📦 deps(thirdparty): update snapshots

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+# Numerical precision & training stability — make it RUN, then stop it diverging
+
+The mechanics of getting a DL run to compute *finite* numbers fast on a rented card, and of debugging it
+when the loss goes NaN or spikes. This layer owns **make-it-run + the mechanics of divergence**; it does
+NOT own *is the converged number real* / cuDNN-nondeterminism-as-a-metric-error — that is
+**verifying-dl-experiments** (cross-link **REQUIRED** at every "is this a bug or a real effect" fork).
+
+To jump: `grep -in '<keyword>' references/training/precision-stability.md` (e.g. `tf32`, `bf16`, `scaler`,
+`nan`, `anomaly`, `z-loss`, `clip`, `warmup`, `qk`, `deterministic`).
+
+## Table of contents
+
+- **Precision choice** — P1 fp32/tf32/fp16/bf16 decision · P2 TF32 default-off footgun · P3 H100/A100/V100 capability
+- **AMP mechanics** — P4 autocast scope · P5 GradScaler (fp16 only) · P6 bf16 needs no scaler · P7 grad-clip under scaler
+- **NaN / Inf** — P8 where NaNs come from · P9 anomaly detection · P10 fp16 overflow vs underflow · P11 bad-data NaN
+- **Loss spikes / divergence** — P12 LR + warmup · P13 grad clipping · P14 skip-the-batch · P15 z-loss · P16 qk-norm · P17 init
+- **Gradients** — P18 explosion/vanishing diagnosis
+- **Repro** — P19 determinism knobs (cross-link)
+- **Pointers** — gotchas_universal.md, multinode.md, spot-resilience.md
+
+---
+
+## Precision choice
+
+### P1 — Which precision: fp32 / TF32 / fp16 / bf16
+
+**Symptom**: unsure which `dtype` to train in; run is either slow (fp32) or NaN-prone (fp16).
+
+**Root cause**: the four modes trade dynamic range against mantissa precision against tensor-core speed.
+fp16 has a 5-bit exponent (max ~65504) so it *overflows* and *underflows* easily; bf16 keeps fp32's 8-bit
+exponent (same range as fp32) but only 7 mantissa bits, so it never needs loss-scaling but is coarser per
+value. TF32 is an fp32-storage mode that runs matmuls at 10 mantissa bits on tensor cores.
+
+**Fix — default ladder (PyTorch 2.x)**:
+1. **bf16 autocast** on Ampere+ (A100/H100/4090/...) — the modern default; same range as fp32, no GradScaler, robust. `torch.autocast("cuda", dtype=torch.bfloat16)`.
+2. **TF32** for the fp32 matmuls that remain (the non-autocast path) — `torch.set_float32_matmul_precision("high")`. Free ~speedup, negligible convergence impact for most nets (P2).
+3. **fp16 autocast + GradScaler** ONLY if stuck on a card with no bf16 tensor cores (V100/T4/2080Ti) — needs the scaler (P5) and is overflow-prone.
+4. **Pure fp32** as the diagnostic fallback: if a run NaNs, *first* prove it's finite in fp32 before blaming the model. fp32 isolates "is this a numerics bug or a model bug."
+
+bf16 handles large dot-products / attention logits better than fp16, which saturates and triggers
+scaler-step-skipping. URLs: https://docs.pytorch.org/docs/2.12/amp.html ·
+https://www.runpod.io/articles/guides/fp16-bf16-fp8-mixed-precision-speed-up-my-model-training
+
+### P2 — TF32 is OFF by default for matmul since PyTorch 1.12 — the "why is my A100 slow" footgun
+
+**Symptom**: an fp32 (or autocast-but-fp32-matmul-heavy) run on an A100/H100 is ~2–4× slower than expected;
+nothing is wrong with the code.
+
+**Root cause**: `torch.backends.cuda.matmul.allow_tf32` defaulted **True in 1.7–1.11**, then flipped to
+**False in 1.12+** (precision-loss complaints from non-DL users). So a fresh PyTorch 2.x box runs fp32
+matmuls at full fp32 on the tensor cores' slow path unless TF32 is re-enabled. Convolutions' TF32
+(`cudnn.allow_tf32`) is a separate knob, enabled by default.
+
+**Fix**: opt back in once at startup —
+```python
+torch.set_float32_matmul_precision("high")   # preferred: enables TF32 (or bf16x3) for fp32 matmul
+# legacy-equivalent, still works:
+torch.backends.cuda.matmul.allow_tf32 = True
+torch.backends.cudnn.allow_tf32 = True
+```
+`"high"` = TF32; `"highest"` = true fp32 (default); `"medium"` = even coarser. HF Trainer exposes `--tf32 1`.
+Most nets converge identically with TF32 as with fp32. URLs:
+https://github.com/pytorch/pytorch/pull/76509 ·
+https://docs.pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html ·
+https://docs.pytorch.org/docs/2.12/notes/numerical_accuracy.html
+
+### P3 — Card capability gates the choice: bf16 needs Ampere+; V100/T4 are fp16-only
+
+**Symptom**: bf16 training is unexpectedly slow (no error), or a config picks bf16 on an old card and falls
+to a slow path.
+
+**Root cause**: fast bf16 tensor cores arrived with **Ampere (A100, RTX 30xx)**; Hopper (H100/H200) adds
+native **FP8**. **V100/T4/RTX 20xx have fp16 tensor cores but no fast bf16** (runs emulated/slow). A rental
+hands whatever card is free, so the right precision is a *per-rental* fact, not a constant.
+
+**Fix**: branch on capability at runtime, never hardcode —
+```python
+use_bf16 = torch.cuda.is_bf16_supported()    # True on Ampere+
+amp_dtype = torch.bfloat16 if use_bf16 else torch.float16
+```
+On V100/T4 use fp16+GradScaler (P5). FP8 (H100) is opt-in via Transformer Engine / `torchao`, not plain
+autocast (out of scope). Record the card next to `nvidia-smi` in Phase 0.
+URL: https://www.e2enetworks.com/blog/nvidia-a100-vs-h100-vs-h200-gpu-comparison
+
+---
+
+## AMP mechanics
+
+### P4 — autocast: wrap ONLY forward + loss, never backward, never `.half()` the model
+
+**Symptom**: dtype-mismatch errors, or AMP gives no speedup, or grads look wrong.
+
+**Root cause**: autocast is a context that casts *eligible ops* per-op inside the region; manually
+`.half()`-ing the model or wrapping the backward pass fights it.
+
+**Fix**:
+```python
+for x, y in loader:
+    optimizer.zero_grad(set_to_none=True)
+    with torch.autocast("cuda", dtype=amp_dtype):   # forward + loss ONLY
+        out = model(x); loss = loss_fn(out, y)
+    # backward is OUTSIDE autocast:
+    loss.backward()                                 # (+ scaler for fp16, P5)
+    optimizer.step()
+```
+Keep the model and optimizer in fp32; do NOT call `model.half()`. Use the new `torch.amp.autocast("cuda",
+...)` / `torch.amp.GradScaler("cuda")` API — `torch.cuda.amp.*` is **deprecated** in PyTorch 2.x. autocast
+state is thread-local (re-enter it inside each DDP/DataParallel worker thread).
+URL: https://docs.pytorch.org/docs/2.12/amp.html
+
+### P5 — GradScaler: required for fp16 to stop gradient *underflow*
+
+**Symptom (no scaler, fp16)**: loss looks fine but the model doesn't learn — small gradients flush to 0 in
+fp16's tiny subnormal range.
+
+**Root cause**: fp16's narrow range underflows small gradients to zero. GradScaler multiplies the loss by a
+large factor before backward (pushing grads into representable range), then unscales before the step and
+**adapts the factor**: on any inf/NaN grad it *skips the optimizer step* and halves the scale (backoff 0.5);
+after `growth_interval` (default 2000) clean steps it doubles it (growth 2.0).
+
+**Fix — canonical fp16 loop**:
+```python
+scaler = torch.amp.GradScaler("cuda")
+for x, y in loader:
+    optimizer.zero_grad(set_to_none=True)
+    with torch.autocast("cuda", dtype=torch.float16):
+        loss = loss_fn(model(x), y)
+    scaler.scale(loss).backward()
+    scaler.step(optimizer)     # internally unscales; SKIPS step if inf/NaN found
+    scaler.update()            # adapts the scale factor
+```
+Early-training "skipped step" warnings as the scaler calibrates are **normal**; *persistent* skips every
+step = a real overflow (go to P10). URLs:
+https://github.com/pytorch/pytorch/blob/main/docs/source/notes/amp_examples.rst ·
+https://docs.pytorch.org/docs/2.12/amp.html
+
+### P6 — bf16 needs NO GradScaler (adding one is pointless, not harmful)
+
+**Symptom**: a copied fp16 recipe carries a GradScaler into a bf16 run — wasted overhead, not a crash or a wrong result.
+
+**Root cause**: bf16 has fp32's exponent range, so gradients don't underflow → loss-scaling is unnecessary
+and the scaler's skip/backoff machinery is dead weight (scale-then-unscale cancels, and it never finds an
+overflow to skip).
+
+**Fix**: for bf16, drop the scaler entirely — plain `loss.backward(); optimizer.step()`. Only fp16 (and the
+V100/T4 path) uses GradScaler.
+URL: https://docs.pytorch.org/docs/2.12/amp.html
+
+### P7 — Gradient clipping under GradScaler: `unscale_` FIRST or you clip scaled grads
+
+**Symptom**: `clip_grad_norm_` under fp16 AMP has no effect, or clips at the wrong magnitude.
+
+**Root cause**: inside the scaler the grads are still multiplied by the (large) scale factor, so clipping to
+`max_norm=1.0` is really clipping to `1.0 × scale` — effectively never.
+
+**Fix**: `scaler.unscale_(optimizer)` once, THEN clip, THEN `scaler.step`:
+```python
+scaler.scale(loss).backward()
+scaler.unscale_(optimizer)                                  # grads now in true scale
+torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+scaler.step(optimizer); scaler.update()
+```
+`unscale_` is idempotent-per-step (call it once). For bf16, just `clip_grad_norm_` directly — no unscale.
+URL: https://github.com/pytorch/pytorch/blob/main/docs/source/notes/amp_examples.rst
+
+---
+
+## NaN / Inf
+
+### P8 — Where NaNs come from: the four arithmetic origins
+
+**Symptom**: loss prints `nan` (or `inf`) after N steps; everything was fine before.
+
+**Root cause** — NaN/Inf is produced by a *finite* set of ops on bad inputs:
+- `log(x)` / `log_softmax` with `x ≤ 0` (e.g. `log` of a `sigmoid` output that hit 0).
+- `sqrt(x)` / `x ** 0.5` with `x < 0`, or its grad at `x = 0` (`d/dx sqrt = 1/(2√x) → inf`).
+- division `a / b` with `b → 0` (un-epsilon'd normalization, variance ≈ 0 in BatchNorm/LayerNorm).
+- `exp(x)` overflow → `inf`, then `inf − inf` / `inf / inf → nan`.
+- fp16 overflow (P10): a value exceeds 65504 → `inf` → grads → NaN.
+
+**Fix — make the op stable, don't paper over it**:
+- Never hand-roll `log(softmax(x))` — use `F.log_softmax` / `F.cross_entropy` (fused, log-sum-exp-stable).
+- Add epsilon *inside* the unstable op: `torch.log(x + 1e-8)`, `torch.sqrt(x + 1e-12)`, `a / (b + 1e-8)`.
+- Clamp before the danger op: `x.clamp(min=1e-7)` before `log`; clamp logits before a manual softmax.
+- Use `eps` in the optimizer/norm (AdamW `eps=1e-8`; raise modestly if `v` is tiny and steps explode).
+
+URLs: https://docs.pytorch.org/docs/stable/generated/torch.log.html ·
+https://medium.com/better-ml/loss-spikes-in-training-causes-detection-and-mitigations-ed66e591b1a1
+
+### P9 — Find the exact op: anomaly detection + a cheap forward hook
+
+**Symptom**: loss is NaN but the stack trace points at `loss.backward()`, not the op that caused it.
+
+**Root cause**: by default the NaN surfaces wherever it's *consumed*, not where it was *born*.
+
+**Fix — two tools, cheap → precise**:
+- **Forward NaN hook (cheap, leave on)** — register on every module to catch the *first* layer to emit NaN:
+  ```python
+  for name, m in model.named_modules():
+      m.register_forward_hook(lambda mod, i, o, n=name:
+          print(f"NaN in {n}") if torch.is_tensor(o) and not torch.isfinite(o).all() else None)
+  ```
+- **`torch.autograd.set_detect_anomaly(True)` (expensive, debug-only)** — records the forward traceback of
+  each backward op and raises at the first backward NaN, pointing at the *forward* line that created it.
+  ```python
+  with torch.autograd.detect_anomaly():   # or set_detect_anomaly(True, check_nan=True)
+      loss.backward()
+  ```
+  The docs warn it "will slow down your program" (roughly an order of magnitude) — enable to *locate*, then
+  turn OFF for the real run, never ship it on. URL: https://docs.pytorch.org/docs/2.12/autograd.html
+
+### P10 — fp16 overflow vs underflow: read the GradScaler signal
+
+**Symptom (fp16)**: loss → inf/NaN; or the scaler skips *every* step and the scale factor collapses toward 0.
+
+**Root cause**: a forward activation exceeds fp16's 65504 max → `inf` → NaN grads → the scaler can't find a
+scale that avoids overflow, so it backs off forever. Common in attention logits and large residual sums.
+(Distinct from underflow, which the scaler *fixes* by P5.)
+
+**Fix**: switch fp16 → **bf16** (P1) — its fp32 range absorbs the large values; this is the single most
+effective fix. If bf16 is unavailable (V100/T4): keep the overflow-prone block (final logits, attention
+scores, the loss) in **fp32** via a nested `torch.autocast("cuda", enabled=False)` region, and apply z-loss
+(P15) / qk-norm (P16) to stop the logits growing.
+URL: https://medium.com/better-ml/loss-spikes-in-training-causes-detection-and-mitigations-ed66e591b1a1
+
+### P11 — NaN from the *data*, not the math
+
+**Symptom**: NaN appears at a specific, reproducible step (always step 4137), not gradually.
+
+**Root cause**: a corrupt sample — NaN/Inf pixel, all-zero target, label outside `[0, C)`, empty sequence,
+divide-by-zero in a custom transform. The math is fine; the input is poison.
+
+**Fix**: guard at the data boundary — `assert torch.isfinite(x).all(), f"non-finite input @ step {step}"`
+(fail loud, with the index). A reproducible-step NaN ⇒ inspect *that batch* (seed the loader, dump the
+index); a *step-varying* NaN ⇒ a numerics/LR problem (P12), not data. Smoke the data first — smoke
+*content* is owned by **verifying-dl-experiments** (cross-link **REQUIRED**).
+URL: https://arxiv.org/pdf/2311.03938
+
+---
+
+## Loss spikes / divergence
+
+### P12 — Loss spike / divergence: LR too high or warmup too short
+
+**Symptom**: training is stable, then the loss jumps orders of magnitude (spike), sometimes recovering,
+sometimes diverging to NaN — most often early, or after a fast LR ramp.
+
+**Root cause**: if the LR ramps too fast or starts too high, early updates land before activation norms and
+the optimizer's second moment (`v`) have stabilized, overshooting into sharp loss regions → gradient-norm
+blowup → spike. A sustained **grad-norm** rise typically *precedes* the loss spike by several steps.
+
+**Fix — in order of cheapness**:
+1. **Lengthen warmup** (linear ramp 0 → peak over e.g. 1–10% of steps); warmup is the single biggest lever on LR-sensitivity of final loss.
+2. **Lower peak LR** ~3–10× and re-check.
+3. **Log grad-norm every step** as the early-warning signal — spikes are predictable from activation/grad-norm scaling before they hit.
+4. Resume from the last good checkpoint *before* the spike (don't train through a diverged region).
+
+URLs: https://arxiv.org/pdf/2309.14322 ·
+https://apxml.com/courses/how-to-build-a-large-language-model/chapter-24-identifying-mitigating-training-instabilities/stabilization-techniques-revisited
+
+### P13 — Gradient clipping: the standard guardrail (and what constant clipping means)
+
+**Symptom**: occasional grad-norm spikes; or NaN right after a single bad batch.
+
+**Root cause**: one pathological batch (rare embedding IDs, an outlier sample) produces an outsized global
+grad norm that overshoots.
+
+**Fix**: clip global grad norm every step — `torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)`
+with `max_norm` ∈ [0.5, 1.0] typical for transformers (under the scaler: P7). **Diagnostic**: if clipping is
+active *every* step or needs an absurdly low threshold to stay stable, that's a symptom of a deeper problem
+(LR too high P12, bad init P17, architecture), not a fix — chase the cause. Global-norm clipping scales
+*all* grads down, so one embedding-heavy batch can throttle everything else that step — consider per-module
+clipping if embeddings dominate.
+URL: https://medium.com/better-ml/loss-spikes-in-training-causes-detection-and-mitigations-ed66e591b1a1
+
+### P14 — Skip-the-batch: drop the update when this step is non-finite
+
+**Symptom**: a single bad batch every few thousand steps NaNs the whole run; restarting wastes hours.
+
+**Root cause**: the optimizer applies a non-finite grad and permanently corrupts the weights.
+
+**Fix**: gate the optimizer step on finiteness (fp16's GradScaler already does this internally, P5; bf16
+needs it explicit):
+```python
+loss.backward()
+gnorm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+if torch.isfinite(gnorm):
+    optimizer.step()
+else:
+    optimizer.zero_grad(set_to_none=True)   # skip this batch, keep weights intact
+    skipped += 1
+```
+Log a `skipped` counter — a *rising* skip rate means a systematic problem (P12/P10), not stray bad data.
+Adaptive spike-clipping (ZClip) and momentum-reset on spike (SPAM) automate this for large runs. URLs:
+https://arxiv.org/pdf/2504.02507 · https://arxiv.org/pdf/2501.06842
+
+### P15 — z-loss: stop softmax logits from drifting unbounded
+
+**Symptom**: training is slowly destabilizing; the softmax normalizer / output logits grow over time and
+eventually overflow (acute in fp16/bf16); the "output logits diverge from log-probs" failure mode.
+
+**Root cause**: nothing pins the absolute scale of pre-softmax logits, so they drift up; large logits cause
+numerical instability and (in low precision) overflow → collapse.
+
+**Fix**: add an auxiliary **z-loss** = `1e-4 · (log Z)²` where `Z` is the softmax denominator
+(`log Z = logsumexp(logits)`), pulling `log Z → 0`:
+```python
+logits = model(x)
+z = torch.logsumexp(logits, dim=-1)
+loss = F.cross_entropy(logits, y) + 1e-4 * (z ** 2).mean()
+```
+Coefficient **1e-4** is the PaLM/ST-MoE value; too large lets z-loss dominate. Standard in LLM pretraining;
+also the recommended fix for MoE router instability. URLs:
+https://medium.com/dair-ai/papers-explained-50-palm-480e72fa3fd5 · https://arxiv.org/pdf/2202.08906 ·
+https://arxiv.org/pdf/2309.14322
+
+### P16 — qk-norm: kill attention-logit growth at high LR
+
+**Symptom**: a transformer diverges only at higher LR; the instability traces to attention scores (Q·Kᵀ)
+growing large before the softmax.
+
+**Root cause**: "growth of logits in attention layers" — one of the two dominant transformer instability
+modes (the other is output-logit divergence, P15). Unbounded attention logits saturate the softmax.
+
+**Fix**: apply **QK-LayerNorm** — LayerNorm query and key per-head before the dot-product. Combined with
+z-loss (P15) + warmup (P12), it lets small models train to similar loss across *orders of magnitude* of LR,
+i.e. removes most LR-sensitivity. URL: https://arxiv.org/pdf/2309.14322
+
+### P17 — Initialization & normalization placement
+
+**Symptom**: divergence in the first few hundred steps regardless of LR; or vanishing signal (P18) in deep
+stacks.
+
+**Root cause**: residual streams accumulate variance with depth; default init can make early
+activations/grads too large (spike) or too small (vanish). Norm/embedding init scale matters.
+
+**Fix**: scale residual-branch init by `1/√(2·n_layers)` (GPT-2-style); prefer pre-LN over post-LN for deep
+transformers; init embeddings at small std (~0.02). When unsure, copy a *known-good* config's init+norm
+scheme rather than tuning blind. URL: https://arxiv.org/pdf/2309.14322
+
+---
+
+## Gradients
+
+### P18 — Gradient explosion vs vanishing: diagnose by logging the norm
+
+**Symptom**: loss NaN/diverges (explosion) OR loss plateaus and the model never learns (vanishing).
+
+**Root cause**: per-layer grad norms blow up (explosion: deep nets, high LR, no clip) or decay to ~0
+(vanishing: saturating activations, bad init P17, too-deep unnormalized stacks).
+
+**Fix — measure first**:
+```python
+total = sum(p.grad.detach().norm()**2 for p in model.parameters() if p.grad is not None) ** 0.5
+# log `total` every step; also log per-layer norms when hunting the culprit layer
+```
+- **Explosion** (norm ↑↑): grad clipping (P13), lower LR (P12), longer warmup, bf16 over fp16 (P10).
+- **Vanishing** (norm → 0): residual connections, normalization layers, better init (P17), non-saturating
+  activations (GELU/SiLU over deep sigmoid/tanh stacks), check the LR isn't *too low*.
+
+A grad-norm trace is the cheapest, highest-signal stability instrument — log it from step 1.
+URL: https://apxml.com/courses/how-to-build-a-large-language-model/chapter-24-identifying-mitigating-training-instabilities/stabilization-techniques-revisited
+
+---
+
+## Reproducibility
+
+### P19 — Deterministic / repro knobs — set them, but the *interpretation* is delegated
+
+**Symptom**: same config + seed gives slightly different loss/metrics run-to-run.
+
+**Root cause**: nondeterministic CUDA kernels + `cudnn.benchmark` autotuning pick different algorithms per
+run; TF32/AMP add low-order noise on top.
+
+**Fix — the mechanical knobs (set these here)**:
+```python
+torch.manual_seed(s); np.random.seed(s); random.seed(s)
+torch.use_deterministic_algorithms(True)        # may need CUBLAS_WORKSPACE_CONFIG=:4096:8
+torch.backends.cudnn.deterministic = True
+torch.backends.cudnn.benchmark = False          # benchmark=True trades determinism for speed
+```
+**Whether a run-to-run delta is "a real effect vs cuDNN nondeterminism," and the full determinism
+methodology, is owned by verifying-dl-experiments (cross-link REQUIRED)** — catalogued as **U36** in
+`references/gotchas_universal.md`. This layer only ensures the knobs are *set and logged*. Determinism costs
+speed — enable for the datapoint that must be clean, not every throwaway run.
+URL: https://docs.pytorch.org/docs/stable/notes/randomness.html
+
+---
+
+## Pointers — adjacent layers, do NOT restate here
+
+- **`references/gotchas_universal.md`** — the *infra* failure modes that masquerade as numerics:
+  **U6** disk-full crashes `torch.save`, **U9** cgroup-OOM (bare `Killed`, not a NaN), **U28** CUDA/driver/
+  torch-build mismatch (`no kernel image` ≠ a precision bug), **U10/U11** VRAM OOM. Rule out infra before
+  chasing a "numerics" ghost.
+- **`verifying-dl-experiments`** (**REQUIRED** cross-link) — owns *is-the-number-real*: smoke **content**,
+  cuDNN-nondeterminism-as-metric-error (U36), collapse/constant-output diagnosis, "bug vs real effect." This
+  file makes training *run and stay finite*; that skill judges whether the converged result is *true*.
+- **`references/spot-resilience.md`** — checkpoint cadence so a divergence-and-resume (P12) loses minimal work.
+- **`references/multinode.md`** — NCCL/precision interactions in DDP (all-reduce dtype, loss-scale sync) for
+  multi-node runs; single-box users skip.