collected activations now return as dict

Author: nathankim7

pyvene/models/intervenable_base.py

@@ -1101,8 +1101,11 @@ def _wait_for_forward_with_serial_intervention(
             unit_locations_base = unit_locations[group_key][1]
 
             if activations_sources != None:
-                for key in keys:
-                    self.activations[key] = activations_sources[key]
+                for passed_in_key, v in activations_sources.items():
+                    assert (
+                        passed_in_key in self.sorted_keys
+                    ), f"{passed_in_key} not in {self.sorted_keys}, {unit_locations}"
+                    self.activations[passed_in_key] = torch.clone(v)
             else:
                 keys_with_source = [
                     k for i, k in enumerate(keys) if unit_locations_source[i] != None

pyvene_101.ipynb

@@ -126,10 +126,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "17c7f2f6-b0d3-4fe2-8e4f-c044b93f3ef0",
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "metadata": {}
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fce745d6f2ca453b98f7b10868b1ab7d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import pyvene as pv\n",
     "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
@@ -144,10 +161,10 @@
     "    \"intervention_type\": pv.CollectIntervention}, model=gpt2)\n",
     "\n",
     "base = \"When John and Mary went to the shops, Mary gave the bag to\"\n",
-    "collected_attn_w = pv_gpt2(\n",
+    "(_, collected_attn_w), _ = pv_gpt2(\n",
     "    base = tokenizer(base, return_tensors=\"pt\"\n",
     "    ), unit_locations={\"base\": [h for h in range(12)]}\n",
-    ")[0][-1][0]"
+    ")"
    ]
   },
   {
@@ -160,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "128be2dd-f089-4291-bfc5-7002d031b1e9",
    "metadata": {
     "metadata": {}
@@ -171,7 +188,7 @@
      "output_type": "stream",
      "text": [
       "loaded GPT2 model gpt2\n",
-      "torch.Size([12, 14, 14])\n"
+      "torch.Size([1, 12, 14, 14])\n"
      ]
     }
    ],
@@ -193,6 +210,7 @@
     "    base = tokenizer(base, return_tensors=\"pt\"\n",
     "    ), unit_locations={\"base\": [h for h in range(12)]}\n",
     ")\n",
+    "collected_attn_w = torch.stack(list(collected_attn_w.values()))\n",
     "print(collected_attn_w[0].shape)"
    ]
   },
@@ -206,16 +224,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 5,
    "id": "678dc46f",
-   "metadata": {},
+   "metadata": {
+    "metadata": {}
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loaded model\n"
+      "loaded GPT2 model gpt2\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [

tests/integration_tests/IntervenableBasicTestCase.py

@@ -591,10 +591,10 @@ def test_customized_intervention_function_get(self):
         )
 
         base = "When John and Mary went to the shops, Mary gave the bag to"
-        collected_attn_w = pv_gpt2(
+        (_, collected_attn_w), _ = pv_gpt2(
             base=tokenizer(base, return_tensors="pt"),
             unit_locations={"base": [h for h in range(12)]},
-        )[0][-1][0]
+        )
 
         cached_w = {}
 
@@ -608,7 +608,7 @@ def pv_patcher(b, s):
 
         base = "When John and Mary went to the shops, Mary gave the bag to"
         _ = pv_gpt2(tokenizer(base, return_tensors="pt"))
-        torch.allclose(collected_attn_w, cached_w["attn_w"].unsqueeze(dim=0))
+        torch.allclose(list(collected_attn_w.values())[0], cached_w["attn_w"].unsqueeze(dim=0))
 
     def test_customized_intervention_function_zeroout(self):