From cb357adadbd881634d9870eeb9457baaa8bb9a58 Mon Sep 17 00:00:00 2001 From: yvesnana Date: Wed, 7 Aug 2024 14:13:53 +0200 Subject: [PATCH 1/4] fixed CPU issue during training added yaml files for granite fixed rank issues Signed-off-by: yvesnana --- configs/deepspeed_config_2.json | 48 + configs/granite_test.yaml | 26 + configs/granite_train.yaml | 26 + poetry.lock | 2921 +++++++++++++++--------------- pyproject.toml | 7 +- src/mtl4ad/data_preprocessing.py | 86 +- src/mtl4ad/model.py | 44 +- src/mtl4ad/train.py | 54 +- src/mtl4ad/train_2.py | 143 ++ 9 files changed, 1882 insertions(+), 1473 deletions(-) create mode 100644 configs/deepspeed_config_2.json create mode 100644 configs/granite_test.yaml create mode 100644 configs/granite_train.yaml create mode 100644 src/mtl4ad/train_2.py diff --git a/configs/deepspeed_config_2.json b/configs/deepspeed_config_2.json new file mode 100644 index 0000000..543e596 --- /dev/null +++ b/configs/deepspeed_config_2.json @@ -0,0 +1,48 @@ +{ + "bf16": { + "enabled": "auto" + }, + "fp16": { + "enabled": "auto", + "loss_scale": 0, + "loss_scale_window": 1000, + "initial_scale_power": 16, + "hysteresis": 2, + "min_loss_scale": 1 + }, + "optimizer": { + "type": "AdamW", + "params": { + "lr": "auto", + "betas": "auto", + "eps": "auto", + "weight_decay": "auto" + } + }, + "scheduler": { + "type": "WarmupLR", + "params": { + "warmup_min_lr": "auto", + "warmup_max_lr": "auto", + "warmup_num_steps": "auto" + } + }, + "zero_optimization": { + "stage": 2, + "offload_optimizer": { + "device": "cpu", + "pin_memory": true + }, + "allgather_partitions": true, + "allgather_bucket_size": 5e8, + "overlap_comm": true, + "reduce_scatter": true, + "reduce_bucket_size": 5e8, + "contiguous_gradients": true + }, + "gradient_accumulation_steps": "auto", + "gradient_clipping": "auto", + "train_batch_size": "auto", + "train_micro_batch_size_per_gpu": "auto", + "steps_per_print": 1e5 +} \ No newline at end of file diff --git a/configs/granite_test.yaml b/configs/granite_test.yaml new file mode 100644 index 0000000..16452ef --- /dev/null +++ b/configs/granite_test.yaml @@ -0,0 +1,26 @@ +model_name: "ibm-granite/granite-7b-base" +dataset_path: "src/mtl4ad/resources/train/dataset" +per_device_train_batch_size: 2 +per_device_eval_batch_size: 2 +gradient_checkpointing: true +gradient_accumulation_steps: 1 +experiment_name: test_no_deepspeed +checkpoint_dir: "models" +# deepspeed: "configs/deepspeed_config.json" #"configs/deepspeed_config.json" +dataset_percentage: 0.2 +dataset_seed: 42 +shuffle: true +enable_peft: true +eval_steps: 4 +do_eval: true +logging_steps: 5 +save_total_limit: 10 +save_steps: 5 +eval_strategy: "steps" +num_train_epochs: 1 +# max_steps: 100 +report_to: "mlflow" +max_seq_length: 512 +process_group_timeout_in_minutes: 50 +resume_from_checkpoint: "/home/ynanateukam/mtl4ad/models/test_no_deepspeed/checkpoint-15" +# optim: "adamw_bnb_8bit" \ No newline at end of file diff --git a/configs/granite_train.yaml b/configs/granite_train.yaml new file mode 100644 index 0000000..68bab98 --- /dev/null +++ b/configs/granite_train.yaml @@ -0,0 +1,26 @@ +model_name: "ibm-granite/granite-7b-base" +dataset_path: "src/mtl4ad/resources/train/dataset" +per_device_train_batch_size: 8 +per_device_eval_batch_size: 8 +gradient_checkpointing: true +gradient_accumulation_steps: 4 +experiment_name: full_train_no_deepspeed +checkpoint_dir: "models" +# deepspeed: "configs/deepspeed_config.json" #"configs/deepspeed_config.json" +dataset_percentage: 100 +dataset_seed: 42 +shuffle: true +enable_peft: true +eval_steps: 5000 +logging_steps: 100 +save_total_limit: 10 +save_steps: 50 +eval_strategy: "steps" +num_train_epochs: 1 +# max_steps: 100 +report_to: "none" +max_seq_length: 4096 +ddp_backend: "nccl" +process_group_timeout_in_minutes: 50 +# resume_from_checkpoint: "/home/ynanateukam/mtl4ad/models/test_no_deepspeed/checkpoint-50" +# optim: "adamw_bnb_8bit" \ No newline at end of file diff --git a/poetry.lock 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language models with reinforcement learning." -optional = false -python-versions = ">=3.7" -files = [ - {file = "trl-0.9.4-py3-none-any.whl", hash = "sha256:a7322f73d818e4ff73441520ae2494ce46ce4c8052058fff7cb80d323ff87e28"}, -] - -[package.dependencies] -accelerate = "*" -datasets = "*" -numpy = ">=1.18.2" -torch = ">=1.4.0" -transformers = ">=4.31.0" -tyro = ">=0.5.11" - -[package.extras] -benchmark = ["deepspeed", "ghapi", "openrlbenchmark (==0.2.1a5)", "requests", "wandb"] -deepspeed = ["deepspeed (>=0.9.5)"] -dev = ["accelerate", "bitsandbytes (<=0.41.1)", "deepspeed", "deepspeed (>=0.9.5)", "diffusers (>=0.18.0)", "ghapi", "openrlbenchmark (==0.2.1a5)", "parameterized", "peft (>=0.4.0)", "pytest", "pytest-cov", "pytest-xdist", "requests", "scikit-learn", "wandb"] -diffusers = ["diffusers (>=0.18.0)"] -peft = ["peft (>=0.4.0)"] -quantization = ["bitsandbytes (<=0.41.1)"] -test = ["accelerate", "parameterized", "pytest", "pytest-cov", "pytest-xdist", "scikit-learn"] +tests = 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avoid serialization errors when saving checkpoints evaluate = '^0.4.1' loguru = '^0.7.2' mlflow = '^2.11.1' @@ -53,12 +53,13 @@ rouge-score = '^0.1.2' tqdm = '^4.25.0' importlib-resources = "^6.3.1" pandas = "^2.2.1" -transformers = "^4.39.3" sacrebleu = "^2.4.2" python-levenshtein = "0.25.1" -trl = "^0.9.4" bitsandbytes = "^0.43.1" +tokenizers = "^0.19.1" +transformers = "^4.44.0" +accelerate = "<0.30.0" [tool.poetry.dev-dependencies] jupyter = "^1.0.0" mypy = "^1.0.0" diff --git a/src/mtl4ad/data_preprocessing.py b/src/mtl4ad/data_preprocessing.py index be32b50..cdfac5b 100644 --- a/src/mtl4ad/data_preprocessing.py +++ b/src/mtl4ad/data_preprocessing.py @@ -2,11 +2,13 @@ import logging from pathlib import Path -from typing import Any, Dict, Optional, Union +from typing import Any, Dict, Optional, Union, List from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset from transformers import PreTrainedTokenizer +import multiprocessing as mp + logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", @@ -14,6 +16,34 @@ ) logger = logging.getLogger(__name__) +def load_dataset_from_folder(folder: Path) -> Dict[str, Optional[Dataset]]: + """Load train and validation datasets from a single folder. + + Args: + folder: The folder to load the datasets from. + + Returns: + A dictionary containing 'train' and 'validation' datasets. + """ + datasets = {"train": None, "validation": None} + + train_file = folder / "train.parquet" + valid_file = folder / "validation.parquet" + + if train_file.exists(): + train_dataset = load_dataset( + "parquet", data_files=str(train_file), split="train", num_proc=mp.cpu_count(), cache_dir="/projects/0/prjs1108/.cache" + ) + datasets["train"] = train_dataset + + if valid_file.exists(): + valid_dataset = load_dataset( + "parquet", data_files=str(valid_file), split="train", num_proc=mp.cpu_count(), cache_dir="/projects/0/prjs1108/.cache" + ) + datasets["validation"] = valid_dataset + + + return datasets def load_dataset_from_folders(base_path: Union[str, Path]) -> DatasetDict: """Load and concatenate datasets from multiple folders. @@ -24,32 +54,27 @@ def load_dataset_from_folders(base_path: Union[str, Path]) -> DatasetDict: Returns: The concatenated dataset with 'train' and 'validation' splits. """ - all_train_datasets = [] - all_valid_datasets = [] - base_path = Path(base_path) if isinstance(base_path, str) else base_path - for folder in base_path.iterdir(): - if folder.is_dir(): - train_file = folder / "train.parquet" - valid_file = folder / "validation.parquet" - - if train_file.exists(): - train_dataset = load_dataset( - "parquet", data_files=str(train_file), split="train", num_proc=32 - ) - all_train_datasets.append(train_dataset) - - if valid_file.exists(): - valid_dataset = load_dataset( - "parquet", data_files=str(valid_file), split="train", num_proc=32 - ) - all_valid_datasets.append(valid_dataset) + + folders = [folder for folder in base_path.iterdir() if folder.is_dir()] + + with mp.Pool(mp.cpu_count()) as pool: + results = pool.map(load_dataset_from_folder, folders) + + all_train_datasets = [res["train"] for res in results if res["train"] is not None] + all_valid_datasets = [res["validation"] for res in results if res["validation"] is not None] train_dataset = concatenate_datasets(all_train_datasets) if all_train_datasets else None valid_dataset = concatenate_datasets(all_valid_datasets) if all_valid_datasets else None dataset = DatasetDict({"train": train_dataset, "validation": valid_dataset}) - + for entry in dataset: + try: + dataset[entry] = dataset[entry].rename_column("target", "labels") + dataset[entry] = dataset[entry].rename_column("source", "text") + except: + pass + return dataset @@ -80,18 +105,19 @@ def process(examples: dict) -> dict: """ formatted_prompts = [ f"{source} \n\n {target}" - for source, target in zip(examples["source"], examples["target"]) + for source, target in zip(examples["text"], examples["labels"]) ] model_inputs = tokenizer( formatted_prompts, truncation=True, max_length=max_length, return_tensors=None, + padding="max_length" ) model_inputs["labels"] = model_inputs["input_ids"].copy() return model_inputs - return dataset.map(process, batched=True) + return dataset.map(process, batched=True, num_proc=mp.cpu_count()) def filter_dataset(dataset: DatasetDict, dataset_percentage: Optional[float]) -> DatasetDict: @@ -145,3 +171,17 @@ def load_dataset_and_preprocess(config: Dict[str, Any], tokenizer) -> Dataset: logger.info("Done shuffling") return dataset + +def generate_formatted_prompts(examples) -> List[str]: + """Generate formatted prompts from examples. + + Args: + examples: A dictionary with 'source' and 'target' lists. + + Returns: + A list of formatted prompts. + """ + return [ + f"{source} \n\n {target}" + for source, target in zip(examples["text"], examples["labels"]) + ] \ No newline at end of file diff --git a/src/mtl4ad/model.py b/src/mtl4ad/model.py index 77002c1..d357173 100644 --- a/src/mtl4ad/model.py +++ b/src/mtl4ad/model.py @@ -1,16 +1,23 @@ """Model and Tokenizer Utilities""" -from typing import Any, Optional, Tuple +from typing import Any, Optional, Tuple, Dict import torch -from peft import LoraConfig -from transformers import AutoModelForCausalLM, AutoTokenizer +from peft import LoraConfig, get_peft_model +from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig +import logging from mtl4ad.utils import find_all_linear_names, is_bf16_available # type: ignore +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) def load_model_and_tokenizer_peft( - model_name: str, use_peft: bool = False + config: Dict[str, Any], use_peft: bool = False ) -> Tuple[AutoModelForCausalLM, AutoTokenizer, Optional[Any]]: """ Loads the model and tokenizer. @@ -22,19 +29,37 @@ def load_model_and_tokenizer_peft( Returns: A tuple containing the loaded model and tokenizer, and optionally the PEFT config. """ + model_name=config["model_name"] + bnb_config = BitsAndBytesConfig( + load_in_4bit=True, + bnb_4bit_use_double_quant=True, + bnb_4bit_quant_type="nf4", + bnb_4bit_compute_dtype=torch.bfloat16 + ) + model = AutoModelForCausalLM.from_pretrained( - model_name, torch_dtype=torch.bfloat16 if is_bf16_available() else torch.float32 + model_name, torch_dtype=torch.bfloat16 if is_bf16_available() else torch.float32, + # use_flash_attention_2=True, + quantization_config=bnb_config, ) model.config.use_cache = False tokenizer = AutoTokenizer.from_pretrained(model_name) + tokenizer.padding_side = 'right' tokenizer.add_eos_token = True tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id + max_length = ( + min(tokenizer.model_max_length, config["max_seq_length"]) + if "max_seq_length" in config and config["max_seq_length"] is not None + else tokenizer.model_max_length +) + tokenizer.model_max_length = max_length peft_config = None if use_peft: - peft_config = enable_peft(model) + model, peft_config = enable_peft(model) + logger.info("Peft enabled") return model, tokenizer, peft_config @@ -70,10 +95,9 @@ def enable_peft(model) -> torch.nn.Module: task_type="CAUSAL_LM", target_modules=target_modules, ) - - return peft_config - # model = get_peft_model(model, peft_config) - # return model + + model = get_peft_model(model, peft_config) + return model, peft_config def prepare_model_for_training( diff --git a/src/mtl4ad/train.py b/src/mtl4ad/train.py index 90f5c92..4813942 100644 --- a/src/mtl4ad/train.py +++ b/src/mtl4ad/train.py @@ -3,6 +3,8 @@ import argparse import logging from pathlib import Path +import json +from datetime import timedelta import mlflow # type: ignore import torch @@ -49,8 +51,9 @@ def initialize_trainer( dataset, experiment_name, output_dir, + max_seq_length, enable_peft=False, - peft_config=None, + peft_config=None ) -> SFTTrainer: """ Initializes the Trainer for model training. @@ -73,6 +76,8 @@ def initialize_trainer( output_dir=str(output_dir), run_name=experiment_name, bf16=IS_BF16_AVAILABLE, + ddp_find_unused_parameters=False, + gradient_checkpointing_kwargs={"use_reentrant":False}, **config, ) @@ -85,6 +90,7 @@ def initialize_trainer( data_collator=collator, callbacks=[PeftSavingCallback()] if enable_peft else None, peft_config=peft_config, + max_seq_length=max_seq_length ) @@ -110,22 +116,46 @@ def main() -> None: output_dir = str(Path(config.get("checkpoint_dir", "tmp")).joinpath(experiment_name)) enable_peft = config.get("enable_peft", False) - model, tokenizer, peft_config = load_model_and_tokenizer_peft(config["model_name"], enable_peft) + if not torch.distributed.is_initialized(): + try: + torch.distributed.init_process_group( + backend="nccl" if IS_CUDA_AVAILABLE else "gloo", + timeout=timedelta( + minutes=float(config.get("process_group_timeout_in_minutes", 30)) + ), + ) + logger.info("Process group has been initialized successfully") + except ValueError: + logger.warning( + "Initializing the process group from the environment was not possible!" + ) + + # logger.info(f"Rank: {torch.distributed.get_rank()}") + # torch.cuda.set_device(torch.distributed.get_rank()) + model, tokenizer, peft_config = load_model_and_tokenizer_peft(config, enable_peft) dataset = load_dataset_and_preprocess(config, tokenizer) - config = extract_valid_training_arguments(config) + clean_config = extract_valid_training_arguments(config) trainer = initialize_trainer( - config, model, tokenizer, dataset, experiment_name, output_dir, enable_peft, peft_config + clean_config, model, tokenizer, dataset, experiment_name, output_dir, config.get("max_seq_length", tokenizer.model_max_length), enable_peft, peft_config ) - is_mlflow_available = config.get("report_to") == "mlflow" - - if is_mlflow_available and (not IS_CUDA_AVAILABLE or torch.distributed.get_rank() == 0): - mlflow.set_experiment(full_experiment_name) - mlflow.start_run() - mlflow.set_tag("mlflow.runName", experiment_name) - mlflow.pytorch.autolog() - trainer.train() + config_json = json.dumps(config) + + # if is_mlflow_available and (not IS_CUDA_AVAILABLE or torch.distributed.get_rank() == 0): + # mlflow.set_experiment(full_experiment_name) + # mlflow.start_run() + # mlflow.set_tag("mlflow.runName", experiment_name) + # mlflow.set_tag("configs", config_json) + # mlflow.pytorch.autolog() + resume_checkpoint = config.get("resume_from_checkpoint", None) + if resume_checkpoint: + logger.info(f"Resuming from Checkpoint: {resume_checkpoint}") + trainer.train(resume_from_checkpoint=resume_checkpoint) + else: + trainer.place_model_on_device = True + trainer.train() + trainer.save_model(str(Path(output_dir).joinpath("last_model"))) dist.destroy_process_group() diff --git a/src/mtl4ad/train_2.py b/src/mtl4ad/train_2.py new file mode 100644 index 0000000..c8e3929 --- /dev/null +++ b/src/mtl4ad/train_2.py @@ -0,0 +1,143 @@ +"""Model Training Script""" + +import argparse +import logging +from pathlib import Path + +import mlflow # type: ignore +import torch +import torch.distributed as dist +from transformers import DataCollatorForSeq2Seq, TrainingArguments +from trl import SFTConfig, SFTTrainer # type: ignore + +from mtl4ad.data_preprocessing import load_dataset_from_folders, filter_dataset, generate_formatted_prompts # type: ignore +from mtl4ad.model import load_model_and_tokenizer_peft # type: ignore +from mtl4ad.utils import ( # type: ignore + PeftSavingCallback, + extract_valid_training_arguments, + format_name, + load_config, +) + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + datefmt="%Y-%m-%d %H:%M:%S", +) +logger = logging.getLogger(__name__) + +IS_CUDA_AVAILABLE = torch.cuda.is_available() +IS_BF16_AVAILABLE = IS_CUDA_AVAILABLE and torch.cuda.is_bf16_supported() + +def parse_arguments() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Training Script") + parser.add_argument("--config", type=str, required=True, help="Path to the configuration file.") + return parser.parse_args() + +def setup_config(config_path: str) -> dict: + config = load_config(config_path) + config["dataset_path"] = str(Path(config["dataset_path"]).resolve()) + config["checkpoint_dir"] = str(Path(config["checkpoint_dir"]).resolve()) + config["deepspeed"] = str(Path(config["deepspeed"]).resolve()) if "deepspeed" in config else None + if "model_name" not in config: + raise KeyError("The 'model_name' key is missing from the configuration. Terminating script.") + return config + +def initialize_distributed(): + if IS_CUDA_AVAILABLE and torch.cuda.device_count() > 1: + if not dist.is_initialized(): + dist.init_process_group(backend='nccl') + +def load_and_prepare_datasets(config: dict) -> dict: + logger.info("Loading dataset...") + dataset = load_dataset_from_folders(config["dataset_path"]) + log_dataset_sizes("Original size", dataset) + + if config.get("dataset_percentage"): + logger.info("Sampling dataset...") + dataset = filter_dataset(dataset, config.get("dataset_percentage")) + log_dataset_sizes("Filtered size", dataset) + + if config.get("shuffle"): + logger.info("Shuffling dataset...") + dataset = dataset.shuffle(seed=config.get("dataset_seed", 42)) + logger.info("Done shuffling") + + return dataset + +def log_dataset_sizes(prefix: str, dataset: dict): + for element in dataset: + logger.info(f"{prefix} of {element}: {len(dataset[element])}") + +def initialize_trainer(config: dict, dataset: dict, model, tokenizer, collator) -> SFTTrainer: + training_args = TrainingArguments( + output_dir=config["checkpoint_dir"], + run_name=config["experiment_name"], + bf16=IS_BF16_AVAILABLE, + ddp_find_unused_parameters=True, + remove_unused_columns=True, + gradient_checkpointing_kwargs={"use_reentrant": False}, + **extract_valid_training_arguments(config), + ) + + return SFTTrainer( + model=model, + tokenizer=tokenizer, + args=training_args, + train_dataset=dataset["train"], + eval_dataset=dataset["validation"], + data_collator=collator, + callbacks=[PeftSavingCallback()] if config.get("enable_peft") else None, + peft_config=config.get("peft_config"), + formatting_func=generate_formatted_prompts, + ) + +def setup_mlflow(config: dict, experiment_name: str): + if config.get("report_to") == "mlflow": + mlflow.set_experiment(experiment_name) + mlflow.start_run() + mlflow.set_tag("mlflow.runName", experiment_name) + mlflow.pytorch.autolog() + +def train_and_save_model(trainer: SFTTrainer, output_dir: str, resume_checkpoint: str): + if resume_checkpoint: + trainer.train(resume_from_checkpoint=resume_checkpoint) + else: + trainer.train() + trainer.save_model(str(Path(output_dir).joinpath("last_model"))) + +def main() -> None: + args = parse_arguments() + config = setup_config(args.config) + experiment_name = format_name(config.get("experiment_name", "train")) + + initialize_distributed() + + local_rank = dist.get_rank() if dist.is_initialized() else 0 + + if local_rank == 0: + dataset = load_and_prepare_datasets(config) + if dist.is_initialized(): + torch.distributed.barrier() + else: + if dist.is_initialized(): + torch.distributed.barrier() + dataset = load_dataset_from_folders(config["dataset_path"]) + + model, tokenizer, peft_config = load_model_and_tokenizer_peft(config["model_name"], config.get("enable_peft")) + collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model) + + trainer = initialize_trainer(config, dataset, model, tokenizer, collator) + + if not IS_CUDA_AVAILABLE or local_rank == 0: + setup_mlflow(config, experiment_name) + + train_and_save_model(trainer, config["checkpoint_dir"], config.get("resume_from_checkpoint")) + + if dist.is_initialized(): + dist.destroy_process_group() + + logger.info("Training completed and model saved.") + +if __name__ == "__main__": + main() \ No newline at end of file From ba86df6ee291a933f5c4fa4b9cab10fa806d2f0d Mon Sep 17 00:00:00 2001 From: yvesnana Date: Wed, 7 Aug 2024 17:23:25 +0200 Subject: [PATCH 2/4] fixed multiprocess Signed-off-by: yvesnana --- src/mtl4ad/data_preprocessing.py | 9 ++---- src/mtl4ad/model.py | 4 +++ src/mtl4ad/train.py | 50 ++++++++++++++++++++++++++++---- 3 files changed, 52 insertions(+), 11 deletions(-) diff --git a/src/mtl4ad/data_preprocessing.py b/src/mtl4ad/data_preprocessing.py index cdfac5b..f23348b 100644 --- a/src/mtl4ad/data_preprocessing.py +++ b/src/mtl4ad/data_preprocessing.py @@ -69,12 +69,9 @@ def load_dataset_from_folders(base_path: Union[str, Path]) -> DatasetDict: dataset = DatasetDict({"train": train_dataset, "validation": valid_dataset}) for entry in dataset: - try: - dataset[entry] = dataset[entry].rename_column("target", "labels") - dataset[entry] = dataset[entry].rename_column("source", "text") - except: - pass - + dataset[entry] = dataset[entry].rename_column("target", "labels") + dataset[entry] = dataset[entry].rename_column("source", "text") + return dataset diff --git a/src/mtl4ad/model.py b/src/mtl4ad/model.py index d357173..4875832 100644 --- a/src/mtl4ad/model.py +++ b/src/mtl4ad/model.py @@ -6,6 +6,8 @@ from peft import LoraConfig, get_peft_model from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig import logging +from accelerate import PartialState + from mtl4ad.utils import find_all_linear_names, is_bf16_available # type: ignore @@ -41,6 +43,8 @@ def load_model_and_tokenizer_peft( model_name, torch_dtype=torch.bfloat16 if is_bf16_available() else torch.float32, # use_flash_attention_2=True, quantization_config=bnb_config, + # load_in_4bit=True, + device_map={"": PartialState().process_index} ) model.config.use_cache = False diff --git a/src/mtl4ad/train.py b/src/mtl4ad/train.py index 4813942..0002959 100644 --- a/src/mtl4ad/train.py +++ b/src/mtl4ad/train.py @@ -12,7 +12,7 @@ from transformers import DataCollatorForSeq2Seq from trl import SFTConfig, SFTTrainer # type: ignore -from mtl4ad.data_preprocessing import load_dataset_and_preprocess # type: ignore +from mtl4ad.data_preprocessing import load_dataset_and_preprocess, load_dataset_from_folders, filter_dataset, generate_formatted_prompts # type: ignore from mtl4ad.model import load_model_and_tokenizer_peft # type: ignore from mtl4ad.utils import ( # type: ignore PeftSavingCallback, @@ -20,6 +20,8 @@ format_name, load_config, ) +import multiprocessing as mp + logging.basicConfig( level=logging.INFO, @@ -43,6 +45,11 @@ def parse_arguments() -> argparse.Namespace: parser.add_argument("--config", type=str, required=True, help="Path to the configuration file.") return parser.parse_args() +def formatting_func(examples): + return [ + f"{source} \n\n {target}" + for source, target in zip(examples["text"], examples["labels"]) + ] def initialize_trainer( config, @@ -87,10 +94,12 @@ def initialize_trainer( args=training_args, train_dataset=dataset["train"], eval_dataset=dataset["validation"], - data_collator=collator, + # data_collator=collator, callbacks=[PeftSavingCallback()] if enable_peft else None, peft_config=peft_config, - max_seq_length=max_seq_length + max_seq_length=max_seq_length, + formatting_func=generate_formatted_prompts, + dataset_num_proc=mp.cpu_count() ) @@ -130,10 +139,41 @@ def main() -> None: "Initializing the process group from the environment was not possible!" ) - # logger.info(f"Rank: {torch.distributed.get_rank()}") + logger.info(f"Rank: {torch.distributed.get_rank()}") + local_rank = torch.distributed.get_rank() + dataset = load_dataset_from_folders(config["dataset_path"]) + if local_rank > 0: + logger.info( + f"rank={local_rank} " + "Waiting for main process to perform the mapping" + ) + torch.distributed.barrier() + # torch.cuda.set_device(torch.distributed.get_rank()) + + for element in dataset: + logger.info(f"Original size of {element}: {len(dataset[element])}") + + if "dataset_percentage" in config: + logger.info("Sampling dataset...") + dataset = filter_dataset(dataset, config.get("dataset_percentage")) + for element in dataset: + logger.info(f"Filtered size of {element}: {len(dataset[element])}") + + if "shuffle" in config: + logger.info("Shuffling dataset...") + dataset = dataset.shuffle(seed=config.get("dataset_seed", 42)) + logger.info("Done shuffling") + + if local_rank == 0: + logger.info(f"rank={local_rank} Loading results from main process") + if IS_CUDA_AVAILABLE: + torch.distributed.barrier() + model, tokenizer, peft_config = load_model_and_tokenizer_peft(config, enable_peft) - dataset = load_dataset_and_preprocess(config, tokenizer) + + # dataset = load_dataset_and_preprocess(config, tokenizer) + clean_config = extract_valid_training_arguments(config) trainer = initialize_trainer( From cab370a3de43f7fcf9021d4b820e37b015b162b7 Mon Sep 17 00:00:00 2001 From: yvesnana Date: Wed, 7 Aug 2024 18:02:26 +0200 Subject: [PATCH 3/4] add sample selection for validation reset params for train and test yaml Signed-off-by: yvesnana --- configs/granite_test.yaml | 1 + configs/granite_train.yaml | 7 ++++--- src/mtl4ad/data_preprocessing.py | 10 +++++++--- src/mtl4ad/train.py | 2 +- 4 files changed, 13 insertions(+), 7 deletions(-) diff --git a/configs/granite_test.yaml b/configs/granite_test.yaml index 16452ef..9be17a3 100644 --- a/configs/granite_test.yaml +++ b/configs/granite_test.yaml @@ -8,6 +8,7 @@ experiment_name: test_no_deepspeed checkpoint_dir: "models" # deepspeed: "configs/deepspeed_config.json" #"configs/deepspeed_config.json" dataset_percentage: 0.2 +n_val_sample: 100 dataset_seed: 42 shuffle: true enable_peft: true diff --git a/configs/granite_train.yaml b/configs/granite_train.yaml index 68bab98..6ece1c1 100644 --- a/configs/granite_train.yaml +++ b/configs/granite_train.yaml @@ -8,13 +8,14 @@ experiment_name: full_train_no_deepspeed checkpoint_dir: "models" # deepspeed: "configs/deepspeed_config.json" #"configs/deepspeed_config.json" dataset_percentage: 100 +n_val_sample: 12000 dataset_seed: 42 shuffle: true enable_peft: true -eval_steps: 5000 +eval_steps: 10000 logging_steps: 100 -save_total_limit: 10 -save_steps: 50 +save_total_limit: 6 +save_steps: 1000 eval_strategy: "steps" num_train_epochs: 1 # max_steps: 100 diff --git a/src/mtl4ad/data_preprocessing.py b/src/mtl4ad/data_preprocessing.py index f23348b..f8421c5 100644 --- a/src/mtl4ad/data_preprocessing.py +++ b/src/mtl4ad/data_preprocessing.py @@ -117,7 +117,7 @@ def process(examples: dict) -> dict: return dataset.map(process, batched=True, num_proc=mp.cpu_count()) -def filter_dataset(dataset: DatasetDict, dataset_percentage: Optional[float]) -> DatasetDict: +def filter_dataset(dataset: DatasetDict, dataset_percentage: Optional[float], n_val_sample: Optional[int]) -> DatasetDict: """ Filters the dataset based on the given percentage. @@ -132,9 +132,13 @@ def filter_dataset(dataset: DatasetDict, dataset_percentage: Optional[float]) -> raise ValueError("dataset_percentage cannot be None") train_new_size = round(len(dataset["train"]) * dataset_percentage / 100) - val_new_size = round(len(dataset["validation"]) * dataset_percentage / 100) dataset["train"] = dataset["train"].select(range(train_new_size)) - dataset["validation"] = dataset["validation"].select(range(val_new_size)) + if not n_val_sample: + val_new_size = round(len(dataset["validation"]) * dataset_percentage / 100) + dataset["validation"] = dataset["validation"].select(range(val_new_size)) + else: + dataset["validation"] = dataset["validation"].shuffle(seed=42) + dataset["validation"] = dataset["validation"].take(n_val_sample) return dataset diff --git a/src/mtl4ad/train.py b/src/mtl4ad/train.py index 0002959..e81ae61 100644 --- a/src/mtl4ad/train.py +++ b/src/mtl4ad/train.py @@ -156,7 +156,7 @@ def main() -> None: if "dataset_percentage" in config: logger.info("Sampling dataset...") - dataset = filter_dataset(dataset, config.get("dataset_percentage")) + dataset = filter_dataset(dataset, config.get("dataset_percentage"), config.get("n_val_sample")) for element in dataset: logger.info(f"Filtered size of {element}: {len(dataset[element])}") From 829c756dda4cb4aa6ba72093290f67600f94d6ef Mon Sep 17 00:00:00 2001 From: yvesnana Date: Mon, 12 Aug 2024 16:31:40 +0200 Subject: [PATCH 4/4] fix: typing Signed-off-by: yvesnana --- src/mtl4ad/data_preprocessing.py | 42 +---- src/mtl4ad/model.py | 42 +++-- .../resources/test/config/test_config.yaml | 4 +- src/mtl4ad/train.py | 42 ++--- src/mtl4ad/train_2.py | 143 ------------------ tests/test_modules.py | 89 +++++++++++ tests/test_trainer.py | 85 ----------- 7 files changed, 139 insertions(+), 308 deletions(-) delete mode 100644 src/mtl4ad/train_2.py create mode 100644 tests/test_modules.py delete mode 100644 tests/test_trainer.py diff --git a/src/mtl4ad/data_preprocessing.py b/src/mtl4ad/data_preprocessing.py index f8421c5..4b39ab1 100644 --- a/src/mtl4ad/data_preprocessing.py +++ b/src/mtl4ad/data_preprocessing.py @@ -1,14 +1,13 @@ """Dataset Utilities: Loading, Preprocessing, and Filtering""" import logging +import multiprocessing as mp from pathlib import Path -from typing import Any, Dict, Optional, Union, List +from typing import Any, Dict, List, Optional, Union from datasets import Dataset, DatasetDict, concatenate_datasets, load_dataset from transformers import PreTrainedTokenizer -import multiprocessing as mp - logging.basicConfig( level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", @@ -69,7 +68,7 @@ def load_dataset_from_folders(base_path: Union[str, Path]) -> DatasetDict: dataset = DatasetDict({"train": train_dataset, "validation": valid_dataset}) for entry in dataset: - dataset[entry] = dataset[entry].rename_column("target", "labels") + dataset[entry] = dataset[entry].rename_column("target", "labels") dataset[entry] = dataset[entry].rename_column("source", "text") return dataset @@ -133,7 +132,7 @@ def filter_dataset(dataset: DatasetDict, dataset_percentage: Optional[float], n_ train_new_size = round(len(dataset["train"]) * dataset_percentage / 100) dataset["train"] = dataset["train"].select(range(train_new_size)) - if not n_val_sample: + if not n_val_sample: val_new_size = round(len(dataset["validation"]) * dataset_percentage / 100) dataset["validation"] = dataset["validation"].select(range(val_new_size)) else: @@ -142,37 +141,6 @@ def filter_dataset(dataset: DatasetDict, dataset_percentage: Optional[float], n_ return dataset -def load_dataset_and_preprocess(config: Dict[str, Any], tokenizer) -> Dataset: - """ - Loads and preprocesses the dataset according to the given configuration. - - Args: - config: The configuration object containing dataset and preprocessing parameters. - tokenizer: The tokenizer to be used for preprocessing the dataset. - - Returns: - The preprocessed dataset. - """ - logger.info("Loading dataset...") - dataset = load_dataset_from_folders(config["dataset_path"]) - for element in dataset: - logger.info(f"Original size of {element}: {len(dataset[element])}") - - if "dataset_percentage" in config: - logger.info("Sampling dataset...") - dataset = filter_dataset(dataset, config.get("dataset_percentage")) - for element in dataset: - logger.info(f"Filtered size of {element}: {len(dataset[element])}") - - dataset = preprocess(dataset, tokenizer, config.get("model_max_length", None)) - - if "shuffle" in config: - logger.info("Shuffling dataset...") - dataset = dataset.shuffle(seed=config.get("dataset_seed", 42)) - logger.info("Done shuffling") - - return dataset - def generate_formatted_prompts(examples) -> List[str]: """Generate formatted prompts from examples. @@ -185,4 +153,4 @@ def generate_formatted_prompts(examples) -> List[str]: return [ f"{source} \n\n {target}" for source, target in zip(examples["text"], examples["labels"]) - ] \ No newline at end of file + ] diff --git a/src/mtl4ad/model.py b/src/mtl4ad/model.py index 4875832..2de64b7 100644 --- a/src/mtl4ad/model.py +++ b/src/mtl4ad/model.py @@ -1,15 +1,14 @@ """Model and Tokenizer Utilities""" -from typing import Any, Optional, Tuple, Dict +import logging +from typing import Any, Dict, Optional, Tuple import torch +from accelerate import PartialState # type: ignore from peft import LoraConfig, get_peft_model from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig -import logging -from accelerate import PartialState - -from mtl4ad.utils import find_all_linear_names, is_bf16_available # type: ignore +from mtl4ad.utils import find_all_linear_names, is_bf16_available, is_cuda_available # type: ignore logging.basicConfig( level=logging.INFO, @@ -39,13 +38,19 @@ def load_model_and_tokenizer_peft( bnb_4bit_compute_dtype=torch.bfloat16 ) - model = AutoModelForCausalLM.from_pretrained( - model_name, torch_dtype=torch.bfloat16 if is_bf16_available() else torch.float32, - # use_flash_attention_2=True, - quantization_config=bnb_config, - # load_in_4bit=True, - device_map={"": PartialState().process_index} - ) + if is_cuda_available(): + model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.bfloat16 if is_bf16_available() else torch.float32, + # use_flash_attention_2=True, + quantization_config=bnb_config, + # load_in_4bit=True, + device_map={"": PartialState().process_index} + ) + else: + model = AutoModelForCausalLM.from_pretrained( + model_name, torch_dtype=torch.bfloat16 if is_bf16_available() else torch.float32 + ) + model.config.use_cache = False tokenizer = AutoTokenizer.from_pretrained(model_name) @@ -68,7 +73,7 @@ def load_model_and_tokenizer_peft( return model, tokenizer, peft_config -def enable_peft(model) -> torch.nn.Module: +def enable_peft(model) -> tuple[Any, LoraConfig]: """ Enables PEFT (Lora) for the model. @@ -79,16 +84,7 @@ def enable_peft(model) -> torch.nn.Module: The model with PEFT enabled. """ model.enable_input_require_grads() - # target_modules = [ - # "q_proj", - # "k_proj", - # "v_proj", - # "o_proj", - # "gate_proj", - # "up_proj", - # "down_proj", - # "lm_head", - # ] + target_modules = find_all_linear_names(model) peft_config = LoraConfig( diff --git a/src/mtl4ad/resources/test/config/test_config.yaml b/src/mtl4ad/resources/test/config/test_config.yaml index ac6e056..462396c 100644 --- a/src/mtl4ad/resources/test/config/test_config.yaml +++ b/src/mtl4ad/resources/test/config/test_config.yaml @@ -4,7 +4,6 @@ per_device_train_batch_size: 1 per_device_eval_batch_size: 1 gradient_checkpointing: true gradient_accumulation_steps: 1 -model_max_length: 128 experiment_name: "test_models" checkpoint_dir: "models" dataset_percentage: 100 @@ -18,4 +17,5 @@ max_steps: 3 num_train_epochs: 1 use_cpu: true eval_strategy: "no" -report_to: none \ No newline at end of file +report_to: none +max_seq_length: 512 \ No newline at end of file diff --git a/src/mtl4ad/train.py b/src/mtl4ad/train.py index e81ae61..c501604 100644 --- a/src/mtl4ad/train.py +++ b/src/mtl4ad/train.py @@ -1,18 +1,22 @@ """Model Training Script""" import argparse -import logging -from pathlib import Path import json +import logging +import multiprocessing as mp from datetime import timedelta +from pathlib import Path import mlflow # type: ignore import torch import torch.distributed as dist -from transformers import DataCollatorForSeq2Seq from trl import SFTConfig, SFTTrainer # type: ignore -from mtl4ad.data_preprocessing import load_dataset_and_preprocess, load_dataset_from_folders, filter_dataset, generate_formatted_prompts # type: ignore +from mtl4ad.data_preprocessing import ( # type: ignore + filter_dataset, + generate_formatted_prompts, + load_dataset_from_folders, +) from mtl4ad.model import load_model_and_tokenizer_peft # type: ignore from mtl4ad.utils import ( # type: ignore PeftSavingCallback, @@ -20,8 +24,6 @@ format_name, load_config, ) -import multiprocessing as mp - logging.basicConfig( level=logging.INFO, @@ -46,6 +48,15 @@ def parse_arguments() -> argparse.Namespace: return parser.parse_args() def formatting_func(examples): + """ + Function to format each example in the dataset. + + Args: + examples: A batch of examples from the dataset. + + Returns: + A list with formated examples. + """ return [ f"{source} \n\n {target}" for source, target in zip(examples["text"], examples["labels"]) @@ -77,7 +88,6 @@ def initialize_trainer( Returns: The initialized Trainer object. """ - collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model) training_args = SFTConfig( output_dir=str(output_dir), @@ -148,9 +158,7 @@ def main() -> None: "Waiting for main process to perform the mapping" ) torch.distributed.barrier() - - # torch.cuda.set_device(torch.distributed.get_rank()) - + for element in dataset: logger.info(f"Original size of {element}: {len(dataset[element])}") @@ -171,8 +179,6 @@ def main() -> None: torch.distributed.barrier() model, tokenizer, peft_config = load_model_and_tokenizer_peft(config, enable_peft) - - # dataset = load_dataset_and_preprocess(config, tokenizer) clean_config = extract_valid_training_arguments(config) @@ -182,12 +188,12 @@ def main() -> None: is_mlflow_available = config.get("report_to") == "mlflow" config_json = json.dumps(config) - # if is_mlflow_available and (not IS_CUDA_AVAILABLE or torch.distributed.get_rank() == 0): - # mlflow.set_experiment(full_experiment_name) - # mlflow.start_run() - # mlflow.set_tag("mlflow.runName", experiment_name) - # mlflow.set_tag("configs", config_json) - # mlflow.pytorch.autolog() + if is_mlflow_available and (not IS_CUDA_AVAILABLE or torch.distributed.get_rank() == 0): + mlflow.set_experiment(full_experiment_name) + mlflow.start_run() + mlflow.set_tag("mlflow.runName", experiment_name) + mlflow.set_tag("configs", config_json) + mlflow.pytorch.autolog() resume_checkpoint = config.get("resume_from_checkpoint", None) if resume_checkpoint: logger.info(f"Resuming from Checkpoint: {resume_checkpoint}") diff --git a/src/mtl4ad/train_2.py b/src/mtl4ad/train_2.py deleted file mode 100644 index c8e3929..0000000 --- a/src/mtl4ad/train_2.py +++ /dev/null @@ -1,143 +0,0 @@ -"""Model Training Script""" - -import argparse -import logging -from pathlib import Path - -import mlflow # type: ignore -import torch -import torch.distributed as dist -from transformers import DataCollatorForSeq2Seq, TrainingArguments -from trl import SFTConfig, SFTTrainer # type: ignore - -from mtl4ad.data_preprocessing import load_dataset_from_folders, filter_dataset, generate_formatted_prompts # type: ignore -from mtl4ad.model import load_model_and_tokenizer_peft # type: ignore -from mtl4ad.utils import ( # type: ignore - PeftSavingCallback, - extract_valid_training_arguments, - format_name, - load_config, -) - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", - datefmt="%Y-%m-%d %H:%M:%S", -) -logger = logging.getLogger(__name__) - -IS_CUDA_AVAILABLE = torch.cuda.is_available() -IS_BF16_AVAILABLE = IS_CUDA_AVAILABLE and torch.cuda.is_bf16_supported() - -def parse_arguments() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Training Script") - parser.add_argument("--config", type=str, required=True, help="Path to the configuration file.") - return parser.parse_args() - -def setup_config(config_path: str) -> dict: - config = load_config(config_path) - config["dataset_path"] = str(Path(config["dataset_path"]).resolve()) - config["checkpoint_dir"] = str(Path(config["checkpoint_dir"]).resolve()) - config["deepspeed"] = str(Path(config["deepspeed"]).resolve()) if "deepspeed" in config else None - if "model_name" not in config: - raise KeyError("The 'model_name' key is missing from the configuration. Terminating script.") - return config - -def initialize_distributed(): - if IS_CUDA_AVAILABLE and torch.cuda.device_count() > 1: - if not dist.is_initialized(): - dist.init_process_group(backend='nccl') - -def load_and_prepare_datasets(config: dict) -> dict: - logger.info("Loading dataset...") - dataset = load_dataset_from_folders(config["dataset_path"]) - log_dataset_sizes("Original size", dataset) - - if config.get("dataset_percentage"): - logger.info("Sampling dataset...") - dataset = filter_dataset(dataset, config.get("dataset_percentage")) - log_dataset_sizes("Filtered size", dataset) - - if config.get("shuffle"): - logger.info("Shuffling dataset...") - dataset = dataset.shuffle(seed=config.get("dataset_seed", 42)) - logger.info("Done shuffling") - - return dataset - -def log_dataset_sizes(prefix: str, dataset: dict): - for element in dataset: - logger.info(f"{prefix} of {element}: {len(dataset[element])}") - -def initialize_trainer(config: dict, dataset: dict, model, tokenizer, collator) -> SFTTrainer: - training_args = TrainingArguments( - output_dir=config["checkpoint_dir"], - run_name=config["experiment_name"], - bf16=IS_BF16_AVAILABLE, - ddp_find_unused_parameters=True, - remove_unused_columns=True, - gradient_checkpointing_kwargs={"use_reentrant": False}, - **extract_valid_training_arguments(config), - ) - - return SFTTrainer( - model=model, - tokenizer=tokenizer, - args=training_args, - train_dataset=dataset["train"], - eval_dataset=dataset["validation"], - data_collator=collator, - callbacks=[PeftSavingCallback()] if config.get("enable_peft") else None, - peft_config=config.get("peft_config"), - formatting_func=generate_formatted_prompts, - ) - -def setup_mlflow(config: dict, experiment_name: str): - if config.get("report_to") == "mlflow": - mlflow.set_experiment(experiment_name) - mlflow.start_run() - mlflow.set_tag("mlflow.runName", experiment_name) - mlflow.pytorch.autolog() - -def train_and_save_model(trainer: SFTTrainer, output_dir: str, resume_checkpoint: str): - if resume_checkpoint: - trainer.train(resume_from_checkpoint=resume_checkpoint) - else: - trainer.train() - trainer.save_model(str(Path(output_dir).joinpath("last_model"))) - -def main() -> None: - args = parse_arguments() - config = setup_config(args.config) - experiment_name = format_name(config.get("experiment_name", "train")) - - initialize_distributed() - - local_rank = dist.get_rank() if dist.is_initialized() else 0 - - if local_rank == 0: - dataset = load_and_prepare_datasets(config) - if dist.is_initialized(): - torch.distributed.barrier() - else: - if dist.is_initialized(): - torch.distributed.barrier() - dataset = load_dataset_from_folders(config["dataset_path"]) - - model, tokenizer, peft_config = load_model_and_tokenizer_peft(config["model_name"], config.get("enable_peft")) - collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model) - - trainer = initialize_trainer(config, dataset, model, tokenizer, collator) - - if not IS_CUDA_AVAILABLE or local_rank == 0: - setup_mlflow(config, experiment_name) - - train_and_save_model(trainer, config["checkpoint_dir"], config.get("resume_from_checkpoint")) - - if dist.is_initialized(): - dist.destroy_process_group() - - logger.info("Training completed and model saved.") - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/tests/test_modules.py b/tests/test_modules.py new file mode 100644 index 0000000..3f25521 --- /dev/null +++ b/tests/test_modules.py @@ -0,0 +1,89 @@ +"""Testing Script""" + +import logging +from pathlib import Path +from typing import Any, Dict, Optional, Tuple + +import pytest +import torch +from datasets import DatasetDict +from mtl4ad.data_preprocessing import load_dataset_from_folders, filter_dataset # type: ignore +from mtl4ad.model import load_model_and_tokenizer_peft # type: ignore +from mtl4ad.train import initialize_trainer # type: ignore +from mtl4ad.utils import extract_valid_training_arguments, load_config # type: ignore +from transformers import PreTrainedTokenizer + +logging.basicConfig(level=logging.INFO) +logger = logging.getLogger(__name__) + +@pytest.fixture(scope="module") +def config_and_tokenizer() -> Tuple[Dict[str, Any], Any, PreTrainedTokenizer, Optional[Any]]: + """ + Pytest fixture to load configuration and tokenizer for testing. + + Returns: + A tuple containing the configuration, model, tokenizer objects, and optionally the PEFT config. + """ + config_path = Path(__file__).resolve().parent.parent / "src/mtl4ad/resources/test/config/test_config.yaml" + config = load_config(config_path) + config["dataset_path"] = str(Path(config["dataset_path"]).resolve()) + config["checkpoint_dir"] = str(Path(config["checkpoint_dir"]).resolve()) + enable_peft = config.get("enable_peft", False) + model, tokenizer, peft_config = load_model_and_tokenizer_peft(config, enable_peft) + return config, model, tokenizer, peft_config + +@pytest.fixture(scope="module") +def dataset(config_and_tokenizer: Tuple[Dict[str, Any], Any, PreTrainedTokenizer, Optional[Any]]) -> DatasetDict: + """ + Pytest fixture to load, filter, and shuffle the dataset for testing. + + Returns: + A DatasetDict object ready for training. + """ + config, _, _, _ = config_and_tokenizer + dataset = load_dataset_from_folders(config["dataset_path"]) + + for element in dataset: + logger.info(f"Original size of {element}: {len(dataset[element])}") + + if "dataset_percentage" in config: + logger.info("Sampling dataset...") + dataset = filter_dataset(dataset, config.get("dataset_percentage"), config.get("n_val_sample")) + for element in dataset: + logger.info(f"Filtered size of {element}: {len(dataset[element])}") + + if "shuffle" in config: + logger.info("Shuffling dataset...") + dataset = dataset.shuffle(seed=config.get("dataset_seed", 42)) + logger.info("Done shuffling") + + return dataset + +def test_training_and_model_saving(config_and_tokenizer: Tuple[Dict[str, Any], Any, PreTrainedTokenizer, Optional[Any]], dataset: DatasetDict): + """ + Test a single training run and verify model saving. + + Ensures that the model is saved correctly after training. + """ + config, model, tokenizer, peft_config = config_and_tokenizer + experiment_name = config.get("experiment_name", "train") + output_dir = Path(config.get("checkpoint_dir", "tmp")) / experiment_name + + clean_config = extract_valid_training_arguments(config) + trainer = initialize_trainer( + clean_config, + model, + tokenizer, + dataset, + experiment_name, + str(output_dir), + config.get("max_seq_length", tokenizer.model_max_length), + config.get("enable_peft", False), + peft_config + ) + + trainer.train() + + model_save_path = output_dir / "last_model" + assert model_save_path.exists(), "Model save path should exist after training" + logger.info("Training run and model saving test completed successfully.") diff --git a/tests/test_trainer.py b/tests/test_trainer.py deleted file mode 100644 index ffcecf7..0000000 --- a/tests/test_trainer.py +++ /dev/null @@ -1,85 +0,0 @@ -"""Testing Script""" - -import logging -from pathlib import Path -from typing import Any, Dict, Optional, Tuple - -import pytest -import torch -from datasets import Dataset -from mtl4ad.data_preprocessing import load_dataset_from_folders, preprocess # type: ignore -from mtl4ad.model import load_model_and_tokenizer_peft # type: ignore -from mtl4ad.train import initialize_trainer # type: ignore -from mtl4ad.utils import extract_valid_training_arguments, load_config # type: ignore -from transformers import PreTrainedTokenizer - -logging.basicConfig(level=logging.INFO) -logger = logging.getLogger(__name__) - -IS_CUDA_AVAILABLE = torch.cuda.is_available() -IS_BF16_AVAILABLE = IS_CUDA_AVAILABLE and torch.cuda.is_bf16_supported() - - -def load_dataset_and_preprocess(config: Dict[str, Any], tokenizer: PreTrainedTokenizer) -> Dataset: - """ - Loads and preprocesses the dataset according to the given configuration. - - Args: - config: The configuration object containing dataset and preprocessing parameters. - tokenizer: The tokenizer to be used for preprocessing the dataset. - - Returns: - The preprocessed dataset. - """ - logger.info("Loading dataset...") - dataset_path = ( - Path(__file__).resolve().parent.parent / "src/mtl4ad" / config.get("dataset_path", None) - ) - dataset = load_dataset_from_folders(dataset_path) - logger.info(f"Original dataset size: {len(dataset)}") - - dataset = preprocess(dataset, tokenizer, config.get("model_max_length", None)) - return dataset - - -@pytest.fixture -def config_and_tokenizer() -> Tuple[Dict[str, Any], Any, PreTrainedTokenizer, Optional[Any]]: - """ - Pytest fixture to load configuration and tokenizer for testing. - - Returns: - A tuple containing the configuration, model, tokenizer objects, and optionally the PEFT config. - """ - config_path = ( - Path(__file__).resolve().parent.parent - / "src/mtl4ad/resources/test/config" - / "test_config.yaml" - ) - config = load_config(config_path) - config["dataset_path"] = str(Path(config["dataset_path"]).resolve()) - config["checkpoint_dir"] = str(Path(config["checkpoint_dir"]).resolve()) - enable_peft = config.get("enable_peft", False) - model, tokenizer, peft_config = load_model_and_tokenizer_peft(config["model_name"], enable_peft) - return config, model, tokenizer, peft_config - - -def test_training_initialization( - config_and_tokenizer: Tuple[Dict[str, Any], Any, PreTrainedTokenizer, Optional[Any]], -): - """ - Test the training setup and initialization. - """ - config, model, tokenizer, peft_config = config_and_tokenizer - experiment_name = config.get("experiment_name", "train") - enable_peft = config.get("enable_peft", False) - dataset = load_dataset_and_preprocess(config, tokenizer) - - output_dir = str(Path(config.get("checkpoint_dir", "tmp")).joinpath(experiment_name)) - - config = extract_valid_training_arguments(config) - trainer = initialize_trainer( - config, model, tokenizer, dataset, experiment_name, output_dir, enable_peft, peft_config - ) - - trainer.train() - logger.info("Test completed successfully.")