 Task 5c. Ran phase_e_fit_fill_model on the ES.FUT 2024-Q1 MBP-10 + trade tape (5.2M trades, 3.9M MBP-10 events, 500K snapshots accumulated at snapshot_interval=50 over a ~24-minute window). Total runtime ~80s. Empirical fill rates within 60s window: - bid_l1: 4.97% (matches L1 maker-side activity in trending market) - ask_l1: 71.34% (high — most 60s windows see an aggressive buy) Fitted L1 cloglog coefficients (all 5 features): BID L1: β_0=-0.213 β_spread=-2.064 β_imbal=-0.099 β_ofi=-0.006 β_logτ=-0.286 ASK L1: β_0=+0.016 β_spread=-40.336 β_imbal=+0.041 β_ofi=+0.652 β_logτ=-0.055 Sanity (sign checks all pass): - β_spread < 0 both sides (wider spread → fewer fills) ✓ - bid β_imbal < 0 (more bid stack → harder to get hit by sell) ✓ - ask β_ofi > 0 (buying pressure correlates with ask fills) ✓ - β_logτ < 0 both sides (quieter markets → slower execution) ✓ L1-only limitation: as documented in the binary header, the parser only populates levels[0]; L2/L3 in the JSON are L1 with β_0 -= ln(L+1) attenuation. Default --out-path bumped to config/ml/phase_e_fill_coeffs.json so future re-runs land in the same committed location. |
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jgrusewski
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3bdf74018d |
feat(alpha): phase_e_fit_fill_model example — cloglog FillModel calibration
Phase E.0 Task 5b. Calibrates FillModel coefficients from historical MBP-10
+ trade tape. Streams snapshots concurrently with time-sorted trades; per
snapshot, determines binary fill outcome ("would a posted L1 limit have
been hit within next --window-seconds?"), accumulates (FillFeatures, y),
calls fit_poisson (cloglog Bernoulli, see
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jgrusewski
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a1e3336b1f |
feat(alpha): ExecutionEnv — 10-dim state, 9-action gating, terminal-only reward
Phase E Task 6. Core environment for the execution-policy DQN.
Episode lifecycle: position-open signal → up to `horizon_snapshots` →
forced close. Within the episode the policy chooses 9 actions; illegal
actions degrade to Wait inside step() (soft masking) — the network's
Q-output is never masked, preserving clean C51 and Munchausen targets.
Reward is **terminal-only**: realized PnL minus fees, accumulated over
all fills and force-closed at terminal mid if still open. No dense
per-snapshot shaping per pearl_event_driven_reward_density_alignment
(the canonical SP11→SP12 lesson — dense shaping on event-driven
objectives creates exposure-positive bias).
Bugs in the plan's sketch, caught + fixed during implementation:
1. Synthetic test's bid/ask were CONSTANT while mid drifted — would
have made the "uptrend" test lose money. Fixed: drift bid/ask
together with mid.
2. L1/L2 closing path used Side::None for fill lookup → 0% fill rate
for closing limits. Fixed: closing-long → Side::Sell (post at ask),
closing-short → Side::Buy (post at bid).
3. `cursor`-based hash for fill PRNG was non-deterministic across
replay seeds. Replaced with self-contained SplitMix64 RNG state
(no `rand` dep added) — fill randomness is now bit-identical across
train/eval replays at the same seed.
4. `max_step_per_episode` and `mid_at_decision` fields stored but
never read — dead per feedback_no_stubs. Removed.
5. Test harness used n==horizon, which made the cursor-exhaustion
guard (`cursor+1 >= len`) fire before the planned FlatMarket call.
Fixed by setting n > horizon so the meaningful `ep.step >= horizon`
path terminates the episode.
6. `.unwrap()` in test bodies → `.expect("…")` per pre-commit policy.
7. Volume-weighted entry_price across multi-fill scaling (the plan
only handled the open-from-flat case).
5 unit tests:
- state_has_expected_dim_and_is_finite (STATE_DIM=10, no NaN)
- market_buy_then_market_sell_on_uptrend_profits (PnL math)
- illegal_action_degrades_to_wait (soft mask, no fee, no fill counted)
- terminal_force_close_pays_out_when_position_open (force-close at mid)
- rng_is_deterministic_across_resets (same seed → identical fills)
`cargo test -p ml --lib env`: 16 passed (4 action_space + 7 fill_model
+ 5 execution_env); full module compiles clean with no new warnings.
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jgrusewski
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d08ab461db |
feat(alpha): fit_poisson via cloglog likelihood + Serde derives on FillCoeffs
Phase E Task 5 (code portion; the 5.2M-trade fit run lands separately).
Diagnosis: the plan's draft used pure Poisson NLL with Bernoulli y∈{0,1},
which converges to λ = empirical rate ȳ. But the runtime fill_prob() uses
`p = 1 − exp(−λ)`, so a trained λ=0.4 → predicted p=0.33 → systematic
~30pp under-fill bias on every passive backtest order. Training and
inference must agree on what λ means.
Fix: switch the fitter to the **cloglog (complementary log-log) binary
likelihood**. Per-sample gradient:
∂L/∂β_k = (p − y) · (μ/p) · x_k
where μ = exp(β·x), p = 1 − exp(−μ)
The μ/p factor is the link derivative; p.max(1e-7) handles the μ→0 limit
in f32 (the analytical limit μ/p → 1 is achieved automatically because
both numerator and denominator vanish proportionally).
Recovery test verifies the fix: 1000 deterministic 40% fills, all-zero
features → fitter recovers β_0 ≈ ln(0.5108) ≈ -0.672 (the value at which
1 − exp(−exp(β_0)) = 0.4), within tolerance 0.05. Slope coefficients
stay near zero (features uninformative).
Also adds Serde derives on FillCoeffs / FillFeatures / FillModel for JSON
serialization (downstream when the calibration example lands).
Deferred: the calibration example (Steps 3-7 of the plan task) is held
back until paired with the actual 5M-trade fit run — the plan's example
has a placeholder loop that violates feedback_no_stubs, and the loader
lift from precompute_features.rs deserves a dedicated commit.
4 new tests (7 total in env::fill_model now):
- fit_recovers_baseline_when_features_uninformative
- fit_rejects_empty_and_mismatched_inputs
- coeffs_round_trip_through_json
`cargo test -p ml --lib env::fill_model`: 7 passed.
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jgrusewski
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a5de50503f |
feat(alpha): Poisson regression fill model scaffold (coeffs fitted in Task 5)
Phase E Task 4. Medium-tier fill simulator per the design memo.
For each (level ∈ {L1,L2,L3}, side ∈ {Bid,Ask}):
λ(features) = exp(β · [1, spread_bps, L1_imb, OFI_5, log(τ+1)])
Per-snapshot Bernoulli fill probability for a posted limit order:
p = 1 − exp(−λ)
Market orders fill immediately at the opposite-side L1 quote (no slippage
modeled at medium tier). Closing actions (Side::None) return λ=0 — they
are handled separately in the env.
Scaffolding only. Task 5 fits the 30 coefficients (6 distributions × 5
features) from the 5.2M-trade historical tape. The skeleton constructor
uses β=0 → λ=1 → p≈0.632, a stable sanity default for early smokes.
Numeric guard: `linear.exp().min(50.0)` caps λ to prevent f32 overflow
under outlier features before fitting lands. Fitted models should stay
well below this cap in practice.
4 unit tests:
- skeleton_has_uniform_fill_prob (β=0 → p≈0.632 within 0.01)
- fill_prob_bounded_under_outlier_features
- lambda_cap_prevents_f32_overflow (β=100 outlier path)
- side_none_returns_zero_rate
`cargo test -p ml --lib env::fill_model`: 4 passed.
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jgrusewski
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94f8f65571 |
feat(alpha): 9-action discrete execution action space + legality gating
Phase E Task 3. Introduces crates/ml/src/env/ for the execution-policy
environment. This commit lands the action space only; fill_model (Task 4)
and execution_env (Task 6) append to mod.rs in their respective commits
per feedback_wire_everything_up (no orphan declarations).
- 9-action discrete space: Wait + {Buy,Sell,Flat} × {Market,L1,L2 / L1}
- Soft action masking: illegal actions degrade to Wait in env::step
(not by masking Q-output) per design memo — preserves clean C51
categorical targets and Munchausen term (Task 10)
- `is_legal(position)` with position ∈ {-1, 0, +1} (sign only;
magnitude is decoupled into the Kelly layer in Task 11)
- repr(u8) discriminants round-trip with from_u8 for replay-buffer
storage by the existing GPU DQN trainer
4 unit tests:
- n_actions_matches_enum_cardinality
- legality_gating_by_position (covers flat/long/short × all 9 actions)
- decode_closing_flag_is_only_flat
- repr_u8_round_trip (replay-buffer contract)
`cargo test -p ml --lib env::action_space`: 4 passed.
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jgrusewski
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8bf8bdf874 |
feat(alpha): state_reset_registry entries + dispatch arms for slots 539..548
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. |
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jgrusewski
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aa5908aa53 |
feat(alpha): reserve ISV slot block 539..550 for alpha trading system
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. |
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jgrusewski
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9c26e78cdc |
docs(phase-e): implementation plan for execution-layer RL policy
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> |
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jgrusewski
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e190ecfa61 |
feat(ml-alpha): backtest cost sweep + annualised Sharpe (Phase 1d.4)
Initial single-cost backtest at τ=0.25 cost=0.25 revealed the binding
constraint: mean_ret = -0.176, pre-cost EV ≈ +0.074, so the model has
a real directional edge but K=6000 price moves are too small to clear
1-tick round-trip cost. The cost-vs-edge balance is the real Phase 1d.4
verdict question, not whether the model has signal.
Two enhancements per the insight block in the previous run:
1. **Cost sweep**: GpuBacktest::run now takes `costs: &[f32]` and
produces (C × T) rows instead of T. The smoke runs at five costs:
- 0.0 frictionless upper bound (theoretical max Sharpe)
- 0.0625 quarter-tick (very aggressive execution)
- 0.125 half-tick (professional desk)
- 0.25 1 tick = $12.50/contract (retail / pessimistic)
- 0.50 2 ticks (very pessimistic)
Tells us the break-even cost where Sharpe crosses zero.
2. **Annualised Sharpe**: per-trade Sharpe × sqrt(trades_per_year).
trades_per_year = n_trades × (seconds_per_year / test_time_span_seconds).
test_time_span_seconds derived from first/last test sequence end-bar
timestamps via FxCacheReader::record_timestamp. Standard Sharpe-time-
scaling assumption (trades roughly i.i.d.); imperfect when signals
cluster in correlated regimes, but the right ballpark for comparison
with industry benchmarks.
Output adds per-cost-band "best operating point" tables plus a clear
"REALISTIC VERDICT" line at cost=0.125 (half-tick — what a professional
desk would actually pay) with three gates:
- Sharpe_ann > 2.0 → deployable
- 0.5 < Sharpe_ann ≤ 2.0 → marginal
- Sharpe_ann ≤ 0.5 → fail at realistic cost
The "FRICTIONLESS UPPER BOUND" line reports the intrinsic edge — what
the model could theoretically achieve at zero cost. Even if realistic
Sharpe fails, this number tells us whether the model has anything to
optimise toward at deployment.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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20c361a300 |
feat(ml-alpha): Phase 1d.4 GPU-native backtest — proper SSM-stacker Sharpe verdict
Four new kernels in mamba2_alpha_kernel.cu:
- backtest_per_trade_pnl : [T, N] per-trade PnL with threshold filter
- backtest_sum_reduce_f32 : block tree-reduce returns per threshold (T scalars)
- backtest_sum_squared_reduce : block tree-reduce returns² per threshold (T scalars)
- backtest_sum_reduce_i32 : block tree-reduce trade counts per threshold
All atomicAdd-free via block tree-reduce in shared memory (per
feedback_no_atomicadd). Single kernel launch handles the full
threshold sweep across all sequences via grid_x=T, grid_y=ceil(N/256).
New module crates/ml-alpha/src/backtest.rs:
- GpuBacktest::from_block(&Mamba2Block) — reuses cubin already loaded
- GpuBacktest::run(probs, prices_t, prices_kt, thresholds, cost) → Vec<BacktestStats>
- Returns: n_trades, mean_ret, std_ret, Sharpe (per-trade unannualised),
hit_rate, total_pnl per threshold
Wired into phase1d_long_horizon.rs after the stacker eval:
- Convert stacker_logits → probs via sigmoid
- Upload probs + end-bar prices + (end-bar + horizon) prices to GPU
- Sweep thresholds [0.00, 0.02, 0.05, 0.10, 0.15, 0.20, 0.25]
- Print per-threshold table + best Sharpe operating point
- GATE: per-trade Sharpe > 1.5 = deployable, 0.5-1.5 = marginal, < 0.5 = fail
Cost model: 0.25 price units round-trip = 1 ES.FUT tick = $12.50/contract.
Tunable via --cost-per-trade. Realistic for retail flow; brokers can
trade at half-tick or better.
GPU-pure on the hot path: kernels do per-trade math + reductions;
host only receives T (= 7 here) scalars per metric for final Sharpe
arithmetic. No GPU↔CPU roundtrip per trade.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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149e1ae7b7 |
fix(ml-alpha): make stacker always-on (clap default_value_t=true bug)
Bool flag with `default_value_t = true` doesn't accept `--stacker true` on the command line in clap — it expects either the flag alone (which inverts) or a custom action. Cleanest fix: drop the flag entirely; the stacker block always runs when cal_frac is in (0, 1). Phase 1d.3 stacker delivered the key result: - Stacker test AUC: 0.7078 (raw Mamba: 0.6619, +4.6pts) - Stacker test accuracy: 0.6683 (raw Mamba: 0.6187, +5.0pts) - Stacker test Brier: 0.2088 (raw Mamba: 0.2299, well below chance 0.25) - Stacker spread-Q4 accuracy: 0.8164 (raw Mamba spread-Q4: 0.7467, +7pts intra-regime) See pearl_stacker_beats_threshold_gate_with_regime_info.md for full write-up and design implications for Phase 1d.4 backtest. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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ab4b7c64cb |
feat(ml-alpha): GPU-native stacked regime head on top of Mamba2 (Phase 1d.3)
Builds a second-stage MLP that takes [mamba_logit, 6 Block-S features] as input (7 dims) and learns the joint alpha-and-regime score in one calibrated output. Trains on the cal half of val (same 50/50 split as Platt/isotonic so all comparisons are on the same held-out test bars). Architecture: 7 → hidden_dim → 1 sigmoid, GELU activation, BCE-with-logits loss, AdamW. Uses the existing GPU-native `MlpModel` from crates/ml-alpha/src/mlp.rs — same primitives used for the Phase 1c MLP baseline. No CPU compute on the hot path (per feedback_cpu_is_read_only); all weights, activations, gradients, optimizer state on GPU; host writes the input matrix to a pinned buffer once per batch via GpuTensor::from_host. The Block-S columns are z-score normalised using cal-half statistics (then applied to the full val matrix) before training; mamba_logit is left raw since it's already close to standard-normal scale via the Mamba's natural calibration (see pearl_mamba_sss_state_yields_native_calibration). After training, reports stacker held-out accuracy + AUC + Brier + log-loss, plus stratified accuracy by Block-S feature so we can see whether the stacker absorbed the regime conditioning (uniform accuracy across quintiles) or just sharpened the Q4-gate (still elevated in Q4). Why this matters for production deployment per pearl_mamba_inherits_regime_structure: - Single calibrated score for conformal coverage gating downstream - Retrainable when market regimes drift - Captures interactions between regime features that a static threshold AND can't (e.g., spread-Q4 only when book is balanced) - Replaces the planned Phase 1d.3 dual-head architecture with a smaller stacked-generalisation approach (no separate regime classifier) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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635e2c8b48 |
feat(ml-alpha): Phase 1d.2 smoke + Block-S stratified accuracy diagnostic
After calibration, also stratify val accuracy across the 6 Block-S features (time_since_trade, time_since_snap, book_event_rate, spread_bps, L1_imbalance, micro_mid_drift) by sampling each val sequence's END BAR feature value, then running `metrics_detail::stratified_accuracy` per column with 5 quintile bins. Tells us whether the K=6000 Mamba alpha concentrates in specific book regimes (justifying an explicit regime head per Phase 1d.3) or is uniform across regimes (allowing direct backtest in Phase 1d.4). The Phase 1c stateless MLP showed strong stratification (spread-Q4 hit 0.752 acc on 76K samples while middle quintiles fell below 0.50); this run tests whether the Mamba inherits or transcends that pattern. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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d57026b0ba |
feat(ml-alpha): Phase 1d.2 smoke + post-hoc Platt/Isotonic calibration
Extends phase1d_long_horizon with a 50/50 val split (cal/test halves):
fit Platt and Isotonic on cal, evaluate on held-out test. Reports both
uncalibrated AND calibrated metrics (accuracy, AUC, Brier, log-loss).
Hypothesis: the K=6000 Mamba result (AUC=0.66 / acc=0.62 from the
uncalibrated
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jgrusewski
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4cf9499b58 |
feat(ml-alpha): Phase 1d.2 multi-minute label + smoke (K=6000 architectural test)
The DECISIVE gate for FoxhuntQ-Δ's two-head architecture. The K-sweep
(commit
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jgrusewski
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ab6922a199 |
feat(ml-alpha): Phase 1d.1 Mamba2 smoke example + first-shot verdict
Trains the from-scratch GPU-pure Mamba2 block against the snapshot fxcache, gathers sequence batches via end-bar lookup into train/val labels, runs AdamW for N epochs, computes val AUC. First-shot result (epochs=3, stride=8, lr=1e-3, hidden=64, state=16, seq_len=32): - Train BCE: 2.338 → 1.164 → 0.957 (monotone, still dropping) - Val accuracy: 0.5645 (beats MLP 0.5241) - Val AUC: 0.5684 (below MLP 0.6849) Interpretation: undertrained (loss curve still descending steeply; stride=8 sees only 1/8 of data; lr=1e-3 conservative given the training-loop unit test converged at lr=1e-2). Not yet a clean GATE FAIL — needs a retry with stride=2, lr=3e-3, epochs=10-20 before declaring the model class has a ceiling below the stateless MLP baseline. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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eb8c251afb |
feat(ml-alpha): Mamba2AdamW optimizer + end-to-end training-loop validation (Phase 1d.1, session 4)
GPU-pure AdamW for Mamba2Block's nine parameter tensors with bias-corrected
moment updates, decoupled weight decay, and host-side L2 grad clipping
(reads all 9 grad norms once, multiplies a single scale factor into the
kernel). Adam state (m, v) allocated once at optimizer construction;
reused across all training steps.
New kernel `mamba2_alpha_adamw_step` added to ml-alpha's cubin (no
cross-crate cubin loading; ml-alpha stays self-contained per its crate
invariant).
Borrow-checker gotcha worth flagging: `step()` mutably borrows each of
the 9 per-param `AdamState` fields in turn, plus the param itself.
Tried `apply()` as a method on `&self` — conflicts with `&mut self.s_*`.
Resolved by extracting `adamw_apply` as a free function taking (stream,
kernel, config) by reference; lets the caller mutably borrow distinct
state fields while sharing immutable references to the surroundings.
**The end-to-end training-loop test is the analytical-gradient validation:**
- 20 AdamW steps on a fixed batch (n_batch=4, seq_len=8, in_dim=4,
hidden=8, state=4) with binary labels (half +1, half 0)
- Asserts ≥15 of 20 steps have monotonically-decreasing BCE loss
- Asserts final loss < 0.65 (below the chance baseline ln(2) ≈ 0.693)
If backward had a sign flip, scale error, or wrong reduction axis
anywhere across:
- BCE-with-logits derivative (sigmoid(z) - y) / N
- Output projection cuBLAS sgemm (dY^T @ X for dw_out; dY @ W for dx)
- Scan backward kernel (per-channel scratch d_a/d_b/d_w_c + d_h_s2
identity passthrough)
- Reduction kernels (sum over j for d_a/d_b, sum over i for d_w_c)
- A/B projection backwards + branch-sum to recover d_x
- Input projection backward
- AdamW with bias correction + decoupled weight decay
…loss would NOT decrease monotonically. It does. The full backward
chain is correct.
Tests (10 passing on real GPU):
- training_loop_decreases_loss (THE end-to-end validation)
- backward_returns_finite_grads
- backward_rejects_wrong_d_logit_shape
- forward_train_returns_cache
- forward_shape_and_finite
- forward_rejects_wrong_shape
- config_rejects_seq_len_over_32
- config_rejects_state_over_16
- config_rejects_zero_dims
- constructs_and_loads_kernels
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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88a6db6eae |
feat(ml-alpha): Mamba2 backward pass — analytical, GPU-pure (Phase 1d.1, session 3)
End-to-end analytical backward through all six stages of the forward
chain. No atomicAdd, no SPSA, no host roundtrip during gradient flow —
each scratch buffer is per-channel-unique so concurrent writes don't
collide; cross-channel reduction is a separate kernel.
New API:
- GpuLinear::backward_with_slices(dy, act, weight, cublas, stream)
→ LinearGrads{dw, db, dx}
Mirror of forward_with_slices added to ml-core; no GpuVarStore lookup.
- Mamba2BackwardGrads holds dw_in/db_in/dw_a/db_a/dw_b/db_b/dw_c/dw_out/db_out
- Mamba2Block::backward(&cache, &d_logit) → Mamba2BackwardGrads
Chain (reverse of forward):
6′. W_out backward (cuBLAS sgemm) → d_h_enriched, dw_out, db_out
5′. mamba2_alpha_scan_bwd kernel → d_a/d_b/d_w_c per-channel/sample
+ d_h_s2 (identity passthrough)
↳ mamba2_alpha_reduce_d_proj × 2 → d_a_proj, d_b_proj [N, K, state]
↳ mamba2_alpha_reduce_d_w_c → dw_c [hidden, state]
3′. W_b backward → d_x_from_b, dw_b, db_b
2′. W_a backward → d_x_from_a, dw_a, db_a
↳ d_x = d_x_from_a + d_x_from_b
1′. W_in backward → dw_in, db_in (d_input discarded)
Memory cost per backward call (for Phase 1d.1 sizes N=64, sh2=32,
K=16, state=8): ~1.1 MiB scratch — fits comfortably on RTX 3050.
Tests (9 passing on real GPU):
- Backward produces all 9 grads with correct shapes
- All grads finite through the 6-stage chain
- dw_in non-zero (gradient flows to the input projection, proving the
full chain is wired — not silently zero-ing somewhere)
- Backward rejects wrong d_logit shape
- + all previously-passing forward / config / shape tests
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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bf6ed42acf |
fix(ml-alpha): backward kernel concerns — atomicAdd-free per-channel scratch + forward cache (Phase 1d.1)
Addresses four concerns surfaced after the forward-pass commit:
1. Backward kernel was scaffolded with `if (j==0)` to dodge atomicAdd,
but that drops contributions from j>0 channels. Rewritten so every
(i, j) thread writes its UNIQUE slot in per-channel scratch:
d_a_per_channel[N, sh2, K, state_d]
d_b_per_channel[N, sh2, K, state_d]
Followed by a unified reduction kernel mamba2_alpha_reduce_d_proj
that sums over j → d_a_proj / d_b_proj [N, K, state_d]. Same kernel
handles both call sites (DRY).
2. d_w_c gradient already had the right pattern (d_w_c_per_sample +
mamba2_alpha_reduce_d_w_c); kept as-is. All three gradient outputs
now follow the same atomicAdd-free scratch+reduce structure per
feedback_no_atomicadd.
3. `forward()` was discarding LinearActivations which the backward path
needs. New `Mamba2ForwardCache` struct carries (input_2d, x, a_proj,
b_proj, h_enriched) — everything backward needs to recover gradients
through the four projections + scan. `forward_train()` returns
`(logit, cache)`; `forward()` thin-wraps and discards the cache for
inference.
4. `x_hist[32 * 16]` in the backward kernel was hardcoded; configs with
seq_len > 32 would silently corrupt. Added MAMBA2_KERNEL_SEQ_MAX=32
constant + config validation. Backward kernel header documents both
limits explicitly.
Tests (7 passing on real GPU):
- forward_train returns cache with correct shapes for all 5 tensors
- seq_len > 32 rejected at config validation
- state_dim > 16 rejected
- forward output [B, 1] all finite
- forward rejects wrong in_dim / seq_len
- kernel handles all 4 functions resolve (fwd / bwd / reduce_d_proj /
reduce_d_w_c) + param-count sanity
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
|
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jgrusewski
|
c3e769b4b6 |
feat(ml-alpha): Mamba2 forward pass — GPU-pure end-to-end (Phase 1d.1, session 2)
Forward inference for the supervised snapshot stream — no ISV, no
temporal_weight, no NULL-pointer dispatch. Clean rewrite of the DQN
mamba2 kernel into a purpose-built alpha kernel.
New kernel `crates/ml-alpha/cuda/mamba2_alpha_kernel.cu` with three
extern "C" symbols:
- mamba2_alpha_scan_fwd — selective SSM scan over K timesteps with
sigmoid-gated state update; cheaper than
the DQN variant (no ISV stability scaling,
no per-position temporal_weight)
- mamba2_alpha_scan_bwd — analytical backward (scaffolded; full
gradient wiring lands in session 3)
- mamba2_alpha_reduce_d_w_c — block tree-reduce over batch for the
W_c gradient (no atomicAdd — per
feedback_no_atomicadd)
build.rs swapped from ../ml/src/cuda_pipeline/mamba2_temporal_kernel.cu
to the local cuda/mamba2_alpha_kernel.cu. ml-alpha no longer depends
on ml's CUDA source — fully self-contained alpha-stack.
Forward pipeline:
1. cuBLAS sgemm: input [B,K,in] @ W_in.T + b_in → x [B,K,hidden]
2. cuBLAS sgemm: x @ W_a.T + b_a → a_proj [B,K,state]
3. cuBLAS sgemm: x @ W_b.T + b_b → b_proj [B,K,state]
4. zero-init h_s2, h_enriched [B, hidden]
5. scan kernel: (a_proj, b_proj, W_c, h_s2) → h_enriched
6. cuBLAS sgemm: h_enriched @ W_out.T + b_out → logit [B, 1]
All on GPU; output is a [N] CudaSlice<f32> of raw logits. Caller
sigmoids + thresholds (or feeds directly into BCE-with-logits).
Tests (5 passing on real GPU):
- forward [4, 16, 81] → logit [4, 1], all finite
- reject wrong in_dim
- reject wrong seq_len
- reject state_dim > 16
- reject zero dims
- + parameter-count sanity
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
1f05d6cb80 |
feat(ml-alpha): from-scratch Mamba2 block — foundation (Phase 1d.1, session 1)
GPU-pure stateful encoder skeleton for the snapshot-stream falsification. This session lands the build infrastructure + weight allocation + kernel loading; forward/backward + training loop in follow-up sessions. - build.rs compiles `../ml/src/cuda_pipeline/mamba2_temporal_kernel.cu` to `mamba2_temporal_kernel.cubin` in OUT_DIR (rerun-if-env-changed=CUDA_COMPUTE_CAP per the L40S/H100 cubin-staleness pattern). Zero header dependencies → single nvcc invocation; no NVRTC. - `Mamba2Block` holds all parameters on GPU (`OwnedGpuLinear` from ml-core for the projection layers, raw `CudaSlice<f32>` for `W_c` which the kernel reads directly). Xavier init via ml-core, which uses pinned host buffers for the seed transfer. - Both `mamba2_scan_projected_fwd` and `mamba2_scan_projected_bwd` kernel symbols resolve at construction; forward and backward paths in follow-up. - State dim hardcoded at ≤16 in the kernel; config validation rejects >16. Tests (3 passing on real GPU): - Reject state_dim > 16 - Reject zero dims - Constructs + loads both kernels + correct param count (8417 for 81×64×16×1) Aligns with project memories: - feedback_no_nvrtc: pre-compiled cubin via build.rs - feedback_no_htod_htoh_only_mapped_pinned: pinned via ml-core init helpers - ml-alpha invariant: no `ml`/`ml-supervised` dep (only the .cu source file) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
6ac9b36782 |
feat(ml-alpha): phase1d_calibrate smoke — gate PASS
Platt scaling drops held-out Brier from 0.346 → 0.221 (chance=0.250); log-loss 1.096 → 0.632. Both Platt and isotonic land below chance baseline. AUC-accuracy gap was pure miscalibration, not fundamental misexpression. Learned: Platt a=0.32 (raw logits too extreme), b=0.89 (positive offset needed). Trained MLP underconfidence on positives compounds with negative prior. Proceed to 1d.1. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
7e77add1e1 |
feat(ml-alpha): Platt + isotonic calibrators for Phase 1d.0
- Calibrator trait + PlattScaler (2-param logistic, BCE gradient descent) - IsotonicCalibrator (pool-adjacent-violators algorithm) - Tests: invariant-based (clean separation, monotonicity) per pearl_tests_must_prove_not_lock_observations Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
5d79bf0b22 |
docs(phase1d): implementation plan for regime-gated tick reasoning + memory accumulator
26 tasks across 5 milestones (1d.0 through 1d.4) with decisive falsification
gates at each. Anchors to commit
|
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jgrusewski
|
db874b1841 |
feat(foxhuntq): Phase 1c snapshot-resolution alpha + leakage fix + variable-dim fxcache
Three things landing atomically because they're load-bearing for each other: 1. **Trend-scanning leakage fix** — trend_scanning.rs was emitting OLS slope+t-stat over a *forward* window [t, t+L]. With the Phase 1a label = sign(price[t+60] − price[t]), the forward feature window overlaps the label window, contaminating it. Purged walk-forward only sterilizes forward-looking *labels* that cross the train/val split, not forward-looking *features* that peek inside the same horizon the label measures. The leak inflated MLP accuracy from 0.49 (legacy 74-dim baseline) to 0.75 — vanished to 0.50 after switching to a trailing window. Bounded the perfect-fit t-stat sentinel from ±1e6 → ±20 (p<1e-30 is already meaningless); eliminated the 16k corruption-cap drops. 2. **Variable-dim alpha column** — fxcache schema now carries the alpha-feature width via metadata (`alpha_feature_dim`), not a compile-time constant. Same on-disk format hosts the 134-dim bar-level stack OR the 81-dim snapshot stack. Reader + auto-detect honor the metadata-declared dim; downstream MLP auto-sizes `in_dim`. Single schema, no forks. 3. **Snapshot pipeline (Phase 1c falsification)** — `snapshot_pipeline.rs`: 81-dim per-MBP10-snapshot extractor reusing 10 snapshot-native alpha blocks + 6 new snapshot-specific features (time-since-trade, time-since-snap, event-rate, spread-bps, L1-imbalance, microprice-mid drift). `precompute_features` gets `--row-unit snapshot` flag; emits one fxcache row per LOB update (1.97M rows from MBP-10 data vs 206K for bar mode). **Smoke verdict on real data** (ES.FUT, 1.97M snapshots, 384K val): - Bar-level honest alpha: accuracy=0.5005, AUC=0.5043 (no signal) - **Snapshot-level alpha**: accuracy=0.5241, AUC=0.6849 (real signal, 384K val) - GBM corroboration: accuracy=0.5401 (non-linear partitioning sees more) - Horizon decay: alpha peaks at K=20-50 snapshots (~5-25ms), gone by K=500 - Regime-conditional: spread-Q4 quintile hits 0.752 accuracy on 76k samples Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
2a2f16b944 |
design(foxhuntq): architectural pivot away from per-bar DQN to decoupled Belief Bus + Conformal DRL
After 16+ SP-runs producing WR pinned at ~0.435 — and a session-end smoke
showing SP22 H6 vNext K=3 head architecture moves WR to 0.458 only via
degenerate Hold-collapse (PF erodes 1.45 → 1.08) — pivot to a research-
honest architecture: distributional supervised alpha + meta-labeling gate +
Coverage-Gated Kelly execution, integrated through a novel GPU-native
publish-subscribe Belief Bus substrate.
This is a DESIGN doc only. No implementation yet. v1 → v4 evolution captured
in the doc itself; v4 is research-honest with explicit prior-work citations:
- Bellemare/Dabney distributional RL (already shipped in foxhunt SP5+)
- Lopez de Prado triple-barrier + purging + meta-labeling
- Vovk/Romano/Gibbs-Candès conformal prediction foundations
- Sun-Yu 2025 NeurIPS CPTC (change-point-aware CP)
- Gan et al. 2025 NeurIPS arXiv:2510.26026 (CP for infinite-horizon RL —
we PORT Algorithm 1 directly in Phase 7, not invent)
- Zhu-Zhu ICML 2025 AlphaQCM (QCM variance estimation, adopted)
- Berti-Kasneci 2025 TLOB (motivates MLP baseline)
Honest novelty narrowed to three claims after literature review:
1. Belief Bus substrate — GPU-native pub/sub bus with per-slot
distributional semantics + conformal coverage + causal DAG metadata.
Extends our existing 539-slot ISV pattern (already novel architecture
vs published trading systems). The substrate integration is not in
literature.
2. Application domain — imbalance-bar HFT futures + MBP-10 microstructure +
triple-barrier labels. Existing distributional CP + DRL papers use
daily stocks, general RL benchmarks, or alpha formula discovery.
3. Adaptive controllers + per-slot conformal coverage — every adaptive
quantity in the system (Kelly priors, reward caps, Adam β1, regime
probabilities) gets conformal coverage attached. Not seen in
literature.
Tiered success criteria recalibrated per CFTC 2014 E-mini HFT study
(median firms hit ~55% WR / PF 1.2-1.4):
- Minimum viable: WR ≥ 50% AND PF ≥ 1.4 → deploy
- Goal: WR ≥ 53% AND PF ≥ 1.7
- Stretch: WR ≥ 55% AND PF ≥ 2.0 (original v1 target — aggressive
top-quartile HFT)
Eight phases with explicit falsification gates:
Phase 0: Purged walk-forward + bar audit (Lopez de Prado hygiene)
Phase 1a: MLP baseline alpha (cheapest falsification)
Phase 1b: TLOB/Mamba2/Liquid encoders
Phase 1C (conditional): tick-resolution if bar fails
Phase 2: Multi-head IQN + QCM + class weights
Phase 3: Belief Bus substrate
Phase 4: CPTC calibration
Phase 5: Coverage-Gated Kelly execution (deployment trigger if viable)
Phase 6: Production wiring + 2-week shadow mode
Phase 7 (optional): Port arXiv:2510.26026 conformal-DRL Q-residual
Phase 7 specifically detailed with concrete Algorithm 1 port (~1100 LOC
total), tunable params (k=5-10 ours vs 1-5 paper, due to γ=0.99 vs 0.8),
and falsification gate (empirical coverage ≥ 88% + PF improvement ≥ 0.2).
Deferred indefinitely (research-grade risk too high):
- Neural SDE (training instability per Kidger 2021)
- Hawkes process bar replacement (O(N²) MLE prohibitive at HFT scale)
- Multi-asset portfolio
- Learned in-trade exit head
Total minimum-viable path: Phases 0-6 ~6-8 weeks engineering + 20 hours
L40S compute. Falsification gates at every step.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
19bb3bc3c8 |
fix(sp22-vnext): aux_outcome CF half mirror on-policy (NOT all -1)
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 (
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jgrusewski
|
878cc9ba72 |
feat(sp22-vnext): F-3c follow-up — K=3 CE EMA in stdout HEALTH_DIAG aux line
The F-3c commit (
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jgrusewski
|
256a5fa5ab |
fix(sp22-vnext): K=3 aux_outcome_labels CF-half buffer underrun
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>
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jgrusewski
|
0acf77e656 |
config(dqn): strip H100-tuned VRAM overrides from dqn-production.toml
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
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jgrusewski
|
8b8bb1af70 |
infra(argo): default GPU pool to ci-training-l40s (sm_89) per feedback_default_to_l40s_pool
SP-chain training has been standardising on L40S since 2026-05-09, but
every invocation required an explicit `--gpu-pool ci-training-l40s`
override. The 2026-05-04 train-mnpf7 incident (sm_90 cubins deployed
to an L40S device, then resubmitted with the explicit override) was
the last incident in a long line of "forgot the pool flag" friction.
`feedback_default_to_l40s_pool.md` codified the user preference; this
commit lands the default in the actual invocation paths.
Changes:
- infra/k8s/argo/train-template.yaml: gpu-pool default H100 → L40S
- infra/k8s/argo/train-multi-seed-template.yaml: same + cuda-compute
-cap default 90 → 89
- scripts/argo-train.sh: docstring / --help / compute-cap fallback
case all flip to L40S as the bare default; H100 becomes opt-in via
`--gpu-pool ci-training-h100` for 80 GB / sm_90 workloads
- scripts/argo-test.sh: --help text aligned
Other architectural defaults (data-source=mbp10 per
feedback_mbp10_mandatory; imbalance-bar-threshold=20.0 per the 2026-
05-10 OOM-prevention fix) are already correct in the template.
Verified via `argo-train.sh dqn --branch sp20-aux-h-fixed --sha HEAD
--baseline --dry-run` — rendered workflow shows cuda-compute-cap=89,
no explicit gpu-pool override (template default L40S in effect).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
bbd52c3aa7 |
feat(sp22-vnext): FoldReset registry entries for B5b/C collector buffers
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>
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jgrusewski
|
4b40710b7c |
feat(sp22-vnext): Phase B5b-2 collector trade plan forward — resolves K=3 asymmetry
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> |
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jgrusewski
|
fb9b62a1f9 |
fix(plan-head): trainer trade plan forward reads from wrong param indices
`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> |
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jgrusewski
|
cdd3dc6edb |
feat(sp22-vnext): Phase F-3c — HEALTH_DIAG snap + console line for K=3 CE EMA
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>
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jgrusewski
|
02479c885d |
feat(sp22-vnext): Phase F-3b — K=3 CE EMA launcher wireup + reset registry
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>
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jgrusewski
|
de64935b78 |
feat(sp22-vnext): Phase F-3 — K=3 CE EMA producer + ISV slot
Adds observability infrastructure for Phase F smoke validation: new ISV slot AUX_TRADE_OUTCOME_CE_EMA_INDEX = 538 tracks the K=3 head's batch-mean sparse cross-entropy EMA. Producer kernel registered + cubin-built; launcher wireup follows in Phase F-3b commit. Changes: - sp22_isv_slots.rs: new pub const AUX_TRADE_OUTCOME_CE_EMA_INDEX = 538 - gpu_dqn_trainer.rs ISV_TOTAL_DIM: 538 → 539 - NEW kernel aux_outcome_ce_ema_kernel.cu: single-thread single-block direct-to-ISV EMA writer with Pearl A first-observation bootstrap. Fixed α=0.05 (slow-moving observability). NULL-tolerant + NaN-guarded. - build.rs: kernel registered; cubin compiles (3.2 KB) Why dedicated kernel (not extension of aux_heads_loss_ema_update): The K=2/K=5 EMA writes through Pearls A+D's 2-stage producer scratch. Extending it would require allocating a new scratch slot, threading Pearls A+D mapping, and touching apply_pearls_ad_kernel. The K=3 head's CE is OBSERVABILITY ONLY in this commit — no Pearls A+D adaptive α needed. Minimum-scope direct-to-ISV path. Re-routable through Pearls A+D if K=3 CE later becomes a controller anchor. Smoke validation signal interpretation: - Cold-start: ISV[538] = 0.0 (FoldReset sentinel) - First step with B_valid > 0: Pearl A bootstrap → ISV[538] = first CE - Typical uniform-K=3 cold-start CE: ln(3) ≈ 1.098 - Learning signal: CE drops from ~1.098 toward 0.5-0.7 over epochs - Falsification signal: CE pinned at ln(3) for many epochs → head can't learn (label noise / under-capacity / outcomes unconditional) Phase F-3b follow-up: - Trainer launcher call after aux_heads_loss_ema_update - HEALTH_DIAG snap layout extension + console column - Reset registry entry (FoldReset sentinel 0) Verification: - cargo check -p ml clean. - cargo test -p ml --lib → 1016/0 green. Audit: docs/dqn-wire-up-audit.md Phase F-3 section. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
93aa4cd6a2 |
feat(sp22-vnext): Phase D — 12-weight W atom-shift (4 actions × 3 outcomes)
THE K=3 HEAD NOW DIRECTLY MODULATES Q-TARGETS. Phase D extends the
Phase 3 atom-shift mechanism from per-action W[4] reading single state
slot 121 to per-(action, outcome) W[4, 3] = 12 weights reading 3 state
slots [121..124).
Mathematical change: shift[a, b] now sums Σ_k W[a*K+k] × state[121+k]
= W[a, 0]*p_Profit + W[a, 1]*p_Stop + W[a, 2]*p_Timeout.
5 atom-shift kernel sites updated (all coordinated):
1. experience_kernels.cu::compute_expected_q (replay path)
2. experience_kernels.cu::mag_concat_qdir (rollout path)
3. experience_kernels.cu::quantile_q_select
4. c51_loss_kernel.cu loss numerator (next_state CVaR side)
5. c51_loss_kernel.cu Bellman target (online + target combined)
Each site replaces `W[a] × state_121` with `Σ_k W[a*K+k] × state[121+k]`
unrolled 3 times. State hoist points lift 1 scalar → 3-element array.
5 supporting kernel updates:
- aux_w_prior_init_kernel.cu: writes 12 K=3 structural priors instead
of 4. Spec prior matrix:
Short × {Profit=+0.5, Stop=-0.5, Timeout=0}
Hold × {Profit= 0, Stop=+0.5, Timeout=0}
Long × {Profit=+0.5, Stop=-0.5, Timeout=0}
Flat × {Profit= 0, Stop=+0.5, Timeout=0}
Block dim bumped 4 → 12.
- c51_aux_dw_kernel.cu: grid bumped (4,1,1) → (b0_size×K=12,1,1).
blockIdx.x decoded as (a, k); reads state slot 121+k, writes
dw_aux[a*K + k]. New kernel arg aux_outcome_k=3.
- adam_w_aux_kernel.cu: W_AUX_DIM 4 → 12. Block dim 12 threads.
Trainer-side buffer resizes:
w_aux_to_q_dir [4] → [12]
adam_m_w_aux [4] → [12]
adam_v_w_aux [4] → [12]
dw_aux_buf [4] → [12]
Rust launcher updates:
- c51_aux_dw_kernel launch: grid (4,1,1) → (12,1,1) + new aux_kto arg
- adam_w_aux_kernel launch: block (4,1,1) → (12,1,1)
- aux_w_prior_init launch: block (4,1,1) → (12,1,1)
Cold-start gracefulness preserved: state[121..124] = 0.0 at step 0
(no K=3 prediction yet from C-1 producer). Σ_k W[a*K+k] × 0 = 0 →
zero atom-shift across all actions. After step 1+ when C-1 producer
fires, real softmax probs activate the prior W's structural bias and
Adam refines from there.
End-to-end K=3 → Q-target chain now active:
K=3 fwd (B3/B4) → softmax → C-1 producer → prev_aux_outcome_probs
→ C-2 state gather → state[121..124) → Phase D atom-shift
→ Q-target z_n + Σ_k W[a*K+k] × prob[k]
→ Bellman target + argmax + action_select all see aux's outcome
prediction.
The K=3 head now influences policy via TWO paths: state input (Phase
C-2) AND Q-target modulation (Phase D).
Verification:
- cargo check -p ml clean.
- cargo test -p ml --lib → 1016/0 green.
Remaining vNext work:
- Phase E: dW backward gradient validation tests
- Phase F: Validation smoke at structural prior — decisive spec test
- B5b-2 (deferred): collector trade plan launch
Audit: docs/dqn-wire-up-audit.md Phase D section.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
d2331f2f62 |
feat(sp22-vnext): Phase B5b — full plan-conditioning integration
The K=3 head's input is now [h_s2_aux (256) || plan_params (6)] = 262- dim, matching the spec's intended architecture. Implements the input concat in both the trainer's replay-batch path and the collector's rollout-step path, with appropriate handling of the backward-side stride mismatch. NEW kernel strided_row_saxpy_kernel.cu: row-truncating SAXPY that accumulates first n_cols_copy columns per row of src [B, src_cols] into dst [B, dst_cols] scaled by alpha. Handles stride mismatch (src_cols != dst_cols). Needed because backward emits dh_s2_aux_to_buf [B, 262] but dh_s2_aux_accum [B, 256] only consumes first 256 cols. PLAN_PARAM_DIM = 6 constant in gpu_aux_heads.rs. Ops struct updates: - AuxTradeOutcomeForwardOps gains concat_kernel + launch_concat() - AuxTradeOutcomeBackwardOps gains strided_saxpy_kernel + launch_strided_row_saxpy() - Both load new cubins in new() Weight tensor resize: - sizes[163] = H × (SH2 + PLAN_PARAM_DIM) = 128 × 262 = 33,536 floats - fan_dims[163] = (H, SH2 + PLAN_PARAM_DIM) Trainer changes: - New aux_to_input_buf [B × 262] field - aux_dh_s2_to_buf resized to [B × 262] - aux_partial_to_w1 resized to [B × H × 262] - max_aux_tensor_len bumped for param_grad_final scratch Trainer forward (aux_heads_forward): - Concat h_s2_aux + plan_params_buf → aux_to_input_buf - forward() with SH2_TOTAL=262 Trainer backward (aux_heads_backward): - backward() with SH2_TOTAL=262; reads aux_to_input_buf - saxpy_f32_kernel SAXPY for dh_s2_aux REPLACED by launch_strided_row_saxpy: copies only first SH2=256 cols per row; trailing 6 cols (plan_params gradient) are dropped — STOP-GRAD on trade plan head from aux loss. Collector forward (rollout): - New exp_aux_to_input_buf [N × 262] field - Concat with plan_params_ptr = 0 (NULL) → zero-fill trailing 6 cols - forward() with SH2_TOTAL=262 Train/inference asymmetry (documented): - Trainer: real plan_params from trade plan head output - Collector: zeros (no trade plan launch in collector) - The head is trained on real plan-conditional outcomes but queried at rollout time with plan_params=0. Phase B5b-2 follow-up would add a trade plan launch to the collector to resolve. Deferred — current state is functional, head still receives plan signal during training. Stop-grad on plan_params: backward writes full [B, 262] gradient, but strided SAXPY only copies first 256 cols. Trade plan head weights NOT trained by K=3 aux loss in this commit. Verification: - cargo check -p ml clean. - cargo test -p ml --lib → 1016/0 green. Phase D next: 12-weight W atom-shift (4 actions × 3 outcomes). Audit: docs/dqn-wire-up-audit.md Phase B5b section. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
53462a28d9 |
feat(sp22-vnext): Phase C-2 — state gather flip K=2 single-slot → K=3 3-slot
THE K=3 HEAD NOW REACHES THE POLICY. Phase C-2 is the consumer-side flip — slot 121's semantic changes from K=2's recentered p_up to K=3's p_Profit, and slots [122, 123] gain new meaning as (p_Stop, p_Timeout). Architectural constraint: aux_dir_prob_per_env (K=2 buffer) is ALSO consumed by experience_env_step's β reward at lines 2335 + 3724-3725. Cannot repurpose that pointer to point at the K=3 [N, 3] buffer — env_step β consumer would read wrong-stride memory. This commit adds SEPARATE arg aux_outcome_probs_per_env [N, 3] to state_gather kernels with NULL fallback. Branching semantic at state_gather read site: - aux_outcome_probs_per_env != NULL → K=3 active path: writes slots [121..124) via new assemble_state_outcome_k3 helper - aux_outcome_probs_per_env == NULL → K=2 fallback: writes slot 121 via existing assemble_state from the recentered scalar Changes: - state_layout.cuh: NEW __device__ helper assemble_state_outcome_k3 — mirrors assemble_state except padding slots [121..124) get p_Profit/p_Stop/p_Timeout (raw softmax probs [0, 1]), slots [124..128) zero for 8-alignment. - experience_kernels.cu: training-side experience_state_gather + eval-side backtest_state_gather both get new trailing arg aux_outcome_probs_per_env (NULL-tolerant). Read site branches: K=3 reads 3 floats / env → assemble_state_outcome_k3; K=2 fallback preserves legacy assemble_state call. - gpu_experience_collector.rs: training launcher passes self.prev_aux_outcome_probs.raw_ptr() → K=3 active in training. - gpu_backtest_evaluator.rs: eval launcher passes NULL → K=2 fallback in eval (eval has no aux producer infra yet). K=2 head still alive: - prev_aux_dir_prob still populated by aux_softmax_to_per_env_kernel - experience_env_step still reads it for β reward (independent consumer untouched) - EGF chain still reads exp_aux_nb_softmax_buf - Only K=2's slot 121 contribution to policy state is suppressed End-to-end K=3 chain now active: Label producer (A2) → per-(env, t) ring (B4b-1) → replay buffer scatter (B4b-2) → PER direct gather → trainer aux_to_label_buf → loss_reduce (B4) sparse CE on real labels → backward (B4) per-sample partials → Adam SAXPY (B1+B4) updates W1/b1/W2/b2 at [163..167) PARALLEL: Collector rollout K=3 forward (B3) → softmax tile → C-1 producer → prev_aux_outcome_probs [N, 3] → C-2 state gather → state[121..124] → policy reads in next step The K=3 head closes the loop: learns from real labels via replay, AND predictions reach policy via state assembly. Trainable + observable. Verification: - cargo check -p ml clean. - cargo test -p ml --lib → 1016/0 green. Remaining vNext work: - Phase D: 12-weight W atom-shift (4 actions × 3 outcomes) - Phase E: dW backward + Adam for W[4, 3] - Phase F: Validation smoke at structural prior - B5b (deferred): plan_params input concat Audit: docs/dqn-wire-up-audit.md Phase C-2 section. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
3286dc7dee |
feat(sp22-vnext): Phase C-1 — K=3 softmax → per-env 3-slot cache (producer)
First half of Phase C. Lands the producer side of the K=3 trade- outcome aux head's state bridge: new kernel populates a per-env 3-slot cache from the K=3 softmax tile every rollout step. The consumer side (state gather reading from this cache → state slots [121..124)) lands in Phase C-2. Mirrors the K=2 head's existing aux_softmax_to_per_env_kernel exactly at K=3: - K=2: prev_aux_dir_prob[env] = 2*softmax[env, 1] - 1 (recentered) - K=3: prev_aux_outcome_probs[env, k] = softmax[env, k] for k in [0, 3) Changes: - state_layout.rs: 3 new constants AUX_OUTCOME_PROFIT_INDEX = 121, AUX_OUTCOME_STOP_INDEX = 122, AUX_OUTCOME_TIMEOUT_INDEX = 123. PROFIT_INDEX aliases AUX_DIR_PROB_INDEX (same value, different semantic). Phase C-2 flips slot 121's meaning from K=2's recentered p_up to K=3's p_Profit. - aux_outcome_softmax_to_per_env_kernel.cu: new kernel + cubin. - gpu_dqn_trainer.rs: new SP22_AUX_OUTCOME_SOFTMAX_TO_PER_ENV_CUBIN embed. - gpu_experience_collector.rs: 2 new struct fields (cache buffer + kernel handle); cubin load + alloc in constructor; struct-init; per-step launch in rollout loop after K=3 forward. - build.rs: kernel registered. Encoding shift K=2 → K=3: K=2 used recentered [-1, +1] to match "no signal = 0" baseline of every other slot. K=3 keeps raw softmax probabilities [0, 1]. Cold-start sentinel 0.0 for all 3 slots = "no prediction yet" (mask). The 3-slot natural distribution is more informative than a scalar. Dead-code status: producer populates cache every step but experience_state_gather doesn't read from it yet — state slot 121 still receives K=2's prev_aux_dir_prob write. Phase C-2 swaps the state gather's source from K=2 cache to K=3 cache (3-slot write). Why split C into C-1 + C-2: experience_state_gather is a hot-path kernel with many consumers. Updating it touches training collector, eval-side backtest evaluator, Rust launcher arg list. C-2 lands that as an atomic state-semantic flip; C-1 lands the GPU-side scaffolding independently so the producer chain can be validated first. Verification: - cargo check -p ml clean. - cargo test -p ml --lib → 1016/0 green. Audit: docs/dqn-wire-up-audit.md Phase C-1 section. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
68f0481a9e |
feat(sp22-vnext): Phase B5a — input concat kernel scaffolding
Lands the plan-conditioning concat kernel for the K=3 trade-outcome forward as reusable scaffolding. Phase B5b (integration) is deferred with rationale: Phase C (state slots) is more critical for testing the K=3 head's effect on policy behavior, and can land independently of the plan-conditioning refinement. NEW kernel aux_to_input_concat_kernel.cu: - Writes [B, SH2+P] from h_s2_aux [B, 256] || plan_params [B, 6] - Pure GPU map; one thread per output element, no atomicAdd - NULL-tolerant on plan_params (zeros trailing P cols when source unavailable, e.g., collector cold-start where the trade plan head doesn't run) - Registered in build.rs; cubin compiles (5.7 KB). Dead code at this commit — no Rust launcher yet. Why B5 is split + B5b deferred: Full Phase B5 (integration) requires three coordinated changes: 1. Forward path: bump aux_to_fwd.forward() to SH2=262 + 262-dim input 2. Backward stride mismatch: backward emits dh_s2_aux_to_buf [B, 262], but dh_s2_aux_accum (input to aux trunk backward) is [B, 256]. A direct SAXPY mismatches row strides (262 vs 256) and corrupts the trunk's upstream gradient. Needs a strided-SAXPY kernel. 3. Collector-path plan_params unavailability: trade plan head only runs trainer-side. Workarounds: zero-fill, add trade plan to collector, or skip K=3 forward in collector. All have trade-offs. Phase B5b would need (1) strided-SAXPY kernel and (2) collector plan_params decision. Real work but NOT on the critical path for testing the K=3 head's effect on WR. Why Phase C should land first: The K=3 head currently trains on real labels (post-B4b) but doesn't influence policy behavior. Phase C wires the head's softmax into state slots [121..124) = (p_Profit, p_Stop, p_Timeout), replacing the K=2 single-slot 121 = 2*p_up - 1. WITH Phase C the policy reads aux's outcome predictions as state features → behavior changes → testable. Without Phase C, validation runs would show "K=3 head trains and converges" but predictions don't reach the policy → WR signal isn't a function of K=3 at all. We'd be testing nothing. Recommendation: skip the full B5 for now, do Phase C next, then Phase D (atom-shift). Phase B5b (plan-conditioning) is a refinement we add IF Phase C/D's no-plan-params version shows promise but plateaus below the WR ≥ 0.55 target. Audit: docs/dqn-wire-up-audit.md Phase B5a section. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
da5e564ccf |
feat(sp22-vnext): Phase B4b-2 — replay-buffer scatter + trainer setter
Second half of Phase B4b. Wires the trade-outcome label column through
the replay buffer (struct field, allocation, scatter on insert, gather
on sample, direct-to-trainer pointer + setter) and connects the
trainer's aux_to_label_buf to receive per-batch sampled labels.
Completes the end-to-end producer → ring → trainer i32 path the K=3
sparse CE consumer reads.
Replay buffer changes (crates/ml-dqn/src/gpu_replay_buffer.rs):
- New struct fields: aux_outcome_labels (capacity-sized ring),
sample_aux_outcome_labels (mbs-sized fallback gather), trainer_aux_
outcome_labels_ptr (direct-path destination)
- New aux_outcome_labels_ptr field on GpuBatchPtrs
- insert_batch signature: new aux_outcome arg between aux_conf_in and
bs. Scatters via existing K-generic scatter_insert_i32 (same kernel
the K=2 aux_sign_labels uses).
- sample_proportional direct gather when trainer_aux_outcome_labels_ptr
!= 0; fallback gather otherwise. Mirrors aux_sign_labels direct/
fallback semantic exactly.
- New setter set_trainer_aux_outcome_labels_ptr mirrors
set_trainer_aux_conf_ptr.
Collector emission:
- New aux_outcome_labels field on GpuExperienceBatch
- Populated at end of collect_experiences_gpu via dtod_clone_i32 from
Phase B4b-1's per-(env, t) producer scratch.
Trainer wireup:
- aux_to_label_buf_ptr() accessor on GpuDqnTrainer (mirrors
aux_nb_label_buf_ptr)
- trainer_aux_to_label_buf_ptr() delegating accessor on
FusedTrainingCtx
- New set_trainer_aux_outcome_labels_ptr call in training_loop at the
same site where set_trainer_buffers + set_trainer_aux_conf_ptr fire.
Two call sites updated (lines ~835 + ~2857).
- insert_batch call in training_loop passes &gpu_batch.aux_outcome
_labels as new arg.
Test fixtures updated: 4 smoke test files + 3 unit-test fixtures in
gpu_replay_buffer.rs alloc zero-init aux_outcome i32 arg.
End-to-end chain complete:
trade_outcome_label_kernel (A2)
→ collector per-step launch (B3)
→ collector emission (B4b-1)
→ replay-buffer insert + scatter (B4b-2)
→ PER sample + direct gather (B4b-2)
→ trainer aux_heads_forward.loss_reduce (B4) reads sparse {-1,0,1,2}
→ trainer aux_heads_backward (B4) computes per-sample partials
→ Adam SAXPY (B1+B4) updates W1, b1, W2, b2 at [163..167)
The "degraded predict-Profit-everywhere" cold-start from Phase B4 is
resolved. K=3 head trains on real sparse trade-outcome labels.
Verification:
- cargo check -p ml clean (21 warnings, none new).
- cargo test -p ml --lib → 1016/0 on clean runs; pre-existing
NoisyLinear flake still surfaces ~30-50% of runs (unrelated to
vNext work — see
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jgrusewski
|
491bf7d3e6 |
feat(sp22-vnext): Phase B4b-1 — per-(env, t) label kernel output + collector buffer
First half of Phase B4b (replay-buffer label scatter chain). Amends the Phase A2 trade_outcome_label_kernel to emit a per-(env, t) output column alongside the existing per-env tile, and adds the collector-side buffer + per-step launch arg. Kernel amendment (trade_outcome_label_kernel.cu): - Added NULL-tolerant `out_labels_per_sample` arg after existing `out_labels`. When non-NULL, writes `out_labels_per_sample[env*L + t] = label` at the same offset as the `trade_close_per_sample[env*L + t]` read. NULL = no-op (preserves Phase A2/A3 contract for callers passing old signature). - Pattern mirrors the K=2 head's `aux_sign_labels` per-(i, t) ring column that threads through the replay buffer. Collector field + alloc + launch: - New struct field `exp_aux_to_label_per_sample: CudaSlice<i32>` sized `[alloc_episodes × alloc_timesteps]`. Sentinel -1 (mask) populated by alloc_zeros + per-step kernel writes — survives until a trade-close event overwrites the env's slot at that t. - Updated Phase B3 launcher in collect_experiences_gpu to pass `self.exp_aux_to_label_per_sample.raw_ptr()` as the new arg. Why split B4b into B4b-1 + B4b-2: the full replay-buffer wireup mirrors the K=2 head's aux_sign_labels pattern across ~8 distinct code sites (replay-buffer struct field, sample destination buffer, direct-to-trainer pointer, setter method, scatter on insert, gather direct, gather fallback, GpuBatchPtrs field). Splitting lets us validate the per-(i, t) producer in isolation before touching the consumer pipeline. B4b-1 (this commit) = producer chain complete. Per-(env, t) column populated correctly every rollout step. Consumer wiring (replay- buffer scatter + trainer setter) is B4b-2's scope. Verification: - cargo check -p ml clean (21 warnings, none new). - cargo test -p ml --lib → 1016/0 on clean runs; pre-existing test_dqn_checkpoint_round_trip NoisyLinear flake still surfaces ~50-70% of full-suite runs (flake predates Phase B4b, unrelated to trade-outcome head — disable_noise() zeros ε but leaves some other randomness source intact). Audit: docs/dqn-wire-up-audit.md Phase B4b-1 section. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
20e1aea27c |
feat(sp22-vnext): Phase B4 — trainer-side replay-batch chain wireup
Wires the trade-outcome head's forward + loss reduce + backward +
per-sample partial reduce + SAXPY into the trainer's aux_heads_forward
and aux_heads_backward methods. Adam SAXPY for the 4 new weight tensors
at [163..167) extends uniformly via the existing aux_param_specs array
iteration.
Changes to aux_heads_forward:
- Steps 7 + 8 appended after K=2 next-bar head's loss reduce
- K=3 forward reads weights at [163..167) (Phase B1) → writes to
aux_to_* save-for-backward tiles (Phase B2)
- K=3 loss_reduce writes aux_to_loss_scalar_buf + aux_to_valid_count_buf
Changes to aux_heads_backward:
- K=3 backward appended after K=5 regime backward, emits per-sample
partials (dW1, db1, dW2, db2) + per-sample dh_s2_aux
- aux_param_specs array extended from 8 → 12 entries. Per-tensor
reduce + SAXPY loop iterates uniformly, scaling each by aux_weight
- K=3 dh_s2_aux SAXPY appended after K=5's, all three heads'
gradients flow into aux trunk's dh_s2_aux_accum (encoder stop-grad
enforced structurally by aux_trunk_backward's missing dx_in output)
Label semantic (cold-start): aux_to_label_buf is alloc_zeros (all 0
= Profit) until Phase B4b lands replay-buffer label scatter. Model
trains on "predict Profit everywhere" — degraded but well-defined
(no NaN). Mirrors K=2 head's known-degraded state between B1.1a
(forward landed) and B1.1b (label producer wired).
Adam SAXPY: existing global SAXPY iterates 0..NUM_WEIGHT_TENSORS
(now 167) — 4 new weight slots get gradient SAXPYs followed by
Adam m/v updates uniformly. Architectural payoff of Phase B1's
NUM_WEIGHT_TENSORS bump.
Test flake mitigation: added bind_to_thread() to
ensemble::adapters::dqn::tests::shared_device() mirroring the
cuda_stream() test-helper pattern from the fix sweep at
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jgrusewski
|
b28b349ac3 |
feat(sp22-vnext): Phase B3 — collector-side rollout buffers + forward chain wireup
Adds collector-side trade-outcome head: 5 struct fields + allocations
+ per-step forward + per-step label producer launches in the rollout
loop. Mirrors the K=2 next-bar head's collector wireup at K=3.
Collector struct additions:
- exp_aux_to_fwd: AuxTradeOutcomeForwardOps (3 kernel handles)
- exp_aux_to_hidden_buf [alloc_episodes × H=128] saved post-ELU
- exp_aux_to_logits_buf [alloc_episodes × K=3] saved logits
- exp_aux_to_softmax_buf [alloc_episodes × K=3] softmax tile
- exp_aux_to_label_buf [alloc_episodes] i32 sparse {-1, 0, 1, 2}
Per-step launches in collect_experiences_gpu rollout loop:
1. aux_trade_outcome_forward — launched immediately after the K=2
sibling's forward_next_bar, parallel on the same stream. Reads
exp_h_s2_aux + weights at flat-buffer indices [163..167) (Phase
B1 additions). Writes hidden/logits/softmax tiles. No consumer
yet — Phase C wires state assembly; Phase B4 wires trainer
scatter.
2. trade_outcome_label_kernel — launched immediately after
experience_env_step on the same stream, reading the save-for-
backward buffers (pnl_vs_target_at_close_per_env, pnl_vs_stop_at_
close_per_env) that env_step just wrote at segment_complete.
Stream-implicit producer→consumer ordering. Emits per-env
{-1, 0, 1, 2} labels — sparse, ~95-99% bars produce -1 (mask).
Dead-code discipline per feedback_wire_everything_up: every kernel arg
+ producer site is real wiring (not NULL placeholder) — only the
absence of consumers reading the produced tiles is "dead". The smoke
run produces softmax tiles + labels every step bit-identical to
pre-vNext baseline (no consumer = no effect on training behavior).
Phase B4 next: trainer-side replay-batch chain (forward + loss_reduce
+ backward + Adam SAXPY for the 4 new weight tensors).
Audit: docs/dqn-wire-up-audit.md Phase B3 section.
Verification:
- cargo check -p ml clean (21 warnings, none new on aux_to_*).
- cargo test -p ml --lib → 1016 passing / 0 failing (unchanged from
post-fix-sweep baseline at
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jgrusewski
|
ebc1b15023 |
fix(tests): repair 14 pre-existing test failures across ml crate
Lib test suite was at 14 failures from accumulated layout/contract drift.
Fixed each by tracing root cause; lib suite now 1016 passing / 0 failing.
Failures fixed (test → root cause → fix):
1. sp14_isv_slots::sp20_isv_slots_reserved_510_to_520 — ISV_TOTAL_DIM
pin drifted from SP20-era 520 to current 538 via SP21/SP22 H6 bus
growth. Slot positions 510-519 still pinned. Fix: relax total-dim
check to >= 520; keep slot-position asserts tight.
2-3. gradient_budget::test_spectral_norm_all_heads_no_panic +
test_spectral_norm_constrains_operator_norm — test config used
cfg.state_dim=16 but trainer uses STATE_DIM=128 when bottleneck
off; also DuelingWeightBacking slices [2]/[3] used pre-GRN w_s2
shape, but post-GRN it's w_b_h_s1 [2*SH1, SH1] = 2048. Fix: add
s1_input_dim_for_test helper; correct slice sizes to GRN layout.
4-9. dqn::trainer::tests::test_feature_vector_to_state +
test_single_sample_batch + test_batched_action_selection +
test_batched_vs_sequential_action_selection_consistency +
test_batch_size_mismatch_larger/smaller_than_configured —
feature_vector_to_state wrapper falsely advertised "no OFI" by
signature but body required strict OFI. Contract violation —
broke 6 unit tests + hyperopt's public convert_to_state APIs.
Fix: wrapper now genuinely produces 45-dim no-OFI state; OFI-
strict callers use _with_ofi with Some(idx).
10. state_kl_monitor::observe_tracks_fire_rate_on_change — last_amp
initialized to 1.0 coincidentally equaled first observation value,
silently suppressing first fire. Test expected first observation
always fires (cold-start semantic). Fix: Option<f32> sentinel —
None means "no prior baseline" → first observe ALWAYS fires.
11. cuda_pipeline::tests::test_eval_action_select_thompson_picks_
proportionally — test launched experience_action_select kernel
with 1 missing arg (v_logits_dir from SP17 Commit C). Kernel
read garbage pointer → SIGSEGV. Also threshold 0.70 calibrated
for pre-SP17 raw-A distribution; post-SP17 sampler uses mean-zero
softmax(V + A_centered), Long still dominates ~4× but P(Long)
drops to ~0.66. Fix: add zero-filled v_logits_buf at correct
arg position; recalibrate threshold 0.70 → 0.60.
12. cuda_pipeline::tests::test_ppo_gpu_data_upload — flaked in parallel
test run with CUDA_ERROR_INVALID_CONTEXT from MappedF32Buffer's
cudaHostAlloc. cuda_stream() test helper used OnceLock to share
a CudaStream but didn't bind_to_thread on subsequent calls. CUDA
contexts are per-thread state. Fix: helper now calls bind_to_
thread() on every invocation (idempotent — mirrors trainer/
constructor.rs:111's pattern).
13. ppo::tests::test_reward_computation — test created hold action
with ExposureLevel::Flat but is_hold() matches only Hold (Flat
= close position = transaction with cost). So hold and buy got
same transaction-cost reduction; reward_hold > reward_buy assert
failed. Fix: use ExposureLevel::Hold for no-cost hold semantic.
14. training_profile::tests::test_production_profile_applies_all_
sections — pinned n_steps==5 and tau==0.005 (pre-TD(0)); production
flipped to n_steps=1, tau=0.01 per dqn-production.toml ("dense
micro-rewards cancel over n>1 bars; faster target tracking for
TD(0)"). Fix: update pins to current production values.
All 14 fixes target root causes — no #[ignore] masking. Verified:
- Lib-only run: cargo test -p ml --lib → 1016/0 (passed/failed)
- Stash-test pre-changes: 1001/15 (confirms 14 fixed atomically)
Remaining flake under `cargo test -p ml --tests`:
- ensemble::adapters::dqn::tests::test_dqn_checkpoint_round_trip —
CUDA-context-race-adjacent flake under parallel `--tests` mode;
passes in isolation and in `--lib` mode (single binary). Predates
this commit; deferred as separate triage.
Audit: docs/dqn-wire-up-audit.md "Fix sweep" section.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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b98dc2730d |
feat(sp22-vnext): Phase B2 — trade-outcome trainer saved-tensor + partial buffers
Adds 11 buffer fields + 2 orchestrator ops handles (fwd + bwd) to the
trainer struct, mirroring the existing aux_nb_* / aux_partial_nb_*
pattern at K=3 instead of K=2.
Trainer struct additions:
- aux_to_fwd: AuxTradeOutcomeForwardOps (Phase B0 scaffold)
- aux_to_bwd: AuxTradeOutcomeBackwardOps
- aux_to_hidden_buf [B, H=128] saved post-ELU
- aux_to_logits_buf [B, K=3] saved logits
- aux_to_softmax_buf [B, K=3] saved softmax (3 future consumers)
- aux_to_label_buf [B] i32 sparse {-1, 0, 1, 2}
- aux_to_loss_scalar_buf [1] mean CE
- aux_to_valid_count_buf [1] B_valid for backward
- aux_dh_s2_to_buf [B, SH2] SAXPYs into dh_s2_aux_accum
- aux_partial_to_w1 [B, H, SH2] per-sample dW1
- aux_partial_to_b1 [B, H] per-sample db1
- aux_partial_to_w2 [B, K=3, H] per-sample dW2
- aux_partial_to_b2 [B, K=3] per-sample db2
Memory: aux_partial_to_w1 = 256 MB at B=2048 — identical to K=2 head's
partial size (same SH2, same H). Total new aux-to footprint ≈ 260 MB.
The existing aux_param_grad_final_buf scratch is sized to the largest
tensor across all aux heads; trade-outcome head's largest is W1 [H, SH2]
= 32,768 floats — identical to K=2/K=5 W1s. No resize needed.
Cold-start label semantics: alloc_zeros yields label 0 (Profit) for
every sample. Until the producer wires in (B3), the trainer's CE loss
treats every sample as "should have predicted Profit" — degraded but
well-defined (no NaN). Mirrors the K=2 head's known-degraded state
between B1.1a and B1.1b.
No FoldReset registration: these buffers are overwritten every batch
— no stale-state-leak risk across folds (matches the existing aux_nb_*
pattern).
Phase B3 next: collector-side rollout buffers + forward chain wireup
into collect_experiences_gpu (per-env softmax → per-(i, t) fan-out
scatter for trainer's aux_to_softmax_buf population).
Audit: docs/dqn-wire-up-audit.md Phase B2 section.
Cargo check clean (21 warnings, none new on aux_to_* fields).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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205e46c171 |
feat(sp22-vnext): Phase B1 — trade-outcome head weight tensors + Xavier init
Adds the 4 weight tensors (W1, b1, W2, b2) for the SP22 H6 vNext
trade-outcome aux head into the trainer's flat params_buf at indices
[163..167). Adam machinery (m/v moment buffers, SAXPY iteration over
0..NUM_WEIGHT_TENSORS) picks up the new tensors uniformly — no
per-tensor wiring needed.
Changes:
- NUM_WEIGHT_TENSORS bumped 163 → 167. Most of the 54 references are
&[u64; NUM_WEIGHT_TENSORS] array-size generics that resize
uniformly with the constant.
- compute_param_sizes() adds 4 new size entries:
[163] aux_to_w1 [H=128, SH2=256] = 32,768 floats
[164] aux_to_b1 [H=128] = 128 floats
[165] aux_to_w2 [K=3, H=128] = 384 floats
[166] aux_to_b2 [K=3] = 3 floats
Total: 33,283 floats = ~133 KB params, ~266 KB Adam state.
- compute_param_sizes() debug_assert updated 163 → 167.
- Xavier fan_dims added: (H, SH2) for W1, (0, 0) for biases (zero-init),
(K=3, H) for W2. Cold-start: logits ≈ 0 → softmax ≈ uniform 1/3 → no
Profit/Stop/Timeout preference per pearl_first_observation_bootstrap.
SH2 stays at 256 in this commit (mirrors K=2 head exactly). The spec's
Phase B input concat (256 → 262 with plan_params 6-dim) will re-shape
slot [163] to 128 × 262 = 33,536 floats later — small touch-up vs the
full B1 commit.
Verification:
- cargo check -p ml clean (21 warnings, none new).
- 14 pre-existing test failures stashed-verified unrelated (OFI features
missing, ISV slot count drift — independent of NUM_WEIGHT_TENSORS).
Phase B2 next: saved-tensor + per-sample partial buffers (hidden_post,
logits, softmax, valid_count, dW*_partial, dh_s2_aux_out).
Audit: docs/dqn-wire-up-audit.md Phase B1 section.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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