mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-12-23 23:01:48 +01:00
99 lines
3.1 KiB
Python
99 lines
3.1 KiB
Python
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from absl import app, flags
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import json
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from typing import Optional
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import os
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import sys
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import torch
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# isort: on
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from tml.common.device import setup_and_get_device
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from tml.common.utils import setup_configuration
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import tml.core.custom_training_loop as ctl
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import tml.machines.environment as env
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from tml.projects.twhin.models.models import apply_optimizers, TwhinModel, TwhinModelAndLoss
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from tml.model import maybe_shard_model
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from tml.projects.twhin.metrics import create_metrics
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from tml.projects.twhin.config import TwhinConfig
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from tml.projects.twhin.data.data import create_dataset
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from tml.projects.twhin.optimizer import build_optimizer
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from tml.ml_logging.torch_logging import logging
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import torch.distributed as dist
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from torch.nn import functional as F
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from torchrec.optim.apply_optimizer_in_backward import apply_optimizer_in_backward
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from torchrec.distributed.model_parallel import get_module
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FLAGS = flags.FLAGS
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flags.DEFINE_bool("overwrite_save_dir", False, "Whether to clear preexisting save directories.")
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flags.DEFINE_string("save_dir", None, "If provided, overwrites the save directory.")
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flags.DEFINE_string("config_yaml_path", None, "Path to hyperparameters for model.")
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flags.DEFINE_string("task", None, "Task to run if this is local. Overrides TF_CONFIG etc.")
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def run(
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all_config: TwhinConfig,
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save_dir: Optional[str] = None,
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):
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train_dataset = create_dataset(all_config.train_data, all_config.model)
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if env.is_reader():
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train_dataset.serve()
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if env.is_chief():
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device = setup_and_get_device(tf_ok=False)
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logging.info(f"device: {device}")
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logging.info(f"WORLD_SIZE: {dist.get_world_size()}")
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# validation_dataset = create_dataset(all_config.validation_data, all_config.model)
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global_batch_size = all_config.train_data.per_replica_batch_size * dist.get_world_size()
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metrics = create_metrics(device)
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model = TwhinModel(all_config.model, all_config.train_data)
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apply_optimizers(model, all_config.model)
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model = maybe_shard_model(model, device=device)
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optimizer, scheduler = build_optimizer(model=model, config=all_config.model)
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loss_fn = F.binary_cross_entropy_with_logits
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model_and_loss = TwhinModelAndLoss(
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model, loss_fn, data_config=all_config.train_data, device=device
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)
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ctl.train(
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model=model_and_loss,
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optimizer=optimizer,
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device=device,
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save_dir=save_dir,
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logging_interval=all_config.training.train_log_every_n,
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train_steps=all_config.training.num_train_steps,
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checkpoint_frequency=all_config.training.checkpoint_every_n,
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dataset=train_dataset.dataloader(remote=False),
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worker_batch_size=global_batch_size,
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num_workers=0,
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scheduler=scheduler,
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initial_checkpoint_dir=all_config.training.initial_checkpoint_dir,
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gradient_accumulation=all_config.training.gradient_accumulation,
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)
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def main(argv):
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logging.info("Starting")
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logging.info(f"parsing config from {FLAGS.config_yaml_path}...")
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all_config = setup_configuration( # type: ignore[var-annotated]
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TwhinConfig,
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yaml_path=FLAGS.config_yaml_path,
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)
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run(
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all_config,
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save_dir=FLAGS.save_dir,
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)
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if __name__ == "__main__":
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app.run(main)
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