Plan 4 Task 2c.3c.2. Additive only — no production callers (2c.3c.4
wires them).
Two infrastructure additions for the GRN trunk backward chain:
1. launch_dw_only_no_bias on CublasBackwardSet: variant of launch_dw_only
that skips the bias-grad kernel call. Linear_residual in h_s1 GRN
block has no bias, so calling launch_dw_only with db=0u64 would
segfault the bias-grad kernel.
2. saxpy_inplace on CublasGemmSet: y += alpha * x for element-wise
gradient accumulation. h_s2 GRN's identity residual needs
d_h_s1 += d_pre_ln_h_s2 after Linear_a_h_s2's backward overwrites
d_h_s1 with d_x = d_linear_a @ W_a. Implementation reuses the
existing dqn_saxpy_f32_kernel (already used by the experience
collector's IQR/ensemble-variance Q-bonus paths) — no new kernel,
no cuBLAS legacy-handle stream-binding work, kernel handle loaded
once at CublasGemmSet::new from DQN_UTILITY_CUBIN.
Both methods sit dead-code until 2c.3c.4's wire-up commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 4 Task 2c.3c.1. Additive only — no production callers (2c.3c.4
wires them). Mirrors 2c.3b's forward phase split for the same
composition reason: cuBLAS Linear_b backward must run BETWEEN GLU
backward and ELU backward.
New methods on GrnBlock:
- backward_raw_phase1: LN_bwd + LN_dgamma_dbeta_p1 + LN_dgamma_dbeta_p2
+ GLU_bwd. Produces d_linear_b_out for caller's Linear_b backward,
plus d_pre_LN (= d_residual = d_glu_out via implicit residual split).
- backward_raw_phase2: ELU_bwd. Takes d_elu_out from caller's Linear_b
backward, produces d_linear_a_out for caller's Linear_a backward.
The existing monolithic backward() is left as-is for surface-area
minimization; will be deleted with 2c.5 cleanup if no production
callers emerge.
No CUDA kernel changes. Pure Rust dispatcher split.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 4 Task 2c.3b. Forward-path runtime restored. Backward path stays
gated by 2c.3a panic markers until 2c.3c swaps it to GRN backward.
Changes:
- CublasGemmSet now owns four GrnBlock instances (online + target,
h_s1 + h_s2) and 14 GRN scratch buffers. Constructor allocates them
alongside the existing branch streams + workspaces.
- New `forward_raw_phase1` + `forward_raw_phase2` methods on GrnBlock
split the existing forward kernel sequence at the cuBLAS Linear_b
boundary so the encoder can dispatch
Linear_a → ELU → Linear_b → Linear_residual → GLU+LN in order.
No new GRN kernels.
- Swap encoder_forward_only to GRN forward composition: cuBLAS Linear_a
+ bias → forward_raw_phase1 (in-place ELU + DtoD save) → cuBLAS
Linear_b + bias → cuBLAS Linear_residual (h_s1 only; h_s2 routes
h_s1_ptr as identity residual) → forward_raw_phase2 (DtoD save +
GLU + residual+LN).
- New target_encoder_forward_only mirrors the online split using
dedicated grn_h_s1_target / grn_h_s2_target instances; called with
save_for_backward=false because the GRN backward only runs against
the online network (target is inference-only).
- Remove panic gates from encoder_forward_only, forward_target_raw,
forward_online_f32. forward_target_raw and forward_online_f32 now
delegate trunk encoding to the new (target_)encoder_forward_only.
- All forward methods on CublasGemmSet migrated from `&self` to
`&mut self`; trainer call sites refactored to direct
`self.cublas_forward.method()` so disjoint-borrow rules let
`self.launch_*` helpers execute alongside.
- Delete orphaned iqn_trunk_forward_kernel + IqnHead::trunk_forward_kernel
field + IqnKernels.trunk_fwd field + load("iqn_trunk_forward_kernel")
call. Replace the cuBLAS fallback in
gpu_iqn_head.rs::execute_training_pipeline with a hard error: IQN
now requires set_cached_target_h_s2 to be called with the buffer
written by BatchedForward::target_encoder_forward_only, so online
and target share ONE trunk implementation. Dead cuBLAS scaffolding
(trunk_l1_gemm/trunk_l2_gemm/trunk_h1_scratch/launch_trunk_bias_relu)
marked #[allow(dead_code)] to keep the diff compact.
- Audit doc updated with Task 2c.3b row.
Backward gates intentionally kept on backward_full,
apply_iqn_trunk_gradient, apply_ensemble_diversity_backward — they
panic loudly until 2c.3c swaps the relu_mask trunk backward to the
GRN backward chain.
Smoke (`cargo test … multi_fold_convergence --ignored`): forward path
runs cleanly through online + target encoder forwards (logs 4×
`GrnBlock initialised` confirming both online + DDQN sets allocate
online + target instances). Smoke then panics inside backward_full,
which is the retained 2c.3a gate. Smoke completion is therefore
deferred to 2c.3c per the spec's "KEEP backward gates" non-negotiable.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 4 Task 2c.3a. Build-clean prep commit. Lays down the GRN param-tensor
layout (86 -> 95 tensors) and migrates all 93 padded_byte_offset call
sites + ancillary index references in lockstep. Does NOT swap the trunk
forward — that's Task 2c.3b. Explicit panics at every trunk-forward and
trunk-backward caller ensure any accidental runtime execution fails
loudly rather than producing silent garbage.
Param-tensor reshuffle:
- Delete: W_S1, b_S1, W_S2, b_S2 (old trunk's 4 Linear tensors)
- Insert: 7 h_s1 GRN tensors (W_a, b_a, W_b, b_b, W_residual, gamma, beta)
- Insert: 6 h_s2 GRN tensors (no W_residual — SH1 == SH2 makes residual
identity)
- All tensor indices >= 4 in OLD layout shift +9 in NEW layout
- NUM_WEIGHT_TENSORS 86->95; FIRST_ISV_TENSOR 68->77
layout_fingerprint_seed() updated. New LAYOUT_FINGERPRINT_CURRENT:
0xcf3a24b0a1f70057 (was 0xa504d3c2f275b8af).
Xavier init paths added for the 13 new tensors:
- W matrices (Linear_a, Linear_b, Linear_residual): Xavier
- LayerNorm gamma_h_s1, gamma_h_s2: 1.0
- LayerNorm beta_h_s1, beta_h_s2: 0.0
- All biases (b_a_h_s1, b_b_h_s1, b_a_h_s2, b_b_h_s2): 0.0
Encoder gates: BatchedForward::encoder_forward_only,
BatchedForward::forward_target_raw, BatchedForward::forward_online_f32,
BatchedBackward::backward_full, GpuDqnTrainer::apply_iqn_trunk_gradient,
GpuDqnTrainer::apply_ensemble_diversity_backward all panic at function
entry. Original bodies preserved (with #[allow(unreachable_code,
unused_variables)]) for Task 2c.3b/c to swap GRN forward + backward
in place.
Spectral-norm descriptor (13 matrices): slots [0]/[1] re-mapped to GRN's
w_a_h_s1/w_a_h_s2 — shapes match legacy W_s1/W_s2 exactly, so the
existing spectral-norm constraint transfers cleanly to the GRN's first
Linear. Linear_b / Linear_residual not yet covered (Task 2c.3c).
Smoke intentionally NOT run. Build-clean is the validation. Task 2c.3b
will swap the encoder forward (gpu_grn::GrnBlock::forward); Task 2c.3c
will swap the trunk backward (gpu_grn::GrnBlock::backward) and run smoke.
Validation:
- cargo check --workspace: 11 baseline warnings (unchanged)
- cargo build -p ml --lib: all 58 cubins compile clean
- 93 padded_byte_offset call sites all migrated; spectral_norm
descriptor + branch_w_base + decoder_forward_only + value_head
indices all updated in lockstep per feedback_no_partial_refactor.md
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The 2c.3+4 dispatch agent surfaced two stale anchors in the
2026-04-25 h_s2 consumer audit:
1. Row 11 (IQN target trunk): the kernel referenced as
`iqn_compute_target_h_s2` in `iqn_dual_head_kernel.cu:1031` is
actually `iqn_trunk_forward_kernel`, and it is ALREADY ORPHANED
(loaded into IqnHead::trunk_forward_kernel but never invoked).
The real IQN target trunk runs through cuBLAS iqn_lt_matmul calls
in gpu_iqn_head.rs::execute_training_pipeline:820-846 (with a
cached fast-path at 803-847). Mitigation reduced to: delete dead
kernel + extract target_encoder_forward_only + replace cuBLAS
fallback path with that extraction.
2. Row 22 (relu_mask sites): line numbers drifted. Audit said
5581/5798; current code is 5655/5697/5872. Crucially, 5697 is
the h_s1 mask, not h_s2/IQN-aux — the audit's "3 sites" claim
needs verification per site before editing. Updated to the
current line numbers with a "verify before editing" note.
These are documentation-only corrections. The mitigation strategy
(GRN backward chain replaces relu_mask; target_encoder_forward_only
unifies trunk implementation) is unchanged in spirit.
Plan 4 Task 2c.2. Additive Rust wrapper around the kernels landed in
2c.1 (`grn_kernel.cu`, commit 68197c2c2). NO production callers in
this commit; dead code until Task 2c.3+4 wires it into the trunk
encoder.
GrnBlock struct holds:
- 5 save-for-backward state buffers per block (elu_post, linear_b_out,
sigmoid_b, ln_mean, ln_rstd, ln_normed) plus 2 partial-reduction
scratch buffers (dgamma_partials, dbeta_partials)
- 8 CudaFunction handles for the kernels in grn_kernel.cu
- has_residual_projection flag (true for h_s1 SD->SH1, false for
h_s2 SH1->SH2)
forward() sequences elu_inplace -> DtoD save post-ELU -> DtoD save
linear_b_out -> glu_forward -> residual+layernorm. cuBLAS GEMMs
(Linear_a, Linear_b, optional Linear_residual) called by caller
outside the wrapper.
backward() sequences LN backward dx (full Jacobian) -> LN backward
dgamma/dbeta two-phase (no atomicAdd) -> GLU backward -> ELU backward.
Residual split is pure pointer aliasing; cuBLAS Linear backward GEMMs
called by caller between steps 4 and 6.
ELU backward saves the POST-activation per the kernel's docstring
(grn_elu_backward recovers f'(x_pre) from x_post via the identity
exp(x_pre) = x_post + 1 for x_post < 0). The wrapper DtoD-copies the
post-ELU buffer into state.elu_post immediately after the in-place
ELU launch.
LN dgamma/dbeta phase-1 partial-reduction allocation is sized for the
maximum batch (num_blocks_b = ceil(B/256)); runtime backward verifies
the runtime batch fits.
Pattern matches gpu_tlob.rs and gpu_attention.rs for idiom
consistency. Module is dead code; production wiring + numerical
validation happens in 2c.3+4.
GRN_CUBIN ref added in gpu_dqn_trainer.rs alongside other Plan 4
kernel cubins. mod.rs declares pub mod gpu_grn. Wire-up audit doc
updated with Task 2c.2 entry + 1 new Orphan-by-design row (will
reclassify Wired at 2c.3+4 alongside the 2c.1 kernel entry).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 4 Task 2c.1. Additive module — kernels compile and load via the
existing kernel-loading infrastructure but have NO production callers
in this commit. Task 2c.3+4 wires them into the trunk encoder.
Eight kernels in grn_kernel.cu (Linear_a/Linear_b/Linear_residual are
cuBLAS GEMMs, not new kernels):
- grn_elu_inplace: element-wise ELU (canonical α=1.0)
- grn_glu_forward: GLU split + sigmoid (saves sigmoid for backward)
- grn_residual_layernorm_forward: residual add + LN, saves mean/rstd/normed
- grn_layernorm_backward_dx: FULL JACOBIAN (not the simplified
attn_layer_norm_bwd_dx which would silently propagate
approximation into trunk gradients)
- grn_layernorm_backward_dgamma_dbeta_p1: per-block partial reduction
(no atomicAdd per feedback_no_atomicadd.md)
- grn_layernorm_backward_dgamma_dbeta_p2: final reduce across blocks
- grn_glu_backward
- grn_elu_backward
LN backward formula (full Jacobian per pearl_cold_path):
d_out_g = d_out * gamma
s1 = sum_d(d_out_g)
s2 = sum_d(d_out_g * normed)
d_x = (1/H) * rstd * (H * d_out_g - s1 - normed * s2)
Layout convention: row-major [B, H] (sample-major, matches spec §4.E.2
and dt_layernorm_kernel) — intentionally different from
attention_kernel.cu's [D, B] col-major; the trunk encoder downstream
of GRN uses row-major buffers, so per-row LN avoids a transpose.
build.rs registers grn_kernel.cu (kernel count: 57 → 58). Verified
nvcc compiles cleanly via `cargo build -p ml --lib`. Module is dead
code until 2c.3+4. Wire-up audit updated with the additive entry per
Invariant 7.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
A first dispatch of monolithic Task 2c was refused with a second scope
assessment that surfaced three architectural facts beyond what 2a/2b
had captured:
(d) attn_layer_norm_bwd_dx is a SIMPLIFIED element-wise approximation
(d_x = d_out * gamma / std), not the full LN Jacobian. Reusing
it in the GRN backward would propagate the approximation into
trunk gradients silently. Task 2c.1 must write a new full
Jacobian: (1/D) * rstd * (D * d_out_g - sum_d(d_out_g) -
normed * sum_d(d_out_g * normed)).
(e) IQN target-trunk migration is not a clean swap. iqn_compute_
target_h_s2 reproduces the legacy LeakyReLU trunk independently
in CUDA. Deleting it requires a target_encoder_forward_only
extraction on the target path (Task 4-style refactor on the
target side). Sub-task in itself.
(f) Three relu_mask removal sites have three different save-buffer
lifetimes (online trunk per-step, IQN-aux per-step in different
trainer, target-sync per-target-update). Three plumbing tasks,
not one.
Decomposition:
- 2c.1: grn_kernel.cu with full LN Jacobian backward + GLU + ELU +
residual-add. Module compiles standalone (additive, dead code).
- 2c.2: gpu_grn.rs Rust wrapper with 5 save-for-backward state
buffers per block (not 3 as the prior plan suggested).
- 2c.3+4: ATOMIC commit — compute_param_sizes 86→95 + fingerprint
rewrite + xavier init for 13 new tensors + all 98 padded_byte_
offset migrations + 3 relu_mask sites with different save-buffer
plumbing each + iqn target trunk delete + target_encoder_forward_
only + mag_concat RMS-match adaptive ISV.
- 2c.5: docs flip.
No code changes. Plan-doc only.
Prerequisite for Plan 4 Task 2c (GRN ADOPT). The current
layout_fingerprint_seed() only covers ISV slot names + indices;
param-tensor layout shifts (which GRN insertion will cause) pass
silently through checkpoint load.
Extends the seed string to include all 86 param-tensor positions
by canonical name. Any structural reshuffle (insert/delete/rename
a tensor) now triggers a different fingerprint and fail-fast at
checkpoint load.
Pragmatic scope (Option A): tensor names + positions, not sizes.
Sizes depend on runtime config (shared_h1, shared_h2, etc.) and
can't be embedded in a const fn. Size mismatches between checkpoint
and current binary are caught by safetensors deserialization
separately. Task 2c's GRN insertion is structural (new tensor
names at new positions) — Option A suffices.
This commit IS a checkpoint break: every existing checkpoint's
fingerprint matches the old seed and fails load. Behavior change
zero; cold-start smoke passes at baseline Sharpe range
(fold-2 best Sharpe = 96.65).
New fingerprint value: 0xa504d3c2f275b8af.
Pearl-aligned: complete fingerprint coverage for what it claims
to protect. Partial coverage was worse than no coverage because
it implied safety where there wasn't.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Prerequisite for Plan 4 Task 2c (GRN ADOPT). Inventories every
trunk-h_s2 consumer and classifies whether each tolerates GRN's
zero-mean signed output or requires the legacy ReLU non-negativity.
Output drives Task 2c mitigation strategy: which consumers need
relu(h_s2) shims at consumption time, and which can take signed
input directly.
No code changes — research only.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
After a first dispatch attempt of monolithic Task 2 (GRN ADOPT) was
correctly refused with a thorough scope assessment, this revision
records the decomposition and the structural facts that drove it:
1. layout_fingerprint_seed() only fingerprints ISV slots, not
param-tensor layout. The "fingerprint will auto-update" assumption
in the original Task 2 spec was wrong for param-tensor reshuffles.
2. compute_param_sizes has 86 tensors (docstring saying 42 is stale).
Inserting GRN's 9 sub-tensors shifts 82 downstream tensors and
98 padded_byte_offset call sites.
3. At least 12 kernels consume h_s2 expecting ReLU non-negativity.
GRN's LayerNorm output is zero-mean (signed). Per-consumer
verification needed before swap.
4. crates/ml-supervised::tft::gated_residual is incompatible as a
port: different formula (sigmoid gate, not GLU split) and
incompatible tensor abstraction. New CUDA kernels from scratch.
Decomposition:
- Task 2a (research, no code): audit h_s2 consumers for ReLU vs
LayerNorm semantics. Output: per-consumer table.
- Task 2b (small, checkpoint break): extend fingerprint seed to
include param-tensor names + sizes. Pearl-aligned: complete
fingerprint coverage rather than partial.
- Task 2c (large, checkpoint break): GRN kernels + 98 call-site
migration + h_s2 consumer shims (per 2a). Blocked on 2a+2b.
Recommended order updated to thread 2a → 2b → 2c. Tasks 1, 6, 3
remain independent of 2c and can land in parallel where useful.
Pearl rules section added documenting the safety constraints
applied throughout the plan.
No code changes. Plan-doc only.
Per pearl_cold_path_no_exception_to_gpu_drives.md and explicit user
direction ("remove the legacy paths!"), the two Plan-1-era host-side
DtoH+CPU-loop helpers are GONE — not just routed around. Replaced
with GPU kernel reductions writing to ISV slots.
Deleted (160 lines):
- GpuDqnTrainer::per_branch_vsn_mean() — DtoH params slice + host abs+mean loop
- GpuDqnTrainer::per_branch_target_drift() — DtoH (target+online) slices + host RMS loop
- FusedTrainingCtx::per_branch_vsn_mean() and per_branch_target_drift() wrappers
Added (GPU-only producers, ~150 lines):
- target_drift_kernel.cu: 2-block reduction RMS(target − online) for mag/dir
branches. 256-thread smem tree-reduce per block; thread 0 EMA-updates
ISV slot via pinned device-mapped (no DtoH).
- ISV slots [92] TARGET_DRIFT_MAG_EMA_INDEX, [93] TARGET_DRIFT_DIR_EMA_INDEX
- Fingerprint shifted [90,91] → [94,95]; ISV_TOTAL_DIM 92 → 96
- Trainer accessors: branch_param_slice_indices(), target_params_buf_device_ptr()
- Collector launcher launch_target_drift_ema_inplace()
- Constructor cold-start writes for the 2 new slots
HEALTH_DIAG site refactor:
- vsn_mag/vsn_dir read from ISV[VSN_MAG_EMA_INDEX=87], ISV[88] (Task 5
GPU-driven kernel produced these, replacing the legacy VSN scalars)
- drift_mag/drift_dir read from ISV[TARGET_DRIFT_MAG_EMA_INDEX=92], ISV[93]
- All 4 reads via FusedTrainingCtx::read_isv_signal_at — pinned device-mapped
so host reads are coherent with GPU kernel writes without explicit DtoH
isv-slots.md: rows for [87..89] updated to reflect GPU-only producers
(replaces stale text claiming host-DtoH); 2 new rows for [92, 93];
fingerprint shifted to [94, 95]; ISV_TOTAL_DIM bumped 92 → 96.
Smoke fold-2 best Sharpe 100.45 at ep5; per-fold best_val_metric
3.75/10.09/20.21 (avg 11.35) — within Plan 3 T5 baseline (95-117 range).
Pearl validation: this commit demonstrates the cold-path-no-exception
rule applied retroactively. The legacy methods existed for an entire
plan generation; "cold path is fine" was the rationale. New rule:
if a value is computed (reduction/EMA/RMS), the compute is in a kernel,
period — regardless of frequency.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 4 Task 4. Pure Rust API refactor — no kernel changes, no parameter
changes, no checkpoint break, no new ISV slot.
New types:
- StateEncoder: wraps the trunk encoder portion (h_s1 + h_s2 GEMMs)
- ValueDecoder: per-branch decoder (4 instances: Dir, Mag, Ord, Urg)
- EncoderOutput: { h_s2_dev_ptr, h_s2_dim, batch_size }
- BranchDecoderOutput: Q-per-action + V_short + V_long (Plan 2 D.3
horizon-decomposed value already landed) + num_atoms + batch_size
BatchedForward gains 2 helper methods:
- encoder_forward_only(...) — extracted trunk encoder dispatch
- decoder_forward_only(branch_idx, ...) — single-stream per-branch
dispatch mirroring the loop body in forward_online_raw
forward_online_raw stays intact and callable; existing call sites
(training_loop, eval, experience collector) unchanged. The trunk
portion of forward_online_raw now routes through encoder_forward_only
internally — lossless extraction (same launch sequence, same
workspace usage). The new wrappers are ADDITIVE attachment points for
Plan 4 Task 1 (Full VSN, attaches pre-encoder), Task 3 (multi-Q IQN,
attaches at decoder), and Task 6 (auxiliary heads, attaches at decoder
peer). Migration of existing callers is deferred — the goal of this
task is establishing a clean type boundary, not migrating call sites.
Smoke (RTX 3050 Ti, 5 epochs, 3 folds, T5 baseline): fold 2 best
val Sharpe = 109.06 at epoch 3 (within RNG noise of T5 landing's
fold-2 best of 95.09; the refactor is behaviorally a no-op since
encoder_forward_only is invoked with byte-identical args).
cargo check --workspace: 11 warnings (matches baseline).
No checkpoint break. No new ISV slot. No new CUDA kernels. No call-site
migration in this commit.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 4 Task 5 Mode A. Light, ISV-only, no model parameters added,
no checkpoint break.
ISV tail-append:
- [87] VSN_MAG_EMA_INDEX
- [88] VSN_DIR_EMA_INDEX
- [89] MAMBA2_RETENTION_EMA_INDEX
- Fingerprint shifted [85,86] → [90,91]; ISV_TOTAL_DIM 87 → 92
Producer (attention_focus_ema_kernel.cu):
- Single kernel, 3-block multi-reduction, fully GPU-driven
- Block 0 reduces magnitude-branch VSN-weight slice of params buffer
- Block 1 reduces direction-branch VSN-weight slice
- Block 2 reduces mamba2_h_enriched buffer
- Each block: 256-thread smem tree-reduce → mean |x| → EMA-update
ISV slot via pinned device-mapped (no explicit DtoH)
- Adaptive α matches Plan 3 Task 3 convention
GPU-only correction (per user direction "no dtoh, pinned memory"):
First-pass agent implementation used host-DtoH + CPU loop for the
Mamba2 retention scalar (mirroring the pre-existing
per_branch_vsn_mean() pattern). User flagged this as a violation
of spec §4.C.6 GPU-drives-CPU-reads even on cold path. Rewrote
the kernel to do all 3 reductions on-device via smem block-reduce,
reading params/mamba2 buffers via raw_ptr() and writing to pinned
ISV slots directly. Removed mamba2_retention_mean() from
GpuDqnTrainer + FusedTrainingCtx (was the host-DtoH culprit).
Read-only AttentionMonitor mirrors PlanThresholdMonitor pattern.
StateResetRegistry: all 3 slots FoldReset.
Smoke fold-2 best Sharpe 95.09, avg best_val_metric 10.98 — within
Plan 3 baseline range. ISV slots populate non-zero from epoch 2.
Pearl: cold-path is not an exception to GPU-drives-CPU-reads. If
the producer is computing a value (not just reading a CPU-side
constant), the compute belongs on GPU regardless of frequency.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Comprehensive revision to match landed Plan 1+2+3 codebase state. Pattern
mirrors the Plan 3 second revision: per-task "Reality reconciliation"
blocks at the top of each task body identifying what's stale vs. landed,
with concrete file paths in the actual cuda_pipeline tree.
Added:
- Task dependency graph with recommended execution order:
5 (light ISV) → 4 (refactor) → 2 (GRN ADOPT) → 1 (Full VSN) →
6 (aux heads) → 3 (multi-Q IQN) → 7 (audit) → 8 (Argo)
- Per-task implementation surface with concrete trunk hooks:
- Task 1: pre-h_s1 VSN gate in batched_forward.rs::forward_online_raw
- Task 2: GRN audit confirms ADOPT branch (GRN absent from DQN trunk;
only in ml-supervised TFT). Replaces h_s1/h_s2 Linear blocks.
- Task 3: re-scoped — IQN already runs num_quantiles=32 with random τ.
Task is CONSTRAINING to fixed τ ∈ {.05, .25, .5, .75, .95}.
- Task 4: pure Rust API split (no kernel changes, no checkpoint break)
around existing forward_online_raw structure
- Task 5: split into Mode A (light, pre-Task-1, 3 ISV slots) and
Mode B (full, post-Task-1, 7 ISV slots). Mode A recommended first.
- Task 6: aux head loss scaled by ISV[LEARNING_HEALTH] per pearl
- Checkpoint-break consolidation note: Tasks 2/1/6/3 each break checkpoint
compatibility; land in sequence with no Argo run between (one fingerprint
shift per commit; final Argo at Task 8 amortizes retraining cost)
- Task 8 absorbs Plan 3's deferred Argo Tier 1 gate (combined validation)
No code changes.
Plan 4 was drafted 2026-04-24 against an earlier Plan 3 design. After
Plan 3 landed (commits 44539d8f4..3cb083f18), the pre-plan gate
referenced slot names that don't exist in the landed implementation:
- PLAN_PARAMS_0_EMA_INDEX → became READINESS_EMA_INDEX (Task 4)
- STATE_KL_THRESHOLD_EMA_INDEX → eliminated; Task 7 uses kernel-internal
trailing-EMA-of-self pattern (no separate threshold slot)
- TEMPORAL_REWARD_{PERSIST,REGIME_SHIFT,CONSISTENCY}_EMA_INDEX → consolidated
into rc[5] → ISV[68] REWARD_BONUS_EMA_INDEX via Plan 3 Task 6a/6b/6c
Updated:
- Pre-plan slot-existence check matches landed slot names
- Validation-doc gate softened: Plan 3 Argo Tier 1 PASS becomes OPTIONAL;
local 5-epoch multi-fold smoke is the de-facto gate (user choice
to proceed without burning Argo cycles)
- Status table at top shows landed Plan 3 baseline (ISV_TOTAL_DIM=87,
PS_STRIDE=43, fingerprint at [85,86]) and per-task difficulty estimate
- Reality-reconciliation note documents the rename trail
No task body changes; subsequent commits will revise individual task
specs as they become next-up for execution.
Plan 3 Task 4.
ISV tail-append:
- [75] READINESS_EMA_INDEX — batch-mean readiness EMA (GPU-written)
- [49] PLAN_THRESHOLD_INDEX — producer upgraded from static constructor
write to GPU kernel (same consumer path unchanged)
- Fingerprint shifted [73,74] → [76,77]; ISV_TOTAL_DIM 75 → 78
Producer (plan_threshold_update_kernel.cu):
- Single-block reduction of readiness_per_sample [N*L]
- Adaptive α = α_base × (1 + 0.5 × |clamp(sharpe, -2, 2)|); α_base=0.05
- Derived: threshold = max(0.1, 0.5 × readiness_ema) → ISV[49]
Consumer sites unchanged (experience_kernels.cu 4 sites + backtest_plan_kernel.cu).
The upgrade is producer-only; consumers keep reading ISV[49] as before but now
receive an adaptive value tracking the policy's actual readiness distribution
rather than a hardcoded 0.5 midpoint.
PlanThresholdMonitor (read-only observer) surfaces plan_threshold.eff +
plan_threshold.readiness_ema for HEALTH_DIAG / controller_activity smoke.
StateResetRegistry: PLAN_THRESHOLD flipped SchemaContract→FoldReset;
READINESS_EMA registered as FoldReset. Both fold-reset arms restore the
cold-start values (0.5 / 1.0) before the first kernel fire on the new fold.
No breaking changes to consumer API.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 3 Task 6b.
Portfolio-state tail-append:
- PS_REGIME_SHIFT_BAR = 40 (hold_time of first detected regime shift, 0 if none)
- PS_STRIDE 40 → 41
- All 6 hardcoded-stride sites migrated in lockstep
Detector (experience_kernels.cu):
- Adaptive threshold = clamp(0.25 × |clamp(sharpe, -2, 2)|, 0.05, 0.5)
- Fires first bar where |regime_now - PS_PLAN_ENTRY_REGIME| > threshold
- First-shift-only (short-circuits on non-zero PS_REGIME_SHIFT_BAR)
- Uses new ISV_SHARPE_EMA_IDX = 22 macro in state_layout.cuh
Consumer (segment_complete block):
- bars_late_frac = clamp(bars_late / hold_time, 0, 1)
- penalty = shaping × conviction × bars_late_frac × |reward|
- All multiplicands except |reward| in [0,1]; max penalty = |reward|
- reward -= penalty; rc[5] -= penalty (cancels with B.2/C.4/D.4a at
other (i,t) slots; ISV[68] REWARD_BONUS_EMA shows net)
- Consumer resets PS_REGIME_SHIFT_BAR after use
**Iteration history.** First pass multiplied by ISV[Q_DIR_ABS_REF] (~5–50,
an absolute Q-magnitude) AND |reward| — produced penalties 5–50× the
reward, destabilising training (smoke: Return swings ±300–900%, Sharpe
oscillating wildly). Root cause: Q_DIR_ABS_REF is an absolute
magnitude, not a [0,1] coefficient; B.1 uses it as a DENOMINATOR to
normalize q_range, not as a multiplier on an already-unbounded signal.
Fix: drop q_scale, keep |reward| as the only unbounded factor. Smoke
now passes cleanly with fold-2 best Sharpe 117.92 (up from T6a's 100.10).
No new ISV slot.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 3 Task 6a.
Portfolio-state tail-append (shared-contract migration, all in same commit):
- PS_INTRA_TRADE_MIN_PNL = 39 (symmetric to PS_INTRA_TRADE_MAX_PNL = 21)
- PS_STRIDE 39 -> 40
- 6 PORTFOLIO_STRIDE hardcoded copies bumped in lockstep
Producer (experience_kernels.cu):
- MIN_PNL tracked per bar (fminf against pnl_pct) in the same block
as MAX_PNL update
- Reset to 0 at all 5 MAX_PNL reset sites (entry, reverse, 2x fold boundary)
Consumer (experience_kernels.cu segment_complete):
- Fires only on reward > 0 AND drawdown_depth > 1e-6
- persist_bonus = shaping x conviction x |min_pnl| x tanh(reward/|min_pnl|)
- reward += persist_bonus; rc[5] += persist_bonus (accumulates with
B.2 entry bonus + C.4 timing bonus — different (i,t) slots per trade)
Self-scaling via tanh: no tuned coefficients. Saturates when recovery
is large relative to drawdown; near-zero when recovery is trivial.
Attribution lands in ISV[68] REWARD_BONUS_EMA via the Task 1 kernel.
No new ISV slot.
Smoke: multi_fold_convergence PASS (fold-2 best Sharpe 100.10, threshold >=80).
HEALTH_DIAG reward_split bonus=17.21 (post-Task-5 rises with new D.4a credit
firing on profitable drawdown recoveries).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Tasks 1, 2, 3, 5 landed on main between a59e7599c and a0abc3da3. The
remaining Task 4/6/7/8/9 bodies in the plan had accumulated drift:
- Task 4: allocated slot 49 (already PLAN_THRESHOLD_INDEX); EMA'd
plan_params[0] (kernel compares against readiness).
- Task 6: allocated 59/60/61 (now collides with Plan 2 Q-quantile
[50..58) and Task 1 reward EMAs [63..69)); required 6 new PS memo
fields and included tuned 0.5e-4f penalty.
- Task 7: allocated 58/63/64 (63/64 collide with REWARD_TRAIL_EMA /
REWARD_MICRO_EMA from Task 1); amplification formula had tuned
2.0× trigger, 0.02 decay, 1.0/2.0 endpoints.
- Task 8: referenced trainer helpers that don't exist
(sample_state_feature_pair, step_scripted, replay_insert_with_
priority_scale); tuned priority_scale=0.5.
- Task 9: used pre-pivot CPU-compute AdaptiveMonitor pattern with
tuned 0.9/0.1 EMA rates.
This revision:
- Adds per-task "Reality reconciliation" sub-header flagging the
stale premise being fixed.
- Marks landed tasks with ✅ LANDED <SHA> and records actual outcomes
(vs. the planned outcomes the original text described).
- Rewrites Task 4/6/7/8/9 bodies to use tail-append slot allocation
(indices recomputed from ISV_TOTAL_DIM at impl time), GPU kernel
producer + read-only AdaptiveMonitor consumer pattern, and
ISV-derived adaptive coefficients in place of tuned constants.
- Splits Task 6 into 6a/6b/6c with independent PS-slot additions
(MIN_PNL / REGIME_SHIFT_BAR / PRE_ENTRY_CONVICTION_EMAs).
- Enumerates concrete trainer-helper prereqs for Task 8.
- Updates Task 10 metric bands to match actual landed ISV slot names.
- Updates exit criteria summary to check off what landed.
No code changes.
Plan 3 Task 5.
Portfolio-state tail-append (shared-contract migration, all in same commit):
- PS_PEAK_PNL_BAR = 38 (hold_time snapshotted when MAX_PNL updates)
- PS_STRIDE 38 -> 39 in state_layout.cuh and ml-core/state_layout.rs
- PORTFOLIO_STRIDE 38 -> 39 in trade_stats_kernel.cu (hardcoded copy)
- PORTFOLIO_STRIDE 38 -> 39 in gpu_experience_collector.rs allocator
- ps_stride 38 -> 39 in gpu_dqn_trainer.rs launch_kelly_cap_update
Producer (experience_kernels.cu):
- Peak bar snapshotted alongside every MAX_PNL update (uses local
hold_time, not ps[PS_HOLD_TIME], because the portfolio-state commit
block runs later in the kernel).
- Peak bar reset to 0 at every MAX_PNL reset site: plan-entry (1856),
entering_trade (2014), reversing_trade (2019), fold hard-reset (2736),
trade-complete soft-reset (2751).
Consumer (experience_kernels.cu segment_complete block):
- bars_early = max(0, segment_hold_time - PS_PEAK_PNL_BAR)
- timing_bonus = shaping_scale x (bars_early / segment_hold_time)
x |final_pnl| x conviction_core
- reward += timing_bonus; rc[5] += timing_bonus
(accumulates with Task 3 B.2 entry bonus — different (i,t) slots).
No new ISV slot — rc[5] bonus semantics unchanged; B.2 and C.4 share it
via += accumulate semantics (defensively idempotent, but the two sites
fire at distinct (i,t) by construction: entry vs exit).
Self-scaling: shaping_scale x conviction_core x |pnl| keeps the bonus
proportional to trade magnitude, no tuned coefficients.
Smoke multi_fold_convergence (RTX 3050 Ti): all 3 folds complete,
fold-2 best Sharpe 84.44 at epoch 1 (expected ~85 range).
cargo check --workspace clean at 11 warnings baseline.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Current Flat opp-cost was conviction-driven but |Q|-scale was implicit.
When training drifts Q magnitudes into ±50, opp-cost becomes invisible
relative to action Q-values → Flat wins argmax. Fix: multiply by
isv_signals_ptr[21] (Q_DIR_ABS_REF_INDEX, EMA of max(|Q_mean|) across
direction bins, populated by update_eval_v_range / q_stats_kernel).
Self-scaling: opp-cost tracks |Q| proportionally throughout training.
No tuned multiplier; relies on existing ISV slot. Floor 1e-3 for cold-
start protection before EMA is warm. rc[4] is now written in the Flat
branch so reward_component_ema kernel picks it up into ISV[67].
No parallel paths — the old formula IS modified in place.
Plan 3 Task 2. Spec §4.B.1.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Aligns Plan 3 with post-Plan-2 reality:
1. AdaptiveController → AdaptiveMonitor throughout (spec §4.C.6
2026-04-24 revision). Tasks 4 (plan-threshold) and 9 (cql_alpha
seed-coupled) become GPU kernel + read-only CPU monitor pairs
instead of CPU-compute controllers — uniform with Plan 1's
tau/epsilon/gamma/kelly_cap and Plan 2's per-branch γ pattern.
2. Stale ISV slot indices [49..65) replaced with tail-append language.
Current post-Plan-2 ISV_TOTAL_DIM = 63; new slots start at 63 and
grow. Fingerprint at 61-62 auto-re-tails.
3. Architecture paragraph updated to document replay-seed orchestration
(Task 8 dqn_replay_seed.rs) as constructor-time cold-path pre-training
— not a GPU-drives violation.
4. Prerequisites section clarified: Plan 2 state — ISV_TOTAL_DIM 63,
TLOB at SL_TLOB_START, layout fingerprint auto-recomputes.
No scope changes to individual Tasks 1-10. Just terminology + slot
indexing aligned with current main state.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
User pearl (2026-04-24): the DQN already has every primitive TLOB needs
(gpu_attention, batched_forward/backward, GpuLinear, cuBLASLt handles,
cuda_autograd). Port TLOB's Q/K/V + attention as a composition of
existing primitives. Random-init, trainable end-to-end from the DQN's
reward signal. Uniform with Mamba2, IQL, atoms/γ/τ/ε — all of which
already follow this pattern.
Eliminates:
- ONNX Runtime dependency (was already dead — stripped from ml-supervised)
- Separate supervised pretraining pipeline
- "Freeze vs fine-tune" false dichotomy
- Pretrained-checkpoint-file-not-found failure mode
Prerequisites for the cuBLAS-native design (all satisfied):
- gpu_attention.rs exists
- GpuLinear trainable layer exists
- cuda_autograd over cuBLAS exists
- MBP-10 data already in the DQN data pipeline
What was "BLOCKED on prerequisites" in the Task 6C audit referred to
the OLD pretrained-ONNX design. The cuBLAS-native design has all
prerequisites satisfied — Task 6C can proceed under the new scope.
Pearl captured in memory: pearl_tlob_no_pretraining.md. Generalises to
any future attention/state-space/Neural ODE module: port to cuBLAS,
random init, let the DQN teach it.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Adds 7th plan_isv dimension: PLAN_ISV_REMAINING_FRACTION = max(0, min(1,
(plan_target_bars - hold_time) / plan_target_bars)) when plan active, else
0. Exposes temporal pressure to the policy.
State vector grows 104 → 112 (105 + 7 padding for 8-alignment).
SL_PORTFOLIO_PLAN_DIM 6 → 7. SL_PADDING_DIM 0 → 7.
Both training (experience_env_step) and val (backtest_plan_state_isv) write
the new slot identically — preserves train/val state-distribution parity.
All hardcoded stride-6 references in backtest_plan_kernel.cu replaced with
SL_PORTFOLIO_PLAN_DIM. plan_isv_buf allocation updated to n_windows * 7.
Stale offset comments ([86..92)) corrected to [98..105) across all files.
No behavioural change to existing dimensions. New signal is additive.
Plan 2 Task 6A. Spec §4.D.6.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The liquid_tau_rk4_step kernel modelled f(x) = (1/tau)*(1-x), which has
fixed point x=1.0 for any tau. liquid_mod_buf was initialised to [1.0;4]
and the dynamics could never move it away from that fixed point since every
kernel call reduces to f(1.0)=0 (no perturbation path exists). The
velocity_mod multiplier in c51_grad_kernel was therefore always 1.0 — a
mathematical identity with zero measurable effect on spread_scale.
Additionally, no ISV slot existed for liquid_mod (violates §4.C.6
GPU-drives spec), and the associated LiquidTrainableAdapter supervised
path was never wired into the DQN backward pass.
Decision C (delete): remove liquid_tau_rk4_step kernel (experience_kernels.cu),
liquid_mod param + velocity_mod line (c51_grad_kernel.cu), liquid_mod_buf
allocation, liquid_tau_rk4_kernel field, update_liquid_tau() method and its
call site in update_eval_v_range() (gpu_dqn_trainer.rs). per_branch_q_gap_ema_buf
is retained — it is used independently for trajectory backtracking state
snapshot/restore. Supervised LiquidTrainableAdapter unchanged.
cargo check -p ml: 8 warnings (baseline), 0 errors.
Audit docs updated: dqn-wire-up-audit.md + ml-supervised-to-dqn-concept-audit.md.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
SoftReset registry category (isv_grad_balance_targets, isv_grad_scale_limit)
implemented via bootstrap-write + existing kernel's EMA. CPU writes bootstrap
values (1.0 for targets, 2.0 for limit) at fold boundary; grad_balance_isv_update
kernel's adaptive-rate EMA (alpha in [0.01, 0.30]) converges from bootstrap
toward observed values over subsequent epochs (implicit decay_bars via EMA time
constant).
Option A chosen (minimal): the existing grad_balance_isv_update kernel already
uses adaptive-rate EMA — not a hard-write — so bootstrap-at-fold-boundary is
sufficient. The EMA time constant ~1/alpha provides the decay, satisfying the
decay_bars=500 intent without a new ISV slot (Option B rejected as over-
engineered: new ISV slot + kernel change for no material behavioral improvement).
Changes:
- training_loop.rs::reset_named_state: add dispatch arms for both SoftReset
entries, writing bootstrap constants (CPU-born input, not adaptive output;
complies with spec §4.C.6 GPU-drives-CPU-reads)
- trainer/mod.rs: fold-boundary reset loop now calls both fold_reset_entries()
and soft_reset_entries(); stale "handled separately (Plan 2)" comment removed
- smoke_tests/soft_reset.rs: 4 CPU-only tests verify registry classification,
entry count, mutual exclusivity from fold_reset, and bootstrap constant invariants
- docs/dqn-wire-up-audit.md: D.5 SoftReset dispatch row added
No CPU-side adaptive computation. No new ISV slot. No behavioral change to
training beyond writing known-good bootstrap values at fold boundaries.
Plan 2 Task 4. Spec §4.D.5.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Plan 1 A.5 audit found mamba2_scan_projected_bwd kernel + host call at
gpu_dqn_trainer.rs::mamba2_backward are ALREADY fully wired in the
adam_grad CUDA-graph child. Plan 2 Task 2 narrows from "implement" to
"validate".
Two smoke tests confirm correctness:
- mamba2_backward_gradients_propagate: grad Frobenius norm 0.25 after 3
epochs (>> 1e-8 threshold), ruling out silent no-op like compute_iqr
had pre-Task-A.6.
- mamba2_backward_grad_check: kernel-level reference check (B=2 K=2
SH2=4 STATE_D=4); max rel_err d_gate=2.6e-7, d_x_out=2.6e-7,
d_context=6.7e-8 — all well within 15% threshold (near machine
epsilon, confirming bit-identical host/GPU results).
No production code change — test-only accessors exposed via #[cfg(test)]
impl blocks on GpuDqnTrainer and DQNTrainer. Audit doc updated.
Plan 2 Task 2. Spec §4.D.1.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Revises Plan 2 to match the post-Plan-1 reality:
1. AdaptiveController → AdaptiveMonitor throughout (read-only observers
per spec §4.C.6 2026-04-24 revision; GPU kernels compute, CPU reads).
Applies to Task 3 (per-branch gamma) and Task 5 (liquid_mod audit).
2. Task 2 (D.1 Mamba2 backward) scope narrowed from "implement" to
"validate existing". Plan 1 A.5 audit confirmed
mamba2_scan_projected_bwd kernel + mamba2_backward host call are
already fully wired. Task 2 now adds a finite-difference grad-check
smoke + non-zero grad-propagation smoke; escalates if either fails.
3. Task 3 (D.2 per-branch gamma) rewritten for GPU-drives compliance:
- Replace scalar GAMMA_EFF_INDEX=43 with 4 per-branch slots at
43-46 (DIR/MAG/ORD/URG).
- New per_branch_gamma_update_kernel.cu reads v-range + health,
writes 4 slots. Deletes the Plan 1 gamma_update_kernel.cu.
- 4 read-only monitors (or one consolidated PerBranchGammaMonitor).
- ISV slots 44-48 shift downstream; fingerprint re-tails at 50-51;
ISV_TOTAL_DIM grows 49 → 52.
- c51_loss_kernel and iql_value_kernel read per-branch γ from ISV.
- No CPU-side γ computation anywhere.
4. Header architecture block updated: new ISV slot count target,
GPU-drives principle explicit, current Plan-1 layout table inline
for reference.
5. Pre-plan verification updated: expects AdaptiveMonitor trait (not
AdaptiveController); checks for 6 Plan-1 monitor files.
6. Removed "schema version bump 1 → 2" language — layout fingerprint
auto-recomputes from seed bytes; no integer version space exists.
Task 4 (D.5 soft fold transitions), Task 5 content (D.7 liquid audit),
Task 6 (D.3+D.6+D.8 coordinated state-layout migration), Task 7
(validation run) structure preserved. Internal slot numbers in those
tasks will reconcile to the new layout when they land (no pre-emptive
edits since those indices depend on whether Task 1 quantile slots or
Task 3 per-branch gamma slots land first — re-compute at exec time).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Local smoke-test run after Plan 1 C.6 completion surfaced three issues:
1. StateResetRegistry missing dispatch arms for the 8 ISV slots
pre-allocated in ac9bcab94 (isv_epoch_idx, isv_epsilon_eff, isv_tau_eff,
isv_gamma_eff, isv_kelly_cap_eff, + 3 SchemaContract which already no-op).
Fold boundary would error "unknown name 'isv_epoch_idx'". Added dispatch
arms in training_loop.rs::reset_named_state — reset each to 0.0; GPU
kernels repopulate on next epoch.
2. controller_activity smoke test's single 50% threshold was designed for
reactive CPU-compute controllers. Under GPU-drives-CPU-reads, tau is a
Polyak-EMA cosine schedule that fires every epoch by design (95%),
gamma is health-coupled monotonic (may fire every epoch as health
drifts). Split threshold per-controller: reactive (anti_lr, grad_clip,
cql_alpha, cost_anneal) = 0.50; schedule-based (tau, gamma) = 1.00.
3. examples/train_baseline_rl was broken since the f64→f32 ABI refactor
(d64adc14f) — hp_f64 returning f64 assigned to f32 fields. Added
hp_f32 helper that narrows JSON-born f64→f32 at ingest boundary. Use
hp_f32 for f32 fields, hp_f64 for f64 fields (learning_rate,
entropy_coefficient, weight_decay). No more "as f32" casts at call
sites. Also fixed replay_buffer_vram_fraction + bars_per_day f64→f32.
Local smoke tests now pass:
- controller_activity: ok (1 passed, 29.5s)
- multi_fold_convergence: ok (1 passed, 3 folds x 20 epochs, 534.9s)
- 24 new monitor + registry unit tests: all passing
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The test observed `mon.observe(0.0)` first but the monitor's initial
last_value is also 0.0, so the first observation didn't fire. Test
expected 2/3 but got 1/3.
Changed observe sequence to (1.0, 1.0, 1.5) mirroring grad_balancer's
correct pattern: first differs from initial 0.0 → fires, second matches
→ no fire, third differs → fires = 2/3.
No production code change.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
atoms_update_kernel.cu: 4-block kernel (grid=(4,1,1), block=(256,1,1)) replaces
the CPU loop that launched adaptive_atom_positions 4× per branch. Each block reads
ISV v-range slots [23..31) and spacing_raw params[52..56); computes softmax +
cumsum; writes atom_positions_buf[branch * num_atoms]. Same formula as the
existing adaptive_atom_positions kernel in experience_kernels.cu.
Deleted recompute_atom_positions and warm_start_atom_positions from GpuDqnTrainer
and their fused_training.rs delegates. step_atom_positions now calls
launch_atoms_update. training_loop.rs: recompute_atom_positions → launch_atoms_update;
warm_start_atom_positions + compute_reward_quantiles block removed.
AtomsMonitor reads ISV[V_CENTER_DIR_INDEX=23] as representative scalar;
diagnose() exposes all 8 v-range slots [23..31). state_reset_registry.rs
"follow-up task" descriptions updated for all 4 adaptive slots.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
kelly_cap_update_kernel.cu: single-thread cold-path kernel aggregates Kelly
win/loss stats across all n_envs from portfolio_states[n_envs, PS_STRIDE].
Computes mean half-Kelly × health-coupled safety multiplier (0.5+0.5*health)
and writes ISV[KELLY_CAP_EFF_INDEX=44]. Matching Bayesian priors from
trade_physics.cuh (prior_wins=2, prior_losses=2) prevent cold-start collapse.
GpuExperienceCollector::portfolio_states_dev_ptr() added — exposes device
pointer for the portfolio_states buffer without a GPU→CPU roundtrip.
GpuDqnTrainer::launch_kelly_cap_update takes ps_dev_ptr + n_envs.
FusedTrainingCtx delegates to trainer. training_loop.rs launches at epoch
boundary after gamma_update (requires both fused_ctx and gpu_experience_collector).
KellyCapMonitor reads ISV[44] for HEALTH_DIAG via AdaptiveMonitor trait.
state_reset_registry.rs description updated; audit docs updated (Invariant 7).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
