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train.py
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train.py
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# Copyright 2023 Cerebras Systems.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import logging
import os
from pathlib import Path
from dataclasses import asdict
import torch
import cerebras_pytorch as cstorch
from configuration import parse_args
from data import get_dataloader
from model import GPTModel
logging.basicConfig()
logger = logging.getLogger(__file__)
logger.setLevel(logging.INFO)
def main(model_config, config, cs_config):
if config.backend == "CSX":
backend = cstorch.backend(config.backend, use_cs_grad_accum=True)
else:
backend = cstorch.backend(config.backend)
out_dir = Path(config.out_dir)
if not backend.is_cpu:
cstorch.amp.use_bfloat16(True)
with backend.device:
model = GPTModel(model_config)
compiled_model = cstorch.compile(model, backend)
decay_params = [p for p in model.parameters() if p.dim() >= 2]
no_decay_params = [p for p in model.parameters() if p.dim() < 2]
param_groups = [
{"params": decay_params, "weight_decay": config.weight_decay},
{"params": no_decay_params, "weight_decay": 0.0},
]
optimizer = cstorch.optim.AdamW(
param_groups,
lr=0.1, # just a placeholder as we are using learning rate scheduling
weight_decay=config.weight_decay,
correct_bias=True,
betas=(0.9, 0.95),
eps=config.adam_epsilon,
)
lr_scheduler = cstorch.optim.lr_scheduler.SequentialLR(
optimizer,
schedulers=[
cstorch.optim.lr_scheduler.LinearLR(
optimizer,
initial_learning_rate=0.0,
end_learning_rate=config.learning_rate,
total_iters=config.warmup_steps,
),
cstorch.optim.lr_scheduler.CosineDecayLR(
optimizer,
initial_learning_rate=config.learning_rate,
end_learning_rate=0.1 * config.learning_rate,
total_iters=config.decay_steps,
),
],
milestones=[config.warmup_steps],
)
all_params = (
p
for param_group in optimizer.param_groups
for p in param_group["params"]
)
if config.checkpoint_path is not None:
logger.info(f"Loading checkpoint from {config.checkpoint_path}")
state_dict = cstorch.load(config.checkpoint_path)
model.load_state_dict(state_dict["model"])
if "optimizer" in state_dict:
optimizer.load_state_dict(state_dict["optimizer"])
if "lr_scheduler" in state_dict:
lr_scheduler.load_state_dict(state_dict["lr_scheduler"])
global_step = state_dict.get("global_step", 0)
else:
global_step = 0
@cstorch.checkpoint_closure
def save_checkpoint(step):
checkpoint_path = out_dir.joinpath(f"checkpoint_{step}.mdl")
state_dict = {
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
"lr_scheduler": lr_scheduler.state_dict(),
"global_step": step,
"model_config": asdict(model_config),
}
cstorch.save(state_dict, checkpoint_path)
logger.info(f"Saved checkpoint to {checkpoint_path}")
@cstorch.trace
def training_step(batch):
input_ids, labels = batch
loss = compiled_model(input_ids, labels)
loss.backward()
torch.nn.utils.clip_grad_norm_(list(all_params), config.max_gradient_norm)
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
return loss
writer = cstorch.utils.tensorboard.SummaryWriter(
log_dir=out_dir.joinpath("train")
)
@cstorch.step_closure
def log_loss(loss, step):
rate = executor.profiler.rate()
global_rate = executor.profiler.global_rate()
logger.info(
f"| Step={step}, "
f"Loss={loss.item():.5f}, "
f"Rate={rate:.2f} samples/sec, "
f"GlobalRate={global_rate:.2f} samples/sec"
)
writer.add_scalar("loss", loss.item(), step)
writer.add_scalar("samples_per_second", global_rate, step)
data_path = os.path.join(config.dataset, "train.bin")
dataloader = cstorch.utils.data.DataLoader(
get_dataloader,
data_path,
config.sequence_length,
config.batch_size,
config.seed,
)
executor = cstorch.utils.data.DataExecutor(
dataloader,
num_steps=config.num_steps - global_step,
checkpoint_steps=config.checkpoint_steps,
cs_config=cs_config,
writer=writer,
)
for step, batch in enumerate(executor, start=global_step + 1):
if step > config.num_steps:
break
loss = training_step(batch)
log_loss(loss, step)
save_checkpoint(step)
logger.info("Training completed successfully!")
if __name__ == '__main__':
model_config, run_config, cs_config = parse_args()
main(model_config, run_config, cs_config)