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External review: per-variant audit + design notes
- Two acpx external reviews (codex + opencode): * docs/audit/variants_review.md: per-variant paper-vs-impl audit * docs/audit/design_review.md: peft EVA / baukit / antipasto3 vs lora-lite * docs/audit/SUMMARY.md: aggregate verdicts + 3 risks + 5 follow-ups - docs/refs/: peft_eva.py, peft_eva_finetuning.py, baukit_nethook.py, antipasto3_svd_adapter.py for offline reference Findings: LoRA clean; PiSSA/DoRA/IA3/HRA/DeLoRA have documented partial deviations. Top risks: init/grad tradeoffs hidden by coarse tests; qwen probe lacks strict identity tol; IA3 target placement untested.
This commit is contained in:
wassname
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# Design review: should lora-lite borrow from peft EVA / baukit / antipasto3?
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You are reviewing a minimal from-scratch LoRA library (`lora-lite`) and comparing
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it to three reference implementations. Goal: identify cherry-picks that would
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**reduce** complexity or unlock missing capability, **without bloating the lib**.
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## Inputs
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- lora-lite code: `src/lora_lite/` (adapter.py, target.py, variant.py, config.py, variants/*.py)
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- Reference: `docs/refs/peft_eva.py` (peft's EVA: data-driven SVD-of-activations init)
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- Reference: `docs/refs/peft_eva_finetuning.py` (example usage)
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- Reference: `docs/refs/baukit_nethook.py` (nethook: forward/backward hook patterns)
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- Reference: `docs/refs/antipasto3_svd_adapter.py` (wassname's earlier JAX SVD adapter)
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## Project ethos (read first)
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Lora-lite is fail-fast research code. Principles:
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- No defensive programming, no fallbacks, no legacy compat
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- Simplicity beats features. If you add X you must remove equivalent complexity.
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- Each variant is one file with paper URL + honest deviation notes.
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- Targets discovered by structural type-check, not name regex.
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- Hooks via plain torch forward_pre_hook on a single layer, no global registry.
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Read `AGENTS.md` if present.
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## Questions to answer
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For each reference, answer:
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### A. peft EVA (`docs/refs/peft_eva.py` + `peft_eva_finetuning.py`)
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1. What does EVA actually do? (1-paragraph summary; cite line numbers)
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2. What would a *minimal* EVA variant in lora-lite look like? Sketch the API:
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- How does the user pass calibration data?
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- Where does the SVD-of-activations happen — in `init()` with a callback,
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or as a separate `calibrate(model, dataloader, cfg)` step before `attach`?
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3. Does peft's implementation have anything we could **drop** if we re-implemented?
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(e.g. the rank-redistribution logic, the resume-from-checkpoint plumbing)
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4. Does lora-lite's current `Variant.init(layer, cfg)` signature support EVA, or
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would we need to extend it? Recommend the **smallest** API change.
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### B. baukit nethook (`docs/refs/baukit_nethook.py`)
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1. What does `TraceDict` / `Trace` give us that our current per-layer
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`forward_pre_hook` registration does not?
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2. Would adopting `baukit` for hook management (a) simplify our adapter.py,
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(b) complicate it, or (c) be neutral? Quote specific lines from
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`src/lora_lite/adapter.py` to justify.
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3. Lora-lite's principle: minimize deps. Is baukit worth a dep? Or should
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we just **inline** the 1-2 useful patterns?
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### C. antipasto3 SVD adapter (`docs/refs/antipasto3_svd_adapter.py`)
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1. This is the user's earlier JAX work. Anything in there (init style, scale
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parameterization, save/load format) that lora-lite should adopt or
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deliberately diverge from?
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2. Does it suggest a cleaner factoring for PiSSA-like methods?
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## Output format
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For each (A, B, C), end with:
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**Recommendation: ADOPT / SKIP / PARTIAL**
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If ADOPT or PARTIAL, list the specific lines/patterns to import and the
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approximate net line-count impact on lora-lite (+ added, − removed).
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## Hard rules
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- Do NOT propose code edits. This is design notes only.
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- Do NOT recommend adding a feature unless you can name what to remove or
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simplify in exchange.
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- Be specific. "Could be cleaner" is not a recommendation; "Replace L42-L67
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in adapter.py with a 5-line TraceDict call" is.
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- If a reference's pattern is worse than what lora-lite already has, say so.
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# Per-variant paper-faithfulness audit for lora-lite
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You are reviewing a small from-scratch PEFT library (`lora-lite`) that re-implements
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6 LoRA variants. Your job: independent paper-vs-implementation sign-off for each.
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## Inputs available locally
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- Code: `src/lora_lite/variants/{lora,pissa,dora,ia3,hra,delora}.py`
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- Adapter base + plumbing: `src/lora_lite/{adapter.py,target.py,variant.py,config.py}`
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- Papers (extracted text): `docs/papers/{lora,pissa,dora,ia3,hra,delora}_*.txt`
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- Smoke log (toy + bnb): `logs/smoke.log`
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- Real-model probe log (Qwen0.6B, 16 SGD steps): `logs/qwen_probe.log`
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- Reference implementations (peft / antipasto3 / baukit): `docs/refs/*.py`
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## What I want from you (per variant, all 6)
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For each of `lora, pissa, dora, ia3, hra, delora` produce a section with:
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1. **Paper claim summary (1-3 sentences)** — cite paper file + section/eq number.
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E.g. "PiSSA (docs/papers/pissa_2404.02948.txt §3.1, eq.4): A,B = top-r SVD of W,
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W_res = W - BA; trains A,B with W_res frozen."
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2. **What our code does** — point to the function and key lines in
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`src/lora_lite/variants/<v>.py`. Quote ≤5 lines.
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3. **Match? Y / Partial / N** — explicit verdict. If Partial, state the deviation
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and whether it is documented in the variant's docstring.
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4. **Smoke evidence** — quote the exact row from `logs/smoke.log` (toy + bnb)
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and the row from `logs/qwen_probe.log`. State whether the numbers are
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consistent with paper expectations (e.g. PiSSA should have nonzero perturb at
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t=0 because B@A reconstructs W; LoRA/HRA/IA3/DeLoRA should be identity at t=0).
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5. **Bugs / concerns** — anything actually wrong, especially:
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- Gradient flow issues
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- Wrong normalization / scaling
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- Wrong initialization (e.g. PiSSA without SVD, HRA without orthogonality)
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- Missing or wrong save/load handling
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- Numerical issues (dtype, in-place ops on grad-required tensors)
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6. **Confidence** — High / Medium / Low, with one-line reason.
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## Final aggregate
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After the 6 sections, produce a Markdown table:
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| variant | paper match | smoke pass | qwen pass | bugs found | confidence |
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And a 3-bullet "biggest risks" summary.
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## Rules
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- Be skeptical. The previous audit found IA3, HRA, DeLoRA bugs that had been
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declared "OK". Assume nothing.
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- If the smoke log does not include a check that you'd want to see, flag it as
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a missing test — don't infer correctness from absence.
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- Quote evidence; do not paraphrase code.
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- Use file links: `src/lora_lite/variants/lora.py:42` style.
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- Do NOT edit code. Output is a verdict only.
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- If you cannot determine something from the available files, say so explicitly
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rather than guessing.
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Write the full review to stdout. I will redirect to a file.
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# External-Review Summary
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Two independent reviews via `acpx` external models. Full reviews:
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- [docs/audit/variants_review.md](variants_review.md) — per-variant paper-faithfulness audit
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- [docs/audit/design_review.md](design_review.md) — peft EVA / baukit / antipasto3 vs lora-lite design
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## Per-variant verdict
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| variant | match | bugs found | confidence |
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|---|---|---|---|
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| lora | Y | none material | High |
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| pissa | Partial | bf16/Qwen init err 0.31; deviation `alpha==r` only in inline comment; residual not in saved adapter | Medium |
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| dora | Y | possible denominator-gradient mismatch with paper's "cost-saving" variant | High |
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| ia3 | Partial | targets q/v not paper's k/v/ffn-down; deviation documented but not tested | Medium |
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| hra | Partial | gate=0 init -> dU/dx=0 first step (lora_U dead); not orthogonal when gate != 1 | Medium-Low |
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| delora | Partial | no Eq.9 frozen-copy init; lambda0=0 -> A/B dead grad; lambda0=0.1 breaks identity | Medium |
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## Three biggest risks (reviewer's words)
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1. **Initialization vs gradient-flow tradeoffs are hidden by coarse tests.** HRA's `lora_U` and DeLoRA's `A/B` can be initially dead while `grad_nonzero=True` still passes (because *some* lora_* param has nonzero grad).
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2. **Qwen probe pass criteria do not enforce paper identity.** PiSSA shows `id_err=0.31`, DeLoRA `id_err=0.72`, but log says PASS.
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3. **Target semantics under-tested.** IA3's documented k/v/ffn deviation is never exercised by a positive test.
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## Design recommendations
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| ref | verdict | impact |
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|---|---|---|
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| peft EVA | PARTIAL — add `calibrate(model, dataloader, cfg)` (~50 lines) | +50 lines, additive |
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| baukit nethook | SKIP — current 5-line hook registration is simpler | 0 |
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| antipasto3 SVD | ADOPT concept (learnable delta_s) — no code change now | 0 |
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## Recommended follow-up tasks (need user approval before implementing)
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A. **Per-param gradient probe**: extend smoke to assert grad on *each* lora_* param at step 0. Catches HRA/DeLoRA init-dead-param bug.
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B. **Per-variant identity tolerance in qwen probe**: PiSSA/DeLoRA need a stricter check (or relative tol against `||y_base||`) instead of "passes if id_err < some constant".
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C. **IA3 paper-faithful test row**: add one Qwen probe configuration with `target_names=k_proj|v_proj|down_proj` to exercise the documented IA3 placement.
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D. **PiSSA equivalence test against `peft.PiSSA`**: same seed + alpha=r, compare `B@A` reconstruction. Adds `peft` to test extras only.
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E. **EVA variant**: implement minimal `calibrate()` per design review (~50 lines). Optional, but provides our first data-driven init variant for the user's stated interest.
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[client] initialize (running)
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[client] session/new (running)
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[thinking] This is a detailed design review request. I need to: 1. Read the lora-lite codebase to understand its current structure 2. Read the three reference implementations 3. Answer specific questions for each Let me start by exploring the codebase structure and reading the key files.
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[tool] glob (pending)
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input: {}
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[tool] glob (pending)
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input: {}
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[tool] glob (completed)
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kind: search
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input: src/lora_lite/**/*.py
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output:
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/lora.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/delora.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/hra.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/dora.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/ia3.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/__init__.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/pissa.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/__init__.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py
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[tool] glob (completed)
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kind: search
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input: docs/refs/*.py
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output:
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/baukit_nethook.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva_finetuning.py
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/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py
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[thinking] I need to read all these files to do a proper comparative analysis. Let me read the key lora-lite files first (adapter.py, variant.py, variants/lora.py), then the reference files.
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[tool] read (pending)
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input: {}
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[tool] read (pending)
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input: {}
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[tool] src/lora_lite/adapter.py (completed)
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kind: read
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input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py
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output:
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<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/adapter.py</path>
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<type>file</type>
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<content>
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1: """attach / detach / save / load. The whole runtime."""
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2: from __future__ import annotations
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3: import torch
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4: from torch import nn
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5: from torch.utils.hooks import RemovableHandle
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6:
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7: from .config import LoraLiteConfig
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8: from .variant import REGISTRY
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9: from .target import find_targets
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10:
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11:
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12: _ATTACHED_ATTR = "_lora_lite_attached"
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13:
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14:
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15: def _hook(layer, args, y):
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16: (x,) = args
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17: cfg: LoraLiteConfig = layer._lora_cfg
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18: x_cast = x.to(cfg.dtype)
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19: out = layer._lora_variant.forward(layer, x_cast, y)
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20: return out.to(y.dtype)
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21:
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22:
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23: def _pre_hook(layer, args):
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24: (x,) = args
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25: cfg: LoraLiteConfig = layer._lora_cfg
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... (92 more lines)
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[tool] read (pending)
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input: {}
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[tool] src/lora_lite/variant.py (completed)
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kind: read
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input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py
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output:
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<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variant.py</path>
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<type>file</type>
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<content>
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1: """Variant protocol + registry. Variants own (x, layer.weight, layer.lora_*) -> y_new."""
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2: from dataclasses import dataclass
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3: from typing import Callable, Protocol, Any
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4: import torch
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5: from torch import nn
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6:
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7: from .config import LoraLiteConfig
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8:
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9:
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10: @dataclass
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11: class ParamSpec:
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12: shape: tuple[int, ...]
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13: init: str | Callable[[torch.Tensor], None] = "zeros" # 'zeros'|'kaiming'|'ones'|callable(t)
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14: trainable: bool = True
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15:
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16: def make(self, dtype: torch.dtype, device) -> nn.Parameter:
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17: t = torch.empty(self.shape, dtype=dtype, device=device)
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18: if callable(self.init):
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19: self.init(t)
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20: elif self.init == "zeros":
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21: t.zero_()
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22: elif self.init == "ones":
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23: t.fill_(1.0)
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24: elif self.init == "kaiming":
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25: # match nn.Linear default: kaiming_uniform_(a=sqrt(5))
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... (34 more lines)
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[tool] src/lora_lite/variants/lora.py (completed)
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kind: read
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input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/lora.py
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/lora.py
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output:
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<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/lora.py</path>
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<type>file</type>
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<content>
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1: """Vanilla LoRA. Hu et al. 2021 https://arxiv.org/abs/2106.09685
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2:
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3: h = W x + (alpha/r) B A x
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4:
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5: Identity at t=0 from B=0. Faithful to the paper.
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6: """
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7: from einops import einsum
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8: from torch import nn
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9: import torch
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10:
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11: from ..variant import register, ParamSpec
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12:
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13:
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14: @register
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15: class LoRA:
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16: name = "lora"
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17:
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18: @staticmethod
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19: def param_specs(d_in, d_out, cfg):
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20: return {
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21: "lora_A": ParamSpec((cfg.r, d_in), init="kaiming", trainable=True),
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22: "lora_B": ParamSpec((d_out, cfg.r), init="zeros", trainable=True),
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23: }
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24:
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25: @staticmethod
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... (14 more lines)
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[thinking] Now let me read the reference implementations to answer the questions.
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[tool] read (pending)
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input: {}
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[tool] read (pending)
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input: {}
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[tool] docs/refs/peft_eva_finetuning.py (completed)
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kind: read
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input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva_finetuning.py
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files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva_finetuning.py
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output:
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<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva_finetuning.py</path>
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<type>file</type>
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<content>
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1: # Copyright 2024-present the HuggingFace Inc. team.
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2: #
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3: # Licensed under the Apache License, Version 2.0 (the "License");
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4: # you may not use this file except in compliance with the License.
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5: # You may obtain a copy of the License at
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6: #
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7: # http://www.apache.org/licenses/LICENSE-2.0
|
||||
8: #
|
||||
9: # Unless required by applicable law or agreed to in writing, software
|
||||
10: # distributed under the License is distributed on an "AS IS" BASIS,
|
||||
11: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
12: # See the License for the specific language governing permissions and
|
||||
13: # limitations under the License.
|
||||
14:
|
||||
15: import torch
|
||||
16: from datasets import load_dataset
|
||||
17: from torch.utils.data import DataLoader
|
||||
18: from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
|
||||
19: from utils import DataCollator, TokenizerMetaMath
|
||||
20:
|
||||
21: from peft import EvaConfig, LoraConfig, get_peft_model, initialize_lora_eva_weights
|
||||
22:
|
||||
23:
|
||||
24: DEVICE = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
|
||||
25:
|
||||
... (74 more lines)
|
||||
|
||||
[tool] docs/refs/peft_eva.py (completed)
|
||||
kind: read
|
||||
input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py
|
||||
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py
|
||||
output:
|
||||
<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/peft_eva.py</path>
|
||||
<type>file</type>
|
||||
<content>
|
||||
1: # Copyright 2024-present the HuggingFace Inc. team.
|
||||
2: #
|
||||
3: # Licensed under the Apache License, Version 2.0 (the "License");
|
||||
4: # you may not use this file except in compliance with the License.
|
||||
5: # You may obtain a copy of the License at
|
||||
6: #
|
||||
7: # http://www.apache.org/licenses/LICENSE-2.0
|
||||
8: #
|
||||
9: # Unless required by applicable law or agreed to in writing, software
|
||||
10: # distributed under the License is distributed on an "AS IS" BASIS,
|
||||
11: # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
12: # See the License for the specific language governing permissions and
|
||||
13: # limitations under the License.
|
||||
14:
|
||||
15: import warnings
|
||||
16: from collections import Counter, defaultdict
|
||||
17: from collections.abc import Iterable, Mapping
|
||||
18: from contextlib import nullcontext
|
||||
19: from copy import deepcopy
|
||||
20: from functools import partial
|
||||
21: from itertools import cycle
|
||||
22: from typing import Optional, Union
|
||||
23:
|
||||
24: import torch
|
||||
25: import torch.distributed as dist
|
||||
... (717 more lines)
|
||||
|
||||
[thinking] Now let me read the baukit and antipasto3 references.
|
||||
|
||||
[tool] read (pending)
|
||||
input: {}
|
||||
|
||||
[tool] read (pending)
|
||||
input: {}
|
||||
|
||||
[tool] docs/refs/baukit_nethook.py (completed)
|
||||
kind: read
|
||||
input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/baukit_nethook.py
|
||||
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/baukit_nethook.py
|
||||
output:
|
||||
<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/baukit_nethook.py</path>
|
||||
<type>file</type>
|
||||
<content>
|
||||
1: """
|
||||
2: Utilities for instrumenting a torch model.
|
||||
3:
|
||||
4: Trace will hook one layer at a time.
|
||||
5: TraceDict will hook multiple layers at once.
|
||||
6: subsequence slices intervals from Sequential modules.
|
||||
7: get_module, replace_module, get_parameter resolve dotted names.
|
||||
8: set_requires_grad recursively sets requires_grad in module parameters.
|
||||
9: """
|
||||
10:
|
||||
11: import contextlib
|
||||
12: import copy
|
||||
13: import inspect
|
||||
14: from collections import OrderedDict
|
||||
15:
|
||||
16: import torch
|
||||
17:
|
||||
18:
|
||||
19: class Trace(contextlib.AbstractContextManager):
|
||||
20: """
|
||||
21: To retain the output of the named layer during the computation of
|
||||
22: the given network:
|
||||
23:
|
||||
24: with Trace(net, 'layer.name') as ret:
|
||||
25: _ = net(inp)
|
||||
... (449 more lines)
|
||||
|
||||
[tool] docs/refs/antipasto3_svd_adapter.py (completed)
|
||||
kind: read
|
||||
input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
|
||||
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py
|
||||
output:
|
||||
<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/docs/refs/antipasto3_svd_adapter.py</path>
|
||||
<type>file</type>
|
||||
<content>
|
||||
1: """SVD adapter for bidirectional steering via block-diagonal Cayley rotations.
|
||||
2:
|
||||
3: Flax NNX port.
|
||||
4:
|
||||
5: kernel = U @ diag(S) @ Vh + W_res (kernel is (in, out), standard Flax convention)
|
||||
6: Learnable: delta_s (additive S scaling), rotation_params (block-diagonal V rotation).
|
||||
7: alpha scales both: S + alpha*delta_s, U @ R(alpha).
|
||||
8:
|
||||
9: Why Cayley (not Givens or matrix exponential):
|
||||
10: Cayley gives exact analytical reversibility: R(-alpha) = R(alpha)^{-1}.
|
||||
11: This is critical -- at alpha=+1 and alpha=-1 the adapter is an exact inverse of
|
||||
12: itself, making bidirectional steering symmetric by construction.
|
||||
13:
|
||||
14: At alpha=0: U_rot = U and S_scaled = S, so the layer is identical to frozen weights.
|
||||
15: """
|
||||
16:
|
||||
17: import math
|
||||
18:
|
||||
19: import jax
|
||||
20: import jax.numpy as jnp
|
||||
21: from jax import Array
|
||||
22: from jaxtyping import Float, Int
|
||||
23: from einops import rearrange
|
||||
24: from flax import nnx
|
||||
25: from loguru import logger
|
||||
... (353 more lines)
|
||||
|
||||
[thinking] Now let me read the lora-lite config and variant protocol more carefully to understand the current API. Then I can compare it to the references.
|
||||
|
||||
[tool] read (pending)
|
||||
input: {}
|
||||
|
||||
[tool] read (pending)
|
||||
input: {}
|
||||
|
||||
[tool] src/lora_lite/config.py (completed)
|
||||
kind: read
|
||||
input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py
|
||||
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py
|
||||
output:
|
||||
<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/config.py</path>
|
||||
<type>file</type>
|
||||
<content>
|
||||
1: from dataclasses import dataclass, field, asdict
|
||||
2: from typing import Any
|
||||
3: import torch
|
||||
4:
|
||||
5:
|
||||
6: @dataclass
|
||||
7: class LoraLiteConfig:
|
||||
8: variant: str = "lora"
|
||||
9: r: int = 8
|
||||
10: alpha: float = 16.0
|
||||
11: dropout: float = 0.0 # currently ignored; variants may use cfg.variant_kwargs
|
||||
12: dtype: torch.dtype = torch.bfloat16
|
||||
13:
|
||||
14: # targeting
|
||||
15: target_roles: tuple[str, ...] = ("reader", "writer")
|
||||
16: target_names: tuple[str, ...] = ()
|
||||
17: exclude_names: tuple[str, ...] = ("lm_head", "embed_tokens")
|
||||
18: layers: tuple[int, ...] | None = None
|
||||
19:
|
||||
20: # variant-specific bag (e.g. lambda0 for DeLoRA)
|
||||
21: variant_kwargs: dict[str, Any] = field(default_factory=dict)
|
||||
22:
|
||||
23: def to_dict(self) -> dict:
|
||||
24: d = asdict(self)
|
||||
25: d["dtype"] = str(self.dtype).removeprefix("torch.")
|
||||
... (16 more lines)
|
||||
|
||||
[tool] src/lora_lite/target.py (completed)
|
||||
kind: read
|
||||
input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py
|
||||
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py
|
||||
output:
|
||||
<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/target.py</path>
|
||||
<type>file</type>
|
||||
<content>
|
||||
1: """Find linear-like targets by shape (reader/writer/inner) + name regex.
|
||||
2:
|
||||
3: Structural matching is deliberate: bnb Linear4bit/8bitLt are not nn.Linear, but
|
||||
4: they expose in_features/out_features/weight and their forward already handles
|
||||
5: dequantization.
|
||||
6: """
|
||||
7: import re
|
||||
8: from torch import nn
|
||||
9:
|
||||
10:
|
||||
11: def is_linear_like(m: nn.Module) -> bool:
|
||||
12: return (
|
||||
13: hasattr(m, "in_features")
|
||||
14: and hasattr(m, "out_features")
|
||||
15: and hasattr(m, "weight")
|
||||
16: and callable(m)
|
||||
17: )
|
||||
18:
|
||||
19:
|
||||
20: def _layer_idx(name: str) -> int | None:
|
||||
21: m = re.search(r"\.layers?\.(\d+)\.", name)
|
||||
22: return int(m.group(1)) if m else None
|
||||
23:
|
||||
24:
|
||||
25: def _classify(m: nn.Module, d_model: int, name: str) -> str:
|
||||
... (37 more lines)
|
||||
|
||||
[thinking] OK now I have a clear picture. Let me write up the design review with specific answers to each question. Let me also check if there's an AGENTS.md file:
|
||||
|
||||
[tool] glob (pending)
|
||||
input: {}
|
||||
|
||||
[tool] glob (completed)
|
||||
kind: search
|
||||
input: AGENTS.md
|
||||
output:
|
||||
No files found
|
||||
|
||||
[thinking] Let me check for AGENTS.md in parent directories or look for any README or docs.
|
||||
|
||||
[tool] glob (pending)
|
||||
input: {}
|
||||
|
||||
[tool] glob (completed)
|
||||
kind: search
|
||||
input: **/AGENTS.md
|
||||
output:
|
||||
No files found
|
||||
|
||||
[thinking] The user wants design notes. I have all the information I need. Let me write up the design notes for each reference.
|
||||
|
||||
[tool] todowrite (pending)
|
||||
input: {}
|
||||
|
||||
[plan]
|
||||
- [in_progress] write design notes for peft EVA
|
||||
- [pending] write design notes for baukit nethook
|
||||
- [pending] write design notes for antipasto3 SVD
|
||||
|
||||
[tool] 3 todos (completed)
|
||||
kind: other
|
||||
input: {"todos":[{"content":"write design notes for peft EVA","status":"in_progress","priority":"high"},{"content":"write design notes for baukit nethook","status":"pending","priority":"high"},{"content":"write design notes ...
|
||||
output:
|
||||
[
|
||||
{
|
||||
"content": "write design notes for peft EVA",
|
||||
"status": "in_progress",
|
||||
"priority": "high"
|
||||
},
|
||||
{
|
||||
"content": "write design notes for baukit nethook",
|
||||
"status": "pending",
|
||||
"priority": "high"
|
||||
},
|
||||
{
|
||||
"content": "write design notes for antipasto3 SVD",
|
||||
"status": "pending",
|
||||
"priority": "high"
|
||||
}
|
||||
]
|
||||
|
||||
[thinking] Let me write out the design notes for each reference. ## A. peft EVA **What EVA does** (1-paragraph): EVA computes SVD-of-activations to initialize LoRA's low-rank decomposition. Rather than random kaiming init, it collects layer inputs during a forward pass through calibration data, runs incremental PCA to find principal components, and uses those as the initial `A` matrix (the left singular vectors). Key features: - Per-layer rank redistribution based on explained variance (lines 302-314) - Convergence checking via cosine similarity between SVD components (lines 147-168) - Distributed support via all-gather (lines 81-102) - Handles non-convergence gracefully (multiple forward passes until all layers converge) A **minimal EVA variant** in lora-lite: The current API is: ```python variant.init(layer, cfg) # called per-layer after params created ``` EVA needs a **separate calibration st...
|
||||
|
||||
|
||||
# Design Review: lora-lite vs peft EVA / baukit / antipasto3
|
||||
|
||||
## A. peft EVA
|
||||
|
||||
**What EVA does** (1-paragraph summary):
|
||||
EVA computes SVD-of-activations to initialize LoRA's low-rank decomposition. Rather than random kaiming init, it registers hooks on target layers, runs incremental PCA on calibration inputs (collected during forward passes), and uses the left singular vectors as the initial `lora_A` matrix. Key features: per-layer rank redistribution based on explained variance (lines 302-314), convergence checking via cosine similarity (lines 147-168), and distributed support via all-gather (lines 81-102).
|
||||
|
||||
**Minimal EVA variant sketch:**
|
||||
|
||||
The current API is:
|
||||
```python
|
||||
variant.init(layer, cfg) # called per-layer after params created (adapter.py:56)
|
||||
```
|
||||
|
||||
EVA needs a **separate calibration step before `attach()`**:
|
||||
|
||||
```python
|
||||
# Option: calibrate() -> eva_state dict
|
||||
def calibrate(model, dataloader, cfg):
|
||||
# Run forward passes, collect activations per target
|
||||
# Compute SVD, return {"layer_name": U_svd}
|
||||
...
|
||||
|
||||
# Then in LoRA variant.init():
|
||||
if "eva_state" in cfg.variant_kwargs:
|
||||
with torch.no_grad():
|
||||
layer.lora_A.weight.copy_(cfg.variant_kwargs["eva_state"][layer_name])
|
||||
```
|
||||
|
||||
**What to drop if re-implementing:**
|
||||
- Rank redistribution logic (lines 302-314) — skip, use fixed rank
|
||||
- Distributed gather (lines 81-102) — skip single-GPU only
|
||||
- Hash deduplication for equal inputs (lines 173-213, 374-386) — skip
|
||||
- Resume-from-checkpoint plumbing (lines 489-559) — skip
|
||||
- Whiten option (lines 475-477) — skip
|
||||
- PEFT model wrapping (lines 612-656) — skip
|
||||
|
||||
**API impact:**
|
||||
Current `Variant.init(layer, cfg)` signature at adapter.py:56 supports EVA via `cfg.variant_kwargs["eva_state"]`. Minimal change: add `calibrate(model, dataloader, cfg)` function (~50 lines) that computes SVD and returns state dict, then LoRA's `init()` checks for `eva_state` in kwargs.
|
||||
|
||||
---
|
||||
|
||||
**Recommendation: PARTIAL**
|
||||
|
||||
- **Import**: Core SVD-of-activations collection + SVD computation (lines 289-486 minus distributed/rank-redist/PEFT plumbing)
|
||||
- **Approximate line-count**: +~80 lines (calibrate function + init hook), −0 removed
|
||||
- **Trade**: Adds data-driven init capability; costs one new function and variant_kwargs passthrough
|
||||
|
||||
---
|
||||
|
||||
## B. baukit nethook
|
||||
|
||||
**What `TraceDict`/`Trace` gives us that lora-lite doesn't:**
|
||||
- Context manager pattern with automatic cleanup (adapter.py:68 already handles this via `RemovableHandle`)
|
||||
- `retain_grad=True` for backward pass (lora-lite doesn't need this — we're not editing grads)
|
||||
- `edit_output` for intercepting/modifying (not needed — we have dedicated `forward_input` hook)
|
||||
- Dictionary interface (`ret['layer.name'].output`) — we just store layer ref directly
|
||||
|
||||
Quote from adapter.py:64-68:
|
||||
```python
|
||||
for _, layer, _ in attached_targets:
|
||||
if hasattr(layer._lora_variant, "forward_input"):
|
||||
handles.append(layer.register_forward_pre_hook(_pre_hook))
|
||||
else:
|
||||
handles.append(layer.register_forward_hook(_hook))
|
||||
```
|
||||
|
||||
**Would baukit simplify or complicate adapter.py?**
|
||||
- **Neutral to negative**. Current code is 5 lines (64-68). Baukit's TraceDict adds ~100 lines of context-management indirection. We don't need:
|
||||
- Per-layer config dicts (retain_output/retain_input per layer)
|
||||
- Edit hooks mid-forward
|
||||
- Stop-after-layer control
|
||||
- clone/detach options
|
||||
|
||||
**Dependencies:**
|
||||
Baukit is a single file (~470 lines). lora-lite currently has no deps beyond torch+einops. Adding baukit adds a dep for ~5 lines of value.
|
||||
|
||||
---
|
||||
|
||||
**Recommendation: SKIP**
|
||||
|
||||
Baukit adds ~400 lines for capabilities we don't need. Current hook registration (adapter.py:64-68) is simpler. The `retain_grad` feature could be useful for ml debugging but that's a separate concern — can inline later if needed.
|
||||
|
||||
---
|
||||
|
||||
## C. antipasto3 SVD adapter
|
||||
|
||||
**1. What to adopt or diverge from?**
|
||||
|
||||
| Feature | antipasto3 | lora-lite | Verdict |
|
||||
|--------|-----------|----------|--------|
|
||||
| SVD init from kernel | `create_svd_adapter()` line 169 | manual param_specs | **Divergen**: lora-lite missing |
|
||||
| Delta-s scaling | `S_scaled = S + alpha*delta_s` (line 160) | direct scaling via `alpha/r` | **Adopt**: antipasto3 better — learnable scaling, not fixed |
|
||||
| Block-diagonal rotation | Cayley transform (lines 135-141) | N/A | **Too complex** for lora-lite ethos |
|
||||
| Residual: `W_res = kernel - U@S@Vh` | line 204 | N/A | **Consider**: adds expressivity |
|
||||
| Dimension selection: L1 trip | lines 219-267 | None | **Skip**: too domain-specific |
|
||||
| Polarity interleave | lines 271-287 | N/A | **Skip**: steering-specific |
|
||||
|
||||
**Key insight**: antipasto3's `W_res` residual (kernel - U@S@Vh) is the cleanest simplification over vanilla LoRA. Instead of:
|
||||
- LoRA: `y = Wx + (α/r)BAx` (learns full rank-r delta)
|
||||
|
||||
You get:
|
||||
- SVD: `y = W_res@x + (α/r)U_scaled@S_scaled@Vh@x` (SVD-based delta, capped)
|
||||
|
||||
This is essentially **PiSSA** (arXiv:2409.13349) — already in lora-lite variants/ (pissa.py).
|
||||
|
||||
**2. PiSSA-like factoring:**
|
||||
PiSSA is in variants/pissa.py. Let me check it:
|
||||
|
||||
[tool] read (pending)
|
||||
input: {}
|
||||
|
||||
[tool] src/lora_lite/variants/pissa.py (completed)
|
||||
kind: read
|
||||
input: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/pissa.py
|
||||
files: /media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/pissa.py
|
||||
output:
|
||||
<path>/media/wassname/SGIronWolf/projects5/2026/lora-lite/src/lora_lite/variants/pissa.py</path>
|
||||
<type>file</type>
|
||||
<content>
|
||||
1: """PiSSA: top-r SVD of W into A,B; replace W with W_res = W - B@A.
|
||||
2:
|
||||
3: Meng et al. 2024 https://arxiv.org/abs/2404.02948
|
||||
4: W_eff(t=0) = W_res + B@A = W (numerically; bf16 round-trip not bit-exact).
|
||||
5: """
|
||||
6: import torch
|
||||
7: from einops import einsum
|
||||
8: from torch import nn
|
||||
9:
|
||||
10: from ..variant import register, ParamSpec
|
||||
11:
|
||||
12:
|
||||
13: @register
|
||||
14: class PiSSA:
|
||||
15: name = "pissa"
|
||||
16:
|
||||
17: @staticmethod
|
||||
18: def param_specs(d_in, d_out, cfg):
|
||||
19: return {
|
||||
20: "lora_A": ParamSpec((cfg.r, d_in), init="zeros", trainable=True),
|
||||
21: "lora_B": ParamSpec((d_out, cfg.r), init="zeros", trainable=True),
|
||||
22: }
|
||||
23:
|
||||
24: @staticmethod
|
||||
25: def init(layer: nn.Linear, cfg) -> None:
|
||||
... (32 more lines)
|
||||
|
||||
[thinking] So PiSSA is already in lora-lite! Let me check what antipasto3 offers beyond this. Comparing antipasto3 to PiSSA: - Both use SVD init from W antipasto3 has: - Delta-s scaling: learnable S + delta_s (additive learnable, not just fixed) - Residual is explicit (W_res stored separately) - PiSSA mutates layer.weight into W_res The key differences: 1. **Delta-s**: antipasto3 has `delta_s` parameters (line 89-91, 160) — fully learnable scaling. PiSSA has no learnable scaling, just fixed `alpha/r`. 2. **Steering coefficient**: antipasto3 uses explicit `alpha` mutated during forward (line 107, 116), not config-based. 3. **Residual**: PiSSA mutates `layer.weight` directly. antipasto3 stores `W_res` separately. antipasto3's delta-s is the main "new" thing worth considering. But that's really a steering/adaptation method (changing alpha at runtime), not an initialization method. For lora-lite's m...
|
||||
|
||||
[tool] todowrite (pending)
|
||||
input: {}
|
||||
|
||||
[plan]
|
||||
- [completed] write design notes for peft EVA
|
||||
- [completed] write design notes for baukit nethook
|
||||
- [completed] write design notes for antipasto3 SVD
|
||||
|
||||
[tool] 0 todos (completed)
|
||||
kind: other
|
||||
input: {"todos":[{"content":"write design notes for peft EVA","priority":"high","status":"completed"},{"content":"write design notes for baukit nethook","priority":"high","status":"completed"},{"content":"write design notes ...
|
||||
output:
|
||||
[
|
||||
{
|
||||
"content": "write design notes for peft EVA",
|
||||
"status": "completed",
|
||||
"priority": "high"
|
||||
},
|
||||
{
|
||||
"content": "write design notes for baukit nethook",
|
||||
"status": "completed",
|
||||
"priority": "high"
|
||||
},
|
||||
{
|
||||
"content": "write design notes for antipasto3 SVD",
|
||||
"status": "completed",
|
||||
"priority": "high"
|
||||
}
|
||||
]
|
||||
|
||||
[thinking] Wait, I wrote the design notes directly as output but the user asked me to answer questions and end with a recommendation. Let me reformat as requested. Actually, looking at the instructions again: the output format says "For each (A, B, C), end with: Recommendation: ADOPT / SKIP / PARTIAL". So I need to restructure my response to match that format better, and NOT write code. Let me restructure: A, B, C sections with each ending in recommendation.
|
||||
|
||||
|
||||
# Design Review: lora-lite vs refs
|
||||
|
||||
## A. peft EVA (`docs/refs/peft_eva.py` + `peft_eva_finetuning.py`)
|
||||
|
||||
### 1. What EVA actually does
|
||||
|
||||
EVA uses SVD-of-activations (incremental PCA on layer inputs during forward passes) to initialize LoRA rather than random kaiming init. Key mechanism:
|
||||
- Registers hooks on target layers, collects inputs across calibration batches
|
||||
- Computes left singular vectors via `IncrementalPCA` (lines 138-143)
|
||||
- Convergence check via cosine similarity between current/previous components (lines 167-168)
|
||||
- Per-layer rank redistribution based on explained variance ratio (lines 302-314, 456)
|
||||
|
||||
The user workflow (peft_eva_finetuning.py:66-76):
|
||||
```python
|
||||
eva_config = EvaConfig(rho=rho)
|
||||
peft_config = LoraConfig(r=rank, init_lora_weights="eva", eva_config=eva_config)
|
||||
peft_model = get_peft_model(model, peft_config)
|
||||
initialize_lora_eva_weights(peft_model, dataloader) # separate calibration step
|
||||
```
|
||||
|
||||
### 2. Minimal EVA variant API sketch
|
||||
|
||||
**Option: separate calibrate() step before attach()**
|
||||
```python
|
||||
# User does:
|
||||
eva_state = calibrate(model, dataloader, cfg) # returns {"layer_name": U_svd}
|
||||
cfg.variant_kwargs["eva_state"] = eva_state
|
||||
attach(model, cfg) # LoRA.init() copies U_svd -> lora_A
|
||||
```
|
||||
|
||||
API change: add `calibrate(model, dataloader, cfg)` function (~50 lines) that computes SVD on calibration inputs, returns state dict. In `LoRA.init()`, check for `eva_state[layer_name]` and copy.
|
||||
|
||||
### 3. What to drop if re-implementing
|
||||
|
||||
- Lines 302-314: rank redistribution logic → use fixed rank r
|
||||
- Lines 81-102: distributed all-gather → single-GPU only
|
||||
- Lines 173-213, 374-386: hash deduplication for equal inputs → skip
|
||||
- Lines 489-559: resume-from-checkpoint plumbing → skip
|
||||
- Lines 475-477: whiten option → skip
|
||||
- Lines 612-656: PEFT model wrapper integration → standalone function
|
||||
|
||||
Droppable: ~60% of the 739-line file.
|
||||
|
||||
### 4. Does current init() signature support EVA?
|
||||
|
||||
Yes. adapter.py:56:
|
||||
```python
|
||||
variant.init(layer, cfg)
|
||||
```
|
||||
|
||||
Add `eva_state` to `cfg.variant_kwargs` before attach, then in `LoRA.init()`:
|
||||
```python
|
||||
if "eva_state" in cfg.variant_kwargs:
|
||||
# copy precomputed U into lora_A
|
||||
```
|
||||
|
||||
Smallest change: add `calibrate()` function (~50 lines), no signature change needed.
|
||||
|
||||
---
|
||||
|
||||
**Recommendation: PARTIAL**
|
||||
|
||||
- **Import**: SVD-of-activations collection + compute
|
||||
- **Lines**: +~50 (calibrate fn), −0
|
||||
- **What to remove in exchange**: Nothing — this is additive capability only. If you add calibration data collection, you must accept the additional complexity.
|
||||
|
||||
---
|
||||
|
||||
## B. baukit nethook (`docs/refs/baukit_nethook.py`)
|
||||
|
||||
### 1. What TraceDict/Trace gives us that we don't have
|
||||
|
||||
| Feature | baukit | lora-lite | Value |
|
||||
|--------|-------|----------|-------|
|
||||
| Context manager cleanup | lines 99-105 | RemovableHandle (adapter.py:64-68) | Same |
|
||||
| retain_grad | line 37-38 | None | Not needed |
|
||||
| edit_output mid-forward | lines 39, 72-75 | None | Not needed |
|
||||
| Dictionary interface | line 111 | None | Not needed |
|
||||
| Stop-after-layer | lines 46-47, 189-202 | None | Not needed |
|
||||
|
||||
TraceDict essentially provides: per-layer output retention + config dicts + cleanup. We already handle cleanup via RemovableHandle.
|
||||
|
||||
### 2. Would baukit simplify or complicate adapter.py?
|
||||
|
||||
Quote adapter.py:64-68:
|
||||
```python
|
||||
for _, layer, _ in attached_targets:
|
||||
if hasattr(layer._lora_variant, "forward_input"):
|
||||
handles.append(layer.register_forward_pre_hook(_pre_hook))
|
||||
else:
|
||||
handles.append(layer.register_forward_hook(_hook))
|
||||
```
|
||||
|
||||
This is 5 lines. Baukit's TraceDict adds ~100 lines of context management we'd use only to replicate these 5 lines. Our current approach is simpler.
|
||||
|
||||
### 3. Is baukit worth a dep?
|
||||
|
||||
No. lora-lite's principle is minimal deps (currently torch+einops). Baukit adds ~400 lines of code for capabilities we don't need. `retain_grad` for debugging is the only useful feature — that's 3 lines to inline if needed later.
|
||||
|
||||
---
|
||||
|
||||
**Recommendation: SKIP**
|
||||
|
||||
Baukit adds a dep for no net simplification. Current hook registration (adapter.py:64-68) is 5 lines and sufficient. If `retain_grad` is needed for ml debugging, inline 3 lines later.
|
||||
|
||||
---
|
||||
|
||||
## C. antipasto3 SVD adapter (`docs/refs/antipasto3_svd_adapter.py`)
|
||||
|
||||
### 1. What to adopt or diverge from
|
||||
|
||||
| Feature | antipasto3 | lora-lite | Verdict |
|
||||
|---------|-----------|----------|--------|
|
||||
| SVD init from kernel | line 169 `create_svd_adapter()` | PiSSA (pissa.py) | Already have |
|
||||
| Delta-s scaling: learnable | line 160 `S + alpha*delta_s` | fixed via alpha/r | **Adopt** |
|
||||
| Residual: `W_res = kernel - U@S@Vh` | line 204 | PiSSA has this | Already have |
|
||||
| Block-diagonal Cayley rotation | lines 135-141 | N/A | Skip (too complex) |
|
||||
| Dimension selection (L1 trip) | lines 219-267 | N/A | Skip (domain-specific) |
|
||||
|
||||
**Key adoption**: antipasto3's learnable `delta_s` is cleaner than fixed scaling. Currently lora-lite uses `scale = alpha/r` at **runtime** — but delta-s makes scaling a **learnable parameter** with its own gradient.
|
||||
|
||||
This is already in PiSSA variant: piSSA `init()` sets `scale=1` via alpha=r convention (pissa.py:43-46), but doesn't expose the scaling as learnable.
|
||||
|
||||
### 2. PiSSA-like factoring
|
||||
|
||||
lora-lite already has PiSSA in `variants/pissa.py`:
|
||||
- Line 31-34: SVD of weight
|
||||
- Line 36-40: decompose into B@A with sqrt(S) scaling
|
||||
- Line 46: residual W_res = W - B@A
|
||||
|
||||
This is exactly antipasto3's core insight, implemented cleanly. The antipasto3 contribution is making `delta_s` learnable — that's a natural extension of PiSSA, not a replacement.
|
||||
|
||||
---
|
||||
|
||||
**Recommendation: ADOPT (concept)**
|
||||
|
||||
- **Import**: learnable delta_s concept (not code — just the idea)
|
||||
- **Lines**: +0 by intent, no code change right now
|
||||
- **What to remove**: Nothing — this is a design note that PiSSA could evolve toward learnable scaling instead of fixed alpha/r
|
||||
|
||||
The antipasto3 patterns ( Cayley rotation, polarity interleave) are steering-specific and too complex for lora-lite ethos. The clean insight is already in PiSSA.
|
||||
|
||||
---
|
||||
|
||||
## Summary
|
||||
|
||||
| Ref | Recommendation | Impact |
|
||||
|-----|--------------|--------|
|
||||
| peft EVA | PARTIAL | +50 lines calibrate fn |
|
||||
| baukit | SKIP | 0 (keep current) |
|
||||
| antipasto3 | ADOPT concept | 0 (design note) |
|
||||
|
||||
[done] end_turn
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,375 @@
|
||||
"""SVD adapter for bidirectional steering via block-diagonal Cayley rotations.
|
||||
|
||||
Flax NNX port.
|
||||
|
||||
kernel = U @ diag(S) @ Vh + W_res (kernel is (in, out), standard Flax convention)
|
||||
Learnable: delta_s (additive S scaling), rotation_params (block-diagonal V rotation).
|
||||
alpha scales both: S + alpha*delta_s, U @ R(alpha).
|
||||
|
||||
Why Cayley (not Givens or matrix exponential):
|
||||
Cayley gives exact analytical reversibility: R(-alpha) = R(alpha)^{-1}.
|
||||
This is critical -- at alpha=+1 and alpha=-1 the adapter is an exact inverse of
|
||||
itself, making bidirectional steering symmetric by construction.
|
||||
|
||||
At alpha=0: U_rot = U and S_scaled = S, so the layer is identical to frozen weights.
|
||||
"""
|
||||
|
||||
import math
|
||||
|
||||
import jax
|
||||
import jax.numpy as jnp
|
||||
from jax import Array
|
||||
from jaxtyping import Float, Int
|
||||
from einops import rearrange
|
||||
from flax import nnx
|
||||
from loguru import logger
|
||||
|
||||
|
||||
# -- Custom variable types for gradient filtering ----------------------------
|
||||
|
||||
class SVDParam(nnx.Param):
|
||||
"""Trainable SVD adapter parameter (base class -- use subclasses for per-group LR)."""
|
||||
pass
|
||||
|
||||
|
||||
class DeltaSParam(SVDParam):
|
||||
"""Trainable delta_s scaling parameters (full LR)."""
|
||||
pass
|
||||
|
||||
|
||||
class RotationParam(SVDParam):
|
||||
"""Block-diagonal rotation parameters (lower LR via rotation_lr_scale config)."""
|
||||
pass
|
||||
|
||||
|
||||
class SVDFrozen(nnx.Variable):
|
||||
"""Frozen SVD component. Not differentiated."""
|
||||
pass
|
||||
|
||||
|
||||
# -- SVD Steering Linear (replaces nnx.Linear) ------------------------------
|
||||
|
||||
class SVDSteeringLinear(nnx.Module):
|
||||
"""SVD steering adapter replacing a linear layer.
|
||||
|
||||
y = ((x @ U_rot) * S_scaled) @ Vh + x @ W_res
|
||||
|
||||
where U_rot, S_scaled depend on alpha (the steering coefficient).
|
||||
Frozen: U, S, Vh, W_res. Learnable: delta_s, rotation_params.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
U: jax.Array, # (in_dim, r) - input singular vectors
|
||||
S: jax.Array, # (r,) - singular values
|
||||
Vh: jax.Array, # (r, out_dim) - output singular vectors
|
||||
W_res: jax.Array, # (in_dim, out_dim) - residual
|
||||
rotation_block_size: int,
|
||||
max_rotation_angle: float,
|
||||
rotate_U: bool = True,
|
||||
rotate_V: bool = False,
|
||||
use_delta_s: bool = True,
|
||||
*,
|
||||
rngs: nnx.Rngs,
|
||||
):
|
||||
r = S.shape[0]
|
||||
bs = min(rotation_block_size, r)
|
||||
assert r % bs == 0, f"r={r} must be divisible by block_size={bs}"
|
||||
|
||||
# Frozen SVD components
|
||||
self.svd_U = SVDFrozen(U.astype(jnp.float32))
|
||||
self.svd_S = SVDFrozen(S.astype(jnp.float32))
|
||||
self.svd_Vh = SVDFrozen(Vh.astype(jnp.float32))
|
||||
self.svd_W_res = SVDFrozen(W_res.astype(jnp.bfloat16))
|
||||
|
||||
# Trainable: delta_s with small positive bias for symmetry breaking.
|
||||
# The +4e-4 nudges the optimizer to scale up selected dims rather than just
|
||||
# rotating them. Rotation alone cannot break sign symmetry at init.
|
||||
key_s, key_r = jax.random.split(rngs.params())
|
||||
self.delta_s = DeltaSParam(
|
||||
jax.random.truncated_normal(key_s, -2.0, 2.0, (r,)) * 4e-4 + 4e-4
|
||||
)
|
||||
|
||||
# Block-diagonal skew-symmetric rotation params (lower LR via RotationParam type)
|
||||
# Upper-triangle parameterization: store only bs*(bs-1)/2 elements per block,
|
||||
# like OFT/PSOFT. Avoids dead diagonal gradients and redundant (i,j)/(j,i) states.
|
||||
n_blocks = r // bs
|
||||
n_triu = bs * (bs - 1) // 2
|
||||
self.rotation_params = RotationParam(
|
||||
jax.random.truncated_normal(key_r, -2.0, 2.0, (n_blocks, n_triu)) * 1e-4
|
||||
)
|
||||
# Pre-compute upper-triangle indices for skew-symmetric reconstruction
|
||||
rows, cols = jnp.triu_indices(bs, k=1)
|
||||
self._triu_rows = rows
|
||||
self._triu_cols = cols
|
||||
|
||||
# Steering coefficient (mutated during 3-pass forward)
|
||||
self.alpha = nnx.Variable(jnp.float32(1.0))
|
||||
self.max_angle = max_rotation_angle
|
||||
self.block_size = bs
|
||||
self.r = r
|
||||
self.rotate_U = rotate_U
|
||||
self.rotate_V = rotate_V
|
||||
self.use_delta_s = use_delta_s
|
||||
|
||||
def __call__(self, x: Float[Array, "*batch in_features"]) -> Float[Array, "*batch out_features"]:
|
||||
alpha = self.alpha.value
|
||||
U = self.svd_U.value
|
||||
S = self.svd_S.value
|
||||
Vh = self.svd_Vh.value
|
||||
W_res = self.svd_W_res.value
|
||||
params = self.rotation_params.value # (n_blocks, n_triu)
|
||||
bs = self.block_size
|
||||
n_blocks = params.shape[0]
|
||||
|
||||
# Reconstruct skew-symmetric from upper-triangle params (like OFT/PSOFT).
|
||||
# 0.5 factor matches BOFT convention: cancels the 2x gradient from A - A^T.
|
||||
A = jnp.zeros((n_blocks, bs, bs), dtype=jnp.float32)
|
||||
A = A.at[:, self._triu_rows, self._triu_cols].set(params.astype(jnp.float32))
|
||||
A = 0.5 * (A - jnp.swapaxes(A, -1, -2))
|
||||
|
||||
# Angle clamping (element-wise tanh, bounds bidirectional symmetry error)
|
||||
a_limit = 2 * math.tan(self.max_angle / 2)
|
||||
A = a_limit * jnp.tanh(A / a_limit)
|
||||
|
||||
# Cayley transform in float32: R = (I - X)^{-1}(I + X)
|
||||
eye = jnp.eye(bs, dtype=jnp.float32)
|
||||
X = alpha * A / 2
|
||||
R_blocks = jnp.linalg.solve(
|
||||
eye[None] - X,
|
||||
eye[None] + X,
|
||||
)
|
||||
|
||||
# Apply rotation to U (input singular vectors)
|
||||
if self.rotate_U:
|
||||
U_reshaped = U.reshape(U.shape[0], n_blocks, bs)
|
||||
U_rot = jnp.einsum('dnb,nbc->dnc', U_reshaped, R_blocks)
|
||||
U_rot = U_rot.reshape(U.shape)
|
||||
else:
|
||||
U_rot = U
|
||||
|
||||
# Apply rotation to Vh (output singular vectors); off by default
|
||||
# (output rotation changes the upstream basis, making adaptation harder)
|
||||
if self.rotate_V:
|
||||
Vh_reshaped = Vh.reshape(n_blocks, bs, Vh.shape[1])
|
||||
Vh_rot = jnp.einsum('nbc,nbj->ncj', R_blocks, Vh_reshaped)
|
||||
Vh_rot = Vh_rot.reshape(Vh.shape)
|
||||
else:
|
||||
Vh_rot = Vh
|
||||
|
||||
S_scaled = S + alpha * self.delta_s.value if self.use_delta_s else S
|
||||
|
||||
dt = x.dtype
|
||||
out = (x @ U_rot.astype(dt)) * S_scaled.astype(dt)
|
||||
out = out @ Vh_rot.astype(dt)
|
||||
out = out + x @ W_res.astype(dt)
|
||||
return out
|
||||
|
||||
|
||||
def create_svd_adapter(
|
||||
kernel: jax.Array,
|
||||
r: int,
|
||||
rotation_block_size: int,
|
||||
max_rotation_angle: float,
|
||||
rngs: nnx.Rngs,
|
||||
selected_indices: jax.Array | None = None,
|
||||
rotate_U: bool = True,
|
||||
rotate_V: bool = False,
|
||||
use_delta_s: bool = True,
|
||||
) -> SVDSteeringLinear:
|
||||
"""Create SVD adapter from a kernel matrix (in_dim, out_dim).
|
||||
|
||||
If selected_indices is provided, uses those SVD dimensions.
|
||||
Otherwise uses top-r by singular value.
|
||||
"""
|
||||
kernel_f32 = kernel.astype(jnp.float32)
|
||||
U_full, S_full, Vh_full = jnp.linalg.svd(kernel_f32, full_matrices=False)
|
||||
|
||||
|
||||
r_actual = min(r, S_full.shape[0])
|
||||
# Ensure divisible by block size
|
||||
bs = min(rotation_block_size, r_actual)
|
||||
r_actual = (r_actual // bs) * bs
|
||||
if r_actual == 0:
|
||||
r_actual = bs
|
||||
|
||||
if selected_indices is not None:
|
||||
indices = selected_indices[:r_actual]
|
||||
else:
|
||||
indices = jnp.arange(r_actual)
|
||||
|
||||
U = U_full[:, indices]
|
||||
S = S_full[indices]
|
||||
Vh = Vh_full[indices, :]
|
||||
W_res = kernel_f32 - U @ jnp.diag(S) @ Vh
|
||||
|
||||
return SVDSteeringLinear(
|
||||
U, S, Vh, W_res,
|
||||
rotation_block_size=bs,
|
||||
max_rotation_angle=max_rotation_angle,
|
||||
rotate_U=rotate_U,
|
||||
rotate_V=rotate_V,
|
||||
use_delta_s=use_delta_s,
|
||||
rngs=rngs,
|
||||
)
|
||||
|
||||
|
||||
# -- Dimension selection (data-aware) ----------------------------------------
|
||||
|
||||
def score_l1_trip(
|
||||
acts_projected: Float[Array, "n k"], S: Float[Array, " k"], r: int,
|
||||
) -> Int[Array, " selected"]:
|
||||
"""L1 trip scoring: union of top dims from 4 pools (cho, rej, diff_pos, diff_neg).
|
||||
|
||||
Why not top-r by singular value? That picks globally "important" dimensions but
|
||||
ignores whether they are active in the contrastive data. This approach takes:
|
||||
r/3 cho-active, r/3 rej-active, r/6 diff_pos, r/6 diff_neg
|
||||
ensuring all signal types (absolute activation and contrastive difference) are
|
||||
represented in the selected subspace.
|
||||
"""
|
||||
k = S.shape[0]
|
||||
assert r < k
|
||||
act_cho = acts_projected[::2]
|
||||
act_rej = acts_projected[1::2]
|
||||
|
||||
l1_cho = jnp.abs(act_cho).mean(axis=0)
|
||||
l1_rej = jnp.abs(act_rej).mean(axis=0)
|
||||
diff = (act_cho - act_rej).mean(axis=0)
|
||||
|
||||
scores_cho = S * l1_cho
|
||||
scores_rej = S * l1_rej
|
||||
scores_diff_pos = S * jax.nn.relu(diff)
|
||||
scores_diff_neg = S * jax.nn.relu(-diff)
|
||||
|
||||
third = r // 3
|
||||
sixth = (r - 2 * third) // 2
|
||||
sixth_rem = r - 2 * third - 2 * sixth
|
||||
|
||||
top_cho = jnp.argsort(-scores_cho)[:third]
|
||||
top_rej = jnp.argsort(-scores_rej)[:third]
|
||||
top_diff_pos = jnp.argsort(-scores_diff_pos)[:sixth + sixth_rem]
|
||||
top_diff_neg = jnp.argsort(-scores_diff_neg)[:sixth]
|
||||
|
||||
combined = jnp.unique(jnp.concatenate([top_cho, top_rej, top_diff_pos, top_diff_neg]))
|
||||
|
||||
if combined.shape[0] < r:
|
||||
scores_union = jnp.maximum(
|
||||
jnp.maximum(scores_cho, scores_rej),
|
||||
jnp.maximum(scores_diff_pos, scores_diff_neg),
|
||||
)
|
||||
# Mask out already-selected indices
|
||||
mask = jnp.zeros(k, dtype=jnp.bool_)
|
||||
mask = mask.at[combined].set(True)
|
||||
scores_union = jnp.where(mask, -jnp.inf, scores_union)
|
||||
extra = jnp.argsort(-scores_union)[:r - combined.shape[0]]
|
||||
combined = jnp.concatenate([combined, extra])
|
||||
|
||||
return jnp.sort(combined[:r])
|
||||
|
||||
|
||||
|
||||
def polarity_interleave(acts_projected: jax.Array, indices: jax.Array) -> jax.Array:
|
||||
"""Reorder indices so consecutive pairs alternate cho/rej-favoring dims.
|
||||
|
||||
Block-diagonal rotation couples dims within each block of block_size.
|
||||
If all dims in a block favor the same direction (all cho-favoring), the block
|
||||
cannot learn bidirectional steering. Interleaving forces each block to have a
|
||||
mix of cho-favoring and rej-favoring dims, enabling bidirectional learning.
|
||||
"""
|
||||
r = indices.shape[0]
|
||||
assert r % 2 == 0
|
||||
diff_signed = (acts_projected[::2, :][:, indices] - acts_projected[1::2, :][:, indices]).mean(axis=0)
|
||||
rank_order = jnp.argsort(-diff_signed)
|
||||
n_half = r // 2
|
||||
cho_ranked = rank_order[:n_half]
|
||||
rej_ranked = rank_order[n_half:][::-1]
|
||||
interleaved = jnp.stack([cho_ranked, rej_ranked], axis=1).reshape(-1)
|
||||
return indices[interleaved]
|
||||
|
||||
|
||||
# -- Attention output adapter ------------------------------------------------
|
||||
|
||||
class SVDAttnOutAdapter(nnx.Module):
|
||||
"""Drop-in for tunix Einsum('BTNH,NHD->BTD').
|
||||
|
||||
Reshapes encoded [b,t,N,H] -> [b,t,N*H], applies SVDSteeringLinear, returns [b,t,D].
|
||||
Exposes .shape = (N, H, D) so tunix Attention.head_dim/.features still work.
|
||||
"""
|
||||
def __init__(self, svd_linear: SVDSteeringLinear, num_heads: int, head_dim: int):
|
||||
self.svd_linear = svd_linear
|
||||
# Tuple attribute: read-only metadata for tunix property access
|
||||
self.shape = (num_heads, head_dim, svd_linear.svd_Vh.value.shape[1])
|
||||
|
||||
def __call__(self, encoded: jax.Array) -> jax.Array:
|
||||
b, t, N, H = encoded.shape
|
||||
return self.svd_linear(encoded.reshape(b, t, N * H))
|
||||
|
||||
|
||||
class SVDAttnQAdapter(nnx.Module):
|
||||
"""Drop-in for tunix Einsum('BTD,NDH->BTNH') -- Q projection in GQA.
|
||||
|
||||
Reshapes weight (N, D, H) -> (D, N*H) for SVD. On forward, applies SVDSteeringLinear
|
||||
then rearranges output back to [b, t, N, H].
|
||||
Exposes .shape = (N, D, H) so tunix Attention.num_heads reads shape[0].
|
||||
"""
|
||||
def __init__(self, svd_linear: SVDSteeringLinear, num_heads: int, features: int, head_dim: int):
|
||||
self.svd_linear = svd_linear
|
||||
self.num_heads = num_heads
|
||||
self.head_dim = head_dim
|
||||
self.shape = (num_heads, features, head_dim)
|
||||
|
||||
def __call__(self, x: Float[Array, "b t D"]) -> Float[Array, "b t N H"]:
|
||||
b, t, _ = x.shape
|
||||
flat = rearrange(x, 'b t d -> (b t) d')
|
||||
out = self.svd_linear(flat)
|
||||
return rearrange(out, '(b t) (N H) -> b t N H', b=b, t=t, N=self.num_heads, H=self.head_dim)
|
||||
|
||||
|
||||
class SVDAttnKVAdapter(nnx.Module):
|
||||
"""Drop-in for tunix Einsum('BSD,CKDH->CBSKH') -- KV projection in GQA.
|
||||
|
||||
K and V share one einsum with weight (C=2, K, D, H). Reshapes to (D, C*K*H) for SVD.
|
||||
On forward, applies SVDSteeringLinear then rearranges to [C, b, t, K, H].
|
||||
Output is tuple-unpacked: key_proj, value_proj = kv_einsum(x).
|
||||
Exposes .shape = (C, K, D, H) so tunix Attention.num_kv_heads reads shape[1].
|
||||
"""
|
||||
def __init__(self, svd_linear: SVDSteeringLinear, C: int, num_kv_heads: int, features: int, head_dim: int):
|
||||
self.svd_linear = svd_linear
|
||||
self.C = C
|
||||
self.num_kv_heads = num_kv_heads
|
||||
self.head_dim = head_dim
|
||||
self.shape = (C, num_kv_heads, features, head_dim)
|
||||
|
||||
def __call__(self, x: Float[Array, "b t D"]) -> Float[Array, "C b t K H"]:
|
||||
b, t, _ = x.shape
|
||||
flat = rearrange(x, 'b t d -> (b t) d')
|
||||
out = self.svd_linear(flat)
|
||||
return rearrange(out, '(b t) (C K H) -> C b t K H', b=b, t=t, C=self.C, K=self.num_kv_heads, H=self.head_dim)
|
||||
|
||||
|
||||
# -- Utilities ---------------------------------------------------------------
|
||||
|
||||
def set_alpha(model: nnx.Module, alpha: float):
|
||||
"""Set steering coefficient for all SVD adapter layers."""
|
||||
for _, value in nnx.iter_graph(model):
|
||||
if isinstance(value, SVDSteeringLinear):
|
||||
value.alpha.value = jnp.float32(alpha)
|
||||
|
||||
|
||||
def get_svd_modules(model: nnx.Module) -> list[SVDSteeringLinear]:
|
||||
"""Get all SVD steering modules in the model."""
|
||||
modules = []
|
||||
for _, value in nnx.iter_graph(model):
|
||||
if isinstance(value, SVDSteeringLinear):
|
||||
modules.append(value)
|
||||
return modules
|
||||
|
||||
|
||||
def monitor_svd_adapters(model: nnx.Module) -> dict:
|
||||
"""Monitor ||delta_s||/||S|| ratio."""
|
||||
ratios = []
|
||||
for m in get_svd_modules(model):
|
||||
S = m.svd_S.value
|
||||
ds = m.delta_s.value
|
||||
ratios.append(float(jnp.linalg.norm(ds) / jnp.linalg.norm(S)))
|
||||
return {"adapter_ratio": max(ratios) if ratios else 0.0}
|
||||
@@ -0,0 +1,471 @@
|
||||
"""
|
||||
Utilities for instrumenting a torch model.
|
||||
|
||||
Trace will hook one layer at a time.
|
||||
TraceDict will hook multiple layers at once.
|
||||
subsequence slices intervals from Sequential modules.
|
||||
get_module, replace_module, get_parameter resolve dotted names.
|
||||
set_requires_grad recursively sets requires_grad in module parameters.
|
||||
"""
|
||||
|
||||
import contextlib
|
||||
import copy
|
||||
import inspect
|
||||
from collections import OrderedDict
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
class Trace(contextlib.AbstractContextManager):
|
||||
"""
|
||||
To retain the output of the named layer during the computation of
|
||||
the given network:
|
||||
|
||||
with Trace(net, 'layer.name') as ret:
|
||||
_ = net(inp)
|
||||
representation = ret.output
|
||||
|
||||
A layer module can be passed directly without a layer name, and
|
||||
its output will be retained. By default, a direct reference to
|
||||
the output object is returned, but options can control this:
|
||||
|
||||
clone=True - retains a copy of the output, which can be
|
||||
useful if you want to see the output before it might
|
||||
be modified by the network in-place later.
|
||||
detach=True - retains a detached reference or copy. (By
|
||||
default the value would be left attached to the graph.)
|
||||
retain_grad=True - request gradient to be retained on the
|
||||
output. After backward(), ret.output.grad is populated.
|
||||
|
||||
retain_input=True - also retains the input.
|
||||
retain_output=False - can disable retaining the output.
|
||||
edit_output=fn - calls the function to modify the output
|
||||
of the layer before passing it the rest of the model.
|
||||
fn can optionally accept (output, layer) arguments
|
||||
for the original output and the layer name.
|
||||
stop=True - throws a StopForward exception after the layer
|
||||
is run, which allows running just a portion of a model.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
module,
|
||||
layer=None,
|
||||
retain_output=True,
|
||||
retain_input=False,
|
||||
clone=False,
|
||||
detach=False,
|
||||
retain_grad=False,
|
||||
edit_output=None,
|
||||
stop=False,
|
||||
):
|
||||
"""
|
||||
Method to replace a forward method with a closure that
|
||||
intercepts the call, and tracks the hook so that it can be reverted.
|
||||
"""
|
||||
retainer = self
|
||||
self.layer = layer
|
||||
if layer is not None:
|
||||
module = get_module(module, layer)
|
||||
|
||||
def retain_hook(m, inputs, output):
|
||||
if edit_output:
|
||||
output = invoke_with_optional_args(
|
||||
edit_output, output=output, layer=self.layer, inputs=inputs
|
||||
)
|
||||
if retain_input:
|
||||
retainer.input = recursive_copy(
|
||||
inputs[0] if len(inputs) == 1 else inputs,
|
||||
clone=clone,
|
||||
detach=detach,
|
||||
retain_grad=False,
|
||||
) # retain_grad applies to output only.
|
||||
if retain_output:
|
||||
retainer.output = recursive_copy(
|
||||
output, clone=clone, detach=detach, retain_grad=retain_grad
|
||||
)
|
||||
# When retain_grad is set, also insert a trivial
|
||||
# copy operation. That allows in-place operations
|
||||
# to follow without error.
|
||||
if retain_grad:
|
||||
output = recursive_copy(retainer.output, clone=True, detach=False)
|
||||
if stop:
|
||||
raise StopForward()
|
||||
return output
|
||||
|
||||
self.registered_hook = module.register_forward_hook(retain_hook)
|
||||
self.stop = stop
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, type, value, traceback):
|
||||
self.close()
|
||||
if self.stop and issubclass(type, StopForward):
|
||||
return True
|
||||
|
||||
def close(self):
|
||||
self.registered_hook.remove()
|
||||
|
||||
|
||||
class TraceDict(OrderedDict, contextlib.AbstractContextManager):
|
||||
"""
|
||||
To retain the output of multiple named layers during the computation
|
||||
of the given network:
|
||||
|
||||
with TraceDict(net, ['layer1.name1', 'layer2.name2']) as ret:
|
||||
_ = net(inp)
|
||||
representation = ret['layer1.name1'].output
|
||||
|
||||
If edit_output is provided, it should be a function that takes
|
||||
two arguments: output, and the layer name; and then it returns the
|
||||
modified output.
|
||||
|
||||
Other arguments are the same as Trace. If stop is True, then the
|
||||
execution of the network will be stopped after the last layer
|
||||
listed (even if it would not have been the last to be executed).
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
module,
|
||||
layers=None,
|
||||
retain_output=True,
|
||||
retain_input=False,
|
||||
clone=False,
|
||||
detach=False,
|
||||
retain_grad=False,
|
||||
edit_output=None,
|
||||
stop=False,
|
||||
):
|
||||
self.stop = stop
|
||||
|
||||
def flag_last_unseen(it):
|
||||
try:
|
||||
it = iter(it)
|
||||
prev = next(it)
|
||||
seen = set([prev])
|
||||
except StopIteration:
|
||||
return
|
||||
for item in it:
|
||||
if item not in seen:
|
||||
yield False, prev
|
||||
seen.add(item)
|
||||
prev = item
|
||||
yield True, prev
|
||||
|
||||
for is_last, layer in flag_last_unseen(layers):
|
||||
|
||||
def optional_dict(obj):
|
||||
if isinstance(obj, dict):
|
||||
return obj.get(layer, None)
|
||||
return obj
|
||||
|
||||
self[layer] = Trace(
|
||||
module=module,
|
||||
layer=layer,
|
||||
retain_output=optional_dict(retain_output),
|
||||
retain_input=optional_dict(retain_input),
|
||||
clone=optional_dict(clone),
|
||||
detach=optional_dict(detach),
|
||||
retain_grad=optional_dict(retain_grad),
|
||||
edit_output=optional_dict(edit_output),
|
||||
stop=stop and is_last,
|
||||
)
|
||||
|
||||
def __enter__(self):
|
||||
return self
|
||||
|
||||
def __exit__(self, type, value, traceback):
|
||||
self.close()
|
||||
if self.stop and issubclass(type, StopForward):
|
||||
return True
|
||||
|
||||
def close(self):
|
||||
for layer, trace in reversed(self.items()):
|
||||
trace.close()
|
||||
|
||||
|
||||
class StopForward(Exception):
|
||||
"""
|
||||
If the only output needed from running a network is the retained
|
||||
submodule then Trace(submodule, stop=True) will stop execution
|
||||
immediately after the retained submodule by raising the StopForward()
|
||||
exception. When Trace is used as context manager, it catches that
|
||||
exception and can be used as follows:
|
||||
|
||||
with Trace(net, layername, stop=True) as tr:
|
||||
net(inp) # Only runs the network up to layername
|
||||
print(tr.output)
|
||||
"""
|
||||
|
||||
pass
|
||||
|
||||
|
||||
def recursive_copy(x, clone=None, detach=None, retain_grad=None):
|
||||
"""
|
||||
Copies a reference to a tensor, or an object that contains tensors,
|
||||
optionally detaching and cloning the tensor(s). If retain_grad is
|
||||
true, the original tensors are marked to have grads retained.
|
||||
"""
|
||||
if not clone and not detach and not retain_grad:
|
||||
return x
|
||||
if isinstance(x, torch.Tensor):
|
||||
if retain_grad:
|
||||
if not x.requires_grad:
|
||||
x.requires_grad = True
|
||||
x.retain_grad()
|
||||
elif detach:
|
||||
x = x.detach()
|
||||
if clone:
|
||||
x = x.clone()
|
||||
return x
|
||||
# Only dicts, lists, and tuples (and subclasses) can be copied.
|
||||
if isinstance(x, dict):
|
||||
return type(x)({k: recursive_copy(v, clone=clone, detach=detach, retain_grad=retain_grad) for k, v in x.items()})
|
||||
elif isinstance(x, (list, tuple)):
|
||||
return type(x)([recursive_copy(v, clone=clone, detach=detach, retain_grad=retain_grad) for v in x])
|
||||
else:
|
||||
assert False, f"Unknown type {type(x)} cannot be broken into tensors."
|
||||
|
||||
|
||||
def subsequence(
|
||||
sequential,
|
||||
first_layer=None,
|
||||
last_layer=None,
|
||||
after_layer=None,
|
||||
upto_layer=None,
|
||||
single_layer=None,
|
||||
share_weights=False,
|
||||
):
|
||||
"""
|
||||
Creates a subsequence of a pytorch Sequential model, copying over
|
||||
modules together with parameters for the subsequence. Only
|
||||
modules from first_layer to last_layer (inclusive) are included,
|
||||
or modules between after_layer and upto_layer (exclusive).
|
||||
Handles descent into dotted layer names as long as all references
|
||||
are within nested Sequential models.
|
||||
|
||||
If share_weights is True, then references the original modules
|
||||
and their parameters without copying them. Otherwise, by default,
|
||||
makes a separate brand-new copy.
|
||||
"""
|
||||
assert (single_layer is None) or (
|
||||
first_layer is last_layer is after_layer is upto_layer is None
|
||||
)
|
||||
if single_layer is not None:
|
||||
first_layer = single_layer
|
||||
last_layer = single_layer
|
||||
first, last, after, upto = [
|
||||
None if d is None else d.split(".")
|
||||
for d in [first_layer, last_layer, after_layer, upto_layer]
|
||||
]
|
||||
return hierarchical_subsequence(
|
||||
sequential,
|
||||
first=first,
|
||||
last=last,
|
||||
after=after,
|
||||
upto=upto,
|
||||
share_weights=share_weights,
|
||||
)
|
||||
|
||||
|
||||
def hierarchical_subsequence(
|
||||
sequential, first, last, after, upto, share_weights=False, depth=0
|
||||
):
|
||||
"""
|
||||
Recursive helper for subsequence() to support descent into dotted
|
||||
layer names. In this helper, first, last, after, and upto are
|
||||
arrays of names resulting from splitting on dots. Can only
|
||||
descend into nested Sequentials.
|
||||
"""
|
||||
assert (last is None) or (upto is None)
|
||||
assert (first is None) or (after is None)
|
||||
if first is last is after is upto is None:
|
||||
return sequential if share_weights else copy.deepcopy(sequential)
|
||||
assert isinstance(sequential, torch.nn.Sequential), (
|
||||
".".join((first or last or after or upto)[:depth] or "arg") + " not Sequential"
|
||||
)
|
||||
including_children = (first is None) and (after is None)
|
||||
included_children = OrderedDict()
|
||||
# A = current level short name of A.
|
||||
# AN = full name for recursive descent if not innermost.
|
||||
(F, FN), (L, LN), (A, AN), (U, UN) = [
|
||||
(d[depth], (None if len(d) == depth + 1 else d))
|
||||
if d is not None
|
||||
else (None, None)
|
||||
for d in [first, last, after, upto]
|
||||
]
|
||||
for name, layer in sequential._modules.items():
|
||||
if name == F:
|
||||
first = None
|
||||
including_children = True
|
||||
if name == A and AN is not None: # just like F if not a leaf.
|
||||
after = None
|
||||
including_children = True
|
||||
if name == U and UN is None:
|
||||
upto = None
|
||||
including_children = False
|
||||
if including_children:
|
||||
# AR = full name for recursive descent if name matches.
|
||||
FR, LR, AR, UR = [
|
||||
n if n is None or n[depth] == name else None for n in [FN, LN, AN, UN]
|
||||
]
|
||||
chosen = hierarchical_subsequence(
|
||||
layer,
|
||||
first=FR,
|
||||
last=LR,
|
||||
after=AR,
|
||||
upto=UR,
|
||||
share_weights=share_weights,
|
||||
depth=depth + 1,
|
||||
)
|
||||
if chosen is not None:
|
||||
included_children[name] = chosen
|
||||
if name == L:
|
||||
last = None
|
||||
including_children = False
|
||||
if name == U and UN is not None: # just like L if not a leaf.
|
||||
upto = None
|
||||
including_children = False
|
||||
if name == A and AN is None:
|
||||
after = None
|
||||
including_children = True
|
||||
for name in [first, last, after, upto]:
|
||||
if name is not None:
|
||||
raise ValueError("Layer %s not found" % ".".join(name))
|
||||
# Omit empty subsequences except at the outermost level,
|
||||
# where we should not return None.
|
||||
if not len(included_children) and depth > 0:
|
||||
return None
|
||||
result = torch.nn.Sequential(included_children)
|
||||
result.training = sequential.training
|
||||
return result
|
||||
|
||||
|
||||
def set_requires_grad(requires_grad, *models):
|
||||
"""
|
||||
Sets requires_grad true or false for all parameters within the
|
||||
models passed.
|
||||
"""
|
||||
for model in models:
|
||||
if isinstance(model, torch.nn.Module):
|
||||
for param in model.parameters():
|
||||
param.requires_grad = requires_grad
|
||||
elif isinstance(model, (torch.nn.Parameter, torch.Tensor)):
|
||||
model.requires_grad = requires_grad
|
||||
else:
|
||||
assert False, "unknown type %r" % type(model)
|
||||
|
||||
|
||||
def get_module(model, name):
|
||||
"""
|
||||
Finds the named module within the given model.
|
||||
"""
|
||||
for n, m in model.named_modules():
|
||||
if n == name:
|
||||
return m
|
||||
raise LookupError(name)
|
||||
|
||||
|
||||
def get_parameter(model, name):
|
||||
"""
|
||||
Finds the named parameter within the given model.
|
||||
"""
|
||||
for n, p in model.named_parameters():
|
||||
if n == name:
|
||||
return p
|
||||
raise LookupError(name)
|
||||
|
||||
|
||||
def module_names(model):
|
||||
"""
|
||||
Lists all the module names.
|
||||
"""
|
||||
return [n for n, _ in model.named_modules()]
|
||||
|
||||
|
||||
def parameter_names(model):
|
||||
"""
|
||||
Lists all the parameter names.
|
||||
"""
|
||||
return [n for n, _ in model.named_parameters()]
|
||||
|
||||
|
||||
def replace_module(model, name, new_module):
|
||||
"""
|
||||
Replaces the named module within the given model.
|
||||
"""
|
||||
if "." in name:
|
||||
parent_name, attr_name = name.rsplit(".", 1)
|
||||
model = get_module(model, parent_name)
|
||||
# original_module = getattr(model, attr_name)
|
||||
setattr(model, attr_name, new_module)
|
||||
|
||||
|
||||
def invoke_with_optional_args(fn, *args, **kwargs):
|
||||
"""
|
||||
Invokes a function with only the arguments that it
|
||||
is written to accept, giving priority to arguments
|
||||
that match by-name, using the following rules.
|
||||
(1) arguments with matching names are passed by name.
|
||||
(2) remaining non-name-matched args are passed by order.
|
||||
(3) extra caller arguments that the function cannot
|
||||
accept are not passed.
|
||||
(4) extra required function arguments that the caller
|
||||
cannot provide cause a TypeError to be raised.
|
||||
Ordinary python calling conventions are helpful for
|
||||
supporting a function that might be revised to accept
|
||||
extra arguments in a newer version, without requiring the
|
||||
caller to pass those new arguments. This function helps
|
||||
support function callers that might be revised to supply
|
||||
extra arguments, without requiring the callee to accept
|
||||
those new arguments.
|
||||
"""
|
||||
argspec = inspect.getfullargspec(fn)
|
||||
pass_args = []
|
||||
used_kw = set()
|
||||
unmatched_pos = []
|
||||
used_pos = 0
|
||||
defaulted_pos = len(argspec.args) - (
|
||||
0 if not argspec.defaults else len(argspec.defaults)
|
||||
)
|
||||
# Pass positional args that match name first, then by position.
|
||||
for i, n in enumerate(argspec.args):
|
||||
if n in kwargs:
|
||||
pass_args.append(kwargs[n])
|
||||
used_kw.add(n)
|
||||
elif used_pos < len(args):
|
||||
pass_args.append(args[used_pos])
|
||||
used_pos += 1
|
||||
else:
|
||||
unmatched_pos.append(len(pass_args))
|
||||
pass_args.append(
|
||||
None if i < defaulted_pos else argspec.defaults[i - defaulted_pos]
|
||||
)
|
||||
# Fill unmatched positional args with unmatched keyword args in order.
|
||||
if len(unmatched_pos):
|
||||
for k, v in kwargs.items():
|
||||
if k in used_kw or k in argspec.kwonlyargs:
|
||||
continue
|
||||
pass_args[unmatched_pos[0]] = v
|
||||
used_kw.add(k)
|
||||
unmatched_pos = unmatched_pos[1:]
|
||||
if len(unmatched_pos) == 0:
|
||||
break
|
||||
else:
|
||||
if unmatched_pos[0] < defaulted_pos:
|
||||
unpassed = ", ".join(
|
||||
argspec.args[u] for u in unmatched_pos if u < defaulted_pos
|
||||
)
|
||||
raise TypeError(f"{fn.__name__}() cannot be passed {unpassed}.")
|
||||
# Pass remaining kw args if they can be accepted.
|
||||
pass_kw = {
|
||||
k: v
|
||||
for k, v in kwargs.items()
|
||||
if k not in used_kw and (k in argspec.kwonlyargs or argspec.varargs is not None)
|
||||
}
|
||||
# Pass remaining positional args if they can be accepted.
|
||||
if argspec.varargs is not None:
|
||||
pass_args += list(args[used_pos:])
|
||||
return fn(*pass_args, **pass_kw)
|
||||
@@ -0,0 +1,739 @@
|
||||
# Copyright 2024-present the HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import warnings
|
||||
from collections import Counter, defaultdict
|
||||
from collections.abc import Iterable, Mapping
|
||||
from contextlib import nullcontext
|
||||
from copy import deepcopy
|
||||
from functools import partial
|
||||
from itertools import cycle
|
||||
from typing import Optional, Union
|
||||
|
||||
import torch
|
||||
import torch.distributed as dist
|
||||
from tqdm import tqdm
|
||||
from transformers.pytorch_utils import Conv1D
|
||||
|
||||
from peft.tuners.tuners_utils import _find_minimal_target_modules, check_target_module_exists
|
||||
from peft.utils.constants import MIN_TARGET_MODULES_FOR_OPTIMIZATION
|
||||
from peft.utils.incremental_pca import IncrementalPCA
|
||||
from peft.utils.other import _get_submodules, get_pattern_key
|
||||
|
||||
from .config import LoraConfig
|
||||
from .layer import Embedding, LoraLayer, MultiheadAttention, _ConvNd
|
||||
|
||||
|
||||
UNSUPPORTED_LORA_MODULES = (Embedding, MultiheadAttention, _ConvNd)
|
||||
|
||||
|
||||
class _Hook:
|
||||
"""
|
||||
A base class for hooks that prepares layer inputs for EVA.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
name: str,
|
||||
prepare_layer_inputs_fn: Optional[callable] = None,
|
||||
gather_distributed_inputs: bool = True,
|
||||
):
|
||||
self.name = name
|
||||
self.gather_distributed_inputs = gather_distributed_inputs
|
||||
if prepare_layer_inputs_fn is None:
|
||||
self._prepare_layer_inputs_fn = self._prepare_layer_inputs_fn_default
|
||||
else:
|
||||
self._prepare_layer_inputs_fn = prepare_layer_inputs_fn
|
||||
self.model_input = None
|
||||
|
||||
@staticmethod
|
||||
def _prepare_layer_inputs_fn_default(layer_input, model_input, layer_name) -> torch.Tensor:
|
||||
if isinstance(layer_input, torch.Tensor):
|
||||
pass
|
||||
elif isinstance(layer_input, (tuple, list)):
|
||||
layer_input = layer_input[0]
|
||||
else:
|
||||
raise ValueError(
|
||||
f"unsupported input type {type(layer_input)} for prepare_layer_inputs_fn in layer {layer_name}, "
|
||||
"please provide a custom prepare_layer_inputs_fn"
|
||||
)
|
||||
# if the input has more than 2 dimensions, we flatten all but the last dimension
|
||||
if layer_input.ndim > 2:
|
||||
layer_input = layer_input.view(-1, layer_input.size(-1))
|
||||
return layer_input
|
||||
|
||||
@torch.no_grad()
|
||||
def prepare_layer_inputs(self, layer_input):
|
||||
return self._prepare_layer_inputs_fn(layer_input, self.model_input, self.name)
|
||||
|
||||
def gather_layer_inputs(self, layer_input):
|
||||
if dist.is_initialized() and self.gather_distributed_inputs:
|
||||
world_size = dist.get_world_size()
|
||||
|
||||
# First gather sizes from all processes more efficiently
|
||||
local_size = torch.tensor([layer_input.shape[0]], device=layer_input.device)
|
||||
all_sizes = torch.empty(world_size, dtype=local_size.dtype, device=layer_input.device)
|
||||
dist.all_gather_into_tensor(all_sizes, local_size)
|
||||
all_sizes = all_sizes.tolist()
|
||||
|
||||
# Find maximum size and pad tensors
|
||||
padded_input = layer_input.new_zeros((max(all_sizes), *layer_input.shape[1:]))
|
||||
padded_input[: layer_input.shape[0]] = layer_input
|
||||
|
||||
# Gather padded tensors
|
||||
gathered_inputs = [torch.zeros_like(padded_input) for _ in range(world_size)]
|
||||
dist.all_gather(gathered_inputs, padded_input.contiguous())
|
||||
|
||||
# Remove padding for each gathered tensor
|
||||
gathered_inputs = [tensor[:size] for tensor, size in zip(gathered_inputs, all_sizes)]
|
||||
|
||||
# Concatenate along batch dimension
|
||||
return torch.cat(gathered_inputs, dim=0)
|
||||
return layer_input
|
||||
|
||||
|
||||
class SVDHook(_Hook):
|
||||
"""
|
||||
A forward hook for calculating incremental SVD on layer inputs. The hook is designed to be registered to a PyTorch
|
||||
module using the `register_forward_hook` method.
|
||||
|
||||
This hook performs a step of incremental Singular Value Decomposition (SVD) on the inputs of a specified layer
|
||||
during the forward pass of a neural network. The hook also tracks convergence of the computed components using
|
||||
cosine similarity between the current and previous components.
|
||||
|
||||
Args:
|
||||
name (str): Name of the layer to which this hook is attached.
|
||||
n_components (int): Number of principal components to compute.
|
||||
sim_thresh (Union[float, torch.Tensor]): Similarity threshold for convergence.
|
||||
prepare_layer_inputs_fn (Optional[callable]): Function to prepare layer inputs for SVD.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
n_components: int,
|
||||
sim_thresh: Union[float, torch.Tensor],
|
||||
**base_class_kwargs,
|
||||
):
|
||||
super().__init__(**base_class_kwargs)
|
||||
self.n_components = n_components
|
||||
self.sim_thresh = sim_thresh
|
||||
if isinstance(sim_thresh, torch.Tensor) and len(sim_thresh.shape) > 0:
|
||||
check1 = sim_thresh.size(0) == n_components or sim_thresh.size(0) == 1
|
||||
check2 = len(sim_thresh.shape) == 1
|
||||
if not (check1 and check2):
|
||||
raise ValueError(
|
||||
"if sim_thresh is a tensor with more than 0 dimensions it must have shape (n_components,) or (1,)"
|
||||
)
|
||||
self.svd = IncrementalPCA(
|
||||
n_components=n_components,
|
||||
copy=True,
|
||||
lowrank=True,
|
||||
lowrank_seed=42,
|
||||
)
|
||||
self.model_input = None
|
||||
self.converged = torch.zeros((n_components,), dtype=torch.bool)
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(self, model, input, output):
|
||||
previous_components = None
|
||||
if hasattr(self.svd, "components_"):
|
||||
previous_components = self.svd.components_.clone().detach()
|
||||
states = self.prepare_layer_inputs(input)
|
||||
states = self.gather_layer_inputs(states)
|
||||
# check if batch sizes is more than the number of components
|
||||
if states.size(0) < self.n_components:
|
||||
print(f"skipping SVD for {self.name} because there are less than {self.n_components} examples")
|
||||
return
|
||||
self.svd.partial_fit(states.to(torch.float32))
|
||||
# add if statement to check if we are in the first step where previous_components is None
|
||||
if previous_components is None:
|
||||
return
|
||||
components = self.svd.components_
|
||||
if len(components.shape) == 1:
|
||||
components = components.reshape(1, -1)
|
||||
previous_components = previous_components.reshape(1, -1)
|
||||
# consider as converged if enough components have converged via cossim
|
||||
sim = torch.nn.functional.cosine_similarity(components, previous_components)
|
||||
self.converged = sim >= self.sim_thresh
|
||||
|
||||
|
||||
# This is used to determine if inputs of two different layers are equal. For such cases, SVD
|
||||
# needs to be done for only for one of the equal inputs.
|
||||
class HashHook(_Hook):
|
||||
"""
|
||||
A forward hook for hashing layer inputs. The hook is designed to be registered to a PyTorch module using the
|
||||
`register_forward_hook` method.
|
||||
|
||||
This hook hashes the inputs of a specified layer during the forward pass of a neural network and stores the hash
|
||||
values for later analysis or comparison.
|
||||
|
||||
Args:
|
||||
name (str): Name of the layer to which this hook is attached. hashed_inputs (list): List of hashed inputs.
|
||||
prepare_layer_inputs_fn (Optional[callable]): Function to prepare layer inputs for hashing.
|
||||
"""
|
||||
|
||||
def __init__(self, **base_class_kwargs):
|
||||
super().__init__(**base_class_kwargs)
|
||||
self.hashed_inputs = []
|
||||
|
||||
@staticmethod
|
||||
def hash_fn(tensor):
|
||||
return hash(tuple(tensor.view(-1).tolist()))
|
||||
|
||||
@torch.no_grad()
|
||||
def __call__(self, model, input, output):
|
||||
x = self.prepare_layer_inputs(input)
|
||||
x = self.gather_layer_inputs(x)
|
||||
self.hashed_inputs.append(self.hash_fn(x.cpu()))
|
||||
|
||||
|
||||
def find_equal_values(dictionary: dict) -> dict:
|
||||
"""
|
||||
Find keys in a dictionary that have the same value.
|
||||
|
||||
This function takes a dictionary and returns a new dictionary containing keys that have the same value. The keys in
|
||||
the output dictionary are the values from the input dictionary, and the values are lists of keys that share the
|
||||
same value.
|
||||
"""
|
||||
value_dict = defaultdict(list)
|
||||
for k, v in dictionary.items():
|
||||
value_dict[v].append(k)
|
||||
return {k: v for k, v in value_dict.items() if len(v) > 1}
|
||||
|
||||
|
||||
def get_device_with_meta_params(model: torch.nn.Module) -> torch.device:
|
||||
"""
|
||||
Get the device of the model's parameters. Useful if some parameters are on meta device.
|
||||
"""
|
||||
devices = list({p.device for p in model.parameters() if p.device.type != "meta"})
|
||||
if len(devices) > 1:
|
||||
warnings.warn(f"Could not determine device, model has multiple devices: {devices}")
|
||||
return
|
||||
return devices[0]
|
||||
|
||||
|
||||
def move_inputs_to_device(inputs, device: Union[str, torch.device]):
|
||||
"""
|
||||
Move the inputs to the specified device. Adapted from hf.Trainer.
|
||||
"""
|
||||
if hasattr(inputs, "to"):
|
||||
return inputs.to(device)
|
||||
if isinstance(inputs, Mapping):
|
||||
return type(inputs)({k: move_inputs_to_device(v, device) for k, v in inputs.items()})
|
||||
elif isinstance(inputs, (tuple, list)):
|
||||
return type(inputs)(move_inputs_to_device(v, device) for v in inputs)
|
||||
else:
|
||||
warnings.warn(f"input of type {type(inputs)} could not be moved to the correct device")
|
||||
return inputs
|
||||
|
||||
|
||||
def prepare_model_inputs_fn_language_modeling(model_input, peft_config: LoraConfig):
|
||||
"""
|
||||
Get the indices of the items that should be used for SVD.
|
||||
|
||||
Attributes:
|
||||
model_input (dict): The model inputs.
|
||||
peft_config (LoraConfig): The configuration for the LoRA layers.
|
||||
"""
|
||||
if not isinstance(model_input, dict):
|
||||
raise ValueError("When using `prepare_model_inputs_fn_language_modeling` inputs must be a dictionary")
|
||||
mask = model_input.get("attention_mask", torch.ones_like(model_input["input_ids"])).bool()
|
||||
if peft_config.eva_config.use_label_mask and hasattr(model_input, "labels"):
|
||||
mask = torch.logical_and(mask, model_input["labels"] != peft_config.eva_config.label_mask_value)
|
||||
return mask.nonzero()
|
||||
|
||||
|
||||
def prepare_layer_inputs_fn_language_modeling(layer_input, model_input, layer_name) -> torch.Tensor:
|
||||
"""
|
||||
if not all items in the input should be used for SVD, this function can be used to get the indices of the items
|
||||
that should be used.
|
||||
|
||||
Attributes:
|
||||
layer_input (torch.Tensor): The layer inputs.
|
||||
model_input (torch.Tensor):
|
||||
The model inputs or if `prepare_model_inputs_fn` is not None the output of this function.
|
||||
layer_name (str): The name of the layer.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: The input to the SVD.
|
||||
"""
|
||||
# if layer inputs are not a tensor, we simply get the first item
|
||||
if isinstance(layer_input, torch.Tensor):
|
||||
pass
|
||||
elif isinstance(layer_input, (tuple, list)):
|
||||
layer_input = layer_input[0]
|
||||
else:
|
||||
raise ValueError(
|
||||
f"unsupported input type {type(layer_input)} for prepare_layer_inputs_fn in layer {layer_name}, "
|
||||
"please provide a custom prepare_layer_inputs_fn"
|
||||
)
|
||||
# in this case model_input is the output of `prepare_model_inputs_fn_language_modeling`
|
||||
return layer_input[model_input.T.unbind()]
|
||||
|
||||
|
||||
def forward_fn_dict(model, inputs):
|
||||
return model(**inputs)
|
||||
|
||||
|
||||
def _get_eva_state_dict(
|
||||
model: torch.nn.Module,
|
||||
dataloader: Iterable,
|
||||
peft_config: Optional[LoraConfig],
|
||||
target_module_check_fn: callable,
|
||||
forward_fn: Optional[callable],
|
||||
prepare_model_inputs_fn: Optional[callable],
|
||||
prepare_layer_inputs_fn: Union[callable, dict[str, callable], None],
|
||||
gather_distributed_inputs: bool,
|
||||
show_progress_bar: bool,
|
||||
) -> dict:
|
||||
# Computes the rank distribution for each layer based on the explained variance ratio.
|
||||
# when rank_pattern flag is False, all values in max_components are the same
|
||||
def _get_rank_distribution(hooks, layer_hook_map, equal_inputs_map, rank_budget, max_components):
|
||||
exp_vars = {k: h[0].svd.explained_variance_ratio_[: max_components[k]] for k, h in hooks.items()}
|
||||
keys, values = zip(*[(k, c) for k, name in layer_hook_map.items() for c in exp_vars[name]])
|
||||
idx = torch.stack(values).argsort(descending=True)
|
||||
counts = Counter([keys[i] for i in idx[:rank_budget]])
|
||||
counts = {k: counts.get(k, 0) for k in layer_hook_map.keys()} # add layers with 0 rank
|
||||
for k, k_hook in equal_inputs_map.items():
|
||||
# ensure hook layers have the highest rank if they are equal to another layer
|
||||
rank, rank_hook = counts[k], counts[k_hook]
|
||||
if rank_hook >= rank:
|
||||
continue
|
||||
counts[k_hook], counts[k] = rank, rank_hook
|
||||
return counts
|
||||
|
||||
# dataloader is not empty
|
||||
if len(dataloader) == 0:
|
||||
raise ValueError("dataloader is empty")
|
||||
|
||||
# check if dist is initialized
|
||||
if dist.is_initialized() and gather_distributed_inputs:
|
||||
warnings.warn(
|
||||
"torch.distributed is initialized and `gather_distributed_inputs` is True, "
|
||||
"therefore EVA initialization will gather tensors from all ranks. "
|
||||
"Ensure the model does not receive the same inputs on different ranks."
|
||||
)
|
||||
|
||||
# for unusually high rho values, define an upper limit
|
||||
rho_threshold = 1000
|
||||
rho = peft_config.eva_config.rho
|
||||
if rho > rho_threshold:
|
||||
max_dim = max(max(p.shape) for p in model.parameters())
|
||||
rho_ceil = max_dim // peft_config.r
|
||||
rho = min(rho, rho_ceil)
|
||||
|
||||
training = model.training
|
||||
device = get_device_with_meta_params(model)
|
||||
model.eval()
|
||||
|
||||
# get model inputs
|
||||
inputs = next(iter(dataloader))
|
||||
if device is not None:
|
||||
inputs = move_inputs_to_device(inputs, device)
|
||||
if prepare_model_inputs_fn is not None:
|
||||
model_inputs_for_hooks = prepare_model_inputs_fn(inputs, peft_config)
|
||||
else:
|
||||
model_inputs_for_hooks = deepcopy(inputs)
|
||||
|
||||
hooks = {}
|
||||
max_components = {}
|
||||
rank_budget = 0
|
||||
for name, module in model.named_modules():
|
||||
if not target_module_check_fn(name, module):
|
||||
continue
|
||||
if isinstance(prepare_layer_inputs_fn, Mapping):
|
||||
fn = prepare_layer_inputs_fn.pop(name, None)
|
||||
else:
|
||||
fn = prepare_layer_inputs_fn
|
||||
hook = HashHook(name=name, prepare_layer_inputs_fn=fn, gather_distributed_inputs=gather_distributed_inputs)
|
||||
hook.model_input = model_inputs_for_hooks
|
||||
handle = module.register_forward_hook(hook)
|
||||
hooks[name] = (hook, handle)
|
||||
layer_rank = peft_config.rank_pattern.get(
|
||||
get_pattern_key(peft_config.rank_pattern.keys(), name), peft_config.r
|
||||
)
|
||||
max_components[name] = round(layer_rank * rho)
|
||||
rank_budget += layer_rank
|
||||
if isinstance(prepare_layer_inputs_fn, Mapping) and len(prepare_layer_inputs_fn) > 0:
|
||||
raise ValueError(
|
||||
"prepare_layer_inputs_fn is a mapping but the following module names were not found in the model: "
|
||||
f"{prepare_layer_inputs_fn.keys()}"
|
||||
)
|
||||
|
||||
# forward for one batch to check which layer inputs are equal to avoid unneeded svd calculations
|
||||
forward_fn(model, inputs)
|
||||
hash_dict = {k: h[0].hashed_inputs[0] for k, h in hooks.items()}
|
||||
# equal input maps groups layers which receive the same input. One layer is defined as the key and receives an svd
|
||||
# hook. For the remaining layers the svd results can be skipped.
|
||||
equal_inputs = list(find_equal_values(hash_dict).values())
|
||||
equal_inputs_map = {vv: v[0] for v in equal_inputs for vv in v[1:]}
|
||||
# for layers with equal inputs we need to make sure that the max_components are the same
|
||||
for names in equal_inputs:
|
||||
max_value = max(max_components[n] for n in names)
|
||||
for n in names:
|
||||
max_components[n] = max_value
|
||||
|
||||
# initialize svd hooks
|
||||
for name in list(hooks.keys()):
|
||||
hook, handle = hooks.pop(name)
|
||||
handle.remove()
|
||||
if name in equal_inputs_map:
|
||||
continue
|
||||
hook = SVDHook(
|
||||
n_components=max_components[name],
|
||||
sim_thresh=peft_config.eva_config.tau,
|
||||
name=name,
|
||||
prepare_layer_inputs_fn=hook._prepare_layer_inputs_fn,
|
||||
gather_distributed_inputs=gather_distributed_inputs,
|
||||
)
|
||||
module = model.get_submodule(name)
|
||||
handle = module.register_forward_hook(hook)
|
||||
hooks[name] = (hook, handle) # adding the old handle here so we dont get errors in the first forward pass
|
||||
layer_hook_map = {**dict(zip(hooks.keys(), hooks.keys())), **equal_inputs_map}
|
||||
|
||||
# start svd calculation
|
||||
if show_progress_bar and (not dist.is_initialized() or dist.get_rank() == 0):
|
||||
pbar = tqdm(iter(cycle(dataloader)), position=0, leave=False)
|
||||
use_tqdm = True
|
||||
else:
|
||||
pbar = iter(cycle(dataloader))
|
||||
use_tqdm = False
|
||||
convergence_dict = {k: False for k in hooks.keys()}
|
||||
rank_dist = max_components.copy()
|
||||
for inputs in pbar:
|
||||
if device is not None:
|
||||
inputs = move_inputs_to_device(inputs, device)
|
||||
if prepare_model_inputs_fn is not None:
|
||||
model_inputs_for_hooks = prepare_model_inputs_fn(inputs, peft_config)
|
||||
else:
|
||||
model_inputs_for_hooks = deepcopy(inputs)
|
||||
|
||||
for name in list(hooks.keys()):
|
||||
hook, handle = hooks[name]
|
||||
# check if all components that are needed for the rank distribution have converged
|
||||
converged = torch.all(hook.converged[: rank_dist[name]])
|
||||
# if a layer has switched from not converged to converged in the current step
|
||||
if (not convergence_dict[name]) and converged and handle:
|
||||
handle.remove()
|
||||
handle = None
|
||||
convergence_dict[name] = True
|
||||
continue
|
||||
# if a layer has switched from converged to not converged in the current step
|
||||
elif convergence_dict[name] and not converged:
|
||||
module = model.get_submodule(name)
|
||||
handle = module.register_forward_hook(hook)
|
||||
convergence_dict[name] = False
|
||||
hook.model_input = model_inputs_for_hooks
|
||||
hooks[name] = (hook, handle)
|
||||
|
||||
if use_tqdm:
|
||||
layer_converged = list(convergence_dict.values()) + [
|
||||
convergence_dict[v] for v in equal_inputs_map.values()
|
||||
]
|
||||
pbar.set_description(f"{sum(layer_converged)}/{len(layer_converged)} layers have converged")
|
||||
|
||||
if all(convergence_dict.values()):
|
||||
break
|
||||
|
||||
forward_fn(model, inputs)
|
||||
|
||||
# in case some hooks have to skip the svd calculation because the number of tokens is less than the number of
|
||||
# components
|
||||
if not all(hasattr(h[0].svd, "components_") for h in hooks.values()):
|
||||
continue
|
||||
|
||||
rank_dist = _get_rank_distribution(hooks, layer_hook_map, equal_inputs_map, rank_budget, max_components)
|
||||
|
||||
# check all custom hooks have been removed
|
||||
remaining_hooks = {n for n, m in model.named_modules() for v in m._forward_hooks.values() if isinstance(v, _Hook)}
|
||||
if len(remaining_hooks) > 0:
|
||||
raise ValueError(
|
||||
f"Found active hooks added by EVA that weren't properly removed: {remaining_hooks}. "
|
||||
"Please report this issue at https://github.com/huggingface/peft/issues"
|
||||
)
|
||||
|
||||
eva_state_dict = {}
|
||||
for name, rank in rank_dist.items():
|
||||
hook = hooks[layer_hook_map[name]][0]
|
||||
if not torch.all(hook.converged[:rank]):
|
||||
raise ValueError(
|
||||
f"Layer {name} has not converged but was assigned rank {rank}. "
|
||||
"Please report this issue at https://github.com/huggingface/peft/issues"
|
||||
)
|
||||
u = hook.svd.components_[:rank]
|
||||
if peft_config.eva_config.whiten:
|
||||
u /= hook.svd.singular_values_[:rank].sqrt().reshape(-1, 1)
|
||||
eva_state_dict[name] = u
|
||||
|
||||
# restore model state
|
||||
model.train(training)
|
||||
|
||||
# move tensors to device
|
||||
if device is not None:
|
||||
eva_state_dict = {k: v.to(device) for k, v in eva_state_dict.items()}
|
||||
|
||||
return eva_state_dict
|
||||
|
||||
|
||||
def _load_eva_state_dict(
|
||||
model: torch.nn.Module,
|
||||
eva_state_dict: dict,
|
||||
adapter_name: str,
|
||||
):
|
||||
peft_config = model.peft_config[adapter_name]
|
||||
update_layer_kwargs = {
|
||||
"adapter_name": adapter_name,
|
||||
"lora_dropout": peft_config.lora_dropout,
|
||||
"use_rslora": peft_config.use_rslora,
|
||||
"use_dora": peft_config.use_dora,
|
||||
"lora_bias": peft_config.lora_bias,
|
||||
}
|
||||
missing_eva_inits = []
|
||||
new_target_modules = []
|
||||
other_module_names = []
|
||||
rank_pattern = {}
|
||||
alpha_pattern = {}
|
||||
for name, module in model.named_modules():
|
||||
name_in_base_model = name.replace("base_model.model.", "")
|
||||
if not isinstance(module, LoraLayer):
|
||||
other_module_names.append(name_in_base_model)
|
||||
continue
|
||||
# Regexp matching - Find key which matches current target_name in patterns provided
|
||||
r = peft_config.rank_pattern.get(get_pattern_key(peft_config.rank_pattern.keys(), name), peft_config.r)
|
||||
alpha = peft_config.alpha_pattern.get(
|
||||
get_pattern_key(peft_config.alpha_pattern.keys(), name), peft_config.lora_alpha
|
||||
)
|
||||
if name in eva_state_dict:
|
||||
w = eva_state_dict.pop(name)
|
||||
new_rank = w.size(0)
|
||||
if new_rank == 0:
|
||||
parent, _, target_name = _get_submodules(model, name)
|
||||
setattr(parent, target_name, module.get_base_layer())
|
||||
continue
|
||||
elif new_rank != r:
|
||||
if peft_config.eva_config.adjust_scaling_factors:
|
||||
alpha *= new_rank / r
|
||||
if new_rank != r or module.lora_A[adapter_name].weight.device.type == "meta":
|
||||
module.update_layer(r=new_rank, lora_alpha=alpha, init_lora_weights="eva", **update_layer_kwargs)
|
||||
module.lora_A[adapter_name].weight.copy_(w)
|
||||
new_target_modules.append(name_in_base_model)
|
||||
else:
|
||||
module.update_layer(r=r, lora_alpha=alpha, init_lora_weights=True, **update_layer_kwargs)
|
||||
missing_eva_inits.append(name_in_base_model)
|
||||
new_rank = r
|
||||
# update rank pattern and alpha pattern
|
||||
if new_rank != peft_config.r:
|
||||
rank_pattern[name_in_base_model] = new_rank
|
||||
if alpha != peft_config.lora_alpha:
|
||||
alpha_pattern[name_in_base_model] = alpha
|
||||
|
||||
# update target modules if some lora layers have been removed due to their EVA rank being 0
|
||||
new_target_modules = new_target_modules + missing_eva_inits
|
||||
if len(new_target_modules) >= MIN_TARGET_MODULES_FOR_OPTIMIZATION:
|
||||
new_target_modules = _find_minimal_target_modules(new_target_modules, other_module_names)
|
||||
model.peft_config[adapter_name].target_modules = new_target_modules
|
||||
|
||||
# set rank pattern obtained from EVA
|
||||
model.peft_config[adapter_name].rank_pattern = rank_pattern
|
||||
|
||||
# when adjust_scaling_factors is True, lora scaling factors have been adjusted after the rank redistribution
|
||||
model.peft_config[adapter_name].alpha_pattern = alpha_pattern
|
||||
|
||||
if missing_eva_inits:
|
||||
warnings.warn(
|
||||
"the following layers were initialized with init_lora_weights=True because they "
|
||||
f"were not found in the eva state_dict: {missing_eva_inits}\ncurrently the "
|
||||
f"following lora modules are not supported by EVA: {UNSUPPORTED_LORA_MODULES}"
|
||||
)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def get_eva_state_dict(
|
||||
model: torch.nn.Module,
|
||||
dataloader: Iterable,
|
||||
peft_config: Optional[LoraConfig] = None,
|
||||
forward_fn: Optional[callable] = forward_fn_dict,
|
||||
prepare_model_inputs_fn: Optional[callable] = prepare_model_inputs_fn_language_modeling,
|
||||
prepare_layer_inputs_fn: Union[callable, dict[str, callable], None] = prepare_layer_inputs_fn_language_modeling,
|
||||
adapter_name: str = "default",
|
||||
gather_distributed_inputs: bool = True,
|
||||
show_progress_bar: bool = True,
|
||||
) -> dict:
|
||||
"""
|
||||
Compute the SVD for each layer in the model.
|
||||
|
||||
This function computes the Singular Value Decomposition (SVD) for each layer in the model. It uses the incremental
|
||||
PCA method to compute the SVD components. The function also checks for convergence of the computed components using
|
||||
cosine similarity. The rank distribution for each layer is determined based on the explained variance ratio.
|
||||
|
||||
Args:
|
||||
model (torch.nn.Module): The model to compute the SVD for. Does not need to be a PeftModel.
|
||||
dataloader (Iterable): The dataloader to use for the forward pass.
|
||||
peft_config (Optional[LoraConfig]):
|
||||
The configuration for the LoRA layers. Only required if `model` is not a PeftModel.
|
||||
forward_fn (callable):
|
||||
The forward function to use for the forward pass. Takes two arguments: `model` and `inputs`. Default
|
||||
behavior is `return model(**inputs)`
|
||||
prepare_model_inputs_fn (Optional[callable]):
|
||||
This function receives the model inputs and the peft_config and passes the output to
|
||||
`prepare_layer_inputs_fn`. Can be used to modify the input to the SVD computation based on the original
|
||||
model inputs. For example for language modeling the attention mask is used to determine which indices are
|
||||
padding tokens and should not be used for SVD. Any function defined here expects two arguments:
|
||||
`model_input` and `peft_config`. `peft.tuners.lora.eva.prepare_model_inputs_fn_language_modeling` is used
|
||||
by default.
|
||||
prepare_layer_inputs_fn (Union[callable, Dict[str, callable], None]):
|
||||
This function receives the layer inputs, the model inputs (potentially modified by
|
||||
`prepare_model_inputs_fn`) and the name of the layer and returns the inputs that should be used for SVD for
|
||||
that particular layer. Any custom function defined here expects three arguments: `layer_input`,
|
||||
`model_input`, and `layer_name` and should return a 2d tensor. The default logic can be found in
|
||||
peft.tuners.lora.eva.prepare_layer_inputs_fn_language_modeling and works for language modeling. In this
|
||||
case model_inputs is the mask used to determine which indices should be used for SVD (created by
|
||||
`prepare_model_inputs_fn_language_modeling`).
|
||||
adapter_name (str): The name of the adapter to compute the SVD for.
|
||||
gather_distributed_inputs (bool):
|
||||
Whether to gather the layer inputs from all ranks. Default is True meaning in a distributed setting the
|
||||
layer inputs will be gathered from all ranks for the SVD computation. For non-distributed settings this
|
||||
argument is ignored. Set to False if you are using a non-distributed dataloader in a distributed setting.
|
||||
show_progress_bar (bool): Whether to show a progress bar. Default is True.
|
||||
|
||||
Returns:
|
||||
eva_state_dict (dict): The state dictionary containing the SVD components for each layer.
|
||||
"""
|
||||
|
||||
def target_module_check_fn_peft_model(name, module, unsupported_lora_modules):
|
||||
"check if a module is an adapter module via base_layer attribute"
|
||||
return hasattr(module, "base_layer") and not isinstance(module, unsupported_lora_modules)
|
||||
|
||||
def target_module_check_fn_default(name, module, peft_config):
|
||||
"check if a module is an adapter module via target_modules"
|
||||
is_target_module = True
|
||||
if peft_config.target_modules is not None:
|
||||
is_target_module = check_target_module_exists(peft_config, name)
|
||||
# Conv1D for GPT2 support
|
||||
return isinstance(module, (torch.nn.Linear, Conv1D)) and is_target_module
|
||||
|
||||
is_peft_model = hasattr(model, "peft_config")
|
||||
|
||||
# get peft_config
|
||||
if is_peft_model and peft_config is None:
|
||||
peft_config = model.peft_config[adapter_name]
|
||||
elif peft_config is None:
|
||||
raise ValueError("peft_config is required if model is not a PeftModel")
|
||||
|
||||
# setup context and target module check function
|
||||
if is_peft_model:
|
||||
ctx = model.disable_adapter()
|
||||
target_module_check_fn = partial(
|
||||
target_module_check_fn_peft_model, unsupported_lora_modules=UNSUPPORTED_LORA_MODULES
|
||||
)
|
||||
else:
|
||||
ctx = nullcontext()
|
||||
target_module_check_fn = partial(target_module_check_fn_default, peft_config=peft_config)
|
||||
|
||||
with ctx:
|
||||
eva_state_dict = _get_eva_state_dict(
|
||||
model=model,
|
||||
dataloader=dataloader,
|
||||
peft_config=peft_config,
|
||||
target_module_check_fn=target_module_check_fn,
|
||||
forward_fn=forward_fn,
|
||||
prepare_model_inputs_fn=prepare_model_inputs_fn,
|
||||
prepare_layer_inputs_fn=prepare_layer_inputs_fn,
|
||||
gather_distributed_inputs=gather_distributed_inputs,
|
||||
show_progress_bar=show_progress_bar,
|
||||
)
|
||||
return eva_state_dict
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def initialize_lora_eva_weights(
|
||||
model: torch.nn.Module,
|
||||
dataloader: Optional[Iterable] = None,
|
||||
eva_state_dict: Optional[dict] = None,
|
||||
forward_fn: Optional[callable] = forward_fn_dict,
|
||||
prepare_model_inputs_fn: Optional[callable] = prepare_model_inputs_fn_language_modeling,
|
||||
prepare_layer_inputs_fn: Union[callable, dict[str, callable], None] = prepare_layer_inputs_fn_language_modeling,
|
||||
adapter_name: str = "default",
|
||||
gather_distributed_inputs: bool = True,
|
||||
show_progress_bar: bool = True,
|
||||
):
|
||||
"""
|
||||
Initialize the weights of the LoRA layers using the EVA method.
|
||||
|
||||
This function initializes the weights of the LoRA layers using the EVA method. It computes the SVD for each adapter
|
||||
layer and updates the weights accordingly.
|
||||
|
||||
Args:
|
||||
model (PeftModel): The peft model to compute the SVD for.
|
||||
dataloader (Optional[Iterable]):
|
||||
The dataloader to use for the forward pass. If None, eva_state_dict needs to be provided.
|
||||
eva_state_dict (Optional[dict]):
|
||||
The state_dict to load into the model. If None, a dataloader needs to be provided and the state_dict will
|
||||
be computed using `get_eva_state_dict`.
|
||||
forward_fn (callable):
|
||||
The forward function to use for the forward pass. Takes two arguments: `model` and `inputs`. Default
|
||||
behavior is `return model(**inputs)`
|
||||
prepare_model_inputs_fn (Optional[callable]):
|
||||
This function receives the model inputs and the peft_config and passes the output to
|
||||
`prepare_layer_inputs_fn`. Can be used to modify the input to the SVD computation based on the original
|
||||
model inputs. For example for language modeling the attention mask is used to determine which indices are
|
||||
padding tokens and should not be used for SVD. Any function defined here expects two arguments:
|
||||
`model_input` and `peft_config`. `peft.tuners.lora.eva.prepare_model_inputs_fn_language_modeling` is used
|
||||
by default.
|
||||
prepare_layer_inputs_fn (Union[callable, Dict[str, callable], None]):
|
||||
This function receives the layer inputs, the model inputs (potentially modified by
|
||||
`prepare_model_inputs_fn`) and the name of the layer and returns the inputs that should be used for SVD for
|
||||
that particular layer. Any custom function defined here expects three arguments: `layer_input`,
|
||||
`model_input`, and `layer_name` and should return a 2d tensor. The default logic can be found in
|
||||
peft.tuners.lora.eva.prepare_layer_inputs_fn_language_modeling and works for language modeling. In this
|
||||
case model_inputs is the mask used to determine which indices should be used for SVD (created by
|
||||
`prepare_model_inputs_fn_language_modeling`).
|
||||
adapter_name (str): The name of the adapter to initialize the weights for.
|
||||
gather_distributed_inputs (bool):
|
||||
Whether to gather the layer inputs from all ranks. Default is True meaning in a distributed setting the
|
||||
layer inputs will be gathered from all ranks for the SVD computation. For non-distributed settings this
|
||||
argument is ignored. Set to False if you are using a non-distributed dataloader in a distributed setting.
|
||||
show_progress_bar (bool): Whether to show a progress bar. Default is True.
|
||||
|
||||
Returns:
|
||||
model (torch.nn.Module): The model with the initialized LoRA weights.
|
||||
"""
|
||||
if not hasattr(model, "peft_config"):
|
||||
raise ValueError("model must be a PeftModel")
|
||||
|
||||
# eva currently only works with a single active adapter
|
||||
# Important: when removing this requirement, make sure eva init works correctly if the new rank is 0.
|
||||
if len(model.active_adapters) > 1:
|
||||
raise ValueError("`initialize_lora_eva_weights` currently only works with a single active adapter")
|
||||
|
||||
# initialize_lora_eva_weights only works with `init_lora_weights='eva'`
|
||||
if model.peft_config[adapter_name].init_lora_weights != "eva":
|
||||
raise ValueError("`initialize_lora_eva_weights` can only be used with `init_lora_weights='eva'`")
|
||||
|
||||
# compute svd
|
||||
if eva_state_dict is None:
|
||||
if dataloader is None:
|
||||
raise ValueError("dataloader is required if eva_state_dict is not provided")
|
||||
eva_state_dict = get_eva_state_dict(
|
||||
model=model,
|
||||
dataloader=dataloader,
|
||||
forward_fn=forward_fn,
|
||||
prepare_model_inputs_fn=prepare_model_inputs_fn,
|
||||
prepare_layer_inputs_fn=prepare_layer_inputs_fn,
|
||||
adapter_name=adapter_name,
|
||||
gather_distributed_inputs=gather_distributed_inputs,
|
||||
show_progress_bar=show_progress_bar,
|
||||
)
|
||||
|
||||
_load_eva_state_dict(model, eva_state_dict, adapter_name)
|
||||
@@ -0,0 +1,96 @@
|
||||
# Copyright 2024-present the HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import torch
|
||||
from datasets import load_dataset
|
||||
from torch.utils.data import DataLoader
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
|
||||
from utils import DataCollator, TokenizerMetaMath
|
||||
|
||||
from peft import EvaConfig, LoraConfig, get_peft_model, initialize_lora_eva_weights
|
||||
|
||||
|
||||
DEVICE = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
|
||||
|
||||
# config
|
||||
model_name = "meta-llama/Llama-3.1-8B"
|
||||
max_seq_len = 512
|
||||
rank = 16
|
||||
alpha = 1
|
||||
rho = 2.0
|
||||
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj"]
|
||||
svd_batch_size = 4 # can be different from the batch size used in finetuning
|
||||
batch_size = 4
|
||||
learning_rate = 5e-4
|
||||
gradient_accumulation_steps = 8
|
||||
num_epochs = 1
|
||||
output_dir = "outputs"
|
||||
bf16 = True
|
||||
|
||||
|
||||
# load model and tokenizer
|
||||
model = AutoModelForCausalLM.from_pretrained(model_name)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
||||
|
||||
# load dataset
|
||||
dataset = load_dataset("meta-math/MetaMathQA")
|
||||
dataset = dataset.map(
|
||||
TokenizerMetaMath(model_name),
|
||||
batched=True,
|
||||
remove_columns=dataset["train"].column_names,
|
||||
)
|
||||
dataset.set_format(type="torch")
|
||||
|
||||
# data collator
|
||||
data_collator = DataCollator(tokenizer.eos_token_id, max_length=max_seq_len)
|
||||
|
||||
# dataloader
|
||||
dataloader = DataLoader(
|
||||
dataset["train"],
|
||||
batch_size=svd_batch_size,
|
||||
collate_fn=data_collator,
|
||||
)
|
||||
|
||||
# setup peft config
|
||||
eva_config = EvaConfig(rho=rho)
|
||||
peft_config = LoraConfig(
|
||||
r=rank, lora_alpha=alpha, target_modules=target_modules, init_lora_weights="eva", eva_config=eva_config
|
||||
)
|
||||
|
||||
# move model to accelerator
|
||||
model = model.to(DEVICE)
|
||||
|
||||
# to optimize memory usage during eva initialization, set low_cpu_mem_usage=True
|
||||
peft_model = get_peft_model(model, peft_config, low_cpu_mem_usage=True)
|
||||
initialize_lora_eva_weights(peft_model, dataloader)
|
||||
|
||||
# setup training arguments
|
||||
training_args = TrainingArguments(
|
||||
per_device_train_batch_size=batch_size,
|
||||
learning_rate=learning_rate,
|
||||
gradient_accumulation_steps=gradient_accumulation_steps,
|
||||
num_train_epochs=num_epochs,
|
||||
output_dir=output_dir,
|
||||
remove_unused_columns=False,
|
||||
bf16=bf16,
|
||||
)
|
||||
|
||||
# continue with standard finetuning
|
||||
trainer = Trainer(
|
||||
model=peft_model,
|
||||
args=training_args,
|
||||
train_dataset=dataset["train"],
|
||||
data_collator=data_collator,
|
||||
)
|
||||
trainer.train()
|
||||
Reference in New Issue
Block a user