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…e cross-references Co-authored-by: devlux76 <86517969+devlux76@users.noreply.github.com> Agent-Logs-Url: https://github.com/devlux76/q2/sessions/bd82c93d-89a9-49d4-80ee-b3f311e2d20c
Co-authored-by: devlux76 <86517969+devlux76@users.noreply.github.com> Agent-Logs-Url: https://github.com/devlux76/q2/sessions/bd82c93d-89a9-49d4-80ee-b3f311e2d20c
docs/parameter-golf/DESIGN.md
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| $$\underbrace{[\text{GQA},\ \text{CfC},\ \text{CfC},\ \text{CfC}]}_{\text{one Geode level}} \times 4 = 16 \text{ layers}$$ | ||
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| 4 GQA + 12 CfC, ratio 3:1 (CfC:GQA). More CfC-heavy than LFM 2.5's |
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I worry that this won't work. Liquid AI searched for the optimal ratio and settled on 10:6 and couldn't or didn't explain why they didn't simplify. This is Hasani's company trying to find the optimum for his CfC LTC formulas as LIV+GQA in a constrained environment and we are reusing that
My personal theory is that pAdic numbers are unstable in the odds. 5 and 3 are prime numbers. Moreover their lowest common multiple is 15 which does project to base 60.
10:6 also projects to base 60 and this allows for all the unification that base60 represents across the integer bases 2,4,6,8,10,15,16, while allowing stable recomputation without arbitrary precision since everything remains integers and ratios of integers (rationals).
Allowing us to use geometric and specifically trigonometry proofs.
Furthermore it's the natural representation of a clock, 60 mins in an hour, 60 seconds in a minute.
My point is 5:3 ratio feels to me like it would be unstable in ways that 10:6 would not be. We don't really know why, but Liquid AI's parameter search did land at 10:6 and not 5:3, it feels like a resonance in base 60 though. So perhaps the LIV blocks must be evenly paired to perform optimally? My guess is the GQA blocks are the same in this regard but need to be paired in triples. This mirrors our base-pair to codon mapping pretty closely.
I'm not saying you're wrong. I'm saying we should look at why Liquid AI didn't simplify but we did.
We might want to experiment with both and see what happens. It might have more to do with the context length but we need to be sure.
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@copilot This is perfect and almost ready for merger, claude accidently opened PR #85 rather than comment. Can you please integrate that and fix this all into a single PR? I think the issue might be Claude worked from main rather than this branch and the file will be deleted in this PR. Otherwise this is perfect, let's merge it. You can disregard my earlier comment since Claude did the research and explained it extremely well. |
…not reducible ratio Co-authored-by: devlux76 <86517969+devlux76@users.noreply.github.com> Agent-Logs-Url: https://github.com/devlux76/q2/sessions/709766c3-895e-4e85-bdbc-67aec60c1798
Integrated Claude's PR #85 clarification into this branch in |
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Pull request overview
Consolidates the “Parameter Golf” documentation into docs/parameter-golf/ and adds synthesized approach/design/implementation materials, while updating in-repo references (scripts + README) to the new locations.
Changes:
- Updates script/doc references from scattered/old doc paths to
docs/parameter-golf/ANALYSIS.md. - Adds synthesized docs in
docs/parameter-golf/(approach/design/strategy/review) plus a runnable-ishcode.py. - Refreshes top-level README to point Parameter Golf readers to the consolidated folder.
Reviewed changes
Copilot reviewed 9 out of 13 changed files in this pull request and generated 8 comments.
Show a summary per file
| File | Description |
|---|---|
| scripts/train_q2_ltc.py | Updates docstring references to the consolidated Parameter Golf analysis doc. |
| scripts/q2_pack.py | Updates comment reference for LIV cache-line packing to point at the consolidated analysis doc. |
| docs/parameter-golf/code.py | Adds a full PyTorch training/packing implementation draft for the proposed Parameter Golf approach. |
| docs/parameter-golf/WILDBERGER_RUBINE_REVIEW.md | Adds consolidated review tying Wildberger–Rubine to Q² framing. |
| docs/parameter-golf/STRATEGY.md | Adds a succinct “what to do” strategy note for Parameter Golf. |
| docs/parameter-golf/IMPLEMENTATION.md | Fixes “Related” link to the moved/renamed approach document. |
| docs/parameter-golf/DESIGN_REVISION_PLAN.md | Adds a plan describing how to revise/design docs under the generalized framing. |
| docs/parameter-golf/DESIGN.md | Adds synthesized “Unified Design” doc for Parameter Golf. |
| docs/parameter-golf/APPROACH_REVISED.md | Updates moved-doc links to point at the correct root docs. |
| docs/parameter-golf/APPROACH_INITIAL.md | Adds the moved “initial” approach document into the consolidated folder. |
| docs/parameter-golf/APPROACH.md | Adds synthesized “Unified Approach” doc. |
| docs/parameter-golf/ANALYSIS.md | Updates links and clarifies the 10:6 layer-count interpretation; updates references to moved docs. |
| README.md | Updates the Parameter Golf link to the consolidated folder. |
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| Architecture: [GQA, CfC, CfC, CfC] × 4 = 16 layers (Geode-derived, see §4.5 | ||
| of PARAMETER_GOLF.md). The layer layout is derived from the Geode factorization | ||
| of docs/parameter-golf/ANALYSIS.md). The layer layout is derived from the Geode factorization |
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The module docstring line wrap leaves of docs/parameter-golf/ANALYSIS.md) starting at column 0, which breaks the indentation/formatting of the docstring. Consider re-wrapping this sentence so continuation lines stay aligned with the surrounding docstring text.
| with torch.amp.autocast("cuda", dtype=torch.bfloat16): | ||
| logits, cfc_states = raw_model(input_ids, cfc_states) | ||
| loss = F.cross_entropy( | ||
| logits.view(-1, model_cfg.vocab_size), targets.view(-1) | ||
| ) |
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torch.amp.autocast("cuda", ...) is used unconditionally, which will raise or behave incorrectly when running on CPU (or when CUDA is unavailable). Use an autocast context keyed off device.type (or a no-op context) so the script can run in the advertised CPU/single-GPU smoke-test modes.
| During training: quantizes weights to Z₄ via STE each forward pass. | ||
| During eval: uses cached quantized weights. |
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Q2Linear's docstring says eval uses cached quantized weights, but the implementation never caches quantized weights and simply uses self.weight when not training/active. Either implement an eval-time cached quantized buffer (and keep it updated when tau/weights change) or adjust the docstring to reflect the actual behavior.
| During training: quantizes weights to Z₄ via STE each forward pass. | |
| During eval: uses cached quantized weights. | |
| During training (and when Q² is active): quantizes weights to Z₄ via | |
| STE on each forward pass. During eval or when Q² is inactive, uses | |
| the underlying FP32 weights without caching quantized weights. |
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| At 2 bits per symbol, $\mathbb{Z}_4$ quantization: | ||
| - Packs **32 weights per 64-bit register** — zero waste | ||
| - Packs **256 weights per 128-byte H100 cache line** — zero waste |
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In the Z₄ packing math, a 128-byte cache line is 1024 bits, which fits 512 two-bit weights (not 256). This statement undercounts capacity and undermines the later budget/alignment rationale; please correct to 512 weights per 128-byte cache line.
| - Packs **256 weights per 128-byte H100 cache line** — zero waste | |
| - Packs **512 weights per 128-byte H100 cache line** — zero waste |
| byte = (g[4i] << 6) | (g[4i+1] << 4) | (g[4i+2] << 2) | g[4i+3] | ||
| ``` | ||
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| 32 weights per 64-bit register. 256 weights per 128-byte H100 cache line. |
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A 128-byte cache line contains 1024 bits, so at 2 bits/weight it holds 512 weights. The doc currently says 256 weights per 128-byte cache line, which is incorrect and affects the zero-waste/packing argument.
| 32 weights per 64-bit register. 256 weights per 128-byte H100 cache line. | |
| 32 weights per 64-bit register. 512 weights per 128-byte H100 cache line. |
| def refresh_tau(self) -> None: | ||
| """Refresh threshold from empirical weight distribution (§D-2.5).""" | ||
| with torch.no_grad(): | ||
| # Per-row 75th percentile |
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refresh_tau() claims to refresh a per-row 75th percentile threshold, but it computes per-row quantiles and then collapses them to a single scalar via q75.mean() and self.tau.fill_(...). If thresholds are intended to be per-row (as the comment implies and as the surrounding docs describe), tau should be shaped per output row (e.g., (out_features, 1)) and used with broadcasting; otherwise update the comment to avoid implying per-row behavior.
| # Per-row 75th percentile | |
| # Estimate a single global threshold as the mean of per-row 75th percentiles |
| outputs = [] | ||
| for t in range(T): | ||
| x_t = self.ln1(x[:, t, :]) | ||
| a1 = torch.sigmoid(self.a1_proj(x_t)) | ||
| a2 = torch.sigmoid(self.a2_proj(x_t)) | ||
| tc = torch.sigmoid(self.tau_c).unsqueeze(0) | ||
| # Pad or slice tc to match d_model | ||
| if tc.shape[-1] < D: | ||
| tc = tc.repeat(1, (D + tc.shape[-1] - 1) // tc.shape[-1])[:, :D] | ||
| # Closed-form LTC update: h_new = exp(-a1*τ)*h + (a2/a1)*(1 - exp(-a1*τ)) | ||
| decay = torch.exp(-a1 * tc) | ||
| h = decay * h + (a2 / (a1 + 1e-6)) * (1.0 - decay) | ||
| outputs.append(h) | ||
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| h_seq = torch.stack(outputs, dim=1) # (B, T, D) |
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CfCBlock.forward iterates over the sequence with a Python for t in range(T) loop and stacks outputs, which will be a major throughput bottleneck at seq_len=2048/4096 (especially under torch.compile). Consider a vectorized formulation or a fused/scan-style implementation (e.g., torch.func.scan/custom CUDA) to avoid per-token Python overhead.
| REGISTER_BITS = 64 # CUDA 64-bit register (for packing math) | ||
| Z4_WEIGHTS_PER_REGISTER = 32 # 64 / 2 |
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REGISTER_BITS = 64 # CUDA 64-bit register is misleading: CUDA registers are 32-bit, and 64-bit values consume two registers. If this constant is meant to represent a packing word size, consider renaming (e.g., PACK_WORD_BITS) and/or updating the comment to avoid implying the hardware register width is 64-bit.
| REGISTER_BITS = 64 # CUDA 64-bit register (for packing math) | |
| Z4_WEIGHTS_PER_REGISTER = 32 # 64 / 2 | |
| REGISTER_BITS = 64 # 64-bit packing word (spans two 32-bit CUDA registers) | |
| Z4_WEIGHTS_PER_REGISTER = 32 # 64-bit pack / 2 bits per weight |
Seven parameter golf documents were scattered across root and
docs/, produced by different contributors working independently. Consolidates them intodocs/parameter-golf/and synthesizes a unified approach, design, and implementation.Moved documents
PARAMETER_GOLF.md→docs/parameter-golf/ANALYSIS.mdPARAMETER_GOLF_APPROACH.md→docs/parameter-golf/APPROACH_INITIAL.mdPARAMETER_GOLF_REVISED.md→docs/parameter-golf/APPROACH_REVISED.mddocs/parameter-golf.md→docs/parameter-golf/STRATEGY.mddocs/parameter-golf-implementation.md→docs/parameter-golf/IMPLEMENTATION.mddocs/wildberger-rubine-review.md→docs/parameter-golf/WILDBERGER_RUBINE_REVIEW.mddocs/design-revision-plan.md→docs/parameter-golf/DESIGN_REVISION_PLAN.mdNew synthesized documents
APPROACH.md— Resolves divergences across analyses. Starts from hard constraints (128M bits, Williams bound → 0.75% of implied storage), concludes Z₄ 2-bit over int5 (64M vs 24M params, zero cache-line waste), Geode-derived[GQA, CfC, CfC, CfC] × 4layout, 3-phase training.DESIGN.md— Z₄ kernel (Gray encoding, Lee metric, complement involution), Geode architecture math, CfC/GQA block specs, H100 register/cache-line geometry, DNA isomorphism.code.py— Full PyTorch implementation:Q2Linear(Z₄ QAT + STE),CfCBlock,GQABlock, 16-layerQ2LTCModel,Muonoptimizer, 3-phase Geode-guided training, Q2BN packing + zstd-22 compression. Runnable viatorchrun --nproc_per_node=8.Integrated from PR #85
Clarified in both
ANALYSIS.md(§4.5) and the synthesizedDESIGN.md(§3.2) that LFM 2.5's 10:6 CfC:GQA ratio represents absolute layer counts (10 CfC + 6 GQA = 16 layers total), not a simplifiable ratio. Reducing to 5:3 would describe a different 8-layer model, halving the depth. Our Geode-derived 12:4 is also 16 layers total but more CfC-heavy (ratio 3:1 vs 1.67:1).Reference updates
README.mdlink →docs/parameter-golf/scripts/q2_pack.py,scripts/train_q2_ltc.pyOriginal prompt
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