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|
- import datetime
- import json
- from typing import Tuple
- import numpy as np
- import ray
- import ray.train as train
- import torch
- import torch.nn as nn
- import torch.nn.functional as F
- import typer
- from ray.air import session
- from ray.air.config import (
- CheckpointConfig,
- DatasetConfig,
- RunConfig,
- ScalingConfig,
- )
- from ray.air.integrations.mlflow import MLflowLoggerCallback
- from ray.data import Dataset
- from ray.train.torch import TorchCheckpoint, TorchTrainer
- from transformers import BertModel
- from typing_extensions import Annotated
- from madewithml import data, models, utils
- from madewithml.config import MLFLOW_TRACKING_URI, logger
- # Initialize Typer CLI app
- app = typer.Typer()
- def train_step(
- ds: Dataset,
- batch_size: int,
- model: nn.Module,
- num_classes: int,
- loss_fn: torch.nn.modules.loss._WeightedLoss,
- optimizer: torch.optim.Optimizer,
- ) -> float: # pragma: no cover, tested via train workload
- """Train step.
- Args:
- ds (Dataset): dataset to iterate batches from.
- batch_size (int): size of each batch.
- model (nn.Module): model to train.
- num_classes (int): number of classes.
- loss_fn (torch.nn.loss._WeightedLoss): loss function to use between labels and predictions.
- optimizer (torch.optimizer.Optimizer): optimizer to use for updating the model's weights.
- Returns:
- float: cumulative loss for the dataset.
- """
- model.train()
- loss = 0.0
- ds_generator = ds.iter_torch_batches(batch_size=batch_size, collate_fn=utils.collate_fn)
- for i, batch in enumerate(ds_generator):
- optimizer.zero_grad() # reset gradients
- z = model(batch) # forward pass
- targets = F.one_hot(batch["targets"], num_classes=num_classes).float() # one-hot (for loss_fn)
- J = loss_fn(z, targets) # define loss
- J.backward() # backward pass
- optimizer.step() # update weights
- loss += (J.detach().item() - loss) / (i + 1) # cumulative loss
- return loss
- def eval_step(
- ds: Dataset, batch_size: int, model: nn.Module, num_classes: int, loss_fn: torch.nn.modules.loss._WeightedLoss
- ) -> Tuple[float, np.array, np.array]: # pragma: no cover, tested via train workload
- """Eval step.
- Args:
- ds (Dataset): dataset to iterate batches from.
- batch_size (int): size of each batch.
- model (nn.Module): model to train.
- num_classes (int): number of classes.
- loss_fn (torch.nn.loss._WeightedLoss): loss function to use between labels and predictions.
- Returns:
- Tuple[float, np.array, np.array]: cumulative loss, ground truths and predictions.
- """
- model.eval()
- loss = 0.0
- y_trues, y_preds = [], []
- ds_generator = ds.iter_torch_batches(batch_size=batch_size, collate_fn=utils.collate_fn)
- with torch.inference_mode():
- for i, batch in enumerate(ds_generator):
- z = model(batch)
- targets = F.one_hot(batch["targets"], num_classes=num_classes).float() # one-hot (for loss_fn)
- J = loss_fn(z, targets).item()
- loss += (J - loss) / (i + 1)
- y_trues.extend(batch["targets"].cpu().numpy())
- y_preds.extend(torch.argmax(z, dim=1).cpu().numpy())
- return loss, np.vstack(y_trues), np.vstack(y_preds)
- def train_loop_per_worker(config: dict) -> None: # pragma: no cover, tested via train workload
- """Training loop that each worker will execute.
- Args:
- config (dict): arguments to use for training.
- """
- # Hyperparameters
- dropout_p = config["dropout_p"]
- lr = config["lr"]
- lr_factor = config["lr_factor"]
- lr_patience = config["lr_patience"]
- batch_size = config["batch_size"]
- num_epochs = config["num_epochs"]
- num_classes = config["num_classes"]
- # Get datasets
- utils.set_seeds()
- train_ds = session.get_dataset_shard("train")
- val_ds = session.get_dataset_shard("val")
- # Model
- llm = BertModel.from_pretrained("allenai/scibert_scivocab_uncased", return_dict=False)
- model = models.FinetunedLLM(llm=llm, dropout_p=dropout_p, embedding_dim=llm.config.hidden_size, num_classes=num_classes)
- model = train.torch.prepare_model(model)
- # Training components
- loss_fn = nn.BCEWithLogitsLoss()
- optimizer = torch.optim.Adam(model.parameters(), lr=lr)
- scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=lr_factor, patience=lr_patience)
- # Training
- batch_size_per_worker = batch_size // session.get_world_size()
- for epoch in range(num_epochs):
- # Step
- train_loss = train_step(train_ds, batch_size_per_worker, model, num_classes, loss_fn, optimizer)
- val_loss, _, _ = eval_step(val_ds, batch_size_per_worker, model, num_classes, loss_fn)
- scheduler.step(val_loss)
- # Checkpoint
- metrics = dict(epoch=epoch, lr=optimizer.param_groups[0]["lr"], train_loss=train_loss, val_loss=val_loss)
- checkpoint = TorchCheckpoint.from_model(model=model)
- session.report(metrics, checkpoint=checkpoint)
- @app.command()
- def train_model(
- experiment_name: Annotated[str, typer.Option(help="name of the experiment for this training workload.")] = None,
- dataset_loc: Annotated[str, typer.Option(help="location of the dataset.")] = None,
- train_loop_config: Annotated[str, typer.Option(help="arguments to use for training.")] = None,
- num_workers: Annotated[int, typer.Option(help="number of workers to use for training.")] = 1,
- cpu_per_worker: Annotated[int, typer.Option(help="number of CPUs to use per worker.")] = 1,
- gpu_per_worker: Annotated[int, typer.Option(help="number of GPUs to use per worker.")] = 0,
- num_samples: Annotated[int, typer.Option(help="number of samples to use from dataset.")] = None,
- num_epochs: Annotated[int, typer.Option(help="number of epochs to train for.")] = 1,
- batch_size: Annotated[int, typer.Option(help="number of samples per batch.")] = 256,
- results_fp: Annotated[str, typer.Option(help="filepath to save results to.")] = None,
- ) -> ray.air.result.Result:
- """Main train function to train our model as a distributed workload.
- Args:
- experiment_name (str): name of the experiment for this training workload.
- dataset_loc (str): location of the dataset.
- train_loop_config (str): arguments to use for training.
- num_workers (int, optional): number of workers to use for training. Defaults to 1.
- cpu_per_worker (int, optional): number of CPUs to use per worker. Defaults to 1.
- gpu_per_worker (int, optional): number of GPUs to use per worker. Defaults to 0.
- num_samples (int, optional): number of samples to use from dataset.
- If this is passed in, it will override the config. Defaults to None.
- num_epochs (int, optional): number of epochs to train for.
- If this is passed in, it will override the config. Defaults to None.
- batch_size (int, optional): number of samples per batch.
- If this is passed in, it will override the config. Defaults to None.
- results_fp (str, optional): filepath to save results to. Defaults to None.
- Returns:
- ray.air.result.Result: training results.
- """
- # Set up
- train_loop_config = json.loads(train_loop_config)
- train_loop_config["num_samples"] = num_samples
- train_loop_config["num_epochs"] = num_epochs
- train_loop_config["batch_size"] = batch_size
- # Scaling config
- scaling_config = ScalingConfig(
- num_workers=num_workers,
- use_gpu=bool(gpu_per_worker),
- resources_per_worker={"CPU": cpu_per_worker, "GPU": gpu_per_worker},
- _max_cpu_fraction_per_node=0.8,
- )
- # Checkpoint config
- checkpoint_config = CheckpointConfig(
- num_to_keep=1,
- checkpoint_score_attribute="val_loss",
- checkpoint_score_order="min",
- )
- # MLflow callback
- mlflow_callback = MLflowLoggerCallback(
- tracking_uri=MLFLOW_TRACKING_URI,
- experiment_name=experiment_name,
- save_artifact=True,
- )
- # Run config
- run_config = RunConfig(
- callbacks=[mlflow_callback],
- checkpoint_config=checkpoint_config,
- )
- # Dataset
- ds = data.load_data(dataset_loc=dataset_loc, num_samples=train_loop_config["num_samples"])
- train_ds, val_ds = data.stratify_split(ds, stratify="tag", test_size=0.2)
- tags = train_ds.unique(column="tag")
- train_loop_config["num_classes"] = len(tags)
- # Dataset config
- dataset_config = {
- "train": DatasetConfig(fit=False, transform=False, randomize_block_order=False),
- "val": DatasetConfig(fit=False, transform=False, randomize_block_order=False),
- }
- # Preprocess
- preprocessor = data.CustomPreprocessor()
- train_ds = preprocessor.fit_transform(train_ds)
- val_ds = preprocessor.transform(val_ds)
- train_ds = train_ds.materialize()
- val_ds = val_ds.materialize()
- # Trainer
- trainer = TorchTrainer(
- train_loop_per_worker=train_loop_per_worker,
- train_loop_config=train_loop_config,
- scaling_config=scaling_config,
- run_config=run_config,
- datasets={"train": train_ds, "val": val_ds},
- dataset_config=dataset_config,
- preprocessor=preprocessor,
- )
- # Train
- results = trainer.fit()
- d = {
- "timestamp": datetime.datetime.now().strftime("%B %d, %Y %I:%M:%S %p"),
- "run_id": utils.get_run_id(experiment_name=experiment_name, trial_id=results.metrics["trial_id"]),
- "params": results.config["train_loop_config"],
- "metrics": utils.dict_to_list(results.metrics_dataframe.to_dict(), keys=["epoch", "train_loss", "val_loss"]),
- }
- logger.info(json.dumps(d, indent=2))
- if results_fp: # pragma: no cover, saving results
- utils.save_dict(d, results_fp)
- return results
- if __name__ == "__main__": # pragma: no cover, application
- if ray.is_initialized():
- ray.shutdown()
- ray.init()
- app()
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