Use pathlib more and have default cache paths be relative to the repo directory

intervention/appendix_plots.ipynb

@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "# %%\n",
+    "from pathlib import Path\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from task import get_acts, get_acts_pca, get_all_acts\n",
@@ -462,7 +463,7 @@
     "\n",
     "for task_name in [\"days_of_week\", \"months_of_year\"]:\n",
     "    results_mistral = pd.read_csv(\n",
-    "        f\"{BASE_DIR}/mistral_{task_name}/results.csv\", skipinitialspace=True\n",
+    "        Path(BASE_DIR) / f\"mistral_{task_name}\" / \"results.csv\", skipinitialspace=True\n",
     "    )\n",
     "\n",
     "    results_mistral = results_mistral.rename(\n",
@@ -480,7 +481,7 @@
     "    print(sum(results_mistral[\"mistral_correct\"]))\n",
     "\n",
     "    results_llama = pd.read_csv(\n",
-    "        f\"{BASE_DIR}/llama_{task_name}/results.csv\", skipinitialspace=True\n",
+    "        Path(BASE_DIR) / f\"llama_{task_name}\" / \"results.csv\", skipinitialspace=True\n",
     "    )\n",
     "\n",
     "    results_llama = results_llama.rename(\n",

intervention/circle_probe_interventions.py

@@ -171,7 +171,7 @@
 probe_projections = {}
 target_to_embeddings = {}
 
-os.makedirs(f"{task.prefix}/circle_probes_{circle_letter}", exist_ok=True)
+(task.prefix / f"circle_probes_{circle_letter}").mkdir(exist_ok=True)
 
 all_maes = []
 all_r_squareds = []
@@ -262,7 +262,7 @@
                 "probe_r": probe_r,
                 "target_to_embedding": target_to_embedding,
             },
-            f"{task.prefix}/circle_probes_{circle_letter}/{probe_file_extension}_layer_{layer}_token_{token}_pca_{pca_k}.pt",
+            task.prefix / f"circle_probes_{circle_letter}" / f"{probe_file_extension}_layer_{layer}_token_{token}_pca_{pca_k}.pt",
         )
 
         mae = (predictions - multid_targets_train).abs().mean()

intervention/days_of_week_task.py

@@ -1,5 +1,6 @@
 # %%
 
+from pathlib import Path
 import os
 from utils import setup_notebook, BASE_DIR
 
@@ -49,9 +50,8 @@ def __init__(self, device, model_name="mistral", n_devices=None):
         # Tokens we expect as possible answers. Best of these can optionally be saved (as opposed to best logit overall)
         self.allowable_tokens = days_of_week
 
-        self.prefix = f"{BASE_DIR}{model_name}_days_of_week/"
-        if not os.path.exists(self.prefix):
-            os.makedirs(self.prefix)
+        self.prefix = Path(BASE_DIR) / f"{model_name}_days_of_week"
+        self.prefix.mkdir(parents=True, exist_ok=True)
 
         self.num_tokens_in_answer = 1
 

intervention/intervene_in_middle_of_circle.py

@@ -40,7 +40,7 @@ def vary_wthin_circle(circle_letter, duration, layer, token, pca_k, all_points):
         model = task.get_model()
 
         circle_projection_qr = torch.load(
-            f"{task.prefix}/circle_probes_{circle_letter}/cos_sin_layer_{layer}_token_{token}_pca_{pca_k}.pt"
+            task.prefix / f"circle_probes_{circle_letter}" / f"cos_sin_layer_{layer}_token_{token}_pca_{pca_k}.pt"
         )
 
         for problem in task.generate_problems():

intervention/main_text_plots.ipynb

@@ -7,6 +7,7 @@
    "outputs": [],
    "source": [
     "# %%\n",
+    "from pathlib import Path\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "from task import get_acts, get_acts_pca\n",
@@ -516,7 +517,7 @@
     "for task_name in [\"days_of_week\", \"months_of_year\"]:\n",
     "    for model_name in [\"mistral\", \"llama\"]:\n",
     "        results = pd.read_csv(\n",
-    "            f\"{BASE_DIR}/{model_name}_{task_name}/results.csv\", skipinitialspace=True\n",
+    "            Path(BASE_DIR) / f\"{model_name}_{task_name}\" / \"results.csv\", skipinitialspace=True\n",
     "        )\n",
     "        number_correct = results[\"best_token\"] == results[\"ground_truth\"]\n",
     "        print(task_name, model_name, np.sum(number_correct))\n",
@@ -560,7 +561,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.11.9"
   }
  },
  "nbformat": 4,

intervention/months_of_year_task.py

@@ -1,6 +1,7 @@
 # %%
 
 import os
+from pathlib import Path
 from utils import setup_notebook, BASE_DIR
 
 setup_notebook()
@@ -71,9 +72,8 @@ def __init__(self, device, model_name="mistral", n_devices=None):
         # Tokens we expect as possible answers. Best of these can optionally be saved (as opposed to best logit overall)
         self.allowable_tokens = months_of_year
 
-        self.prefix = f"{BASE_DIR}{model_name}_months_of_year/"
-        if not os.path.exists(self.prefix):
-            os.makedirs(self.prefix)
+        self.prefix = Path(BASE_DIR) / f"{model_name}_months_of_year"
+        self.prefix.mkdir(parents=True, exist_ok=True)
 
         self.num_tokens_in_answer = 1
 

intervention/task.py

@@ -1,3 +1,4 @@
+from pathlib import Path
 from utils import BASE_DIR  # Need this import to set the huggingface cache directory
 import os
 import numpy as np
@@ -24,7 +25,6 @@ def __str__(self):
     def __repr__(self):
         return str(self)
 
-
 def generate_and_save_acts(
     task,
     names_filter,
@@ -39,10 +39,10 @@ def generate_and_save_acts(
     forward_batch_size = 2
     num_tokens_to_generate = task.num_tokens_in_answer
     all_problems = task.generate_problems()
-    output_file = task.prefix + "results.csv"
+    output_file = task.prefix / "results.csv"
 
     if save_results_csv:
-        os.makedirs(task.prefix, exist_ok=True)
+        task.prefix.mkdir(parents=True, exist_ok=True)
         model_best_addition = "" if not save_best_logit else ", best_token"
         with open(output_file, "w") as f:
             f.write(
@@ -98,7 +98,7 @@ def generate_and_save_acts(
                     print(tensors.shape)
                 torch.save(
                     tensors,
-                    f"{task.prefix}{save_file_prefix}{current_problem_index}.pt",
+                    task.prefix / f"{save_file_prefix}{current_problem_index}.pt",
                 )
 
                 if save_results_csv:
@@ -146,7 +146,7 @@ def get_all_acts(
     all_problems = task.generate_problems()
     all_problems_already_generated = True
     for i in range(len(all_problems)):
-        if not os.path.exists(f"{task.prefix}{save_file_prefix}{i}.pt"):
+        if not (task.prefix / f"{save_file_prefix}{i}.pt").exists():
             all_problems_already_generated = False
             break
     if not all_problems_already_generated or force_regenerate:
@@ -163,7 +163,7 @@ def get_all_acts(
     all_acts = []
     for i in range(0, len(all_problems)):
         tensors = torch.load(
-            f"{task.prefix}{save_file_prefix}{i}.pt", map_location="cpu"
+            task.prefix / f"{save_file_prefix}{i}.pt", map_location="cpu"
         )
         all_acts.append(tensors)
         if len(all_acts) > 1:
@@ -186,17 +186,17 @@ def get_acts(
     if save_file_prefix != "" and save_file_prefix[-1] != "_":
         save_file_prefix += "_"
     file_name = (
-        f"{task.prefix}{save_file_prefix}layer{layer_fetch}_token{token_fetch}.pt"
+        task.prefix / f"{save_file_prefix}layer{layer_fetch}_token{token_fetch}.pt"
     )
-    if not os.path.exists(file_name) or force_regenerate:
+    if not file_name.exists() or force_regenerate:
         print(file_name, "not exists")
         all_acts = get_all_acts(
             task, names_filter=names_filter, save_file_prefix=save_file_prefix
         )
         for layer in range(all_acts.shape[1]):
             for token in range(all_acts.shape[2]):
                 file_name = (
-                    f"{task.prefix}{save_file_prefix}layer{layer}_token{token}.pt"
+                    task.prefix / f"{save_file_prefix}layer{layer}_token{token}.pt"
                 )
                 torch.save(
                     all_acts[:, layer, token, :].detach().cpu().clone(), file_name
@@ -218,11 +218,11 @@ def get_acts_pca(
     names_filter=lambda x: "resid_post" in x or "hook_embed" in x,
     save_file_prefix="",
 ):
-    act_file_name = f"{task.prefix}pca/{save_file_prefix}/layer{layer}_token{token}_pca{pca_k}{'_normalize' if normalize_rms else ''}.pt"
-    pca_pkl_file_name = f"{task.prefix}pca/{save_file_prefix}/layer{layer}_token{token}_pca{pca_k}{'_normalize' if normalize_rms else ''}.pkl"
-    os.makedirs(f"{task.prefix}/pca/{save_file_prefix}", exist_ok=True)
+    act_file_name = task.prefix / "pca" / save_file_prefix / f"layer{layer}_token{token}_pca{pca_k}{'_normalize' if normalize_rms else ''}.pt"
+    pca_pkl_file_name = task.prefix / "pca" / save_file_prefix / f"layer{layer}_token{token}_pca{pca_k}{'_normalize' if normalize_rms else ''}.pkl"
+    (task.prefix / "pca" / save_file_prefix).mkdir(parents=True, exist_ok=True)
 
-    if not os.path.exists(act_file_name) or not os.path.exists(pca_pkl_file_name):
+    if not act_file_name.exists() or not pca_pkl_file_name.exists():
         acts = get_acts(
             task,
             layer,
@@ -239,9 +239,9 @@ def get_acts_pca(
 
 
 def get_acts_pls(task, layer, token, pls_k, normalize_rms=False):
-    act_file_name = f"{task.prefix}/pls/layer{layer}_token{token}_pls{pls_k}{'_normalize' if normalize_rms else ''}.pt"
-    pls_pkl_file_name = f"{task.prefix}/pls/layer{layer}_token{token}_pls{pls_k}{'_normalize' if normalize_rms else ''}.pkl"
-    os.makedirs(f"{task.prefix}/pls", exist_ok=True)
+    act_file_name = task.prefix / "pls" / f"layer{layer}_token{token}_pls{pls_k}{'_normalize' if normalize_rms else ''}.pt"
+    pls_pkl_file_name = task.prefix / "pls" / f"layer{layer}_token{token}_pls{pls_k}{'_normalize' if normalize_rms else ''}.pkl"
+    (task.prefix / "pls").mkdir(parents=True, exist_ok=True)
 
     # if not os.path.exists(act_file_name) or not os.path.exists(pls_pkl_file_name):
     if True:

intervention/utils.py

@@ -1,9 +1,10 @@
 import os
 import dill as pickle
+from pathlib import Path
 
-BASE_DIR = "/data/scratch/jae/"
+BASE_DIR = Path(__file__).parent.parent / "cache"
 
-os.environ["TRANSFORMERS_CACHE"] = f"{BASE_DIR}/.cache/"
+os.environ["TRANSFORMERS_CACHE"] = f"{(Path(BASE_DIR) / '.cache').absolute()}/"
 
 
 def setup_notebook():

sae_multid_feature_discovery/generate_feature_occurence_data.py

@@ -1,12 +1,12 @@
 # %%
-
+from pathlib import Path
 import os
 from utils import BASE_DIR
 
 
 # hopefully this will help with memory fragmentation
 os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
-os.environ["TRANSFORMERS_CACHE"] = f"{BASE_DIR}.cache/"
+os.environ["TRANSFORMERS_CACHE"] = f"{(Path(BASE_DIR) / '.cache').absolute()}/"
 
 import einops
 import numpy as np
@@ -52,8 +52,8 @@
 
 num_sae_activations_to_save = 10**9
 
-save_folder = f"{BASE_DIR}{model_name}"
-os.makedirs(save_folder, exist_ok=True)
+save_folder = Path(BASE_DIR) / model_name
+save_folder.mkdir(exist_ok=True, parents=True)
 
 t.set_grad_enabled(False)
 

sae_multid_feature_discovery/utils.py

@@ -1,7 +1,9 @@
+
+from pathlib import Path
 from huggingface_hub import hf_hub_download
 import os
 
-BASE_DIR = "/data/scratch/jae/"
+BASE_DIR = Path(__file__).parent.parent / "cache"
 
 def get_gpt2_sae(device, layer):
     from sae_lens import SAE