utils/distributed.py

from datetime import timedelta
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(50000000.0), 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)
    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:
        init_method = f'tcp://[{host}]:{port}'
    else:
        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)

    def state_dict(self):
        return self.shadow

    def load_state_dict(self, sd):
        self.shadow = {k: v.clone() for k, v in sd.items()}

    def copy_to(self, fsdp_module):
        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))