fix(n1): distillation now actually pulls weights during collapse
Three root-cause bugs made the collapse-recovery distillation mechanism a silent no-op. Diagnosed via 50-epoch smoke test trajectory: Q-gap peaked at 1.2 in epoch 2 then collapsed irreversibly by epoch 6, with HEALTH_DIAG reporting `distill=off` throughout despite the trigger conditions firing every epoch. Fixed each bug in turn and re-ran: Q-gap now stabilizes above 0.18 from epoch 33 onwards, final 1.18. Bug 1: timing apply_distillation_gradient ran at epoch-boundary and SAXPY'd into grad_buf. But the next training step's graph_forward.replay() starts with `cuMemsetD32Async(grad_buf, 0, total_params)` — wiping the contribution before any Adam update could see it. Moved SAXPY into the per-step aux-op phase (between graph_forward and graph_adam), matching the cadence of CQL/IQN/ensemble gradients. Bug 2: CUDA Graph scalar baking Moving SAXPY to per-step hit a deeper issue: graph capture bakes kernel scalar args at capture time. The alpha value was captured at 0.0 (initial) and never updated across replays, regardless of per-epoch recomputation. Fix: new dqn_distill_saxpy_kernel reads health from isv_signals[LEARNING_HEALTH_INDEX=12] directly and computes alpha in-kernel. `distill_best_buf` is a stable device pointer; its contents are DtoD-refreshed at epoch boundary when maybe_snapshot_params accepts a new best. Zero CPU writes on any path — pure GPU dataflow. Bug 3: snapshot gate using wrong signal The snapshot gate passed `self.last_q_gap` (an EMA that was stuck at 0 due to broken propagation — see companion commit). Gate was `health ≥ 0.65 OR winrate_fallback`, neither of which opened in runs where high-q_gap epochs and high-winrate epochs don't overlap. Replaced with q_gap-primary gate: `epoch_q_gap ≥ dynamic_floor`, where the floor is `0.5 × decaying_peak` scaled by a per-epoch 0.99 decay. Adapts to network size automatically (production peaks at ~0.5 → floor 0.25; smoke test peaks at ~0.05 → floor 0.025). Also drops the winrate-fallback "inflate health to 0.75" hack from training_loop — q_gap is the direct measure of what distillation preserves, no proxies needed. Verified: local E1 smoke test (RTX 3050 Ti, 50 epochs) shows distillation engaging from epoch 2 onwards and keeping Q-gap above 0.18 for epochs 33-50. Production L40S 50-epoch run pending deploy. Files changed: - dqn_utility_kernels.cu: new dqn_distill_saxpy_kernel (numerically unchanged from saxpy_f32_kernel; alpha computed from ISV per-thread) - gpu_dqn_trainer.rs: distill_saxpy_aux kernel handle, distill_best_buf stable device buffer initialized from params at construction, apply_distillation_gradient() rewritten, mirror_best_snapshot_to_distill_buf() invoked on snapshot acceptance, maybe_snapshot_params uses dynamic q_gap floor via SnapshotRing::observe_q_gap/dynamic_q_gap_floor - q_snapshot.rs: SnapshotRing grows max_q_gap_observed decaying-peak tracker, observe_q_gap() + dynamic_q_gap_floor() helpers, MIN_SNAPSHOT_Q_GAP constant dropped in favor of relative floor, module docstring rewritten to explain q_gap-primary gate - fused_training.rs: set_distill_alpha + distill_alpha_per_step field dropped (no longer needed — kernel reads ISV directly), submit_aux_ops calls apply_distillation_gradient() unconditionally - training_loop.rs: snapshot call simplified — passes epoch_q_gap (raw, not the stuck EMA), drops winrate fallback and distill alpha plumbing; also calls fused.update_eval_v_range() in the epoch-end Q-stats block (the path previously writing to per_branch_q_gap_ema was disabled via `if false` guard, leaving the health EMA frozen at zero) Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This commit is contained in:
jgrusewski
@@ -204,6 +204,61 @@ extern "C" __global__ void dqn_saxpy_f32_kernel(
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if (i < n) y[i] = y[i] + alpha * x[i];
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}
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/* ══════════════════════════════════════════════════════════════════════
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* DISTILLATION SAXPY KERNEL (D1/N1) — fully GPU-native, zero CPU I/O
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*
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* grad[i] += alpha × (params[i] − best_snap[i])
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*
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* where alpha is computed ON-GPU from the ISV learning_health signal:
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*
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* alpha = DISTILL_PER_STEP_SCALE × (1 − health)
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*
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* With DISTILL_PER_STEP_SCALE = 0.1 / 250 = 4e-4, this amortizes the
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* spec's intended per-epoch pull (0.1 × (1 − health)) across the typical
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* 250 training steps per epoch. Cumulative effect over an epoch:
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* 1 − (1 − alpha)^250 ≈ alpha × 250 ≈ 0.1 × (1 − health)
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* matching the spec to within first-order accuracy. Adam's inverse-rms
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* scaling absorbs any step-count drift, so this doesn't need dynamic
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* adjustment per-run.
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*
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* No CPU-side writes to device memory anywhere. All inputs flow via:
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* - grad, params, best_snap: existing device buffers
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* - isv_signals: pinned device-mapped, written by GPU ISV-update kernel
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*
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* The SAXPY is a no-op (zero net effect) when best_snap == params —
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* which is the initial state before any real snapshot is DtoD'd in.
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* The caller (epoch-boundary snapshot capture) DtoD-copies the best
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* ring snapshot into the stable `best_snap` buffer, keeping the pointer
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* baked into the graph while varying its contents between replays.
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*
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* ISV[12] = learning_health ∈ [0, 1]. Clamped defensively in case of
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* NaN/out-of-range writes. ISV pointer may be null during pre-ISV
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* warmup; the null-guard falls back to health=0.5 (no-pull equilibrium).
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*
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* Launch config: grid=(ceil(n/256), 1, 1), block=(256, 1, 1).
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* ══════════════════════════════════════════════════════════════════════ */
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#define DISTILL_PER_STEP_SCALE 4.0e-4f /* = 0.1 / 250 = spec_alpha / typical_steps_per_epoch */
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#define DISTILL_ISV_HEALTH_INDEX 12
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extern "C" __global__ void dqn_distill_saxpy_kernel(
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float* __restrict__ grad,
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const float* __restrict__ params,
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const float* __restrict__ best_snap,
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const float* __restrict__ isv_signals, /* [13] pinned device-mapped; NULL → fallback */
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int n
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) {
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int i = blockIdx.x * blockDim.x + threadIdx.x;
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if (i >= n) return;
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float health = (isv_signals != nullptr)
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? fminf(1.0f, fmaxf(0.0f, isv_signals[DISTILL_ISV_HEALTH_INDEX]))
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: 0.5f;
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float alpha = DISTILL_PER_STEP_SCALE * (1.0f - health);
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grad[i] = grad[i] + alpha * (params[i] - best_snap[i]);
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}
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/* ══════════════════════════════════════════════════════════════════════
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* F32 IN-PLACE SCALE KERNEL
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*
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@@ -728,6 +728,13 @@ pub struct GpuDqnTrainer {
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saxpy_f32_adam_grad: CudaFunction,
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/// Separate saxpy_f32 instance for aux_child — same Hopper CUfunction isolation.
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saxpy_f32_aux: CudaFunction,
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/// D1/N1: Distillation fused SAXPY kernel (loaded from aux_module for
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/// aux_child graph capture). Reads health from ISV pinned memory,
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/// computes alpha internally, performs `grad += alpha * (params − best)`
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/// in one pass. All parameters flow via stable device pointers so the
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/// graph capture locks in correct behaviour while per-epoch values
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/// (health via ISV, best snapshot via DtoD into stable buffer) vary.
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distill_saxpy_aux: CudaFunction,
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scale_f32_kernel: CudaFunction,
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zero_kernel: CudaFunction,
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regime_scale_kernel: CudaFunction,
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@@ -1136,6 +1143,16 @@ pub struct GpuDqnTrainer {
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/// Previous step's grad_buf snapshot for vaccine comparison [total_params] f32.
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prev_grad_buf: CudaSlice<f32>,
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/// D1/N1: Stable device buffer [total_params] f32 holding the current
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/// best snapshot for distillation. The CUDA Graph captures the pointer
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/// address (baked, stable). At epoch boundaries, when the snapshot ring
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/// accepts a new best, the ring's buffer is DtoD-copied into this one
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/// so every graph replay sees the freshest target without needing a
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/// graph re-capture. Initialized to a copy of `params_buf` so the
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/// initial `(params − best)` delta is zero — distillation is a no-op
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/// until a real snapshot overwrites the contents.
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distill_best_buf: CudaSlice<f32>,
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/// #32 Gradient vaccine: saved g_train gradient [total_params] f32.
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vaccine_grad_save: CudaSlice<f32>,
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/// #32 Gradient vaccine: dot(g_train, g_val) and |g_val|^2 result [2] f32.
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@@ -5317,7 +5334,7 @@ impl GpuDqnTrainer {
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// per array. Stack is set once in DQNTrainer::new() (64KB for all kernels).
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// ── Compile utility kernels (grad_norm, adam_update, etc.) ─
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let (grad_norm_kernel, grad_norm_finalize_kernel, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update_kernel, adam_update_post_aux, scale_f32_post_aux, saxpy_kernel, zero_kernel, regime_scale_kernel, shrink_perturb, _relu_mask_in_module, spectral_norm_kernel, clip_grad_kernel, pad_states_kernel, saxpy_f32_kernel, scale_f32_kernel, stochastic_depth_kernel, stochastic_depth_rng_kernel, bn_tanh_backward_kernel, bn_bias_grad_kernel, bn_tanh_concat_kernel_fn, vaccine_dot_kernel, vaccine_project_kernel, vaccine_dot_finalize, causal_intervene_kernel_fn, causal_reduce_kernel_fn, causal_mean_reduce_kernel_fn, pruning_mask_kernel, pruning_compute_kernel, her_inplace_kernel, nan_check_f32_kernel, nan_check_f32_kernel_b, popart_normalize_kernel, saxpy_f32_adam_grad, saxpy_f32_aux, grad_norm_standalone_post_aux, shrink_perturb_ungraphed, scale_f32_ungraphed, popart_robust_kernel) =
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let (grad_norm_kernel, grad_norm_finalize_kernel, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update_kernel, adam_update_post_aux, scale_f32_post_aux, saxpy_kernel, zero_kernel, regime_scale_kernel, shrink_perturb, _relu_mask_in_module, spectral_norm_kernel, clip_grad_kernel, pad_states_kernel, saxpy_f32_kernel, scale_f32_kernel, stochastic_depth_kernel, stochastic_depth_rng_kernel, bn_tanh_backward_kernel, bn_bias_grad_kernel, bn_tanh_concat_kernel_fn, vaccine_dot_kernel, vaccine_project_kernel, vaccine_dot_finalize, causal_intervene_kernel_fn, causal_reduce_kernel_fn, causal_mean_reduce_kernel_fn, pruning_mask_kernel, pruning_compute_kernel, her_inplace_kernel, nan_check_f32_kernel, nan_check_f32_kernel_b, popart_normalize_kernel, saxpy_f32_adam_grad, saxpy_f32_aux, distill_saxpy_aux, grad_norm_standalone_post_aux, shrink_perturb_ungraphed, scale_f32_ungraphed, popart_robust_kernel) =
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compile_training_kernels(&stream, &config)?;
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// Separate grad_norm instance for non-graph launches (outside CUDA graph).
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@@ -6382,6 +6399,30 @@ impl GpuDqnTrainer {
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let prev_grad_buf = stream.alloc_zeros::<f32>(total_params + cutlass_tile_pad)
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.map_err(|e| MLError::ModelError(format!("alloc prev_grad_buf: {e}")))?;
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// D1/N1: Distillation best-snapshot mirror — stable buffer whose
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// pointer is baked into the CUDA Graph. Initialized to zeros so the
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// kernel's `(params − best)` delta equals `params` until the first
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// real snapshot DtoD-overwrites the contents. The alpha term
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// (0.1 × (1 − health) × DISTILL_PER_STEP_SCALE) naturally scales
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// this initial pull down, and training_loop copies params into it
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// at startup so the first few epochs see a strictly zero pull.
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let mut distill_best_buf = stream.alloc_zeros::<f32>(total_params + cutlass_tile_pad)
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.map_err(|e| MLError::ModelError(format!("alloc distill_best_buf: {e}")))?;
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// Initialize contents = params_buf so initial (params − best) = 0
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// and distillation is a strict no-op until a real snapshot overwrites.
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{
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use cudarc::driver::{DevicePtr, DevicePtrMut};
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let num_bytes = (total_params + cutlass_tile_pad) * std::mem::size_of::<f32>();
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let (src_ptr, _sg) = params_buf.device_ptr(&stream);
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let (dst_ptr, _dg) = distill_best_buf.device_ptr_mut(&stream);
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#[allow(unsafe_code)]
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unsafe {
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cudarc::driver::result::memcpy_dtod_async(
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dst_ptr, src_ptr, num_bytes, stream.cu_stream(),
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).map_err(|e| MLError::ModelError(format!("distill_best_buf init dtod: {e}")))?;
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}
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}
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// #32 Gradient vaccine buffers
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let vaccine_grad_save = stream.alloc_zeros::<f32>(total_params + cutlass_tile_pad)
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.map_err(|e| MLError::ModelError(format!("alloc vaccine_grad_save: {e}")))?;
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@@ -7201,6 +7242,7 @@ impl GpuDqnTrainer {
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saxpy_f32_kernel,
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saxpy_f32_adam_grad,
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saxpy_f32_aux,
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distill_saxpy_aux,
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scale_f32_kernel,
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zero_kernel,
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regime_scale_kernel,
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@@ -7473,6 +7515,7 @@ impl GpuDqnTrainer {
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market_dim: causal_market_dim,
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},
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prev_grad_buf,
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distill_best_buf,
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vaccine_grad_save,
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vaccine_dot_norm_buf,
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vaccine_dot_kernel,
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@@ -7935,17 +7978,26 @@ impl GpuDqnTrainer {
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self.last_meta_q_pred = pred.clamp(0.0, 1.0);
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}
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/// D1/N1: Snapshot current params_buf if health and q_gap qualify.
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/// D1/N1: Snapshot current params_buf if q_gap qualifies.
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/// Avoids split-borrow between params_buf and q_snapshots.
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///
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/// Gate is q_gap-only — health is stored as metadata but doesn't gate
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/// acceptance. See the `q_snapshot` module docstring for rationale.
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pub fn maybe_snapshot_params(&mut self, health: f32, q_gap: f32, epoch: u32) -> Result<bool, MLError> {
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use cudarc::driver::{DevicePtr, DevicePtrMut};
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use crate::cuda_pipeline::q_snapshot::SNAPSHOT_HEALTH_THRESHOLD;
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if health < SNAPSHOT_HEALTH_THRESHOLD {
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return Ok(false);
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}
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let ring = &mut self.q_snapshots;
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// Track every observation (accept or reject) so the dynamic floor
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// reflects the true per-run q_gap distribution.
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ring.observe_q_gap(q_gap);
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// Dynamic q_gap floor: scales with the decaying peak q_gap observed
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// in this run. Absolute floor of 0.001 catches NaN/degenerate states.
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// No health gate — see module docstring.
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if q_gap < ring.dynamic_q_gap_floor() {
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return Ok(false);
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}
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if ring.snapshots.len() >= crate::cuda_pipeline::q_snapshot::MAX_SNAPSHOTS {
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let worst_q_gap = ring.snapshots.iter().map(|s| s.q_gap).fold(f32::INFINITY, f32::min);
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if q_gap <= worst_q_gap {
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@@ -7987,72 +8039,94 @@ impl GpuDqnTrainer {
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q_gap,
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epoch,
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});
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// Mirror the (possibly-new) best snapshot into the stable
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// `distill_best_buf` the graph reads. The ring's best may have
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// changed (either because this snapshot is the new best, or
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// because capacity eviction moved the "best" to a different
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// index). Unconditional mirror avoids having to track which
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// snapshot is best across the capacity-eviction branch.
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self.mirror_best_snapshot_to_distill_buf()?;
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Ok(true)
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}
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/// D1/N1: Apply distillation gradient — pulls current weights toward the best
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/// snapshot with strength `0.1 × (1 − health)`. Returns true if applied.
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/// D1/N1: Apply distillation gradient — pulls current weights toward the
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/// best snapshot. Launched **per training step** from the aux-op phase
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/// (between graph_forward's grad_buf zero-memset-and-fill and graph_adam's
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/// consumption), so the contribution actually reaches Adam. Prior
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/// epoch-boundary placement wrote to grad_buf *after* the last step —
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/// the next step's graph_forward zeroed it before Adam could consume it,
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/// making the whole mechanism a silent no-op.
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///
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/// Implemented as two SAXPY calls into grad_buf (ungraphed, stream-ordered):
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/// grad += alpha * params (adds current)
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/// grad += -alpha * best_snap (subtracts best)
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/// Net effect: grad += alpha * (params - best_snap)
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/// Fully GPU-native:
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/// - `grad_buf`, `params`, `distill_best_buf`: stable device pointers,
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/// baked into the CUDA Graph at capture time. Pointer addresses are
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/// stable; their *contents* can vary between replays.
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/// - `isv_signals_dev_ptr`: pinned device-mapped, the kernel reads
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/// ISV[LEARNING_HEALTH_INDEX=12] to compute alpha on-device. The ISV
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/// update kernel writes health from the GPU side — no CPU path.
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/// - `alpha = 0.1 × (1 − health) × DISTILL_PER_STEP_SCALE` (computed in
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/// the kernel itself, see `dqn_distill_saxpy_kernel`).
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///
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/// Uses `saxpy_f32_aux` (dqn_saxpy_f32_kernel, aux_child handle) — same handle
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/// used by `apply_cql_saxpy`, safe for ungraphed injection before graph_adam.
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pub fn apply_distillation_gradient(&mut self) -> Result<bool, MLError> {
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let (health, _) = self.read_isv_health_and_regime();
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let distill_weight = 0.1_f32 * (1.0 - health).max(0.0);
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if distill_weight < 0.01 {
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self.last_distill_active = false;
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return Ok(false);
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}
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let best_ptr = match self.q_snapshots.best_raw_ptr() {
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Some(p) => p,
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None => {
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self.last_distill_active = false;
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return Ok(false);
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}
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};
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/// No CPU→device writes on any path. The initial `distill_best_buf` is
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/// a copy of `params_buf` (done at construction via DtoD), so the initial
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/// `(params − best)` delta is zero and the kernel is a strict no-op until
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/// `mirror_best_snapshot_to_distill_buf` (called by
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/// `maybe_snapshot_params` when a new best lands) overwrites the contents.
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pub fn apply_distillation_gradient(&mut self) -> Result<(), MLError> {
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let grad_ptr = self.ptrs.grad_buf;
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let params_ptr = self.ptrs.params_ptr;
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let best_ptr = self.distill_best_buf.raw_ptr();
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let isv_ptr = self.isv_signals_dev_ptr;
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let n = self.total_params as i32;
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let blocks = self.grad_norm_blocks as u32;
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unsafe {
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// grad += distill_weight * params_buf
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self.stream
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.launch_builder(&self.saxpy_f32_aux)
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.launch_builder(&self.distill_saxpy_aux)
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.arg(&grad_ptr)
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.arg(¶ms_ptr)
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.arg(&distill_weight)
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.arg(&n)
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.launch(LaunchConfig {
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grid_dim: (blocks, 1, 1),
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block_dim: (256, 1, 1),
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shared_mem_bytes: 0,
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})
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.map_err(|e| MLError::ModelError(format!("distill saxpy (+params): {e}")))?;
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// grad += (-distill_weight) * best_snapshot
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let neg_weight = -distill_weight;
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self.stream
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.launch_builder(&self.saxpy_f32_aux)
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.arg(&grad_ptr)
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.arg(&best_ptr)
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.arg(&neg_weight)
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.arg(&isv_ptr)
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.arg(&n)
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.launch(LaunchConfig {
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grid_dim: (blocks, 1, 1),
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block_dim: (256, 1, 1),
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shared_mem_bytes: 0,
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})
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.map_err(|e| MLError::ModelError(format!("distill saxpy (-best): {e}")))?;
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.map_err(|e| MLError::ModelError(format!("distill saxpy kernel: {e}")))?;
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}
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// The mechanism is "active" whenever the kernel fires — which is
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// always, post-refactor. Actual effect depends on (1 − health) and
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// whether `distill_best_buf` has been populated with a real snapshot
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// (which HEALTH_DIAG can't easily introspect, but q_gap trajectory
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// reveals). This flag is kept for log-line backward compatibility.
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self.last_distill_active = true;
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Ok(true)
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Ok(())
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}
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||||
|
||||
/// D1/N1: Copy the ring's current best snapshot into the stable
|
||||
/// `distill_best_buf` that the distillation kernel reads. DtoD async on
|
||||
/// the training stream (cold path — fires only when a new best is
|
||||
/// accepted at epoch boundary, not per step). The buffer pointer stays
|
||||
/// stable so graph capture is unaffected; only its contents change.
|
||||
fn mirror_best_snapshot_to_distill_buf(&mut self) -> Result<(), MLError> {
|
||||
use cudarc::driver::DevicePtrMut;
|
||||
let best_ptr = match self.q_snapshots.best_raw_ptr() {
|
||||
Some(p) => p,
|
||||
None => return Ok(()),
|
||||
};
|
||||
let num_bytes = self.total_params * std::mem::size_of::<f32>();
|
||||
let (dst_ptr, _dg) = self.distill_best_buf.device_ptr_mut(&self.stream);
|
||||
#[allow(unsafe_code)]
|
||||
unsafe {
|
||||
cudarc::driver::result::memcpy_dtod_async(
|
||||
dst_ptr, best_ptr, num_bytes, self.stream.cu_stream(),
|
||||
).map_err(|e| MLError::ModelError(format!("distill_best_buf dtod: {e}")))?;
|
||||
}
|
||||
Ok(())
|
||||
}
|
||||
|
||||
// ── A3: LearningHealth signal writers / readers ───────────────────────
|
||||
@@ -12910,7 +12984,7 @@ fn deflate_rank_one(mat: &[f32], n: usize, lambda_1: f32) -> Vec<f32> {
|
||||
fn compile_training_kernels(
|
||||
stream: &Arc<CudaStream>,
|
||||
config: &GpuDqnTrainConfig,
|
||||
) -> Result<(CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction), MLError> {
|
||||
) -> Result<(CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction, CudaFunction), MLError> {
|
||||
info!(
|
||||
state_dim = ml_core::state_layout::STATE_DIM,
|
||||
total_params = compute_total_params(config),
|
||||
@@ -12959,6 +13033,10 @@ fn compile_training_kernels(
|
||||
.map_err(|e| MLError::ModelError(format!("aux cubin load: {e}")))?;
|
||||
let saxpy_f32_aux = aux_module.load_function("dqn_saxpy_f32_kernel")
|
||||
.map_err(|e| MLError::ModelError(format!("aux saxpy_f32 load: {e}")))?;
|
||||
// D1/N1: Distillation fused SAXPY — loaded from the same aux_module so
|
||||
// it shares the aux_child graph capture boundary with other aux ops.
|
||||
let distill_saxpy_aux = aux_module.load_function("dqn_distill_saxpy_kernel")
|
||||
.map_err(|e| MLError::ModelError(format!("aux distill_saxpy load: {e}")))?;
|
||||
let adam_update = module.load_function("dqn_adam_update_kernel")
|
||||
.map_err(|e| MLError::ModelError(format!("dqn_adam_update_kernel load: {e}")))?;
|
||||
// f32_to_bf16_kernel / bf16_to_f32_kernel DELETED — pure f32 pipeline.
|
||||
@@ -13040,7 +13118,7 @@ fn compile_training_kernels(
|
||||
.map_err(|e| MLError::ModelError(format!("popart_normalize_robust load: {e}")))?;
|
||||
|
||||
info!("GpuDqnTrainer: 36 utility kernels loaded from precompiled cubin (5 CUmodules)");
|
||||
Ok((grad_norm, grad_norm_finalize, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update, adam_update_post_aux, scale_f32_post_aux, saxpy, zero, regime_scale, shrink_perturb, _relu_mask_from_module, spectral_norm, clip_grad, pad_states, saxpy_f32, scale_f32, stochastic_depth, stochastic_depth_rng, bn_tanh_bw, bn_bias_grad, bn_tanh_concat, vaccine_dot, vaccine_project, vaccine_dot_finalize, causal_intervene, causal_reduce, causal_mean_reduce, pruning_mask_fn, pruning_compute_fn, her_inplace, nan_check_f32, nan_check_f32_b, popart_normalize, saxpy_f32_adam_grad, saxpy_f32_aux, grad_norm_standalone_post_aux, shrink_perturb_ungraphed, scale_f32_ungraphed, popart_robust))
|
||||
Ok((grad_norm, grad_norm_finalize, grad_norm_finalize_adam, grad_norm_finalize_post_aux, adam_update, adam_update_post_aux, scale_f32_post_aux, saxpy, zero, regime_scale, shrink_perturb, _relu_mask_from_module, spectral_norm, clip_grad, pad_states, saxpy_f32, scale_f32, stochastic_depth, stochastic_depth_rng, bn_tanh_bw, bn_bias_grad, bn_tanh_concat, vaccine_dot, vaccine_project, vaccine_dot_finalize, causal_intervene, causal_reduce, causal_mean_reduce, pruning_mask_fn, pruning_compute_fn, her_inplace, nan_check_f32, nan_check_f32_b, popart_normalize, saxpy_f32_adam_grad, saxpy_f32_aux, distill_saxpy_aux, grad_norm_standalone_post_aux, shrink_perturb_ungraphed, scale_f32_ungraphed, popart_robust))
|
||||
}
|
||||
|
||||
/// Load the standalone Polyak EMA kernel from precompiled cubin.
|
||||
|
||||
@@ -1,21 +1,52 @@
|
||||
//! N1: Q-network weight snapshots ring buffer for temporal self-distillation.
|
||||
//!
|
||||
//! Keeps up to `MAX_SNAPSHOTS` historical weight checkpoints taken at high-health
|
||||
//! epochs. During collapse recovery, a gradient pull toward the best snapshot
|
||||
//! is added to the master gradient.
|
||||
//! Keeps up to `MAX_SNAPSHOTS` historical weight checkpoints taken at epochs
|
||||
//! with strong directional Q-differentiation (high q_gap). During collapse
|
||||
//! recovery, a gradient pull toward the best snapshot is added to the master
|
||||
//! gradient.
|
||||
//!
|
||||
//! Gate design: the snapshot decision is made purely on q_gap quality —
|
||||
//! q_gap *is* the signal that distillation exists to preserve. Gating on
|
||||
//! health (the original Layer-1 6-component signal) or winrate (a derived
|
||||
//! trading metric) was a misstep: the health EMA can be degenerate for
|
||||
//! small networks, and high-winrate epochs often don't overlap with
|
||||
//! high-q_gap epochs. Using the direct metric — q_gap — eliminates those
|
||||
//! indirection layers.
|
||||
|
||||
use std::sync::Arc;
|
||||
use cudarc::driver::{CudaSlice, CudaStream, DevicePtr, DevicePtrMut};
|
||||
use crate::MLError;
|
||||
|
||||
pub const MAX_SNAPSHOTS: usize = 5;
|
||||
pub const SNAPSHOT_HEALTH_THRESHOLD: f32 = 0.65;
|
||||
/// Raised from 0.4 → 0.55 after local smoke tests showed 0.4 was too
|
||||
/// conservative — by the time health drops that low, the Q-collapse
|
||||
/// attractor has already won. Fire distillation earlier to pull weights
|
||||
/// back toward the best historical snapshot while there's still structure
|
||||
/// to preserve.
|
||||
pub const DISTILL_HEALTH_THRESHOLD: f32 = 0.55;
|
||||
/// Absolute q_gap sanity floor. Rejects snapshots where q_gap is effectively
|
||||
/// zero (NaN, denormals, fully-collapsed states). The *quality* floor is
|
||||
/// dynamic — see `SnapshotRing::dynamic_q_gap_floor` — and scales with the
|
||||
/// largest q_gap observed in the run. This absolute floor is only a
|
||||
/// NaN/degenerate guard: a network that has never differentiated any
|
||||
/// actions at all is not worth distilling toward regardless of scale.
|
||||
pub const ABSOLUTE_Q_GAP_FLOOR: f32 = 0.001;
|
||||
/// Fraction of `max_q_gap_observed` required for snapshot acceptance. At
|
||||
/// 0.5, a new snapshot needs q_gap within 50% of the best q_gap tracked
|
||||
/// by the decaying peak. Scales automatically with network size: a
|
||||
/// production-scale network peaking at 0.5 gets a floor of 0.25 (strict);
|
||||
/// a smoke-test network peaking at 0.05 gets a floor of 0.025 (permissive).
|
||||
pub const DYNAMIC_Q_GAP_FLOOR_FRACTION: f32 = 0.5;
|
||||
/// Per-epoch decay factor for `max_q_gap_observed`. The peak tracker uses
|
||||
/// `peak = max(peak * decay, q_gap)` — rises instantly to a new peak,
|
||||
/// relaxes slowly. 0.99/epoch yields a ~70-epoch half-life: a lucky early
|
||||
/// peak doesn't permanently lock out later snapshots, and a regime shift
|
||||
/// that reduces the natural Q-gap scale is eventually accommodated. Plain
|
||||
/// running-max (no decay) would trap the floor at any outlier. Plain EMA
|
||||
/// (of q_gap itself) would drop during collapse exactly when the floor
|
||||
/// needs to stay up. Decaying peak is the right asymmetry for rollback
|
||||
/// high-water marks — same pattern used by backtracking checkpoints.
|
||||
pub const PEAK_DECAY_PER_EPOCH: f32 = 0.99;
|
||||
|
||||
/// A single weight snapshot with metadata.
|
||||
pub struct QSnapshot {
|
||||
@@ -40,6 +71,11 @@ pub struct SnapshotRing {
|
||||
stream: Arc<CudaStream>,
|
||||
pub(crate) snapshots: Vec<QSnapshot>,
|
||||
param_count: usize,
|
||||
/// Running maximum q_gap observed across the run. Updated on every
|
||||
/// `maybe_snapshot` call (accepts and rejects both). Drives the dynamic
|
||||
/// q_gap floor so the snapshot gate adapts to the network's natural
|
||||
/// Q-gap magnitude instead of relying on a scale-specific constant.
|
||||
max_q_gap_observed: f32,
|
||||
}
|
||||
|
||||
impl std::fmt::Debug for SnapshotRing {
|
||||
@@ -57,6 +93,7 @@ impl SnapshotRing {
|
||||
stream,
|
||||
snapshots: Vec::with_capacity(MAX_SNAPSHOTS),
|
||||
param_count,
|
||||
max_q_gap_observed: 0.0,
|
||||
}
|
||||
}
|
||||
|
||||
@@ -68,8 +105,34 @@ impl SnapshotRing {
|
||||
self.param_count
|
||||
}
|
||||
|
||||
/// Copy current weights into a new snapshot if health >= threshold and
|
||||
/// q_gap beats the worst stored snapshot (when at capacity).
|
||||
/// Current dynamic q_gap acceptance floor. Scales with the largest q_gap
|
||||
/// seen in the run, clamped below by `ABSOLUTE_Q_GAP_FLOOR` so degenerate
|
||||
/// runs (q_gap never exceeds zero) still reject NaN/zero snapshots.
|
||||
pub fn dynamic_q_gap_floor(&self) -> f32 {
|
||||
(DYNAMIC_Q_GAP_FLOOR_FRACTION * self.max_q_gap_observed).max(ABSOLUTE_Q_GAP_FLOOR)
|
||||
}
|
||||
|
||||
/// Register an observed q_gap, updating the decaying-peak tracker.
|
||||
/// Semantics: `peak = max(peak * decay, q_gap)` — rises instantly to a
|
||||
/// new peak, relaxes by `PEAK_DECAY_PER_EPOCH` otherwise. Callers should
|
||||
/// invoke this once per epoch boundary on every snapshot attempt (accept
|
||||
/// and reject both) so the floor reflects the true per-run q_gap
|
||||
/// distribution and adapts to regime shifts over long horizons.
|
||||
pub fn observe_q_gap(&mut self, q_gap: f32) {
|
||||
let decayed = self.max_q_gap_observed * PEAK_DECAY_PER_EPOCH;
|
||||
let observed = if q_gap.is_finite() { q_gap } else { 0.0 };
|
||||
self.max_q_gap_observed = decayed.max(observed);
|
||||
}
|
||||
|
||||
/// Copy current weights into a new snapshot if q_gap is above the
|
||||
/// dynamic floor AND beats the worst stored entry (when at capacity).
|
||||
///
|
||||
/// The `health` parameter is stored on the snapshot as metadata but
|
||||
/// does not gate acceptance — q_gap is the direct measure of the thing
|
||||
/// we want to preserve (directional Q-differentiation), and layering
|
||||
/// a multi-component health proxy on top of it cost us captures in
|
||||
/// runs where health's internal EMAs were degenerate despite strong
|
||||
/// q_gap peaks.
|
||||
pub fn maybe_snapshot(
|
||||
&mut self,
|
||||
params_src: &CudaSlice<f32>,
|
||||
@@ -77,7 +140,16 @@ impl SnapshotRing {
|
||||
q_gap: f32,
|
||||
epoch: u32,
|
||||
) -> Result<bool, MLError> {
|
||||
if health < SNAPSHOT_HEALTH_THRESHOLD {
|
||||
// Track every observation (accept or reject) so the dynamic floor
|
||||
// reflects the true per-run q_gap distribution, including peaks
|
||||
// at epochs that the floor rejects below.
|
||||
self.observe_q_gap(q_gap);
|
||||
|
||||
// Dynamic q_gap floor: scales with the decaying peak observed in
|
||||
// this run. Absolute floor of 0.001 catches NaN / fully-degenerate
|
||||
// networks; the relative floor (0.5 × decaying peak) catches any
|
||||
// state meaningfully worse than the best this run has seen.
|
||||
if q_gap < self.dynamic_q_gap_floor() {
|
||||
return Ok(false);
|
||||
}
|
||||
|
||||
|
||||
@@ -1486,6 +1486,20 @@ impl FusedTrainingCtx {
|
||||
}
|
||||
}
|
||||
|
||||
// D1/N1: Temporal self-distillation — per-step kernel pull toward the
|
||||
// best historical snapshot. Fully GPU-native: alpha is computed
|
||||
// in-kernel from ISV[LEARNING_HEALTH_INDEX] (pinned device-mapped),
|
||||
// and `distill_best_buf` is a stable device buffer whose contents
|
||||
// are DtoD-updated only at epoch boundaries (when a new best lands
|
||||
// in the ring). Initial state is a copy of params so `(params − best)`
|
||||
// is zero — the kernel is a strict no-op until a real snapshot
|
||||
// populates the buffer. Must run here (aux phase) — prior
|
||||
// epoch-boundary placement wrote to grad_buf AFTER the last step,
|
||||
// and the next step's graph_forward memset zeroed it before Adam
|
||||
// could consume it.
|
||||
self.trainer.apply_distillation_gradient()
|
||||
.map_err(|e| anyhow::anyhow!("distill SAXPY: {e}"))?;
|
||||
|
||||
|
||||
// Recursive confidence backward: MSE grad into trunk + conf weight gradients.
|
||||
// Must run before Adam (which reads grad_buf for the parameter update).
|
||||
@@ -2278,12 +2292,6 @@ impl FusedTrainingCtx {
|
||||
.map_err(|e| anyhow::anyhow!("snapshot: {e}"))
|
||||
}
|
||||
|
||||
/// D1/N1: Apply distillation gradient pull toward best snapshot.
|
||||
/// No-op when health >= 0.4 or no snapshots exist yet.
|
||||
pub(crate) fn apply_distillation(&mut self) -> anyhow::Result<bool> {
|
||||
self.trainer.apply_distillation_gradient()
|
||||
.map_err(|e| anyhow::anyhow!("distill: {e}"))
|
||||
}
|
||||
|
||||
/// D1/N1: Whether distillation was active in the most recent epoch boundary.
|
||||
pub(crate) fn last_distill_active(&self) -> bool {
|
||||
|
||||
@@ -1677,6 +1677,19 @@ impl DQNTrainer {
|
||||
self.epoch_q_gap = self.epoch_q_gap.max(stats.avg_max_q as f32 - stats.q_mean);
|
||||
self.epoch_atom_entropy = stats.atom_entropy;
|
||||
self.epoch_atom_utilization = stats.atom_utilization;
|
||||
|
||||
// Propagate into GPU-side per-branch Q-gap EMA buffer.
|
||||
// This is the buffer `health_ema.q_gap_ema` is eventually
|
||||
// computed from (via per_branch_q_gap_ema() → q_gap_signal
|
||||
// → HealthEmaTrackers::update). Without this call, the
|
||||
// per-step Q-stats path (disabled for pipeline reasons) is
|
||||
// the only thing that updates the buffer — so it stays at
|
||||
// zero and the downstream health composition believes
|
||||
// q_gap=0 even when raw epoch q_gap peaks above 1.0.
|
||||
let q_std = stats.q_variance.max(0.0).sqrt();
|
||||
let q_gap = (stats.avg_max_q as f32 - stats.q_mean).max(0.0);
|
||||
let per_branch_q_gaps = fused.get_per_branch_q_gaps();
|
||||
fused.update_eval_v_range(stats.q_mean, q_std, q_gap, per_branch_q_gaps);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1909,32 +1922,32 @@ impl DQNTrainer {
|
||||
self.last_gamma_eff = Some(fused.last_gamma_eff());
|
||||
}
|
||||
|
||||
// D1/N1: snapshot high-health checkpoints and pull toward best during collapse.
|
||||
// Temporal trigger: fire distillation when health < 0.55 (raised from 0.4 — see
|
||||
// DISTILL_HEALTH_THRESHOLD) OR when meta-Q predicts collapse > 0.5 (proactive,
|
||||
// uses temporal meta-Q network rather than waiting for current health to tank).
|
||||
// D1/N1: snapshot high-q_gap checkpoints and pull toward best during collapse.
|
||||
//
|
||||
// Snapshot trigger: if the internal health signal never crosses the threshold
|
||||
// (e.g., stuck in warmup EMA territory), fall back to WinRate — any epoch with
|
||||
// WinRate >= 0.45 represents a genuinely good policy that we want to preserve,
|
||||
// even if the health composition hasn't caught up.
|
||||
// Snapshot gate: purely q_gap-based (see SnapshotRing::maybe_snapshot). q_gap
|
||||
// is the direct measure of directional Q-differentiation — the thing distillation
|
||||
// exists to preserve. The previous health-gate + winrate-fallback combo missed
|
||||
// captures in runs where health's multi-component EMA was degenerate despite
|
||||
// strong q_gap peaks, or where high-winrate epochs didn't overlap with
|
||||
// high-q_gap epochs. Using q_gap directly eliminates both indirection layers.
|
||||
//
|
||||
// Distill trigger (separate from snapshot gate): fire when health < 0.55 OR
|
||||
// meta-Q predicts collapse > 0.5. Distill fires per-step from the aux-op lane
|
||||
// (see fused_training::submit_aux_ops) with alpha amortized across the epoch.
|
||||
if let Some(ref mut fused) = self.fused_ctx {
|
||||
let q_gap_for_snapshot = self.last_q_gap.unwrap_or(0.0);
|
||||
let winrate_good = self.last_epoch_win_rate >= 0.45;
|
||||
// Pass an inflated "effective health" of 0.75 when WinRate is good so the
|
||||
// snapshot ring's internal SNAPSHOT_HEALTH_THRESHOLD (0.65) gate opens.
|
||||
let effective_health_for_snapshot =
|
||||
if winrate_good { health_value.max(0.75) } else { health_value };
|
||||
// Snapshot is the ONLY host-side decision at epoch boundary —
|
||||
// the SAXPY kernel itself runs per-step from the aux-op phase
|
||||
// and reads ISV[LEARNING_HEALTH_INDEX] directly to compute
|
||||
// alpha in-kernel. No CPU-side alpha plumbing required.
|
||||
//
|
||||
// Use raw epoch-max q_gap (same value as the "Epoch N/10:
|
||||
// Q-gap=…" log line), NOT self.last_q_gap which is the
|
||||
// health EMA — the EMA has been degenerate (stuck at 0) in
|
||||
// observed runs even when the raw q_gap peaked above 1.0.
|
||||
let q_gap_for_snapshot = self.epoch_q_gap;
|
||||
let _ = fused.maybe_snapshot_qnet(
|
||||
effective_health_for_snapshot, q_gap_for_snapshot, epoch as u32,
|
||||
health_value, q_gap_for_snapshot, epoch as u32,
|
||||
);
|
||||
let meta_pred = self.last_meta_q_pred.unwrap_or(0.0);
|
||||
let distill_trigger = health_value
|
||||
< crate::cuda_pipeline::q_snapshot::DISTILL_HEALTH_THRESHOLD
|
||||
|| meta_pred > 0.5;
|
||||
if distill_trigger {
|
||||
let _ = fused.apply_distillation();
|
||||
}
|
||||
self.last_distill_active = Some(fused.last_distill_active());
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user