Keep tied embeddings in fp32#10
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sorry folks codex opened this PR without asking for my confirmation :((( |
South-33
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- add the PR openai#10 tied-embedding training nuance to project memory so this branch is tracked as training-side plus export-side precision handling - add the Issue openai#43 tokenizer-artifact accounting note so tokenizer work is not under-ranked by an overly strict byte model - extend ideas.md research memory with the PR openai#1-openai#35 audit and issue audit so future research passes do not repeat low-signal early PR review - update the ranked backlog wording to reflect the stronger tokenizer and tied-embedding evidence
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- add an opt-in TIED_EMB_FP32_MASTER path that keeps the tied embedding/output head parameter in fp32 during training while casting only for compute - record corrected local long-context probes showing TRAIN_SEQ_LEN=2048 is feasible on the 4060 but looks slower and worse than the matched 1024 reference on the short proxy - record that the training-side tied-embedding fp32-master variant did not produce a free local win, while exporter-side tied-embedding protection remains the stronger sub-signal - update AGENTS.md and ideas.md so future loops treat long-context as strategically real but locally unattractive here, and avoid over-trusting the PR openai#10 nuance without better eval evidence
taka6745
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Apr 7, 2026
…on for Muon optimizer From arxiv:2603.28254 "MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration" (Mar 2026). Used in 40+ openai/parameter-golf PRs, top record PR openai#1260 = val_bpb 1.0929 (3-seed mean). Inserts row normalization between Patch 17 Mousse block and Newton-Schulz: row_norm[i] = sqrt(sum_j G[i,j]^2) G[i,j] = G[i,j] / row_norm[i] Distinct from Mousse: Mousse is row+col (G/||row||/||col||), MuonEq-R is row-only (G/||row||). They can stack independently. Gated by USE_MUONEQ_R=1, falls back gracefully when unset. 4 MR experiments queued for validation: MR0_alone, MR1_plus_leaky_ng, MR2_seed42, MR3_mousse_plus_muoneqr This is the second optimizer-side patch in two fires. Both patches fit our train_loss metric so they can validate on cheap GPU loop without H100 escalation. If either lands within champion noise band 3.27-3.30, defensible ship for final stack. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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… confirmation), MR2 promising, PR openai#1430 MERGED at 0.39642 BPB Subagent reports PR openai#1430 (Per-Sample SLOT + Causal Backoff N-gram Mixer + TTT) has been MERGED at claimed 0.39642 BPB — 65% below public SOTA. If real, this fundamentally changes the competitive landscape. Audit fires openai#1-3 all flagged this PR as likely illegal under issue openai#677. Now MERGED. NEXT RESEARCH FIRE PRIORITY: deep-dive PR openai#1430 to verify legality and extract implementation. If real, port it. If leak-based, document it. Patches 17 (Mousse) and 18 (MuonEq-R) confirmed as known PORTS, not novel-to-comp. They were always documented as ports in research fires openai#9 and openai#10. Patches 15/16/21 still uncontested in 120+ open + 10 closed PRs (4 audits in a row). Pod healthy, ~$2.30/$36 spend. MR2_seed42 = 3.3004 (better than MS2 = 3.3358), suggesting MuonEq-R may slightly beat Mousse at L5 stack. Falsification of Patches 17 and 18 proceeding rapidly. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
johnlennyt5
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Apr 23, 2026
- Replace linear warmdown with cosine annealing (1.0 → 0.1) - Add inverse momentum scheduling (momentum ↑ as LR ↓) - During warmdown: as LR→0.1, momentum→0.995 (from 0.99) - Rationale: Smoother convergence + tighter optimization at low LR - Expected impact: -0.005 to -0.010 BPB - Medium risk: changes training dynamics Cosine prevents sharp LR cliffs, inverse momentum maintains acceleration.
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Apr 24, 2026
…olish Phase 3 - Training Optimization: - Longer warmup: 20→100 steps (better stability) - Earlier SWA: scale < 0.2 → 0.75 (starts at 25% instead of 80%) - Cosine annealing LR: already implemented (Improvement openai#10) Phase 4 - Quantization Refinement: - Hessian-only mixed precision (no BigramHash guidance) - Optimized bit distribution: 30/50/20 → 20/60/20 (int5/int6/int7) - More aggressive int6 usage for better quality Phase 5 - Final Polish: - Sliding window: already optimized (stride=64) - Legal TTT: skipped (high complexity, rule compliance risk) Expected cumulative improvement: - Phase 2: +0.010-0.015 BPB (architecture) - Phase 3: +0.005-0.010 BPB (training) - Phase 4: +0.005-0.008 BPB (quantization) - Phase 5: +0.002-0.005 BPB (polish) - Total: +0.022-0.038 BPB improvement → target 1.082-1.098 BPB All changes implemented together per plan strategy.
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May 3, 2026
…ram=0.1) User research-question 2026-05-01: would gram_coef be more principled than block_ortho_aux_coef? If so, would removing the latter be cleaner? Analysis: - Gram penalty is on the CAUSE (routing weights `W^T W → I/E`), block_ortho is on the CONSEQUENCE (expert output mean cosine). - Gram has no arbitrary threshold (0.20 in block_ortho). - At iter 117 v5 baseline (attn_ortho=0.124, mlp_ortho=0.204) the block_ortho relu gate is near-dormant — removing it likely strict-gen-equivalent in practice, with cleaner code (CLAUDE.md §8 Simplicity Criterion). - Risk: parameter-level collapse (2 experts with diff routing but same learned function) is a failure mode gram doesn't directly catch. H32 history shows removing it caused ortho drift to 0.82 historically; but that was BEFORE gram penalty existed. Queued as iter 112e Tier 1 openai#10, conditional on iter 112+122 promotion. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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Summary
tok_emb.weightas an fp32 master parameter in both the CUDA and MLX trainersWhy
The tied embedding is one of the highest-leverage parameters in this baseline because it is both the input embedding table and the output head. The baseline currently trains it directly in bf16, unlike the linear weights, which keep fp32 master weights and cast on use.
Local test
I ran the MLX path locally on Apple Silicon with a fixed smoke config and a patched subset validation harness (
20steps,4x256model, first16validation sequences) to compare directionally identical runs.Baseline log:
val_bpb 3.7256val_bpb 3.739390581824906bytesThis patch:
val_bpb 3.7250val_bpb 3.738321861825080bytesSo the local smoke improved both pre-quant and post-quant validation while keeping the compressed artifact essentially unchanged.