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train.py
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train.py
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import argparse
import json
import time
import numpy as np
import wandb
from tqdm import tqdm
from mesh_transformer.build_model import build_model
from lm_eval import evaluator, tasks
from tasks.eval_harness import EvalHarnessAdaptor
from tfrecord_loader import TFRecordNewInputs
import multiprocessing
def parse_args():
# Parse command line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--tpu", type=str, help="Name of TPU to train on.")
parser.add_argument("--tpu_region", type=str, help="Region of TPU to train on.")
parser.add_argument("--preemptible", action="store_true")
parser.add_argument("--config", type=str, default=None, help="Config file location")
parser.add_argument("--new", action="store_true", help="If set, deletes previous checkpoint, if it exists, and "
"starts a new training run")
parser.add_argument("--version", type=int, default=1, help="Choose which model version to use")
args = parser.parse_args()
return args
if __name__ == "__main__":
# huggingface tokenizers gets very angry if you fork
multiprocessing.set_start_method("spawn")
args = parse_args()
params = json.load(open(args.config))
if args.new:
print(f"Starting experiment {params['name']} from scratch! "
f"all data in gs://{params['bucket']}/{params['model_dir']}/ will be deleted")
input("Hit enter to continue")
tpu_name = args.tpu
region = args.tpu_region
preemptible = args.preemptible
clean_start = args.new
gradient_accumulation_steps = params.get("gradient_accumulation_steps", 1)
per_replica_batch = params["per_replica_batch"]
tpu_size = params["tpu_size"]
cores_per_replica = params["cores_per_replica"]
bucket = params["bucket"]
model_dir = params["model_dir"]
layers = params["layers"]
d_model = params["d_model"]
n_heads = params["n_heads"]
n_vocab = params["n_vocab"]
seq = params["seq"]
norm = params["norm"]
val_batches = params["val_batches"]
val_every = params["val_every"]
ckpt_every = params["ckpt_every"]
keep_every = params["keep_every"]
eval_tasks = params["eval_harness_tasks"]
total_steps = params["total_steps"]
pe = params["pe"]
assert pe in ["fixed", "rotary", "t5"]
t = build_model(params, tpu_name, region, preemptible, version=args.version)
try:
t.save(0, bucket, model_dir, init=True, overwrite=clean_start)
step = 0
train_load_restore = None
except Exception as e:
print(f"Save failed with error {e}, trying to load instead...", e)
step, aux = t.load(bucket, model_dir)
train_load_restore = aux.get("train_loader", None)
if train_load_restore is None:
print("Failed to restore train loader state")
train_dataset = TFRecordNewInputs(f"data/{params['train_set']}",
batch_size=(
gradient_accumulation_steps,
per_replica_batch * tpu_size // cores_per_replica),
sample_size=params['seq'],
restore_state=train_load_restore)
global_val_batch = int(per_replica_batch * tpu_size // cores_per_replica * params.get("val_batch_multiplier", 1))
val_sets = {}
for k, v in params['val_set'].items():
val_sets[k] = TFRecordNewInputs(f"data/{v}",
batch_size=(global_val_batch,),
sample_size=seq)
# use dynamic seq length unless pe is fixed
adaptor = EvalHarnessAdaptor(t,
seq,
global_val_batch,
shrink=pe != "fixed",
min_seq=1024 if args.version == 2 else None) # work around suboptimal pjit layout
start = time.time()
t.train(train_dataset.get_samples())
print(f"Train fn compiled in {time.time() - start:.06}s")
start = time.time()
for val_set in val_sets.values():
t.eval(val_set.get_samples())
print(f"Eval fn compiled in {time.time() - start:.06}s")
project = params.get("wandb_project", "mesh-transformer-jax")
wandb.init(project=project, entity="eleutherai", name=params["name"], config=params)
eval_task_dict = tasks.get_task_dict(eval_tasks)
pbar = tqdm(initial=step, total=total_steps, desc="Training progress")
while True:
loss, last_loss = t.train(train_dataset.get_samples())
wandb.log({'train/loss': loss, 'train/last_loss': last_loss}, step)
if (step % ckpt_every == 0 and step) or step == total_steps:
t.save(step, bucket, model_dir,
aux={"train_loader": train_dataset.get_state()},
init=False,
delete_old=step % keep_every != 0)
if step == total_steps:
print("training completed!")
exit()
if step % val_every == 0:
for name, val_set in val_sets.items():
val_loss = []
for i, _ in tqdm(zip(val_set.sample_once(), range(val_batches)),
desc=f"validation for step {step}, set {name}",
total=val_batches):
val_loss.append(t.eval(i))
val_loss = np.array(val_loss).mean()
print(f"validation loss for step {step}, set {name}: {val_loss}")
wandb.log({f'val/loss_{name}': float(val_loss)}, step)
results = evaluator.evaluate(adaptor, eval_task_dict, False, 0, None)
flat_results = {}
for task_name, task_res in results["results"].items():
version = results["versions"][task_name]
for metric_name, metric_res in task_res.items():
flat_results[f"{task_name}-v{version}/{metric_name}"] = float(metric_res)
dumped = json.dumps(results, indent=2)
print(f"step {step} val results: {dumped}")
wandb.log(flat_results, step)
step += 1
pbar.set_postfix({'loss': loss, 'last_loss': last_loss})
pbar.update()