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Port the inference-time forward pass of the CELO2 / ELO-CELO2 learned optimizers from the original JAX/optax implementation into pure PyTorch, as drop-in pylo optimizers. - pylo/models/CELO2_MLP.py: CELO2MLP, the split-input per-parameter MLP backbone (14 split first-layer weights + dense layers, HF Hub mixin). - pylo/optim/CELO2_naive.py: CELO2_naive optimizer — momentum / RMS / factored Adafactor accumulators, CELO2 feature stack, Newton-Schulz orthogonalization for 2D+ params, AdamW for 1D params over the shared accumulators, and a warmup + cosine LR schedule. Loads its meta-model from HuggingFace (default DiamondXL/celo2); a local converted checkpoint or an explicit network take precedence. - pylo/optim/ELO_CELO2_naive.py: ELO_CELO2_naive — at inference the ELO expert mechanism is disabled, so it reduces to the CELO2 forward with the ELO default hyper-parameters (weight_decay=0.1, clip_grad=True); default meta-model DiamondXL/elo-celo2. - scripts/convert_celo2_checkpoint.py: convert a JAX/Haiku theta checkpoint into a CELO2MLP state_dict. - tests/test_celo2.py: step/update, higher-rank param, state-dict resume, and a JAX numerical-alignment test (2D update matches the reference to ~3e-6; auto-skips when the JAX source is unavailable). - Register the new classes in the pylo / pylo.optim / pylo.models inits. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Port the inference-time forward pass of the ELO learned optimizer from the original JAX implementation (ELO_AdafacMLPLOpt) into pure PyTorch. At inference the ELO expert mechanism is disabled, so the update reduces to the Adafactor-MLP forward — identical features and meta-model (MetaMLP, 39 inputs / 2 outputs) to AdafacLO. ELO differs only in using raw accumulator decays, a warmup-then-constant (optionally cosine) LR schedule, and the update rule p -= lr * (dir*exp(mag*exp_mult) + wd*p). - pylo/optim/ELO_naive.py: ELO_naive optimizer (reuses MetaMLP and the AdafacLO feature helpers); default meta-model DiamondXL/elo. - scripts/convert_elo_checkpoint.py: convert a JAX/Haiku ELO theta into a MetaMLP state_dict (transposes the dense weights). - tests/test_elo.py: step/update, state-dict resume, and a JAX numerical-alignment test (matches the reference to ~1.5e-8; auto-skips when the JAX source is unavailable). - Register ELO_naive / ELO in the pylo and pylo.optim inits. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ests End-to-end comparison against the real JAX optimizers revealed that the standalone Celo2LOpt drives its LR schedule through an optax chain whose step count starts at 0 (so the first update uses schedule(0)), whereas ELO_Celo2LOpt evaluates the schedule at iteration+1. CELO2_naive was 1-indexed (matching ELO-CELO2), leaving a ~1.8e-3 warmup-phase discrepancy vs Celo2LOpt. - Add a per-class LR-schedule offset: CELO2_naive is 0-indexed (matches Celo2LOpt), ELO_CELO2_naive overrides to 1-indexed (matches ELO_Celo2LOpt). AdamW bias correction stays 1-indexed in both. - Add test_jax_end_to_end_alignment: drives the real Celo2LOpt / ELO_Celo2LOpt over a multi-step trajectory with a 2D weight + 1D bias, nonzero weight decay and enabled gradient clipping, exercising the full step() (1D AdamW, schedule, weight decay, global-norm clipping). Both match the reference to ~6e-8 (was only the 2D core verified before). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Keep the existing VeLO_CUDA Quick Start intact and append an ELO-CELO2 example plus an "ELO series" entry with the arXiv link. Pure additions (no content removed from the xiao_elo README). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Merge the CUDA path from the ELO-torch line into xiao_elo (additive; the existing naive optimizers and VeLO/AdafacLO kernels are untouched): - pylo/csrc/celo2_kernel.cu: fused feature-construction + split-input MLP forward kernel for CELO2 / ELO-CELO2 - pylo/optim/CELO2_cuda.py, ELO_CELO2_cuda.py: Python wrappers - tests/test_celo2_cuda.py: CUDA-vs-naive numerical alignment tests - setup.py: register the celo2_cuda_kernel CUDAExtension - pylo/optim/__init__.py: override CELO2 / ELO_CELO2 to the CUDA variants when the extension is available, falling back to naive otherwise - .gitignore: ignore *.pickle LO checkpoints Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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add LOs trained with ELO