70d5fc29cf
Decision-stride S lets a length-K sequence span ((K-1)*S + 1) raw snapshots instead of K consecutive ones — expands the effective time-window covered by each sequence at the same K-positions compute cost. With K=64 and S=4, the window covers 256 ticks (~5s on ES MBP-10 at 20ms-tick) instead of 64 ticks (~1.3s). Loader (crates/ml-alpha/src/data/loader.rs): - `MultiHorizonLoaderConfig.decision_stride: usize` (default 1, must pre-existing call sites add the new field). - `next_sequence` reads snapshot at `anchor + k * stride`; labels at the same indices (labels stay in absolute-snapshot horizons regardless of stride, e.g. h=6000 always means "predict 6000 raw snapshots forward"). - `prev` snapshot for microstructure features (prev_mid, prev_ts_ns) now points to the prior K-position (`anchor + (k-1)*stride`), NOT the consecutive-snapshot prior, so `Δt = ts_ns - prev_ts_ns` carries the actual elapsed time between K-positions (consumed by Mamba2's dt_s and the planned Phase 2C TGN Fourier features). - New `#[ignore]` real-data test: `loader_stride_4_yields_correct_spacing` asserts Δt monotonicity at stride=4. Mamba2 dt_s (crates/ml-alpha/src/trainer/perception.rs): - `PerceptionTrainerConfig.decision_stride: usize` plumbs the stride through. dispatch_train_step + evaluate_batched now use `dt_s = decision_stride as f32` so Mamba2's selective scan `exp(-dt * sigmoid(a))` reflects the real elapsed time. With stride=1 the behaviour is identical to before. CLI (crates/ml-alpha/examples/alpha_train.rs): - `--decision-stride <S>` flag (default 1) wired into both train and val loaders + PerceptionTrainerConfig. Argo workflow: - `decision-stride` parameter on the template (default "1") + `--decision-stride` script flag + propagation into the train pod's alpha_train invocation. Synthetic smoke (tests/perception_overfit.rs): - `stacked_trainer_loss_shrinks_with_stride_4` proves the trainer-level dt_s=4.0 keeps the Mamba2+LN+CfC+GRN chain numerically stable. Converges 0.32 → 0.0000 (matches stride=1 smoke trajectory — dt_s scaling didn't break the SSM dynamics). Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
155 lines
5.3 KiB
Bash
Executable File
155 lines
5.3 KiB
Bash
Executable File
#!/usr/bin/env bash
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# Submit the alpha-perception workflow.
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#
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# Trains the stacked Mamba2 -> CfC -> heads perception model on MBP-10
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# from the training-data PVC. Emits alpha_train_summary.json with
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# per-horizon validation AUC to the feature-cache PVC.
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#
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# Defaults match the validated synthetic-overfit smoke config; cluster
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# runs can override for sweep work.
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#
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# Usage:
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# ./scripts/argo-alpha-perception.sh # current HEAD on ml-alpha-phase-a
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# ./scripts/argo-alpha-perception.sh --branch main
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# ./scripts/argo-alpha-perception.sh --epochs 1 --n-train-seqs 1000 # quick smoke
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# ./scripts/argo-alpha-perception.sh --watch
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set -euo pipefail
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SHA=HEAD
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BRANCH=$(git symbolic-ref --short HEAD 2>/dev/null || echo "ml-alpha-phase-a")
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GPU_POOL=ci-training-l40s
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EPOCHS=5
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SEQ_LEN=32
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MAMBA2_STATE_DIM=16
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LR_CFC=3e-3
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LR_MAMBA2=1e-3
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N_TRAIN_SEQS=8000
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N_VAL_SEQS=1000
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SEED=16962
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BATCH_SIZE=1
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AUTO_HORIZON_WEIGHTS=false
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EARLY_STOP_METRIC=mean_auc
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EARLY_STOP_PATIENCE=5
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CV_FOLD=0
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CV_N_FOLDS=1
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CV_TRAIN_WINDOW=0
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DECISION_STRIDE=1
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WATCH=false
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usage() {
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cat <<EOF
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Usage: $0 [OPTIONS]
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--sha <commit> Git SHA (default: HEAD on the current branch)
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--branch <branch> Git branch (default: $BRANCH)
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--gpu-pool <pool> GPU pool (default: $GPU_POOL)
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--epochs <n> Training epochs (default: $EPOCHS)
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--seq-len <n> Snapshots per sequence window (default: $SEQ_LEN)
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--mamba2-state-dim <n> Mamba2 SSM state dim (default: $MAMBA2_STATE_DIM)
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--lr-cfc <f> CfC learning rate (default: $LR_CFC)
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--lr-mamba2 <f> Mamba2 learning rate (default: $LR_MAMBA2)
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--n-train-seqs <n> Train sequences per epoch (default: $N_TRAIN_SEQS)
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--n-val-seqs <n> Val sequences per epoch (default: $N_VAL_SEQS)
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--seed <n> Random seed (default: $SEED)
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--cv-fold <k> CV fold index (default: $CV_FOLD)
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--cv-n-folds <N> Total CV folds (default: $CV_N_FOLDS)
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--cv-train-window <W> Files per train window (default: $CV_TRAIN_WINDOW = auto)
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--decision-stride <S> Snapshot stride for sequence sampling (default: $DECISION_STRIDE)
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--watch Follow logs via argo watch
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EOF
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}
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while [[ $# -gt 0 ]]; do
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case "$1" in
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--sha) SHA="$2"; shift 2 ;;
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--branch) BRANCH="$2"; shift 2 ;;
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--gpu-pool) GPU_POOL="$2"; shift 2 ;;
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--epochs) EPOCHS="$2"; shift 2 ;;
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--seq-len) SEQ_LEN="$2"; shift 2 ;;
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--mamba2-state-dim) MAMBA2_STATE_DIM="$2"; shift 2 ;;
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--lr-cfc) LR_CFC="$2"; shift 2 ;;
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--lr-mamba2) LR_MAMBA2="$2"; shift 2 ;;
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--n-train-seqs) N_TRAIN_SEQS="$2"; shift 2 ;;
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--n-val-seqs) N_VAL_SEQS="$2"; shift 2 ;;
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--seed) SEED="$2"; shift 2 ;;
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--batch-size) BATCH_SIZE="$2"; shift 2 ;;
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--auto-horizon-weights) AUTO_HORIZON_WEIGHTS=true; shift ;;
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--early-stop-metric) EARLY_STOP_METRIC="$2"; shift 2 ;;
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--early-stop-patience) EARLY_STOP_PATIENCE="$2"; shift 2 ;;
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--cv-fold) CV_FOLD="$2"; shift 2 ;;
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--cv-n-folds) CV_N_FOLDS="$2"; shift 2 ;;
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--cv-train-window) CV_TRAIN_WINDOW="$2"; shift 2 ;;
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--decision-stride) DECISION_STRIDE="$2"; shift 2 ;;
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--watch) WATCH=true; shift ;;
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-h|--help) usage; exit 0 ;;
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*) echo "Unknown option: $1"; usage; exit 1 ;;
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esac
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done
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case "$GPU_POOL" in
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ci-training-l40s)
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SM=89 ;;
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ci-training-h100)
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SM=90 ;;
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*)
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echo "Unknown gpu-pool: $GPU_POOL"
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exit 1 ;;
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esac
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# Resolve commit-sha=HEAD to an actual SHA locally before submission.
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# The in-cluster check-cache pod uses this SHA to look up the binary
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# cache without needing git inside the alpine pod.
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if [ "$SHA" = "HEAD" ]; then
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echo "Resolving HEAD for branch $BRANCH..."
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if ! git rev-parse --git-dir >/dev/null 2>&1; then
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echo "ERROR: not in a git repo; cannot resolve HEAD"
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exit 1
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fi
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git fetch --quiet origin "$BRANCH" 2>/dev/null || true
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SHA=$(git rev-parse "origin/$BRANCH" 2>/dev/null \
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|| git rev-parse "$BRANCH" 2>/dev/null \
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|| git rev-parse HEAD)
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echo " resolved: $SHA"
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fi
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echo "Submitting alpha-perception workflow..."
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echo " branch: $BRANCH"
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echo " sha: $SHA"
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echo " gpu-pool: $GPU_POOL (sm_$SM)"
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echo " epochs: $EPOCHS"
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echo " seq-len: $SEQ_LEN"
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echo " mamba2-state-dim: $MAMBA2_STATE_DIM"
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echo " lr-cfc: $LR_CFC"
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echo " lr-mamba2: $LR_MAMBA2"
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echo " n-train-seqs: $N_TRAIN_SEQS"
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echo " n-val-seqs: $N_VAL_SEQS"
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echo " seed: $SEED"
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WATCH_FLAG=""
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if [[ "$WATCH" == "true" ]]; then
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WATCH_FLAG="--watch"
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fi
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argo submit -n foxhunt --from=wftmpl/alpha-perception \
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-p commit-sha="$SHA" \
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-p git-branch="$BRANCH" \
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-p cuda-compute-cap="$SM" \
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-p gpu-pool="$GPU_POOL" \
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-p epochs="$EPOCHS" \
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-p seq-len="$SEQ_LEN" \
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-p mamba2-state-dim="$MAMBA2_STATE_DIM" \
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-p lr-cfc="$LR_CFC" \
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-p lr-mamba2="$LR_MAMBA2" \
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-p n-train-seqs="$N_TRAIN_SEQS" \
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-p n-val-seqs="$N_VAL_SEQS" \
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-p seed="$SEED" \
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-p batch-size="$BATCH_SIZE" \
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-p auto-horizon-weights="$AUTO_HORIZON_WEIGHTS" \
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-p early-stop-metric="$EARLY_STOP_METRIC" \
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-p early-stop-patience="$EARLY_STOP_PATIENCE" \
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-p cv-fold="$CV_FOLD" \
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-p cv-n-folds="$CV_N_FOLDS" \
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-p cv-train-window="$CV_TRAIN_WINDOW" \
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-p decision-stride="$DECISION_STRIDE" \
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$WATCH_FLAG
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