|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "markdown", |
| 5 | + "id": "ab195250-6a0f-4176-a09d-3696d911203d", |
| 6 | + "metadata": {}, |
| 7 | + "source": [ |
| 8 | + "# Running the official evaluator\n", |
| 9 | + "\n", |
| 10 | + "This flightpath walks through running the official ensemble, either directly or using different combiner logic and seeing the results in MLCommons' MLFlow server.\n", |
| 11 | + "\n", |
| 12 | + "## Requirements\n", |
| 13 | + "To run this flightpath, you must:\n", |
| 14 | + "* Have access to the AIRR MLFlow server.\n", |
| 15 | + " * Modify `.env.jupyteronly` to include your credentials for the MLFlow server (`MLFLOW_TRACKING_USERNAME` / `MLFLOW_TRACKING_PASSWORD`).\n", |
| 16 | + " * Alternatively, you can put the credentials in `~/.mlflow/credentials` as described [here](https://mlflow.org/docs/latest/ml/auth/#credentials-file).\n", |
| 17 | + " * Note that if you want to use a locally running mlflow server, you can modify .env.jupyteronly to set `MLFLOW_TRACKING_URI` appropriately.\n", |
| 18 | + "* Have access to the modelbench-private repository *and* set `USE_PRIVATE_MODELBENCH=true` in `.env.jupyteronly`.\n", |
| 19 | + "\n", |
| 20 | + "Once modifications (if any) are made to the `.env.jupyteronly` configuration, start jupyter with the `./start_jupyter.sh` script." |
| 21 | + ] |
| 22 | + }, |
| 23 | + { |
| 24 | + "cell_type": "markdown", |
| 25 | + "id": "28f35ba7-cf70-49ed-80e7-518d7886161f", |
| 26 | + "metadata": {}, |
| 27 | + "source": [ |
| 28 | + "## MLFlow server login" |
| 29 | + ] |
| 30 | + }, |
| 31 | + { |
| 32 | + "cell_type": "markdown", |
| 33 | + "id": "3d2d5865-2cd7-4b81-a588-dfec27727643", |
| 34 | + "metadata": {}, |
| 35 | + "source": [ |
| 36 | + "## Import runways" |
| 37 | + ] |
| 38 | + }, |
| 39 | + { |
| 40 | + "cell_type": "code", |
| 41 | + "execution_count": null, |
| 42 | + "id": "f44e837c-05e9-4e62-916d-9884bb47839e", |
| 43 | + "metadata": {}, |
| 44 | + "outputs": [], |
| 45 | + "source": [ |
| 46 | + "import datetime\n", |
| 47 | + "from modelplane.runways import responder, annotator, scorer" |
| 48 | + ] |
| 49 | + }, |
| 50 | + { |
| 51 | + "cell_type": "markdown", |
| 52 | + "id": "726c8897-db04-4435-8d67-7a05309ef740", |
| 53 | + "metadata": {}, |
| 54 | + "source": [ |
| 55 | + "## Settings\n", |
| 56 | + "\n", |
| 57 | + "* The `sut_id` refers to the model that generates the responses to the prompts. It is currently set to a demo SUT.\n", |
| 58 | + "* The `experiment` variable will be used to organize the various runs in mlflow.\n", |
| 59 | + "* The `prompts` should point to a location in `/flightpaths/data`. A sample dataset is provided.\n", |
| 60 | + "* You can cache prompt responses via `cache_dir`.\n", |
| 61 | + "\n", |
| 62 | + "Finally, `n_jobs` can adjust the parallelism." |
| 63 | + ] |
| 64 | + }, |
| 65 | + { |
| 66 | + "cell_type": "code", |
| 67 | + "execution_count": null, |
| 68 | + "id": "414f9c85-d146-4119-854b-e009235aa4c4", |
| 69 | + "metadata": {}, |
| 70 | + "outputs": [], |
| 71 | + "source": [ |
| 72 | + "sut_id = \"demo_yes_no\"\n", |
| 73 | + "experiment = \"fp_private_\" + datetime.date.today().strftime(\"%Y%m%d\")\n", |
| 74 | + "prompts = \"data/airr_official_1.0_demo_en_us_prompt_set_release_reduced.csv\"\n", |
| 75 | + "ground_truth = \"data/fakegroundtruth.csv\"\n", |
| 76 | + "cache_dir = None\n", |
| 77 | + "n_jobs = 4" |
| 78 | + ] |
| 79 | + }, |
| 80 | + { |
| 81 | + "cell_type": "markdown", |
| 82 | + "id": "43bc96aa-7a5c-4ac4-ab2d-ad89c96b4a28", |
| 83 | + "metadata": {}, |
| 84 | + "source": [ |
| 85 | + "### Combiner logic - `AnyUnsafeStrategy`\n", |
| 86 | + "\n", |
| 87 | + "This implementation will return is_safe = \"unsafe\" if any of the ensemble members decide it's unsafe.\n", |
| 88 | + "\n", |
| 89 | + "Combiner logic must be declared via `ENSEMBLE_STRATEGIES`." |
| 90 | + ] |
| 91 | + }, |
| 92 | + { |
| 93 | + "cell_type": "code", |
| 94 | + "execution_count": null, |
| 95 | + "id": "a9170875-d822-4e49-8445-37639caee836", |
| 96 | + "metadata": {}, |
| 97 | + "outputs": [], |
| 98 | + "source": [ |
| 99 | + "from typing import Dict\n", |
| 100 | + "\n", |
| 101 | + "from modelgauge.safety_model_response import SafetyModelResponse, EnsembleSafetyModelResponse\n", |
| 102 | + "from modelgauge.ensemble_annotator_set import EnsembleStrategy, ENSEMBLE_STRATEGIES\n", |
| 103 | + "\n", |
| 104 | + "\n", |
| 105 | + "class AnyUnsafeEnsembleStrategy(EnsembleStrategy):\n", |
| 106 | + " def compute_response(self, annotations: Dict[str, SafetyModelResponse]) -> EnsembleSafetyModelResponse:\n", |
| 107 | + " response = EnsembleSafetyModelResponse(\n", |
| 108 | + " joined_responses=annotations, \n", |
| 109 | + " is_safe=True,\n", |
| 110 | + " safety_categories=[],\n", |
| 111 | + " is_valid=False,\n", |
| 112 | + " )\n", |
| 113 | + " for annotation in annotations.values():\n", |
| 114 | + " if annotation.is_valid:\n", |
| 115 | + " response.is_valid = True\n", |
| 116 | + " if not annotation.is_safe:\n", |
| 117 | + " response.is_safe = False\n", |
| 118 | + " current_categories = set(response.safety_categories)\n", |
| 119 | + " current_categories.update(annotation.safety_categories)\n", |
| 120 | + " response.safety_categories = sorted(current_categories)\n", |
| 121 | + " return response\n", |
| 122 | + "\n", |
| 123 | + "ENSEMBLE_STRATEGIES[\"anyunsafe\"] = AnyUnsafeEnsembleStrategy()" |
| 124 | + ] |
| 125 | + }, |
| 126 | + { |
| 127 | + "cell_type": "markdown", |
| 128 | + "id": "17760cd3-23fe-4c79-8882-475d8d7096ea", |
| 129 | + "metadata": {}, |
| 130 | + "source": [ |
| 131 | + "## Run the model\n", |
| 132 | + "\n", |
| 133 | + "This step will get responses to the prompts from the given SUT.\n", |
| 134 | + "\n", |
| 135 | + "Save this run_id to avoid having to re-run the model later. The results are saved as an artifact in mlflow.\n", |
| 136 | + "\n", |
| 137 | + "You can see the the runs associated with the experiment you specified above at your MLFlow tracking server. There will be a link below after running. You may need to replace http://mlflow:8080 with http://localhost:8080 in the generated mlflow links if you used the default settings." |
| 138 | + ] |
| 139 | + }, |
| 140 | + { |
| 141 | + "cell_type": "code", |
| 142 | + "execution_count": null, |
| 143 | + "id": "b70d76d5-a3e1-4cc0-aeff-e71b6ff64825", |
| 144 | + "metadata": {}, |
| 145 | + "outputs": [], |
| 146 | + "source": [ |
| 147 | + "run_id = responder.respond(\n", |
| 148 | + " sut_id=sut_id,\n", |
| 149 | + " experiment=experiment,\n", |
| 150 | + " prompts=prompts,\n", |
| 151 | + " cache_dir=cache_dir,\n", |
| 152 | + " n_jobs=n_jobs,\n", |
| 153 | + ")" |
| 154 | + ] |
| 155 | + }, |
| 156 | + { |
| 157 | + "cell_type": "markdown", |
| 158 | + "id": "740a8a85-c171-4d11-b094-cd617b14b6ed", |
| 159 | + "metadata": {}, |
| 160 | + "source": [ |
| 161 | + "## Annotate the model by ensembling with the new combiner logic\n", |
| 162 | + "\n", |
| 163 | + "This will grab the annotators from the `PRIVATE_ANNOTATOR_SET` and apply the new combiner logic to them. You'll be able to see the details of the annotations in mlflow.\n", |
| 164 | + "\n", |
| 165 | + "If you want to run the official annotator directly (without modifications), you can instead run:\n", |
| 166 | + "\n", |
| 167 | + "```python\n", |
| 168 | + "annotation_run_id = annotator.annotate(\n", |
| 169 | + " ensemble_id=\"official-1.0\",\n", |
| 170 | + " experiment=experiment,\n", |
| 171 | + " response_run_id=run_id,\n", |
| 172 | + " cache_dir=cache_dir,\n", |
| 173 | + " n_jobs=n_jobs,\n", |
| 174 | + ")\n", |
| 175 | + "```" |
| 176 | + ] |
| 177 | + }, |
| 178 | + { |
| 179 | + "cell_type": "code", |
| 180 | + "execution_count": null, |
| 181 | + "id": "06632c4d-90bd-4c2d-9c36-84e59dd8f190", |
| 182 | + "metadata": {}, |
| 183 | + "outputs": [], |
| 184 | + "source": [ |
| 185 | + "from modelgauge.private_ensemble_annotator_set import PRIVATE_ANNOTATOR_SET\n", |
| 186 | + "\n", |
| 187 | + "annotator_ids = PRIVATE_ANNOTATOR_SET.annotators\n", |
| 188 | + "annotation_run_id = annotator.annotate(\n", |
| 189 | + " annotator_ids=annotator_ids,\n", |
| 190 | + " ensemble_strategy=\"anyunsafe\",\n", |
| 191 | + " experiment=experiment,\n", |
| 192 | + " response_run_id=run_id,\n", |
| 193 | + " cache_dir=cache_dir,\n", |
| 194 | + " n_jobs=n_jobs,\n", |
| 195 | + ")" |
| 196 | + ] |
| 197 | + } |
| 198 | + ], |
| 199 | + "metadata": { |
| 200 | + "kernelspec": { |
| 201 | + "display_name": "Python 3 (ipykernel)", |
| 202 | + "language": "python", |
| 203 | + "name": "python3" |
| 204 | + }, |
| 205 | + "language_info": { |
| 206 | + "codemirror_mode": { |
| 207 | + "name": "ipython", |
| 208 | + "version": 3 |
| 209 | + }, |
| 210 | + "file_extension": ".py", |
| 211 | + "mimetype": "text/x-python", |
| 212 | + "name": "python", |
| 213 | + "nbconvert_exporter": "python", |
| 214 | + "pygments_lexer": "ipython3", |
| 215 | + "version": "3.12.11" |
| 216 | + } |
| 217 | + }, |
| 218 | + "nbformat": 4, |
| 219 | + "nbformat_minor": 5 |
| 220 | +} |
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