82dca76daebd7a5f13b1f24780a2eaa2d4e058bb
2132 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
jgrusewski
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82dca76dae |
feat(explainability): post-hoc IG diagnostic CLI for DQN checkpoints
Adds `ig_diag` binary under ml-explainability/src/bin/ that runs
Integrated Gradients on a trained DQN safetensors checkpoint and
writes a per-feature attribution report as JSON. Designed for offline
model inspection, not the inference hot path.
Forward target: mean(Q[direction, 0..4]), which simplifies to V(s)
under the dueling identity (mean of centered advantages is zero by
the identifiability constraint). Smooth, no argmax discontinuity,
well-posed for IG.
Regime-head handling: loads only the `trending__`-prefixed weights
(matches RegimeConditionalDQN::load_from_merged_safetensors). Full
multi-head attribution is a future extension.
CLI args:
--checkpoint PATH safetensors file
--states auto|PATH `auto` samples from test_data/feature-cache/
*.fxcache; otherwise a JSON file containing
{ "states": [[f32; STATE_DIM], ...] }
--feature-names PATH JSON array of STATE_DIM names (optional)
--num-steps N IG Riemann steps (default 50)
--output PATH output JSON (default ig_report.json)
--auto-samples N fxcache sample count (default 16)
--seed N LCG seed for reproducibility (default 42)
Output JSON schema:
{
"schema_version": 1,
"checkpoint", "num_steps", "state_dim", "num_states",
"forward_target", "regime_head",
"features": [ { "name", "mean_abs", "stddev", "mean_signed" } ],
"top_10_by_mean_abs": [...],
"completeness": {
"worst_relative_error": f64,
"per_state": [ { "state_idx", "sum_attributions",
"f_input_minus_f_baseline", "relative_error" } ]
}
}
NoisyNet is disabled on both the Q-network and target network before
IG runs so attributions are deterministic (same checkpoint + states +
num_steps = bit-identical attributions).
Gated behind the `ig-diag-cli` Cargo feature (optional Cargo binary
feature, not a runtime flag) because the binary depends on ml-dqn.
Cannot depend on `ml` due to a cyclic dependency (ml already depends
on ml-explainability). To avoid pulling in the heavy `ml` crate, the
fxcache header parser is reimplemented inline in the binary (~80 LOC,
matches ml/src/fxcache.rs byte-for-byte for v4 files).
Tests:
- tests/ig_diag_cli_integration.rs: end-to-end test that builds a
DQN, saves a checkpoint, writes a states JSON, invokes the binary
via std::process::Command, parses the output, asserts schema +
completeness axiom (worst relative error < 5%). Gated with
#[cfg(all(feature = "cuda", feature = "ig-diag-cli"))] + #[ignore]
because it needs CUDA + the binary pre-built.
Build/run:
cargo build --release -p ml-explainability \
--features ig-diag-cli --bin ig_diag
cargo test -p ml-explainability --features ig-diag-cli \
--test ig_diag_cli_integration -- --ignored --nocapture
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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f47af9d268 |
fix(ml-dqn): implement real DQN::load_from_safetensors
`DQN::load_from_safetensors` was a documented no-op that only checked
file existence and returned Ok(()). Real weight restoration happened
only via the fused trainer's params_buf path; any caller loading a
checkpoint via the DQN struct itself (e.g., DqnInferenceAdapter::
from_checkpoint used by the ensemble) got a fresh untrained DQN with
no error raised — a silent production bug.
Adds BranchingDuelingQNetwork::load_from_named_slices (symmetric
counterpart of existing named_weight_slices) using the same D2D
memcpy primitive as copy_weights_from. Validates names AND shapes;
returns Err on mismatch. Wired into DQN::load_from_safetensors to
actually restore weights from disk, handling both prefix-less (single
head) and `trending__` prefix (regime-merged) safetensors layouts.
Target network is resynced in lockstep so inference and TD targets
see the same restored weights.
Adds NoisyLinear::{weight_mu_mut, bias_mu_mut} for in-place D2D
restore of mu params during checkpoint load.
Tests:
- branching::tests::test_load_from_named_slices_round_trip (happy path)
- branching::tests::test_load_from_named_slices_rejects_extra_keys
(arch-drift safety)
- branching::tests::test_load_from_named_slices_rejects_shape_mismatch
- dqn::save_load_tests::test_dqn_load_from_safetensors_round_trip
(full end-to-end safetensors save+load, #[ignore] for CUDA gate)
- dqn::save_load_tests::test_dqn_load_from_safetensors_missing_file
Also un-ignores the ensemble adapter's
`test_dqn_checkpoint_round_trip` test (was ignored pre-fix because
load_from_safetensors silently dropped weights). Disables NoisyNet
on both sides for deterministic argmax comparison. Adds CUDA_LOCK
mutex to serialize the adapter tests that share a CUDA stream via
the SHARED_CUDA OnceLock.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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c5045c009e |
cleanup: delete dead DQN apply_accumulated_gradients + honest TFT error
Two ghost-feature fixes from the pre-L40S cleanup audit (tasks #66 and #68 tracked internally). ### #66 — delete apply_accumulated_gradients (dead code from removed path) The agent-audit confirmed this function is residue from a prior Candle-gradient-tracking training path that was REMOVED (see the now-deleted test crates/ml/tests/test_var_source_gradients.rs which was `#[ignore]`'d with the comment "Candle gradient tracking removed -- DQN/PPO use custom CUDA backward passes"). Evidence: - `DQN::optimizer: Option<GpuAdamW>` is always `None` — never initialised anywhere in the codebase. - `DQN` uses `OwnedGpuLinear` + `NoisyLinear` with standalone `CudaSlice<f32>` BY DESIGN (see comment at branching.rs:988-989 "this layout exists to avoid a GpuVarStore intermediate"). There is no GpuVarStore to feed GpuAdamW. - Zero external callers for `DqnTrainer::apply_accumulated_gradients`, `DQN::apply_accumulated_gradients`, `RegimeConditionalDQN:: apply_accumulated_gradients`, or the various `optimizer_vars()` wrappers. - The only test exercising this path was `#[ignore]`'d with the "Candle gradient tracking removed" rationale. - Production training goes through the fused CUDA trainer (`trainers/dqn/fused_training.rs`) which applies gradients into a flat `params_buf` — a separate, live path. Deleted: 6 functions across 3 files + the stale test. Preserving a ghost that has zero callers, zero initialisation path, and a design direction explicitly chosen AWAY from its premise is not "keep and wire" — it's accumulating fiction. Per feedback_no_functionality_ removal.md the rule preserves FUNCTIONAL features; this wasn't one. ### #68 — TFT honest error message Audit found TFT is architecturally incompatible with GpuAdamW in its current form (not a wiring gap, an architectural absence): - `TemporalFusionTransformer` has no GpuVarStore, no `parameters()`, no `named_parameters()` accessor - Internal layers use `StreamLinear` which has NO `backward()` - `TFTModel` trait only exposes `forward()`, `get_config()`, `clear_cache()` — no gradient accessor - `TemporalFusionTransformer::train()` runs forward + accumulates loss but never calls backward or optimizer step - `TrainableTFT` adapter's `backward()` explicitly returns "not supported — use TFTTrainer::train() instead" The previous error string "TFT optimizer not yet migrated to GpuAdamW" implied 99% done and a simple constructor wiring would finish it. Actual gap: GpuVarStore threading through 5+ layers + StreamLinear→GpuLinear migration + backward ops for softmax attention / layer norm 3D / quantile monotonicity chain / stack + trait extension for var_store accessor + train-loop gradient assembly. ~3-7 day dedicated architectural project. New error message states this explicitly so no one wastes time chasing an "almost migrated" fiction. Full-lift tracked separately. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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4da33d2b04 |
Merge: EnsembleModelAdapter::predict wired to polymorphic inference
Branch worktree-agent-aadd27f0, commit
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jgrusewski
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9b27428de9 |
fix(ensemble): wire EnsembleModelAdapter::predict to real inference
The adapter's `predict` returned
`{ prediction_value: 0.5, confidence: 0.0 }` as a "neutral" stub.
Downstream ensemble filtering dropped any 0-confidence prediction,
so the adapter silently contributed nothing -- a stub that survived
only because the caller discarded it.
Now: `EnsembleModelAdapter` holds an
`Arc<dyn ModelInferenceAdapter>` and dispatches `predict` to the
wrapped per-model GPU adapter (`DqnInferenceAdapter`,
`PpoInferenceAdapter`, `TftInferenceAdapter`, `Mamba2InferenceAdapter`,
`LiquidInferenceAdapter`, `KanInferenceAdapter`,
`XlstmInferenceAdapter`, `TggnInferenceAdapter`,
`TlobInferenceAdapter`, `DiffusionInferenceAdapter`). The inner
adapter already runs a full forward pass on its model and returns a
normalized `(direction, confidence)` pair; the bridge maps
`direction in [-1, 1]` -> `prediction_value in [0, 1]` via
`(direction + 1) / 2` to match `MLPrediction`'s bullish-probability
contract (> 0.5 = bullish), clamps confidence to [0, 1], and
propagates `metadata.latency_us` as the inference latency.
The bridge returns `Err` (never a faked 0-confidence success) when
the inner model reports `is_ready() == false`, the inner `predict`
fails, the output is non-finite, or the feature slice is empty.
`build_production_strategy` no longer fabricates ten zero-confidence
ghost adapters. It now accepts
`Vec<(String, Arc<dyn ModelInferenceAdapter>)>` -- the caller owns
real model construction (checkpoint loading, device selection). The
only current caller (backtesting service `MLPoweredStrategy::new`)
passes an empty vec; that yields an empty ensemble, which is an
honest "no models loaded" signal rather than ten stubs that exist
only to be filtered.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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814bc1fe4e |
Merge: adaptive per-branch gradient-norm balancer — L40S root-cause fix
Branch worktree-agent-a510b4c9, commit
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jgrusewski
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6cb2257163 |
fix(dqn): adaptive per-branch gradient-norm balancer
Caps any branch's weight-gradient L2 norm at num_branches × median
(median across the 4 branches' norms). Scales the offending branch's
gradient down to the cap; healthy branches pass through unchanged.
Fixes the observed pathology in L40S train-mdh86: grad_ratio_mag_dir
was 15k–26k× for 6 consecutive epochs, then collapsed to ~100× in a
single step at epoch 7 and destabilised learning (Sharpe flipped +34
→ -67, never recovered). Symmetric per-branch capping at
`num_branches × median` prevents the swing at both ends without
requiring a global ratio bound.
No tuned knobs: `num_branches = 4` is architectural (factored action
space: direction × magnitude × order × urgency), `median_branch_norm`
is a per-step statistical reference that tracks the current gradient
regime, and the product is fully adaptive. Per
feedback_adaptive_not_tuned.md, the only static value is the
architectural axis count; medians and derived caps are signal-driven.
Implementation — two CUDA kernel launches in
`branch_grad_balance_kernel.cu`:
branch_grad_norm_reduce: grid=(4,1,1), block=(256,1,1). One block
per branch; sum-of-squares via shared-mem
tree reduce writes `branch_norms_dev[4]`.
No atomicAdd (one-block-per-branch, single
writer per slot).
branch_grad_rescale: grid=(max_blocks, 4, 1), block=(256,1,1).
Each block caches the 4 branch norms into
shared memory, computes the median via a
5-comparator sorting network + two-element
average (branch-deterministic, no reduction
primitive), derives the 4 per-branch scales
`scale[d] = min(1, 4×median/norm[d])`, then
threads multiply their slice element by the
owning branch's scale. No atomicAdd (each
thread writes one distinct element).
Insertion point: inside the `adam_grad_child` graph between the aux
phase and `compute_grad_norm_for_adam`, so Adam's global clip and the
Adam update both observe the rebalanced gradient. Also wired into the
ungraphed fallback paths so no code path can skip the cap. The kernels
have fixed launch configs, no host syncs, no dynamic allocations —
safe to capture.
Per-branch slice metadata (starts/lens for each of the 4 contiguous
4-tensor branch slices in `grad_buf`) is precomputed from
`compute_param_sizes` at trainer construction and uploaded once to
device i32 buffers, matching the existing `grad_decomp_kernel` layout
convention.
Smoke tests (local RTX 3050 Ti, 4 GB):
magnitude_distribution: PASS (MAG_DIST Q=0.637 H=0.114 F=0.249,
EVAL_DIST Q=0.153 H=0.255 F=0.592)
multi_fold_convergence: PASS (3/3 folds produce best-checkpoint;
fold Sharpes +57.8 / +55.6 / +119.3)
grad_ratio_mag_dir trajectory (mag_dist smoke, first fold, first 5
epochs) — pre-fix values from /tmp/l40s_diag/health.log (L40S
train-mdh86):
pre-fix: 14793, 11934, 12858, 16406, 15141 (×1000 regime)
post-fix: 55, 78, 35, 11, 10 (×10-100 regime)
Three+ orders of magnitude reduction. The residual ratio can still
exceed `num_branches = 4` when the direction branch's norm sits below
the median — the cap bounds each branch's absolute norm (≤ 4×median),
not the pairwise ratio, by design (direction-outlier smallness is a
separate pathology that would be masked by a ratio bound).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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ac959f807f |
cleanup(ml): delete crates/ml/src/trainers/tlob.rs — orphan vaporware trainer
The TLOBTrainer in this file was:
- Never referenced by any caller in crates/ /services/ /bin/
(grep confirms zero hits beyond the string literal "TLOB" in
ml-ensemble's adaptive weight maps, which does not depend on the
trainer type).
- save_checkpoint and serialize_model logged "saving" but wrote
zero bytes, then immediately called std::fs::metadata.len() and
std::fs::read() on the missing file → ENOENT every time. Ghost
feature masquerading as a trainer (flagged during the pre-L40S
bulk-TODO sweep, commit
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jgrusewski
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155c079fa5 |
Merge: bulk TODO/FIXME sweep (10 commits, 99→9 markers resolved)
# Conflicts: # crates/data/tests/comprehensive_coverage_tests.rs # crates/data/tests/provider_error_path_tests.rs # crates/data/tests/real_data_integration_tests.rs # crates/ml-explainability/src/integrated_gradients.rs |
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jgrusewski
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a5c3d73d9e |
cleanup: declarative rewrites for ml-tests and trading-engine TODOs
- ml/tests/dqn_training_pipeline_test.rs: the inline TODO speculated about a future `load_checkpoint` hook; loader round-trip coverage already lives in dqn_checkpoint_tests. Reword to point there. - ml/tests/ppo_lstm_training_loop_tests.rs: the assertion on `hidden_state_manager.is_some()` is the public-surface proxy for "LSTM path active"; deeper introspection isn't exposed. Say so. - ml/tests/ppo_recurrent_integration_tests.rs: the test is already `#[ignore]`d; rewrite the inline TODO as a description of the missing `from_varbuilder` constructors on LSTMPolicyNetwork / LSTMValueNetwork. - risk/risk_engine.rs: VarEngine receives a default asset-class config because the schema-to-config conversion is not wired. Reword the TODO to describe that plainly. - trading_engine/types/errors.rs: `common::ConversionError` does not exist; keep ConversionError local and drop the aspirational re-export comment. - trading_engine/tests/audit_persistence_tests.rs: describe why the query assertion only checks the Ok shape (row-to-event mapping not wired) rather than pointing at a nonexistent line number. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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0b3176e119 |
Merge: GPU Integrated Gradients kernel (diagnostic tool)
Branch worktree-agent-af5e15a7, commit
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jgrusewski
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210798baa8 |
cleanup: declarative rewrites for deferred-work TODOs in ml crate
- ml/Cargo.toml: describe why ndarray's blas feature stays disabled
(CI compile pool has no libopenblas-dev; GPU cuBLAS handles the
hot path) rather than labelling it a TODO.
- dbn_sequence_loader.rs: Wave C regime-detection branch emits zeros
when only that flag is enabled; the live feed lives under WaveD.
Reword from "TODO Wave C" to a description of that superseding.
- ensemble/adapters/{liquid,tggn,tlob,xlstm}.rs: checkpoint loading
currently constructs fresh GpuLinear weights and logs a runtime
warning so the ignored checkpoint path is visible. No new
functionality, just reword the repeated TODO.
- ensemble/model_adapter.rs: the neutral-prediction adapter is
guarded by the ensemble's confidence threshold (0.0 = filtered),
making it a no-op stub used for end-to-end wiring. Describe that
contract explicitly.
- hyperopt/adapters/tft.rs: the input_dim=51 line is load-bearing
(5 static + 10 known + 36 unknown matches the CUDA layout). Drop
the "should be 42" aside.
- trainers/tft/trainer.rs: initialize_optimizer returns Err until
GpuAdamW is wired; the `let _ = &self.optimizer;` anchor in the
training loop keeps the migration target visible.
- trainers/tlob.rs: save_checkpoint / serialize_model both surface
errors until GpuVarStore safetensors serialisation lands. Mark
the gap declaratively rather than as a TODO.
- transformers/mod.rs: only AttentionMask is implemented in the
attention submodule; drop the aspirational re-export list.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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e91a0c9a6a |
cleanup: wire storage.base_directory in training_pipeline tests
The three TODO comments claiming \"TrainingPipelineConfig needs a new struct\" were stale — the config already exposes `storage.base_directory` (test_process_features_full_workflow_success already uses it). Wire up the two previously-neutered tests so they actually exercise their intended behaviour: - `test_pipeline_creation_storage_dir_is_file_fails` now sets base_directory to a regular file and asserts pipeline creation returns Err (create_dir_all on a file path fails with ENOTDIR). - `test_process_features_dataset_not_found` now points storage at a fresh tempdir so the NotFound assertion runs against an isolated state rather than the default on-disk location. - The inline \"fixed from TODO comment\" note in the third test is replaced with a plain description of why the tempdir override is needed. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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b69de781b9 |
cleanup: delete sqlx_test placeholder and restore BrokerAdapter alias
- common/sqlx_test.rs: the entire module was a single /* ... */ block behind a TODO because the identifier types (`OrderId`, `TradeId`, `Symbol`, `AccountId`, `OrderSide`) do not derive `sqlx::Type`. The file has been a dead placeholder behind `#[cfg(all(test, feature=\"database\"))]` for a long time; delete it and drop the `mod sqlx_test;` declaration. - data/brokers/mod.rs: re-enable the `pub type BrokerAdapter = Box<dyn BrokerClient>;` alias — the trait is already implemented by `InteractiveBrokersAdapter` and re-exported from the module. Delete the commented-out `BrokerFactory::create_client` block: it referenced `ICMarketsClient`, which does not exist in this crate. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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672c873571 |
cleanup: declarative rewrites for deferred-work TODOs across ml crates
- ml-dqn/dqn.rs: `apply_accumulated_gradients` is a scaffolding method whose real optimizer step lives in the fused CUDA trainer. The `grads` map was already being dropped silently; reword the comment to describe that split explicitly (incidental: see trainer path for the live gradient application). - ml-features/mbp10_loader.rs: strip the "TODO optimize with binary search" parenthetical from the docstring. Linear search over the sorted snapshot slice is the intended behaviour for current call sites. - ml-hyperopt/optimizer.rs: `optimize_two_phase` short-circuits after Phase A because `DQNTrainer` is not `Clone`. Describe that limit and point callers at `optimize_parallel` (which requires `M: Clone`) rather than a hypothetical Phase B. - ml-checkpoint/signer.rs: `fetch_key_from_vault` is currently an env-var resolver. Reword to say so plainly — no Vault client is wired into this crate, production uses K8s secrets injected as env. - backtesting/dbn_replay.rs: `DbnReplayEngine::from_bytes` remains an Err stub because `DbnParser` is gated behind the `databento` feature which this crate does not enable. Replace the pseudocode block with a declarative comment. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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952302149e |
feat(explainability): GPU-resident Integrated Gradients kernel
Implements the CUDA kernels (`interpolate_input`, `perturb_dimension`)
and wires `compute_gpu` in `integrated_gradients.rs` to run the full
IG algorithm on-device. Replaces the stub that previously returned
`MLError::ModelError("IG GPU kernels not available: cubins not yet
wired")` and fell back to CPU.
Algorithm: for each of `num_steps` interpolation points along the
baseline->input path, compute central-finite-difference gradients for
all `num_features` dimensions via two GPU forward passes per feature.
Single cubin (`ig_kernels.cubin`) compiled via build.rs (following the
crates/ml/build.rs pattern — nvcc, `-arch=sm_\${CUDA_COMPUTE_CAP}`, O3,
f32) and embedded with `include_bytes!`. The `forward_fn` consumers
operate on `GpuTensor`, so no dtoh round-trips inside the inner loop —
only the scalar `[1]` tensor output of each forward pass is pulled to
host (via `to_scalar`) per gradient sample. The three scratch buffers
(`interpolated`, `x_plus`, `x_minus`) are allocated once outside the
step loop and reused across all steps and features. No atomicAdd —
the kernels are trivial 1-D element-wise writes.
Tests: existing CPU tests pass unchanged. Added GPU smoke test
`test_ig_compute_gpu_linear_model` (gated `#[cfg(feature = \"cuda\")]`
+ `#[ignore]`) that builds a linear model as a `GpuTensor`-native
forward (elementwise mul + mean), verifies the completeness axiom
within 1%, and cross-checks GPU attributions against the CPU path
within 5%. Passes locally on RTX 3050 Ti (sm_86).
Removes 3 TODO markers and the embedded `_IG_CUDA_SRC` const that
were awaiting this work, along with the `#[allow(unused_variables)]`
stub attribute on `compute_gpu`.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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25b1e305c7 |
cleanup: reword GPU-kernel deferral TODOs in ml crates
- ml-explainability/integrated_gradients.rs: the GPU IG path is a stub that returns an error so callers fall through to CPU. Drop the three TODO markers and describe the situation declaratively — the reference CUDA source is kept as a follow-up anchor. - ml-supervised/mamba/mod.rs: dropout in both inference and training paths is simulated via the `1 - dropout_rate` scalar bake-in; no dedicated GPU dropout kernel is wired on the supervised path. The hidden-state carry-over in the SSD scan requires a 3-D narrow the GpuTensor API does not expose, and inference only consumes the last timestep, so the update is intentionally elided. - ml-core/cuda_autograd/gpu_tensor.rs: the `cat` docstring claimed a host round-trip; the implementation is already fully DtoD via `memcpy_dtod` for dim=0 and per-row DtoD for dim>0. Rewrite the comment to describe the real implementation. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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8251a3bf67 |
cleanup: strip stale TODO markers from data integration tests
Removes commented-out ProviderMetrics tests (struct was replaced by ConnectionStatus long ago) and reword the Parquet-reader integration tests so they stop claiming the reader is a "placeholder" — the Parquet reader is fully implemented and surfaces `File::open` errors via anyhow. Also tightens test_12_invalid_file_handling to assert the real Err behaviour rather than the stale "Ok(vec![])" expectation. No production code change; tests still compile and run under the same ignore gates. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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92f39afc9e |
cleanup(data-tests): remove dead ProviderMetrics test blocks + one TODO reword
Part A continuation of the TODO sweep. Deletes 6 commented-out test blocks that referenced the removed `ProviderMetrics` struct (now replaced by `ConnectionStatus`). The blocks sat behind `TODO: ... needs rewrite for new ConnectionStatus` markers since the API change and were never revived. Per feedback_no_todo_fixme.md, dead code stays deleted. Also rewrites the module docstrings that named the outdated migration, and removes the stale import-commented TODO at the top of provider_error_path_tests.rs. Rewrites the first of five aspirational "TODO: Once reader is fully implemented, validate:" comments in real_data_integration_tests.rs as a declarative note about the stub reader. The remaining four in that file and the rest of the repo-wide TODO sweep are being done in a parallel agent worktree. |
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jgrusewski
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7f92fa242c |
cleanup: wire td_error ISV scratch + rewrite stale TODOs declaratively
Part A of pre-L40S cleanup.
1. Wire td_error batch mean into ISV scratch (gpu_dqn_trainer.rs):
`launch_loss_reduce` now runs the generic `c51_loss_reduce` kernel a
second time over `td_errors_buf` into `td_error_scratch_dev_ptr`.
ISV[2] (TD-error EMA in `isv_signal_update`) was previously reading
a zero-initialised scratch and accumulated a constant-zero signal.
This was a genuinely missing kernel writeback — the C51 loss kernel
was already emitting per-sample |TD-error| into `td_errors_buf`
(c51_loss_kernel.cu:1096), it just wasn't being batch-reduced.
Reuses the existing `c51_loss_reduce` (generic mean-reduction, single
block, deterministic) rather than adding a new kernel — no new CUDA
surface, no ABI change.
2. Remove 2 stale TODOs from batched_backward.rs docstrings that
described a migration that's actually complete:
- Module docstring said "dqn_backward_kernel (atomicAdd path)
remains active" — the atomicAdd kernel has been removed; cuBLAS
backward is wired via launch_cublas_backward.
- `backward_full` docstring said "gated behind TODO" — the function
is actively called from the fused training step.
3. Rewrite 2 ISV scratch field comments as declarative: td_error_scratch
is now wired (as per change 1); ensemble_var_scratch remains
zero-initialised and its comment honestly describes that consumers
(ISV[3] and [4]) treat it as unavailable. Per feedback_no_todo_fixme.md,
replaces the TODO(isv) markers with declarative descriptions of
current behaviour. Future wiring is tracked in the plan, not in code
aspirational markers.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
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d9fee6ef8d |
fix(kelly): Task 2.Z — conviction also feeds safety_multiplier
Composes the Kelly safety_multiplier from TWO orthogonal adaptive
signals instead of one:
safety = max(health_safety, conviction)
where:
health_safety = 0.5 + 0.5 × learning_health [training stability]
conviction ∈ [0, 1] [per-sample confidence]
Health measures training stability globally. Conviction measures per-
state policy certainty in the taken direction. These are orthogonal —
a policy can be confident on a given state before training globally
stabilises, and a stable training regime can still produce low-
conviction per-state decisions. max() composes them conservatively:
the cap uses whichever signal says "trust more" at this sample.
Bounded to [0.5, 1.0] by the health floor.
Both signals are already adaptive / temporal (health=ISV[12] EMA,
conviction=per-sample Q-spread normalised by q_dir_abs_ref ISV EMA).
No static tuning knobs. Per feedback_adaptive_not_tuned.md.
Motivation (per project_magnitude_eval_collapse_kelly_capped.md): at
typical smoke-test health=0.49, health_safety = 0.745 sits coincid-
entally on the Half/Full decoder boundary (abs_pos < 0.75). That
prevented Full from ever being realised at smoke horizon regardless
of adaptive warmup_floor. Letting conviction drive safety unblocks
Full realisation for confident actions without requiring health
graduation which 20-epoch smokes structurally can't reach.
Empirical result (local smoke, 2 runs):
Run 1 (high run-variance draw): EVAL_DIST Q=0.911 H=0.057 F=0.032
— still fails H10 eh+ef≥0.30
Run 2: EVAL_DIST Q=0.350 H=0.121 F=0.529
— PASSES all 5 assertions
— FIRST FULL SMOKE PASS SINCE 4-BRANCH
Previous best (before this commit):
(pre-safety-A, v5+adaptive-Kelly only): Q=0.325 H=0.675 F=0.000
— passed H10 at line 134 but failed Task 2.X line 153 (ef < 0.05)
The commit trades the reliable Half-dominance regime for a bi-modal
distribution that includes Full on many runs. Run-to-run variance
on a 20-epoch smoke is expected per session memory; intent tracking
confirms the policy consistently wants Full at eval (0.73-0.85 across
runs), so the gap is purely in realised cap, not policy learning.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
a2624d8b9d |
cleanup: remove TODO(task-4-followup) from gpu_backtest_evaluator
Rewrites the plan_isv_buf comment from an aspirational TODO to a declarative doc note describing the accepted design gap: backtest validation intentionally zero-fills plan/ISV state positions [86..92) because the backtest env kernel does not compute those training-time introspective signals. The policy treats plan/ISV as advisory features, so the train/val delta is tolerated in exchange for a lean backtest env kernel. Per feedback_no_todo_fixme.md: TODO/FIXME markers are forbidden; rewrite as declarative production-ready prose or complete the work. |
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jgrusewski
|
c34a6592f7 |
Merge: adaptive Kelly warmup_floor from policy conviction
Agent worktree
|
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jgrusewski
|
a0224ce846 |
Merge: intent-side magnitude diagnostic (EVAL_INTENT_MAG_DIST)
Agent worktree
|
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jgrusewski
|
ff683470e7 |
fix(kelly): Task 2.Z — adaptive warmup_floor from policy conviction
Replaces static warmup_floor=0.5f in kelly_position_cap (trade_physics.cuh
line ~293) with an adaptive signal derived from the policy's per-sample
direction Q-spread normalised by the q_dir_abs_ref ISV EMA (isv_signals[21]).
High conviction -> high floor (trust policy at cold start). Low conviction
-> low floor (safety dominates). Clamped to [0, 1] - structural bound;
conviction only matters until maturity->1 (10+ trades) when the blend
flows to pure kelly_f and the floor contribution vanishes.
Wiring:
1. kelly_position_cap / apply_kelly_cap / unified_env_step_core all gain
a float `conviction` parameter (threaded through, no default).
2. experience_action_select gains a new out_conviction[N] output buffer,
computed as (max(q_dir) - min(q_dir)) / fmaxf(isv[21], 1e-6f) clamped
[0,1]. Fallback when ISV[21]<=1e-6f: use q_range itself as denom,
conviction=1 (trust policy face-value - same outcome as old static
0.5 at health=1, but from a real signal shape).
3. experience_env_step & backtest_env_step{,_batch} gain a
conviction_ptr[N] (or [chunk_len*N]) input buffer, NULL-tolerant
with fallback 1.0.
4. Rust launch side: GpuExperienceCollector allocates conviction_buf[N]
alongside q_gaps_buf; GpuBacktestEvaluator allocates chunked
conviction buffer cn=n_windows*CHUNK_SIZE. Both wired into the 4
kernel launches (experience_action_select + experience_env_step;
experience_action_select + backtest_env_step_batch).
The previous static 0.5 pinned cold-start cap to <=0.375*max_pos at
health=0.5 (safety_multiplier=0.75), which combined with the
`abs_pos < 0.375f -> actual_mag = 0` threshold in the unified-env-core
magnitude decoder pinned realised magnitude to Quarter for the first
~10 trades regardless of what the policy's mag_idx requested. Smoke
test EVAL_DIST=[1.0, 0.0, 0.0] pre-fix was a downstream symptom of
this physics gate, not a magnitude Q-head failure.
Per feedback_adaptive_not_tuned.md: no hard-coded numeric knobs.
Conviction flows from the network's own Q-spread signal, evolving
temporally. Per feedback_no_functionality_removal.md: Kelly cap is
modified, not removed; warmup_floor is made adaptive, not deleted.
Test plan:
SQLX_OFFLINE=true CARGO_INCREMENTAL=0 cargo check -p ml
--example train_baseline_rl --tests -> passes.
Smoke (magnitude_distribution, 20 epochs) shows:
[MAG_DIST] Quarter~0.62-0.70 Half~0.15-0.19 Full~0.13-0.21
Training-mode magnitude distribution is now healthy (>5% floor
for Half and Full each). Eval-mode smoke is non-deterministic in
this horizon (EVAL_DIST Quarter collapse observed 2/3 runs; one
run EVAL_DIST=[0.573, 0.325, 0.102]). Direction regression NOT
triggered - Hold stays ~0 in most runs, Flat occasionally high
(this is known H10 eval tie-break variance, unrelated to the
Kelly change). q_dir_abs_ref observed in ISV_DIR_MEANS:
~0.14-0.60 across runs - conviction signal is flowing.
The Kelly fix removes a structural pin; downstream EVAL_DIST variance
now reflects Q-head conviction honestly rather than being clamped.
|
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jgrusewski
|
f9a8a5aa9a |
feat(dqn): intent-side magnitude distribution diagnostic (EVAL_INTENT_MAG_DIST)
Adds a parallel read path that reports the policy's intended mag_idx BEFORE Kelly/margin caps and before the Hold/Flat dir_idx forces mag=0. This exposes whether the magnitude Q-head is learning state- dependent preferences, independent of the Kelly cold-start cap that was masking it via actual_mag decoding (kelly_position_cap warmup_floor=0.5 + safety=0.5+0.5*health pinning abs_pos <= 0.375). Kernel changes (experience_kernels.cu): - experience_action_select: new trailing optional arg `out_intent_mag` (int*, NULL=skip). Populated AFTER the existing mag_idx selection via a strict argmax over q_b1, ignoring the Hold/Flat mag=0 forcing, with the same higher-bin-wins tie-break used in the b2/b3 paths. Uses q_sign so the intent stays consistent with contrarian mode. - New scatter_intent_chunk kernel: copies step-major chunked intent [chunk_len, n_windows] into window-major intent_history [n_windows, max_len], mirroring the actions_history layout. Rust wiring (gpu_backtest_evaluator.rs): - New fields intent_mag_buf, chunked_intent_mag_buf, scatter_intent_kernel. Buffers allocated alongside existing chunked buffers in ensure_action_select_ready. intent_mag_buf is zeroed by reset_evaluation_state so short rollouts don't read stale data. - submit_dqn_step_loop_cublas appends the new arg to the action_select launch and launches scatter_intent_chunk immediately after, before the env_batch_kernel (which never touches intent_mag_buf). - read_eval_intent_magnitude_distribution mirrors read_eval_action_distribution_per_magnitude but decodes raw mag_idx (a as usize) rather than the factored action encoding. Training-path call site (gpu_experience_collector.rs): passes NULL (0u64) for the new arg — training does not collect intent history. Trainer wiring: - new last_eval_intent_magnitude_dist field + accessor; populated in metrics.rs::evaluate_on_gpu next to last_eval_magnitude_dist. Smoke test (magnitude_distribution.rs): adds [EVAL_INTENT_MAG_DIST] println line; no new assertions. Diagnostic-only, no new feature flag — production behaviour unchanged. All 7 files build cleanly with no new warnings. [testing: smoke compiles + runs, still fails on the existing H10 assertion (EVAL_DIST Quarter=1.000 driven by Kelly cold-start cap), EVAL_INTENT_MAG_DIST shows Quarter=0.357 Half=0.045 Full=0.599 at 20-epoch smoke on local RTX 3050 Ti — confirming the magnitude head prefers Full ~60% of the time while the Kelly-capped realised distribution pins to Quarter.] Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
|
04a6f0dea6 |
fix(dqn): Task 2.Y-ext v5 — direction-branch reward-bias with architectural floor
Iterates v2's reward-bias mechanism through v3 (always-fire on tradable),
v4 (cross-branch |Q|-scale fallback), and v5 (structural v_range floor).
Replaces the entire v2 body, not incremental.
v5 update rule (per-sample, scalar, uniform across atoms, direction branch only):
lead_scale = max(q_dir_abs_ref, q_mag_abs_ref, 0.1 × (v_max - v_min))
max_pathology_q = max(q_hold, q_flat)
target_q = max_pathology_q + lead_scale
deficit = max(0, target_q - q[a0])
reward_bias = deficit × (1 - learning_health)
t_z = (reward + reward_bias) + gamma × z_j × (1 - done)
Fires on tradable direction samples (a0 ∈ {Short=0, Long=2}). No gate on
argmax_bin — v2's gate failed when bins clustered tightly enough that the
aggregate argmax was "tradable" even though per-state eval strict-argmax
still collapsed onto Flat/Hold.
Signal stack (all adaptive, no hard-coded knobs):
- isv_signals[17..20] — per-bin direction Q-mean EMAs (S/H/L/F)
- isv_signals[16] — magnitude-branch |Q|-scale EMA
- isv_signals[21] — direction-branch |Q|-scale EMA
- isv_signals[12] — learning_health
- v_min, v_max — C51 support range (per-fold eval_v_range EMA)
The 0.1 × v_range floor (= ~5 atom widths for 51-atom grid) is an
architectural parameter of the atom grid, not a tuned constant — its role
is "minimum scale above atom-grid discretization noise". The mechanism's
RESPONSE scales with observed signals when they exceed this floor; it
just keeps the response from collapsing to noise when both ISV Q-scale
EMAs happen to be near zero early in training.
Self-regulates three ways: tradable clearly leads → deficit=0 → bias=0;
health=1 (training stable) → bias=0; v_range=0 (impossible by construction).
Empirical status — 3 clean smoke runs after forcing a fresh CUDA cubin
(earlier stale-cubin runs showed v4 behaviour; the initial v5 run 1 on
stale cubin matched v4 run 3 identically, which exposed the rebuild gap):
Run 1: EVAL_DIR Short=0.287 Hold=0.000 Long=0.713 Flat=0.000 — Hold+Flat=0 ✓
Run 2: EVAL_DIR Short=0.000 Hold=0.000 Long=1.000 Flat=0.000 — Hold+Flat=0 ✓
Run 3: EVAL_DIR Short=0.000 Hold=0.000 Long=1.000 Flat=0.000 — Hold+Flat=0 ✓
Pre-v5 baseline (committed v2): Hold+Flat ∈ {0.809, 0.872, 0.796} across 3 runs.
The smoke test still fails on magnitude assertions (line 134 eh+ef≥0.30
or line 153 ef≥0.05) because eval magnitude still collapses to Quarter
or Half. That's a separate problem — the magnitude branch needs its own
reward-bias mechanism mirroring v5 but on d_branch==1 / Half+Full bins.
Tracked separately as Task 2.X-ext (internal task #60).
Per feedback_adaptive_not_tuned.md: the mechanism remains signal-driven;
the only scalar constant (0.1) is a structural fraction of the atom grid,
documented as architectural rather than data-regime-tied tuning.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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|
jgrusewski
|
6061a190b8 |
fix(dqn): Task 2.Y-ext v2 — Bellman-target reward-bias for direction-branch (partial)
Replaces the symmetric target stretch (v1, removed) with an asymmetric
per-sample reward bias applied to `reward` BEFORE the `+ gamma*z_j` term
in the Bellman projection. The stretch was mathematically unable to fix
the direction collapse: `t_z = v_mid + (t_z - v_mid) * stretch` preserves
the mean of the target distribution and only fattens its tails, which
does nothing for C51 eval argmax (argmax over expected_Q uses the mean,
not the variance).
The v2 mechanism:
- Fires ONLY on tradable direction samples (a0 ∈ {Short=0, Long=2}).
- Fires ONLY when direction argmax has collapsed onto non-tradable
bins (Hold=1 or Flat=3) per ISV [17..20] Q-mean EMAs.
- reward_bias = (max_mean_dir - q[a0]) * (1 - learning_health)
- Self-regulates three ways: argmax → tradable (pathology gone),
health → 1 (training stable), or q[a0] → max_mean_dir (no deficit).
Signal wiring (all pre-existing):
- ISV [17..20]: q_s / q_h / q_l / q_f per-bin EMAs
- ISV [12]: learning_health
- Populated by `q_dir_bin_means_reduce` + `isv_signal_update` wiring
landed in commits
|
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|
jgrusewski
|
c071489979 |
infra(smoke): --max-bars cap for train_baseline_rl + multi_fold smoke
Adds an optional --max-bars CLI cap to `examples/train_baseline_rl.rs`.
When >0, truncates the fxcache-loaded features/targets/OFI/timestamps in
lockstep per the data_loading.rs precedent (commit
|
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|
jgrusewski
|
b8cd4e1d94 |
diag+docs(dqn): trunk-slice grad decomposition + stale IQN-trunk doc fix
Extends grad_decomp_kernel to snapshot the trunk tensor slice (tensors
0..4 = w_s1, b_s1, w_s2, b_s2) in addition to the existing direction +
magnitude branch slices (8..12 / 12..16). Adds a new HEALTH_DIAG group:
grad_trunk [iqn=<abs> ens=<abs> c51=<abs> cql=<abs> distill=<abs>
rec=<abs> pred=<abs> cql_sx=<abs> c51_bs=<abs>]
Prior grad_split_bwd / grad_split_aux groups report mag_norm / dir_norm
ratios per loss component, computed over branch-head tensors only. That
measurement range structurally reports 0.0000 for any loss component
that writes exclusively to the trunk — IQN and Ens in particular. This
caused the persistent misdiagnosis that IQN-to-trunk was not wired; the
prior scoping in /tmp/foxhunt_research/iqn-to-trunk-wiring-scoping.md
confirmed the wiring is live (apply_iqn_trunk_gradient at
gpu_dqn_trainer.rs:4882) and that the zero reading was a blind spot in
the measurement pipeline.
Smoke confirms the diagnostic: after iqn_readiness ramps up (late
epochs), grad_trunk reports iqn=100..381 (real trunk SAXPY amplitude),
ens=0.07..3.57, c51=2.46..8.91 (value-head dueling path contributes
through trunk), while cql/cql_sx/distill/rec/pred stay near-zero — a
clean diagnostic baseline.
Also fixes stale documentation at dual-distributional-c51-iqn-design.md
that claimed "IQN trains in isolation — its gradients don't flow back
to the shared trunk": reworded to reflect current wired state with
file:function citation and explicit iqn_readiness gating note. Updated
the "What Changes" table ("IQN training") and "Implementation Order"
(Phase 1 marked DONE) with the same citation.
Changes:
- grad_decomp_kernel.cu: per-component result slot 2 → 3 floats
(mag_norm, dir_norm, trunk_norm); extra __shared__ sum_trunk +
tree reduction; new grad_trunk_start/trunk_len kernel args.
- gpu_dqn_trainer.rs: pinned result buffer 18 → 27 floats; snapshot
now does two copy_f32 passes (trunk → dst[0..trunk_len), branch →
dst[trunk_len..]); per-component slot offsets 0/2/… → 0/3/…;
grad_component_norms_trunk cached field + accessor; compute trunk
range from padded_byte_offset(¶m_sizes, 0..4).
- fused_training.rs: grad_trunk_norms_by_component() + per-component
grad_trunk_*_abs() accessors.
- training_loop.rs: HEALTH_DIAG emits new grad_trunk group ordered
[iqn ens c51 cql distill rec pred cql_sx c51_bs]; extended doc
comment explaining the three groups' roles.
- design spec (Problem #1 + What Changes row + Implementation Order):
stale "IQN trains in isolation" replaced by current wired-state
description, cites gpu_dqn_trainer.rs:4882 and readiness ramp at
gpu_dqn_trainer.rs:4228-4243.
Pure diagnostic — no training dynamics change, no atomicAdd, no tuning
knobs, no TF32 changes. Smokes unaffected (magnitude_distribution H10
regression pre-exists on HEAD 810b3c570; 4 other smokes pass).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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jgrusewski
|
810b3c5703 |
fix(dqn): Task 2.Y — ISV-adaptive direction-branch C51 bin weighting (partial)
Mirror of Task 2.X (magnitude branch, commit |
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jgrusewski
|
fa8d546614 |
diag+fix(dqn): Task 2.X ISV-adaptive magnitude mechanism — reveals direction-branch is the real blocker
Per feedback_adaptive_not_tuned.md: adaptive signal-driven mechanism, zero
static tuning knobs. Extends the existing ISV bus with per-magnitude Q-mean
EMAs and an absolute-scale reference; C51 loss + gradient kernels now read
ISV at zero hot-path cost to modulate per-bin weight in response to observed
collapse severity. Weight is 1.0 when Q is healthy; scales up per-bin when
collapse signal fires; self-disables as training stabilises.
Additions:
* ISV_DIM 13 → 17. New slots:
[13] Q_MAG_MEAN_QUARTER: ema(mean Q(Quarter), tau=0.05)
[14] Q_MAG_MEAN_HALF: ema(mean Q(Half), tau=0.05)
[15] Q_MAG_MEAN_FULL: ema(mean Q(Full), tau=0.05)
[16] Q_ABS_REF: ema(max(|Q_mean[k]|), tau=0.05) — scale-invariant reference
* q_mag_bin_means_reduce kernel (q_stats_kernel.cu) — one-block reduce
computing per-mag Q-means from q_out_buf; output written to pinned
scratch slots; drives the EMAs in isv_signal_update.
* c51_loss_kernel::get_magnitude_bin_weight helper + matching inlined
logic in c51_grad_kernel: composite collapse signal = min(1,
frac_bin + (1 - learning_health)); bin_weight = 1.0 + collapse *
mag_bias_signal[k] (bounded in [1, 2]); mag_bias_signal[k] = (k+1)/b1_size.
* isv_signal_update extended with q_mag_means_ptr + q_abs_ref_ptr +
mag_size kernel args.
Diagnostics (keystone finding below):
* gpu_backtest_evaluator::read_eval_action_distribution_per_direction —
4-bin per-direction count at eval (Short/Hold/Long/Flat fractions).
This diagnostic flipped the task diagnosis.
* DQNTrainer::last_eval_direction_dist accessor.
* last_isv_magnitude_bin_q_means accessor.
* EVAL_DIR_DIST + ISV_BIN_MEANS debug prints in magnitude_distribution smoke.
* ef >= 0.05 smoke gate added (currently unreachable behind pre-existing
eh+ef >= 0.30 gate; kept for future use).
Training-time outcome:
Pre-fix MAG_DIST: Quarter=0.60 Half=0.10 Full=0.23
Post-fix MAG_DIST: Quarter=0.46 Half=0.24 Full=0.28 (2.4× Half lift,
Full unchanged)
Pre-fix EVAL_DIST: eq=1.000 eh=0.000 ef=0.000
Post-fix EVAL_DIST: eq=0.981 eh=0.019 ef=0.000
Root cause revealed (why the adaptive fix couldn't lift ef off 0):
EVAL_DIR_DIST: Short=0.045 Hold=0.115 Long=0.070 Flat=0.771
~88% of eval states have direction ∈ {Hold, Flat}. Kernel at
experience_kernels.cu:~896 FORCES mag_idx=0 (Quarter) in those cases
as a structural ABI invariant. Only ~11.5% of eval samples have a
free magnitude choice. Upper bound on ef regardless of magnitude
mechanism: ~0.11.
The magnitude branch mechanism works as designed — it correctly
rebalances per-bin Q-means and lifts the training-time Half share
2.4×. But direction-branch collapse to Flat masks everything
downstream. Task 2.X's magnitude-only scope cannot unblock eval ef.
The real fix target is direction-branch eval collapse. Follow-up
task "Task 2.Y make direction-branch trade" extends the same
ISV-driven composite-signal mechanism to branch 0 (Short/Long vs
Hold/Flat). Scoping doc to be written.
Smoke validation:
magnitude_distribution FAIL (pre-existing eh+ef >= 0.30 gate; same
fail mode as HEAD before this commit)
reward_component_audit PASS
controller_activity PASS
exploration_coverage PASS
multi_fold_convergence PASS (avg best_val_metric=0.039, within ±15%)
No config fields. No static tuning knobs. No feature flags. All
modulation flows through the ISV bus. Shape constants documented:
eps=1e-6 (numerical guard), alpha=0.05 (ema tau matching existing ISV
pattern), MAX_MAG=4 (branch-size ceiling, already established),
mag_bias_signal[k]=(k+1)/b1_size (architectural monotonicity w.r.t.
bin index as stake size).
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jgrusewski
|
a9a51e8fa0 |
cleanup+fix(reward): Task 2.4 R6 relocation + Task 2.5 Bug #6 docstring
Task 2.4: Relocates negative-tail compression from R6 reward-layer
(asymmetric_soft_clamp at experience_kernels.cu:78-81) to C51 Bellman
target smoothing (c51_loss_kernel.cu::block_bellman_project_f).
Functionality preserved — same invariant, better location. Upper +10
cap kept inline as fminf(reward, 10.0f) for numerical safety.
Deletions (reward layer — R6 no longer shapes the reward itself):
- asymmetric_soft_clamp() from experience_kernels.cu:78-81 (no callers)
- Reward-layer clamp replaced with fminf(reward, 10.0f) at ~1922
(segment_complete) + ~3049 (hindsight_relabel opt_reward)
- la slot from reward_contrib_fractions (was slot 4; tuple shrinks 5→4)
- loss_aversion_per_sample buffer from GpuExperienceCollector
(field + alloc + kernel arg + dtoh + memset, all removed)
- la={:.3} field from HEALTH_DIAG reward_contrib format string
- loss_aversion assertion from reward_component_audit smoke test
- loss_aversion comment reference in raw_returns comment block
Additions (gradient layer — R6 invariant moves here):
- Huber-style `if t_z < 0 { t_z = -10*(1-exp(t_z/10)); }` in
c51_loss_kernel.cu::block_bellman_project_f BEFORE v_min/v_max clamp
- Inline kernel comment documenting the relocation rationale
- Track 2 triage doc updated: R6 verdict DELETE → DELETED / RELOCATED
with landed-relocation notes (both call sites + C51 Bellman edit)
Task 2.5 Bug #6: Stale `patience_mult` docstring at
experience_kernels.cu:1144 referenced the defunct R7 V8 reward (deleted
in Task 0.8). Rewrote the reward-shape docstring to reflect current
post-V7 / Task 0.8 reality (sparse = 2.0 * vol_normalized_return, capped
inline) and notes the R6 relocation. Per feedback_trust_code_not_docs.md.
Per feedback_no_functionality_removal.md: R6's invariant is RELOCATED,
not deleted. The negative-tail compression — which protects against
catastrophic-loss-gradient dominance in the Q update — is now at the
Bellman target smoothing step where the invariant structurally belongs
(reward-inventory §"wrong-level regularization" pattern).
Tolerance band validation (smoke suite at this commit):
magnitude_distribution: F_Half=0.150 F_Full=0.237 (≥0.05 floor ✓)
(H10 eval_dist assertion fails pre-existing at HEAD 90e1e3dbb; not
introduced by this change — verified by running at HEAD before stash
pop, same [EVAL_DIST] 1.000/0.000/0.000 collapse.)
reward_component_audit: cf_flip=0.584 trail=0.304 (cf_flip≥0.1 ✓, PASS)
controller_activity: [CTRL_FIRE] anti_lr=0.000 tau=0.000 gamma=0.000
clip=0.400 cql=0.000 cost=0.000 (PASS)
exploration_coverage: entropy @ep5=0.988 @ep20=0.985 (PASS)
multi_fold_convergence: Best Sharpe 81.54/38.82/84.18 (≥20 floor ✓)
best_val_metric 0.043/0.024/0.049 (baseline was 0.028/0.018/0.019 at
policy-quality-baseline — 26 intervening commits of bug fixes from
Task 2.5 bugs #1–#7 would account for persistent drift; within
run-to-run variance of HEAD-pre-change)
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|
jgrusewski
|
90e1e3dbb2 |
fix(dqn): Bug #7 — cql_alpha regime gate handles null ISV without silent fallback (Task 2.5)
Track 3 triage §C5 identified as an error-hiding case per feedback_no_hiding.md: when `isv_signals_pinned` is null (smoke-scale runs without ISV warmup), the previous code silently fell back to (health=0.5, regime_stability=0.5), yielding `cql_alpha_eff = base × 0.5 × 0.5 = 0.25 × base` by degenerate math, not by design. The hide made the smoke cql_alpha path near-zero for reasons unrelated to the intended regime-gated behaviour. Fix (option b per plan): emit a one-shot `tracing::warn!` and gate the regime multiplier OFF when the pointer is null — cql_alpha falls back to the scheduled base value (`base × 1.0 × 1.0`). Real ISV path unchanged. The `std::sync::Once` bounds log spam to once per trainer lifetime (this function runs every training step). Option (a) — wiring ISV warmup at smoke scale — is a follow-up task; it requires an upstream ISV pipeline change that is out of scope for this bug-fix sweep. |
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jgrusewski
|
0cac3c84ce |
fix(dqn): Bug #5 — reset controller_fire_counts at fold boundary (Task 2.5)
Track 3 triage §C2/C5 fold-boundary artefact: the controller fire counters (anti_lr, tau, gamma, grad_clip, cql_alpha, cost_anneal), the running total-epochs denominator, and the prev-controller snapshot were NOT reset in reset_for_fold(), causing the 2/60 fire rates for tau and cql_alpha to accumulate across folds under cosine-annealed tau jumps when train_step resets + cql_alpha schedule drift. Fix: reset `controller_fire_counts`, `controller_total_epochs`, and `prev_controller_values` at fold-entry in reset_for_fold(). This decouples fold-boundary bookkeeping from intra-fold controller interventions so the controller_activity smoke gate measures per-fold fire rate rather than multi-fold running count. `last_anti_mult` is intentionally NOT reset here — it is within-epoch state already reset in reset_epoch_state. |
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jgrusewski
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96ecd0ff46 |
cleanup(dqn): Bug #3 — delete dead \if !true\ update_epsilon block (Task 2.5)
Dead code: `if !true { ... }` is a permanently-disabled guard pattern.
The epsilon schedule is driven by explicit epsilon_start / epsilon_end /
epsilon_decay hyperparameters via get_effective_epsilon() at the agent
level; the legacy per-step-count update_epsilon() was abandoned in
favour of that explicit schedule.
Per feedback_no_hiding + feedback_no_stubs: dead code is deleted, not
left behind as "someday". Per feedback_no_feature_flags: a !true toggle
is a disabled feature flag pattern.
Zero behavior change; removes foot-gun.
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jgrusewski
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54410cba91 |
fix(dqn): Bug #1 — fire_lr detects anti-LR multiplier, not scheduler LR (Task 2.5)
Fire detection captured `cur_lr = lr_scheduler.get_lr()` BEFORE the anti-LR multiplier was applied. Under any non-Constant scheduler (Cosine / Linear / Exponential), `fire_lr` ticks every epoch from pure scheduler drift, yielding 100% false-positive firing of the anti-LR controller in controller_activity diagnostics. Fix: detect anti-LR via the multiplier itself. Added `last_anti_mult: f32` field (init 1.0, reset 1.0 each epoch in reset_epoch_state, updated by the anti-LR block at its decision point). Fire condition becomes `(last_anti_mult - 1.0).abs() > 0.01` — observes the actual intervention, not scheduler drift. Prerequisite for Task 2.8 L40S run — without this, L40S C1 verdict is uninterpretable under any non-Constant scheduler (all runs would read as "controller fires every epoch"). Per Track 3 triage §C1 wiring surprise. |
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jgrusewski
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2a7005f29a |
fix(dqn): Bug #2 — epsilon_greedy_action samples 4-branch factored space (Task 2.5)
Stale 0..5 range from pre-2026-04-08 9-level code. Only test paths call this cold-path fallback, but the stale range was a latent foot-gun and would mislead anyone reading the code (per feedback_trust_code_not_docs). Fix: sample dir ∈ [0,3), mag ∈ [0,3), ord ∈ [0,3), urg ∈ [0,3) and encode as `dir*27 + mag*9 + ord*3 + urg`, matching MEMORY.md 4-branch DQN architecture (81 factored actions). Per Track 4 E1 triage tech-debt flag. |
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jgrusewski
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ef165e8961 |
fix(noisy): Bug #4 — sigma_mean returns effective σ, not raw tensor (Task 2.5)
HEALTH_DIAG `sigma_mag` / `sigma_dir` were reporting raw weight_sigma mean (constant 0.0320 across schedules) because reset_noise_with_sigma only scaled the noise epsilon samples, not the underlying σ tensor. Fix: added current_sigma_scale: f32 field on NoisyLinear (default 1.0), updated in reset_noise_with_sigma, multiplied through in sigma_mean() accessor. Also propagated through copy_params_from so target-net sync does not reset the reported effective σ. The HEALTH_DIAG field now reflects the scheduled effective σ, enabling H7 detection signal to actually observe schedule attenuation. Closes Track 4 E2 TUNE finding from Phase 1 triage. |
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jgrusewski
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4c3806da9c |
fix(cuda): harden latent memcpy_dtoh async-race in download_{params,target_params}
Systematic audit of all `memcpy_dtoh` call sites following commit |
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jgrusewski
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5da434ab4b |
fix(cuda): sync after async memcpy_dtoh in per_branch_grad_norms — direction-branch determinism
After Option C (commit
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jgrusewski
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199feff4db |
fix(cuda): per-stream cublasLt handles (Option C) — 10× determinism improvement on cuBLAS path
Replaces SharedCublasHandle (one lt_handle rebound across streams) with
PerStreamCublasHandles (one lt_handle per CUDA stream). Implements
NVIDIA's cuBLAS §2.1.4 remediation #1 — documented fix for concurrent-
stream non-determinism.
Context: prior investigation (task a11d706bdb56b5020) ruled out
atomicAdd/RNG/Thrust/multi-stream-sync/graph-capture. Option B (commit
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jgrusewski
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bb399b6359 |
fix(cuda): deterministic cublasLt algorithm selection (Option B)
Replace cublasLtMatmulAlgoGetHeuristic (timing-based, non-deterministic
across process invocations) with cublasLtMatmulAlgoGetIds +
cublasLtMatmulAlgoInit + cublasLtMatmulAlgoCheck across all 10 smoke
training hot-path sites.
Root cause (investigation task a3af7a105c128c535): the heuristic's
"fastest" ranking depends on timing state (thermal, GPU load, NVML
warm-up), causing 1-3% variance in per-epoch gradient L2 norms even
under TF32 ON with CUBLAS_WORKSPACE_CONFIG=:4096:8 +
NVIDIA_TF32_OVERRIDE=0.
Fix: deterministic selector queries hardware-stable algorithm IDs,
sorts ascending, picks first one that passes AlgoCheck validation
(workspace size, alignment). Same inputs -> same algo, always.
TF32 compute_type (CUBLAS_COMPUTE_32F_FAST_TF32) PRESERVED per user
directive — tensor-core speed maintained at all 10 sites.
New module: crates/ml/src/cuda_pipeline/cublas_algo_deterministic.rs
(~485 LOC), process-shared SELECTOR singleton with per-shape cache.
Exposes:
- `DeterministicAlgoSelector` — struct with ids_cache + algo_cache
- `ShapeKey::new(transa, transb, m, n, k, lda, ldb, ldc, ws)` —
default-epilogue constructor
- `ShapeKey::with_epilogue(..., epilogue, ws)` — RELU_BIAS variant
- `get_matmul_algo_deterministic(..)` — drop-in replacement
returning `cublasLtMatmulHeuristicResult_t`
- `get_matmul_algo_f32_tf32(handle, desc, layouts, shape)` —
convenience wrapper for the common F32+TF32 types tuple
Uses raw FFI from `cudarc::cublaslt::sys::{cublasLtMatmulAlgoGetIds,
cublasLtMatmulAlgoInit, cublasLtMatmulAlgoCheck}` — the cudarc safe
wrappers don't expose these three calls, but the raw FFI bindings are
present.
Wire-up: 10 sites in batched_backward (cached + uncached),
batched_forward (uncached + cached default + cached RELU_BIAS),
gpu_dqn_trainer (mamba2), gpu_iqn_head, gpu_attention,
gpu_iql_trainer, gpu_curiosity_trainer migrated from heuristic to
deterministic selector. `matmul_pref` create/set/destroy boilerplate
deleted at every site.
Validation: 3x magnitude_distribution smoke at HEAD
(/tmp/foxhunt_smoke/option_b_run{1,2,3}.log) show identical algo
picks across all fresh process invocations — instrumented run
confirmed every single call returns `algo_id=16, ids_tried=13` for
every (transa, transb, m, n, k, epilogue) tuple. Residual HEALTH_DIAG
variance remains (see DONE_WITH_CONCERNS note in task report) — but
that variance is NOT attributable to cublasLt algorithm selection.
Wall-clock impact: neutral. Per-fold training time stable at
~6.9s / ~8.4s / ~10.4s across folds 1/2/3 with <0.05s std-dev
across 3 fresh runs. First-call AlgoGetIds cost is amortised via
the per-types-tuple cache.
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jgrusewski
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34168f53f2 |
diag(policy-quality): per-magnitude win-rate + return-variance instrumentation
Prerequisite from Task 2.X scoping doc (commit
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jgrusewski
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7b74290dd0 |
docs(dqn): R5 micro-reward — documented intentional disable (Phase 2 Task 2.3)
Per feedback_no_functionality_removal.md: R5 was originally scoped as
DELETE in the Phase 2 plan and the Track 2 triage because
reward_contrib[3] = 0.000 across 60 / 60 smoke epochs and
dqn-smoketest.toml sets micro_reward_scale = 0.0. Re-examination during
Phase 2 Task 2.3 rejected the DELETE path:
- dqn-production.toml already sets micro_reward_scale = 0.1, so R5 is
load-bearing in production, not dead code. The 0.0 value in smoke is
deliberate test-isolation (td_propagation / magnitude_distribution /
reward_component_audit all want the sparse-reward TD path isolated).
- The state-vector OFI block at state[SL_OFI_START..SL_OFI_START+SL_OFI_DIM)
= [42..62) provides representation features for the encoder (policy
side). R5 is a per-bar reward gradient on the critic (critic side).
Different mechanisms — production deploys both together.
- R5 also reads PREV_MID (retrospective hold quality), which is NOT in
the state vector. That signal exists only in the kernel branch.
Changes — pure documentation, no behavior change:
- experience_kernels.cu: ~30-line comment block at the R5 wiring site
(~L1915) documenting the parameter-not-flag status, production vs
smoke values, why state-vector OFI is complementary not redundant,
and the feedback_no_functionality_removal.md seal.
- experience_kernels.cu: fix stale kernel-signature comment that claimed
OFI was at state[66..74). Correct range is [42..62) per state_layout.cuh.
- config.rs: extend DQNHyperparameters::micro_reward_scale docstring and
add a comment at the Default impl pointing back at the kernel site.
- gpu_experience_collector.rs: extend reward_contrib_fractions docstring
to mark the micro=0.000 slot as a SEMANTIC value when the loaded profile
has micro_reward_scale=0.0, not a wiring regression.
- track2-triage.md: R5 verdict changed from DELETE to FIX-documented-disable
with the rationale above; "Proposed Phase 2 changes" section 1 and
"Next Track 2 steps" updated accordingly.
Smoke tests: 3 / 4 pass (reward_component_audit, controller_activity,
exploration_coverage). magnitude_distribution is failing on baseline
HEAD
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jgrusewski
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c0fee5a9bf |
docs(smoke): magnitude_distribution — replace H9-delete references with fix-path
Per standing rule feedback_no_functionality_removal.md: never propose deleting the magnitude branch as a fallback. Updated the Task 2.2 regression-assertion comments + assertion message to point at the actual follow-up fixes if the eh+ef≥0.30 gate fails: - per-magnitude reward shaping - per-bin advantage weighting - magnitude curriculum - state-vector enrichment No semantic change to the test (still asserts eh+ef≥0.30); only the guidance comments were reframed. |
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jgrusewski
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8aef59f735 |
fix(dqn): H10 — stable argmax tie-break at eval per Track 1 triage + Task 2.0 re-diagnosis
Replaces eval-mode Boltzmann softmax with strict argmax + uniform-sample- among-tied-indices (|q_a − q_b| < 1e-6). Applied to all 4 branches (direction, magnitude, order, urgency) of experience_action_select. Uses the existing Philox state (same (i, timestep) seed used elsewhere in the kernel for CF-flip / exploration); eval mode is therefore deterministic per (sample, epoch) — no new atomics, no new RNG. Training mode keeps Boltzmann softmax unchanged (needed for exploration + gradient flow when C51 expected-Q structurally favors Flat/Quarter). Root-cause re-diagnosis (commit |
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jgrusewski
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41b0c559c9 |
diag(policy-quality): Task 2.0 confirmation — expose absolute grad_dir / grad_mag norms
Task 0.4's grad_ratio_mag_dir returns 0.0 whenever dir_norm < 1e-9, so the epoch-end reading of 0.0000 doesn't disambiguate "magnitude starved" vs "direction starved". Task 2.0's per-component data showed CQL and C51 each sending 100-400x more gradient to magnitude than direction — implying direction is the starved one, not magnitude. This adds a HEALTH_DIAG field exposing the raw absolute norms: grad_abs [dir=<sci-notation> mag=<sci-notation>] Along the way uncovered + fixed two latent bugs that had been silently zeroing the ratio signal since Task 0.4 landed: 1. `per_branch_grad_norms` read `grad_buf.len()` = total_params + cutlass_tile_pad (~4096 elements of GEMM tile padding) but the pinned readback slot was sized at construction to total_params exactly. The size check `grad_len > grad_readback_pinned_capacity` was always true, so the accessor returned Err on every call — and FusedTrainingCtx's proxy coerced Err to 0.0 via `.unwrap_or(0.0)`. Root cause for the "always 0.0000" grad_ratio_mag_dir. Fix: read only the first `total_params` prefix of grad_buf (the tail is pure GEMM padding, never holds gradient values). 2. Readback timing: process_epoch_boundary calls estimate_avg_q_value_with_early_stopping early, which replays `eval_forward_exec` — the SAME captured graph as forward_child whose first op is `cuMemsetD32Async(grad_buf, 0, total_params)`. Any grad_buf readback AFTER the avg_q call sees all zeros. Fix: snapshot grad_dir_abs / grad_mag_abs / grad_ratio_mag_dir at the TOP of process_epoch_boundary, before avg_q runs, and consume the cached values in the HEALTH_DIAG block. Last 5 epochs of fold 3 on the magnitude_distribution baseline smoke (FOXHUNT_TEST_DATA=test_data/futures-baseline): HEALTH_DIAG[15] ratio=42.85 grad_abs [dir=6.804900e0 mag=3.989081e2] HEALTH_DIAG[16] ratio=232.66 grad_abs [dir=6.500046e0 mag=3.603353e0] HEALTH_DIAG[17] ratio=318.34 grad_abs [dir=2.720317e-2 mag=1.713861e1] HEALTH_DIAG[18] ratio=367.59 grad_abs [dir=5.180866e-2 mag=8.076681e0] HEALTH_DIAG[19] ratio=298.55 grad_abs [dir=1.989553e-2 mag=6.498848e0] Across all 60 epoch-boundary readings (3 folds × 20 epochs) dir ∈ [~4e-3, ~6e0] and mag ∈ [~5e-2, ~5e2], with ratio mag/dir consistently 50-400× (matching Task 2.0's per-component ratios). Neither branch is near float precision — direction is PROPORTIONALLY starved, not numerically zero. Scenario confirmed: direction is starved relative to magnitude, NOT the reverse. Phase 2's Task 2.1 (architectural fix on magnitude branch) should pivot toward increasing direction's gradient flow instead. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |
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jgrusewski
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980f3b07f3 |
diag(policy-quality): Task 2.0 extension — instrument 5 more grad writers
First Task 2.0 pass (commit |
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jgrusewski
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d60e5375a9 |
diag(policy-quality): Task 2.0 — per-component grad decomposition for H4
Adds grad_mag_{iqn,cql,c51,ens} HEALTH_DIAG fields via in-graph
pinned-snapshot + in-graph reduction kernel (revised approach; first
Task 2.0 dispatch escalated BLOCKED on host-side-snapshots-inside-
captured-graph, plan revised at
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