mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-11-16 21:29:24 +01:00
109 lines
3.2 KiB
Python
109 lines
3.2 KiB
Python
import datetime
|
|
import os
|
|
from typing import Callable, List, Optional, Tuple
|
|
import tensorflow as tf
|
|
|
|
import tml.common.checkpointing.snapshot as snapshot_lib
|
|
from tml.common.device import setup_and_get_device
|
|
from tml.core import config as tml_config_mod
|
|
import tml.core.custom_training_loop as ctl
|
|
from tml.core import debug_training_loop
|
|
from tml.core import losses
|
|
from tml.core.loss_type import LossType
|
|
from tml.model import maybe_shard_model
|
|
|
|
|
|
import tml.projects.home.recap.data.dataset as ds
|
|
import tml.projects.home.recap.config as recap_config_mod
|
|
import tml.projects.home.recap.optimizer as optimizer_mod
|
|
|
|
|
|
# from tml.projects.home.recap import feature
|
|
import tml.projects.home.recap.model as model_mod
|
|
import torchmetrics as tm
|
|
import torch
|
|
import torch.distributed as dist
|
|
from torchrec.distributed.model_parallel import DistributedModelParallel
|
|
|
|
from absl import app, flags, logging
|
|
|
|
flags.DEFINE_string("config_path", None, "Path to hyperparameters for model.")
|
|
flags.DEFINE_bool("debug_loop", False, "Run with debug loop (slow)")
|
|
|
|
FLAGS = flags.FLAGS
|
|
|
|
|
|
def run(unused_argv: str, data_service_dispatcher: Optional[str] = None):
|
|
print("#" * 100)
|
|
|
|
config = tml_config_mod.load_config_from_yaml(recap_config_mod.RecapConfig, FLAGS.config_path)
|
|
logging.info("Config: %s", config.pretty_print())
|
|
|
|
device = setup_and_get_device()
|
|
|
|
# Always enable tensorfloat on supported devices.
|
|
torch.backends.cuda.matmul.allow_tf32 = True
|
|
torch.backends.cudnn.allow_tf32 = True
|
|
|
|
loss_fn = losses.build_multi_task_loss(
|
|
loss_type=LossType.BCE_WITH_LOGITS,
|
|
tasks=list(config.model.tasks.keys()),
|
|
pos_weights=[task.pos_weight for task in config.model.tasks.values()],
|
|
)
|
|
|
|
# Since the prod model doesn't use large embeddings, for now we won't support them.
|
|
assert config.model.large_embeddings is None
|
|
|
|
train_dataset = ds.RecapDataset(
|
|
data_config=config.train_data,
|
|
dataset_service=data_service_dispatcher,
|
|
mode=recap_config_mod.JobMode.TRAIN,
|
|
compression=config.train_data.dataset_service_compression,
|
|
vocab_mapper=None,
|
|
repeat=True,
|
|
)
|
|
|
|
train_iterator = iter(train_dataset.to_dataloader())
|
|
|
|
torch_element_spec = train_dataset.torch_element_spec
|
|
|
|
model = model_mod.create_ranking_model(
|
|
data_spec=torch_element_spec[0],
|
|
config=config,
|
|
loss_fn=loss_fn,
|
|
device=device,
|
|
)
|
|
|
|
optimizer, scheduler = optimizer_mod.build_optimizer(model, config.optimizer, None)
|
|
|
|
model = maybe_shard_model(model, device)
|
|
|
|
datetime_str = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
|
|
print(f"{datetime_str}\n", end="")
|
|
|
|
if FLAGS.debug_loop:
|
|
logging.warning("Running debug mode, slow!")
|
|
train_mod = debug_training_loop
|
|
else:
|
|
train_mod = ctl
|
|
|
|
train_mod.train(
|
|
model=model,
|
|
optimizer=optimizer,
|
|
device=device,
|
|
save_dir=config.training.save_dir,
|
|
logging_interval=config.training.train_log_every_n,
|
|
train_steps=config.training.num_train_steps,
|
|
checkpoint_frequency=config.training.checkpoint_every_n,
|
|
dataset=train_iterator,
|
|
worker_batch_size=config.train_data.global_batch_size,
|
|
enable_amp=False,
|
|
initial_checkpoint_dir=config.training.initial_checkpoint_dir,
|
|
gradient_accumulation=config.training.gradient_accumulation,
|
|
scheduler=scheduler,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
app.run(run)
|