diff --git a/.gitignore b/.gitignore index 331728a..39acf9e 100644 --- a/.gitignore +++ b/.gitignore @@ -12,6 +12,7 @@ logs/ .DS_Store tmp/ elm-stuff/ +models--*/ # Testing .coverage diff --git a/contrib/mimics/exps/003-baselines/baseline_bioclip.py b/contrib/mimics/exps/003-baselines/baseline_bioclip.py new file mode 100644 index 0000000..8508a80 --- /dev/null +++ b/contrib/mimics/exps/003-baselines/baseline_bioclip.py @@ -0,0 +1,9 @@ +"""Baseline classifier sweep for BioCLIP2.5 ViT-H/14 on Cambridge butterflies (256p, v1.6).""" + + +def make_cfgs() -> list[dict]: + return [ + { + "shards_dpath": "/fs/scratch/PAS2136/jbeattie/saev/shards/2e66437c", + } + ] diff --git a/contrib/mimics/exps/003-baselines/baseline_dinov3.py b/contrib/mimics/exps/003-baselines/baseline_dinov3.py new file mode 100644 index 0000000..47656ef --- /dev/null +++ b/contrib/mimics/exps/003-baselines/baseline_dinov3.py @@ -0,0 +1,15 @@ +"""Baseline classifier sweep for DINOv3 ViT-L/16 on Cambridge butterflies (384p, v1.6). + +The DINOv3 activation shards for the Cambridge dataset were cleaned from +/fs/scratch, so shards_dpath below currently has no recorded activations. +Regenerate them first with `launch.py shards` (family=dinov3, layers=[-2], +dataset cambridge-segfolder-v1.6), then point shards_dpath at the new hash. +""" + + +def make_cfgs() -> list[dict]: + return [ + { + "shards_dpath": "/fs/scratch/PAS2136/jbeattie/saev/shards/a1da4c41", + } + ] diff --git a/contrib/mimics/exps/003-baselines/baselines.csv b/contrib/mimics/exps/003-baselines/baselines.csv new file mode 100644 index 0000000..c5d0433 --- /dev/null +++ b/contrib/mimics/exps/003-baselines/baselines.csv @@ -0,0 +1,49 @@ +model,shard_id,task_name,n_erato,n_melpomene,bal_acc_mean,bal_acc_std,auc_mean,auc_std +bioclip2.5-vith14,2e66437c,lativitta_dorsal_vs_malleti_dorsal,1636,891,0.98302196015358,0.00737219849964053,0.9986968146672579,0.0003511235869817655 +bioclip2.5-vith14,2e66437c,lativitta_ventral_vs_malleti_ventral,1637,893,0.9827393933379357,0.009525820925983789,0.996903508139499,0.0022787005502441066 +bioclip2.5-vith14,2e66437c,cyrbia_dorsal_vs_cythera_dorsal,370,55,0.9232186732186731,0.05503630659607398,0.9864864864864863,0.0076916343206380935 +bioclip2.5-vith14,2e66437c,cyrbia_ventral_vs_cythera_ventral,370,55,0.8777641277641278,0.08918614326636368,0.9751842751842752,0.033295783029945826 +bioclip2.5-vith14,2e66437c,notabilis_dorsal_vs_plesseni_dorsal,309,241,0.9979166666666666,0.004166666666666697,1.0,0.0 +bioclip2.5-vith14,2e66437c,notabilis_ventral_vs_plesseni_ventral,307,239,0.9885774722369117,0.008380015055501564,0.9998641615191032,0.0001663828438319494 +bioclip2.5-vith14,2e66437c,hydara_dorsal_vs_melpomene_dorsal,109,229,0.93861158165506,0.037442895749045385,0.9837618420227117,0.022318468187199975 +bioclip2.5-vith14,2e66437c,hydara_ventral_vs_melpomene_ventral,109,227,0.9637185519794217,0.018130152873928586,0.9933414894284459,0.007190838769793013 +bioclip2.5-vith14,2e66437c,venus_dorsal_vs_vulcanus_dorsal,207,102,0.9227409988385598,0.057558479724736634,0.9845238095238095,0.012762319947820351 +bioclip2.5-vith14,2e66437c,venus_ventral_vs_vulcanus_ventral,206,103,0.9173228803716608,0.03407028712074117,0.9563298490127758,0.02277101157538431 +bioclip2.5-vith14,2e66437c,ssp.nov.P_dorsal_vs_malleti_dorsal,168,891,0.929491414120901,0.03730958878190825,0.9896223083706236,0.0036714959436587574 +bioclip2.5-vith14,2e66437c,ssp.nov.P_ventral_vs_malleti_ventral,158,893,0.9241313471806845,0.018797049459278355,0.9869168399610416,0.008224553298665255 +dinov3-vitl16,3756d5b9,lativitta_dorsal_vs_malleti_dorsal,1636,891,0.9991355303845694,0.0017289392308612152,0.9999589974885961,0.00008200502280764077 +dinov3-vitl16,3756d5b9,lativitta_ventral_vs_malleti_ventral,1637,893,0.9965897383944167,0.0032030888606340096,0.9998493871349808,0.00020534225891704125 +dinov3-vitl16,3756d5b9,cyrbia_dorsal_vs_cythera_dorsal,370,55,0.97002457002457,0.02196029705795918,0.9992628992628994,0.0009828009828009633 +dinov3-vitl16,3756d5b9,cyrbia_ventral_vs_cythera_ventral,370,55,0.9764127764127764,0.023886689272108814,0.9992628992628994,0.0009828009828009633 +dinov3-vitl16,3756d5b9,notabilis_dorsal_vs_plesseni_dorsal,309,241,0.9917091836734693,0.007784212579146788,0.9996639784946236,0.0005205622777160503 +dinov3-vitl16,3756d5b9,notabilis_ventral_vs_plesseni_ventral,307,239,0.9946379781420764,0.006833674172100678,1.0,0.0 +dinov3-vitl16,3756d5b9,hydara_dorsal_vs_melpomene_dorsal,109,229,0.9772055963360311,0.016045493102670942,0.9992007026789634,0.0007536160223566593 +dinov3-vitl16,3756d5b9,hydara_ventral_vs_melpomene_ventral,109,227,0.9636219336219337,0.019430827541366824,0.9955768868812347,0.003954363302864833 +dinov3-vitl16,3756d5b9,venus_dorsal_vs_vulcanus_dorsal,207,102,0.9560162601626017,0.005716345011033408,0.9945063879210221,0.004070777435308824 +dinov3-vitl16,3756d5b9,venus_ventral_vs_vulcanus_ventral,206,103,0.8956678281068525,0.03649535597185299,0.958931475029036,0.02279378961790591 +dinov3-vitl16,3756d5b9,ssp.nov.P_dorsal_vs_malleti_dorsal,168,891,0.9398349678890392,0.01644217808546759,0.9934171495590778,0.002530140740667018 +dinov3-vitl16,3756d5b9,ssp.nov.P_ventral_vs_malleti_ventral,158,893,0.9505699161808685,0.020351160246502353,0.9847742583439469,0.011082548387451065 +bioclip2.5-vith14,46bcd3db,lativitta_dorsal_vs_malleti_dorsal,1636,891,0.9974012464326174,0.0023298014380150355,, +bioclip2.5-vith14,46bcd3db,lativitta_ventral_vs_malleti_ventral,1637,893,0.9914546084416772,0.004146557071709601,, +bioclip2.5-vith14,46bcd3db,cyrbia_dorsal_vs_cythera_dorsal,370,55,0.9595823095823096,0.03344909037772437,, +bioclip2.5-vith14,46bcd3db,cyrbia_ventral_vs_cythera_ventral,370,55,0.97002457002457,0.03617597169207131,, +bioclip2.5-vith14,46bcd3db,notabilis_dorsal_vs_plesseni_dorsal,309,241,0.9906092275619924,0.006544308644715273,, +bioclip2.5-vith14,46bcd3db,notabilis_ventral_vs_plesseni_ventral,307,239,0.9860851467008711,0.014261852431014492,, +bioclip2.5-vith14,46bcd3db,hydara_dorsal_vs_melpomene_dorsal,109,229,0.9633339607252651,0.02736056650225681,, +bioclip2.5-vith14,46bcd3db,hydara_ventral_vs_melpomene_ventral,109,227,0.9524537298450342,0.033661532193360044,, +bioclip2.5-vith14,46bcd3db,venus_dorsal_vs_vulcanus_dorsal,207,102,0.9735133565621371,0.024227851067502797,, +bioclip2.5-vith14,46bcd3db,venus_ventral_vs_vulcanus_ventral,206,103,0.9028048780487804,0.027212208950103726,, +bioclip2.5-vith14,46bcd3db,ssp.nov.P_dorsal_vs_malleti_dorsal,168,891,0.9011971021196468,0.016108159771161937,, +bioclip2.5-vith14,46bcd3db,ssp.nov.P_ventral_vs_malleti_ventral,158,893,0.9195288438953471,0.03817699564487849,, +dinov3-vitl16,a1da4c41,lativitta_dorsal_vs_malleti_dorsal,1636,891,0.9907195071199049,0.005006835619635315,0.9989628839251422,0.001081629014746479 +dinov3-vitl16,a1da4c41,lativitta_ventral_vs_malleti_ventral,1637,893,0.9953633582692707,0.0028327215815540396,0.9997260569569437,0.00042195852979583025 +dinov3-vitl16,a1da4c41,cyrbia_dorsal_vs_cythera_dorsal,370,55,0.97002457002457,0.024598877407799202,0.9948402948402949,0.006879606879606887 +dinov3-vitl16,a1da4c41,cyrbia_ventral_vs_cythera_ventral,370,55,0.9673218673218674,0.03603803694839543,0.9732186732186733,0.0314784112788394 +dinov3-vitl16,a1da4c41,notabilis_dorsal_vs_plesseni_dorsal,309,241,0.9983870967741936,0.0032258064516129,1.0,0.0 +dinov3-vitl16,a1da4c41,notabilis_ventral_vs_plesseni_ventral,307,239,1.0,0.0,1.0,0.0 +dinov3-vitl16,a1da4c41,hydara_dorsal_vs_melpomene_dorsal,109,229,0.9795771378380074,0.013133002394586164,0.9980105401844532,0.0018819838034961117 +dinov3-vitl16,a1da4c41,hydara_ventral_vs_melpomene_ventral,109,227,0.9797935880544577,0.013167875270855053,0.9961697722567289,0.005763239496210127 +dinov3-vitl16,a1da4c41,venus_dorsal_vs_vulcanus_dorsal,207,102,0.9632752613240418,0.026335879665141195,0.993472706155633,0.008467576036107372 +dinov3-vitl16,a1da4c41,venus_ventral_vs_vulcanus_ventral,206,103,0.8909639953542394,0.030562997811800288,0.9526074332171893,0.006649191809858322 +dinov3-vitl16,a1da4c41,ssp.nov.P_dorsal_vs_malleti_dorsal,168,891,0.9603370808894999,0.016896932610991146,0.9950845563829128,0.003783597180852484 +dinov3-vitl16,a1da4c41,ssp.nov.P_ventral_vs_malleti_ventral,158,893,0.9666360385310846,0.018140630344994885,0.9904828737235782,0.010554644666379915 diff --git a/contrib/mimics/exps/003-baselines/export_csv.py b/contrib/mimics/exps/003-baselines/export_csv.py new file mode 100644 index 0000000..ef18b21 --- /dev/null +++ b/contrib/mimics/exps/003-baselines/export_csv.py @@ -0,0 +1,58 @@ +"""Export baseline classifier stats to a readable CSV. + +Reads every baseline_cls_*.parquet from DEFAULT_OUT_DPATH and writes a single +CSV with one row per (shard, task) and a short model label. +""" + +import pathlib + +import beartype +import polars as pl +from mimics.baselines import DEFAULT_OUT_DPATH + +MODEL_LABELS = { + "clip": "bioclip2.5-vith14", + "dinov3": "dinov3-vitl16", +} + + +@beartype.beartype +def get_model_label(family: str) -> str: + """Short human-readable model name for a family.""" + return MODEL_LABELS.get(family, family) + + +@beartype.beartype +def main(out_fpath: pathlib.Path) -> None: + dfs = [] + for pq_fpath in sorted(DEFAULT_OUT_DPATH.glob("baseline_cls_*.parquet")): + df = pl.read_parquet(pq_fpath).with_columns( + pl.col("family").map_elements(get_model_label).alias("model") + ) + dfs.append(df) + assert dfs, f"No baseline parquets found in '{DEFAULT_OUT_DPATH}'." + cols = [ + "model", + "shard_id", + "task_name", + "n_erato", + "n_melpomene", + "bal_acc_mean", + "bal_acc_std", + "auc_mean", + "auc_std", + ] + padded = [] + for df in dfs: + df = df.select(c for c in cols if c in df.columns) + for c in cols: + if c not in df.columns: + df = df.with_columns(pl.lit(None, dtype=pl.Float64).alias(c)) + padded.append(df) + result = pl.concat(padded).select(cols) + result.write_csv(out_fpath) + print(f"Wrote {result.height} rows to '{out_fpath}'.") + + +if __name__ == "__main__": + main(pathlib.Path("baselines.csv")) diff --git a/contrib/mimics/exps/005-iclr-discovery/dino_viewer.py b/contrib/mimics/exps/005-iclr-discovery/dino_viewer.py new file mode 100644 index 0000000..05ef1e6 --- /dev/null +++ b/contrib/mimics/exps/005-iclr-discovery/dino_viewer.py @@ -0,0 +1,687 @@ +import marimo + +__generated_with = "0.23.11" +app = marimo.App(width="full") + + +@app.cell +def _(): + import pathlib + import typing as tp + + import marimo as mo + import numpy as np + import polars as pl + import scipy.sparse + from PIL import Image + from tdiscovery.classification import apply_grouping, load_image_labels + from tdiscovery.visuals import make_seg + + import saev.data + import saev.data.datasets + import saev.data.models + import saev.viz + + from sklearn import linear_model + from sklearn.metrics import mean_squared_error, r2_score + + return ( + Image, + apply_grouping, + linear_model, + load_image_labels, + make_seg, + mean_squared_error, + mo, + np, + pathlib, + pl, + r2_score, + saev, + scipy, + tp, + ) + + +@app.cell +def _(pathlib): + run_root_dpath = pathlib.Path("/fs/ess/PAS2136/jbeattie/saev/runs") + shards_dpath = pathlib.Path( + "/fs/scratch/PAS2136/jbeattie/saev/shards/93a3db75/" + ) + + shard_id = "93a3db75" + + all_run_ids = [ + # Layer 21, k=16 + 'vnycpqlu', + # Layer 21, k=32 + 'nw11ywf5', + # Layer 21, k=64 + '6se2d0jo', + # Layer 21, k=128 + '25ma6cta', + # Layer 23, k=16 + '3rbr8lbq', + # Layer 23, k=32 + 'mblk8qj9', + # Layer 23, k=64 + 'ap6fx1xz', + # Layer 23, k=128 + '9la0oibn' + ] + + run_metadata = { + # Layer 21, k=16 + 'vnycpqlu': {'layer': 21, 'k': 16}, + # Layer 21, k=32 + 'nw11ywf5': {'layer': 21, 'k': 32}, + # Layer 21, k=64 + '6se2d0jo': {'layer': 21, 'k': 64}, + # Layer 21, k=128 + '25ma6cta': {'layer': 21, 'k': 128}, + # Layer 23, k=16 + '3rbr8lbq': {'layer': 23, 'k': 16}, + # Layer 23, k=32 + 'mblk8qj9': {'layer': 23, 'k': 32}, + # Layer 23, k=64 + 'ap6fx1xz': {'layer': 23, 'k': 64}, + # Layer 23, k=128 + '9la0oibn': {'layer': 23, 'k': 128}, + } + return all_run_ids, run_metadata, run_root_dpath, shard_id, shards_dpath + + +@app.cell +def _(mo): + mo.md(""" + # 002-wider-saes: Feature Viewer + + Browse SAE features scored for mimic pair discrimination. Select a run and task, then pick features by selectivity to view per-class activation grids. + """) + return + + +@app.cell +def _(all_run_ids, mo, pl, run_root_dpath, shard_id): + _dfs = [] + for _rid in all_run_ids: + _pq = ( + run_root_dpath / _rid / "inference" / shard_id / "cambridge-mimics.parquet" + ) + if _pq.exists(): + _dfs.append(pl.read_parquet(_pq)) + scores_df = pl.concat(_dfs) if _dfs else pl.DataFrame() + mo.stop(scores_df.height == 0, mo.md("No score parquets found.")) + + scores_df = scores_df.with_columns( + pl.col("auroc").sub(0.5).abs().mul(2).clip(0, 1).alias("selectivity"), + (pl.col("support_erato") - pl.col("support_melpomene")) + .abs() + .alias("support_diff"), + ) + return (scores_df,) + + +@app.cell +def _(mo, scores_df): + _tasks = sorted(scores_df.get_column("task").unique().to_list()) + task_dropdown = mo.ui.dropdown( + _tasks, value=_tasks[0], label="Task", searchable=True + ) + n_per_class_slider = mo.ui.slider(3, 20, value=6, label="Images per class") + show_seg_toggle = mo.ui.switch(value=False, label="Show seg masks") + mo.hstack( + [task_dropdown, n_per_class_slider, show_seg_toggle], + justify="start", + ) + return n_per_class_slider, show_seg_toggle, task_dropdown + + +@app.cell +def _(mo, scores_df): + _runs = scores_df.get_column("run_id").unique().to_list() + run_dropdown = mo.ui.dropdown( + _runs + ["all", "layer_22", "layer_24"], value=_runs[0], label="Run", searchable=True + ) + + layer_22 = ['vnycpqlu', 'nw11ywf5', '6se2d0jo', '25ma6cta'] + layer_24 = ['3rbr8lbq', 'mblk8qj9', 'ap6fx1xz', '9la0oibn'] + run_dropdown + return layer_22, layer_24, run_dropdown + + +@app.cell +def _(layer_22, layer_24, mo, pl, run_dropdown, scores_df, task_dropdown): + if run_dropdown.value == "all": + filtered_df = scores_df.filter(pl.col("task") == task_dropdown.value).sort( + "selectivity", descending=True + ) + elif run_dropdown.value == "layer_22": + filtered_df = scores_df.filter(pl.col("run_id").is_in(layer_22)).filter(pl.col("task") == task_dropdown.value).sort( + "selectivity", descending=True + ) + elif run_dropdown.value == "layer_24": + filtered_df = scores_df.filter(pl.col("run_id").is_in(layer_24)).filter(pl.col("task") == task_dropdown.value).sort( + "selectivity", descending=True + ) + else: + filtered_df = scores_df.filter(pl.col("run_id") == run_dropdown.value).filter(pl.col("task") == task_dropdown.value).sort( + "selectivity", descending=True + ) + mo.stop(filtered_df.height == 0, mo.md("No features for this task.")) + + feature_table = mo.ui.table( + filtered_df + .select( + "run_id", + "feature_id", + "selectivity", + "auroc", + "support_diff", + "support_erato", + "support_melpomene", + "mean_act_erato", + "mean_act_melpomene", + "w1_act", + "w1_firing" + ) + .to_pandas(), + selection="single", + label="Top features by selectivity", + ) + feature_table + return feature_table, filtered_df + + +@app.cell +def _(filtered_df): + random_sample_df = filtered_df + random_sample_df["w1_act"].max() + return (random_sample_df,) + + +@app.cell +def _( + filtered_df, + linear_model, + mean_squared_error, + r2_score, + random_sample_df, +): + import matplotlib.pyplot as plt + w1_dist_fig = plt.figure() + plt.yscale("log") + plt.title("W1 distance distribution") + plt.xlabel("W1") + plt.ylabel("count") + plt.hist(filtered_df["w1_act"]) + + + + _regr = linear_model.LinearRegression() + _regr.fit(random_sample_df["w1_act"].reshape((-1, 1)), random_sample_df["selectivity"].reshape((-1, 1))) + w1_y = _regr.predict(random_sample_df["w1_act"].to_numpy().reshape((-1, 1))) + + print("Coefficients: \n", _regr.coef_) + print("Mean squared error: %.5f" % mean_squared_error(random_sample_df["selectivity"].reshape((-1, 1)), w1_y)) + print("Coefficient of determination: %.5f" % r2_score(random_sample_df["selectivity"].reshape((-1, 1)), w1_y)) + + + + w1_selectivity_fig = plt.figure(figsize=(5, 5), dpi=1000) + plt.scatter(x = random_sample_df["w1_act"], y = random_sample_df["selectivity"]) + + plt.plot([random_sample_df["w1_act"].min(), random_sample_df["w1_act"].max()], [w1_y.min(), w1_y.max()], linestyle="--", color="red", linewidth=2) + plt.xlabel("W1") + plt.ylabel("Selectivity") + plt.title("W1 vs Selectivity") + + #mo.hstack([w1_dist_fig, w1_selectivity_fig]) + w1_selectivity_fig + return (plt,) + + +@app.cell +def _( + all_run_ids, + linear_model, + mean_squared_error, + np, + pl, + plt, + r2_score, + run_metadata, + scores_df, +): + + + k = 10 + fw = 3 + fh = 3 + figpad = 15 + + tasks_of_interest = [ + "cyrbia_dorsal_vs_cythera_dorsal", + #"cyrbia_ventral_vs_cythera_ventral", + "lativitta_dorsal_vs_malleti_dorsal", + #"lativitta_ventral_vs_malleti_ventral", + "notabilis_dorsal_vs_plesseni_dorsal", + #"notabilis_ventral_vs_plesseni_ventral", + #"ssp.nov.P_dorsal_vs_malleti_dorsal", + #"ssp.nov.P_ventral_vs_malleti_ventral", + ] + + top_k_selectivity_dfs = {id: scores_df.filter(pl.col("run_id") == id).filter(pl.col("task").is_in(tasks_of_interest)).sort(by='selectivity', descending=True).group_by("task").head(k) for id in all_run_ids} + top_k_w1_act_dfs = {id: scores_df.filter(pl.col("run_id") == id).filter(pl.col("task").is_in(tasks_of_interest)).sort(by='w1_act', descending=True).group_by('task').head(k) for id in all_run_ids} + + for id in all_run_ids: + top_k_selectivity_dfs[id] = top_k_selectivity_dfs[id].with_columns(k = pl.lit(run_metadata[id]['k']), layer = pl.lit(run_metadata[id]['layer'])) + top_k_w1_act_dfs[id] = top_k_w1_act_dfs[id].with_columns(k = pl.lit(run_metadata[id]['k']), layer = pl.lit(run_metadata[id]['layer'])) + + ### SELECTIVITY + top_k_select_fig_21 = plt.figure(dpi=1000, figsize=(fw, fh)) + plt.tight_layout() + plt.title(f"Selectivity vs k in Layer 21 @ {k}", pad=figpad) + plt.ylabel("Selectivity") + plt.xlabel("k") + plt.ylim(bottom=0.4, top=1) + plt.xticks([16, 32, 64, 128]) + + reg_x = [] + reg_y = [] + labels = [] + for id in all_run_ids: + if int(top_k_selectivity_dfs[id]['layer'][0]) != 21: + continue + reg_x.extend(list(top_k_selectivity_dfs[id]['k'])) + reg_y.extend(list(top_k_selectivity_dfs[id]['selectivity'])) + labels.extend(list(top_k_selectivity_dfs[id]['task'])) + plt.scatter(reg_x, reg_y, alpha = 0.8) + + regr = linear_model.LinearRegression() + regr.fit(np.array(reg_x).reshape(-1, 1), np.array(reg_y).reshape(-1, 1)) + pred_y = regr.predict(np.array(reg_x).reshape(-1, 1)) + + # The coefficients + print("Coefficients: \n", regr.coef_) + # The mean squared error + print("Mean squared error: %.5f" % mean_squared_error(reg_y, pred_y)) + # The coefficient of determination: 1 is perfect prediction + print("Coefficient of determination: %.5f" % r2_score(reg_y, pred_y)) + + plt.plot(reg_x, pred_y, linestyle="--", linewidth=2) + + plt.savefig(f"./figures/selectivity_vs_k_21_at_{k}.png", bbox_inches="tight") + + top_k_select_fig_23 = plt.figure(dpi=1000, figsize=(fw, fh)) + plt.title(f"Selectivity vs k in Layer 23 @ {k}", pad=figpad) + plt.ylabel("Selectivity") + plt.xlabel("k") + plt.ylim(bottom=0.4, top=1) + plt.xticks([16, 32, 64, 128]) + + reg_x = [] + reg_y = [] + for id in all_run_ids: + if int(top_k_selectivity_dfs[id]['layer'][0]) != 23: + continue + + reg_x.extend(list(top_k_selectivity_dfs[id]['k'])) + reg_y.extend(list(top_k_selectivity_dfs[id]['selectivity'])) + + plt.scatter(reg_x, reg_y, alpha = 0.8) + + regr = linear_model.LinearRegression() + regr.fit(np.array(reg_x).reshape(-1, 1), np.array(reg_y).reshape(-1, 1)) + pred_y = regr.predict(np.array(reg_x).reshape(-1, 1)) + + # The coefficients + print("Coefficients: \n", regr.coef_) + # The mean squared error + print("Mean squared error: %.5f" % mean_squared_error(reg_y, pred_y)) + # The coefficient of determination: 1 is perfect prediction + print("Coefficient of determination: %.5f" % r2_score(reg_y, pred_y)) + + plt.plot(reg_x, pred_y, linestyle="--", linewidth=2) + plt.savefig(f"./figures/selectivity_vs_k_23_at_{k}.png", bbox_inches="tight") + + + ### W1 ACT + + top_k_w1_fig_21 = plt.figure(dpi=1000, figsize=(fw, fh)) + plt.title(f"W1 Distance vs k in Layer 21 @ {k}", pad=figpad) + plt.ylabel("W1") + plt.xlabel("k") + plt.xticks([16, 32, 64, 128]) + + reg_x = [] + reg_y = [] + for id in all_run_ids: + if int(top_k_w1_act_dfs[id]['layer'][0]) != 21: + continue + reg_x.extend(list(top_k_w1_act_dfs[id]['k'])) + reg_y.extend(list(top_k_w1_act_dfs[id]['w1_act'])) + plt.scatter(reg_x, reg_y, alpha = 0.8) + + regr = linear_model.LinearRegression() + regr.fit(np.array(reg_x).reshape(-1, 1), np.array(reg_y).reshape(-1, 1)) + pred_y = regr.predict(np.array(reg_x).reshape(-1, 1)) + + # The coefficients + print("Coefficients: \n", regr.coef_) + # The mean squared error + print("Mean squared error: %.5f" % mean_squared_error(reg_y, pred_y)) + # The coefficient of determination: 1 is perfect prediction + print("Coefficient of determination: %.5f" % r2_score(reg_y, pred_y)) + + plt.plot(reg_x, pred_y, linestyle="--", linewidth=2) + + plt.savefig(f"./figures/w1_act_vs_k_21_at_{k}.png", bbox_inches="tight") + + top_k_w1_fig_23 = plt.figure(dpi=1000, figsize=(fw, fh)) + plt.title(f"W1 Distance vs k in Layer 23 @ {k}", pad=figpad) + plt.ylabel("W1") + plt.xlabel("k") + plt.xticks([16, 32, 64, 128]) + + reg_x = [] + reg_y = [] + for id in all_run_ids: + if int(top_k_w1_act_dfs[id]['layer'][0]) != 23: + continue + reg_x.extend(list(top_k_w1_act_dfs[id]['k'])) + reg_y.extend(list(top_k_w1_act_dfs[id]['w1_act'])) + plt.scatter(reg_x, reg_y, alpha = 0.8) + + regr = linear_model.LinearRegression() + regr.fit(np.array(reg_x).reshape(-1, 1), np.array(reg_y).reshape(-1, 1)) + pred_y = regr.predict(np.array(reg_x).reshape(-1, 1)) + + # The coefficients + print("Coefficients: \n", regr.coef_) + # The mean squared error + print("Mean squared error: %.5f" % mean_squared_error(reg_y, pred_y)) + # The coefficient of determination: 1 is perfect prediction + print("Coefficient of determination: %.5f" % r2_score(reg_y, pred_y)) + + plt.plot(reg_x, pred_y, linestyle="--", linewidth=2) + plt.savefig(f"./figures/w1_act_vs_k_23_at_{k}.png", bbox_inches="tight") + + #mo.vstack([mo.hstack([top_k_select_fig_21, top_k_select_fig_23]), mo.hstack([top_k_w1_fig_21, top_k_w1_fig_23]),]) + return + + +@app.cell +def _(feature_table, mo): + _sel = feature_table.value + mo.stop( + _sel is None or len(_sel) == 0, + mo.md("Select a feature from the table above."), + ) + selected_run_id = str(_sel.iloc[0]["run_id"]) + selected_feature_id = int(_sel.iloc[0]["feature_id"]) + mo.md(f"Selected: run=`{selected_run_id}` feature=**{selected_feature_id}**") + return selected_feature_id, selected_run_id + + +@app.cell +def _( + apply_grouping, + load_image_labels, + mo, + run_root_dpath, + saev, + scipy, + selected_run_id, + shard_id, + shards_dpath, + task_dropdown, + tp, +): + # Load render context: image dataset, labels, patch size. + _md = saev.data.Metadata.load(shards_dpath) + _model_cls = tp.cast( + tp.Callable[[str], tp.Any], saev.data.models.load_model_cls(_md.family) + ) + _vit = _model_cls(_md.ckpt) + _resize_tr = _vit.make_resize(_md.ckpt, _md.content_tokens_per_example, scale=1.0) + _data_cfg = tp.cast(saev.data.datasets.Config, _md.make_data_cfg()) + img_ds = saev.data.datasets.get_dataset( + _data_cfg, data_transform=_resize_tr, mask_transform=_resize_tr + ) + patch_size = int(_vit.patch_size) + n_patches = _md.content_tokens_per_example + pixel_agg = _md.pixel_agg + bg_label = _data_cfg.bg_label + + _, _labels_by_col = load_image_labels(shards_dpath) + sv_labels = _labels_by_col["subspecies_view"] + n_images = len(sv_labels) + + from mimics import tasks as _tasks_mod + + _grouping = _tasks_mod.make_label_grouping(task_dropdown.value) + _targets, _names, include_mask = apply_grouping(sv_labels, _grouping) + _c2i = {name: i for i, name in enumerate(_names)} + erato_i = _c2i["erato"] + melp_i = _c2i["melpomene"] + _inc_tgt = _targets[include_mask] + erato_class_mask = _inc_tgt == erato_i + melp_class_mask = _inc_tgt == melp_i + + # Load token_acts for selected run. + _ta_fpath = ( + run_root_dpath / selected_run_id / "inference" / shard_id / "token_acts.npz" + ) + msg = f"Missing token_acts: '{_ta_fpath}'." + assert _ta_fpath.exists(), msg + ta_csc = scipy.sparse.load_npz(str(_ta_fpath)).tocsc() + tpi = ta_csc.shape[0] // n_images + assert ta_csc.shape[0] == n_images * tpi + + mo.md( + f"Loaded token_acts for `{selected_run_id}` (d_sae={ta_csc.shape[1]}, tpi={tpi}, patch_size={patch_size})." + ) + return ( + bg_label, + erato_class_mask, + img_ds, + include_mask, + melp_class_mask, + n_images, + n_patches, + patch_size, + pixel_agg, + sv_labels, + ta_csc, + tpi, + ) + + +@app.cell +def _( + Image, + bg_label, + erato_class_mask, + img_ds, + include_mask, + make_seg, + melp_class_mask, + mo, + n_images, + n_patches, + n_per_class_slider, + np, + patch_size, + pixel_agg, + run_root_dpath, + saev, + selected_feature_id, + selected_run_id, + shard_id, + show_seg_toggle, + sv_labels, + ta_csc, + tpi, +): + # Extract per-patch activations for this feature. + _col = ta_csc[:, selected_feature_id].toarray().ravel() + _by_image = _col.reshape(n_images, tpi) + _max_acts = _by_image.max(axis=1) + _upper = float(_by_image.max()) + + _inc_acts = _by_image[include_mask] + _inc_max = _max_acts[include_mask] + inc_max_acts = _inc_max + _inc_i = np.where(include_mask)[0] + + _n = n_per_class_slider.value + _n_cols = 3 + + import glasbey + + _palette = [ + tuple(rgb) for rgb in glasbey.create_palette(palette_size=256, as_hex=False) + ] + + # Cache dir: runs/{run_id}/inference/{shard_id}/images/{feature_id}/ + _cache_dpath = ( + run_root_dpath + / selected_run_id + / "inference" + / shard_id + / "images" + / str(selected_feature_id) + ) + _cache_dpath.mkdir(parents=True, exist_ok=True) + + def _render_images(img_i, patches): + """Render highlighted + seg images, caching to disk.""" + _sae_fpath = _cache_dpath / f"{img_i}_sae_img.png" + _seg_fpath = _cache_dpath / f"{img_i}_seg.png" + + if _sae_fpath.exists() and _seg_fpath.exists(): + return Image.open(_sae_fpath), Image.open(_seg_fpath) + + _sample = img_ds[img_i] + assert isinstance(_sample, dict) and "data" in _sample + _img = _sample["data"] + assert isinstance(_img, Image.Image) + + if _sae_fpath.exists(): + _sae = Image.open(_sae_fpath) + else: + _sae = saev.viz.add_highlights(_img, patches, patch_size, upper=_upper) + _sae.save(_sae_fpath) + + _seg = None + if _seg_fpath.exists(): + _seg = Image.open(_seg_fpath) + else: + _seg_raw = _sample.get("patch_labels", None) + if _seg_raw is not None: + _seg = make_seg( + _seg_raw, n_patches, patch_size, pixel_agg, bg_label, _palette + ) + _seg.save(_seg_fpath) + + return _sae, _seg + + def _get_top_bot(class_mask): + _cls_max = _inc_max[class_mask] + _order = np.argsort(-_cls_max) + _top_i = _order[:_n] + _nonzero = _order[_cls_max[_order] > 0] + _bot_i = _nonzero[-_n:] if len(_nonzero) >= _n else _nonzero + return _top_i, _bot_i + + _e_top, _e_bot = _get_top_bot(erato_class_mask) + _m_top, _m_bot = _get_top_bot(melp_class_mask) + + # Pre-render all images with a single progress bar. + _render_jobs = [] + for _indices, _mask in [ + (_e_top, erato_class_mask), + (_m_top, melp_class_mask), + (_e_bot, erato_class_mask), + (_m_bot, melp_class_mask), + ]: + _cls_acts = _inc_acts[_mask] + _cls_global_i = _inc_i[_mask] + for _idx in _indices: + _img_i = int(_cls_global_i[_idx]) + _patches = _cls_acts[_idx].astype(np.float32) + _render_jobs.append((_img_i, _patches)) + + _rendered = {} + for _img_i, _patches in mo.status.progress_bar( + _render_jobs, title="Rendering images" + ): + if _img_i not in _rendered: + _rendered[_img_i] = _render_images(_img_i, _patches) + + def _build_grid(indices, class_mask): + """Build grid from pre-rendered images.""" + _cls_max = _inc_max[class_mask] + _cls_global_i = _inc_i[class_mask] + + _imgs = [] + for _idx in indices: + _img_i = int(_cls_global_i[_idx]) + _sae, _seg = _rendered[_img_i] + _act_val = float(_cls_max[_idx]) + _sv = sv_labels[_img_i] + _ssp, _view = _sv.rsplit("_", 1) + _cells = [mo.image(_sae)] + if show_seg_toggle.value and _seg is not None: + _cells.append(mo.image(_seg)) + _cells.append(mo.md(f"{_ssp} ({_view}) | act={_act_val:.3f}")) + _imgs.append(mo.vstack(_cells)) + + _rows = [] + for _r in range(0, len(_imgs), _n_cols): + _rows.append(mo.hstack(_imgs[_r : _r + _n_cols], justify="start")) + return mo.vstack(_rows) + + mo.vstack([ + mo.md(f"## Feature {selected_feature_id} (upper={_upper:.3f})"), + mo.md(f"### Erato Top {len(_e_top)}"), + _build_grid(_e_top, erato_class_mask), + mo.md(f"### Melpomene Top {len(_m_top)}"), + _build_grid(_m_top, melp_class_mask), + mo.md(f"### Erato Bottom {len(_e_bot)}"), + _build_grid(_e_bot, erato_class_mask), + mo.md(f"### Melpomene Bottom {len(_m_bot)}"), + _build_grid(_m_bot, melp_class_mask), + ]) + return (inc_max_acts,) + + +@app.cell +def _( + erato_class_mask, + inc_max_acts, + melp_class_mask, + plt, + selected_feature_id, +): + plt.figure(figsize=(3, 3), dpi=1000) + #plt.yscale('log') + plt.title(f"Feature {selected_feature_id} Max Activations") + plt.xlabel("Activation Value") + plt.ylabel("Count") + plt.hist(inc_max_acts[melp_class_mask], alpha=0.6, label="Melpomene") + plt.hist(inc_max_acts[erato_class_mask], alpha=0.6, label="Erato") + + plt.legend() + return + + +@app.cell +def _(): + return + + +if __name__ == "__main__": + app.run() diff --git a/contrib/mimics/exps/005-iclr-discovery/generate_dino_shards.sh b/contrib/mimics/exps/005-iclr-discovery/generate_dino_shards.sh new file mode 100644 index 0000000..acc4e14 --- /dev/null +++ b/contrib/mimics/exps/005-iclr-discovery/generate_dino_shards.sh @@ -0,0 +1,13 @@ +#!/usr/bin/env bash + +uv run launch.py shards \ + --family dinov3 \ + --ckpt /fs/scratch/PAS2136/jbeattie/models/encoders/dinov3/dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth \ + --shards-root /fs/scratch/PAS2136/jbeattie/saev/shards/ \ + --batch-size 128 \ + --n-workers 1 \ + --d-model 1024 \ + --layers 19 21 23 \ + --content-tokens-per-example 384 \ + --max-tokens-per-shard 1200000 \ + data:img-seg-folder --data.root /fs/ess/PAS2136/jbeattie/datasets/cambridge-segfolder-v1.6 diff --git a/contrib/mimics/exps/005-iclr-discovery/inference_dino.py b/contrib/mimics/exps/005-iclr-discovery/inference_dino.py new file mode 100644 index 0000000..b7733d0 --- /dev/null +++ b/contrib/mimics/exps/005-iclr-discovery/inference_dino.py @@ -0,0 +1,22 @@ +"""Inference for Pareto-optimal 16K BioCLIP SAEs on Cambridge butterflies (256p, v1.6).""" + +def make_cfgs(): + import os.path + + run_root = "/fs/ess/PAS2136/jbeattie/saev/runs" + shards_384 = "/fs/scratch/PAS2136/jbeattie/saev/shards/93a3db75" + + # Pareto-optimal run IDs from notebook.py, keyed by (layer, d_sae). + run_ids = { + (21, 16384): ['vnycpqlu', 'nw11ywf5', '6se2d0jo', '25ma6cta'], + (23, 16384): ['3rbr8lbq', 'mblk8qj9', 'ap6fx1xz', '9la0oibn'] + } + + cfgs = [] + for (layer, _), ids in run_ids.items(): + for run_id in ids: + cfgs.append({ + "run": os.path.join(run_root, run_id), + "data": {"shards": shards_384, "layer": layer}, + }) + return cfgs diff --git a/contrib/mimics/exps/005-iclr-discovery/inference_dino.sh b/contrib/mimics/exps/005-iclr-discovery/inference_dino.sh new file mode 100644 index 0000000..2e4db06 --- /dev/null +++ b/contrib/mimics/exps/005-iclr-discovery/inference_dino.sh @@ -0,0 +1,4 @@ +#!/usr/bin/env bash + +uv run launch.py inference \ + --sweep ~/sae-trait-discovery/saev/contrib/mimics/exps/005-workshop-features/inference_dino.py diff --git a/contrib/mimics/exps/005-iclr-discovery/score_dino.py b/contrib/mimics/exps/005-iclr-discovery/score_dino.py new file mode 100644 index 0000000..9c59051 --- /dev/null +++ b/contrib/mimics/exps/005-iclr-discovery/score_dino.py @@ -0,0 +1,24 @@ +"""Scoring sweep for Pareto-optimal 16K/32K SAEs on Cambridge butterflies (384p, v1.6).""" + +def make_cfgs() -> list[dict]: + # Pareto-optimal run IDs from notebook.py, keyed by (layer, d_sae). + run_ids = [ + # Layer 21, k=16 + 'vnycpqlu', + # Layer 21, k=32 + 'nw11ywf5', + # Layer 21, k=64 + '6se2d0jo', + # Layer 21, k=128 + '25ma6cta', + # Layer 23, k=16 + '3rbr8lbq', + # Layer 23, k=32 + 'mblk8qj9', + # Layer 23, k=64 + 'ap6fx1xz', + # Layer 23, k=128 + '9la0oibn' + ] + + return [{"run_ids": run_ids, "run_root_dpath": "/fs/ess/PAS2136/jbeattie/saev/runs", "shards_dpath": "/fs/scratch/PAS2136/jbeattie/saev/shards/93a3db75"}] diff --git a/contrib/mimics/exps/005-iclr-discovery/train_16k_dino.py b/contrib/mimics/exps/005-iclr-discovery/train_16k_dino.py new file mode 100644 index 0000000..fb60c56 --- /dev/null +++ b/contrib/mimics/exps/005-iclr-discovery/train_16k_dino.py @@ -0,0 +1,50 @@ +"""Training sweep for 16K-latent SAEs on Cambridge butterflies (384p, v1.6).""" + + +def make_cfgs() -> list[dict]: + n_train = 100_000_000 + batch_size = 1024 * 8 + + + # Cambridge butterfly v1.6 shards, DINOv3 ViT-L/16, 384 patches, prefer-fg + shards_384 = "/fs/scratch/PAS2136/jbeattie/saev/shards/93a3db75" + + cfgs = [] + for layer in [21, 23]: + for k in [16, 32, 64, 128]: + for lr in [3e-4, 1e-3, 3e-3]: + cfgs.append({ + "tags": ["mimics-16k-dino-384p-v1.6"], + # "slurm_acct": "PAS2136", + # "slurm_partition": "nextgen", + # "n_hours": 8.0, + "lr": lr, + "n_lr_warmup": 500, + "n_sparsity_warmup": n_train // batch_size, + "runs_root": "/fs/ess/PAS2136/jbeattie/saev/runs", + "n_train": n_train, + "sae": { + "d_model": 1024, + "d_sae": 1024 * 16, + "normalize_w_dec": True, + "remove_parallel_grads": True, + "activation": {"top_k": k}, + "reinit_blend": 0.8, + }, + "train_data": { + "layer": layer, + "shards": shards_384, + "batch_size": batch_size, + "min_buffer_fill": 0.2, + "ignore_labels": [0], + "use_tmpdir": True, + }, + "val_data": { + "layer": layer, + "shards": shards_384, + "batch_size": batch_size, + "ignore_labels": [0], + "use_tmpdir": True, + }, + }) + return cfgs diff --git a/contrib/mimics/exps/005-iclr-discovery/train_dino_saes.sh b/contrib/mimics/exps/005-iclr-discovery/train_dino_saes.sh new file mode 100644 index 0000000..dca0149 --- /dev/null +++ b/contrib/mimics/exps/005-iclr-discovery/train_dino_saes.sh @@ -0,0 +1,6 @@ +#!/usr/bin/env bash + +uv run launch.py train \ + --sweep ~/sae-trait-discovery/saev/contrib/mimics/exps/002-wider-saes/train_16k_dino.py \ + --max-parallel 4 \ + sae.activation:batch-top-k \ No newline at end of file diff --git a/contrib/trait_discovery/notebooks/__marimo__/session/figures.py.json b/contrib/trait_discovery/notebooks/__marimo__/session/figures.py.json new file mode 100644 index 0000000..f08d997 --- /dev/null +++ b/contrib/trait_discovery/notebooks/__marimo__/session/figures.py.json @@ -0,0 +1,136 @@ +{ + "version": "1", + "metadata": { + "marimo_version": "0.24.2", + "script_metadata_hash": null + }, + "cells": [ + { + "id": "Hbol", + "code_hash": "2e819f135b6cd1f608f6ced3860bf60d", + "outputs": [ + { + "type": "data", + "data": { + "text/markdown": "

Figures for \"Towards Open-Ended Visual Scientific Discovery with Sparse Autoencoders\"

\nThis notebook hardcodes run ids for reproducible figures.\n

Coding Style For This Notebook

\n\n

Typical Pattern

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    \n
  1. Write plot_<figure_name>().
    2. \n
  2. Inside it, define specs = [RunSpec(...), ...].
    3. \n
  3. df, skipped = load_df(specs).
    4. \n
  4. Plot from df, then write csv_df and save both pdf and csv.
    5. \n
\n

Table Pattern

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    \n
  1. Write <table_name>().
    2. \n
  2. Inside it, define specs = [RunSpec(...), ...].
    3. \n
  3. df, skipped = load_df(specs).
    4. \n
  4. Select rows with explicit shard columns (for example best by 3802cb66/train_probe_r__train-614861a0).
    5. \n
  5. Return a tidy table DataFrame and save a CSV artifact for reproducibility.
    6. \n
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method: str = pa.Field(isin=["k-means", "pca", "semi-nmf"]) + method: str = pa.Field(isin=["k-means", "pca", "semi-nmf", "nmf"]) # From baselines::train fit_data: str = pa.Field() @@ -277,7 +277,7 @@ def load_baseline_probe_results_df() -> pa.typing.DataFrame[ method = None if method_raw == "kmeans": method = "k-means" - elif method_raw in {"pca", "semi-nmf"}: + elif method_raw in {"pca", "semi-nmf", "nmf"}: method = method_raw if method is None: @@ -291,7 +291,7 @@ def load_baseline_probe_results_df() -> pa.typing.DataFrame[ if method == "k-means": fit_val_mse = wandb_run["summary/eval/inertia"] fit_val_l0 = 1.0 - elif method in {"pca", "semi-nmf"}: + elif method in {"pca", "semi-nmf", "nmf"}: fit_val_mse = wandb_run["summary/eval/recon_mse"] fit_val_l0 = wandb_run["config/k"] else: diff --git a/contrib/trait_discovery/src/tdiscovery/baselines.py b/contrib/trait_discovery/src/tdiscovery/baselines.py index 5d90d15..3c143f3 100644 --- a/contrib/trait_discovery/src/tdiscovery/baselines.py +++ b/contrib/trait_discovery/src/tdiscovery/baselines.py @@ -29,7 +29,7 @@ log_format = "[%(asctime)s] [%(levelname)s] [%(name)s] %(message)s" -BaselineMethod = tp.Literal["kmeans", "pca", "semi-nmf"] +BaselineMethod = tp.Literal["kmeans", "pca", "semi-nmf", "nmf"] BASELINE_SCHEMA_VERSION = 1 @@ -462,6 +462,199 @@ def _update_last_batch_metrics(self, acts: Tensor, z: Tensor) -> None: self.last_batch_nmse_ = recon_sse / sse_baseline +@jaxtyped(typechecker=beartype.beartype) +class MiniBatchNMF(torch.nn.Module): + """Mini-batch NMF estimator with non-negative codes and dictionary.""" + + method = "nmf" + + def __init__( + self, + n_concepts: int, + device: str = "cuda", + z_iters: int = 10, + encode_iters: int = 300, + d_iters: int = 10, + batch_size: int = 16384, + ridge: float = 1e-6, + eps: float = 1e-8, + forget_factor: float = 0.7, + d_update_every: int = 10, + ): + super().__init__() + assert n_concepts > 0, "n_concepts must be positive" + assert z_iters >= 0, "z_iters must be non-negative" + assert encode_iters >= 0, "encode_iters must be non-negative" + assert d_iters >= 0, "d_iters must be non-negative" + assert batch_size > 0, "batch_size must be positive" + assert ridge >= 0.0, "ridge must be non-negative" + assert eps > 0.0, "eps must be positive" + assert 0.0 <= forget_factor < 1.0, "forget_factor must be in [0, 1)" + assert d_update_every > 0, "d_update_every must be positive" + + self.n_concepts = n_concepts + self.device = torch.device(device) + self.z_iters = z_iters + self.encode_iters = encode_iters + self.d_iters = d_iters + self.batch_size = batch_size + self.ridge = float(ridge) + self.eps = float(eps) + self.forget_factor = float(forget_factor) + self.d_update_every = d_update_every + + self.D_: Tensor | None = None + self.n_features_in_: int | None = None + self.n_samples_seen_: int = 0 + self.n_steps_: int = 0 + self.ZtZ_acc_: Tensor | None = None + self.ZtA_acc_: Tensor | None = None + self.last_batch_recon_mse_: float | None = None + self.last_batch_nmse_: float | None = None + self._ddt_: Tensor | None = None + self._ddt_pos_: Tensor | None = None + self._ddt_neg_: Tensor | None = None + self._ddt_reg_inv_: Tensor | None = None + + def partial_fit(self, batch: Float[Tensor, "batch d_model"]) -> tp.Self: + assert batch.ndim == 2, f"batch must be 2D, got {batch.shape}" + if batch.shape[0] == 0: + return self + + acts = batch.to(self.device, dtype=torch.float32) + n_batch, n_features = acts.shape + + if self.n_features_in_ is None: + self._initialize_dictionary(n_features) + else: + msg = f"Expected {self.n_features_in_} features, got {n_features}" + assert n_features == self.n_features_in_, msg + + z = self._encode(acts, self.z_iters) + self._update_last_batch_metrics(acts, z) + + zt = z.mT + ztz = zt @ z + zta = zt @ acts + + assert self.ZtZ_acc_ is not None + assert self.ZtA_acc_ is not None + forget = self.forget_factor + self.ZtZ_acc_ = forget * self.ZtZ_acc_ + (1.0 - forget) * ztz + self.ZtA_acc_ = forget * self.ZtA_acc_ + (1.0 - forget) * zta + + self.n_samples_seen_ += int(n_batch) + self.n_steps_ += 1 + + if self.n_steps_ % self.d_update_every == 0: + self._update_dictionary_from_accumulators() + + return self + + def transform( + self, batch: Float[Tensor, "batch d_model"], *, n_iters: int | None = None + ) -> Float[Tensor, "batch k"]: + assert self.D_ is not None, "MiniBatchNMF has not been fitted" + assert batch.ndim == 2, f"batch must be 2D, got {batch.shape}" + acts = batch.to(self.device, dtype=torch.float32) + n_iters = self.encode_iters if n_iters is None else n_iters + assert n_iters >= 0, "n_iters must be non-negative" + return self._encode(acts, n_iters) + + def _initialize_dictionary(self, n_features: int) -> None: + assert self.n_features_in_ is None, "Dictionary already initialized" + D = torch.randn( + self.n_concepts, n_features, device=self.device, dtype=torch.float32 + ).abs() + self.D_ = D.clamp_min(self.eps) + self.n_features_in_ = n_features + self.ZtZ_acc_ = torch.zeros( + (self.n_concepts, self.n_concepts), device=self.device, dtype=torch.float32 + ) + self.ZtA_acc_ = torch.zeros( + (self.n_concepts, n_features), device=self.device, dtype=torch.float32 + ) + self._refresh_cache() + + def _update_dictionary_from_accumulators(self) -> None: + assert self.D_ is not None + assert self.ZtZ_acc_ is not None + assert self.ZtA_acc_ is not None + ztz_pos = _pos_part(self.ZtZ_acc_) + ztz_neg = _neg_part(self.ZtZ_acc_) + zta_pos = _pos_part(self.ZtA_acc_) + zta_neg = _neg_part(self.ZtA_acc_) + D = self.D_ + for _ in range(self.d_iters): + numerator = zta_pos + ztz_neg @ D + denominator = zta_neg + ztz_pos @ D + self.eps + D = D * torch.sqrt(numerator / denominator) + self.D_ = D.clamp_min(self.eps) + self._refresh_cache() + + def _refresh_cache(self) -> None: + assert self.D_ is not None + ddt = self.D_ @ self.D_.mT + self._ddt_ = ddt + self._ddt_pos_ = _pos_part(ddt) + self._ddt_neg_ = _neg_part(ddt) + eye = torch.eye(self.n_concepts, device=ddt.device, dtype=ddt.dtype) + self._ddt_reg_inv_ = torch.linalg.solve(ddt + self.eps * eye, eye) + + def _ensure_cache(self) -> None: + if ( + self._ddt_pos_ is None + or self._ddt_neg_ is None + or self._ddt_reg_inv_ is None + ): + self._refresh_cache() + + def _encode(self, acts: Tensor, n_iters: int) -> Tensor: + assert self.D_ is not None + self._ensure_cache() + assert self._ddt_pos_ is not None + assert self._ddt_neg_ is not None + assert self._ddt_reg_inv_ is not None + + z = acts @ self.D_.mT @ self._ddt_reg_inv_ + z = z.clamp_min(self.eps) + if n_iters == 0: + return z + + atd = acts @ self.D_.mT + atd_pos = _pos_part(atd) + atd_neg = _neg_part(atd) + ddt_pos = self._ddt_pos_ + ddt_neg = self._ddt_neg_ + + for _ in range(n_iters): + numerator = atd_pos + z @ ddt_neg + denominator = atd_neg + z @ ddt_pos + self.eps + z = z * torch.sqrt(numerator / denominator) + + return z + + def _update_last_batch_metrics(self, acts: Tensor, z: Tensor) -> None: + assert self.D_ is not None + n_batch = acts.shape[0] + recon = z @ self.D_ + diff = (acts - recon).to(torch.float64) + recon_sse = diff.pow(2).sum().item() + self.last_batch_recon_mse_ = recon_sse / n_batch + + acts64 = acts.to(torch.float64) + sum_sq = (acts64 * acts64).sum().item() + sum_vec = acts64.sum(dim=0) + sum_vec_sq = torch.dot(sum_vec, sum_vec).item() + sse_baseline = sum_sq - sum_vec_sq / n_batch + msg = ( + f"Baseline variance is non-positive (sse_baseline={sse_baseline:.6e});" + " cannot compute normalized MSE." + ) + assert sse_baseline > 0.0, msg + self.last_batch_nmse_ = recon_sse / sse_baseline + + @beartype.beartype @dataclasses.dataclass(frozen=True) class TrainConfig: @@ -474,6 +667,7 @@ class TrainConfig: collapse_tol: float = 0.5 z_iters: int = 10 encode_iters: int = 300 + d_iters: int = 10 ridge: float = 1e-6 eps: float = 1e-8 forget_factor: float = 0.7 @@ -504,7 +698,7 @@ class InferenceConfig: n_dists: int = 25 """Number of latent dimensions to snapshot for distributions.""" n_iters: int = 300 - """Number of multiplicative update iterations during Semi-NMF inference.""" + """Number of multiplicative update iterations during NMF/Semi-NMF inference.""" save: bool = True """Whether to write token_acts/statistics files. If False, only metrics.json is written.""" force: bool = False @@ -581,6 +775,32 @@ def _materialize_model( model.last_batch_nmse_ = state["last_batch_nmse"] model._refresh_cache() return model + elif method == "nmf": + D = state["D"] + n_concepts, n_features_in = D.shape + msg = f"n_features_in mismatch: {state['n_features_in']} vs {n_features_in}" + assert state["n_features_in"] == n_features_in, msg + + model = MiniBatchNMF( + n_concepts=n_concepts, + device=device, + z_iters=state["z_iters"], + encode_iters=state["encode_iters"], + d_iters=state["d_iters"], + batch_size=state["batch_size"], + ridge=state["ridge"], + eps=state["eps"], + forget_factor=state["forget_factor"], + d_update_every=state["d_update_every"], + ) + model.D_ = D.to(device) + model.n_features_in_ = n_features_in + model.n_samples_seen_ = state["n_samples_seen"] + model.n_steps_ = state["n_steps"] + model.last_batch_recon_mse_ = state["last_batch_recon_mse"] + model.last_batch_nmse_ = state["last_batch_nmse"] + model._refresh_cache() + return model else: tp.assert_never(method) @@ -650,6 +870,26 @@ def _serialize_model_state(model: torch.nn.Module) -> dict[str, tp.Any]: "last_batch_recon_mse": model.last_batch_recon_mse_, "last_batch_nmse": model.last_batch_nmse_, } + if isinstance(model, MiniBatchNMF): + assert model.D_ is not None, "D_ missing" + assert model.n_features_in_ is not None, "n_features_in_ missing" + return { + "D": model.D_.cpu(), + "n_features_in": model.n_features_in_, + "n_samples_seen": model.n_samples_seen_, + "n_steps": model.n_steps_, + "n_concepts": model.n_concepts, + "z_iters": model.z_iters, + "encode_iters": model.encode_iters, + "d_iters": model.d_iters, + "batch_size": model.batch_size, + "ridge": model.ridge, + "eps": model.eps, + "forget_factor": model.forget_factor, + "d_update_every": model.d_update_every, + "last_batch_recon_mse": model.last_batch_recon_mse_, + "last_batch_nmse": model.last_batch_nmse_, + } raise TypeError(f"Unsupported baseline model type: {type(model).__name__}") @@ -689,7 +929,7 @@ def get_training_metrics(model: torch.nn.Module, n_samples: int) -> dict[str, fl "train/var_ratio": model.last_batch_var_ratio_ or 0.0, "train/n_samples": n_samples, } - if isinstance(model, MiniBatchSemiNMF): + if isinstance(model, (MiniBatchSemiNMF, MiniBatchNMF)): return { "train/recon_mse": model.last_batch_recon_mse_ or 0.0, "train/nmse": model.last_batch_nmse_ or 0.0, @@ -798,11 +1038,13 @@ def eval_pca(cfg: TrainConfig, model: MiniBatchPCA) -> dict[str, float]: @beartype.beartype -def eval_semi_nmf(cfg: TrainConfig, model: MiniBatchSemiNMF) -> dict[str, float]: +def eval_factorization( + cfg: TrainConfig, model: MiniBatchSemiNMF | MiniBatchNMF +) -> dict[str, float]: if cfg.n_val <= 0: return {} - assert model.D_ is not None, "D_ missing for Semi-NMF eval" + assert model.D_ is not None, f"D_ missing for {model.method} eval" dataloader = saev.data.ShuffledDataLoader(cfg.val_data) loader_like = tp.cast(saev.utils.scheduling.DataLoaderLike, dataloader) @@ -816,7 +1058,7 @@ def eval_semi_nmf(cfg: TrainConfig, model: MiniBatchSemiNMF) -> dict[str, float] ) n_samples = 0 - for batch in saev.helpers.progress(limiter, desc="semi-nmf-eval", every=20): + for batch in saev.helpers.progress(limiter, desc=f"{model.method}-eval", every=20): acts = batch["act"].to(model.device, non_blocking=True) codes = model.transform(acts) recon = codes @ model.D_ @@ -876,6 +1118,19 @@ def train_worker_fn(cfg: TrainConfig): forget_factor=cfg.forget_factor, d_update_every=cfg.d_update_every, ) + elif cfg.method == "nmf": + model = MiniBatchNMF( + n_concepts=cfg.k, + device=cfg.device, + z_iters=cfg.z_iters, + encode_iters=cfg.encode_iters, + d_iters=cfg.d_iters, + batch_size=cfg.train_data.batch_size, + ridge=cfg.ridge, + eps=cfg.eps, + forget_factor=cfg.forget_factor, + d_update_every=cfg.d_update_every, + ) else: tp.assert_never(cfg.method) @@ -912,7 +1167,7 @@ def train_worker_fn(cfg: TrainConfig): elapsed, ) - if isinstance(model, MiniBatchSemiNMF) and model.n_steps_ > 0: + if isinstance(model, (MiniBatchSemiNMF, MiniBatchNMF)) and model.n_steps_ > 0: if model.n_steps_ % model.d_update_every != 0: model._update_dictionary_from_accumulators() @@ -927,7 +1182,10 @@ def train_worker_fn(cfg: TrainConfig): eval_metrics = eval_pca(cfg, model) elif cfg.method == "semi-nmf": assert isinstance(model, MiniBatchSemiNMF) - eval_metrics = eval_semi_nmf(cfg, model) + eval_metrics = eval_factorization(cfg, model) + elif cfg.method == "nmf": + assert isinstance(model, MiniBatchNMF) + eval_metrics = eval_factorization(cfg, model) else: tp.assert_never(cfg.method) @@ -1243,7 +1501,9 @@ def _run_pca_inference(cfg: InferenceConfig, model: MiniBatchPCA): @beartype.beartype -def _run_semi_nmf_inference(cfg: InferenceConfig, model: MiniBatchSemiNMF): +def _run_factorization_inference( + cfg: InferenceConfig, model: MiniBatchSemiNMF | MiniBatchNMF +): logger = logging.getLogger("inference") md = saev.data.Metadata.load(cfg.data.shards) @@ -1269,7 +1529,7 @@ def _run_semi_nmf_inference(cfg: InferenceConfig, model: MiniBatchSemiNMF): sum_vec_d = torch.zeros((d_model,), dtype=torch.float64, device=device) n_tokens = 0 - for batch in saev.helpers.progress(dl, desc="semi-nmf-inference", every=1): + for batch in saev.helpers.progress(dl, desc=f"{model.method}-inference", every=1): acts = batch["act"].to(device) codes = model.transform(acts, n_iters=cfg.n_iters) codes = codes.to(torch.float32) @@ -1305,7 +1565,7 @@ def _run_semi_nmf_inference(cfg: InferenceConfig, model: MiniBatchSemiNMF): token_acts_blocks.append(scipy.sparse.csr_matrix(codes.cpu().numpy())) if n_tokens == 0: - logger.warning("No tokens processed during Semi-NMF inference.") + logger.warning("No tokens processed during %s inference.", model.method) return if cfg.save: @@ -1348,7 +1608,8 @@ def _run_semi_nmf_inference(cfg: InferenceConfig, model: MiniBatchSemiNMF): if cfg.save: logger.info( - "Wrote Semi-NMF baseline inference artifacts: %s, %s, %s, %s, %s", + "Wrote %s baseline inference artifacts: %s, %s, %s, %s, %s", + model.method, fpaths.token_acts, fpaths.mean_values, fpaths.sparsity, @@ -1356,7 +1617,9 @@ def _run_semi_nmf_inference(cfg: InferenceConfig, model: MiniBatchSemiNMF): fpaths.metrics, ) else: - logger.info("Wrote Semi-NMF baseline inference metrics: %s", fpaths.metrics) + logger.info( + "Wrote %s baseline inference metrics: %s", model.method, fpaths.metrics + ) @beartype.beartype @@ -1371,8 +1634,8 @@ def inference_worker_fn(cfg: InferenceConfig): _run_kmeans_inference(cfg, model) elif isinstance(model, MiniBatchPCA): _run_pca_inference(cfg, model) - elif isinstance(model, MiniBatchSemiNMF): - _run_semi_nmf_inference(cfg, model) + elif isinstance(model, (MiniBatchSemiNMF, MiniBatchNMF)): + _run_factorization_inference(cfg, model) else: tp.assert_never(model) diff --git a/contrib/trait_discovery/sweeps/011_iclr/baselines_nmse.py b/contrib/trait_discovery/sweeps/011_iclr/baselines_nmse.py new file mode 100644 index 0000000..32fc68d --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/baselines_nmse.py @@ -0,0 +1,32 @@ +def make_cfgs() -> list[dict]: + import os.path + + cfgs: list[dict] = [] + + runs_root = "/fs/ess/PAS2136/samuelstevens/tdiscovery/saev/runs" + dinov3_vitl_in1k_val = "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/10d802bd" + + baseline_run_ids = [ + "myy5btgw", # kmeans, k=1 + "qmbo5jxw", # pca, k=1 + "kwh4twl0", # pca, k=4 + "za1xuhhn", # pca, k=16 + "a1x1laxm", # pca, k=64 + "unu6dbfb", # pca, k=256 + "dzv7ha4u", # pca, k=1024 + "lm51bf37", # semi-nmf, k=1 + "em7hzdw0", # semi-nmf, k=4 + "cmf1j0gd", # semi-nmf, k=16 + "q6qtn8f6", # semi-nmf, k=64 + "rv1wfbws", # semi-nmf, k=256 + "k9sot7dd", # semi-nmf, k=1024 + ] + + for run_id in baseline_run_ids: + cfgs.append({ + "run": os.path.join(runs_root, run_id), + "save": False, + "data": {"shards": dinov3_vitl_in1k_val, "layer": 23}, + }) + + return cfgs diff --git a/contrib/trait_discovery/sweeps/011_iclr/baselines_train_0.py b/contrib/trait_discovery/sweeps/011_iclr/baselines_train_0.py new file mode 100644 index 0000000..85c5cdd --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/baselines_train_0.py @@ -0,0 +1,34 @@ +# uv run scripts/launch.py baseline::train --method kmeans --sweep sweeps/train_kmeans.py --runs-root /fs/ess/PAS2136/samuelstevens/tdiscovery/runs --train-data.shards /fs/scratch/PAS2136/samuelstevens/saev/shards/51567c6c --train-data.min-buffer-fill 0.2 --val-data.shards /fs/scratch/PAS2136/samuelstevens/saev/shards/3e27794f +def make_cfgs() -> list[dict]: + cfgs = [] + runs_root = "/fs/ess/PAS2136/jbeattie/tdiscovery/saev/runs" + dinov3_vitl_in1k_train = ( + "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/fe341cf9" + ) + dinov3_vitl_in1k_val = "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/8ac8e813" + + methods = [ + # ("kmeans", [1024 * 16]), + # ("pca", [1, 4, 16, 64, 256, 1024]), + # ("semi-nmf", [1, 4, 16, 64, 256, 1024]), + ("nmf", [1, 4]), + ] + for layer in [13, 15, 17, 19, 21, 23]: + for method, ks in methods: + for k in ks: + cfgs.append({ + "method": method, + "k": k, + "runs_root": runs_root, + "train_data": { + "shards": dinov3_vitl_in1k_train, + "layer": layer, + "min_buffer_fill": 0.2, + }, + "val_data": { + "shards": dinov3_vitl_in1k_val, + "layer": layer, + }, + }) + + return cfgs diff --git a/contrib/trait_discovery/sweeps/011_iclr/baselines_train_1.py b/contrib/trait_discovery/sweeps/011_iclr/baselines_train_1.py new file mode 100644 index 0000000..45e2e2c --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/baselines_train_1.py @@ -0,0 +1,34 @@ +# uv run scripts/launch.py baseline::train --method kmeans --sweep sweeps/train_kmeans.py --runs-root /fs/ess/PAS2136/samuelstevens/tdiscovery/runs --train-data.shards /fs/scratch/PAS2136/samuelstevens/saev/shards/51567c6c --train-data.min-buffer-fill 0.2 --val-data.shards /fs/scratch/PAS2136/samuelstevens/saev/shards/3e27794f +def make_cfgs() -> list[dict]: + cfgs = [] + runs_root = "/fs/ess/PAS2136/jbeattie/tdiscovery/saev/runs" + dinov3_vitl_in1k_train = ( + "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/fe341cf9" + ) + dinov3_vitl_in1k_val = "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/8ac8e813" + + methods = [ + # ("kmeans", [1024 * 16]), + # ("pca", [1, 4, 16, 64, 256, 1024]), + # ("semi-nmf", [1, 4, 16, 64, 256, 1024]), + ("nmf", [16, 64]), + ] + for layer in [13, 15, 17, 19, 21, 23]: + for method, ks in methods: + for k in ks: + cfgs.append({ + "method": method, + "k": k, + "runs_root": runs_root, + "train_data": { + "shards": dinov3_vitl_in1k_train, + "layer": layer, + "min_buffer_fill": 0.2, + }, + "val_data": { + "shards": dinov3_vitl_in1k_val, + "layer": layer, + }, + }) + + return cfgs diff --git a/contrib/trait_discovery/sweeps/011_iclr/baselines_train_2.py b/contrib/trait_discovery/sweeps/011_iclr/baselines_train_2.py new file mode 100644 index 0000000..cf4d11d --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/baselines_train_2.py @@ -0,0 +1,34 @@ +# uv run scripts/launch.py baseline::train --method kmeans --sweep sweeps/train_kmeans.py --runs-root /fs/ess/PAS2136/samuelstevens/tdiscovery/runs --train-data.shards /fs/scratch/PAS2136/samuelstevens/saev/shards/51567c6c --train-data.min-buffer-fill 0.2 --val-data.shards /fs/scratch/PAS2136/samuelstevens/saev/shards/3e27794f +def make_cfgs() -> list[dict]: + cfgs = [] + runs_root = "/fs/ess/PAS2136/jbeattie/tdiscovery/saev/runs" + dinov3_vitl_in1k_train = ( + "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/fe341cf9" + ) + dinov3_vitl_in1k_val = "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/8ac8e813" + + methods = [ + # ("kmeans", [1024 * 16]), + # ("pca", [1, 4, 16, 64, 256, 1024]), + # ("semi-nmf", [1, 4, 16, 64, 256, 1024]), + ("nmf", [256, 1024]), + ] + for layer in [13, 15, 17, 19, 21, 23]: + for method, ks in methods: + for k in ks: + cfgs.append({ + "method": method, + "k": k, + "runs_root": runs_root, + "train_data": { + "shards": dinov3_vitl_in1k_train, + "layer": layer, + "min_buffer_fill": 0.2, + }, + "val_data": { + "shards": dinov3_vitl_in1k_val, + "layer": layer, + }, + }) + + return cfgs diff --git a/contrib/trait_discovery/sweeps/011_iclr/butterflies_shards.sh b/contrib/trait_discovery/sweeps/011_iclr/butterflies_shards.sh new file mode 100644 index 0000000..33b1b0d --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/butterflies_shards.sh @@ -0,0 +1,11 @@ +#!/bin/sh + +uv run launch.py shards \ + --shards-root /fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/ \ + --family dinov3 \ + --ckpt /fs/scratch/PAS2136/jbeattie/models/encoders/dinov3/dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth \ + --batch-size 128 \ + --n-workers 1 \ + --layers -1 \ + --content-tokens-per-example 256 \ + data:img-seg-folder --data.root /fs/ess/PAS2136/jbeattie/datasets/cambridge-segfolder-v1.6 \ No newline at end of file diff --git a/contrib/trait_discovery/sweeps/011_iclr/imagenet_train_shards.sh b/contrib/trait_discovery/sweeps/011_iclr/imagenet_train_shards.sh new file mode 100644 index 0000000..3c403ee --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/imagenet_train_shards.sh @@ -0,0 +1,11 @@ +#!/bin/sh + +uv run launch.py shards \ + --shards-root /fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/ \ + --family dinov3 \ + --ckpt /fs/scratch/PAS2136/jbeattie/models/encoders/dinov3/dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth \ + --batch-size 128 \ + --n-workers 1 \ + --layers 13 15 17 19 21 23 \ + --content-tokens-per-example 196 \ + data:imagenet --data.split train \ No newline at end of file diff --git a/contrib/trait_discovery/sweeps/011_iclr/imagenet_val_shards.sh b/contrib/trait_discovery/sweeps/011_iclr/imagenet_val_shards.sh new file mode 100644 index 0000000..b136e05 --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/imagenet_val_shards.sh @@ -0,0 +1,11 @@ +#!/bin/sh + +uv run launch.py shards \ + --shards-root /fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/ \ + --family dinov3 \ + --ckpt /fs/scratch/PAS2136/jbeattie/models/encoders/dinov3/dinov3_vitl16_pretrain_lvd1689m-8aa4cbdd.pth \ + --batch-size 128 \ + --n-workers 1 \ + --layers 13 15 17 19 21 23 \ + --content-tokens-per-example 196 \ + data:imagenet --data.split validation \ No newline at end of file diff --git a/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_0.sh b/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_0.sh new file mode 100644 index 0000000..c3631b5 --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_0.sh @@ -0,0 +1,4 @@ +#!/bin/sh + +uv run scripts/launch.py baseline::train \ + --sweep sweeps/011_iclr/baselines_train_0.py \ \ No newline at end of file diff --git a/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_1.sh b/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_1.sh new file mode 100644 index 0000000..d06e3cb --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_1.sh @@ -0,0 +1,4 @@ +#!/bin/sh + +uv run scripts/launch.py baseline::train \ + --sweep sweeps/011_iclr/baselines_train_1.py \ \ No newline at end of file diff --git a/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_2.sh b/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_2.sh new file mode 100644 index 0000000..fc32e56 --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/nmf_baseline_2.sh @@ -0,0 +1,4 @@ +#!/bin/sh + +uv run scripts/launch.py baseline::train \ + --sweep sweeps/011_iclr/baselines_train_2.py \ \ No newline at end of file diff --git a/contrib/trait_discovery/sweeps/011_iclr/train_butterfly_saes.py b/contrib/trait_discovery/sweeps/011_iclr/train_butterfly_saes.py new file mode 100644 index 0000000..ce002b9 --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/train_butterfly_saes.py @@ -0,0 +1,44 @@ +def make_cfgs() -> list[dict]: + batch_size: int = 1024 * 16 + n_train = 100_000_000 + + shards_dpath = "/fs/scratch/PAS2136/jbeattie/iclr2026/saev/shards/45090eb7/" + + version = "v1.6" + + cfgs = [] + for k in [64]: + for lr in [3e-4, 1e-3, 3e-3, 1e-2, 3e-2, 1e-1]: + cfgs.append({ + "tags": ["cambridge-256p-v1"], + "slurm_acct": "PAS2136", + "slurm_partition": "nextgen", + "n_hours": 12.0, + "lr": lr, + "n_lr_warmup": 500, + "n_sparsity_warmup": n_train // batch_size, + "runs_root": "/fs/ess/PAS2136/samuelstevens/saev/runs", + "n_train": n_train, + "sae": { + "d_model": 1024, + "d_sae": 1024 * 16, + "normalize_w_dec": True, + "remove_parallel_grads": True, + "activation": {"top_k": k}, + "reinit_blend": 0.8, + }, + "train_data": { + "layer": -1, + "shards": shards_dpath, + "min_buffer_fill": 0.2, + "ignore_labels": [0], + "use_tmpdir": True, + }, + "val_data": { + "layer": -1, + "shards": shards_dpath, + "ignore_labels": [0], + "use_tmpdir": True, + }, + }) + return cfgs diff --git a/contrib/trait_discovery/sweeps/011_iclr/train_butterfly_saes.sh b/contrib/trait_discovery/sweeps/011_iclr/train_butterfly_saes.sh new file mode 100644 index 0000000..d3b9540 --- /dev/null +++ b/contrib/trait_discovery/sweeps/011_iclr/train_butterfly_saes.sh @@ -0,0 +1,4 @@ +#!/bin/sh + +uv run launch.py train \ + --sweep contrib/trait_discovery/sweeps/011_iclr/train_butterfly_saes.py \ No newline at end of file diff --git a/contrib/trait_discovery/sweeps/baselines_train.py b/contrib/trait_discovery/sweeps/baselines_train.py index 8c49e70..db9ce22 100644 --- a/contrib/trait_discovery/sweeps/baselines_train.py +++ b/contrib/trait_discovery/sweeps/baselines_train.py @@ -8,7 +8,8 @@ def make_cfgs() -> list[dict]: methods = [ # ("kmeans", [1024 * 16]), # ("pca", [1, 4, 16, 64, 256, 1024]), - ("semi-nmf", [1, 4, 16, 64, 256, 1024]), + # ("semi-nmf", [1, 4, 16, 64, 256, 1024]), + ("nmf", [1, 4, 16, 64, 256, 1024]), ] for layer in [13, 15, 17, 19, 21, 23]: for method, ks in methods: diff --git a/contrib/trait_discovery/tests/test_nmf.py b/contrib/trait_discovery/tests/test_nmf.py new file mode 100644 index 0000000..3025bb6 --- /dev/null +++ b/contrib/trait_discovery/tests/test_nmf.py @@ -0,0 +1,157 @@ +"""Tests for MiniBatchNMF, including agreement with sklearn's NMF on full data.""" + +import itertools + +import torch +import torch.nn.functional as F +from sklearn.decomposition import NMF as SklearnNMF +from tdiscovery.baselines import MiniBatchNMF, TrainConfig, dump, load + +import saev.data +import saev.disk + + +def _generate_nmf_data( + *, + n_samples: int = 256, + n_features: int = 8, + n_concepts: int = 4, + seed: int = 0, +) -> torch.Tensor: + torch.manual_seed(seed) + z = torch.rand(n_samples, n_concepts) + D = torch.rand(n_concepts, n_features) + return (z @ D).to(torch.float32) + + +def _fit_full_batch( + acts: torch.Tensor, + *, + n_concepts: int, + epochs: int, + seed: int, +) -> MiniBatchNMF: + torch.manual_seed(seed) + model = MiniBatchNMF( + n_concepts=n_concepts, + device="cpu", + z_iters=10, + encode_iters=200, + d_iters=10, + d_update_every=1, + ) + for _ in range(epochs): + model.partial_fit(acts) + return model + + +def _best_permutation_similarity(a: torch.Tensor, b: torch.Tensor) -> float: + assert a.shape == b.shape + n = a.shape[0] + a_norm = F.normalize(a, dim=1) + b_norm = F.normalize(b, dim=1) + similarity = torch.abs(a_norm @ b_norm.mT) + best = max( + sum(similarity[i, perm[i]].item() for i in range(n)) + for perm in itertools.permutations(range(n)) + ) + return best / n + + +def test_minibatch_nmf_matches_sklearn_nmf_on_full_data() -> None: + acts = _generate_nmf_data() + n_concepts = 4 + epochs = 100 + + model = _fit_full_batch(acts, n_concepts=n_concepts, epochs=epochs, seed=0) + assert model.D_ is not None + assert model.n_samples_seen_ == acts.shape[0] * epochs + assert torch.all(model.D_ >= 0) + + codes = model.transform(acts) + assert codes.shape == (acts.shape[0], n_concepts) + assert torch.all(codes >= 0) + + ours_mse = float(((acts - codes @ model.D_) ** 2).mean().item()) + assert ours_mse < 1e-3, f"MiniBatchNMF did not converge: mse={ours_mse:.3e}" + + reference = SklearnNMF( + n_components=n_concepts, init="random", random_state=0, max_iter=500 + ) + ref_codes = torch.tensor(reference.fit_transform(acts.numpy())) + ref_dict = torch.tensor(reference.components_) + assert torch.all(ref_codes >= 0) + assert torch.all(ref_dict >= 0) + ref_mse = float(((acts - ref_codes @ ref_dict) ** 2).mean().item()) + assert ref_mse < 1e-3, f"sklearn NMF did not converge: mse={ref_mse:.3e}" + + assert ours_mse < 100 * ref_mse, ( + f"Recon gap too large: ours={ours_mse:.3e} vs sklearn={ref_mse:.3e}" + ) + similarity = _best_permutation_similarity(ref_dict, model.D_) + assert similarity > 0.8, f"Dictionaries disagree: cosine sim={similarity:.3f}" + + +def test_minibatch_nmf_round_trip(tmp_path) -> None: + train_shards = tmp_path / "train-shards" + val_shards = tmp_path / "val-shards" + train_shards.mkdir(parents=True, exist_ok=True) + val_shards.mkdir(parents=True, exist_ok=True) + runs_root = tmp_path / "saev" / "runs" + runs_root.mkdir(parents=True, exist_ok=True) + + cfg = TrainConfig( + method="nmf", + k=3, + device="cpu", + runs_root=runs_root, + train_data=saev.data.ShuffledConfig(shards=train_shards), + val_data=saev.data.ShuffledConfig(shards=val_shards), + z_iters=7, + encode_iters=11, + d_iters=5, + ridge=1e-5, + eps=1e-6, + forget_factor=0.5, + d_update_every=2, + ) + run = saev.disk.Run.new( + "nmf-round-trip", + train_shards_dir=train_shards, + val_shards_dir=val_shards, + runs_root=cfg.runs_root, + ) + + model = MiniBatchNMF( + n_concepts=cfg.k, + device="cpu", + z_iters=cfg.z_iters, + encode_iters=cfg.encode_iters, + d_iters=cfg.d_iters, + batch_size=cfg.train_data.batch_size, + ridge=cfg.ridge, + eps=cfg.eps, + forget_factor=cfg.forget_factor, + d_update_every=cfg.d_update_every, + ) + model.D_ = torch.rand(cfg.k, 5) + model.n_features_in_ = 5 + model.n_samples_seen_ = 128 + model.n_steps_ = 7 + + dump(run, cfg, model=model) + loaded = load(run, device="cpu") + + assert isinstance(loaded, MiniBatchNMF) + assert loaded.D_ is not None + assert torch.allclose(loaded.D_, model.D_) + assert loaded.n_features_in_ == model.n_features_in_ + assert loaded.n_samples_seen_ == model.n_samples_seen_ + assert loaded.n_steps_ == model.n_steps_ + assert loaded.z_iters == cfg.z_iters + assert loaded.encode_iters == cfg.encode_iters + assert loaded.d_iters == cfg.d_iters + assert loaded.ridge == cfg.ridge + assert loaded.eps == cfg.eps + assert loaded.forget_factor == cfg.forget_factor + assert loaded.d_update_every == cfg.d_update_every diff --git a/uv.lock b/uv.lock index a3a84fd..9d39f29 100644 --- a/uv.lock +++ b/uv.lock @@ -2,14 +2,18 @@ version = 1 revision = 3 requires-python = ">=3.12" resolution-markers = [ - "python_full_version >= '3.13' and sys_platform == 'linux'", + "python_full_version >= '3.14' and sys_platform == 'linux'", + "python_full_version == '3.13.*' and sys_platform == 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