{ "cells": [ { "cell_type": "markdown", "id": "035c3466", "metadata": { "id": "colab-badge", "papermill": { "duration": 0.00384, "end_time": "2026-07-30T22:44:59.555349+00:00", "exception": false, "start_time": "2026-07-30T22:44:59.551509+00:00", "status": "completed" }, "tags": [] }, "source": [ "\n", " \"Open\n", "" ] }, { "cell_type": "markdown", "id": "7fb27b941602401d91542211134fc71a", "metadata": { "papermill": { "duration": 0.003367, "end_time": "2026-07-30T22:44:59.573734+00:00", "exception": false, "start_time": "2026-07-30T22:44:59.570367+00:00", "status": "completed" }, "tags": [] }, "source": [ "# Circuit Tracer Analysis Backend Demo\n", "\n", "This notebook demonstrates the **Interpretune analysis ops pipeline** for circuit-tracer analysis,\n", "running the full **semantic concept intervention** flow as a single composite pipeline:\n", "\n", "- **Concept Direction** — Compute a directional concept vector (e.g. \"Capitals − States\")\n", "- **Attribution Graph** — Generate a circuit-level attribution graph from the concept direction\n", "- **Node Influence** — Score graph nodes by their causal influence on the concept\n", "- **Top Features** — Extract the most influential transcoder features\n", "- **Feature Intervention** — Amplify those features and measure how predictions shift\n", "\n", "The five ops above are composed into the `intervention_from_concept` pipeline, which chains\n", "them automatically and returns results in a single `AnalysisBatch`.\n", "\n", "> **Tip:** For individual op dispatching and the `DISPATCHER` API, see the\n", "> [op_collection_example](../example_op_collections/op_collection_example.ipynb) notebook.\n", "\n", "The `backend` parameter selects the circuit-tracer backend: **NNsight** (default) or\n", "**TransformerLens** — both are validated by the parameterized notebook tests. (The\n", "TransformerLens backend uses the legacy `HookedTransformer` path; circuit-tracer does not yet\n", "support `TransformerBridge` — see the tracking notes in `docs/circuit_tracer_backend_support.md`.)\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "acae54e37e7d407bbb7b55eff062a284", "metadata": { "execution": { "iopub.execute_input": "2026-07-30T22:44:59.582657Z", "iopub.status.busy": "2026-07-30T22:44:59.582456Z", "iopub.status.idle": "2026-07-30T22:45:09.565166Z", "shell.execute_reply": "2026-07-30T22:45:09.564349Z" }, "papermill": { "duration": 9.989441, "end_time": "2026-07-30T22:45:09.566813+00:00", "exception": false, "start_time": "2026-07-30T22:44:59.577372+00:00", "status": "completed" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "# @title Imports { display-mode: \"form\" }\n", "from pprint import pformat\n", "\n", "import interpretune as it\n", "from it_examples import _ACTIVE_PATCHES # noqa: F401\n", "from it_examples.example_module_registry import MODULE_EXAMPLE_REGISTRY\n", "from it_examples.utils.example_helpers import required_os_env\n", "from it_examples.utils.nb_ui_utils import (\n", " display_target_gap,\n", " display_top_features_comparison,\n", " display_topk_token_predictions,\n", " resolve_feature_explanations,\n", ")\n", "from interpretune import ITSession, ITSessionConfig\n", "from interpretune.analysis.ops.base import AnalysisBatch\n", "from interpretune.config import AnalysisCfg, init_analysis_cfgs" ] }, { "cell_type": "markdown", "id": "9a63283cbaf04dbcab1f6479b197f3a8", "metadata": { "papermill": { "duration": 0.003284, "end_time": "2026-07-30T22:45:09.573799+00:00", "exception": false, "start_time": "2026-07-30T22:45:09.570515+00:00", "status": "completed" }, "tags": [] }, "source": [ "## Notebook Parameters\n", "\n", "This cell contains parameters that can be injected by papermill during parameterized test runs.\n", "\n", "`dashboard_mode` selects where feature-dashboard links point: `\"public\"` uses\n", "[neuronpedia.org](https://www.neuronpedia.org), `\"local\"` uses a local Neuronpedia dev webapp\n", "(`local_webapp_url`) — see the Neuronpedia\n", "[localhost guide](https://github.com/hijohnnylin/neuronpedia?tab=readme-ov-file#localhost-installation).\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "8dd0d8092fe74a7c96281538738b07e2", "metadata": { "execution": { "iopub.execute_input": "2026-07-30T22:45:09.581725Z", "iopub.status.busy": "2026-07-30T22:45:09.581580Z", "iopub.status.idle": "2026-07-30T22:45:09.584648Z", "shell.execute_reply": "2026-07-30T22:45:09.583847Z" }, "papermill": { "duration": 0.008299, "end_time": "2026-07-30T22:45:09.585422+00:00", "exception": false, "start_time": "2026-07-30T22:45:09.577123+00:00", "status": "completed" }, "tags": [ "parameters" ] }, "outputs": [], "source": [ "# Parameters - These will be injected by papermill during parameterized test runs\n", "backend = \"nnsight\" # Options: \"transformerlens\", \"nnsight\"\n", "core_log_dir = None # Directory to save analysis logs (if None, a temp directory will be created)\n", "intervention_scale_factor = 10.0 # Multiplier for feature intervention amplitudes\n", "dashboard_mode = \"public\" # Options: \"public\" (neuronpedia.org links), \"local\" (local Neuronpedia dev webapp)\n", "local_webapp_url = \"http://localhost:3000\" # Feature-dashboard base URL used when dashboard_mode=\"local\"" ] }, { "cell_type": "code", "execution_count": 4, "id": "72eea5119410473aa328ad9291626812", "metadata": { "execution": { "iopub.execute_input": "2026-07-30T22:45:09.593404Z", "iopub.status.busy": "2026-07-30T22:45:09.593277Z", "iopub.status.idle": "2026-07-30T22:45:09.598223Z", "shell.execute_reply": "2026-07-30T22:45:09.597438Z" }, "papermill": { "duration": 0.010171, "end_time": "2026-07-30T22:45:09.599073+00:00", "exception": false, "start_time": "2026-07-30T22:45:09.588902+00:00", "status": "completed" }, "tags": [ "hide-cell" ] }, "outputs": [], "source": [ "# @title Environment Setup { display-mode: \"form\" }\n", "env_path: str | None = None # set to '/full/path/to/.env' to override\n", "os_env_reqs = None\n", "assert required_os_env(env_path=env_path, env_reqs=os_env_reqs)" ] }, { "cell_type": "markdown", "id": "8edb47106e1a46a883d545849b8ab81b", "metadata": { "papermill": { "duration": 0.003373, "end_time": "2026-07-30T22:45:09.605996+00:00", "exception": false, "start_time": "2026-07-30T22:45:09.602623+00:00", "status": "completed" }, "tags": [] }, "source": [ "## Session Configuration\n", "\n", "We load a registered example module configuration for Gemma-2-2b with the RTE task,\n", "then configure it for the selected backend. The `\"gemma\"` transcoder set uses Gemma Scope\n", "transcoders, matching the upstream\n", "[attribution_targets_demo](https://github.com/safety-research/circuit-tracer/blob/main/demos/attribution_targets_demo.ipynb).\n", "\n", "> **Note:** the first `MODULE_EXAMPLE_REGISTRY` access hydrates *every* example registry entry.\n", "> Per-entry config-normalization feedback (categorized as `ITInstantiationFeedbackWarning`) is\n", "> suppressed during that bulk hydration so this cell only surfaces messages relevant to the\n", "> requested configuration; directly instantiating a config still shows its feedback.\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "10185d26023b46108eb7d9f57d49d2b3", "metadata": { "execution": { "iopub.execute_input": "2026-07-30T22:45:09.613900Z", "iopub.status.busy": "2026-07-30T22:45:09.613775Z", "iopub.status.idle": "2026-07-30T22:45:09.854741Z", "shell.execute_reply": "2026-07-30T22:45:09.853843Z" }, "papermill": { "duration": 0.246046, "end_time": "2026-07-30T22:45:09.855432+00:00", "exception": false, "start_time": "2026-07-30T22:45:09.609386+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Backend: nnsight\n", "CircuitTracerConfig(backend='nnsight',\n", " model_name=None,\n", " transcoder_set='gemma',\n", " dtype=torch.bfloat16,\n", " max_n_logits=10,\n", " desired_logit_prob=0.95,\n", " batch_size=256,\n", " max_feature_nodes=8192,\n", " offload='cpu',\n", " lazy_encoder=None,\n", " lazy_decoder=True,\n", " verbose=True,\n", " default_node_threshold=0.8,\n", " default_edge_threshold=0.98,\n", " save_graphs=True,\n", " graph_output_dir=None,\n", " analysis_target_tokens=['▁Dallas', '▁Austin'],\n", " target_token_ids=None,\n", " use_neuronpedia=False,\n", " intervention_scale_factor=10.0,\n", " intervention_max_influence_norm_scale=False,\n", " intervention_sign_aware_scale=True,\n", " intervention_value=None,\n", " intervention_value_source='top_feature_activation_values',\n", " intervention_constrained_layers=None,\n", " intervention_freeze_attention=None,\n", " intervention_apply_activation_function=None,\n", " intervention_sparse=False,\n", " intervention_return_activations=False,\n", " nnsight_remote=False,\n", " ndif_api_key=None)\n" ] } ], "source": [ "# Load the demo configuration from the example module registry\n", "base_itdm_cfg, base_it_cfg, dm_cls, m_cls = MODULE_EXAMPLE_REGISTRY.get(\"gemma2.rte_demo.circuit_tracer\")\n", "\n", "# Configure backend\n", "base_it_cfg.circuit_tracer_cfg.backend = backend\n", "print(f\"Backend: {backend}\")\n", "\n", "# Optionally override core_log_dir\n", "if core_log_dir:\n", " base_it_cfg.core_log_dir = core_log_dir\n", "\n", "# Use Gemma Scope transcoders (matches upstream attribution_targets_demo)\n", "base_it_cfg.circuit_tracer_cfg.transcoder_set = \"gemma\"\n", "\n", "# Configure intervention settings\n", "base_it_cfg.circuit_tracer_cfg.intervention_value_source = \"top_feature_activation_values\"\n", "base_it_cfg.circuit_tracer_cfg.intervention_scale_factor = intervention_scale_factor\n", "\n", "print(pformat(base_it_cfg.circuit_tracer_cfg))" ] }, { "cell_type": "code", "execution_count": 6, "id": "8763a12b2bbd4a93a75aff182afb95dc", "metadata": { "execution": { "iopub.execute_input": "2026-07-30T22:45:09.864116Z", "iopub.status.busy": "2026-07-30T22:45:09.863982Z", "iopub.status.idle": "2026-07-30T22:45:38.869593Z", "shell.execute_reply": "2026-07-30T22:45:38.868658Z" }, "papermill": { "duration": 29.011537, "end_time": "2026-07-30T22:45:38.870939+00:00", "exception": false, "start_time": "2026-07-30T22:45:09.859402+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[INFO] interpretune.utils.logging: Loading ReplacementModel with backend: nnsight\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "INFO:interpretune.utils.logging:Loading ReplacementModel with backend: nnsight\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8224c40232094d4e846b32515dbd1835", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading (incomplete total...): 0.00B [00:00, ?B/s]" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "69fb1c322fb44265a0ed03ebe51dd830", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Fetching 26 files: 0%| | 0/26 [00:00 For interactive node influence visualization, see the\n", "> [attribution_analysis](../../analysis_injection/attribution_analysis.ipynb) notebook." ] }, { "cell_type": "code", "execution_count": 10, "id": "c629780d", "metadata": { "execution": { "iopub.execute_input": "2026-07-30T22:45:56.551857Z", "iopub.status.busy": "2026-07-30T22:45:56.551666Z", "iopub.status.idle": "2026-07-30T22:46:00.407433Z", "shell.execute_reply": "2026-07-30T22:46:00.406107Z" }, "papermill": { "duration": 3.865798, "end_time": "2026-07-30T22:46:00.408257+00:00", "exception": false, "start_time": "2026-07-30T22:45:56.542459+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", "
Top Features (by node influence)
#NodeScoreExplanation
1(21, 10, 5943)0.0039a mix of location names, political words, and parts of code
2(24, 10, 6394)0.0021place names and words describing geographic locality
3(23, 10, 12237)0.0019locations
4(0, 2, 16200)0.0014code syntax elements
5(24, 10, 5999)0.0013language related to institutions, negative situations, the internet, and programming languages
6(20, 10, 15589)0.0012references to geographic locations, especially in addresses
7(19, 10, 2695)0.0011locations in North America
8(24, 10, 6044)0.0010locations and legal case identifiers
9(22, 10, 4999)0.0010references to metropolitan areas and travel between locations
10(18, 10, 6101)9.49e-04words, phrases, and names related to governments and political entities
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Build feature tuples and scores for display\n", "features = [tuple(f.tolist()) for f in results.top_feature_ids]\n", "scores = results.top_feature_scores.tolist()\n", "\n", "# Feature-dashboard links: public neuronpedia.org or a local Neuronpedia dev webapp\n", "neuronpedia_base_url = \"https://www.neuronpedia.org\" if dashboard_mode == \"public\" else local_webapp_url\n", "\n", "# Best-effort explanation text from the feature API (public or local webapp); unmapped features\n", "# simply render an empty Explanation cell\n", "feature_explanations = resolve_feature_explanations(\n", " model_id=\"gemma-2-2b\",\n", " source_set=\"gemmascope-transcoder-16k\",\n", " feature_tuples=[(f[0], f[-1]) for f in features],\n", " base_url=neuronpedia_base_url,\n", ")\n", "\n", "display_top_features_comparison(\n", " {\"Top Features (by node influence)\": features},\n", " {\"Top Features (by node influence)\": scores},\n", " neuronpedia_model=\"gemma-2-2b\",\n", " neuronpedia_base_url=neuronpedia_base_url,\n", " feature_explanations=feature_explanations,\n", ")" ] }, { "cell_type": "markdown", "id": "f3ae5fe7", "metadata": { "papermill": { "duration": 0.008388, "end_time": "2026-07-30T22:46:00.425490+00:00", "exception": false, "start_time": "2026-07-30T22:46:00.417102+00:00", "status": "completed" }, "tags": [] }, "source": [ "## Results: Feature Intervention\n", "\n", "Amplifying the top features by the configured scale factor and measuring how\n", "predictions shift. If the pipeline correctly identifies \"capital\" features,\n", "amplifying them should increase the probability of **Austin** relative to **Dallas**." ] }, { "cell_type": "code", "execution_count": 11, "id": "9e82f8cc", "metadata": { "execution": { "iopub.execute_input": "2026-07-30T22:46:00.443880Z", "iopub.status.busy": "2026-07-30T22:46:00.443639Z", "iopub.status.idle": "2026-07-30T22:46:00.471071Z", "shell.execute_reply": "2026-07-30T22:46:00.470118Z" }, "papermill": { "duration": 0.038058, "end_time": "2026-07-30T22:46:00.471938+00:00", "exception": false, "start_time": "2026-07-30T22:46:00.433880+00:00", "status": "completed" }, "tags": [] }, "outputs": [ { "data": { "text/html": [ "\n", " \n", "\n", "
\n", "
Input Sentence:
\n", "
Fact: the capital of the state containing Dallas is
\n", "\n", "
\n", "
Original Top 5 Tokens
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", " \n", "
TokenProbabilityDistribution
▁Austin42.045%
42.0%
▁not5.690%
5.7%
▁the5.690%
5.7%
▁Texas5.022%
5.0%
▁Fort3.911%
3.9%
\n", "\n", "
New Top 5 Tokens
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", "\n", "\n", " \n", "
TokenProbabilityDistribution
▁Austin64.504%
64.5%
▁San11.209%
11.2%
Austin2.834%
2.8%
▁Fort2.501%
2.5%
▁Irving1.948%
1.9%
\n", "
\n", " \n", "
\n", "
Key Tokens
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", " \n", "
TokenOriginalNewChange
Austin42.0452%64.5045%
+0.2246
Dallas3.0457%0.2987%
0.0275
\n", "
\n", " \n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "
\n", "
Feature intervention — Austin vs Dallas
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", "\n", "\n", " \n", "
TokenPre probPost probPre logitPost logitΔ
Austin42.045%64.504%26.125026.1250+0.0000
Dallas3.046%0.299%23.500020.7500-2.7500
Gap (Austin − Dallas)+2.6250+5.3750+2.7500
\n", "
\n", " " ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pre_logits = results.pre_intervention_logits.float().cpu()\n", "post_logits = results.post_intervention_logits.float().cpu()\n", "\n", "# Rich pre/post comparison with key tokens\n", "display_topk_token_predictions(\n", " prompt,\n", " pre_logits,\n", " post_logits,\n", " tokenizer,\n", " k=5,\n", " key_tokens=key_tokens,\n", ")\n", "\n", "# Consolidated pre/post probabilities, logits, and the Austin - Dallas gap in one table\n", "pre_gap, post_gap = display_target_gap(\n", " pre_logits,\n", " post_logits,\n", " (\"Austin\", austin_id),\n", " (\"Dallas\", dallas_id),\n", " title=\"Feature intervention — Austin vs Dallas\",\n", ")\n", "\n", "# Sanity gate (both backends): amplifying \"capital\" features must widen the Austin-Dallas gap\n", "assert post_gap > pre_gap, \"feature intervention should widen the Austin-Dallas logit gap\"" ] }, { "cell_type": "markdown", "id": "84f7a364", "metadata": { "papermill": { "duration": 0.008795, "end_time": "2026-07-30T22:46:00.489906+00:00", "exception": false, "start_time": "2026-07-30T22:46:00.481111+00:00", "status": "completed" }, "tags": [] }, "source": [ "## Summary\n", "\n", "The `intervention_from_concept` pipeline ran all five analysis ops in a single call:\n", "\n", "| Op | Result |\n", "|----|---------|\n", "| `concept_direction` | Computed a \"Capitals − States\" concept vector via paired rejection |\n", "| `compute_attribution_graph` | Generated a circuit-level attribution graph for the prompt |\n", "| `graph_node_influence` | Scored graph nodes by causal influence |\n", "| `extract_top_features` | Ranked and extracted the top-10 most influential features |\n", "| `feature_intervention_forward` | Amplified top features and measured prediction shifts |\n", "\n", "Virtual logit target IDs from the attribution graph are automatically resolved to real\n", "vocabulary token IDs, so no manual override is needed.\n", "\n", "**Key result:** Amplifying the identified \"capital\" features shifts the model's prediction\n", "towards the correct answer (Austin), validating that the circuit-tracer pipeline identifies\n", "causally relevant features.\n", "\n", "### What's Next\n", "\n", "- **Concept-Direction Steering Demo**: See `ct_concept_steering_demo.ipynb` for store- and embed-based,\n", " sign-aware multi-feature concept steering with public or locally served feature dashboards\n", "- **RTE research direction**: RTE-focused concept-direction research continues in\n", " [interpretune#220](https://github.com/speediedan/interpretune/issues/220)\n", "- **Analysis Injection**: See `attribution_analysis.ipynb` for node influence visualization via injection hooks\n", "- **Op Dispatching**: See `op_collection_example.ipynb` for the `DISPATCHER` API and individual op usage\n", "- **AnalysisRunner**: For batch analysis workflows, use `AnalysisRunner` with `AnalysisCfg` objects\n", "- **Basic CT Tutorial**: See `circuit_tracer_adapter_example_basic.ipynb` for the foundational adapter tutorial" ] } ], "metadata": { "kernelspec": { "display_name": "it_latest (3.13.11.final.0)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.13.11" }, "papermill": { "default_parameters": {}, "duration": 65.264553, "end_time": "2026-07-30T22:46:03.928454+00:00", "environment_variables": {}, "exception": null, "input_path": "/home/speediedan/repos/interpretune/src/it_examples/notebooks/publish/circuit_tracer_examples/ct_analysis_backend_demo.ipynb", "output_path": "/home/speediedan/repos/interpretune/docs/notebook_artifacts/circuit_tracer_examples/ct_analysis_backend_demo.ipynb", "parameters": {}, "start_time": "2026-07-30T22:44:58.663901+00:00", "version": "2.7.0" }, "widgets": { 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