Spaces:
Running
on
L40S
Running
on
L40S
File size: 4,470 Bytes
4f6613a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 |
import os
os.environ["USE_LIBUV"] = "0"
import sys
from typing import Optional
import hydra
import lightning as L
import pyrootutils
import torch
from lightning import Callback, LightningDataModule, LightningModule, Trainer
from lightning.pytorch.loggers import Logger
from lightning.pytorch.strategies import DDPStrategy
from omegaconf import DictConfig, OmegaConf
os.environ.pop("SLURM_NTASKS", None)
os.environ.pop("SLURM_JOB_NAME", None)
os.environ.pop("SLURM_NTASKS_PER_NODE", None)
# register eval resolver and root
pyrootutils.setup_root(__file__, indicator=".project-root", pythonpath=True)
# Allow TF32 on Ampere GPUs
torch.set_float32_matmul_precision("high")
torch.backends.cudnn.allow_tf32 = True
# register eval resolver
OmegaConf.register_new_resolver("eval", eval)
import fish_speech.utils as utils
log = utils.RankedLogger(__name__, rank_zero_only=True)
@utils.task_wrapper
def train(cfg: DictConfig) -> tuple[dict, dict]:
"""Trains the model. Can additionally evaluate on a testset, using best weights obtained during
training.
This method is wrapped in optional @task_wrapper decorator, that controls the behavior during
failure. Useful for multiruns, saving info about the crash, etc.
Args:
cfg (DictConfig): Configuration composed by Hydra.
Returns:
Tuple[dict, dict]: Dict with metrics and dict with all instantiated objects.
""" # noqa: E501
# set seed for random number generators in pytorch, numpy and python.random
if cfg.get("seed"):
L.seed_everything(cfg.seed, workers=False)
if cfg.get("deterministic"):
torch.use_deterministic_algorithms(True)
log.info(f"Instantiating datamodule <{cfg.data._target_}>")
datamodule: LightningDataModule = hydra.utils.instantiate(cfg.data)
log.info(f"Instantiating model <{cfg.model._target_}>")
model: LightningModule = hydra.utils.instantiate(cfg.model)
log.info("Instantiating callbacks...")
callbacks: list[Callback] = utils.instantiate_callbacks(cfg.get("callbacks"))
log.info("Instantiating loggers...")
logger: list[Logger] = utils.instantiate_loggers(cfg.get("logger"))
log.info(f"Instantiating trainer <{cfg.trainer._target_}>")
trainer: Trainer = hydra.utils.instantiate(
cfg.trainer,
callbacks=callbacks,
logger=logger,
)
object_dict = {
"cfg": cfg,
"datamodule": datamodule,
"model": model,
"callbacks": callbacks,
"logger": logger,
"trainer": trainer,
}
if logger:
log.info("Logging hyperparameters!")
utils.log_hyperparameters(object_dict)
if cfg.get("train"):
log.info("Starting training!")
ckpt_path = cfg.get("ckpt_path")
auto_resume = False
resume_ckpt_path = utils.get_latest_checkpoint(cfg.paths.ckpt_dir)
if resume_ckpt_path is not None:
ckpt_path = resume_ckpt_path
auto_resume = True
if ckpt_path is not None:
log.info(f"Resuming from checkpoint: {ckpt_path}")
# resume weights only is disabled for auto-resume
if cfg.get("resume_weights_only") and auto_resume is False:
log.info("Resuming weights only!")
ckpt = torch.load(ckpt_path, map_location=model.device)
if "state_dict" in ckpt:
ckpt = ckpt["state_dict"]
err = model.load_state_dict(ckpt, strict=False)
log.info(f"Error loading state dict: {err}")
ckpt_path = None
trainer.fit(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
train_metrics = trainer.callback_metrics
if cfg.get("test"):
log.info("Starting testing!")
ckpt_path = trainer.checkpoint_callback.best_model_path
if ckpt_path == "":
log.warning("Best ckpt not found! Using current weights for testing...")
ckpt_path = cfg.get("ckpt_path")
trainer.test(model=model, datamodule=datamodule, ckpt_path=ckpt_path)
log.info(f"Best ckpt path: {ckpt_path}")
test_metrics = trainer.callback_metrics
# merge train and test metrics
metric_dict = {**train_metrics, **test_metrics}
return metric_dict, object_dict
@hydra.main(
version_base="1.3", config_path="./configs", config_name="llama_pretrain.yaml"
)
def main(cfg: DictConfig) -> Optional[float]:
# train the model
train(cfg)
if __name__ == "__main__":
main()
|