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import os
import random
import subprocess
from dataclasses import asdict, dataclass, field
import torch
import torch.distributed as dist
from veomni.checkpoint import build_checkpointer
from veomni.distributed.parallel_state import init_parallel_state
from veomni.distributed.torch_parallelize import build_parallelize_model
from veomni.models import build_foundation_model, build_tokenizer
from veomni.optim import build_lr_scheduler, build_optimizer
from veomni.utils import helper
from veomni.utils.arguments import DataArguments, ModelArguments, TrainingArguments, parse_args
from veomni.utils.device import get_device_type, get_nccl_backend, get_torch_device
logger = helper.create_logger(__name__)
@dataclass
class Arguments:
model: "ModelArguments" = field(default_factory=ModelArguments)
data: "DataArguments" = field(default_factory=DataArguments)
train: "TrainingArguments" = field(default_factory=TrainingArguments)
"""
torchrun --nnodes=1 --nproc-per-node=8 --master-port=4321 tests/utils/test_checkpointer.py \
--model.model_path qwen2-1_5b-instruct \
--data.train_path None \
--train.global_batch_size 16 \
--train.micro_batch_size 2 \
--train.data_parallel_mode fsdp1 \
--train.output_dir "ckpt_test" \
--train.rmpad False \
--train.rmpad_with_pos_ids False \
--train.ckpt_manager "omnistore" \
--train.max_steps 10 \
"""
def run_checkpointer_test():
args = parse_args(Arguments)
logger.info(f"Process rank: {args.train.global_rank}, world size: {args.train.world_size}")
logger.info_rank0(json.dumps(asdict(args), indent=2))
helper.set_seed(args.train.seed, args.train.enable_full_determinism)
get_torch_device().set_device(f"{get_device_type()}:{args.train.local_rank}")
dist.init_process_group(backend=get_nccl_backend())
init_parallel_state(
dp_size=args.train.data_parallel_size,
dp_replicate_size=args.train.data_parallel_replicate_size,
dp_shard_size=args.train.data_parallel_shard_size,
tp_size=args.train.tensor_parallel_size,
ep_size=args.train.expert_parallel_size,
pp_size=args.train.pipeline_parallel_size,
cp_size=args.train.context_parallel_size,
ulysses_size=args.train.ulysses_parallel_size,
dp_mode=args.train.data_parallel_mode,
)
Checkpointer = build_checkpointer(dist_backend=args.train.data_parallel_mode, ckpt_manager=args.train.ckpt_manager)
args.train.compute_train_steps()
logger.info_rank0("Prepare model")
model = build_foundation_model(
config_path=args.model.config_path,
weights_path=args.model.model_path,
attn_implementation=args.model.attn_implementation,
moe_implementation=args.model.moe_implementation,
init_device=args.train.init_device,
force_use_huggingface=args.model.force_use_huggingface,
)
model = build_parallelize_model(
model,
enable_full_shard=args.train.enable_full_shard,
enable_mixed_precision=args.train.enable_mixed_precision,
enable_gradient_checkpointing=args.train.enable_gradient_checkpointing,
init_device=args.train.init_device,
enable_fsdp_offload=args.train.enable_fsdp_offload,
basic_modules=model._no_split_modules + args.model.basic_modules,
enable_reentrant=args.train.enable_reentrant,
enable_forward_prefetch=args.train.enable_forward_prefetch,
)
optimizer = build_optimizer(
model,
lr=args.train.lr,
weight_decay=args.train.weight_decay,
fused=True,
optimizer_type=args.train.optimizer,
)
lr_scheduler = build_lr_scheduler(
optimizer,
train_steps=args.train.train_steps * args.train.num_train_epochs,
lr=args.train.lr,
lr_min=args.train.lr_min,
lr_decay_style=args.train.lr_decay_style,
lr_decay_ratio=args.train.lr_decay_ratio,
lr_warmup_ratio=args.train.lr_warmup_ratio,
lr_start=args.train.lr_start,
)
# prepare data
tokenizer = build_tokenizer(args.model.tokenizer_path)
raw_text = "Hello, how are you?, I am fine, thank you."
input_ids = tokenizer.encode(raw_text, return_tensors="pt")
micro_batch = {
"input_ids": input_ids,
"attention_mask": torch.ones_like(input_ids),
"labels": input_ids,
}
micro_batch = {
k: v.to(get_device_type(), non_blocking=True) if isinstance(v, torch.Tensor) else v
for k, v in micro_batch.items()
}
helper.print_example(micro_batch, rank=args.train.local_rank)
loss: "torch.Tensor" = model(**micro_batch, use_cache=False).loss
logger.info(f"rank {args.train.local_rank} loss: {loss}")
# save checkpoint
global_step = 1
state = {
"model": model,
"optimizer": optimizer,
"extra_state": {
"global_step": global_step,
"lr_scheduler": lr_scheduler.state_dict(),
},
}
logger.info_rank0(f"Distributed checkpoint saving... global_step {global_step}")
Checkpointer.save(args.train.save_checkpoint_path, state, global_steps=global_step)
logger.info_rank0("Distributed checkpoint saved successfully!")
# load checkpoint
state = {"model": model, "optimizer": optimizer, "extra_state": {}}
load_checkpoint_path = os.path.join(args.train.save_checkpoint_path, f"global_step_{global_step}")
Checkpointer.load(load_checkpoint_path, state)
lr_scheduler.load_state_dict(state["extra_state"]["lr_scheduler"])
global_step = state["extra_state"]["global_step"]
global_step += 1
logger.info_rank0("load checkpoint successfully!")
# for dropout, reset seed
helper.set_seed(args.train.seed, args.train.enable_full_determinism)
helper.print_example(micro_batch, rank=args.train.local_rank)
resume_loss: "torch.Tensor" = model(**micro_batch, use_cache=False).loss.mean()
logger.info(f"rank {args.train.local_rank} loss: {loss}, resume_loss: {resume_loss}")
assert torch.allclose(loss, resume_loss), (
f"rank {args.train.local_rank} loss is not equal, loss: {loss}, resume_loss: {resume_loss}"
)
logger.info(f"[rank{args.train.local_rank}] finish!!!!!")
dist.destroy_process_group()
def test_omnistore_checkpointer():
port = 12345 + random.randint(0, 100)
command = [
"torchrun",
"--nnodes=1",
"--nproc_per_node=8",
f"--master_port={port}",
"tests/utils/test_checkpointer.py",
"--model.model_path=qwen2-1_5b-instruct",
"--data.train_path=None",
"--train.global_batch_size=16",
"--train.micro_batch_size=2",
"--train.data_parallel_mode=fsdp1",
"--train.output_dir=omnistore_test",
"--train.rmpad=False",
"--train.rmpad_with_pos_ids=False",
"--train.ckpt_manager=omnistore",
"--train.max_steps=10",
]
result = subprocess.run(command, check=True)
assert result.returncode == 0
def test_dcp_checkpointer():
port = 12345 + random.randint(0, 100)
command = [
"torchrun",
"--nnodes=1",
"--nproc_per_node=8",
f"--master_port={port}",
"tests/utils/test_checkpointer.py",
"--model.model_path=qwen2-1_5b-instruct",
"--data.train_path=None",
"--train.global_batch_size=16",
"--train.micro_batch_size=2",
"--train.data_parallel_mode=fsdp1",
"--train.output_dir=dcp_test",
"--train.rmpad=False",
"--train.rmpad_with_pos_ids=False",
"--train.ckpt_manager=dcp",
"--train.max_steps=10",
]
result = subprocess.run(command, check=True)
assert result.returncode == 0
if __name__ == "__main__":
run_checkpointer_test()
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