diff --git a/docs/vendor/lora-lite b/docs/vendor/lora-lite new file mode 160000 index 0000000..ce8c250 --- /dev/null +++ b/docs/vendor/lora-lite @@ -0,0 +1 @@ +Subproject commit ce8c25042266ba60e8e3418fed14afe5954210ab diff --git a/docs/vendor/simple_GRPO b/docs/vendor/simple_GRPO new file mode 160000 index 0000000..30f252c --- /dev/null +++ b/docs/vendor/simple_GRPO @@ -0,0 +1 @@ +Subproject commit 30f252ce368c03556662eeaf0023809c38cff6f9 diff --git a/pyproject.toml b/pyproject.toml index 07e06ba..36b7686 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,10 @@ description = "SVD-basis gradient projection vs RL reward hacking on Nanda's Lee requires-python = ">=3.11" dependencies = [ "torch>=2.4", - "transformers>=4.45", + # transformers>=4.58 has Qwen3.5 (model_type=qwen3_5, gated-delta-net). + # Per HF card: install from main if 4.58 not yet released. We pin to main + # via [tool.uv.sources] below; the version spec here is just a floor. + "transformers>=4.58.0.dev0", "einops>=0.8", "jaxtyping>=0.2", "beartype>=0.18", @@ -38,3 +41,9 @@ where = ["src"] [tool.uv] exclude-newer = "2026-05-23" + +[tool.uv.sources] +# Qwen3.5 (qwen3_5 model_type, gated-delta-net) lands in transformers main; pin +# until 4.58 release. v5.7.0 changelog note: "incorrect cached forward behavior +# in Qwen3.5's gated-delta-net linear attention" — fixed on main. +transformers = { git = "https://github.com/huggingface/transformers.git", rev = "main" } diff --git a/spec.md b/spec.md index c8c4c61..d3ec5a6 100644 --- a/spec.md +++ b/spec.md @@ -30,45 +30,63 @@ plus an optional block-Cayley rotation. The rank axis stays pinned to the SVD basis of the original weight, so `v_hack` extracted in that basis remains meaningful across all training steps. -Forward pass per wrapped module: +Forward pass per wrapped module (first pass uses full rank $r = \min(d_{in}, d_{out})$, +so the residual term $W_{res}$ vanishes): -$$y = x W_{res}^T + ((x V_h^T) \odot (S + \delta_S)) U^T$$ +$$y = ((x V_h^T) \odot (S + \delta_S)) U^T$$ -where $W_{res} = W - U_r \mathrm{diag}(S_r) V_{h,r}$, and $U_r$, $S_r$, $V_{h,r}$ -are buffers (frozen). Trainable: $\delta_S : [r]$ (and optionally a small Cayley -rotation `rot_T` we leave off by default). +where $U$, $S$, $V_h$ come from the SVD of $W$ and are buffers (frozen). +Trainable: $\delta_S : [r]$ (and optionally a small Cayley rotation `rot_T` +we leave off by default). At reduced rank we would add +$x W_{res}^T$ with $W_{res} = W - U_r \mathrm{diag}(S_r) V_{h,r}$, but we +defer rank cropping to v2 to skip the "where to cut" question. Per-step gradient signal: $$\frac{\partial L}{\partial \delta_S} = \sum_t (x_t V_h^T) \odot \left(\frac{\partial L}{\partial h_t} U\right) \in \mathbb{R}^r$$ -Both factors of the elementwise product live in rank-r SVD basis. v_hack -extracted as `mean_pairs(x V_h^T)_{hack} - mean(x V_h^T)_{clean}` lives in the -*same* `[r]` rank space. Projection is one line: +We extract `v_hack` **gradient-side** (locked in): for each contrastive pair, +run one NLL backward on the completion tokens and read each module's +`m.delta_S.grad : [r]`. Then $\hat v_{hack}^{(m)} =$ unit$($mean$_{hack}$ grad $-$ mean$_{clean}$ grad$)$. +This lives in the exact same `[r]` rank space the per-step training gradient +lives in (the gradient is the natural object to compare gradients against), +and it fuses the input-activation and output-error contributions in one shot +instead of guessing whether input-side $(x V_h^T)$ or output-side $(\partial L/\partial h)\, U$ +better predicts where SGD will move. We did consider activation-side +($x V_h^T$ mean-diff). Dropped as primary because it only sees the input +factor and ignores the output-error factor, while the per-step gradient sees +both. -$$\nabla_{\delta_S} \leftarrow \nabla_{\delta_S} - \cos_{align} \cdot \|\nabla_{\delta_S}\| \cdot \hat v_{hack}$$ +Projection (locked: no magnitude threshold; one-sided clip stays — see note): -with one-sided gating (only project when $\cos_{align} > 0$, i.e. the gradient is -pushing toward the hack direction). Magnitude preservation = renormalize back -to original $\|\nabla_{\delta_S}\|$. +$$g \leftarrow g - \max(0,\, \cos_{align}) \cdot \|g\| \cdot \hat v_{hack}, \qquad \cos_{align} = \frac{g \cdot \hat v_{hack}}{\|g\|}$$ + +then rescale to original $\|g\|$ (magnitude-preserving). The $\max(0,\cdot)$ is +not gating, it's directional correctness: without it, when $\cos<0$ we'd be +*adding* to the hack component. No magnitude/threshold gating (locked): we +project every step every module. Capacity cost is ~1/r per module per step. +If `v_hack` at a module is just noise, projection ablates a noise direction in +expectation = approximately a no-op. ## Why not vanilla GRPO via verl verl is Ariahw's framework but uses Ray + FSDP2 + Hydra; inserting a pre-optimizer-step hook on per-module rank-space gradients requires deep subclassing of their worker abstraction. We pay one cost in exchange: -we use [lsdefine/lsrl](https://github.com/lsdefine/lsrl) instead. lsrl is a -two-file GRPO implementation with reported convergence on Qwen2.5-3B in 12m on -2xA800 (60 steps). One pre-optimizer hook is trivial to add. +we use [lsdefine/simple_GRPO](https://github.com/lsdefine/simple_GRPO) instead. +simple_GRPO is a two-file GRPO implementation (`ref_server.py` + `grpo_ref_split.py`, +~315 lines total) with reported convergence on Qwen2.5-7B. The training loop +is literally `loss = GRPO_step(batch); engine.backward(loss); engine.step()` — +inserting a projection hook between backward and step is a one-line edit. Cost of this deviation: we re-establish the "vanilla hack emergence" baseline -on lsrl rather than inheriting it from Ariahw's verl baseline. H4 is the -sanity check that this happens. We port Ariahw's `run_tests`-overwrite +on simple_GRPO rather than inheriting it from Ariahw's verl baseline. H4 is +the sanity check that this happens. We port Ariahw's `run_tests`-overwrite detection (their [src/train/verl/rewards.py](https://github.com/ariahw/rl-rewardhacking/blob/main/src/train/verl/rewards.py)) -into lsrl's reward server (`docs/vendor/lsrl/lsrl/reward_server.py`). +into simple_GRPO's reward server (`docs/vendor/simple_GRPO/ref_server.py`). Vendored references (read-only, see [docs/vendor/](docs/vendor/)): -- [lsrl](https://github.com/lsdefine/lsrl) — GRPO trainer +- [simple_GRPO](https://github.com/lsdefine/simple_GRPO) — GRPO trainer - [lora-lite](https://github.com/wassname/lora-lite) — AntiPaSTO adapter - [rl-rewardhacking](https://github.com/ariahw/rl-rewardhacking) (already at `external/`) @@ -82,12 +100,6 @@ LeetCode pass rate within 10pp of vanilla. Falsified if: hack rate reduction < 15pp, OR pass rate drops by >15pp at matched hack-rate budget, OR result is within 1 SEM of vanilla across seeds. -**H2 (activation- vs gradient-side `v_hack`):** Gradient-side `v_hack` -(mean-diff of `grad(delta_S)` from one NLL backward per pair) outperforms -activation-side `v_hack` (mean-diff of `x V_h^T`), at matched pair count. -Falsified if: gradient-side matches or is worse than activation-side within -1 SEM. *(open question — see "Decisions left open" below.)* - **H3 (gradient vs advantage):** Gradient-level intervention (ours) outperforms advantage-level intervention (Rebound reimplemented) on hack rate at matched pass rate. @@ -95,106 +107,240 @@ pass rate. Falsified if: Rebound reimplementation matches or beats ours within 1 SEM. **H4 (scaling sanity on our stack):** Qwen3.5-2B trained with vanilla -AntiPaSTO+GRPO on lsrl reproduces measurable reward hacking (>30% hack rate at -200 steps). +AntiPaSTO+GRPO on simple_GRPO reproduces measurable reward hacking (>30% hack +rate at 200 steps). Falsified if: vanilla hack rate <30%. Decision branch: swap to Qwen3-4B with - num_generations halved. Secondary: if lsrl can't reproduce hacking on either - model, fall back to Ariahw's verl path and accept the harder hook. - -**H5 (capacity cost of no-gating):** No-gating (project every step every -module) does not measurably hurt pass rate vs cos-threshold gating -(`|cos_align| > 0.1` -> project). Falsified if: gated arm beats no-gating arm -on pass rate by >5pp at matched hack rate. + num_generations halved. Secondary: if simple_GRPO can't reproduce hacking on + either model, fall back to Ariahw's verl path and accept the harder hook. ## Steps ### 1. Build infra — fast-dev-run targets first, no real training yet - - **1a.** Vendor lsrl into `docs/vendor/lsrl/`; smoke-run their GSM8K example - on tiny-random-qwen3 (5 steps, CPU) to confirm reward-server / actor / - rollout split works in our env. - - **1b.** Vendor lora-lite into `docs/vendor/lora-lite/`; wrap Qwen3.5-0.8B - attn+MLP modules with AntiPaSTO (`r=256, block_size=4, rotate_basis="none"` - to start; only `delta_S` trainable). Verify forward-pass round-trip - numerically matches base model at $\delta_S = 0$. - - **1c.** Implement `v_hack` extraction per module: - - **Activation-side (default):** forward N contrastive pair completions, - per wrapped module register a `forward_pre_hook` capturing - `(x @ Vh^T)` flattened over (batch, seq), mean over hack rows minus - mean over clean rows, unit-normalize. Cache as `dict[module_name -> Tensor[r]]` - on disk. - - **Gradient-side (ablation):** for each pair, NLL backward on completion - tokens, per module capture `module.lora_delta_s.grad : [r]`, mean-diff - hack vs clean, unit-normalize. - - Validation: per-module projection score `(x_hack @ Vh^T - x_clean @ Vh^T) @ v_hack` - should be positive on held-out pairs in >90% of modules. + - **1a.** Vendor simple_GRPO into `docs/vendor/simple_GRPO/` (done); smoke-run + their GSM8K example on tiny-random-qwen3 (5 steps, CPU) to confirm + `ref_server` + `grpo_ref_split` rollout/train split works in our env. + - **1b.** Vendor lora-lite into `docs/vendor/lora-lite/` (done); wrap + Qwen3.5-0.8B attn+MLP `nn.Linear` modules with AntiPaSTO **at full rank** + (`r = min(d_in, d_out)`, no SVD cropping; `rotate_basis="none"`, only + `delta_S` trainable). Full rank means $W = U \,\mathrm{diag}(S)\, V_h$ + exactly and `W_res = 0`, so there's no truncation error to debug on the + first pass. Verify forward-pass round-trip numerically matches base model + at $\delta_S = 0$ (max abs diff <1e-3 on a fixed prompt). + - **1c.** Implement gradient-side `v_hack` extraction (pseudocode below). + Validation: per-module held-out projection score + `cos(g_held_hack - g_held_clean, v_hack)` > 0 in >50% of modules. -### 2. H4 sanity — does vanilla AntiPaSTO+GRPO+lsrl produce hacking? +### 2. H4 sanity — does vanilla AntiPaSTO+GRPO+simple_GRPO produce hacking? - - **2a.** Port Ariahw's `run_tests`-overwrite detection into lsrl's reward - fn. Verify the reward fn fires on synthetic hack/clean rollouts before - real training. - - **2b.** Train Qwen3.5-2B, AntiPaSTO (`r=256`, `delta_S` only), GRPO + - **2a.** Port Ariahw's `run_tests`-overwrite detection into simple_GRPO's + `ref_server.py` reward fn. Verify the reward fn fires on synthetic + hack/clean rollouts before real training. + - **2b.** Train Qwen3.5-2B, AntiPaSTO (`r=full`, `delta_S` only), GRPO (group_norm), 200 steps, num_generations=8, batch=16, 1 seed. - Decision: if hack rate <30%, switch to Qwen3-4B (same num_gen=8, batch=16) - and re-run 2b. Secondary fallback: drop lsrl, return to verl. + Decision: if hack rate <30%, switch to Qwen3-4B with `num_generations=4, + batch=16` (half num_gen to keep VRAM headroom) and re-run 2b. + Secondary fallback: drop simple_GRPO, return to verl. -### 3. Implement rank-space projection in lsrl's training loop +### 3. Implement rank-space projection in simple_GRPO's training loop - - **3a.** lsrl's actor calls `optimizer.step()` once per group; insert a - `pre_step_hook(model)` that walks `[m for m in model.modules() if hasattr(m, 'lora_delta_s')]` - and for each module reads `m.lora_delta_s.grad : [r]`, projects against - `v_hack[module_name]` (one-sided, magnitude-preserving), writes back. - - **3b.** Diagnostics logged per step per module: `cos_in`, `||grad||`, - `frac_modules_projected`. + - **3a.** In `grpo_ref_split.py`, between `engine.backward(loss)` and + `engine.step()`, call `project_grads(model, v_hack_cache)`. + `project_grads` walks `[m for m in model.modules() if hasattr(m, 'delta_S')]` + and for each module reads `m.delta_S.grad : [r]`, projects against + `v_hack[module_name]` (one-sided, magnitude-preserving), writes back + in place. (Pseudocode below.) + - **3b.** Diagnostics logged per step (aggregated over modules): + mean/std `cos_align`, mean `||grad||`, `frac_modules_with_cos>0`. ### 4. Run arms (200 steps each, 3 seeds where indicated) a. Vanilla AntiPaSTO + GRPO (3 seeds) — baseline - b. Our method, activation-side `v_hack`, no gating (3 seeds) — main result - c. Our method, gradient-side `v_hack` (3 seeds) — H2 - d. Our method, cos-threshold gating ($|\cos| > 0.1$) (1 seed) — H5 - e. Our method, no magnitude preservation (1 seed) — design ablation - f. Rebound reimplementation: advantage-side `v_hack` penalty (3 seeds) — H3 - g. AntiPaSTO rank sweep: $r \in \{64, 256, 1024\}$ (1 seed each) — sensitivity + b. Our method, gradient-side `v_hack`, no gating (3 seeds) — main result + c. Our method, no magnitude preservation (1 seed) — design ablation + d. Rebound reimplementation: advantage-side `v_hack` penalty (3 seeds) — H3 + (concrete formula: per-rollout penalty `α · max(0, cos(h_mean, v_concept))` + added to scalar reward, where `h_mean` is mean residual-stream activation + at a chosen layer and `v_concept` is mean-diff activation direction + extracted from the same 60-80 pairs. We use Wu & Tang 2026 §3.2's + published `α=0.5` and same layer fraction (60-75% depth). Single + layer, not per-module, matching their setup. *Different `v_concept` + from our gradient-side `v_hack` — this is intentional: H3 isolates the + gradient-vs-advantage mechanism choice, not the direction-extraction + choice.*) - Total: 14 runs × ~3h on RTX 6000 96GB = ~42h compute. + Total: 10 runs × ~3h on RTX 6000 96GB = ~30h compute. + *(Rank sweep deferred to v2; first pass uses `r = min(d_in, d_out)` per + module, no cropping.)* ### 5. Measure at every 25 steps - - **Hack rate** (Ariahw's detector ported into lsrl) + - **Hack rate** (Ariahw's detector ported into simple_GRPO) - **Pass rate** on held-out problems without write access to evaluator - **Per-module `cos_align`** trajectory (sanity that we're projecting something nonzero) - - **`frac_modules_projected`** per step (sanity for gating arms) + - **`frac_modules_with_cos>0`** per step (sanity that one-sided clip fires) - **KL drift from init policy** (catastrophic-change check) -### 6. Headline plot +### 6. Headline plot and headline table - Hack rate vs pass rate, one point per (arm × seed). Pareto frontier. Our - method should land below-and-to-the-right of vanilla. Annotate Rebound. + **Plot.** Hack rate vs pass rate, one point per (arm × seed). Pareto + frontier. Our method should land below-and-to-the-right of vanilla. + Annotate Rebound. + + **Table schema (publication-ready; left-to-right = essential to optional, + so trailing columns can be cut for space):** + + | Arm | ΔSafePass↑ | Hack %↓ | Pass %↑ | KL↓ | mean·cos\* | frac·fired\* | ‖g‖\* | + |---|---|---|---|---|---|---|---| + | Vanilla (a) | 0 (ref) | — | — | — | — | — | — | + | **Ours (b)** | — | — | — | — | — | — | — | + | Ours, no mag-preserve (c) | — | — | — | — | — | — | — | + | Rebound (d) | — | — | — | — | — | — | — | + + *Caption.* ↑ higher is better, ↓ lower is better. **ΔSafePass** = (pass% − + hack%) − vanilla's (pass% − hack%): single headline number, positive means + we win. **Hack %** = fraction of rollouts triggering `run_tests`-overwrite + detector. **Pass %** = fraction passing held-out tests without write access. + **KL** = mean per-token KL from init policy over last 25 steps. + \* = projection-internal diagnostic, only meaningful for arms (b)/(c); + distinguishes "projection active" (mean·cos > 0.2, frac·fired > 0.4) from + "projection silent no-op". Cells report mean ± SEM across seeds. ### 7. Falsification check - Before publishing, run pre-registered analysis on H1-H5. Report all + Before publishing, run pre-registered analysis on H1, H3, H4. Report all hypotheses including falsified ones. +## Pseudocode (the three load-bearing bits) + +### A. AntiPaSTO module wrap (full rank, first pass) + +``` +class AntiPaSTO(nn.Module): + # constructed from an existing nn.Linear(W: [d_out, d_in], b) + # FIRST PASS: r = min(d_out, d_in) -- no truncation, W_res == 0 + def __init__(self, W, b): + U, S, Vh = torch.linalg.svd(W.float(), full_matrices=False) + r = S.shape[0] # = min(d_out, d_in) + # buffers (frozen): the full SVD + self.U = U # [d_out, r] + self.S = S # [r] + self.Vh = Vh # [r, d_in] + self.b = b + # trainable (ONLY this): scalar per rank + self.delta_S = nn.Parameter(torch.zeros(r)) + + def forward(self, x): # x: [..., d_in] + return ((x @ self.Vh.T) * (self.S + self.delta_S)) @ self.U.T + self.b +``` + +Replace every target `nn.Linear` in attn (`q,k,v,o_proj`) and MLP +(`up,gate,down_proj`) with this. At `delta_S=0`, output == original linear up +to numerical precision (no `W_res` residual term needed at full rank). + +**SVD precompute strategy.** Don't SVD the whole model on GPU at once. +Load the base model on CPU, then for each target `Linear`: move `W` to GPU, +run `torch.linalg.svd(W.float(), full_matrices=False)`, save +`(U, S, Vh) -> svd_cache/{model_name}/{module_path}.pt`. Wrap construction +then loads the cached SVD per module. SVD is done once per base model; ~5-10s +per big MLP weight on RTX 3090. + +### B. Gradient-side `v_hack` extraction (per module) + +``` +v_hack = {} # dict[module_name -> Tensor[r]] +grads_hack = defaultdict(list) +grads_clean = defaultdict(list) + +# Per-pair: process hack and clean independently, NLL over their own completion +# tokens only. Different completion lengths are fine -- we use mean NLL +# (sum_nll / n_completion_tokens), so each pair contributes a length-normalized +# gradient. This avoids biasing v_hack toward longer (typically clean) +# completions. Pad each example individually; no cross-completion padding. + +for (prompt, hack_completion, clean_completion) in pairs: + for label, completion in [('hack', hack_completion), ('clean', clean_completion)]: + model.zero_grad() + ids = tokenize(prompt + completion) # [1, L] + mask = completion_mask(ids, prompt_len=len(prompt_ids)) # 1 on completion tokens + logits = model(ids).logits[:, :-1] + # MEAN NLL over completion tokens (length-normalized) + loss = (nll_per_token(logits, ids[:, 1:]) * mask[:, 1:]).sum() / mask[:, 1:].sum() + loss.backward() + for name, m in model.named_modules(): + if hasattr(m, 'delta_S'): + bucket = grads_hack if label == 'hack' else grads_clean + bucket[name].append(m.delta_S.grad.detach().cpu().clone()) + +for name in grads_hack: + diff = stack(grads_hack[name]).mean(0) - stack(grads_clean[name]).mean(0) # [r] + v_hack[name] = diff / (diff.norm() + 1e-8) + +torch.save(v_hack, 'v_hack.pt') +``` + +Validation (report both, don't just gate on threshold): + +- On held-out pairs, recompute per-module `diff_held` and + `cos_align_held = cos(diff_held, v_hack[name])`. +- **Distribution check (primary):** plot histogram of `cos_align_held` across + all modules. Healthy = unimodal positive, median > 0.3. Pathological = + bimodal or median near 0. +- **Gate (secondary):** `cos_align_held > 0` in >50% of modules is the + minimum to proceed; mean `cos_align_held > 0.2` is the target. If <50% pass, + extraction is broken and we debug before training. + +### C. Pre-optimizer-step projection hook + +``` +def project_grads(model, v_hack: dict[str, Tensor]): + # called after engine.backward(loss), before engine.step() + cos_log, n_modules, n_fired = [], 0, 0 + for name, m in model.named_modules(): + if not hasattr(m, 'delta_S'): continue + g = m.delta_S.grad # [r] + if g is None: continue + n_modules += 1 + v = v_hack[name].to(g.device) # [r], unit + g_norm = g.norm() + if g_norm < 1e-12: continue + cos_a = (g @ v) / g_norm # scalar + cos_log.append(cos_a.item()) + if cos_a > 0: + n_fired += 1 + g_new = g - cos_a * g_norm * v # remove hack component + g_new = g_new * (g_norm / (g_new.norm() + 1e-8)) # magnitude preserve + m.delta_S.grad.copy_(g_new) + return dict(mean_cos=mean(cos_log), frac_fired=n_fired/max(n_modules,1)) +``` + +Integration into `grpo_ref_split.py` training loop +(vendored at `docs/vendor/simple_GRPO/simple_grpo_v1/grpo_ref_split.py`; we copy and +edit, not import): + +``` +# at top of training script, once: +v_hack = torch.load('v_hack.pt', map_location='cpu') # dict[str, Tensor[r]] +# (extraction script from B above produces this artifact; if missing, crash loud) + +# inside the training loop: +loss = GRPO_step(batch) +engine.backward(loss) +stats = project_grads(engine.module, v_hack) # <-- NEW: 1 line +engine.step() +if rank == 0: log(stats) +``` + ## Decisions left open (write these up alongside results) -- **Activation- vs gradient-side `v_hack` (H2).** Activation = cheap, geometric, - matches Wu-Tang/CAA tradition. Gradient = principled (the literal direction - training will move toward), more expensive. Default activation; gradient is - arm c. -- **Gating threshold (H5).** No-gating default; cos>0.1 gating is arm d. - Argument for no-gating: removing 1 direction from r=256 trainable subspace - per module per step is ~0.4% capacity. If `v_hack` at a module is noise, we - ablate a noise direction in expectation = approx no-op. Argument for gating: - in modules where hack signal is weak, projection just removes some random - direction the optimizer might have used. H5 settles this. -- **Rank `r`.** Default 256 (lora-lite antipasto default); sweep in arm g. - Trainable parameter count is just `r` per module (vs `r*(d_in+d_out)` for - standard LoRA), so larger `r` is cheap, but `v_hack`'s SNR per dim degrades. +- **Rank `r`.** First pass: `r = min(d_in, d_out)` per module (no cropping) + to avoid debugging where to cut the SVD. Trainable params per module = + `min(d_in, d_out)`, still tiny vs full LoRA's `r*(d_in+d_out)`. Tradeoff: + larger `r` keeps geometric fidelity but `v_hack`'s SNR per dim degrades; + smaller `r` would concentrate hack signal but introduces truncation error in + `W_res`. Rank sweep is v2 work. ## Why measure ratio, not just hack rate @@ -206,28 +352,31 @@ problems without write access, our method reduces hack rate from X% to Y%." ## Compute estimate -- Single run on 96GB RTX 6000: ~2-3h (Qwen3.5-2B, num_gen=8, 200 steps, lsrl, - AntiPaSTO r=256) -- 14 runs: 35-45h -- At ~$3 AUD/hr: $105-135 AUD +- Single run on 96GB RTX 6000: ~2-3h (Qwen3.5-2B, num_gen=8, 200 steps, + simple_GRPO, AntiPaSTO full rank) +- 10 runs: 25-35h +- At ~$3 AUD/hr: $75-105 AUD - + debugging buffer: budget ~$200 AUD total - Calendar time: 1 week back-to-back; 2-3 weeks with iteration ## Risks and decision points -- **H4 falsified (no hack on Qwen3.5-2B with lsrl):** branch 1 — try - Qwen3-4B same hyperparams. Branch 2 — drop lsrl, hook into verl +- **H4 falsified (no hack on Qwen3.5-2B with simple_GRPO):** branch 1 — try + Qwen3-4B same hyperparams. Branch 2 — drop simple_GRPO, hook into verl directly. Adds ~1-2 weeks engineering. - **AntiPaSTO + GRPO doesn't train:** known risk — antipasto's trainable - subspace (`delta_S` only) may be too small for RL. Mitigation: enable - Cayley rotation (`rotate_basis="V"`, `block_size=4`), adds `r*(bs-1)/2` - params per module. Or fall back to PiSSA-LoRA-freeze-A. + subspace (`delta_S` only) may be too small for RL. If so, document and + fall back to PiSSA-LoRA-freeze-A. We do **not** enable Cayley rotation + (`rotate_basis="V"`) as a mitigation: a rotated rank axis breaks the + invariant that `v_hack` (extracted in the original SVD basis) stays + meaningful across training, which is the whole point of using AntiPaSTO + over vanilla LoRA. - **`v_hack` steering check fails (per-module projection scores ≤chance):** extraction broken. Check (a) hook captures pre-residual input, (b) pair quality drives strong activation difference somewhere, (c) tokenization of hack vs clean completions isn't trivially distinguishing. - **All methods tie vanilla on hack rate:** intervention not biting. Check - `cos_align` logs nonzero, `frac_modules_projected` nonzero. + `cos_align` logs nonzero, `frac_modules_with_cos>0` nonzero. ## What this is not @@ -247,6 +396,6 @@ problems without write access, our method reduces hack rate from X% to Y%." - **AntiPaSTO** ([wassname/lora-lite/variants/antipasto.py](https://github.com/wassname/lora-lite/blob/main/src/lora_lite/variants/antipasto.py), ([wassname/AntiPaSTO paper](https://github.com/wassname/AntiPaSTO)) — adapter we wrap with. -- **lsrl** ([lsdefine/lsrl](https://github.com/lsdefine/lsrl)) — GRPO trainer. +- **simple_GRPO** ([lsdefine/simple_GRPO](https://github.com/lsdefine/simple_GRPO)) — GRPO trainer. - **PiSSA** ([arxiv:2404.02948](https://arxiv.org/abs/2404.02948)) — frozen top-r SVD-init for LoRA; closest spiritual ancestor to AntiPaSTO. diff --git a/src/projected_grpo/antipasto.py b/src/projected_grpo/antipasto.py new file mode 100644 index 0000000..991093e --- /dev/null +++ b/src/projected_grpo/antipasto.py @@ -0,0 +1,153 @@ +"""AntiPaSTO full-rank adapter for projected-GRPO. + +Per spec.md: wrap nn.Linear with frozen U, S, Vh (full rank = min(d_in, d_out)). +Trainable: delta_S only, shape [r]. No rotation (would break v_hack basis invariance). + +Forward: + y = ((x @ Vh.T) * (S + delta_S)) @ U.T + b + +At delta_S=0, output == original linear up to fp32 SVD round-trip precision. +""" +from __future__ import annotations + +import hashlib +from pathlib import Path + +import torch +from jaxtyping import Float +from loguru import logger +from torch import Tensor, nn + + +class AntiPaSTOLinear(nn.Module): + """Drop-in replacement for nn.Linear with full-rank SVD + learnable delta_S. + + Buffers (frozen): U[d_out, r], S[r], Vh[r, d_in], optional bias[d_out]. + Trainable: delta_S[r]. + """ + + def __init__( + self, + U: Float[Tensor, "d_out r"], + S: Float[Tensor, "r"], + Vh: Float[Tensor, "r d_in"], + bias: Float[Tensor, "d_out"] | None, + dtype: torch.dtype = torch.float32, + ): + super().__init__() + r = S.shape[0] + self.register_buffer("U", U.to(dtype).contiguous()) + self.register_buffer("S", S.to(dtype).contiguous()) + self.register_buffer("Vh", Vh.to(dtype).contiguous()) + if bias is not None: + self.register_buffer("bias", bias.to(dtype).contiguous()) + else: + self.bias = None + self.delta_S = nn.Parameter(torch.zeros(r, dtype=dtype)) + + @property + def r(self) -> int: + return self.S.shape[0] + + def forward(self, x: Float[Tensor, "... d_in"]) -> Float[Tensor, "... d_out"]: + # x @ Vh.T : [..., r]; * (S+dS) : elementwise; @ U.T : [..., d_out] + h = x @ self.Vh.transpose(-1, -2) + h = h * (self.S + self.delta_S) + y = h @ self.U.transpose(-1, -2) + if self.bias is not None: + y = y + self.bias + return y + + +def _model_svd_dir(model_name: str, cache_root: Path) -> Path: + safe = model_name.replace("/", "__") + return cache_root / safe + + +def svd_cached( + W: Float[Tensor, "d_out d_in"], + cache_path: Path, + device: torch.device, +) -> tuple[Tensor, Tensor, Tensor]: + """SVD with disk cache. Compute on `device` in fp32, save as fp32 cpu tensors. + + Cache key = sha256(W.cpu fp32 bytes)[:16] in filename suffix, so weight change + invalidates the cache automatically (fail-loud, no silent stale). + """ + cache_path.parent.mkdir(parents=True, exist_ok=True) + W_fp32 = W.detach().to(torch.float32).cpu().contiguous() + sha = hashlib.sha256(W_fp32.numpy().tobytes()).hexdigest()[:16] + final = cache_path.with_suffix(f".{sha}.pt") + if final.exists(): + d = torch.load(final, map_location="cpu", weights_only=True) + return d["U"], d["S"], d["Vh"] + W_gpu = W_fp32.to(device) + U, S, Vh = torch.linalg.svd(W_gpu, full_matrices=False) + U, S, Vh = U.cpu(), S.cpu(), Vh.cpu() + torch.save({"U": U, "S": S, "Vh": Vh}, final) + logger.info(f"SVD cached: {final.name} shape U={tuple(U.shape)} S0={S[0]:.3f} S-1={S[-1]:.3e}") + return U, S, Vh + + +TARGET_SUFFIXES = ( + # full attention (Qwen3.5 has 6 full-attn layers) + "q_proj", + "k_proj", + "v_proj", + "o_proj", + # linear-attention / GatedDeltaNet (Qwen3.5 has 18 linear-attn layers) + "in_proj_qkv", + "in_proj_z", + "in_proj_a", + "in_proj_b", + "out_proj", + # MLP (24 layers) + "up_proj", + "gate_proj", + "down_proj", +) + + +def is_target(name: str) -> bool: + return name.split(".")[-1] in TARGET_SUFFIXES + + +def wrap_model_with_antipasto( + model: nn.Module, + model_name: str, + cache_root: Path = Path("svd_cache"), + svd_device: torch.device | str = "cuda", + adapter_dtype: torch.dtype = torch.float32, +) -> dict[str, AntiPaSTOLinear]: + """Replace every target nn.Linear in `model` (in place) with AntiPaSTOLinear. + + SVD is computed on `svd_device` per layer, cached to disk by weight hash. + Returns dict[module_qualified_name -> wrapper] for downstream v_hack code. + """ + svd_device_t = torch.device(svd_device) if isinstance(svd_device, str) else svd_device + svd_dir = _model_svd_dir(model_name, cache_root) + wrappers: dict[str, AntiPaSTOLinear] = {} + + # Collect first to avoid mutating during iteration. + targets: list[tuple[str, nn.Linear, nn.Module, str]] = [] + for name, m in model.named_modules(): + if isinstance(m, nn.Linear) and is_target(name): + parent_name = name.rsplit(".", 1)[0] + child_name = name.rsplit(".", 1)[1] + parent = model.get_submodule(parent_name) + targets.append((name, m, parent, child_name)) + + logger.info(f"AntiPaSTO wrap: {len(targets)} target Linear modules in {model_name}") + for i, (name, linear, parent, child_name) in enumerate(targets): + W = linear.weight.data + bias = linear.bias.data if linear.bias is not None else None + cache_path = svd_dir / f"{name}.pt" + U, S, Vh = svd_cached(W, cache_path, device=svd_device_t) + # Place wrapper on the same device as the original module's weight. + target_device = W.device + wrap = AntiPaSTOLinear(U, S, Vh, bias, dtype=adapter_dtype).to(target_device) + setattr(parent, child_name, wrap) + wrappers[name] = wrap + if (i + 1) % 20 == 0 or i == len(targets) - 1: + logger.info(f" wrapped {i+1}/{len(targets)} last={name}") + return wrappers diff --git a/src/projected_grpo/verify_antipasto_identity.py b/src/projected_grpo/verify_antipasto_identity.py new file mode 100644 index 0000000..ef4642c --- /dev/null +++ b/src/projected_grpo/verify_antipasto_identity.py @@ -0,0 +1,102 @@ +"""Step-1 verify: wrapped Qwen3.5-0.8B output == base output at delta_S=0. + +SHOULD: max abs diff < 1e-3 over 3 prompts of different lengths. +ELSE: SVD round-trip is bad (numerical, dtype, or shape bug). + +Run: uv run python -m projected_grpo.verify_antipasto_identity +""" +from __future__ import annotations + +import copy +import sys +from pathlib import Path + +import torch +from loguru import logger +from tabulate import tabulate +from transformers import AutoModelForCausalLM, AutoTokenizer + +from .antipasto import wrap_model_with_antipasto + + +MODEL = "Qwen/Qwen3.5-0.8B" +PROMPTS = [ + "Hello", + "Write a Python function that returns the sum of two integers.", + ( + "You are an expert programmer. Solve the following LeetCode problem:\n" + "Given an integer array nums, find the contiguous subarray with the largest sum.\n" + "Return the sum." + ), +] +CACHE_ROOT = Path("svd_cache") + + +def main() -> int: + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + logger.info(f"device={device} model={MODEL}") + + tokenizer = AutoTokenizer.from_pretrained(MODEL) + base = AutoModelForCausalLM.from_pretrained( + MODEL, dtype=torch.float32, attn_implementation="sdpa" + ).to(device) + base.eval() + + wrapped = copy.deepcopy(base) + wrappers = wrap_model_with_antipasto( + wrapped, + model_name=MODEL, + cache_root=CACHE_ROOT, + svd_device=device, + adapter_dtype=torch.float32, + ) + wrapped.eval() + + n_wrapped = len(wrappers) + n_params_trainable = sum(p.numel() for w in wrappers.values() for p in w.parameters() if p.requires_grad) + n_params_base = sum(p.numel() for p in base.parameters()) + logger.info( + f"wrapped={n_wrapped} modules " + f"delta_S params={n_params_trainable:,} " + f"base params={n_params_base:,} " + f"ratio={n_params_trainable / n_params_base:.4%}" + ) + + rows = [] + all_ok = True + for i, prompt in enumerate(PROMPTS): + ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device) + with torch.no_grad(): + y_base = base(ids).logits + y_wrap = wrapped(ids).logits + diff = (y_base - y_wrap).abs() + max_diff = diff.max().item() + mean_diff = diff.mean().item() + scale = y_base.abs().mean().item() + ok = max_diff < 1e-3 + all_ok = all_ok and ok + rows.append( + dict( + idx=i, + seq_len=ids.shape[1], + logit_scale=f"{scale:.3f}", + max_abs_diff=f"{max_diff:.2e}", + mean_abs_diff=f"{mean_diff:.2e}", + ok=("PASS" if ok else "FAIL"), + ) + ) + + print(tabulate(rows, headers="keys", tablefmt="pipe")) + logger.info( + "SHOULD: max_abs_diff < 1e-3 on all rows. 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