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322 changes: 224 additions & 98 deletions neuron-explainer/demos/generate_and_score_explanation.ipynb
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Original file line number Diff line number Diff line change
@@ -1,101 +1,227 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%load_ext autoreload\n",
"%autoreload 2"
]
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os \n",
"\n",
"os.environ[\"NEURON_EXPLAINER_API_KEY\"] = \"EMPTY\"\n",
"os.environ[\"NEURON_EXPLAINER_API_BASE\"] = \"http://localhost:8000/v1\" # Paste here your vLLM API base URL, with /v1 at the end"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "uj0EX7BdBeo6"
},
"outputs": [],
"source": [
"from neuron_explainer.activations.activation_records import calculate_max_activation\n",
"from neuron_explainer.activations.activations import ActivationRecordSliceParams, load_neuron\n",
"from neuron_explainer.explanations.calibrated_simulator import UncalibratedNeuronSimulator\n",
"from neuron_explainer.explanations.explainer import TokenActivationPairExplainer\n",
"from neuron_explainer.explanations.prompt_builder import PromptFormat\n",
"from neuron_explainer.explanations.scoring import simulate_and_score\n",
"from neuron_explainer.explanations.simulator import ExplanationNeuronSimulator, ExplanationTokenByTokenSimulator"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "s3cxrE6MBhdn"
},
"outputs": [],
"source": [
"EXPLAINER_MODEL_NAME = \"Qwen/Qwen3-Coder-30B-A3B-Instruct\"\n",
"SIMULATOR_MODEL_NAME = \"Qwen/Qwen3-Coder-30B-A3B-Instruct\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "OGBG65ecDEW4"
},
"outputs": [],
"source": [
"from neuron_explainer.api_client import ApiClient"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "syncikDxDIib"
},
"outputs": [],
"source": [
"client = ApiClient(model_name=\"Qwen/Qwen3-Coder-30B-A3B-Instruct\", max_concurrent=1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "4JZBbRnXCldP",
"outputId": "554dc778-e35e-4eaf-c83d-511cd9d043e9"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Response: {'role': 'assistant', 'content': 'Nothing much', 'refusal': None, 'annotations': None, 'audio': None, 'function_call': None, 'tool_calls': [], 'reasoning_content': None}\n"
]
}
],
"source": [
"test_response = await client.make_request(messages=[{\"role\": \"user\", \"content\": \"What's up?\"}], max_tokens=2)\n",
"print(\"Response:\", test_response[\"choices\"][0][\"message\"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "cCYOJ4kZCjvc"
},
"outputs": [],
"source": [
"# Load a neuron record.\n",
"neuron_record = load_neuron(9, 6236)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "aSnM2HneGAkY"
},
"outputs": [],
"source": [
"# Grab the activation records we'll need.\n",
"slice_params = ActivationRecordSliceParams(n_examples_per_split=5)\n",
"train_activation_records = neuron_record.train_activation_records(\n",
" activation_record_slice_params=slice_params\n",
")\n",
"valid_activation_records = neuron_record.valid_activation_records(\n",
" activation_record_slice_params=slice_params\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "7sX5EvHvGSE9"
},
"outputs": [],
"source": [
"# Generate an explanation for the neuron.\n",
"explainer = TokenActivationPairExplainer(\n",
" model_name=EXPLAINER_MODEL_NAME,\n",
" prompt_format=PromptFormat.HARMONY_V4,\n",
" max_concurrent=1,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2dSp0pPzGZdS",
"outputId": "32333e37-d923-41aa-9d07-8b228cc903e8"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"explanation=' phrases indicating repetition or multiple occurrences.'\n"
]
}
],
"source": [
"explanations = await explainer.generate_explanations(\n",
" all_activation_records=train_activation_records,\n",
" max_activation=calculate_max_activation(train_activation_records),\n",
" num_samples=1,\n",
")\n",
"assert len(explanations) == 1\n",
"explanation = explanations[0]\n",
"print(f\"{explanation=}\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 339
},
"id": "ANFzVlEKGhv6",
"outputId": "1e689aef-667d-4cdc-df25-ac6b046ca6f1"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"score=0.08\n"
]
}
],
"source": [
"# Simulate and score the explanation.\n",
"simulator = UncalibratedNeuronSimulator(\n",
" ExplanationNeuronSimulator(\n",
" SIMULATOR_MODEL_NAME,\n",
" explanation,\n",
" max_concurrent=1,\n",
" prompt_format=PromptFormat.INSTRUCTION_FOLLOWING,\n",
" )\n",
")\n",
"scored_simulation = await simulate_and_score(simulator, valid_activation_records)\n",
"print(f\"score={scored_simulation.get_preferred_score():.2f}\")"
]
}
],
"metadata": {
"accelerator": "GPU",
"colab": {
"gpuType": "T4",
"provenance": []
},
"kernelspec": {
"display_name": "neuron-explainer",
"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": "3.12.4"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"OPENAI_API_KEY\"] = \"put-key-here\"\n",
"\n",
"from neuron_explainer.activations.activation_records import calculate_max_activation\n",
"from neuron_explainer.activations.activations import ActivationRecordSliceParams, load_neuron\n",
"from neuron_explainer.explanations.calibrated_simulator import UncalibratedNeuronSimulator\n",
"from neuron_explainer.explanations.explainer import TokenActivationPairExplainer\n",
"from neuron_explainer.explanations.prompt_builder import PromptFormat\n",
"from neuron_explainer.explanations.scoring import simulate_and_score\n",
"from neuron_explainer.explanations.simulator import ExplanationNeuronSimulator\n",
"\n",
"EXPLAINER_MODEL_NAME = \"gpt-4\"\n",
"SIMULATOR_MODEL_NAME = \"text-davinci-003\"\n",
"\n",
"\n",
"# test_response = await client.make_request(prompt=\"test 123<|endofprompt|>\", max_tokens=2)\n",
"# print(\"Response:\", test_response[\"choices\"][0][\"text\"])\n",
"\n",
"# Load a neuron record.\n",
"neuron_record = load_neuron(9, 6236)\n",
"\n",
"# Grab the activation records we'll need.\n",
"slice_params = ActivationRecordSliceParams(n_examples_per_split=5)\n",
"train_activation_records = neuron_record.train_activation_records(\n",
" activation_record_slice_params=slice_params\n",
")\n",
"valid_activation_records = neuron_record.valid_activation_records(\n",
" activation_record_slice_params=slice_params\n",
")\n",
"\n",
"# Generate an explanation for the neuron.\n",
"explainer = TokenActivationPairExplainer(\n",
" model_name=EXPLAINER_MODEL_NAME,\n",
" prompt_format=PromptFormat.HARMONY_V4,\n",
" max_concurrent=1,\n",
")\n",
"explanations = await explainer.generate_explanations(\n",
" all_activation_records=train_activation_records,\n",
" max_activation=calculate_max_activation(train_activation_records),\n",
" num_samples=1,\n",
")\n",
"assert len(explanations) == 1\n",
"explanation = explanations[0]\n",
"print(f\"{explanation=}\")\n",
"\n",
"# Simulate and score the explanation.\n",
"simulator = UncalibratedNeuronSimulator(\n",
" ExplanationNeuronSimulator(\n",
" SIMULATOR_MODEL_NAME,\n",
" explanation,\n",
" max_concurrent=1,\n",
" prompt_format=PromptFormat.INSTRUCTION_FOLLOWING,\n",
" )\n",
")\n",
"scored_simulation = await simulate_and_score(simulator, valid_activation_records)\n",
"print(f\"score={scored_simulation.get_preferred_score():.2f}\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "openai",
"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": "3.9.9"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat": 4,
"nbformat_minor": 0
}
13 changes: 6 additions & 7 deletions neuron-explainer/neuron_explainer/api_client.py
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Original file line number Diff line number Diff line change
Expand Up @@ -80,13 +80,12 @@ async def f_retry(*args: Any, **kwargs: Any) -> None:
return decorate


API_KEY = os.getenv("OPENAI_API_KEY")
assert API_KEY, "Please set the OPENAI_API_KEY environment variable"
API_HTTP_HEADERS = {
"Content-Type": "application/json",
"Authorization": "Bearer " + API_KEY,
}
BASE_API_URL = "https://api.openai.com/v1"
API_KEY = os.getenv("NEURON_EXPLAINER_API_KEY", os.getenv("OPENAI_API_KEY", ""))
BASE_API_URL = os.getenv("NEURON_EXPLAINER_API_BASE", "https://api.openai.com/v1")

API_HTTP_HEADERS = {"Content-Type": "application/json"}
if API_KEY:
API_HTTP_HEADERS["Authorization"] = f"Bearer {API_KEY}"


class ApiClient:
Expand Down
9 changes: 0 additions & 9 deletions neuron-explainer/neuron_explainer/explanations/explainer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,9 +62,6 @@ def from_int(cls, i: int) -> ContextSize:
raise ValueError(f"{i} is not a valid ContextSize")


HARMONY_V4_MODELS = ["gpt-3.5-turbo", "gpt-4", "gpt-4-1106-preview"]


class NeuronExplainer(ABC):
"""
Abstract base class for Explainer classes that generate explanations from subclass-specific
Expand All @@ -82,12 +79,6 @@ def __init__(
max_concurrent: Optional[int] = 10,
cache: bool = False,
):
if prompt_format == PromptFormat.HARMONY_V4:
assert model_name in HARMONY_V4_MODELS
elif prompt_format in [PromptFormat.NONE, PromptFormat.INSTRUCTION_FOLLOWING]:
assert model_name not in HARMONY_V4_MODELS
else:
raise ValueError(f"Unhandled prompt format {prompt_format}")

self.model_name = model_name
self.prompt_format = prompt_format
Expand Down