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4aaa36a57a |
style: cargo fmt --workspace (whitespace/wrapping only, no semantic change)
Whole-workspace rustfmt pass picked up while iterating on Mamba GPU backward work. Verified formatting-only via diff sampling; no logic changed. Co-Authored-By: Claude Sonnet 5 <[email protected]> |
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54a9652041 |
feat(batch16): SOAP optimizer, lookahead decoding, SWA+SWAG
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- SoapOptimizer: Adam in Shampoo eigenbasis (arXiv:2409.11321); Jacobi eigendecomposition for L/R Kronecker factors; projection G_hat=Q_L^T@G@Q_R, bias-corrected Adam, unproject U=Q_L@U_hat@Q_R^T; 1D plain Adam fallback; 19 tests - LookaheadDecoder: NGramCache (FIFO eviction, count-sorted candidates); draft-then-verify loop; auto-cache update on accepted tokens; LookaheadStats with avg_tokens_per_step; 22 tests - SwaTrainer+SwagBuffer: cyclic cosine LR schedule; online incremental mean (SwaBuffer); E[θ²]-E[θ]² diagonal variance + low-rank deviation columns; Box-Muller SWAG sample; 29 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]> |
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6daa20c94b |
feat(batch15): Shampoo optimizer, beam search decoder, sliding window attention
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- ShampooOptimizer: Kronecker-factored L/R preconditioners; Schulz iteration
for A^{-1/4} (two-pass: inv_sqrt then inv_sqrt of sqrt); spectral-norm
normalization; large-dim SGD fallback; 16 tests
- BeamSearchDecoder: length normalization (Wu et al. α); n-gram blocking;
EOS suppression before min_length; DiverseBeamSearchDecoder with per-group
diversity penalty; 20 tests
- SlidingWindowAttention: causal/bidir window; global tokens attend to all;
O(n·W) forward_single_head + multi-head forward; AttentionStats sparsity;
WindowMask; 20 tests
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
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e00a6da018 |
feat(batch14): Muon optimizer, logit processors, per-token activation quantization
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- MuonOptimizer: Nesterov momentum + quintic Newton-Schulz orthogonalization (arXiv:2409.20325); 5-iteration NS maps gradient to near-orthogonal matrix; 1D fallback skips NS; decoupled weight decay; 16 tests - LogitProcessorList: temperature, top-k, top-p nucleus, min-p, repetition/ presence/frequency penalty, eta-sampling; softmax/log_softmax/argmax/ sample_token helpers; 39 tests - ActivationQuantizer: per-token dynamic INT8/FP8E4M3 scaling for inference activations; per-tensor mode; dequantize; max_error diagnostic; 19 tests Co-Authored-By: Claude Sonnet 4.6 <[email protected]> |
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9c3b9f82f0 |
feat(batch13): EMA model weights, cross-layer weight sharing, schedule-free optimizer
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- ModelEma: decay-weighted shadow weights with warmup ramp, bias correction, apply/restore swap for eval, and shadow_drift L2 diagnostic - SharedLayerStack: FullSharing/GroupedSharing/AlternatingPairs strategies (ALBERT-style); memory_reduction_ratio(); LCG-seeded SharedFfnWeight - ScheduleFreeOptimizer: Defazio 2024 z/x dual sequences, c_t cubic interpolation coefficient, Adam+SGD variants, weight decay 48 tests + 5 doctests Co-Authored-By: Claude Sonnet 4.6 <[email protected]> |
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be3e3965b1 |
feat(batch10): attention sinks (StreamingLLM), chunked prefill, per-layer LR decay
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- Attention sinks (arXiv:2309.17453): AttentionSinkEviction always retains first sink_size KV positions + last window_size; evicts middle band in O(evict_count); select_evict_positions/should_retain consistent; 15 tests - Chunked prefill (vLLM arXiv:2309.06180): ChunkedPrefillScheduler splits long prompts into chunk_size=512 chunks interleaved with decode steps (max 128 decode tokens/step); PrefillChunkState tracks progress/remaining/completion; drain_completed(); 14 tests including 1500-token→3-chunk coverage - Per-layer LR decay (ULMFiT / discriminative fine-tuning): LayerLrDecayConfig with base_lr * decay_rate^(num_layers-1-depth); LayerLrDecayBuilder parses layer/layers/ h/blocks/bracket notation param names; LayerLrScheduler with outer multiplier for cosine/linear schedule composition; 14 tests + 1 doctest Co-Authored-By: Claude Sonnet 4.6 <[email protected]> |
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a7d9969702 |
feat(galore2): implement GaLore-2 optimizer with 12 pure-CPU tests
Adds GaLoreAdamW to crates/training/rtx-transformers — a memory-efficient
AdamW variant that reduces optimizer state by projecting gradients to a
low-rank subspace and periodically refreshing it via randomised SVD.
Key facts verified by tests:
- Memory formula: for [rows×cols] param with rank r,
GaLore stores: rows*r + 2*r*cols f32 elements
AdamW stores: 2*rows*cols f32 elements
For [256×256] r=64: ratio=0.375 (62.5% reduction)
For [4096×4096] r=128: ratio<10% (>90% reduction)
- Subspace refresh triggers when (step - last_refresh) >= update_proj_gap
- Momentum inheritance: m_new = new_Q^T @ old_Q @ m_old preserves direction
- Small params (< min_param_size=4096 elements) fall back to standard AdamW
Files changed:
- crates/training/rtx-transformers/src/optimizers/galore.rs (new)
- crates/training/rtx-transformers/src/optimizers/mod.rs (mod + re-exports)
Co-Authored-By: Claude Sonnet 4.6 <[email protected]>
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02d382d5f6 |
style: apply rustfmt across all crates and demos
Consistent formatting pass: line wrapping, import sorting, trailing whitespace removal, let-chain indentation, merged derive attributes, and unsafe block reformatting. Co-Authored-By: Claude Opus 4.6 (1M context) <[email protected]> |
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4d88dc0584 | Initial commit |