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Add the Aida optimizer - #551
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shaneraphel wants to merge 5 commits into
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shaneraphel wants to merge 5 commits into
shaneraphel wants to merge 5 commits into
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get_trace averaged the Hessian diagonal over the spatial dims only for 4D kernels. Conv1d (3D) and Conv3d (5D) weights left tmp_output unbound. Average over dims 2..ndim-1 instead; 4D is unchanged.
Each mode factor of an order-k tensor enters to the power -1/(2k): -1/4 per side for matrices. The code used -1/k, twice the published exponent. The 7 pre-existing test_optimizer Shampoo failures are a state-dict precision issue on this torch version and fail identically without this change.
warmup_rate (default 1e-6) replaces the hard-coded warm-up slope and min_step_size (default 1e-2) the floor. Old checkpoints without the new group keys fall back to the old values. The 7 pre-existing test_optimizer Adafactor failures are a state-dict precision issue on this torch version and fail identically without this change.
Second-order optimizer with a diagonal Hessian estimate (SophiaG). Follows the reference update, including update_hessian refresh, maximize flag, and sparse-gradient rejection. CUDA-graph capturable execution is not ported. Wired into the test harness lists and README.
Layerwise-gradient-statistics optimizer with K mutual projections of the gradient and its running average. Modernizes the reference in-place add_ calls, keeps eps out of the state, and holds K/xi in the group defaults. Wired into the test harness lists and README.
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Summary
Adds
Aida, which exploits layerwise gradient statistics through mutual vector projections of the gradient and its running average, from https://arxiv.org/abs/2203.13273.The port follows https://github.com/guoqiang-zhang-x/Aida-Optimizer with three modernizations:
add_(scalar, tensor)calls use the pre-1.5 torch signature and fail on current torch; the port usesadd_(tensor, alpha=...)/addcmul_epsintoexp_avg_varin place, soepsaccumulates into the state every step; the port computes the denom out of placeKandximove from instance attributes into the group defaults (with validation and__setstate__fallbacks), so checkpoints round-trip; the deadamsgradgroup key is droppedFixes #538.
Test plan
pytest tests/test_aida.py: 4 passed — two steps match a hand-computed reference for K in {1, 2, 3},exp_avg_varafter one step is exactly(1-b2)*residual^2with no eps,reset()clears state, invalid K/xi rejectedpytest tests/test_optimizer_with_nn.py -k Aida: 1 passedpytest tests/test_param_validation.py -k Aida: 2 passedtests/test_optimizer.py -k Aida: 7 failures in the state-dict round-trip assertion, the same pre-existing torch-2.11 harness issue documented in Add the Sophia optimizer #550 (verified identical on Lion, Apollo, Adahessian without any change)