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Running on Zero
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from time import sleep
import traceback
from typing import List, Optional
import lightning as L
from lightning.pytorch.loggers import WandbLogger
from lightning.pytorch.callbacks import OnExceptionCheckpoint
import wandb
from torch import Tensor
import torch
import hyperparameters as hp
from conditioning.clap_embedder import LinearClapEmbedder
from conditioning.condition_type import ConditionType
from conditioning.conditioning_method import ConditioningMethod
from conditioning.prompt_processor import InterleavedContextPromptProcessor, StraightContextPromptProcessor
from conditioning.t5embedder import T5EmbedderCPU, T5EmbedderGPU
import config as cfg
from data.stem import Stem
from models.lightning_musicgen import LightningMusicgen
from training.callback import PrintLossesCallback, SaveDemoOnValidationCallback
from lightning.pytorch.callbacks import TQDMProgressBar, ModelCheckpoint
from utils.inspection import print_params
from utils.logging import get_or_create_run_id
def train(model_params: hp.ModelParams,
dataset_params: hp.DatasetParams,
max_steps: Optional[int] = None,
max_time: Optional[str] = None,
max_epochs: Optional[int] = None,
log: Optional[bool] = None,
accumulate_grad_batches: int = 1,
validate_every_n_steps: Optional[int] = None,
distributed_strategy: Optional[str] = None,
seed: Optional[int] = None,
run_name: Optional[str] = None,
kill_on_end: bool = False,
n_demos_per_epoch: int = 6,
devices: int = -1):
if log is None:
log = run_name is not None
if log and run_name is None:
raise ValueError("Need a run name to log on wandb")
# init model and datamodule
full_run_params = {
"model_params": model_params.to_dict(),
"data_params": dataset_params.to_dict()
}
model = model_params.instantiate()
datamodule = dataset_params.instantiate()
n_train_batches, n_valid_batches = (datamodule.lengths["train"],
datamodule.lengths["valid"])
# setup callbacks and logger
logger: WandbLogger | bool = False
resume_from_checkpoint: Optional[Path] = None
if log:
assert run_name is not None
output_dir = cfg.output_dir() / run_name
if output_dir.exists():
print(
f"Output directory for a run named {run_name} exists. Resuming training..."
)
resume_from_checkpoint = output_dir / "last.ckpt"
wandb_run_id = get_or_create_run_id(output_dir)
logger = WandbLogger(
entity=cfg.ENTITY,
project=cfg.PROJECT,
name=run_name,
id=wandb_run_id,
resume="allow",
config=full_run_params,
# settings=wandb.Settings(start_method="fork"),
)
callbacks: List[L.Callback] = []
if not cfg.running_locally():
# printlossescallback = PrintLossesCallback()
# callbacks.append(printlossescallback)
progbar: L.Callback = TQDMProgressBar(refresh_rate=n_train_batches // 2)
callbacks.append(progbar)
if run_name is not None:
output_dir = cfg.output_dir() / run_name
savedemocallback = SaveDemoOnValidationCallback(
output_dir, save_model=False, n_demos=n_demos_per_epoch)
interruptcallback = OnExceptionCheckpoint(output_dir,
filename="interrupted")
modelcheckpoint = ModelCheckpoint(dirpath=output_dir,
save_last=True,
every_n_epochs=1,
save_top_k=-1)
callbacks += [
interruptcallback,
savedemocallback,
modelcheckpoint,
]
# init trainer
trainer = L.Trainer(
enable_model_summary=True,
accelerator="auto",
max_steps=max_steps or -1,
max_epochs=max_epochs,
max_time=max_time,
devices=devices,
strategy=distributed_strategy or "auto",
gradient_clip_val=1.0,
accumulate_grad_batches=accumulate_grad_batches,
gradient_clip_algorithm="value",
precision="16-mixed",
callbacks=callbacks,
logger=logger,
log_every_n_steps=10,
val_check_interval=validate_every_n_steps or 1.0,
# check_val_every_n_epoch=None,
limit_train_batches=n_train_batches,
limit_val_batches=n_valid_batches,
num_sanity_val_steps=-1,
enable_progress_bar=True,
)
# set seed
if seed is not None:
L.seed_everything(seed)
# run training
if kill_on_end:
try:
trainer.fit(model, datamodule=datamodule)
print("Training is finished. Killing myself in five minutes.")
try:
wandb.finish()
sleep(300)
cfg.shutdown()
except KeyboardInterrupt:
print("You saved me! I'll never forget that.")
return
except Exception:
cfg.shutdown()
except KeyboardInterrupt:
print("Received keyboard interrupt. Stopping training "
"without shutting down...")
wandb.finish()
except Exception as e:
(cfg.output_dir() / "exception.txt").write_text(
f"Exception: {str(e)}\n\n "
f"Stacktrace: {traceback.format_exc()}\n")
print(f"training broke with exception {e}")
print(f"Killing myself in five minutes")
try:
wandb.finish()
sleep(300)
cfg.shutdown()
except KeyboardInterrupt:
print("You saved me! I'll never forget that.")
return
except Exception:
cfg.shutdown()
else:
trainer.fit(model, datamodule=datamodule)
if run_name is not None:
wandb.finish()
return
if __name__ == "__main__":
from time import time
encodec_params = hp.pretrained_encodec_meta_32khz_params
lm_params = hp.FioraSmallLmParams()
prompt_processor_params = hp.PromptProcessorParams(
keep_only_valid_steps=True,
model_class=InterleavedContextPromptProcessor,
context_dropout=0.5)
conditioning_params = hp.ConditioningParams(
embedder_types={
ConditionType.DESCRIPTION: T5EmbedderGPU,
ConditionType.STYLE: LinearClapEmbedder
},
conditioning_methods={
ConditionType.DESCRIPTION: ConditioningMethod.CROSS_ATTENTION,
ConditionType.STYLE: ConditioningMethod.INPUT_SUM,
},
conditioning_dropout=0.5)
model_params: hp.MusicgenParams = hp.MusicgenParams(
encodec_params=encodec_params,
lm_params=lm_params,
prompt_processor_params=prompt_processor_params,
conditioning_params=conditioning_params)
dataset_params = hp.MixDatasetParams(clip_length_in_seconds=10,
sample_rate=32_000,
root_dir=cfg.mixdata_path(),
single_stem=True,
target_stem=Stem.DRUMS,
min_context_seconds=5,
use_style_conditioning=True,
use_beat_conditioning=False,
type_of_context="stems",
add_click=False,
sync_chunks=False,
bpm_in_caption=False,
batch_size_train=2,
batch_size_test=12,
num_workers=8,
speed_transform_p=0.5,
pitch_transform_p=0.5,
n_samples_per_epoch=2000)
device = "cuda"
model: LightningMusicgen = model_params.instantiate().to(device)
datamodule = dataset_params.instantiate()
# validation step test
# model.eval()
# vd = iter(datamodule.val_dataloader())
# for i in range(2):
# batch = next(vd)
# batch = {
# k: v.to(device) if isinstance(v, Tensor) else v
# for k, v in batch.items()
# }
# t0 = time()
# with torch.autocast(device_type="cuda"):
# val_loss = model.validation_step(batch, i)
# t1 = time()
# print(f"val step in {t1 - t0} seconds")
# training step test
# td = iter(datamodule.train_dataloader())
# model.train()
# for i in range(10):
# batch = next(td)
# batch = {
# k: v.to(device) if isinstance(v, Tensor) else v
# for k, v in batch.items()
# }
# t0 = time()
# with torch.autocast(device_type="cuda"):
# train_loss = model.training_step(batch, i)
# t1 = time()
# print(f"training step in {t1 - t0} seconds")
trainer = L.Trainer(accelerator="auto",
precision="16-mixed",
enable_model_summary=True,
logger=None,
enable_checkpointing=False,
num_sanity_val_steps=2,
limit_train_batches=50,
limit_val_batches=2,
max_epochs=10)
trainer.fit(model, datamodule=datamodule)
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