update tutorials (no test)

stanfordnlp · Feb 3, 2025 · 262ed44 · 262ed44
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diff --git a/tutorials/advanced_tutorials/IOI_with_DAS.ipynb b/tutorials/advanced_tutorials/IOI_with_DAS.ipynb
@@ -7301,7 +7301,7 @@
    ],
    "source": [
     "intervention = boundless_das_intervenable.interventions[\n",
-    "    \"layer.8.repr.attention_value_output.unit.pos.nunit.1#0\"\n",
+    "    \"layer_8_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
     "]\n",
     "boundary_mask = sigmoid_boundary(\n",
     "    intervention.intervention_population.repeat(1, 1),\n",
@@ -12475,7 +12475,7 @@
    ],
    "source": [
     "intervention = das_intervenable.interventions[\n",
-    "    \"layer.8.repr.attention_value_output.unit.pos.nunit.1#0\"\n",
+    "    \"layer_8_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
     "]\n",
     "learned_weights = intervention.rotate_layer.weight\n",
     "headwise_learned_weights = torch.chunk(learned_weights, chunks=12, dim=0)\n",
@@ -17400,7 +17400,7 @@
    ],
    "source": [
     "intervention = boundless_das_intervenable.interventions[\n",
-    "    \"layer.9.repr.attention_value_output.unit.pos.nunit.1#0\"\n",
+    "    \"layer_9_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
     "]\n",
     "boundary_mask = sigmoid_boundary(\n",
     "    intervention.intervention_population.repeat(1, 1),\n",
@@ -23343,7 +23343,7 @@
    ],
    "source": [
     "intervention = das_intervenable.interventions[\n",
-    "    \"layer.9.repr.attention_value_output.unit.pos.nunit.1#0\"\n",
+    "    \"layer_9_repr_attention_value_output_unit_pos_nunit_1#0\"\n",
     "]\n",
     "learned_weights = intervention.rotate_layer.weight\n",
     "headwise_learned_weights = torch.chunk(learned_weights, chunks=12, dim=0)\n",

diff --git a/tutorials/advanced_tutorials/Voting_Mechanism.ipynb b/tutorials/advanced_tutorials/Voting_Mechanism.ipynb
@@ -481,7 +481,7 @@
    "source": [
     "torch.save(\n",
     "    pv_llama.interventions[\n",
-    "        f\"layer.{layer}.comp.block_output.unit.pos.nunit.1#0\"].state_dict(), \n",
+    "        f\"layer_{layer}_comp_block_output_unit_pos_nunit_1#0\"].state_dict(), \n",
     "    f\"./tutorial_data/layer.{layer}.pos.{token_position}.bin\"\n",
     ")"
    ]
@@ -522,9 +522,9 @@
     "pv_llama = pv.IntervenableModel(pv_config, llama)\n",
     "pv_llama.set_device(\"cuda\")\n",
     "pv_llama.disable_model_gradients()\n",
-    "pv_llama.interventions[f'layer.{layer}.comp.block_output.unit.pos.nunit.1#0'].load_state_dict(\n",
+    "pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#0'].load_state_dict(\n",
     "    torch.load('./tutorial_data/layer.15.pos.75.bin'))\n",
-    "pv_llama.interventions[f'layer.{layer}.comp.block_output.unit.pos.nunit.1#1'].load_state_dict(\n",
+    "pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#1'].load_state_dict(\n",
     "    torch.load('./tutorial_data/layer.15.pos.80.bin'))"
    ]
   },
@@ -665,11 +665,11 @@
     "for loc in [78, 75, 80, [75, 80]]:\n",
     "    if loc == 78:\n",
     "        print(\"[control] intervening location: \", loc)\n",
-    "        pv_llama.interventions[f'layer.{layer}.comp.block_output.unit.pos.nunit.1#0'].load_state_dict(\n",
+    "        pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#0'].load_state_dict(\n",
     "            torch.load('./tutorial_data/layer.15.pos.78.bin'))\n",
     "    else:\n",
     "        print(\"intervening location: \", loc)\n",
-    "        pv_llama.interventions[f'layer.{layer}.comp.block_output.unit.pos.nunit.1#0'].load_state_dict(\n",
+    "        pv_llama.interventions[f'layer_{layer}_comp_block_output_unit_pos_nunit_1#0'].load_state_dict(\n",
     "            torch.load('./tutorial_data/layer.15.pos.75.bin'))\n",
     "    # evaluation on the test set\n",
     "    collected_probs = []\n",