diff --git a/llmtune/cli/toolkit.py b/llmtune/cli/toolkit.py index 7451722..36fbe2e 100644 --- a/llmtune/cli/toolkit.py +++ b/llmtune/cli/toolkit.py @@ -15,7 +15,8 @@ from llmtune.finetune.lora import LoRAFinetune from llmtune.inference.lora import LoRAInference from llmtune.pydantic_models.config_model import Config -from llmtune.qa.generics import LLMTestSuite, QaTestRegistry +from llmtune.qa.generics import LLMTestSuite +from llmtune.qa.qa_tests import QaTestRegistry from llmtune.ui.rich_ui import RichUI from llmtune.utils.ablation_utils import generate_permutations from llmtune.utils.save_utils import DirectoryHelper @@ -92,6 +93,7 @@ def run_one_experiment(config: Config, config_path: Path) -> None: tests = QaTestRegistry.create_tests_from_list(llm_tests) test_suite = LLMTestSuite.from_csv(results_file_path, tests) test_suite.save_test_results(qa_file_path) + test_suite.print_test_results() @app.command("run") diff --git a/llmtune/config.yml b/llmtune/config.yml index 01def93..f77eab6 100644 --- a/llmtune/config.yml +++ b/llmtune/config.yml @@ -38,14 +38,16 @@ lora: r: 32 lora_alpha: 64 lora_dropout: 0.1 - target_modules: - - q_proj - - v_proj - - k_proj - - o_proj - - up_proj - - down_proj - - gate_proj + target_modules: "all-linear" + # to target specific modules + # target_modules: + # - q_proj + # - v_proj + # - k_proj + # - o_proj + # - up_proj + # - down_proj + # - gate_proj # Training ------------------- training: diff --git a/llmtune/pydantic_models/config_model.py b/llmtune/pydantic_models/config_model.py index 9938b67..fe1ab82 100644 --- a/llmtune/pydantic_models/config_model.py +++ b/llmtune/pydantic_models/config_model.py @@ -125,7 +125,9 @@ class LoraConfig(BaseModel): lora_alpha: Optional[int] = Field(16, description="The alpha parameter for Lora scaling") bias: Optional[str] = Field("none", description="Bias type for Lora. Can be 'none', 'all' or 'lora_only'") lora_dropout: Optional[float] = Field(0.1, description="The dropout probability for Lora layers") - target_modules: Optional[List[str]] = Field(None, description="The names of the modules to apply Lora to") + target_modules: Optional[Union[List[str], Literal["all-linear"]]] = Field( + "all-linear", description="The names of the modules to apply Lora to" + ) fan_in_fan_out: Optional[bool] = Field( False, description="Flag to indicate if the layer to replace stores weight like (fan_in, fan_out)", @@ -242,3 +244,4 @@ class Config(BaseModel): lora: LoraConfig training: TrainingConfig inference: InferenceConfig + qa: QaConfig diff --git a/llmtune/qa/generics.py b/llmtune/qa/generics.py index 8efabc2..0f9d6d3 100644 --- a/llmtune/qa/generics.py +++ b/llmtune/qa/generics.py @@ -1,5 +1,6 @@ import statistics from abc import ABC, abstractmethod +from pathlib import Path from typing import Dict, List, Union import pandas as pd @@ -18,23 +19,6 @@ def get_metric(self, prompt: str, grount_truth: str, model_pred: str) -> Union[f pass -class QaTestRegistry: - registry = {} - - @classmethod - def register(cls, *names): - def inner_wrapper(wrapped_class): - for name in names: - cls.registry[name] = wrapped_class - return wrapped_class - - return inner_wrapper - - @classmethod - def create_tests_from_list(cls, test_names: List[str]) -> List[LLMQaTest]: - return [cls.create_test(test) for test in test_names] - - class LLMTestSuite: def __init__( self, @@ -51,11 +35,17 @@ def __init__( self._results = {} @staticmethod - def from_csv(file_path: str, tests: List[LLMQaTest]) -> "LLMTestSuite": + def from_csv( + file_path: str, + tests: List[LLMQaTest], + prompt_col: str = "Prompt", + gold_col: str = "Ground Truth", + pred_col="Predicted", + ) -> "LLMTestSuite": results_df = pd.read_csv(file_path) - prompts = results_df["prompt"].tolist() - ground_truths = results_df["ground_truth"].tolist() - model_preds = results_df["model_prediction"].tolist() + prompts = results_df[prompt_col].tolist() + ground_truths = results_df[gold_col].tolist() + model_preds = results_df[pred_col].tolist() return LLMTestSuite(tests, prompts, ground_truths, model_preds) def run_tests(self) -> Dict[str, List[Union[float, int, bool]]]: @@ -84,5 +74,11 @@ def print_test_results(self): def save_test_results(self, path: str): # TODO: save these! + path = Path(path) + dir = path.parent + + if not dir.exists(): + dir.mkdir(parents=True, exist_ok=True) + resultant_dataframe = pd.DataFrame(self.test_results) resultant_dataframe.to_csv(path, index=False) diff --git a/llmtune/qa/qa_tests.py b/llmtune/qa/qa_tests.py index 46236d4..9a01861 100644 --- a/llmtune/qa/qa_tests.py +++ b/llmtune/qa/qa_tests.py @@ -9,7 +9,7 @@ from rouge_score import rouge_scorer from transformers import DistilBertModel, DistilBertTokenizer -from llmtune.qa.generics import LLMQaTest, QaTestRegistry +from llmtune.qa.generics import LLMQaTest model_name = "distilbert-base-uncased" @@ -21,6 +21,23 @@ nltk.download("averaged_perceptron_tagger") +class QaTestRegistry: + registry = {} + + @classmethod + def register(cls, *names): + def inner_wrapper(wrapped_class): + for name in names: + cls.registry[name] = wrapped_class + return wrapped_class + + return inner_wrapper + + @classmethod + def create_tests_from_list(cls, test_names: List[str]) -> List[LLMQaTest]: + return [cls.registry[test]() for test in test_names] + + @QaTestRegistry.register("summary_length") class LengthTest(LLMQaTest): @property diff --git a/poetry.lock b/poetry.lock index c6927bd..c9969ee 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. 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[package.dependencies] @@ -4010,13 +4027,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"] [[package]] name = "trl" -version = "0.7.11" +version = "0.8.6" description = "Train transformer language models with reinforcement learning." optional = false python-versions = ">=3.7" files = [ - {file = "trl-0.7.11-py3-none-any.whl", hash = "sha256:640235f43e734af4d37afeccd56831764df76ac09529df7f884c1809bdeeac36"}, - {file = "trl-0.7.11.tar.gz", hash = "sha256:eac98ca3d3cbe6498dcce65e939e487b5f50c6c6358e557487303bcfad0aa4db"}, + {file = "trl-0.8.6-py3-none-any.whl", hash = "sha256:0c9ee3faa7a6ddc158debaea0f725edb3143b4cc43babc5b5a05374de3e6352c"}, + {file = "trl-0.8.6.tar.gz", hash = "sha256:3785683f5b3e20d4b28481070dbdf736def42bcd6d3323e8a92f458925683da3"}, ] [package.dependencies] @@ -4059,13 +4076,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=6.2,<7.0)", "isort (>=5.0.6,<6. [[package]] name = "typing-extensions" -version = "4.10.0" +version = "4.11.0" description = "Backported and Experimental Type Hints for Python 3.8+" optional = false python-versions = ">=3.8" files = [ - {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"}, - {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"}, + {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"}, + {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"}, ] [[package]] @@ -4085,24 +4102,25 @@ typing-extensions = ">=3.7.4" [[package]] name = "tyro" -version = "0.7.3" +version = "0.8.3" description = "Strongly typed, zero-effort CLI interfaces" optional = false python-versions = ">=3.7" files = [ - {file = "tyro-0.7.3-py3-none-any.whl", hash = "sha256:4502347518b2364f9219486f9e6b97788146f67ee7900f3c499043c2c7c0be4b"}, - 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{file = "wandb-0.16.5-py3-none-any.whl", hash = "sha256:023b6c72a6ef13085c9a970f6714548eca64f56d3d8698e42372764950dfd004"}, - {file = "wandb-0.16.5.tar.gz", hash = "sha256:c317d55af93a688f3eafcdfec897f7b72da1fe1525140e076ecdaab8b09aa46e"}, + {file = "wandb-0.16.6-py3-none-any.whl", hash = "sha256:5810019a3b981c796e98ea58557a7c380f18834e0c6bdaed15df115522e5616e"}, + {file = "wandb-0.16.6.tar.gz", hash = "sha256:86f491e3012d715e0d7d7421a4d6de41abef643b7403046261f962f3e512fe1c"}, ] [package.dependencies] @@ -4482,4 +4500,4 @@ multidict = ">=4.0" [metadata] lock-version = "2.0" python-versions = ">=3.9, <=3.12" -content-hash = "a219bb2a51335cec61f5490e834262b8449fbdbc96ad2b4880a20504f9bbe5ca" +content-hash = "5d8db23dbf9d5b88e80c91f5fed6e82632dfe596d15ec2ab475e78b049cd4ed9" diff --git a/pyproject.toml b/pyproject.toml index 513058f..76ed3e1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -32,7 +32,7 @@ folders = [ [tool.poetry.dependencies] python = ">=3.9, <=3.12" -transformers = "~4.37.2" +transformers = "~4.40.2" datasets = "^2.17.0" peft = "^0.8.2" pandas = "^2.2.0" @@ -44,7 +44,7 @@ einops = "^0.7.0" bitsandbytes = "^0.42.0" nltk = "^3.8.1" accelerate = "^0.27.0" -trl = "~0.7.10" +trl = "~0.8.6" rouge-score = "^0.1.2" absl-py = "^2.1.0" py7zr = "^0.20.8" diff --git a/test_utils/test_config.py b/test_utils/test_config.py index 1e972b9..cdb58d3 100644 --- a/test_utils/test_config.py +++ b/test_utils/test_config.py @@ -6,6 +6,7 @@ InferenceConfig, LoraConfig, ModelConfig, + QaConfig, SftArgs, TrainingArgs, TrainingConfig, @@ -70,4 +71,16 @@ def get_sample_config(): test_size=0.1, train_test_split_seed=42, ), + qa=QaConfig( + llm_tests=[ + "jaccard_similarity", + "dot_product", + "rouge_score", + "word_overlap", + "verb_percent", + "adjective_percent", + "noun_percent", + "summary_length", + ] + ), ) diff --git a/tests/data/test_ingestor.py b/tests/data/test_ingestor.py index 9abf813..ce6b2f2 100644 --- a/tests/data/test_ingestor.py +++ b/tests/data/test_ingestor.py @@ -1,5 +1,7 @@ +from unittest.mock import MagicMock, mock_open + import pytest -from unittest.mock import patch, MagicMock, mock_open +from datasets import Dataset from llmtune.data.ingestor import ( CsvIngestor, @@ -9,8 +11,6 @@ get_ingestor, ) -from datasets import Dataset - def test_get_ingestor(): assert isinstance(get_ingestor("json")(""), JsonIngestor) @@ -31,9 +31,7 @@ def test_json_ingestor_to_dataset(mocker): def test_jsonl_ingestor_to_dataset(mocker): - mock_generator = mocker.patch( - "llmtune.data.ingestor.JsonlIngestor._jsonl_generator" - ) + mock_generator = mocker.patch("llmtune.data.ingestor.JsonlIngestor._jsonl_generator") mock_dataset = mocker.patch("llmtune.data.ingestor.Dataset") JsonlIngestor("").to_dataset() @@ -52,9 +50,7 @@ def test_huggingface_to_dataset(mocker): # Setup path = "some_path" ingestor = HuggingfaceIngestor(path) - mock_concatenate_datasets = mocker.patch( - "llmtune.data.ingestor.concatenate_datasets" - ) + mock_concatenate_datasets = mocker.patch("llmtune.data.ingestor.concatenate_datasets") mock_load_dataset = mocker.patch("llmtune.data.ingestor.load_dataset") mock_dataset = mocker.patch("llmtune.data.ingestor.Dataset") @@ -108,9 +104,7 @@ def test_jsonl_ingestor_generator(file_content, expected_output, mocker): mocker.patch("builtins.open", mock_open(read_data=file_content)) mocker.patch( "ijson.items", - side_effect=lambda f, prefix, multiple_values: ( - iter(expected_output) if multiple_values else iter([]) - ), + side_effect=lambda f, prefix, multiple_values: (iter(expected_output) if multiple_values else iter([])), ) ingestor = JsonlIngestor("dummy_path.jsonl") diff --git a/tests/finetune/test_finetune_lora.py b/tests/finetune/test_finetune_lora.py index 0a2bcb0..6c29939 100644 --- a/tests/finetune/test_finetune_lora.py +++ b/tests/finetune/test_finetune_lora.py @@ -18,15 +18,11 @@ def test_lora_finetune_initialization(mocker): ) # Initialize LoRAFinetune with the sample configuration - lora_finetune = LoRAFinetune( - config=get_sample_config(), directory_helper=MagicMock() - ) + lora_finetune = LoRAFinetune(config=get_sample_config(), directory_helper=MagicMock()) # Assertions to ensure that LoRAFinetune is initialized as expected mock_lora_config.assert_called_once_with(**get_sample_config().lora.model_dump()) - assert ( - lora_finetune.config == get_sample_config() - ), "Configuration should match the input configuration" + assert lora_finetune.config == get_sample_config(), "Configuration should match the input configuration" def test_model_and_tokenizer_loading(mocker): @@ -37,9 +33,7 @@ def test_model_and_tokenizer_loading(mocker): "llmtune.finetune.lora.AutoModelForCausalLM.from_pretrained", return_value=MagicMock(), ) - mock_tokenizer = mocker.patch( - "llmtune.finetune.lora.AutoTokenizer.from_pretrained", return_value=MagicMock() - ) + mock_tokenizer = mocker.patch("llmtune.finetune.lora.AutoTokenizer.from_pretrained", return_value=MagicMock()) mock_inject_lora = mocker.patch( "llmtune.finetune.lora.LoRAFinetune._inject_lora", return_value=None, # _inject_lora doesn't return a value @@ -89,18 +83,14 @@ def test_model_finetune(mocker): "llmtune.finetune.lora.AutoModelForCausalLM.from_pretrained", return_value=MagicMock(), ) - mocker.patch( - "llmtune.finetune.lora.AutoTokenizer.from_pretrained", return_value=MagicMock() - ) + mocker.patch("llmtune.finetune.lora.AutoTokenizer.from_pretrained", return_value=MagicMock()) mocker.patch( "llmtune.finetune.lora.LoRAFinetune._inject_lora", return_value=None, # _inject_lora doesn't return a value ) mock_trainer = mocker.MagicMock() - mock_sft_trainer = mocker.patch( - "llmtune.finetune.lora.SFTTrainer", return_value=mock_trainer - ) + mock_sft_trainer = mocker.patch("llmtune.finetune.lora.SFTTrainer", return_value=mock_trainer) directory_helper = MagicMock() @@ -144,9 +134,7 @@ def test_save_model(mocker): mocker.patch("llmtune.finetune.lora.AutoModelForCausalLM.from_pretrained") mock_tok = mocker.MagicMock() - mocker.patch( - "llmtune.finetune.lora.AutoTokenizer.from_pretrained", return_value=mock_tok - ) + mocker.patch("llmtune.finetune.lora.AutoTokenizer.from_pretrained", return_value=mock_tok) mocker.patch( "llmtune.finetune.lora.LoRAFinetune._inject_lora", return_value=None, diff --git a/tests/inference/test_inference_lora.py b/tests/inference/test_inference_lora.py index 56dd300..a0f2983 100644 --- a/tests/inference/test_inference_lora.py +++ b/tests/inference/test_inference_lora.py @@ -1,13 +1,11 @@ -import pytest from unittest.mock import MagicMock + from datasets import Dataset +from transformers import BitsAndBytesConfig from llmtune.inference.lora import LoRAInference -from llmtune.utils.save_utils import DirectoryHelper from test_utils.test_config import get_sample_config # Adjust import path as needed -from transformers import BitsAndBytesConfig - def test_lora_inference_initialization(mocker): # Mock dependencies @@ -15,15 +13,11 @@ def test_lora_inference_initialization(mocker): "llmtune.inference.lora.AutoPeftModelForCausalLM.from_pretrained", return_value=MagicMock(), ) - mock_tokenizer = mocker.patch( - "llmtune.inference.lora.AutoTokenizer.from_pretrained", return_value=MagicMock() - ) + mock_tokenizer = mocker.patch("llmtune.inference.lora.AutoTokenizer.from_pretrained", return_value=MagicMock()) # Mock configuration and directory helper config = get_sample_config() - dir_helper = MagicMock( - save_paths=MagicMock(results="results_dir", weights="weights_dir") - ) + dir_helper = MagicMock(save_paths=MagicMock(results="results_dir", weights="weights_dir")) test_dataset = Dataset.from_dict( { "formatted_prompt": ["prompt1", "prompt2"], @@ -31,7 +25,7 @@ def test_lora_inference_initialization(mocker): } ) - inference = LoRAInference( + _ = LoRAInference( test_dataset=test_dataset, label_column_name="label_column_name", config=config, @@ -45,9 +39,7 @@ def test_lora_inference_initialization(mocker): device_map=config.model.device_map, attn_implementation=config.model.attn_implementation, ) - mock_tokenizer.assert_called_once_with( - "weights_dir", device_map=config.model.device_map - ) + mock_tokenizer.assert_called_once_with("weights_dir", device_map=config.model.device_map) def test_infer_all(mocker): @@ -55,24 +47,16 @@ def test_infer_all(mocker): "llmtune.inference.lora.AutoPeftModelForCausalLM.from_pretrained", return_value=MagicMock(), ) - mocker.patch( - "llmtune.inference.lora.AutoTokenizer.from_pretrained", return_value=MagicMock() - ) + mocker.patch("llmtune.inference.lora.AutoTokenizer.from_pretrained", return_value=MagicMock()) mocker.patch("os.makedirs") mock_open = mocker.patch("builtins.open", mocker.mock_open()) mock_csv_writer = mocker.patch("csv.writer") - mock_infer_one = mocker.patch.object( - LoRAInference, "infer_one", return_value="predicted" - ) + mock_infer_one = mocker.patch.object(LoRAInference, "infer_one", return_value="predicted") config = get_sample_config() - dir_helper = MagicMock( - save_paths=MagicMock(results="results_dir", weights="weights_dir") - ) - test_dataset = Dataset.from_dict( - {"formatted_prompt": ["prompt1"], "label_column_name": ["label1"]} - ) + dir_helper = MagicMock(save_paths=MagicMock(results="results_dir", weights="weights_dir")) + test_dataset = Dataset.from_dict({"formatted_prompt": ["prompt1"], "label_column_name": ["label1"]}) inference = LoRAInference( test_dataset=test_dataset, diff --git a/tests/qa/test_generics.py b/tests/qa/test_generics.py index 05258b9..73f4fd3 100644 --- a/tests/qa/test_generics.py +++ b/tests/qa/test_generics.py @@ -1,7 +1,7 @@ import pytest -from unittest.mock import Mock from pandas import DataFrame -from llmtune.qa.generics import LLMQaTest, QaTestRegistry, LLMTestSuite + +from llmtune.qa.generics import LLMQaTest, LLMTestSuite @pytest.fixture @@ -12,9 +12,9 @@ def mock_rich_ui(mocker): @pytest.fixture def example_data(): data = { - "prompt": ["What is 2+2?", "What is the capital of France?"], - "ground_truth": ["4", "Paris"], - "model_prediction": ["3", "Paris"], + "Prompt": ["What is 2+2?", "What is the capital of France?"], + "Ground Truth": ["4", "Paris"], + "Predicted": ["3", "Paris"], } return DataFrame(data) @@ -65,9 +65,7 @@ def test_save_test_results(mock_csv, mock_tests, mocker): def test_print_test_results(capfd, example_data): tests = [MockQaTest()] - test_suite = LLMTestSuite( - tests, example_data["prompt"], example_data["ground_truth"], example_data["model_prediction"] - ) + test_suite = LLMTestSuite(tests, example_data["Prompt"], example_data["Ground Truth"], example_data["Predicted"]) test_suite.print_test_results() out, err = capfd.readouterr() diff --git a/tests/qa/test_qa_tests.py b/tests/qa/test_qa_tests.py index 9d9000d..22e12de 100644 --- a/tests/qa/test_qa_tests.py +++ b/tests/qa/test_qa_tests.py @@ -1,14 +1,14 @@ import pytest + from llmtune.qa.qa_tests import ( - QaTestRegistry, - LengthTest, - JaccardSimilarityTest, + AdjectivePercent, DotProductSimilarityTest, + JaccardSimilarityTest, + LengthTest, + NounPercent, RougeScoreTest, - WordOverlapTest, VerbPercent, - AdjectivePercent, - NounPercent, + WordOverlapTest, ) diff --git a/tests/test_ablation_utils.py b/tests/test_ablation_utils.py index 0324409..005d7d2 100644 --- a/tests/test_ablation_utils.py +++ b/tests/test_ablation_utils.py @@ -1,12 +1,12 @@ import pytest from pydantic import BaseModel + from llmtune.utils.ablation_utils import ( - get_types_from_dict, + generate_permutations, get_annotation, get_model_field_type, - validate_and_get_ablations, + get_types_from_dict, patch_with_permutation, - generate_permutations, ) diff --git a/tests/test_cli.py b/tests/test_cli.py index fb348b0..c8e2f6b 100644 --- a/tests/test_cli.py +++ b/tests/test_cli.py @@ -1,14 +1,9 @@ -import pytest -from unittest.mock import MagicMock, patch -from pathlib import Path +from unittest.mock import patch -from pydantic import ValidationError from typer.testing import CliRunner -from llmtune.cli.toolkit import app, cli, run_one_experiment -from llmtune.pydantic_models.config_model import Config +from llmtune.cli.toolkit import app, cli -from test_utils.test_config import get_sample_config runner = CliRunner()