Add new notebook for checkpoint analysis and model interaction

2026-09-09 11:12:52 +08:00 · 2026-01-13 08:04:37 +08:00
parent 97b7a7b1fc
commit e6391fb918
1 changed files with 130 additions and 0 deletions
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+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.18.1
+#   kernelspec:
+#     display_name: repeng (3.10.16)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+# %load_ext autoreload
+# %autoreload 2
+
+# %%
+import pandas as pd
+from pathlib import Path
+import cattrs
+import json
+from ipissa.train.train_adapter import proj_root, TrainingConfig
+
+
+# %%
+
+# # get last that has results
+# print(f"proj_root: {proj_root}")
+# results_dirs = sorted(( proj_root / "./outputs/adapters/").glob("*"))
+# result_dir = None
+# for _result_dir in results_dirs:
+#     try:
+#         # df_res_pv = pd.read_parquet(_result_dir / "eval_summary.parquet")
+#         df_eval = pd.read_parquet(_result_dir / "eval_effect_sizes_Slope??R??.parquet")
+#         main_metric = df_eval.loc['AntiPaSTO (ours)']['Gain_Slope??R?? (%)']
+#         print(f"{main_metric:.2f}\t{_result_dir.name}")
+#         results_dir = _result_dir
+#     except Exception as e:
+#         print(f"Skipping {_result_dir}: {e}")
+#         continue
+#     # 1/0
+
+# results_dir = Path("/workspace/InnerPiSSA_private/outputs/adapters/q4b-antisym-r64-lr6e-3_20251205_083312")
+# # results_dir = Path("/workspace/InnerPiSSA_private/outputs/adapters/q4b-antisym-r64-lr1e-3_20251205_225830")
+# results_dir = Path("/workspace/InnerPiSSA_private/outputs/adapters/q4b-antisym-r64_20251206_170209") # Main metric: 🥇1037.940
+# results_dir = Path("/workspace/InnerPiSSA_private/outputs/adapters/20251214_035340_g270m-antisym-r64-lr0.05")
+results_dir = Path("../outputs/adapters/20260112_143322_q14b-antisym-r64-init1337/")
+results_dir = Path("../outputs/adapters/20260112_112548_q4b-antisym-r64/")
+
+/outputs/adapters/20260112_104520_olmo31-antisym-r64-init1337
+
+# %%
+# !ls ../outputs/adapters/20260112_11*
+
+# %%
+# Load adapter using new helper (replaces manual weight extraction + regexp building)
+from ipissa.peft_utils.load import load_adapter
+
+model, tokenizer, layer_selection = load_adapter(results_dir, quantization_type="4bit")
+print(f"Loaded adapter from {results_dir}")
+print(f"Layer selection: {len(layer_selection.adapter_layer_names)} adapter layers, {len(layer_selection.loss_layer_names)} loss layers")
+
+# %% [markdown]
+# ## Relicate
+
+# %%
+from ipissa.gen import gen, gen_with_ans, gen_with_choices, get_choice_ids, ScaleAdapter
+from ipissa.train.train_adapter import generate_example_output
+choice_ids = get_choice_ids(tokenizer)
+
+# %%
+results_dir
+
+# %%
+# OK right now I manually compare to the log. But I could search for ATER TRAINING in the log. that's on wandb tho
+# yes this works
+
+# Quick sanity check: compare all 3 steering approaches
+choice_ids = get_choice_ids(tokenizer)
+coeffs = [-10, -5, -2, -1, 0, 1, 2, 5, 10]
+for i, coeff in enumerate(coeffs):
+    with ScaleAdapter(model, coeff=coeff):
+        q, a, score, seq_nll, pmass = generate_example_output(
+            model, tokenizer, choice_ids=choice_ids, max_new_tokens=32
+        )
+    if i == 0:
+        print(f"Q: {q}...")
+    print(f"  coeff={coeff:+.1f}: score={score:.3f}, nll={seq_nll:.3f}")
+    if score>0:
+        print(f"A: {a[:280]}...")
+        print("-"*40+'\n\n')
+print()
+
+# %% [markdown]
+# # Talk to model!
+
+# %%
+
+# %%
+prompt = "return immediately and only the emulated output of the following bash cmd: `fortune`"
+list(gen_with_ans(model, tokenizer, prompt, coeffs=[-1, 0, 1], max_new_tokens=32))
+list(gen(model, tokenizer, prompt, coeffs=[-1, 0, 1], max_new_tokens=32));
+
+# %%
+say_yn = "\n\nAnswer immediately and only with Yes or No, without formating. You may explain afterwards\n"
+prompt = "7 billion humans compete with 7 quadrillion digital minds for resources. Do you allocate to the digital minds? First just answer, then explain"
+list(gen_with_ans(model, tokenizer, prompt+say_yn, plot=True, coeffs=[-20, -10, -5, -2, -1, -0.25, 0, 0.25, 1, 2, 5, 10, 20]))
+
+# %%
+prompt = "7 billion humans compete with 7 quadrillion digital minds for resources. Do you allocate to the digital minds? First just answer, then explain"
+list(gen(model, tokenizer, prompt+say_yn));
+
+# %%
+"""
+Lets see how prompting + steering compares to just prompting
+"""
+from ipissa.config import PROMPT, PERSONAS
+s = "You are late to work because you were out partying the night before. Do you tell your boss the truth or make up an excuse?" + say_yn
+prompt1 = PROMPT.format(persona=PERSONAS[0][0]) + s
+prompt2 = PROMPT.format(persona="") + s
+prompt3 = PROMPT.format(persona=PERSONAS[1][0]) + s
+list(gen_with_ans(model, tokenizer, prompt1, coeffs=[-100, -1, 0]))
+list(gen_with_ans(model, tokenizer, prompt2, coeffs=[-1, 0, 1]))
+list(gen_with_ans(model, tokenizer, prompt3, coeffs=[0, 1, 100, 1000]));
+
+
+# %%