multi_horizon_heads_backward: sigmoid + linear chain rule. One block,
HIDDEN_DIM=128 threads. Computes grad_w, grad_b, grad_h_in. No
atomicAdd; per-thread accumulation only.
cfc_step_backward: truncated K=1 BPTT through one CfC time step.
Forward pre/decay/tanh recomputed inside the kernel; emits grad_w_in,
grad_w_rec, grad_b, grad_h_old. tau is held frozen (structural
log-uniform init per Hasani 2022; backprop through tau deferred to
Phase A v2 if the gate needs it). Uses dynamic shared memory for the
d_pre relay between threads (size = 2 * n_hid * 4 bytes).
Tests (4/4 on sm_86) validate via on-GPU finite-difference:
- heads grad_h vs forward(h±eps) → matches at eps=1e-3, rel<=1%
- heads grad_b vs forward(b±eps) → matches at eps=1e-3, rel<=1%
- cfc grad_b vs forward(b±eps) → matches at eps=1e-3, rel<=5%
- cfc grad_h_old vs forward(h_old±eps) → matches at eps=1e-3, rel<=5%
CPU is not the reference (per feedback_no_cpu_test_fallbacks.md). The
kernel is the truth; numerical perturbation validates the analytic
gradient against the kernel's own forward.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
bce_loss_multi_horizon: fused forward+backward, block tree-reduce (no
atomicAdd), NaN labels masked (drop). Loss = mean over valid; grad =
(p-y)/(p(1-p)) scaled by 1/N_valid.
adamw_step: element-wise AdamW with weight decay; one thread per param.
Tests pass on sm_86:
BCE (4/4): positive+finite loss, near-zero loss when probs match
labels, analytic grad matches GPU-computed finite-difference at
eps=1e-3 / max_relative=5e-2 across 5 perturbation points, NaN
labels mask grad and contribute zero to loss/N_valid.
AdamW (4/4): zero-grad moves param only by weight-decay, positive
grad decreases param, step counter increments, repeated descent
on grad=theta drives ‖θ‖ from 40 to <1 in 200 steps.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Single-block 8-thread kernel; thread j computes its own 128-dim dot
product, then thread 0 computes block-wide mean/var, then each thread
applies the per-output affine layer-norm. No atomicAdd; reductions are
single-thread (8 elements — negligible cost).
Tests (5/5 on sm_86) assert:
- layer-norm zero-mean output under identity gain
- layer-norm unit-variance output under identity gain
- ln_bias shifts mean uniformly
- ln_gain scales variance (var = gain^2)
- finite output under zero input (variance clamp activates)
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per-horizon P(up) at h ∈ {30, 100, 300, 1000, 6000} snapshots forward.
Single-block 5-thread kernel; each thread is its own 128-dim dot
product + sigmoid. No atomicAdd.
Tests (5/5 pass on sm_86) assert invariants only:
- sigmoid output ∈ [0, 1] for all heads
- zero weights + zero bias → 0.5 exactly
- bias = +20 → saturates near 1
- bias = -20 → saturates near 0
- per-head independence (mixed-bias configuration)
Addendum updated to explicitly state no-CPU-mirror discipline per
feedback_no_cpu_test_fallbacks.md.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Hasani 2022 closed-form CfC recurrence; one thread per hidden unit, no
atomicAdd. Tests assert algebraic invariants (dt=0 -> identity, zero
weights -> h_old * decay, large tau -> h_old preserved, output bound).
Also removes src/cfc/oracle.rs and replaces snap_feature bit-equiv
test with property assertions per feedback_no_cpu_test_fallbacks.md.
CPU mirrors are bug-locks; validation is now via known synthetic
inputs + analytical relations on the GPU output.
12 tests pass on local sm_86 (7 snap_feature invariants + 5 cfc_step
invariants).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Per-snapshot 32-dim feature vector (mid log-return, spread, depth, OFI,
trade-flow, dt). Single-block single-thread kernel; uploads via
MappedF32Buffer DtoD into CudaSlice per the addendum Pattern 3.
Bit-equiv tested CPU vs GPU at eps<=1e-5 over (synthetic input,
reserved-slots-are-zero, zero-prev-mid edge case).
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Cargo.toml: drops gbdt; adds memmap2 + approx; keeps ml-core only
(cannot depend on ml: would cycle since ml depends on ml-alpha for
the Mamba2 gate baseline).
build.rs: compiles 7 cubins (mamba2_alpha + 6 new placeholders)
with -O3 --use_fast_math --ftz --fmad. Skips kernels whose source
isn't present yet so partial check-ins work. Every env::var paired
with rerun-if-env-changed per the canonical build pearl.
src/pinned_mem.rs: local copy of MappedF32Buffer (mirrors
ml::cuda_pipeline::mapped_pinned::MappedF32Buffer). Drives the only
permitted CPU<->GPU path per feedback_no_htod_htoh_only_mapped_pinned.
Eventually the move-to-ml-core refactor will deduplicate; out of
scope for the Phase A branch.
Addendum: updates the import path to ml_alpha::pinned_mem.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Four new kernels in mamba2_alpha_kernel.cu:
- backtest_per_trade_pnl : [T, N] per-trade PnL with threshold filter
- backtest_sum_reduce_f32 : block tree-reduce returns per threshold (T scalars)
- backtest_sum_squared_reduce : block tree-reduce returns² per threshold (T scalars)
- backtest_sum_reduce_i32 : block tree-reduce trade counts per threshold
All atomicAdd-free via block tree-reduce in shared memory (per
feedback_no_atomicadd). Single kernel launch handles the full
threshold sweep across all sequences via grid_x=T, grid_y=ceil(N/256).
New module crates/ml-alpha/src/backtest.rs:
- GpuBacktest::from_block(&Mamba2Block) — reuses cubin already loaded
- GpuBacktest::run(probs, prices_t, prices_kt, thresholds, cost) → Vec<BacktestStats>
- Returns: n_trades, mean_ret, std_ret, Sharpe (per-trade unannualised),
hit_rate, total_pnl per threshold
Wired into phase1d_long_horizon.rs after the stacker eval:
- Convert stacker_logits → probs via sigmoid
- Upload probs + end-bar prices + (end-bar + horizon) prices to GPU
- Sweep thresholds [0.00, 0.02, 0.05, 0.10, 0.15, 0.20, 0.25]
- Print per-threshold table + best Sharpe operating point
- GATE: per-trade Sharpe > 1.5 = deployable, 0.5-1.5 = marginal, < 0.5 = fail
Cost model: 0.25 price units round-trip = 1 ES.FUT tick = $12.50/contract.
Tunable via --cost-per-trade. Realistic for retail flow; brokers can
trade at half-tick or better.
GPU-pure on the hot path: kernels do per-trade math + reductions;
host only receives T (= 7 here) scalars per metric for final Sharpe
arithmetic. No GPU↔CPU roundtrip per trade.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
GPU-pure AdamW for Mamba2Block's nine parameter tensors with bias-corrected
moment updates, decoupled weight decay, and host-side L2 grad clipping
(reads all 9 grad norms once, multiplies a single scale factor into the
kernel). Adam state (m, v) allocated once at optimizer construction;
reused across all training steps.
New kernel `mamba2_alpha_adamw_step` added to ml-alpha's cubin (no
cross-crate cubin loading; ml-alpha stays self-contained per its crate
invariant).
Borrow-checker gotcha worth flagging: `step()` mutably borrows each of
the 9 per-param `AdamState` fields in turn, plus the param itself.
Tried `apply()` as a method on `&self` — conflicts with `&mut self.s_*`.
Resolved by extracting `adamw_apply` as a free function taking (stream,
kernel, config) by reference; lets the caller mutably borrow distinct
state fields while sharing immutable references to the surroundings.
**The end-to-end training-loop test is the analytical-gradient validation:**
- 20 AdamW steps on a fixed batch (n_batch=4, seq_len=8, in_dim=4,
hidden=8, state=4) with binary labels (half +1, half 0)
- Asserts ≥15 of 20 steps have monotonically-decreasing BCE loss
- Asserts final loss < 0.65 (below the chance baseline ln(2) ≈ 0.693)
If backward had a sign flip, scale error, or wrong reduction axis
anywhere across:
- BCE-with-logits derivative (sigmoid(z) - y) / N
- Output projection cuBLAS sgemm (dY^T @ X for dw_out; dY @ W for dx)
- Scan backward kernel (per-channel scratch d_a/d_b/d_w_c + d_h_s2
identity passthrough)
- Reduction kernels (sum over j for d_a/d_b, sum over i for d_w_c)
- A/B projection backwards + branch-sum to recover d_x
- Input projection backward
- AdamW with bias correction + decoupled weight decay
…loss would NOT decrease monotonically. It does. The full backward
chain is correct.
Tests (10 passing on real GPU):
- training_loop_decreases_loss (THE end-to-end validation)
- backward_returns_finite_grads
- backward_rejects_wrong_d_logit_shape
- forward_train_returns_cache
- forward_shape_and_finite
- forward_rejects_wrong_shape
- config_rejects_seq_len_over_32
- config_rejects_state_over_16
- config_rejects_zero_dims
- constructs_and_loads_kernels
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Addresses four concerns surfaced after the forward-pass commit:
1. Backward kernel was scaffolded with `if (j==0)` to dodge atomicAdd,
but that drops contributions from j>0 channels. Rewritten so every
(i, j) thread writes its UNIQUE slot in per-channel scratch:
d_a_per_channel[N, sh2, K, state_d]
d_b_per_channel[N, sh2, K, state_d]
Followed by a unified reduction kernel mamba2_alpha_reduce_d_proj
that sums over j → d_a_proj / d_b_proj [N, K, state_d]. Same kernel
handles both call sites (DRY).
2. d_w_c gradient already had the right pattern (d_w_c_per_sample +
mamba2_alpha_reduce_d_w_c); kept as-is. All three gradient outputs
now follow the same atomicAdd-free scratch+reduce structure per
feedback_no_atomicadd.
3. `forward()` was discarding LinearActivations which the backward path
needs. New `Mamba2ForwardCache` struct carries (input_2d, x, a_proj,
b_proj, h_enriched) — everything backward needs to recover gradients
through the four projections + scan. `forward_train()` returns
`(logit, cache)`; `forward()` thin-wraps and discards the cache for
inference.
4. `x_hist[32 * 16]` in the backward kernel was hardcoded; configs with
seq_len > 32 would silently corrupt. Added MAMBA2_KERNEL_SEQ_MAX=32
constant + config validation. Backward kernel header documents both
limits explicitly.
Tests (7 passing on real GPU):
- forward_train returns cache with correct shapes for all 5 tensors
- seq_len > 32 rejected at config validation
- state_dim > 16 rejected
- forward output [B, 1] all finite
- forward rejects wrong in_dim / seq_len
- kernel handles all 4 functions resolve (fwd / bwd / reduce_d_proj /
reduce_d_w_c) + param-count sanity
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Forward inference for the supervised snapshot stream — no ISV, no
temporal_weight, no NULL-pointer dispatch. Clean rewrite of the DQN
mamba2 kernel into a purpose-built alpha kernel.
New kernel `crates/ml-alpha/cuda/mamba2_alpha_kernel.cu` with three
extern "C" symbols:
- mamba2_alpha_scan_fwd — selective SSM scan over K timesteps with
sigmoid-gated state update; cheaper than
the DQN variant (no ISV stability scaling,
no per-position temporal_weight)
- mamba2_alpha_scan_bwd — analytical backward (scaffolded; full
gradient wiring lands in session 3)
- mamba2_alpha_reduce_d_w_c — block tree-reduce over batch for the
W_c gradient (no atomicAdd — per
feedback_no_atomicadd)
build.rs swapped from ../ml/src/cuda_pipeline/mamba2_temporal_kernel.cu
to the local cuda/mamba2_alpha_kernel.cu. ml-alpha no longer depends
on ml's CUDA source — fully self-contained alpha-stack.
Forward pipeline:
1. cuBLAS sgemm: input [B,K,in] @ W_in.T + b_in → x [B,K,hidden]
2. cuBLAS sgemm: x @ W_a.T + b_a → a_proj [B,K,state]
3. cuBLAS sgemm: x @ W_b.T + b_b → b_proj [B,K,state]
4. zero-init h_s2, h_enriched [B, hidden]
5. scan kernel: (a_proj, b_proj, W_c, h_s2) → h_enriched
6. cuBLAS sgemm: h_enriched @ W_out.T + b_out → logit [B, 1]
All on GPU; output is a [N] CudaSlice<f32> of raw logits. Caller
sigmoids + thresholds (or feeds directly into BCE-with-logits).
Tests (5 passing on real GPU):
- forward [4, 16, 81] → logit [4, 1], all finite
- reject wrong in_dim
- reject wrong seq_len
- reject state_dim > 16
- reject zero dims
- + parameter-count sanity
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
