add sae encode function

evals/autointerp/README.md

@@ -7,6 +7,7 @@ There are 4 Python files in this folder:
 - `config.py` - this contains the config class for AutoInterp.
 - `main.py` - this contains the main `AutoInterp` class, as well as the functions which are the interface to the rest of the SAEBench codebase.
 - `demo.py` - you can run this via `python demo.py --api_key YOUR_API_KEY` to see an example output & how the function works. It creates & saves a log file (I've left the output of those files in the repo, so you can see what they look like).
+- `sae_encode.py` - this contains a temporary replacement for the `encode` method in SAELens, until [my PR](https://github.com/jbloomAus/SAELens/pull/334) is merged.
 
 ## Summary of how it works
 

evals/autointerp/demo.py

@@ -2,25 +2,18 @@
 from pathlib import Path
 
 import torch
+
 from evals.autointerp.config import AutoInterpConfig
 from evals.autointerp.main import run_eval
 
 # Set up command-line argument parsing
 parser = argparse.ArgumentParser(description="Run AutoInterp evaluation.")
-parser.add_argument(
-    "--api_key", type=str, required=True, help="API key for the evaluation."
-)
+parser.add_argument("--api_key", type=str, required=True, help="API key for the evaluation.")
 args = parser.parse_args()
 
 api_key = args.api_key  # Use the API key supplied via command line
 
-device = torch.device(
-    "mps"
-    if torch.backends.mps.is_available()
-    else "cuda"
-    if torch.cuda.is_available()
-    else "cpu"
-)
+device = torch.device("mps" if torch.backends.mps.is_available() else "cuda" if torch.cuda.is_available() else "cpu")
 
 selected_saes_dict = {
     "gpt2-small-res-jb": ["blocks.7.hook_resid_pre"],
@@ -31,18 +24,14 @@
 cfg = AutoInterpConfig(model_name="gpt2-small", override_latents=[9, 11, 15, 16873])
 save_logs_path = Path(__file__).parent / "logs_4.txt"
 save_logs_path.unlink(missing_ok=True)
-results = run_eval(
-    cfg, selected_saes_dict, device, api_key, save_logs_path=save_logs_path
-)
+results = run_eval(cfg, selected_saes_dict, str(device), api_key, save_logs_path=save_logs_path)
 print(results)
 
 # ! Demo 2: 100 randomly chosen latents
 cfg = AutoInterpConfig(model_name="gpt2-small", n_latents=100)
 save_logs_path = Path(__file__).parent / "logs_100.txt"
 save_logs_path.unlink(missing_ok=True)
-results = run_eval(
-    cfg, selected_saes_dict, device, api_key, save_logs_path=save_logs_path
-)
+results = run_eval(cfg, selected_saes_dict, str(device), api_key, save_logs_path=save_logs_path)
 print(results)
 
 # python demo.py --api_key "YOUR_API_KEY"