Giorgio Gilestro
01d87e504f
The real-weight image of E12, and the answer to the mode-connectivity reviewer. Small no-BN MLPs on MNIST, forked from a shared base and trained independently, are weight-averaged; we measure the linear-mode-connectivity barrier before and after in-house deterministic Git Re-Basin permutation alignment (neural/rebasin.py, scipy linear_sum_assignment), decomposing it into removable (coordinate artefact) and residual (reproductive isolation). kind: speciation_real. Result (3 reps): - shared (same task, shared fork): no barrier — trivially mergeable. - independent (same task, different init): naive 0.056, alignment removes 98% (residual 0.001) — the incompatibility is a coordinate artefact. - conflict (conflicting label maps): naive 0.496, alignment removes 0% (residual 0.496) — genuine reproductive isolation. Because alignment demonstrably works on the independent case, the conflict residual is real, not a failure to align. - Isolation cliff (speciation_real_cliff): residual rises 0.00->0.13->0.19->0.28-> 0.40->0.49 with the fraction of conflicting classes — the real-weight mirror of E12's cliff; residual==naive throughout (functional, not coordinate). rebasin.py sanity-gated (recovers a known permutation exactly). plot_speciation_real.py (2-panel), +4 pure-NumPy tests (142 green), README with honest positioning vs Git Re-Basin / Entezari / Frankle / Pari 2024 / Zhou 2026. Wired into make mnist (needs torchvision). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
47 lines
2.4 KiB
Makefile
47 lines
2.4 KiB
Makefile
# Layer 1 + Layer 1.5 automation. The uv venv (built from the committed uv.lock) is the
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# reproducibility source of truth; every target runs inside it via `uv run`.
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.PHONY: env env-neural env-mnist env-llm test layer1 layer2 neural mnist llm figures clean
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env: ## build .venv from the committed lockfile
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uv sync --extra dev
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env-neural: ## add the Layer 1.5 torch stack (GPU; Stage C onward)
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uv sync --extra dev --extra neural
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env-mnist: ## add torchvision for the real-MNIST confirmation tier
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uv sync --extra dev --extra neural --extra mnist
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test: ## correctness tests + scientific-validation tests (the spine of trust)
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uv run pytest
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layer1: ## run experiments E1-E6 + the learning-kernel bridge (analytic)
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for e in E1 E2 E3 E4 E5 E6 E7 E8 E9 E10 E11 E12 E12_nk kernel_sharpen kernel_smooth; do uv run python -m knowledge.experiment configs/layer1/$$e.yaml; done
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neural: ## run Layer 1.5 synthetic neural experiments (excludes the MNIST/torchvision tiers)
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for c in configs/neural/*.yaml; do case "$$c" in *mnist*|*speciation_real*) ;; \
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*) uv run python -m neural.experiment "$$c" ;; esac; done
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mnist: ## run the torchvision tiers: MNIST collapse + E13 real-weight speciation (needs env-mnist)
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uv run python -m neural.experiment configs/neural/mnist_collapse.yaml
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uv run python -m neural.experiment configs/neural/speciation_real.yaml
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uv run python -m neural.experiment configs/neural/speciation_real_cliff.yaml
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env-llm: ## add the LLM stack for the Layer-2 prototype (GPU; transformers/peft)
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uv sync --extra dev --extra neural --extra llm
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llm: ## run the LLM prototypes: merge (fusion) + moe (union) + directed (offspring select)
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uv run python -m llm.experiment configs/llm/merge.yaml
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uv run python -m llm.experiment configs/llm/moe.yaml
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uv run python -m llm.experiment configs/llm/directed.yaml
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layer2: neural ## alias: Layer 1.5 is the current Layer-2 deliverable (LLM rung deferred)
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figures: ## regenerate figures from committed results
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for e in E1 E2 E3 E4 E5 E6; do MPLBACKEND=Agg uv run python figures/plot_$$e.py; done
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for p in figures/plot_*.py; do case "$$p" in */plot_E[1-6].py|*/_*) ;; \
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*) [ -e "$$p" ] && MPLBACKEND=Agg uv run python "$$p" ;; esac; done
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clean: ## remove caches and generated results (keeps committed manifests)
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rm -rf .pytest_cache **/__pycache__
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find results -type f ! -name '.gitkeep' -delete 2>/dev/null || true
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