diff --git a/scripts/eval/callibration/README.md b/scripts/eval/callibration/README.md new file mode 100644 index 0000000000..3ba8ff4ab2 --- /dev/null +++ b/scripts/eval/callibration/README.md @@ -0,0 +1,10 @@ +# Callibration + +A good benchmark is one that clearly shows which models are better and which are worse. We test our benchmark tasks by using a series of progressively more advanced models to see if the benchmarks effectively differentiate between them, and at which number of shots they performed best at. + +To run the code: +* Select an independant variable and a series of models that correspond to the settings of that variable +* Select clusters +* Edit the list of tasks in the base_callibration.yaml to reflect the ones you want to see +* Run the launcher script +* When everything is done, run the analyze_output notebook which collates the results from wandb diff --git a/scripts/eval/callibration/analyze_output.ipynb b/scripts/eval/callibration/analyze_output.ipynb new file mode 100644 index 0000000000..abd8c9ae1f --- /dev/null +++ b/scripts/eval/callibration/analyze_output.ipynb @@ -0,0 +1,548 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [], + "source": [ + "import wandb\n", + "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "\n", + "ENTITY = \"mosaic-ml\" # your wandb entity (user or team name)\n", + "PROJECT = \"eval-llama2-callibrate\" # your project name\n", + "\n", + "GROUPS = [\"7\", \"13\", \"70\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting seaborn\n", + " Downloading seaborn-0.13.2-py3-none-any.whl.metadata (5.4 kB)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/homebrew/lib/python3.11/site-packages (from seaborn) (1.25.2)\n", + "Requirement already satisfied: pandas>=1.2 in /opt/homebrew/lib/python3.11/site-packages (from seaborn) (2.1.0)\n", + "Collecting matplotlib!=3.6.1,>=3.4 (from seaborn)\n", + " Downloading matplotlib-3.8.2-cp311-cp311-macosx_11_0_arm64.whl.metadata (5.8 kB)\n", + "Collecting contourpy>=1.0.1 (from matplotlib!=3.6.1,>=3.4->seaborn)\n", + " Downloading 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fonttools-4.47.2 kiwisolver-1.4.5 matplotlib-3.8.2 pyparsing-3.1.1 seaborn-0.13.2\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.3.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython3.11 -m pip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "!pip3 install seaborn" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "eval-llama2-13-SDjghA\n", + "13\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/tessa.barton/work/llm-foundry/scripts/eval/callibration/artifacts/run-bc814veq-history:v0\n", + "1\n", + "\n", + "\n", + "\n", + "eval-llama2-13-KHLBCd\n", + "70\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: 1 of 1 files downloaded. \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/Users/tessa.barton/work/llm-foundry/scripts/eval/callibration/artifacts/run-fzh5qjql-history:v0\n", + "1\n", + "\n", + "\n" + ] + } + ], + "source": [ + "api = wandb.Api()\n", + "def extract_wandb_icl_data(entity,\n", + " project,\n", + " group):\n", + " \n", + " \"\"\" \n", + " Extract ICL data from the end of each run and combine into a pandas dataframe\n", + " \"\"\"\n", + " runs = api.runs(f\"{entity}/{project}\", {\"group\": group})\n", + "\n", + " icl_df_list = []\n", + "\n", + " for i, run in enumerate(runs):\n", + " if run.state == 'finished':\n", + " print(run)\n", + " print(run.name)\n", + " print(run.group) \n", + "\n", + " run_link = api.run(f\"{entity}/{project}/{run.id}\")\n", + " data_list = run_link.logged_artifacts()\n", + "\n", + "\n", + " idx = len(data_list) # this can sometimes be empty\n", + " if idx > 0:\n", + " \n", + " data_dir = data_list[idx-1].download()\n", + " print(data_dir)\n", + " \n", + " df = pd.read_parquet(f'{data_dir}/0000.parquet', engine='pyarrow') # this seems to be the format for data\n", + "\n", + " tmp_df = df.iloc[-1] # only select the last step, as this is the step when the model was evaluated in these runs\n", + " tmp_df = tmp_df.to_frame().T # conver to single-row DataFrame\n", + " tmp_df['run_name'] = run.name\n", + " tmp_df['run_group'] = run.group\n", + " icl_df_list.append(tmp_df)\n", + " print(len(tmp_df))\n", + " print('\\n')\n", + " if len(icl_df_list) == 0:\n", + " return None\n", + " \n", + " icl_df = pd.concat(icl_df_list,ignore_index=True,axis=0)\n", + " #icl_df.to_csv(\"wandb_group_metrics.csv\", index=False)\n", + "\n", + " return icl_df\n", + "\n", + "icl_df_groups = {group: extract_wandb_icl_data(ENTITY, PROJECT, group) for group in GROUPS}" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "def process_df(df, group):\n", + " if df is None:\n", + " return None\n", + " \n", + " df['index1'] = df.index\n", + " df = df.T\n", + " df['index1'] = df.index\n", + "\n", + " def split_path_and_expand(row):\n", + " # Split the 'path' column\n", + " parts = row['index1'].split('/')\n", + " if len(parts) == 5:\n", + " # Assuming the structure is always the same and you want specific parts\n", + " row['benchmark'] = parts[1]\n", + " nshots = int(parts[2].split('-')[0])\n", + " row['shots'] = nshots\n", + " row['subtask'] = parts[3]\n", + " elif len(parts) == 4:\n", + " row['benchmark'] = parts[1]\n", + " nshots = int(parts[2].split('-')[0])\n", + " row['shots'] = nshots\n", + " row['subtask'] = ''\n", + " else:\n", + " return np.nan\n", + " return row\n", + "\n", + " # Apply the function to each row\n", + " df = df.apply(split_path_and_expand, axis=1)\n", + " df['variable'] = group\n", + " df.drop('index1', axis=1, inplace=True)\n", + " df.reset_index(drop=True, inplace=True)\n", + " df.rename(columns={0: 'accuracy'}, inplace=True)\n", + "\n", + " return df\n", + "\n", + "icl_df_groups = [process_df(icl_df_groups[group], group) for group in GROUPS]" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "26\n", + "470\n" + ] + } + ], + "source": [ + "for i, g in icl_df_groups.items():\n", + " if g is not None:\n", + " print(len(g.columns))" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "icl_df_groups = [group for group in icl_df_groups if group is not None]\n", + "icl_df_groups = pd.concat(icl_df_groups, ignore_index=True)" + ] + }, + { + "cell_type": "code", + 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "g = sns.relplot(\n", + " data=dfg,\n", + " y='accuracy',\n", + " x='variable',\n", + " hue='shots',\n", + " col='benchmark',\n", + " col_wrap=6,\n", + " height=3,\n", + " facet_kws=dict(sharey=False),\n", + " kind='line',\n", + " palette='tab10'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/scripts/eval/callibration/base_callibration.yaml b/scripts/eval/callibration/base_callibration.yaml new file mode 100644 index 0000000000..d86edc513f --- /dev/null +++ b/scripts/eval/callibration/base_callibration.yaml @@ -0,0 +1,225 @@ +name: +compute: + gpus: 8 + cluster: +scheduling: + priority: low + preemptible: true +image: mosaicml/llm-foundry:2.1.0_cu121-8e96a95 +integrations: +- integration_type: git_repo + git_repo: mosaicml/llm-foundry + git_branch: main + pip_install: .[gpu-flash2] + ssh_clone: true + +command: |- + cd llm-foundry/scripts + composer eval/eval.py /mnt/config/parameters.yaml || \ + (echo "Command failed - killing python" && pkill python && exit 1) +parameters: + model_name: + model_path: + max_seq_len: 4096 + models: + - model_name: + model: + name: hf_causal_lm + pretrained: true + init_device: mixed + use_auth_token: true + use_flash_attention_2: true + pretrained_model_name_or_path: + tokenizer: + name: + kwargs: + model_max_length: 4096 + icl_tasks: + - + label: jeopardy + dataset_uri: eval/local_data/world_knowledge/jeopardy_all.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + continuation_delimiter: "\nAnswer: " + has_categories: true + - + label: bigbench_qa_wikidata + dataset_uri: eval/local_data/world_knowledge/bigbench_qa_wikidata.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: arc_easy + dataset_uri: eval/local_data/world_knowledge/arc_easy.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + continuation_delimiter: "\nAnswer: " + - + label: arc_challenge + dataset_uri: eval/local_data/world_knowledge/arc_challenge.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + continuation_delimiter: "\nAnswer: " + - + label: mmlu + dataset_uri: eval/local_data/world_knowledge/mmlu.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + continuation_delimiter: "\nAnswer: " + has_categories: true + - + label: bigbench_misconceptions + dataset_uri: eval/local_data/world_knowledge/bigbench_misconceptions.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: copa + dataset_uri: eval/local_data/commonsense_reasoning/copa.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: piqa + dataset_uri: eval/local_data/commonsense_reasoning/piqa.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + continuation_delimiter: "\nAnswer: " + - + label: openbook_qa + dataset_uri: eval/local_data/commonsense_reasoning/openbook_qa.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: bigbench_novel_concepts + dataset_uri: eval/local_data/commonsense_reasoning/bigbench_novel_concepts.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: bigbench_strange_stories + dataset_uri: eval/local_data/commonsense_reasoning/bigbench_strange_stories.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: bigbench_strategy_qa + dataset_uri: eval/local_data/commonsense_reasoning/bigbench_strategy_qa.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: lambada_openai + dataset_uri: eval/local_data/language_understanding/lambada_openai.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: hellaswag + dataset_uri: eval/local_data/language_understanding/hellaswag.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: winograd + dataset_uri: eval/local_data/language_understanding/winograd_wsc.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: schema + - + label: winogrande + dataset_uri: eval/local_data/language_understanding/winogrande.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: schema + - + label: bigbench_conlang_translation + dataset_uri: eval/local_data/language_understanding/bigbench_conlang_translation.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: bigbench_language_identification + dataset_uri: eval/local_data/language_understanding/bigbench_language_identification.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: bigbench_conceptual_combinations + dataset_uri: eval/local_data/language_understanding/bigbench_conceptual_combinations.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: bigbench_elementary_math_qa + dataset_uri: eval/local_data/symbolic_problem_solving/bigbench_elementary_math_qa.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: bigbench_dyck_languages + dataset_uri: eval/local_data/symbolic_problem_solving/bigbench_dyck_languages.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: bigbench_cs_algorithms + dataset_uri: eval/local_data/symbolic_problem_solving/bigbench_cs_algorithms.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: bigbench_logical_deduction + dataset_uri: eval/local_data/symbolic_problem_solving/bigbench_logical_deduction.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: bigbench_operators + dataset_uri: eval/local_data/symbolic_problem_solving/bigbench_operators.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: bigbench_repeat_copy_logic + dataset_uri: eval/local_data/symbolic_problem_solving/bigbench_repeat_copy_logic.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: simple_arithmetic_nospaces + dataset_uri: eval/local_data/symbolic_problem_solving/simple_arithmetic_nospaces.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: simple_arithmetic_withspaces + dataset_uri: eval/local_data/symbolic_problem_solving/simple_arithmetic_withspaces.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: math_qa + dataset_uri: eval/local_data/symbolic_problem_solving/math_qa.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: logi_qa + dataset_uri: eval/local_data/symbolic_problem_solving/logi_qa.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + continuation_delimiter: "\nAnswer: " + - + label: pubmed_qa_labeled + dataset_uri: eval/local_data/reading_comprehension/pubmed_qa_labeled.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: squad + dataset_uri: eval/local_data/reading_comprehension/squad.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: language_modeling + - + label: bigbench_understanding_fables + dataset_uri: eval/local_data/reading_comprehension/bigbench_understanding_fables.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + - + label: boolq + dataset_uri: eval/local_data/reading_comprehension/boolq.jsonl + num_fewshot: [0, 1, 3, 5, 10] + icl_task_type: multiple_choice + continuation_delimiter: "\nAnswer: " + device_eval_batch_size: 1 + seed: 42 + precision: amp_bf16 + dist_timeout: 7200 + fsdp_config: + verbose: false + mixed_precision: PURE + limit_all_gathers: true + sharding_strategy: FULL_SHARD + activation_cpu_offload: false + activation_checkpointing: true + activation_checkpointing_reentrant: false + loggers: + wandb: {} diff --git a/scripts/eval/callibration/run_callibration.py b/scripts/eval/callibration/run_callibration.py new file mode 100644 index 0000000000..e409dcb7ee --- /dev/null +++ b/scripts/eval/callibration/run_callibration.py @@ -0,0 +1,69 @@ +# Copyright 2024 MosaicML LLM Foundry authors +# SPDX-License-Identifier: Apache-2.0 + +"""Run the evaluation callibration.""" +import copy + +from mcli.sdk import RunConfig, create_run + +# Edit the yaml file to change the model and the benchmarks + +YAML_FILE = 'base_callibration.yaml' +NAME = 'eval-callibration' + +# Edit the clusters, priority, preemptible, retry_on_system_failure + +clusters = ['rxzx', 'rxzx', 'rxzx', 'rxzx'] +n_gpus = [8, 8, 8, 8] +priority = 'low' +preemptible = True +retry_on_system_failure = False + +# Edit the independant variable + +independant_variable_to_load_path = { + 7: 'meta-llama/Llama-2-7b-hf', + 13: 'meta-llama/Llama-2-13b-hf', + 70: 'meta-llama/Llama-2-70b-hf', + 71: 'meta-llama/Llama-2-70b-chat-hf', +} +independant_variable = list(independant_variable_to_load_path.keys()) + +for c, n_gpu, independant_variable in zip(clusters, n_gpus, + independant_variable): + config = RunConfig.from_file(YAML_FILE) + parameters = copy.deepcopy(config.parameters) + config.name = f'{NAME}-{independant_variable}' + config.gpu_num = n_gpu + config.cluster = c + config.scheduling = {'priority': priority, 'preemptible': preemptible} + if retry_on_system_failure: + config.scheduling = config.scheduling | { + 'retry_on_system_failure': False, + 'max_retries': 1 + } + + # Edit the wandb integrations + config.integrations.append({ + 'integration_type': 'wandb', + 'project': 'eval-llama2-callibrate', + 'group': f'{independant_variable}', + 'entity': 'mosaic-ml' + }) + + run_params = copy.deepcopy(parameters) + + run_params['models'][0]['model_name'] = independant_variable_to_load_path[ + independant_variable] + run_params['models'][0]['model'][ + 'pretrained_model_name_or_path'] = independant_variable_to_load_path[ + independant_variable] + run_params['models'][0]['tokenizer'][ + 'name'] = independant_variable_to_load_path[independant_variable] + + config.parameters = run_params + print(config.name) + print(config.gpu_num) + + run = create_run(config) + print(run)