d45dde8458e3bc8036dbbe4fc4e2c1d7cfa63f84
90 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
jgrusewski
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410ab6b0ea |
arch(ml-alpha): ISV-driven σ + adaptive Z_SCALE — both controllers anchor on loss_ema
Per pearl_controller_anchors_isv_driven, every controller anchor/target/
cap derives from a tracked signal, not hardcoded constants. The σ-only
revert kept Kendall σ as a free Adam-learned scalar — that violated ISV
discipline and fought Adam's m/√v normalization
(pearl_adam_normalizes_loss_weights).
Single source of truth for both per-horizon controllers:
log_sigma_h[h] ← max(log(0.5), 0.5 * log(loss_ema[h]))
Kendall equilibrium (∂L/∂log σ = 0 ⟹ σ_h² = mean_bce_h)
in closed form. Asymmetric floor at log(0.5) prevents
collapse. No Adam state, no gradient delay.
lambda[h] ← clamp(1.0, 2.0, 1.0 + Z_SCALE_ISV * z_h)
Z_SCALE_ISV = (LAMBDA_CEILING - LAMBDA_FLOOR) / z_max_ema
Adaptive scale auto-uses the full clamp envelope:
the historical-max-z horizon maps exactly to
LAMBDA_CEILING. Replaces hardcoded Z_SCALE=0.5 which
rarely engaged on real data (max observed λ ~1.04).
Both anchor on the same ISV (loss_ema). z_max_ema is a new single-scalar
EMA state tracking max |z| across horizons, with first-obs bootstrap.
Removes:
- opt_log_sigma AdamW optimizer (σ no longer learned)
- grad_log_sigma_h_d memset (BCE kernel writes; output ignored — kept
only to preserve BCE kernel signature)
Kernel signature change (horizon_ema_and_lambda):
+z_max_ema [1] (read+write EMA state)
+log_sigma_h [5] (closed-form output, overwrite)
Discipline:
- First-obs bootstrap (sentinel <= 0) per pearl_first_observation_bootstrap
- Permanent floor (max(real, floor)) per pearl_blend_formulas_must_have_permanent_floor
- Asymmetric clamp per pearl_audit_unboundedness_for_implicit_asymmetry
- Z-score normalisation per pearl_zscore_normalization_for_magnitude_asymmetric_signals
- No nvrtc, no atomicAdd, no host branches in graph capture
All 9 perception_overfit tests pass — including
horizon_ema_and_lambda_track_after_training which validates the kernel
end-to-end through 64 K-loop iterations of capture/replay.
Submit local smoke; cluster A/B vs σ-only baseline (0.7506/0.7519) and
vs Phase 1+2+3 (0.7749/0.7591) follows once the perf-only 3-fold A/B
confirms no regression at
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jgrusewski
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b23f8f2efa |
perf(ml-alpha): NVIDIA-grade rewrite of CfC K-loop hot kernels — 2.15× faster
Local L40S profile (perception_overfit smoke) GPU kernel time:
1589ms → 739ms (53.5% reduction, 2.15× speedup).
Wall-clock smoke: 9.6s → 4.94s (1.94× faster).
Per-kernel deltas (nsys --cuda-graph-trace=node):
reduce_axis0: 362ms → 10ms (36× faster)
Block layout: per-column (1 block / output) → 32-wide column tile
(block_dim = 32 × 8). Cross-thread reads were strided by n_tail
(~40K floats = 160KB stride) — one cache line per thread, 8× HBM
bandwidth wasted. New tile gives coalesced 128B transactions per
warp. Block tree-reduce kept (no atomicAdd, per feedback_no_atomicadd).
+1 shared-mem pad to eliminate 32-way bank conflict on the ty reduce.
multi_horizon_heads_grn_bwd_batched: 540ms → 113ms (4.8× faster)
1. Stage h_row[HIDDEN] and a1[5,HEAD_MID] in shared at block entry.
Eliminates ~28K redundant DRAM reads/block across Pass 3 + Pass 5.
2. Pass 5 reorder: k outer / i inner with d_z1[k,m] pinned in
register; writes to grad_w1_scratch are sequential per-thread.
3. Block size 64 → 128 threads. Pass 5/6 now partition over i
(output column): cross-thread writes become COALESCED 128B/warp
(was stride-128 = 512B). Passes 2/3/4 gate on (tid < HEAD_MID).
4. Pass 3 thread role: m_out → m_in/n. Same coalescing fix on
grad_w2 writes AND w2 reads in the d_eta_2 sum.
cfc_step_backward_batched: 351ms → 271ms (1.3× faster)
1. Stage x_b[n_in] and h_old_b[n_hid] in shared (was 128× redundant
DRAM reads per block; now 1× cooperative load).
2. Pass 1 thread role: i (output row) → k (output col). For each
i loop iteration, the warp writes grad_w_in[..., tid] /
grad_w_rec[..., tid] — COALESCED 128B/warp (was stride-128
non-coalesced).
multi_horizon_heads_grn_fwd_batched: 211ms → 204ms
Stage h_row[HIDDEN] in shared — Pass 1 and Pass 3 both consume.
cfc_step_batched (fwd): 95ms → 94ms
Stage x_b and h_old_b in shared.
Shared-mem budgets fit comfortably under the 48KB SM cap (~6KB / ~2KB
respectively). All 9 perception_overfit tests pass — gradient
correctness validated end-to-end (constant-signal overfit, K-loop
capture/replay, stride-4 path, evaluate-only paths).
Discipline:
- Block tree-reduce only, never atomicAdd
- No nvrtc; pre-compiled cubins via build.rs
- Mapped-pinned-only is unaffected (CPU↔GPU contract untouched)
- Single source of truth: replaced kernels in place, no v2 suffixes
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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1a465cf7d5 |
refactor(ml-alpha): revert axes B/C/D/E — keep only Kendall σ (axis A)
Post-A/B verdict (see project_ml_alpha_v2_ab_verdict.md): v2 with all
5 axes was marginally tied on h6000 (+0.0013 vs 0.7591 baseline mean,
fails the +0.01 win threshold) and slightly below on mean_auc
(−0.0208 vs 0.7749 baseline mean, within 1σ) at ~5× the wall-time
cost. Per `feedback_v7_gem_methodology` (measure before delete or
wire), the architecture has been measured — it doesn't earn its
compute cost. This commit reverts the axes that didn't lift:
- axis B (L2 anchor + Wiener-α controller) — DROPPED
- axis C (horizon-token attention pool) — DROPPED
- axis D (regime-MoE gate + experts) — DROPPED
- axis E (inverted cross-variate attn) — DROPPED
- axis A (Kendall σ-weighted BCE) — KEPT
Files deleted (kernels, host bindings, numgrad tests, trainer state):
- cuda/{horizon_token_attention_pool, inverted_attention_pool,
inv_pooled_merge, regime_moe_gate, anchor_l2,
horizon_mean_collapse}.cu
- src/{horizon_token_attention_pool, inverted_attention_pool,
inv_pooled_merge, regime_moe_gate, anchor_l2,
horizon_mean_collapse}.rs
- src/trainer/{multi_horizon_attention, anchor_controller}.rs
- tests/{horizon_token_attention_pool_numgrad,
inverted_attention_pool_numgrad,
regime_moe_gate_numgrad,
anchor_l2_numgrad}.rs
Files restored (from V1 commit
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jgrusewski
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4ae9a27f48 |
fix(ml-alpha): wire axis E into loss — real add_inv_broadcast kernel
CRITICAL ARCHITECTURAL FIX discovered while planning fused kernel:
The previous Stage 2 `add_inv_broadcast` helper in
`multi_horizon_attention.rs` was a STUB that returned Ok(()) without
doing anything. Practical consequences:
- Forward: `inv_pooled_d` (output of inverted_attention_pool.forward)
was never added into `ctx_h_d`. Downstream MoE + heads never saw
the inverted-attention signal. Axis E contributed ZERO to the
forward output and the loss.
- Backward: `inv_pool.backward` was being fed `grad_ctx_mean` as
its "upstream gradient", but that's the gradient at the CHAIN
TERMINUS — not the gradient w.r.t. inv_pooled_d (which is zero
by construction since inv_pooled wasn't in the loss). The bwd
was injecting incorrect noise into `grad_ln_out`.
Net: paying inverted_attention compute for no gain, plus polluting
ln_b's gradient. Two perf rewrite rounds earlier today showed no
wall-time movement precisely because the slow path was wired into
training while the optimized one was dead.
FIX:
(a) New kernel `cuda/inv_pooled_merge.cu`:
fwd: ctx_h[b, h, d] += inv_pooled[b, d] (broadcast over h)
bwd: grad_inv_pooled[b, d] = Σ_h grad_ctx_h[b, h, d]
Tiny — single block-per-batch, no syncthreads, coalesced reads.
(b) Host binding `src/inv_pooled_merge.rs` (InvPooledMerge).
(c) `MultiHorizonAttention` adds:
- `merge: InvPooledMerge` field.
- `grad_inv_pooled_d [B, H]` buffer for the real upstream of inv_pool.bwd.
(d) `MultiHorizonAttention.forward` now calls `merge.forward(...)`
between inv_pool.forward and moe.forward. Axis E is now actually
in the model's forward output.
(e) `MultiHorizonAttention.backward` now calls `merge.backward` after
moe.bwd writes `grad_ctx_h_d`, producing `grad_inv_pooled_d`.
`inv_pool.backward` consumes the REAL upstream gradient
(`grad_inv_pooled_d`) instead of the prior `grad_ctx_mean` fake.
(f) Old stub `add_inv_broadcast` deleted.
CORRECTNESS:
- perception_overfit 9/9 PASS after the wiring. Loss still shrinks
on the constant-signal test (0.67 → -0.99 over 250 steps), now
with axis E actually contributing.
- All numgrad kernel tests still pass (kernels themselves unchanged
in this commit; only the wiring).
PERF IMPACT (expected):
- +2 tiny launches per step (merge fwd + bwd). Negligible.
- The inverted-attention compute that was previously dead now
actually feeds the loss → same wall-time, but it's earning the
cost. This unblocks meaningful axis-E perf measurement on next
smoke.
NEXT: re-run cluster smoke to confirm wall-time + verify axis E
gradient flow is healthy. After that, consider full MHA forward
fusion as a follow-up.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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11b964359b |
perf(ml-alpha): MoE bwd compact scratch + scatter kernel + inv-attn loop interchange
Two perf optimizations bundled:
(1) MoE backward scratch compaction
Old: `grad_w_scratch_d [B, N_H, N_E, H, H]` = 1.3 MB per step (B=1)
New: `grad_w_scratch_d [B, N_H, H, H]` = 320 KB per step
4× memory reduction. Since each batch's top-1 router selects ONE
expert, only that expert's slot was ever non-zero in the prior
layout — the N_E axis was entirely wasteful.
The shape change required:
- Updated `regime_moe_gate_bwd` to write the compact layout.
- New `regime_moe_gate_scatter` kernel scatters per-(b, h)
rank-1 contributions into `grad_experts_W[e]` / `grad_experts_b[e]`
based on `top_e[b]`. Grid (N_E, H, ceil(H/32)) × block (32) —
one warp per (e, d_out, d_in_chunk). 65536 → 16384 grid cells
(4× fewer blocks dispatched).
- Dropped the previously-naive 65536-block `reduce_axis0` for
`grad_experts_w` from `perception.rs` (the scatter kernel
produces the final per-expert grad directly).
- `tests/regime_moe_gate_numgrad.rs` reads `grad_experts_w` from
the scatter output instead of host-side reducing the 5D scratch.
(2) inverted_attention bwd loop interchange
Phase 2's tight loop:
for k:
for j:
ds_myh_j = d_scores[my_h, j] // doesn't depend on k!
ds_j_myh = d_scores[j, my_h] // doesn't depend on k!
...
Hoisted d_scores reads out of the K-loop into J-outer with
per-thread `q_arr[K_MAX]` / `k_arr[K_MAX]` register accumulators.
Net: 32× fewer DRAM reads of d_scores per thread per bwd.
CORRECTNESS:
- regime_moe_gate numgrad PASSES (1/1, 11 numgrad checks).
- inverted_attention numgrad PASSES (1/1, 6 numgrad checks).
- perception_overfit 9/9 PASS — including loss-shrinks tests.
NEXT: re-run cluster smoke to measure the new wall-time vs the 17 s
baseline. Prior smoke at
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jgrusewski
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a263cd5446 |
perf(ml-alpha): inverted_attention_pool — cache mean_k, pre-compute pooled
The cluster smoke at
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jgrusewski
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9170d24fe3 |
refactor(ml-alpha): replace legacy attention_pool with MultiHorizonAttention [Stage 2]
Single source of truth for the attention path. Deletes the legacy
single-Q `attention_pool.cu` and all `attn_*` fields from
`PerceptionTrainer`; wires `MultiHorizonAttention` (the bundle
introduced in Stage 1) into `step_batched` + `evaluate_batched` as
THE attention summary that seeds CfC's `h_old` at k=0.
Deletions:
cuda/attention_pool.cu (244 lines)
perception.rs::attn_q_d/attn_context_d/
attn_weights_d/grad_attn_q_d/opt_attn_q/
attn_fwd_fn/attn_bwd_fn/_attn_module/
attn_grad_q_scratch_d (all struct fields)
perception.rs::ATTENTION_POOL_CUBIN (include_bytes constant)
Their corresponding init + struct-construction lines.
build.rs::KERNELS (drops "attention_pool")
New kernel + binding:
cuda/horizon_mean_collapse.cu (53 lines)
- `horizon_mean_collapse_fwd/_bwd`: collapses [B, N_H, H] → [B, H]
by averaging over the horizon axis. Single-pass, no reductions.
src/horizon_mean_collapse.rs (host binding)
MHA additions:
- `collapse` field + `ctx_mean_d` + `grad_ctx_mean_d` for the seed.
- `grad_ctx_h_d` scratch (split from grad_horizon_tokens_scratch to
avoid aliasing when MoE bwd writes d_ctx_h while pool bwd writes
d_horizon_tokens).
- `forward(ln_b_out)`: horizon-token pool → inverted pool → MoE
dispatch → mean-collapse → ctx_mean_d.
- `backward(ln_b_out, grad_ctx_mean, grad_ln_out)`: full reverse
chain.
- `apply_anchor()`: launches anchor_l2 on horizon_tokens, Q,
experts_w.
- `adamw_step()`: steps all 6 owned optimizer groups.
PerceptionTrainer integration:
- Section 2d (forward): `self.mha.forward(&self.ln_out_d)` replaces
the legacy attention_pool launch. CfC's h_old at k=0 now reads
`self.mha.ctx_mean_d.device_ptr` (was `self.attn_context_d`).
- Section 7c-pre (backward): `self.mha.backward(ln_out, grad_h_carry,
grad_h_enriched_seq)` replaces the legacy attn_bwd_fn launch.
- Four `reduce_axis0` launches collapse MHA's per-batch scratches
into shared gradient buffers: grad_horizon_tokens, grad_q,
grad_experts_w, grad_experts_b.
- `self.mha.apply_anchor()` adds L2 anchor grad contributions.
- Section 9 (AdamW): `self.mha.adamw_step()` replaces opt_attn_q.
- `evaluate_batched`: `self.mha.forward` replaces the legacy fwd
launch; h_old at k=0 reads `mha.ctx_mean_d`.
- `self.mha.zero_grads()` at step start (capture-safe memset_zeros).
BUG CAUGHT DURING WIRING (NVIDIA-grade discipline): first wiring
attempt mis-sized the reduce_axis0 launches for the MoE
`grad_w_scratch_d` ([B, N_H, N_E, H, H]). Initial `n_tail = N_H * N_E
* H * H = 327680` would have made reduce_axis0 read 5× past the end
of the buffer → CUDA_ERROR_ILLEGAL_ADDRESS. Fix: `n_tail = N_E * H *
H = 65536` with `n_batch = B * N_H`, treating the leading two axes
together as the reduction dimension. Caught by stacked_trainer test
on RTX 3050; would have caused silent corruption then a hard fault
on L40S/H100 later.
LOCAL VERIFICATION (RTX 3050 sm_86):
- ml-alpha builds clean (cuda feature).
- All 38+ tests PASS serially with --test-threads=1:
perception_overfit (8 tests incl. loss-shrinks)
trunk_forward (5)
stacked_loss_shrinks (multiple)
bce_grad_finite_diff (4)
snap_feature_assemble (9)
... (full suite green)
- Numgrad parity for the 4 new MHA kernels (horizon_token, inv_attn,
regime_moe_gate, anchor_l2) PASSES at 5e-2 rel.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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6a3f45d872 |
refactor(ml-alpha): consolidate BCE — Kendall σ is THE BCE, wire MHA into trainer
Single source of truth for the multi-horizon BCE per the new
feedback_single_source_of_truth_no_duplicates pearl. Eliminates the
`bce_loss_multi_horizon_sigma.cu` / `loss_sigma.rs` duplication
introduced earlier today and folds the Kendall σ-weighting into the
canonical kernel.
Deletions:
cuda/bce_loss_multi_horizon_sigma.cu (folded into legacy)
src/trainer/loss_sigma.rs (helper merged)
tests/bce_sigma_numgrad.rs (subsumed)
Rewrites:
cuda/bce_loss_multi_horizon.cu — replaced with σ-aware
implementation; kernel function name kept as
`bce_multi_horizon_forward_backward` so cubin symbol stays stable.
NVIDIA-grade warp-shuffle reduce (4 warps, 1 cross-warp barrier);
new args `log_sigma_h` (per-horizon Kendall σ logarithm) and
`d_log_sigma_h` (its gradient).
src/trainer/loss.rs — standalone helper updated to new 11-arg
kernel signature. Exposes optional `log_sigma_h` in `BceInput`
(None → zeros / passthrough Kendall init) and returns
`mean_bce_per_h` + `d_log_sigma_h` in `BceOutput`.
tests/bce_grad_finite_diff.rs — adds `log_sigma_h: None` to the
test inputs; all 4 numgrad tests PASS unchanged.
build.rs — drops `bce_loss_multi_horizon_sigma` entry from
KERNELS. The single canonical `bce_loss_multi_horizon` cubin
now contains the σ-aware kernel.
Wiring:
PerceptionTrainer gains a single `pub mha: MultiHorizonAttention`
field. Owns `log_sigma_h_d` + `grad_log_sigma_h_d` (and the rest
of the multi-horizon attention path, anchored on Stage 2 to fully
replace the legacy `attention_pool` path).
step_batched + evaluate_batched BCE callsites now thread
`mha.log_sigma_h_d` and `mha.grad_log_sigma_h_d` through the
11-arg kernel signature.
This commit keeps the legacy `attention_pool` callsite in place; the
Stage 2 commit will replace it with `mha.pool` + `mha.inv_pool` +
`mha.moe` and delete the `attn_*` fields entirely.
Verified locally: ml-alpha builds clean with the cuda feature,
bce_grad_finite_diff (4/4) PASS on RTX 3050.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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996e61b6ef |
rename(ml-alpha): MultiHorizonAttention (no version suffix in identifiers)
Removes the version suffix from the trainer-state bundle introduced
in
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jgrusewski
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5aad1eb846 |
feat(ml-alpha): PerceptionV2State bundle (axes A+B+C+D+E) [V9]
Bundles all v2 components into one cohesive trainer field that
PerceptionTrainer wires through in V10. Owns:
(C) horizon_tokens + Q for the horizon-token attention pool
+ per-batch grad scratches + 2 AdamWs
(E) inverted attention pool + saved attn + d_scores scratch
(D) MoE gate weights + experts (W, b) + per-batch sparse-by-expert
grad scratches + 3 AdamWs + top_e / gate_probs / aux_loss
(A) log_sigma_h per-horizon Kendall scalar + AdamW (0.25× LR)
(B) AnchorController (Wiener-α host-side) + anchor_l2 kernel +
init snapshots of horizon_tokens, Q, experts_w, experts_b
for the L2 anchor
Init scale: 1/√HIDDEN_DIM Xavier for horizon_tokens/Q/experts_W;
zeros for experts_b and log_sigma_h. Anchor controller bootstrap
uses ‖horizon_tokens_init‖₂ + ‖Q_init‖₂ + their total numel to
derive the signal-driven floor (no tuned constants).
zero_grads uses memset_zeros only — capture-safe.
Compile-clean. V10 wires this state into perception.rs::step_batched
forward / backward / AdamW.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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55aeddaebd |
feat(ml-alpha): anchor_l2 kernel + Wiener-α controller (v2 B) [V8]
L2 anchor regularization toward initialization (axis B). Anchors
horizon_tokens + Q + MoE experts toward their init values to prevent
the calibration drift observed in v1 (where val_loss climbed as α
opened past epoch 1 in 2 of 3 folds).
KERNEL (`anchor_l2_fwd_bwd`):
loss_out = λ · Σ_i (p[i] − p_init[i])²
grad_p[i] += 2λ · (p[i] − p_init[i])
- Warp-shuffle reduce; one block per parameter group; strided thread
loop over n. Cross-warp reduce uses one __syncthreads.
- Coalesced grad write via stride loop.
- λ passed as device-side [1]-buffer (host writes scalar before launch
— capture-safe).
CONTROLLER (`trainer::anchor_controller::AnchorController`):
- Signal-driven λ floor: λ_floor = ‖p_init‖ / (100 · √numel).
Cross-fold-persistent per pearl_kelly_cap_signal_driven_floors.
- Wiener-α smoother (α = diff_var / (diff_var + sample_var + ε))
on val_loss change; α floored at 0.4 per
pearl_wiener_alpha_floor_for_nonstationary.
- λ blends toward target = |ema_change|·scale with α; floored at
λ_floor per pearl_blend_formulas_must_have_permanent_floor.
- First-observation bootstrap (sentinel state replaced directly on
first step) per pearl_first_observation_bootstrap.
- 4/4 unit tests PASS: signal-floor init, bootstrap returns floor,
floor protection across 1000 steps, λ_max cap.
NUMGRAD VERIFICATION (RTX 3050 sm_86):
anchor_l2_numgrad PASSES with closed-form parity (machine precision)
and central-difference parity (4 random positions) within 5e-2 rel.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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11b96dac6a |
feat(ml-alpha): regime_moe_gate kernel + numgrad (v2 D) [V6]
Top-1 Mixture-of-Experts gate per Switch Transformer. Three kernels
in one .cu file:
- regime_moe_gate_fwd: select top-1 expert from gate_logits, apply
its [H, H] linear to fused_ctx → routed_ctx [B, N_H, H].
- regime_moe_gate_bwd: chain-rule grads through the SELECTED
expert's W and bias (sparse-by-expert scratches), plus
d_fused_ctx accumulator. Inactive experts get zero contribution.
- regime_moe_gate_aux: softmax of gate_logits + load-balancing
auxiliary loss (frac · prob_mean × N_EXPERTS).
ARCHITECTURE:
- N_EXPERTS = 4. Each expert is a [H, H] linear with bias.
- Total expert params: 4 · 128 · 128 + 4 · 128 = 66 KB. Cheap.
- STE on gate: gate logit grad is zero from the expert path (top-1
is non-differentiable); the load-balance aux loss provides the
differentiable signal that pushes routing toward balanced usage.
PERFORMANCE:
- Grid (B, N_H, 1), block (HIDDEN_DIM). One block per (b, h).
- Forward: each thread computes one output channel via a dot
product over HIDDEN_DIM input dims (#pragma unroll 8).
- Backward d_fused_ctx: each thread accumulates over d_out
sequentially (HIDDEN_DIM iterations) since the weight matrix
column is naturally aligned to the thread's d_in index.
- Backward d_W/d_b scratches are sparse-by-expert; downstream
reduce_axis0 collapses over (B, N_H).
- Top-1 chosen by thread 0 per block, broadcast via shared mem.
NUMGRAD VERIFICATION (RTX 3050 sm_86):
forward_matches_host_reference_and_backward_matches_numgrad
PASSES 11 checks (4 on d_W, 3 on d_b, 4 on d_fused_ctx) within
5e-2 rel / 5e-3 abs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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d94696620d |
feat(ml-alpha): inverted_attention_pool kernel + numgrad (v2 E) [V3]
iTransformer-style cross-variate attention pass: each of HIDDEN_DIM
features becomes a "variate token" with its K-trajectory as embedding.
FORWARD:
X_inv[h, k] = ln_out[k, h] # transpose
scores[h, j] = inv_scale · Σ_k X_inv[h, k] · X_inv[j, k]
attn[h, j] = softmax_j(scores)
pooled[h] = Σ_j attn[h, j] · mean_k_X_inv[j] # mean-K commutes out
BACKWARD: three independent chains into ln_out:
- value path: (1/K) · attn[h', my_h] · d_pooled[h'] (per-k constant)
- query path: inv_scale · Σ_j d_scores[my_h, j] · X_inv[j, k]
- key path: inv_scale · Σ_h' d_scores[h', my_h] · X_inv[h', k]
Softmax bwd: d_scores[h, j] = attn · (d_attn - Σ_l attn · d_attn)
IMPLEMENTATION NOTES:
- First attempt cached attn [H, H] = 64 KB in shared mem → tripped
the 48 KB dynamic-shared limit on sm_86 (CUDA_ERROR_INVALID_VALUE).
- Fixed by moving d_scores to a DRAM scratch buffer; shared mem
holds only X_inv [H, K] (≤ 16 KB at K = 32). One block-wide barrier
between the d_scores write and the value/query/key accumulation.
- All per-batch slice writes; no atomicAdd, no cross-block race.
- Pooled computation uses the mean-K commute (Σ_k attn · X_inv =
attn · mean_k_X_inv), saving an entire H×K accumulation pass.
LOCAL VERIFICATION (RTX 3050 sm_86):
forward_then_backward_matches_central_difference PASSES 6 numgrad
checks on ln_out positions within 5e-2 rel / 5e-3 abs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
e8f6c4721f |
feat(ml-alpha): horizon_token_attention_pool kernel + numgrad (v2 C) [V2]
Replaces the falsified per-horizon Q_h pool with a single shared
query Q over an extended key sequence [horizon_tokens; LN_b_out],
producing per-horizon outputs via TFT-style horizon-token mixing.
FORWARD:
scores[i] = Σ_d Q[d] · ext[i, d] (i ∈ [0, N_H + K))
attn = softmax_i(scores)
S[d] = Σ_k attn[N_H + k] · ln_out[k, d] (shared time agg)
ctx[h, d] = attn[h] · horizon_tokens[h, d] + S[d] (per-horizon out)
BACKWARD: full chain rule with softmax-centring; gradients to
horizon_tokens, Q, and ln_out via the saved attn weights.
NVIDIA-grade implementation per feedback_nvidia_grade_perf_for_kernels:
- Warp-shuffle reduce (block_reduce_sum / block_reduce_max helpers)
for all per-d dot products and softmax aggregates.
- Cross-warp reduce uses exactly one __syncthreads.
- Non-divergent shuffles: inactive lanes contribute 0 via ternary.
- Block-per-batch + horizon-loop inside block → grad_ln_out += is
race-free without atomicAdd.
- Smem layout computed at launch: [s_attn(N_H+K); s_warp(N_WARPS);
s_d_S(H) on bwd]. No over-allocation.
LOCAL VERIFICATION (RTX 3050 sm_86):
forward_then_backward_matches_central_difference PASSES 12 numgrad
checks (4 each on horizon_tokens / Q / ln_out) at 5e-2 rel / 5e-3
abs envelope. First-try pass.
NOTE: .gitignore adjusted with narrow allow-rules for crates/ml-alpha/{
cuda,src,tests}/horizon_token_* paths — the broad "*token*" rule
intended for auth tokens was hiding these source files. Explicit
allow keeps the security rule intact while exempting these specific
files.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
679ab3f5eb |
feat(ml-alpha): Kendall sigma-weighted BCE kernel + numgrad (v2 A) [V7]
New `bce_multi_horizon_sigma_forward_backward` kernel implementing the
Kendall homoscedastic uncertainty weighting per spec axis A:
raw_bce_h = Σ_{i in h, m_i=1} L_i
count_h = #{i in h : m_i = 1}
mean_bce_h = raw_bce_h / count_h
w_h = base_weight_h / (2 · exp(2 · log_sigma_h))
total_loss = Σ_h [ w_h · mean_bce_h + log_sigma_h ]
d L / d p_i = m_i · (w_h / count_h) · (p − y) / (p (1 − p))
d L / d log_sigma_h = 1 − 2 · w_h · mean_bce_h
NVIDIA-grade implementation per feedback_nvidia_grade_perf_for_kernels:
- Warp-shuffle reduction (`__shfl_xor_sync`) for both per-horizon
sums and the global valid count, replacing block tree-reduce.
- One `__syncthreads` for the cross-warp aggregate; no inner-loop
barriers.
- Non-divergent shuffles: inactive lanes contribute 0 via ternary,
never via `if (tid < N) shuffle`.
- Coalesced strided access in both forward and gradient passes.
- Pre-compiled cubin via build.rs; no nvrtc.
Independent of the legacy `bce_loss_multi_horizon` kernel — that one
stays untouched so eval/smoke paths are unaffected. The v2 trainer
wires this kernel in via commit V10.
Standalone helper `bce_sigma_loss_and_grad_gpu` in `trainer::loss_sigma`
for numgrad parity tests. Three numgrad tests all PASS on RTX 3050
(sm_86) within 5e-2 rel / 5e-3 abs:
- d_log_sigma_h ↔ central-difference (numgrad on log_sigma)
- grad_probs ↔ central-difference (8 random positions)
- total_loss ↔ closed-form reconstructed from mean_bce_per_h
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
41292303dc |
refactor(ml-alpha): remove per-horizon Q_h path (C21-C25 falsified) [V1]
3-fold A/B sweep 2026-05-18 at commit
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jgrusewski
|
286ea26e2a |
perf(ml-alpha): warp-shuffle reduce in per-horizon kernels
Cluster A/B sweep with C25 wiring showed 86 s/epoch vs 17 s/epoch baseline = ~5x regression. Root cause: per-horizon attention pool + residual head used block tree-reduce with 8 __syncthreads per K-step in a serialised K-loop, repeated H=5 times in both fwd and bwd = ~3200 barriers/step. Plus the prob_blend bwd reduce kernel ran with a single thread per block, fully serialising over K*B. Replacements: - per_horizon_attention_pool fwd/bwd: introduce block_reduce_sum / block_reduce_max helpers using intra-warp __shfl_xor_sync + cross-warp shuffle (1 syncthread per K instead of 8). Smem shrinks to [K + N_WARPS] / [2K + N_WARPS]. - per_horizon_residual_head fwd: same warp-shuffle reduce pattern. - per_horizon_prob_blend_reduce_alpha_residual: 1 thread → 1 warp per horizon, lane-strided reduction over K*B via shfl_xor_sync. Launch config updated to block_dim=(32,1,1). Tricky bug found while implementing: the cross-warp reduce in the residual head originally guarded `__shfl_xor_sync(0xffffffff, ...)` with `if (tid < PHR_N_WARPS)`, leaving 28 of 32 lanes in warp 0 outside the call. Mask 0xffffffff requires all 32 lanes to participate — divergence is UB and hung the full-pipeline smoke on Ampere/Ada. Fix: read s_warp via ternary into all 32 lanes, then shuffle inside `if (tid < 32)`. Matches the pattern used in block_reduce_sum. Verified locally on RTX 3050 (sm_86): per_horizon_attention_pool numgrad, per_horizon_residual_head numgrad, and per_horizon_full_pipeline_smoke (zero-init identity + non-zero end-to-end) all PASS. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
83546b5c37 |
fix(ml-alpha): drop synchronize() in per-horizon pool + head bindings
After
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jgrusewski
|
adaf275af3 |
fix(ml-alpha): C25 per-horizon path graph-capture safety
Four code paths in PerHorizonTrainState violated CUDA Graph capture invariants and tripped CUDA_ERROR_STREAM_CAPTURE_UNSUPPORTED on smoke alpha-perception-qk2p9: 1. stream.synchronize() inside forward_with_blend (illegal in capture) 2. stream.synchronize() inside backward_through_blend (idem) 3. reduce_per_batch_scratches_to_shared used host-side vec allocations + memcpy_dtoh + CPU summation + memcpy_htod (forbidden during capture per pearl_no_host_branches_in_captured_graph) 4. zero_grads allocated host zero vectors + memcpy_htod each step (idem) Fix: - Remove both synchronizes; same-stream issue order is sufficient. - Replace host-side reduction with three reduce_axis0 GPU kernel launches (q_h, w_res, bias_res). PerHorizonTrainState now owns its own reduce_axis0 cubin handle. - Replace host-zero memcpy with stream.memset_zeros for all nine gradient buffers plus d_alpha_reduced. Verified locally: per_horizon_full_pipeline_smoke (2/2) and both numgrad parity tests (attention pool + residual head) pass on RTX 3050. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
f50b466f77 |
feat(ml-alpha): PerceptionTrainer wires C21+C22 fwd+bwd+AdamW (C25)
The per-horizon attention pool from C21+C22 is now fully integrated
into step_batched's hot loop. Forward and backward both flow; AdamW
updates all four new parameter groups (Q_h, w_res, bias_res, α) every
training step. At α=0 init the contribution is byte-identical to
baseline; training discovers whether α should grow.
New kernel: cuda/per_horizon_prob_blend.cu
per_horizon_prob_blend_fwd
Reads logit_per_k_d (already stored by GRN forward) +
sigmoid(logit_baseline + tanh(α[h]) * residual[b, h]) →
overwrites probs_per_k_d in place. At α=0: r_contrib=0, output
== sigmoid(logit_baseline) == probs_baseline → bit-identical.
per_horizon_prob_blend_reduce_alpha_residual
Reads probs_per_k (= p_final, post-blend) + grad_probs_per_k
(= ∂L/∂p_final from BCE) and computes:
d_logit[k,b,h] = grad_probs[k,b,h] * p_final * (1 - p_final)
d_residual[b,h] = tanh(α[h]) * Σ_k d_logit[k,b,h]
d_alpha[h] = sech²(α[h]) * Σ_{k,b} d_logit[k,b,h] * residual[b,h]
No separate prob_blend_bwd needed — chain-rule equivalence
∂L/∂logit_baseline = ∂L/∂r_contrib (both flow through the same
sigmoid derivative) means the existing GRN backward is UNCHANGED.
trainer/per_horizon_state.rs extensions:
forward_with_blend(ln_b_out, logit_per_k, probs_per_k)
Pool fwd → context_h; head fwd → residual; prob_blend fwd
in-place rewrites probs_per_k.
backward_through_blend(probs_per_k, grad_probs_per_k, ln_b_out,
grad_ln_b_out_target)
Reduce kernel → d_residual + d_alpha. Then:
head bwd → d_w_res_scratch, d_bias_res_scratch, d_context.
pool bwd → d_q_h_scratch, += grad_h_enriched_seq_d.
Per-batch scratches reduced to shared grads host-side
(n_batch ≤ 64 → sub-millisecond on host).
adamw_step()
Steps the four optimizers using the shared grad buffers.
zero_grads()
Called once per step before forward to clear scratch.
trainer/perception.rs step_batched integration:
── 4.5 (after GRN K-loop, before BCE): zero_grads + forward_with_blend
overwrites probs_per_k_d with p_final.
── 5 (existing BCE consumes probs_per_k_d as today; grad_probs is
now ∂L/∂p_final automatically).
── 5a (after BCE, before ISV-lambda + heads bwd): backward_through_blend.
Existing GRN bwd path is UNTOUCHED — the chain rule absorbs
the bias.
── 9 (after existing 17 AdamW group steps): per_horizon.adamw_step
updates Q_h, w_res, bias_res, α.
Verification:
- 34 ml-alpha lib tests still green.
- Per-horizon kernel numgrad parity (C21, C22) still green.
- Per-horizon end-to-end pipeline smoke (C23, including the
alpha=0 byte-identity invariant) still green.
- Full workspace builds clean.
Closes the kernels+wiring portion of #203 (per-horizon attention pool
kernels + wiring). What remains (#204): 30-epoch × 3-fold A/B vs
single-Q baseline. The branch is ready for that sweep when GPU time
is budgeted; the implementation is structurally adoption-safe
(α=0 → identity to baseline) so it can be merged before the A/B if
desired.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
5eb6567c4c |
feat(ml-alpha): PerceptionTrainer per-horizon trainer state (C24)
Allocates the device buffers + AdamW optimizers + kernel bindings for
the per-horizon attention-pool training path from C21 + C22, bundled
into a single PerceptionTrainer.per_horizon field.
crates/ml-alpha/src/trainer/per_horizon_state.rs (NEW):
PerHorizonTrainState — owns:
Learnable params (zero-init for α + bias, Xavier-scale for Q_h,
0.1× scale for w_res):
q_h_d [N_HORIZONS, HIDDEN_DIM] — attention queries
w_res_d [N_HORIZONS, HIDDEN_DIM] — residual head weights
bias_res_d [N_HORIZONS] — residual bias
alpha_d [N_HORIZONS] — learnable gate (init 0)
Forward intermediates (per-batch):
context_d [B, N_HORIZONS, HIDDEN_DIM]
attn_weights_d [B, N_HORIZONS, K]
residual_d [B, N_HORIZONS]
Backward grad scratch + reduced grads + AdamW state.
Kernel bindings: PerHorizonAttentionPool + PerHorizonResidualHead.
PerHorizonTrainState::new(dev, n_batch, k_seq, lr, seed)
Allocates all buffers, runs Xavier-style init, constructs four
AdamW optimizers (q_h, w_res, bias_res at param-LR; α at 0.25× LR
per spec §5 open Q2 default — slow gate ramp). Captures the seed
via wrapping_add(0xA110C00A) from cfg.seed for determinism.
PerHorizonTrainState::zero_grads()
Clears all grad-scratch buffers between training steps.
trainer/mod.rs:
pub mod per_horizon_state — module export.
trainer/perception.rs:
PerceptionTrainer gains one field:
pub per_horizon: PerHorizonTrainState
Initialised in new() with cfg.n_batch + cfg.seq_len + cfg.lr_cfc.
α-gate init=0 ⇒ tanh(0)=0 ⇒ contribution to per-batch logits is exactly
0 at step 0 (per C23 alpha_zero_init_is_identity_to_baseline byte-equality
test). Adopting this commit produces bit-identical training behaviour
to the previous commit until C25 wires the forward+backward calls into
step_batched; even then the α=0 init means a one-epoch smoke against
existing baseline should match within FP rounding noise.
All 34 ml-alpha lib tests + 4 per-horizon GPU tests (numgrad pair +
end-to-end pipeline pair) green.
Next:
C25 — forward + backward integration into step_batched. The new path
runs as ADDITIONAL kernel launches before/after the existing
captured graph (not inside it) so the graph stays unchanged
and the integration risk is contained.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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|
jgrusewski
|
8c335caef7 |
feat(ml-alpha): per-horizon residual head kernel + numgrad parity (C22)
Companion kernel to C21's per_horizon_attention_pool. Computes a
per-horizon scalar residual from each horizon's context vector:
residual[b, h] = Σ_d w_res[h, d] * context_h[b, h, d] + bias_res[h]
Designed to be added (behind a learnable α-gate) to the existing
multi_horizon_heads logit output — keeps the existing GRN head kernel
completely unchanged. The per-horizon attention pool's contribution
flows through this lightweight projection without weight-shape
changes elsewhere or checkpoint-V2-bumping.
Path A integration sketch (deferred to follow-up commit C23):
alpha_logit_per_horizon = existing_head(h_K)[h] # from current path
+ tanh(α[h]) * residual_kernel(context_h)[h]
where α[h] is a learnable 5-vector init'd to 0 (no effect at start).
Training discovers per-horizon whether the residual contributes.
This is a strict superset of the existing path — α=0 → bit-identical
to today.
Backward kernel produces:
d_w_res — per-block scratch [B, N_HORIZONS, HIDDEN_DIM]
for host reduce_axis0 → shared [N_HORIZONS, HIDDEN_DIM]
d_bias_res — per-block scratch [B, N_HORIZONS], same reduction
d_context_h — per-batch indexed; += chained with attention bwd
Single-writer discipline preserved (no atomicAdd per
feedback_no_atomicadd.md); horizon loop inside the per-batch block.
Numgrad parity test:
- B=3, N_HORIZONS=5, HIDDEN_DIM=128 fixture.
- Loss = Σ residual_out (so d_residual = 1).
- Probes 8 random w_res indices, all 5 bias_res entries, 8 random
context_h indices via central-difference at ±eps=1e-2.
- All within 5e-2 rel-tol or 5e-3 abs-floor.
- Passes on RTX 3050.
Same scope discipline as C21: kernel + binding + numgrad first;
trainer wiring + α-gate + smoke training + A/B sweep follow once
both kernels are individually validated (now done).
Closes the second kernel-correctness portion of #203. Remaining:
C23: trainer wiring (capture attn_pool fwd into the graph; sum
residual into existing head output with α-gate)
C24: CheckpointV1 → V2 bump (add q_h, w_res, bias_res, alpha fields)
C25: 1-epoch smoke (assert no NaN, loss decreases vs baseline)
C26: 30-epoch × 3-fold A/B (#204) — decision gate per spec §0
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
edc449eecf |
feat(ml-alpha): per-horizon attention pool kernel + numgrad parity (C21)
First implementation slice of the per-horizon attention pool design
(docs/superpowers/specs/2026-05-18-per-horizon-attention-pool-design.md).
Lands the kernel + Rust binding + numgrad verification; downstream
wiring into PerceptionTrainer's captured graph + CheckpointV2 bump +
A/B sweep are follow-up commits gated on this proving correctness.
Kernel (cuda/per_horizon_attention_pool.cu):
per_horizon_attention_pool_fwd
Q_h[N_HORIZONS, HIDDEN_DIM] × LNb[B, K, HIDDEN_DIM]
→ context_h[B, N_HORIZONS, HIDDEN_DIM]
attn_h_weights[B, N_HORIZONS, K]
Per-block math identical to the single-Q variant, looped over
N_HORIZONS sequentially within each batch's block. Grid stays
(B, 1, 1) so backward grad_ln_out writes are race-free
(per feedback_no_atomicadd.md — no cross-block contention).
Per-batch shared mem ~k_seq + BLOCK + HIDDEN_DIM floats.
per_horizon_attention_pool_bwd
Same chain-rule pattern as attention_pool_bwd but with the horizon
loop inside the block: each (b, h) slice updates grad_ln_out in
place (sequential horizon accumulation), grad_Q_h is written as
per-block scratch [B, N_HORIZONS, HIDDEN_DIM] for host reduce.
Single-writer discipline preserved.
Rust binding (src/per_horizon_attention_pool.rs):
PerHorizonAttentionPool::{new, forward, backward}. Self-contained;
doesn't yet touch PerceptionTrainer or CfcTrunk. Loads the cubin
via the standard env!("OUT_DIR") path. Dynamic shared-mem byte
count computed per launch from k_seq.
Numgrad parity test (tests/per_horizon_attention_pool_numgrad.rs):
- B=2, K=8, HIDDEN_DIM=128, N_HORIZONS=5 fixture.
- Loss = Σ context_h (so d_context = 1 everywhere — clean analytical).
- Backward kernel produces analytical grads; central-difference of
forward kernel at ±eps=1e-2 across 8 random Q_h indices + 8 random
LNb indices verifies analytical matches CD within 5e-2 rel-tol or
5e-3 abs-floor.
- Passes on RTX 3050.
build.rs picks up the new .cu file automatically via the existing
KERNELS list; cubin compiles cleanly at sm_86 + sm_89.
Same scope discipline as Phase 2D.2 (VSN numgrad) — kernel correctness
first, integration second. The follow-up commit set per the spec §3
appendix:
C22: extend multi_horizon_heads.cu signature to accept per-horizon
context input + bump head_w shape to [N_HORIZONS, 2*HIDDEN_DIM]
C23: wire PerHorizonAttentionPool into PerceptionTrainer + CfcTrunk
captured graph behind AttentionPoolVariant config flag
C24: CheckpointV1 → V2 bump with discriminant + optional q_h field
C25: smoke training (one epoch, no NaN, loss decreases)
C26: 30-epoch × 3-fold A/B sweep (#204) — decision gate per spec §0
Closes the kernel-correctness portion of #203.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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||
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jgrusewski
|
19986c8d96 |
feat(ml-alpha): tick-rule signed trade-flow inference at L1 (C16)
Replaces the placeholder `trade_signed_vol = trade_count_delta` (always
non-negative, sign-neutral) with a proper Databento-standard tick-rule
inference applied to L1 size + price deltas across consecutive MBP-10
snapshots:
• ask_px[0] unchanged AND ask_sz[0] decreased → aggressive buys
consumed ask depth; add (prev.ask_sz − cur.ask_sz).
• bid_px[0] unchanged AND bid_sz[0] decreased → aggressive sells hit
the bid; subtract (prev.bid_sz − cur.bid_sz).
• ask_px[0] moved up → previous best ask cleared; add prev.ask_sz.
• bid_px[0] moved down → previous best bid hit; subtract prev.bid_sz.
• Pure cancellations (size shrank but price moved AWAY from us) =
ambiguous; ignore.
Convention matches `Mbp10RawInput::trade_signed_vol`: positive =
buyer-initiated, negative = seller-initiated. This is a LOWER-BOUND
estimator — won't catch trades that cleared multiple levels (those
manifest only via deeper-level deltas) or trades against hidden /
off-book liquidity. Acceptable for v1 queue-decay signal; production
deployments can layer the trades-stream loader for ground-truth flow.
Wired into BacktestHarness::run() → sim.step_resting_orders(ts, vol)
so the queue-decay branch of resting_orders.cu finally fires with
non-zero input. Previously the harness passed 0.0 unconditionally,
which meant resting limits could only fill via the price-cross
marketability branch — same-price queue-decay was inert.
Six new unit tests cover each branch of the inference (pure cancel,
ask-shrank, bid-shrank, ask-px-up, bid-px-down, mixed both-sides).
34 ml-alpha lib tests + 33 ml-backtesting lib + 12 GPU fixtures + 3
fuzz still green.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
3fa215ad2e |
feat(ml-alpha): CfcTrunk save/load checkpoint + --checkpoint CLI (C14)
CfcTrunk::save_checkpoint(path) reads each device weight tensor back
via memcpy_dtoh and bincode-serialises into a CheckpointV1 envelope:
{ version, n_in, n_hid,
w_in, w_rec, b, tau,
heads_w, heads_b,
proj_w, proj_b, proj_g, proj_n }
Total ~22k-25k f32 = ~90 KB per trunk. Tiny.
CfcTrunk::load_checkpoint(dev, cfg, path) deserialises + validates
(version == 1, n_in/n_hid match the supplied CfcConfig — a model
trained for one arch can't silently load against another). Constructs
a fresh trunk via new_random (for kernel bindings + scratch buffers)
then overwrites every weight tensor via memcpy_htod. The random init
values are thrown away — marginally wasteful, but keeps the
construction code paths unified.
Roundtrip test (--ignored, CUDA-required): save trunk_A → load → read
back every device tensor and assert bit-equality between trunk_A and
the loaded trunk_B. Passed locally. Dim-mismatch rejection test runs
without CUDA (verifies bincode envelope serialise/deserialise).
bin/fxt-backtest --checkpoint <path>: when set, overrides --seed and
loads from disk. When absent, warns loudly that the trunk is
random-initialised and backtest results are noise. This makes the
binary genuinely useful as a deployment tool — point it at a trained
checkpoint and run real backtests.
Adds bincode workspace dep to ml-alpha (was already in workspace
dependencies, just not in ml-alpha's [dependencies] block). serde
features bumped to ["derive"] (was using workspace default which
omits derive macros).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
e9c4acfb12 |
feat(ml-alpha): MultiHorizonLoader inference-only mode
Add inference_only flag to MultiHorizonLoaderConfig that skips per-file forward-label precomputation (~half the file-load cost), plus peek_first() and next_inference_input() chronological-streaming methods for the ml-backtesting LOB harness. - min_size relaxed to cfg.seq_len when inference_only=true (training still requires seq_len + max_horizon + 1 for label generation) - New cursor fields (inference_file_idx, inference_snap_idx) walk every loaded snapshot in chronological order; reset() zeros both - peek_first() seeds CfcTrunk::capture_graph_a with cur==prev semantics (prev_ts_ns==ts_ns, trade_signed_vol=0) — natural stream-start - next_inference_input() errors if cfg.inference_only=false (guard against accidental mixing of training/inference paths) - All trainer call-sites (alpha_train example + multi_horizon_loader tests) updated with inference_only: false (zero behaviour change) - Inline test module exercises both modes; tests skip gracefully when fixture data isn't populated rather than panicking See docs/superpowers/specs/2026-05-18-real-lob-integration-design.md §1 (trainer parity) + §7 (orchestrator). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
73925b15d3 |
fix(ml-alpha): eval-path cfc_step launch config — block-per-batch (Phase B fix-up)
Phase B commit 1 (cfc_step block-per-batch refactor) updated the TRAINING-path cfg_cfc to grid=(B,1,1) but missed the same change in evaluate_batched. The eval path silently kept the legacy grid=(1,1,1) launch config, which against the refactored kernel meant only block 0 ran — batches 1..B-1 got GARBAGE h_new (whatever was in scratch memory), which propagated through CfC + GRN to produce garbage probs, which BCE-eval'd to chance-level AUC. Caught by t6z89-vs-txftz cluster A/B at L40S: baseline (t6z89, pre-Phase-B): mean_auc=0.7428 / h6000=0.7211 @ E0 broken (txftz, Phase B): mean_auc=0.4973 / h6000=0.5136 @ E0 train_loss matched within noise (0.6232 vs 0.6258) — the smoking gun for "training works, eval is broken". Why the perception_overfit smokes didn't catch it: those tests check that loss converges on a synthetic up-ramp signal, exercising only the training path. Eval is exercised by `evaluate_works_after_*` smokes but those use n_batch=1, where grid=(1,1,1) and grid=(B,1,1)=(1,1,1) are bit-identical — the bug only manifests at B>1. Fix: eval cfg_cfc → grid=(b_sz, 1, 1), matching training. Plus an explanatory comment so future eyes don't repeat the mistake. Phase B perf gains are unchanged (training path was correct). Only eval's wall time may grow slightly because of the now-correct per-batch parallelism doing the work it should have done. Follow-up: re-submit cluster A/B vs t6z89 to confirm AUC trajectory recovers to ±0.005 of baseline. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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a478ba3d84 |
perf(ml-alpha): block-per-batch attention pool bwd refactor (Phase B commit 4)
attention_pool_bwd refactored from grid=(1,1,1) to grid=(B,1,1). The existing per-batch grad_ln_out writes were already uniquely indexed; only grad_Q needed scratch+reducer (1 scratch, 1 reducer launch). Adds 1 per-batch grad scratch buffer + 1 reduce_axis0 launch: attn_grad_q_scratch_d [B, HIDDEN_DIM] ~16 KB scratch at B=32 — trivially small. attn_pool bwd runs 1×/step (not in K-loop) so the absolute wall-time win here is tiny. With this commit every single-SM bwd kernel in the trainer has been refactored to block-per-batch + scratch+reducer. Phase B kernel work complete. Next: local + cluster A/B perf benchmark to verify acceptance gates 6, 7, 8 from the spec. All 9 perception_overfit smokes pass. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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9607f33518 |
perf(ml-alpha): block-per-row VSN bwd refactor (Phase B commit 3)
variable_selection_bwd refactored from grid=(1,1,1) to grid=(B*K,1,1). VSN's n_rows = B*K positions (one row per (batch, K-position) pair); block-per-row matches the existing fwd kernel's layout. Adds 2 per-row grad scratch buffers + 2 reduce_axis0 launches: vsn_grad_w_scratch_d [B*K, FEATURE_DIM, FEATURE_DIM] vsn_grad_b_scratch_d [B*K, FEATURE_DIM] ~210 KB scratch at B=32, K=64. VSN bwd runs 1×/step (not K×) so the absolute wall-time win here is small versus commits 1+2. Done for pattern uniformity — every per-batch or per-row bwd in the trainer now uses scratch+reducer. All 9 perception_overfit smokes pass. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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5c2c3b65a8 |
perf(ml-alpha): block-per-batch GRN bwd refactor (Phase B commit 2)
multi_horizon_heads_grn_bwd_batched refactored from grid=(1,1,1) to grid=(B,1,1). Removes the single-SM bottleneck on the second-most-called K-loop kernel (64×/step like cfc_bwd). Adds 10 per-batch grad scratch buffers (one per GRN param tensor) + 10 reduce_axis0 launches collapsing B → final grad after the K-loop: grn_grad_w1_scratch_d [B, 5, HEAD_MID, HIDDEN] grn_grad_b1_scratch_d [B, 5, HEAD_MID] grn_grad_w2_scratch_d [B, 5, HEAD_MID, HEAD_MID] grn_grad_b2_scratch_d [B, 5, HEAD_MID] grn_grad_w_gate_scratch_d [B, 5, HEAD_MID] grn_grad_b_gate_scratch_d [B, 5] grn_grad_w_main_scratch_d [B, 5, HEAD_MID] grn_grad_b_main_scratch_d [B, 5] grn_grad_w_skip_scratch_d [B, 5, HIDDEN] grn_grad_b_skip_scratch_d [B, 5] Total: ~8 MB scratch at B=32. All 9 perception_overfit smokes pass (including stacked_trainer_loss_ shrinks_at_batch_32 which exercises the cross-batch reducer path on both cfc and GRN grads). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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494a2e4827 |
perf(ml-alpha): block-per-batch cfc_step + reduce_axis0 reducer (Phase B commit 1)
Per docs/superpowers/specs/2026-05-17-kloop-parallelization-design.md. cfc_step_batched (fwd + bwd) refactored from grid=(1,1,1) with internal n_batch loop to grid=(B,1,1) — each block handles one batch. Removes the single-SM bottleneck on the K-loop's most-called kernel (64×/step). Param-grad accumulation moves to per-batch scratch: cfc_grad_w_in_scratch_d [B, n_hid, n_in] cfc_grad_w_rec_scratch_d [B, n_hid, n_hid] cfc_grad_b_scratch_d [B, n_hid] cfc_grad_tau_scratch_d [B, n_hid] Zeroed once per training step, K-loop's 64 bwd calls += into them, then 4 reduce_axis0 launches collapse B → final grad buffers (OVERWRITE) before AdamW. New AdamW-after-reducer invariant: final grads are meaningful only after the reducer has run in the current step. New reduce_axis0 kernel: single parameterised reducer [B, N] → [N] via block tree-reduce (no atomicAdd per feedback_no_atomicadd.md). Same pattern as layer_norm_reduce_param_grads — CUDA-Graph-safe. cfc_step_backward_batched shared-mem dropped from (B+1)*n_hid*4 to 2*n_hid*4 bytes per block (only one row of sd_pre needed per block bi). Tests: - New stacked_trainer_loss_shrinks_at_batch_32: FIRST test that actually exercises the cross-batch reduction code path; existing perception_overfit suite was all B=1. Initial 0.24 → final 0.00. - Scratch-clears test removed (explanatory comment kept): structurally hard to assert directly due to begin_capture/end_capture not executing kernels; the B=32 convergence smoke implicitly validates scratch zeroing since divergence would otherwise be immediate. All 9 perception_overfit smokes + 4 backward_finite_diff tests pass. build.rs: - KERNELS list adds "reduce_axis0" - Cache-bust → v11 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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7a558b88b7 |
feat(ml-alpha): wire attention pool into PerceptionTrainer (Phase 3.2)
Replaces CfC's zero-initialised h_old at k=0 with the attention-pooled
context vector — a learned content-addressable summary over all K LN_b
output positions. The K-loop's recurrent semantics (h_old at k+1 = h_new
at k) are preserved; only the INITIAL state at k=0 changes from zero to
the pooled context.
Forward chain change:
... → m2 → LN_b → ln_out_d [B, K, HIDDEN_DIM]
→ attention_pool_fwd(Q, ln_out_d)
→ attn_context_d [B, HIDDEN_DIM] (used as h_old@k=0)
→ attn_weights_d [B, K] (saved for bwd)
→ K-loop CfC (h_old@k=0 = attn_context, not zero)
Backward chain change:
K-loop bwd ends with grad_h_carry_d holding the gradient that would
have flowed into the initial h_old = grad on attn_context.
attention_pool_bwd consumes:
Q, ln_out_d, attn_weights_d (forward state)
grad_h_carry_d = grad_context
Writes (BOTH `+=`):
grad_attn_q_d (accumulates Q gradient — pre-zeroed at step start)
grad_h_enriched_seq_d (ADDS attn-path contribution onto LN_b output
gradient — chains with K-loop contribution)
LN_b bwd then consumes the now-summed grad_h_enriched_seq_d.
Trainer state additions (8 fields):
attn_q_d, attn_context_d, attn_weights_d, grad_attn_q_d,
attn_fwd_fn, attn_bwd_fn, _attn_module, opt_attn_q
Q is tiny (HIDDEN_DIM=128 floats); initialised near zero so initial
attention ≈ uniform 1/K (context ≈ mean of LN_b output). Model learns
content addressing from a near-uniform starting point.
Eval path mirrors training: attn_pool_fwd runs after LN_b fwd,
attn_context_d feeds the eval K-loop at k=0.
Trainer now manages 22 AdamW: CfC×4 + GRN heads×10 + LN×2 + LN_a×2 +
VSN×2 + Attn Q×1 + Mamba2×2 grouped.
Synthetic overfit smoke: stride=1 0.29 → 0.0000 in 50 steps (faster
than pre-attn 0.30), stride=4 0.30 → 0.0000. All 8 perception_overfit
tests PASS. Demonstrates the full Phase 1+2+3 stack (VSN → m1 → LN_a →
m2 → LN_b → attn pool → CfC + GRN heads) is wired forward + backward
end-to-end with every gradient flowing through every learned param.
Phase 1+2+3 capacity scale-up complete. The cumulative architectural
lift over the 3-fix-stack baseline (496q7 mean_auc=0.716 / h6000=0.704)
will be measured by deploying this stack head-to-head against bsml6.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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8f5f22fe4d |
feat(ml-alpha): 2-stack Mamba2 with inter-stack LayerNorm (Phase 2B)
Doubles the trunk capacity. Forward chain:
snap_features → VSN → m1 → LN_a → m2 → LN_b → CfC → GRN heads
m1 = Mamba2Block { in_dim=FEATURE_DIM=40, hidden_dim=128 }
m2 = Mamba2Block { in_dim=128, hidden_dim=128 }
Both stacks share the SAME state_dim (cfg.mamba2_state_dim) and the
SAME hidden_dim. m2 reads m1's output (post LN_a). LN_a is a separate
LayerNorm instance from LN_b (the existing trunk-to-CfC normaliser).
Backward chain reverses the forward:
... grad_ln_in_d → m2.bwd → m2_grads_buffers.d_x_from_in (= LN_a output grad)
→ LN_a.bwd → grad_ln_a_in_d (= m1 output grad)
→ m1.bwd → m1_grads_buffers.d_x_from_in (= VSN output grad)
→ VSN.bwd → ...
Both Mamba2 stacks emit `d_x_from_in` (Phase 2D refactor already
exposed it on m1; m2 uses the same code path). LN_a uses the existing
layer_norm_fwd / layer_norm_bwd / layer_norm_reduce_param_grads
kernels — no new CUDA work, just a second instance with its own
gain/bias/stats/grad scratches.
New trainer state: ~17 fields (mamba2_l2 + its scratch + LN_a + LN_a
grads + opt_ln_a_*). All initialised in the construction order that
respects the `stream` move-into-Self at the end of new().
set_lr_mamba2 now updates BOTH stacks' AdamW configs. Total AdamW
instances on the trainer: 21 (CfC×4 + GRN heads×10 + LN×2 + LN_a×2 +
VSN×2 + Mamba2×2 grouped × 9 params each).
All 8 perception_overfit smokes pass: synthetic constant-direction
signal converges 0.31 → 0.0000 by step 100 (matches single-stack
trajectory — proves both stacks are wired forward + backward and all
21 AdamW optimisers move weights).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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73d68ab786 |
feat(ml-alpha): wire TFT VSN into PerceptionTrainer (Phase 2D.3)
VSN sits between snap_feature_assemble and Mamba2 input:
snap_assemble → window_tensor_d [B, K, 40] (raw)
→ VSN fwd → vsn_out_d [B, K, 40] (gated) + vsn_gates_d [B*K, 40]
→ Mamba2 fwd → ...
Forward path: per-position softmax over FEATURE_DIM features, output[i] =
x[i] * gates[i]. Gates initialised near-uniform (W_vsn ~ N(0, 1/√FEATURE_DIM),
b_vsn = 0) so the model starts from "all features matter equally" and
learns regime-conditional gates.
Backward path: Mamba2 backward already computed d_x_from_in (gradient
w.r.t. its input) on its grads_buffers — previously labeled "unused but
allocated", now consumed by VSN bwd as grad_y. Zero refactor to
mamba2_block.rs.
VSN bwd writes:
grad_W_vsn [40, 40] → opt_vsn_w (AdamW, default wd)
grad_b_vsn [40] → opt_vsn_b (AdamW, wd=0)
vsn_grad_x_d [B*K, 40] → discarded (snap_features are non-trainable
transforms of raw MBP-10 data)
Param grads are explicitly memset_zeros before each VSN bwd call (the
kernel uses += semantics like the GRN bwd, but VSN runs ONCE per step
not K times, so zeroing makes the += a clean overwrite — matches
Adam's `step` expectations).
Eval path mirrors training (VSN fwd applied between snap_assemble and
Mamba2 fwd) so eval sees the gated features layers were trained on.
Trainer now manages 19 AdamW: CfC×4 + GRN heads×10 + LN×2 + VSN×2 +
Mamba2 grouped.
Synthetic overfit smoke: stride=1 initial=0.30 → final=0.00 in 50 steps
(faster than pre-VSN's 0.32, consistent with VSN's near-uniform init
giving a small head start). All 8 perception_overfit tests pass.
Note: the smoke proves VSN's W/b actually move via the Mamba2 input
gradient — if `d_x_from_in` were zero, VSN params wouldn't update and
the chain still converges but VSN remains identity. The fact that
initial loss DIFFERS (0.30 vs 0.32) shows VSN is in the forward path
end-to-end.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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005ded9722 |
feat(ml-alpha): TGN Δt Fourier features in snap_feature_assemble (Phase 2C)
Bumps FEATURE_DIM 32→40. Slots [32..40] now carry 8 TGN-style Fourier
features encoding the elapsed time Δt = ts_ns - prev_ts_ns:
(cos(ω_k · Δt_ns), sin(ω_k · Δt_ns))_{k=0..3}
at log-spaced periods [60s, 6s, 600ms, 60ms].
This gives Mamba2's input vector explicit Δt encoding that's
discriminative across temporal scales — particularly important once
decision-stride > 1 (Phase 2A) lands and the gap between K-positions
becomes irregular. Without these features the model has no way to
distinguish "1ms gap" from "1s gap" between consecutive K-positions.
Slots [0..32] unchanged (bit-equivalent for the first 32 features).
Reserved-zero slots [26..32] kept for future macro context. Slots
[20..26] still hold the loader-precomputed EMA regime cascade.
Frequencies stored in __constant__ memory (SNAP_DT_OMEGAS[4]) — small
table, broadcast read pattern, no register pressure. Frequency
selection rationale (one per log-decade):
60s — minute-scale macro session context
6s — 10s-scale liquidity windows
600ms — sub-second microstructure
60ms — tick-cluster spacing
Δt clamped to >= 0 so the rare out-of-order timestamp doesn't produce
nonsense angles. Each (cos, sin) pair satisfies cos²+sin² = 1
(verified by new test `dt_fourier_features_are_bounded`).
New tests in snap_feature_bit_equiv.rs:
- dt_fourier_features_are_bounded: |slot| <= 1 + cos²+sin² == 1
- dt_fourier_discriminates_scales: Δt=1ms vs Δt=1s produce L2-distinct
Fourier vectors (>0.1)
- reserved_slots_are_zero updated to check [20..32] (regime + reserved)
instead of [20..FEATURE_DIM]
All 8 perception_overfit smokes still pass (synthetic stride=1 and
stride=4 both converge 0.32 → 0.0000) — proves the wider FEATURE_DIM=40
input doesn't break the Mamba2+LN+GRN chain.
build.rs cache-bust → v8.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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70d5fc29cf |
feat(ml-alpha): decision-stride loader + CLI + Mamba2 dt_s scaling (Phase 2A)
Decision-stride S lets a length-K sequence span ((K-1)*S + 1) raw snapshots instead of K consecutive ones — expands the effective time-window covered by each sequence at the same K-positions compute cost. With K=64 and S=4, the window covers 256 ticks (~5s on ES MBP-10 at 20ms-tick) instead of 64 ticks (~1.3s). Loader (crates/ml-alpha/src/data/loader.rs): - `MultiHorizonLoaderConfig.decision_stride: usize` (default 1, must pre-existing call sites add the new field). - `next_sequence` reads snapshot at `anchor + k * stride`; labels at the same indices (labels stay in absolute-snapshot horizons regardless of stride, e.g. h=6000 always means "predict 6000 raw snapshots forward"). - `prev` snapshot for microstructure features (prev_mid, prev_ts_ns) now points to the prior K-position (`anchor + (k-1)*stride`), NOT the consecutive-snapshot prior, so `Δt = ts_ns - prev_ts_ns` carries the actual elapsed time between K-positions (consumed by Mamba2's dt_s and the planned Phase 2C TGN Fourier features). - New `#[ignore]` real-data test: `loader_stride_4_yields_correct_spacing` asserts Δt monotonicity at stride=4. Mamba2 dt_s (crates/ml-alpha/src/trainer/perception.rs): - `PerceptionTrainerConfig.decision_stride: usize` plumbs the stride through. dispatch_train_step + evaluate_batched now use `dt_s = decision_stride as f32` so Mamba2's selective scan `exp(-dt * sigmoid(a))` reflects the real elapsed time. With stride=1 the behaviour is identical to before. CLI (crates/ml-alpha/examples/alpha_train.rs): - `--decision-stride <S>` flag (default 1) wired into both train and val loaders + PerceptionTrainerConfig. Argo workflow: - `decision-stride` parameter on the template (default "1") + `--decision-stride` script flag + propagation into the train pod's alpha_train invocation. Synthetic smoke (tests/perception_overfit.rs): - `stacked_trainer_loss_shrinks_with_stride_4` proves the trainer-level dt_s=4.0 keeps the Mamba2+LN+CfC+GRN chain numerically stable. Converges 0.32 → 0.0000 (matches stride=1 smoke trajectory — dt_s scaling didn't break the SSM dynamics). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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b6dfd89903 |
feat(ml-alpha): wire TFT GRN heads into PerceptionTrainer (Phase 1.7)
Replaces the single-linear heads (5 × 128) with per-horizon TFT GRN:
trunk h[B, 128]
→ W1 (HIDDEN→HEAD_MID) + GELU = eta_2 [B, 5, HEAD_MID]
→ W2 (HEAD_MID→HEAD_MID) = eta_1 [B, 5, HEAD_MID]
→ (W_gate, W_main) split = (gate_lin, main) [B, 5]
→ W_skip (HIDDEN→1) = skip [B, 5]
→ skip + sigma(gate_lin) * main = logit [B, 5]
→ sigma(logit) = p [B, 5]
10 trainable param tensors per horizon (5 weight + 5 bias), 10 AdamW
state objects (biases get wd=0), 6 per-K-position intermediate buffers
sized [K, B, 5, HEAD_MID] (z1, a1, z2) and [K, B, 5] (gate_logit, main,
logit). All saved during fwd, consumed by GRN bwd for the chain rule.
K-loop forward replaces `multi_horizon_heads_batched` with the new
`multi_horizon_heads_grn_fwd_batched` kernel. K-loop backward replaces
`multi_horizon_heads_backward_batched` with `multi_horizon_heads_grn_bwd_batched`.
ISV `lambda_d` is consumed by the GRN bwd kernel to scale ONLY the
trunk gradient (per pearl_adam_normalizes_loss_weights.md — the
effective lever is the trunk gradient, not loss weight).
Eval path also uses the GRN fwd kernel so eval sees the same
distribution downstream layers trained on (same pattern as Phase 1.6
LN wiring).
Xavier-uniform init: scale = sqrt(1 / fan_in) per tensor:
W1, W_skip: scale = sqrt(1/HIDDEN) ≈ 0.088
W2, W_gate, W_main: scale = sqrt(1/HEAD_MID) ≈ 0.125
Parameter count per horizon: W1 (8192) + b1 (64) + W2 (4096) + b2 (64)
+ W_gate (64) + b_gate (1) + W_main (64) + b_main (1) + W_skip (128)
+ b_skip (1) = 12,675 params × 5 horizons = 63,375 head params total
(vs single-linear's 5*(128+1)=645). Trunk grad dimensionality is
unchanged.
Synthetic overfit smoke: loss 0.32 → 0.0000 by step 50 (faster than
single-linear's 0.37 → 0.0006 in 250 steps); all 7 perception_overfit
tests PASS. Confirms the full Mamba2 + LN + CfC + GRN backward chain
is wired correctly and all 17 AdamW optimisers move weights.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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c24cf7423b |
feat(ml-alpha): wire LayerNorm into PerceptionTrainer (Phase 1.6)
LN sits between Mamba2 encoder output and the K-loop CfC consumer: Mamba2.h_enriched_seq [B,K,H] → LN fwd → ln_out_d [B,K,H] → transpose → h_enriched_seq_t_d [K,B,H] → CfC + heads Forward path: LN consumes raw Mamba2 output, writes ln_out_d + per-row stats (mean, inv_std). Transpose now reads from ln_out_d. Backward path: post-K-loop grad transpose produces grad_h_enriched_seq_d (now the LN OUTPUT grad). LN bwd consumes it + saved stats + Mamba2 fwd output (for normalised) + gain, writes: grad_ln_in_d (Mamba2's input gradient) per-row grad_gain/grad_bias scratches Two reducer launches collapse per-row scratches into [HIDDEN] param grads (block tree-reduce, no atomicAdd per feedback_no_atomicadd.md). AdamW state for ln_gain + ln_bias added (wd=0, lr=lr_cfc). Mamba2 bwd now consumes grad_ln_in_d, NOT grad_h_enriched_seq_d. Eval path mirrors training: LN fwd applied after Mamba2 fwd so eval sees the same distribution downstream layers trained on. Synthetic overfit smoke passes: BCE 0.37 → 0.0006 over 250 steps, confirming end-to-end Mamba2+LN+CfC+heads chain is wired correctly and all 9 AdamW optimisers (CfC×4 + heads×2 + LN×2 + Mamba2 grouped) move weights. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
|
00da163078 |
feat(ml-alpha): h6000-aligned ISV — uniform BCE + z-score lambda + K=64
Three correlated fixes addressing the architectural inconsistency
surfaced by the 3-fold ISV CV: we built a horizon-aware gradient
controller (ISV) but suppressed its target horizon (h6000) to 0.36%
of the loss via auto-horizon-weights, then used a ratio formula
that never approached its own clamp ceiling. ISV's lambda was
operating on rounding error.
(1) Uniform BCE weights as auto-default
trainer/perception.rs: `auto_horizon_weights` now returns
[1.0; 5] regardless of seq_len. Prior schedule `min(1, K/h)`
gave h6000 weight 0.0053 at K=32 — combined with lambda ~1.04,
h6000's effective loss contribution was ~0.37%, indistinguishable
from zero. With uniform weights, each horizon contributes 20% and
ISV's lambda actually has something to scale.
(2) Z-score lambda derivation
cuda/horizon_lambda.cu: replace `ratio = ema_h / mean(ema)` with
`z_h = (ema_h - mean) / std(ema); lambda = clamp(1.0 + 0.5*z, 1.0, 2.0)`.
Per `pearl_zscore_normalization_for_magnitude_asymmetric_signals.md`
z-score makes lambda spread scale-invariant of the absolute EMA
level. The ratio formula gave lambdas ≤ 1.04 in our data because
per-horizon BCE clusters tightly (range ~0.04) while mean is
~0.65. Z-score fills the [1.0, 2.0] envelope: 1σ → 1.5, 2σ →
ceiling. Boost-only asymmetric clamp preserved.
Test verification on the existing smoke (after 5 steps):
ema = [0.526, 0.522, 0.608, 0.641, 0.553]
lambda = [1.00, 1.00, 1.40, 1.76, 1.00]
Previously with ratio formula, max lambda on the same data
would have been ~1.05. h1000 (1.5σ above mean BCE here) now
gets a 76% trunk-gradient boost vs uniform.
(3) Default --seq-len 32 → 64
examples/alpha_train.rs: K=32 gives the model 0.5% of the
h6000 prediction window as in-window context. K=64 doubles
that, giving Mamba2's SSM state more material to build
long-horizon predictions. Within the kernel's MAMBA2_KERNEL_SEQ_MAX
cap of 96. Per-epoch wall scales ~K (more K-loop launches in
the captured graph, ~2× wall at K=64 vs K=32 for the K-loop
portion of dispatch).
Cache-bust v5 in build.rs to force nvcc recompile against the new
horizon_lambda.cu formula on the cluster's /cargo-target PVC. Old
cubins compute a numerically different lambda; running them against
the new Rust loop would silently apply the wrong gradient scaler.
Validation: 7 perception_overfit tests + 26 lib + 23 integration
ml-alpha tests pass. Synthetic overfit still converges to 0.0006.
horizon_ema_and_lambda_track_after_training observes the new
lambda spread (1.0-1.76) and asserts the asymmetric clamp envelope.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
|
a45fd85986 |
feat(ml-alpha): raise Mamba2 state cap 16→32 + add auc_h6000 early-stop
Three correlated changes for the next CV round: 1. Mamba2 state_dim cap: 16 → 32 cuda/mamba2_alpha_kernel.cu: MAMBA2_ALPHA_MAX_STATE_D 16 → 32. Per-thread state register `float x[32]` (128 B/thread) and per-thread x_hist replay cache `float x_hist[K*32]` (up to 12 KiB/thread of local memory at K=96). L40S/H100 register file (256 KiB/SM) absorbs this without occupancy collapse for our block dims (32-128 threads). Update Rust-side MAMBA2_KERNEL_STATE_MAX + validation message + test name. Kernel header doc updated. 2. New early-stop option: auc_h6000 examples/alpha_train.rs: add the long-horizon AUC as a third early-stop metric. The ISV CV ( |
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jgrusewski
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5d42ab0e98 |
feat(ml-alpha): ISV horizon weighting Phase 3 — wire lambda into trunk grad
Connects the per-horizon lambda computed by horizon_ema_and_lambda (landed in |
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jgrusewski
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37c3a8f4d7 |
feat(ml-alpha): ISV-driven per-horizon EMA + lambda (Phase 1+2)
Foundation for replacing the static `--auto-horizon-weights` formula (`min(1, K/h)`) with a signal-driven per-horizon gradient scaler. Per `feedback_isv_for_adaptive_bounds.md`: adaptive bounds live in ISV, not hardcoded constants. Per `pearl_adam_normalizes_loss_weights.md`: Adam normalizes per-loss weight lifts (SP13 saw 13× aux_w produce only 0.6%/epoch divergence), so the effective lever is scaling the GRADIENT into the shared trunk, not the BCE coefficient. This commit sets up the EMA + lambda infrastructure; Phase 3 (wiring lambda into heads_bwd to actually scale the trunk gradient) is gated on the 3-fold CV results from |
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jgrusewski
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eb51c0f9cd |
feat(ml-alpha): walk-forward CV via file-list-driven loader
mhzs7 reported val mean_auc=0.726 on
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jgrusewski
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3a196382f0 |
fix(ml-alpha): captured graph poisoned eval via SyncOnDrop events
z2w9w cluster run hit CUDA_ERROR_INVALID_VALUE at "eval snap_batched
fwd" the first time validation ran after a captured training step.
Training itself succeeded (epoch 0 train_loss=0.69 over 250 captured
graph replays); only the subsequent direct eval kernel launch failed.
Root cause (vendor/cudarc/src/driver/safe/core.rs:920):
CudaSlice::device_ptr_mut() returns a `SyncOnDrop::Record` guard.
On drop, that guard UNCONDITIONALLY calls `event.record(stream)` on
the slice's `.read` event (the check at line 953 only gates the
cuStreamWaitEvent on .write — the unconditional event.record at the
end runs no matter what). Inside a stream-capture region, those
event.record(stream) calls turn the CudaEvents into "captured
events" per the CUDA Driver API. Captured events can ONLY be waited
on by streams in the same capture sequence; any later
cuStreamWaitEvent from outside fails with CUDA_ERROR_INVALID_VALUE.
The trainer's eval path then called `device_ptr_mut()` again to
stage the eval DtoDs — which inserted exactly that
cuStreamWaitEvent on the now-captured `.write` event of every
trainer CudaSlice. First kernel launch after the dtods failed.
Why this hit z2w9w now: a) all our work is on a SINGLE stream
(`self.stream`), so the event-based multi-stream sync that cudarc
inserts is pure overhead, b) the capture region is exactly where
those overhead events become poisonous.
Fix: disable cudarc's read/write event tracking BEFORE the trainer
allocates ANY device memory. With tracking off at alloc time,
CudaSlice::new returns `read: None, write: None` (core.rs:1283).
SyncOnDrop::record_event with `event: None` produces a `Record(None)`
that does nothing on drop. launch_builder skips its event waits/
records too. The capture region runs clean; the eval direct launches
have no stale captured events to wait on.
Validation:
- 6 perception_overfit tests pass, including 3 NEW regression tests
that pin the exact failure modes:
* evaluate_alone_succeeds — eval with no prior training
* evaluate_works_after_warmup_only — eval after 1 uncaptured step
* evaluate_works_after_capture_no_replay — eval right after capture
(the minimal repro that pinpointed `device_ptr_mut` inside capture)
* evaluate_works_after_captured_training_step — full warmup +
capture + replay + eval
- 26 ml-alpha lib + 23 ml-alpha integration + 306 ml-core lib all pass.
Also drops the now-redundant disable/enable_event_tracking dance
around the capture region — events are globally disabled for the
trainer's lifetime so no per-capture flipping needed.
Honors: feedback_no_quickfixes.md (root-cause traced through
cudarc's safe wrappers to the SyncOnDrop record contract, not a
symptom-suppress sleep/retry hack).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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87ca1f5f55 |
perf(ml-alpha): CUDA Graph capture of training step (#162 finale)
Captures the full Mamba2 fwd → K-loop fwd → BCE → K-loop bwd →
Mamba2 bwd → AdamW × 7 → loss DtoD chain into a single CUDA Graph.
First step_batched call runs uncaptured (cuBLAS warmup); second
call captures; third+ replays. Replaces ~155 individual kernel
launches per step with one graph launch.
Four root-cause issues had to be fixed in concert to make the
capture region capture-compatible:
1. cuBLAS lazy workspace allocation
crates/ml-alpha/src/mamba2_block.rs — pre-allocate an 8 MiB
workspace via cublasSetWorkspace_v2 at Mamba2Block::new.
cuBLAS would otherwise allocate on first gemm call with each
new shape, breaking capture.
2. cuBLAS heuristic plan-cache lookup
crates/ml-core/src/cuda_autograd/linear.rs — switch
gemm_ex_f32 from CUBLAS_GEMM_DEFAULT_TENSOR_OP to
CUBLAS_GEMM_DFALT. The heuristic algo path triggers
plan-cache allocs; DFALT is deterministic with negligible
perf delta for our shapes (Mamba2: 128×32, 128×state_dim).
3. Per-call cuModuleLoadData in bias kernels
crates/ml-core/src/cuda_autograd/linear.rs — add a
`BiasKernels` struct (add_bias_2d_kernel + reduce_sum_axis0
handles) cached at construction. `BiasKernels::shared(stream)`
uses a per-context OnceLock cache so the cubin loads exactly
once per CUDA context for the process lifetime. Every
`GpuLinear` and `OwnedGpuLinear` constructor now stores its
`BiasKernels`. Removed the per-call `get_bias_kernels`
helper entirely (no legacy aliases — greenfield).
4. Per-call CudaSlice::clone() in Mamba2 fwd + bwd
crates/ml-alpha/src/mamba2_block.rs — three sites cloned
the input slice to build a fresh `GpuTensor` view. Each
`CudaSlice::clone()` does cuMemAlloc + dtod copy
(vendor/cudarc/src/driver/safe/core.rs:1437); cuMemAlloc
is forbidden during capture.
Refactored `forward_with_slices_into` and
`backward_with_slices_into` to take raw `x_data: &CudaSlice<f32>,
batch: usize` instead of `&GpuTensor` / `&LinearActivations`.
All three Mamba2 fwd call sites + three bwd call sites now
pass the underlying CudaSlice directly.
Trainer changes (trainer/perception.rs):
- Three-state machine in step_batched: warmup → capture → replay.
- Labels staging fill moved BEFORE the captured region (host writes
only; replays read whatever the host last wrote).
- Removed the post-snap_batched sync that was inside dispatch (it
would trip STREAM_CAPTURE_ISOLATION; kernels are stream-ordered
so the sync was unnecessary).
- Dispatch extracted into `dispatch_train_step` method; the
captured region brackets exactly this method.
- Final sync + mapped-pinned loss read happens once per step in
step_batched, outside the captured region.
Validation:
- Synthetic overfit smoke: 0.397 → 0.0007 (matches pre-refactor
trajectory; capture+replay produces equivalent loss).
- 26 ml-alpha lib tests + 23 integration tests pass.
- 306 ml-core lib tests pass.
Also fixed (orthogonal but required for green workspace):
crates/ml-core/src/action_space.rs — tests assumed 7-exposure
layout (63 actions). Production `ExposureLevel` enum has 8
variants (Hold inserted at idx 3, Flat moved to idx 7 → 72
total actions). Updated tests + `get_valid_action_mask` to
match the 8-level layout.
Honors:
- feedback_no_legacy_aliases.md (no `add_bias_2d_with_fn` /
legacy `get_bias_kernels` fallback; one canonical API).
- feedback_no_partial_refactor.md (signature change propagated
to every caller atomically; no half-migrated state).
- feedback_wire_everything_up.md (BiasKernels wired into all
GpuLinear/OwnedGpuLinear constructors in same commit).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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ab94ce2a49 |
perf(ml-alpha): device-resident AdamW step counter (capture prep)
Stage 1+2 of #162 (CUDA Graph capture of training step). The AdamW kernels previously took the step counter as a host scalar arg, which gets baked into kernel args at CUDA Graph capture time — replays would freeze the counter and produce wrong bias-correction values. Both AdamW variants now read the step from a device pointer, advanced by a tiny 1-thread `increment_counter` kernel that goes inside the captured region. Each replay correctly increments and observes the new step value. Kernel changes: adamw_step.cu: - adamw_step: int step → const int* step_ptr - adamw_increment_counter: new, +=1 on step_ptr[0] mamba2_alpha_kernel.cu: - mamba2_alpha_adamw_step_devscale: int t → const int* step_ptr - mamba2_alpha_increment_step_counter: new Rust changes: trainer/optim.rs (AdamW): - host `step: i32` → device `step_count_d: CudaSlice<i32>` - step(): launch increment kernel BEFORE adamw kernel; both read device counter via pointer arg. - step_count(): test-only accessor, mapped-pinned readback (sync). mamba2_block.rs (Mamba2AdamW): - kept host `step_count: i32` for legacy paths (`step`, `step_from_buffers`) which aren't capture-compatible anyway (host grad-norm dtoh, host scalar grad_scale). - added device `step_count_d: CudaSlice<i32>` for the production gpu_clip path; advances via `kernel_increment_step` kernel inside the captured region. - adamw_apply_devscale: `t: i32` → `step_d: &CudaSlice<i32>`. Validation: - 4 adamw_invariants tests pass (step_count_increments specifically exercises the device counter). - 10 mamba2_block lib tests pass (training_loop_decreases_loss exercises legacy host-counter path). - Synthetic overfit smoke: initial=0.25 → final=0.0006 (matches pre-refactor trajectory bit-for-bit-equivalent). Stage 3+4 (capture brackets + first-call-capture / subsequent-replay state machine in step_batched) follows in the next commit. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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b6fb720acd |
perf(ml-alpha): eliminate all dtoh from training hot path
GPU-resident grad-norm + clip-scale; mapped-pinned loss readback.
Replaces 9× memcpy_dtoh per Mamba2 AdamW step (grad-norm host roundtrip)
+ 1× per-step download() (loss). Saves ~10 stream-sync barriers/step.
New kernels (cuda/grad_norm.cu):
- grad_norm_sq_phase1: per-block tree-reduce of x[i]^2 (no atomicAdd)
- grad_norm_sq_phase2: cross-tensor accumulator (sequential stream-ordered)
- grad_clip_scale: writes min(1, max_norm/sqrt(norm_sq)) to device ptr
- mamba2_alpha_adamw_step_devscale: reads grad_scale from device pointer
instead of host scalar, allowing AdamW kernels to launch async without
waiting for a CPU-side norm computation.
Trainer changes (perception.rs):
- loss_d kept device-side; mapped-pinned MappedF32Buffer shadow.
- Single stream.synchronize() at end of step (was 2: post-bwd + download).
- DtoD copy loss_d → loss_host_d queued, then sync flushes both kernels
+ copy in one barrier. Loss read via host_ptr (no dtoh).
Mamba2 AdamW (mamba2_block.rs):
- step_from_buffers_gpu_clip(): all grad-norm tensors processed via
phase1+phase2 chain, scale computed on-device, AdamW launches with
devscale variant. Zero host roundtrips.
- Pre-allocated block_partials_d, grad_norm_sq_d, grad_scale_d.
Optimizer (optim.rs): removed redundant stream.synchronize() per AdamW step.
Each per-tensor AdamW kernel is stream-ordered; sync only needed before
host reads, which the trainer handles centrally.
Synthetic overfit smoke: initial=0.30 → final=0.0006 (matches pre-refactor
trajectory). Full ml-alpha test suite passes (45 tests across lib +
integration).
Honors:
- feedback_no_htod_htoh_only_mapped_pinned.md (MappedF32Buffer only)
- feedback_no_atomicadd.md (block tree-reduce only)
- feedback_no_legacy_aliases.md (step_from_buffers replaced, not aliased)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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jgrusewski
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eb0e4b6328 |
perf(ml-alpha): full zero-alloc training step (#3 foundation)
Eliminates ALL per-step allocations from the training hot path — foundation for CUDA Graph capture (next commit). Before this commit, each step_batched call allocated: Mamba2 forward: input_2d view, x, a_proj, b_proj, h_s2, h_enriched_seq Mamba2 backward: d_a_per_channel/d_b_per_channel/d_w_c/d_h_s2 (#2 covered) d_a_proj_flat, d_b_proj_flat, dw_c LinearGrads.{dw,db,dx} × 3 projections (cuBLAS internal) d_x_from_a + d_x_from_b + d_x (elementwise add) dw_out, db_out (zero-init shells) Trainer wrapper: window_tensor, h_enriched_seq_t, grad_h_enriched_seq_t, grad_h_enriched_seq ~20-25 cudaMalloc / GpuTensor::zeros calls per step × 2000 steps/epoch = 40-50K allocations per epoch. This commit adds zero-alloc `_into` variants throughout the chain: ml-core/cuda_autograd/linear.rs: OwnedGpuLinear::forward_with_slices_into OwnedGpuLinear::backward_with_slices_into reduce_sum_axis0_into ml-core/cuda_autograd/elementwise.rs + gpu_tensor.rs: ElementwiseKernels::binary_into GpuTensor::add_into ml-alpha/mamba2_block.rs: Mamba2BlockForwardScratch (pre-allocated forward cache) Mamba2BackwardGradsBuffers (pre-allocated backward outputs) Mamba2Block::forward_train_seq_into (zero-alloc forward) Mamba2Block::backward_from_h_enriched_seq_full_into (zero-alloc backward) Mamba2AdamW::step_from_buffers (reads grads_buffers directly) ml-alpha/trainer/perception.rs: PerceptionTrainer pre-allocates: window_tensor_d, h_enriched_seq_t_d, grad_h_enriched_seq_t_d, grad_h_enriched_seq_d, mamba2_fwd_scratch, mamba2_grads_buffers step_batched + evaluate_batched fully wired through _into variants Original `forward_with_slices` / `backward_with_slices` / `binary` / `add` / `backward_from_h_enriched_seq` paths preserved unchanged — Phase E.3 ml/examples callers unaffected. The captured-graph commit (next) only needs to wrap this zero-alloc training step in cuGraph capture/replay; no further refactoring of buffer management. 77 ml-alpha tests pass. Synthetic overfit converges identically (0.29 → 0.0007 in 250 steps) — gradients are bit-identical to the allocating path. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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c70c5cdf21 |
perf(ml-alpha): fused batched snap_feature_assemble kernel (#4)
Previously the per-step snap_feature path did B*K = 768 single-snapshot kernel launches (at B=8, K=96) + 768 DtoD copies into the window tensor. New `snap_feature_assemble_batched` processes all B*K snapshots in a SINGLE launch and writes outputs directly into the window tensor's storage. Per-step CPU work: pack 12 mapped-pinned staging buffers (~150 KB total host writes), then 10 DtoD copies of the staging → device buffers. Per-step GPU work: 1 batched kernel launch with B*K threads (each writes 32 floats to its output row). Mapped-pinned staging buffers cover the full B*K capacity at trainer init — no per-step allocation. New `MappedI32Buffer` and `MappedI64Buffer` types parallel `MappedF32Buffer` to stage `trade_count` (i32) and `ts_ns` / `prev_ts_ns` (i64) without violating the no-htod rule (`feedback_no_htod_htoh_only_mapped_pinned.md`). Dead per-snapshot scratch + helpers (`bid_px_d`, `snap_feat_d`, `stg_bid_px`, `snap_fn`, `upload_into`, etc.) removed per `feedback_no_legacy_aliases.md` — the only callers were the per-snapshot path, gone. Expected per-step savings: ~5-10 ms launch + DtoD overhead at B=8, K=96. Over 2000 steps/epoch = 10-20 sec/epoch. 77 ml-alpha tests pass. Synthetic overfit unchanged. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |
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jgrusewski
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ebae67cb6b |
perf(ml-alpha): pre-allocate Mamba2 backward scratch (#2)
The four biggest per-call scratch buffers in Mamba2Block::backward_from_h_enriched_seq were allocated fresh on every training step: d_a_per_channel [N, sh2, K, state_d] ~6 MB at B=8, K=96 d_b_per_channel [N, sh2, K, state_d] ~6 MB d_w_c_per_sample [N, sh2, state_d] ~64 KB d_h_s2 [N, sh2] ~4 KB For 2000 optimizer steps/epoch × 15 epochs = 30 000 alloc_zeros calls per training run, all on the hot path. New `Mamba2BackwardScratch` struct holds these as device-resident buffers, constructed once per (n_batch, seq_len, hidden_dim, state_dim) at trainer init. New `backward_from_h_enriched_seq_into` method takes the scratch by &mut and reuses the buffers each call. The smaller per-call buffers (d_a_proj_flat, d_b_proj_flat, dw_c) still allocate per call — they feed into ownership-transferring LinearGrads outputs, where pre-allocation would require refactoring ml-core's cuBLAS wrappers without proportional gain. The original `backward_from_h_enriched_seq` is preserved (Phase E.3 callers still use it). Trainer switches to the `_into` variant. Expected per-step savings: ~10-20 ms on L40S (4 cudaMalloc latencies per call × 2.5-5 μs each + cache pressure reduction). Over 2000 steps/epoch that's 20-40 sec/epoch. 77 ml-alpha tests pass. Synthetic overfit unchanged (0.27 → 0.0006). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com> |