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from functools import partial
import os
import torch
import torch.distributed as dist
from torch.distributed.fsdp import (
FullStateDictConfig,
FullyShardedDataParallel as FSDP,
MixedPrecision,
ShardingStrategy,
StateDictType,
)
from torch.distributed.fsdp.api import CPUOffload
from torch.distributed.fsdp.wrap import (
size_based_auto_wrap_policy,
transformer_auto_wrap_policy,
)
def fsdp_state_dict(
model,
):
fsdp_fullstate_save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, fsdp_fullstate_save_policy):
checkpoint = model.state_dict()
return checkpoint
def fsdp_wrap(
module,
sharding_strategy="full",
mixed_precision=False,
wrap_strategy="size",
min_num_params=int(5e7),
transformer_module=None,
cpu_offload=False,
):
if mixed_precision:
mixed_precision_policy = MixedPrecision(
param_dtype=torch.bfloat16,
reduce_dtype=torch.float32,
buffer_dtype=torch.float32,
cast_forward_inputs=False,
)
else:
mixed_precision_policy = None
if wrap_strategy == "transformer":
auto_wrap_policy = partial(
transformer_auto_wrap_policy,
transformer_layer_cls=transformer_module,
)
elif wrap_strategy == "size":
auto_wrap_policy = partial(size_based_auto_wrap_policy, min_num_params=min_num_params)
else:
raise ValueError(f"Invalid wrap strategy: {wrap_strategy}")
os.environ["NCCL_CROSS_NIC"] = "1"
sharding_strategy = {
"full": ShardingStrategy.FULL_SHARD,
"hybrid_full": ShardingStrategy.HYBRID_SHARD,
"hybrid_zero2": ShardingStrategy._HYBRID_SHARD_ZERO2,
"no_shard": ShardingStrategy.NO_SHARD,
}[sharding_strategy]
module = FSDP(
module,
auto_wrap_policy=auto_wrap_policy,
sharding_strategy=sharding_strategy,
mixed_precision=mixed_precision_policy,
device_id=torch.cuda.current_device(),
limit_all_gathers=True,
use_orig_params=True,
cpu_offload=CPUOffload(offload_params=cpu_offload),
sync_module_states=False, # Load ckpt on rank 0 and sync to other ranks
)
return module
def barrier():
if dist.is_initialized():
dist.barrier()
def launch_distributed_job(
backend: str = "nccl",
):
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
host = os.environ["MASTER_ADDR"]
port = int(os.environ["MASTER_PORT"])
if ":" in host: # IPv6
init_method = f"tcp://[{host}]:{port}"
else: # IPv4
init_method = f"tcp://{host}:{port}"
dist.init_process_group(
rank=rank,
world_size=world_size,
backend=backend,
init_method=init_method,
timeout=timedelta(minutes=30),
)
torch.cuda.set_device(local_rank)
class EMA_FSDP:
def __init__(
self,
fsdp_module: torch.nn.Module,
decay: float = 0.999,
):
self.decay = decay
self.shadow = {}
self._init_shadow(fsdp_module)
@torch.no_grad()
def _init_shadow(
self,
fsdp_module,
):
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
with FSDP.summon_full_params(fsdp_module, writeback=False):
for n, p in fsdp_module.module.named_parameters():
self.shadow[n] = p.detach().clone().float().cpu()
@torch.no_grad()
def update(
self,
fsdp_module,
):
d = self.decay
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
with FSDP.summon_full_params(fsdp_module, writeback=False):
for n, p in fsdp_module.module.named_parameters():
self.shadow[n].mul_(d).add_(p.detach().float().cpu(), alpha=1.0 - d)
# Optional helpers ---------------------------------------------------
def state_dict(
self,
):
return self.shadow # picklable
def load_state_dict(
self,
sd,
):
self.shadow = {k: v.clone() for k, v in sd.items()}
def copy_to(
self,
fsdp_module,
):
# load EMA weights into an (unwrapped) copy of the generator
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
with FSDP.summon_full_params(fsdp_module, writeback=True):
for n, p in fsdp_module.module.named_parameters():
if n in self.shadow:
p.data.copy_(self.shadow[n].to(p.dtype, device=p.device))
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