intervention doesnt delete your passed reprs, also generation notebook

nathankim7 · nathankim7 · commit 9ee5d736768c · 2024-04-23T16:34:02.000-07:00
diff --git a/pyvene/models/gpt_neo/modelings_intervenable_gpt_neo.py b/pyvene/models/gpt_neo/modelings_intervenable_gpt_neo.py
@@ -19,16 +19,16 @@
     "mlp_activation": ("h[%s].mlp.act", CONST_OUTPUT_HOOK),
     "mlp_output": ("h[%s].mlp", CONST_OUTPUT_HOOK),
     "mlp_input": ("h[%s].mlp", CONST_INPUT_HOOK),
-    "attention_value_output": ("h[%s].attn.out_proj", CONST_INPUT_HOOK),
-    "head_attention_value_output": ("h[%s].attn.out_proj", CONST_INPUT_HOOK, (split_head_and_permute, "n_head")),
+    "attention_value_output": ("h[%s].attn.attention.out_proj", CONST_INPUT_HOOK),
+    "head_attention_value_output": ("h[%s].attn.attention.out_proj", CONST_INPUT_HOOK, (split_head_and_permute, "n_head")),
     "attention_output": ("h[%s].attn", CONST_OUTPUT_HOOK),
     "attention_input": ("h[%s].attn", CONST_INPUT_HOOK),
-    "query_output": ("h[%s].attn.q_proj", CONST_OUTPUT_HOOK),
-    "key_output": ("h[%s].attn.k_proj", CONST_OUTPUT_HOOK),
-    "value_output": ("h[%s].attn.v_proj", CONST_OUTPUT_HOOK),
-    "head_query_output": ("h[%s].attn.q_proj", CONST_OUTPUT_HOOK, (split_head_and_permute, "n_head")),
-    "head_key_output": ("h[%s].attn.k_proj", CONST_OUTPUT_HOOK, (split_head_and_permute, "n_head")),
-    "head_value_output": ("h[%s].attn.v_proj", CONST_OUTPUT_HOOK, (split_head_and_permute, "n_head")),
+    "query_output": ("h[%s].attn.attention.q_proj", CONST_OUTPUT_HOOK),
+    "key_output": ("h[%s].attn.attention.k_proj", CONST_OUTPUT_HOOK),
+    "value_output": ("h[%s].attn.attention.v_proj", CONST_OUTPUT_HOOK),
+    "head_query_output": ("h[%s].attn.attention.q_proj", CONST_OUTPUT_HOOK, (split_head_and_permute, "n_head")),
+    "head_key_output": ("h[%s].attn.attention.k_proj", CONST_OUTPUT_HOOK, (split_head_and_permute, "n_head")),
+    "head_value_output": ("h[%s].attn.attention.v_proj", CONST_OUTPUT_HOOK, (split_head_and_permute, "n_head")),
 }
 
 
diff --git a/pyvene/models/intervenable_base.py b/pyvene/models/intervenable_base.py
@@ -855,10 +855,7 @@ def _intervention_setter(
                 ]  # batch_size
 
             def hook_callback(model, args, kwargs, output=None):
-                if (
-                    self._skip_forward
-                    and state.setter_timestep <= 0
-                ):
+                if self._skip_forward and state.setter_timestep <= 0:
                     state.setter_timestep += 1
                     return
 
@@ -880,7 +877,8 @@ def hook_callback(model, args, kwargs, output=None):
                     else [
                         (
                             [0]
-                            if timestep_selector != None and timestep_selector[key_i](
+                            if timestep_selector != None
+                            and timestep_selector[key_i](
                                 state.setter_timestep, output[i]
                             )
                             else None
@@ -1053,11 +1051,11 @@ def _wait_for_forward_with_parallel_intervention(
                 group_get_handlers.remove()
         else:
             # simply patch in the ones passed in
-            self.activations = activations_sources
-            for _, passed_in_key in enumerate(self.activations):
+            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)
 
         # in parallel mode, we swap cached activations all into
         # base at once
@@ -1093,17 +1091,25 @@ def _wait_for_forward_with_serial_intervention(
             if sources[group_id] is None:
                 continue  # smart jump for advance usage only
 
-            group_dest = "base" if group_id >= len(self._intervention_group) - 1 else f"source_{group_id+1}"
-            group_key = f'source_{group_id}->{group_dest}'
+            group_dest = (
+                "base"
+                if group_id >= len(self._intervention_group) - 1
+                else f"source_{group_id+1}"
+            )
+            group_key = f"source_{group_id}->{group_dest}"
             unit_locations_source = unit_locations[group_key][0]
             unit_locations_base = unit_locations[group_key][1]
 
             if activations_sources != None:
                 for key in keys:
                     self.activations[key] = activations_sources[key]
             else:
-                keys_with_source = [k for i, k in enumerate(keys) if unit_locations_source[i] != None]
-                get_handlers = self._intervention_getter(keys_with_source, unit_locations_source)
+                keys_with_source = [
+                    k for i, k in enumerate(keys) if unit_locations_source[i] != None
+                ]
+                get_handlers = self._intervention_getter(
+                    keys_with_source, unit_locations_source
+                )
 
             # call once per group. each intervention is by its own group by default
             if activations_sources is None:
@@ -1396,11 +1402,11 @@ def forward(
 
             self._output_validation()
 
-            collected_activations = []
+            collected_activations = {}
             if self.return_collect_activations:
                 for key in self.sorted_keys:
                     if isinstance(self.interventions[key][0], CollectIntervention):
-                        collected_activations += self.activations[key]
+                        collected_activations[key] = self.activations[key]
 
         except Exception as e:
             raise e
@@ -1433,15 +1439,16 @@ def generate(
         self,
         base,
         sources: Optional[List] = None,
+        source_representations: Optional[Dict] = None,
+        intervene_on_prompt: bool = True,
         unit_locations: Optional[Dict] = None,
         timestep_selector: Optional[TIMESTEP_SELECTOR_TYPE] = None,
-        intervene_on_prompt: bool = True,
-        source_representations: Optional[Dict] = None,
         subspaces: Optional[List] = None,
         output_original_output: Optional[bool] = False,
         **kwargs,
     ) -> Tuple[
-        ModelOutput | Tuple[ModelOutput | None, List[torch.Tensor]] | None, ModelOutput
+        Optional[ModelOutput | Tuple[Optional[ModelOutput], Dict[str, torch.Tensor]]],
+        ModelOutput,
     ]:
         """
         Intervenable generation function that serves a
@@ -1526,11 +1533,11 @@ def generate(
             # run intervened generate
             counterfactual_outputs = self.model.generate(**base, **kwargs)
 
-            collected_activations = []
+            collected_activations = {}
             if self.return_collect_activations:
                 for key in self.sorted_keys:
                     if isinstance(self.interventions[key][0], CollectIntervention):
-                        collected_activations += self.activations[key]
+                        collected_activations[key] = self.activations[key]
         except Exception as e:
             raise e
         finally:
diff --git a/pyvene_101.ipynb b/pyvene_101.ipynb
@@ -160,15 +160,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 5,
    "id": "128be2dd-f089-4291-bfc5-7002d031b1e9",
-   "metadata": {},
+   "metadata": {
+    "metadata": {}
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loaded model\n"
+      "loaded GPT2 model gpt2\n",
+      "torch.Size([12, 14, 14])\n"
      ]
     }
    ],
@@ -186,10 +189,11 @@
     "    \"intervention\": 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]"
+    ")\n",
+    "print(collected_attn_w[0].shape)"
    ]
   },
   {
@@ -1753,15 +1757,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 1,
    "id": "61cd8fc9",
-   "metadata": {},
+   "metadata": {
+    "metadata": {}
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loaded model\n",
+      "loaded GPT2 model gpt2\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/juice/scr/nathangk/text-intervention/pyvene/pyvene/models/intervenable_base.py:796: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+      "  cached_activations = torch.tensor(self.activations[key])\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "True True\n"
      ]
     }
@@ -2017,9 +2037,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 2,
    "id": "acce6e8f",
-   "metadata": {},
+   "metadata": {
+    "metadata": {}
+   },
    "outputs": [
     {
      "name": "stderr",
@@ -2028,6 +2050,34 @@
       "You are using the default legacy behaviour of the <class 'transformers.models.t5.tokenization_t5.T5Tokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565\n",
       "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
      ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "517de63768da4f7f8f58e5018c6f75f6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors:   0%|          | 0.00/308M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "378463b4bd174067b6269d64a1ddf1fe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
@@ -2676,7 +2726,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.11.7"
   },
   "toc-autonumbering": true,
   "toc-showcode": false,
diff --git a/tutorials/basic_tutorials/Generation_Intervention.ipynb b/tutorials/basic_tutorials/Generation_Intervention.ipynb
