ViperEkura
0b6a17330f
- New FSDP2Executor registers as 'fsdp2' in ExecutorFactory, using per-module fully_shard() instead of FSDP1 FlatParameter wrapper - FSDP2 preserves original Parameter objects as DTensors, eliminating use_orig_params=True hack - FSDP2Executor implements _no_sync via set_requires_gradient_sync, clip_grad_norm via unshard, unwrap_model via DTensor.full_tensor - Drop **_extra/**_ddp_only_kwargs fallbacks in BaseExecutor/FSDPExecutor/FSDP2Executor, replaced by parallel_mode-aware executor_kwargs dispatch in train.py (ddp-only kwargs only passed when parallel_mode=ddp) - Export FSDP2Executor in astrai.parallel.__init__
391 lines
13 KiB
Python
391 lines
13 KiB
Python
"""Unified training executor — parallel strategy + gradient accumulation."""
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import contextlib
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import logging
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import os
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from contextlib import contextmanager
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from typing import Any, Callable, Optional, Tuple
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import torch
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import torch.distributed as dist
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import torch.nn as nn
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from torch.distributed.fsdp import (
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FSDPModule,
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FullStateDictConfig,
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StateDictType,
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fully_shard,
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)
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from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
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from torch.distributed.tensor import DTensor
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from torch.nn.parallel import DistributedDataParallel as DDP
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from torch.optim import Optimizer
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from torch.optim.lr_scheduler import LRScheduler
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from astrai.factory import BaseFactory
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from astrai.parallel.setup import get_rank, get_world_size
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logger = logging.getLogger(__name__)
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class GradientState:
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def __init__(self, grad_accum_steps: int = 1):
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self.num_steps = max(grad_accum_steps, 1)
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self._step: int = 0
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self._sync_gradients: bool = True
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@property
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def sync_gradients(self) -> bool:
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return self._sync_gradients
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def _do_sync(self):
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self._step += 1
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self._sync_gradients = self._step % self.num_steps == 0
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class AccumOptimizer:
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def __init__(self, optimizer: Optimizer, gradient_state: GradientState):
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self.optimizer = optimizer
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self.gradient_state = gradient_state
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def step(self, closure=None):
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if self.gradient_state.sync_gradients:
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self.optimizer.step(closure)
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def zero_grad(self):
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if self.gradient_state.sync_gradients:
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self.optimizer.zero_grad()
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@property
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def param_groups(self):
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return self.optimizer.param_groups
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def state_dict(self):
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return self.optimizer.state_dict()
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def load_state_dict(self, d):
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self.optimizer.load_state_dict(d)
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class AccumScheduler:
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def __init__(self, scheduler: LRScheduler, gradient_state: GradientState):
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self.scheduler = scheduler
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self.gradient_state = gradient_state
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def step(self):
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if self.gradient_state.sync_gradients:
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self.scheduler.step()
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def state_dict(self):
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return self.scheduler.state_dict()
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def load_state_dict(self, d):
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self.scheduler.load_state_dict(d)
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def get_last_lr(self):
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return self.scheduler.get_last_lr()
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class BaseExecutor:
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def __init__(self, grad_accum_steps: int = 1):
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self.gradient_state = GradientState(grad_accum_steps)
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def prepare(
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self,
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model_fn: Callable[[], nn.Module],
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optimizer_fn: Optional[Callable[[nn.Module], Optimizer]] = None,
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scheduler_fn: Optional[Callable[[Optimizer], LRScheduler]] = None,
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before_wrap: Optional[Callable[[nn.Module], nn.Module]] = None,
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) -> Tuple[nn.Module, Optional[Optimizer], Optional[LRScheduler]]:
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model = model_fn()
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if before_wrap is not None:
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model = before_wrap(model)
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model = self._prepare_model(model)
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optimizer = None
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scheduler = None
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if optimizer_fn is not None:
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optimizer = optimizer_fn(model)
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if scheduler_fn is not None:
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scheduler = scheduler_fn(optimizer)
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optimizer = AccumOptimizer(optimizer, self.gradient_state)
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if scheduler is not None:
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scheduler = AccumScheduler(scheduler, self.gradient_state)
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return model, optimizer, scheduler
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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return model
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def _no_sync(self, model: nn.Module):
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return contextlib.nullcontext()
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@contextmanager
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def accumulate(self, model: nn.Module):
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self.gradient_state._do_sync()
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if not self.gradient_state.sync_gradients:
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with self._no_sync(model):
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yield
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else:
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yield
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def backward(self, loss: torch.Tensor):
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loss.backward()
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def unwrap_model(self, model: nn.Module):
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return model.state_dict()
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@contextmanager
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def checkpoint_context(self, model: nn.Module):
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if self.use_distributed:
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dist.barrier()
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state_dict = self._gather_state_dict(model)
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yield state_dict
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if self.use_distributed:
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dist.barrier()
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def _gather_state_dict(self, model: nn.Module):
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state_dict = self.unwrap_model(model)
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if self.use_distributed and get_rank() != 0:
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return None
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return state_dict
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@property
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def use_distributed(self) -> bool:
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return get_world_size() > 1
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@property
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def sync_gradients(self) -> bool:
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return self.gradient_state.sync_gradients
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@property
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def grad_accum_steps(self) -> int:
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return self.gradient_state.num_steps
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def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
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total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
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if isinstance(total_norm, torch.Tensor):
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return total_norm.item()
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return total_norm
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class ExecutorFactory(BaseFactory[BaseExecutor]):
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pass
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@ExecutorFactory.register("none")
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class NoneExecutor(BaseExecutor):
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pass
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@ExecutorFactory.register("ddp")
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class DDPExecutor(BaseExecutor):
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def __init__(
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self,
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grad_accum_steps: int = 1,
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dim: int = 0,
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broadcast_buffers: bool = True,
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init_sync: bool = True,
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process_group=None,
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bucket_cap_mb: int = 25,
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find_unused_parameters: bool = False,
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check_reduction: bool = False,
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gradient_as_bucket_view: bool = False,
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static_graph: bool = False,
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delay_all_reduce_named_params=None,
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param_to_hook_all_reduce=None,
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mixed_precision=None,
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device_mesh=None,
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):
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super().__init__(grad_accum_steps=grad_accum_steps)
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self._ddp_kwargs = dict(
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dim=dim,
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broadcast_buffers=broadcast_buffers,
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init_sync=init_sync,
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process_group=process_group,
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bucket_cap_mb=bucket_cap_mb,
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find_unused_parameters=find_unused_parameters,
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check_reduction=check_reduction,
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gradient_as_bucket_view=gradient_as_bucket_view,
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static_graph=static_graph,
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delay_all_reduce_named_params=delay_all_reduce_named_params,
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param_to_hook_all_reduce=param_to_hook_all_reduce,
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mixed_precision=mixed_precision,
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device_mesh=device_mesh,
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)
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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if not self.use_distributed:
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logger.warning("DDP backend selected but world_size=1, model not wrapped")
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return model
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local_rank = int(os.environ.get("LOCAL_RANK", get_rank()))
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model = DDP(
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model,
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device_ids=[local_rank],
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output_device=local_rank,
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**self._ddp_kwargs,
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)
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logger.info("Model wrapped with DDP (world_size=%d)", get_world_size())
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return model
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def _no_sync(self, model: nn.Module):
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if isinstance(model, DDP):
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return model.no_sync()
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return contextlib.nullcontext()
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def unwrap_model(self, model: nn.Module):
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if isinstance(model, DDP):
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return model.module.state_dict()
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return model.state_dict()
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@ExecutorFactory.register("fsdp")
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class FSDPExecutor(BaseExecutor):
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def __init__(
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self,
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grad_accum_steps: int = 1,
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process_group=None,
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sharding_strategy=None,
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cpu_offload=None,
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auto_wrap_policy=None,
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backward_prefetch=None,
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mixed_precision=None,
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ignored_modules=None,
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param_init_fn=None,
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sync_module_states: bool = False,
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forward_prefetch: bool = False,
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limit_all_gathers: bool = True,
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ignored_states=None,
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device_mesh=None,
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):
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super().__init__(grad_accum_steps=grad_accum_steps)
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self._fsdp_kwargs = {
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k: v
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for k, v in dict(
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process_group=process_group,
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sharding_strategy=sharding_strategy,
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cpu_offload=cpu_offload,
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auto_wrap_policy=auto_wrap_policy,
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backward_prefetch=backward_prefetch,
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mixed_precision=mixed_precision,
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ignored_modules=ignored_modules,
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param_init_fn=param_init_fn,
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sync_module_states=sync_module_states,
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forward_prefetch=forward_prefetch,
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limit_all_gathers=limit_all_gathers,
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use_orig_params=True,
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ignored_states=ignored_states,
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device_mesh=device_mesh,
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).items()
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if v is not None
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}
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self._original_model: Optional[nn.Module] = None
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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if not self.use_distributed:
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logger.warning("FSDP backend selected but world_size=1, model not wrapped")
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return model
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self._original_model = model
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device_id = torch.device("cuda", get_rank())
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model = FSDP(model, device_id=device_id, **self._fsdp_kwargs)
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logger.info("Model wrapped with FSDP (world_size=%d)", get_world_size())
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return model
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def _no_sync(self, model: nn.Module):
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if isinstance(model, FSDP):
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return model.no_sync()
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return contextlib.nullcontext()
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def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
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if isinstance(model, FSDP) and self.use_distributed:
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total_norm = model.clip_grad_norm_(max_norm)
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if isinstance(total_norm, torch.Tensor):
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return total_norm.item()
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return total_norm
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return super().clip_grad_norm(model, max_norm)
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def unwrap_model(self, model: nn.Module):
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if isinstance(model, FSDP) and self.use_distributed:
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with FSDP.state_dict_type(
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model,
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StateDictType.FULL_STATE_DICT,
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FullStateDictConfig(offload_to_cpu=True, rank0_only=True),
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):
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return model.state_dict()
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return model.state_dict()
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@ExecutorFactory.register("fsdp2")
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class FSDP2Executor(BaseExecutor):
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"""FSDP2 executor using `torch.distributed.fsdp.fully_shard` (per-module API).
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Unlike FSDP1's `FSDP(model, ...)` wrapper, FSDP2 wraps each submodule
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individually via `fully_shard(module)`. Original `Parameter` objects are
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preserved (as DTensors) — no `FlatParameter`, no `use_orig_params=True` hack.
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"""
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def __init__(
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self,
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grad_accum_steps: int = 1,
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mesh: Optional[Any] = None,
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mp_policy: Optional[Any] = None,
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reshard_after_forward: bool = True,
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):
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super().__init__(grad_accum_steps=grad_accum_steps)
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self._mesh = mesh
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self._mp_policy = mp_policy
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self._reshard_after_forward = reshard_after_forward
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def _prepare_model(self, model: nn.Module) -> nn.Module:
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if not self.use_distributed:
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logger.warning("FSDP2 backend selected but world_size=1, model not wrapped")
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return model
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kwargs = dict(
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mesh=self._mesh,
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mp_policy=self._mp_policy,
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reshard_after_forward=self._reshard_after_forward,
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)
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kwargs = {k: v for k, v in kwargs.items() if v is not None}
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model = fully_shard(model, **kwargs)
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logger.info("Model wrapped with FSDP2 (world_size=%d)", get_world_size())
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return model
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@contextmanager
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def _no_sync(self, model: nn.Module):
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if isinstance(model, FSDPModule):
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model.set_requires_gradient_sync(False, recurse=True)
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try:
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yield
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finally:
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model.set_requires_gradient_sync(True, recurse=True)
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else:
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yield
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def clip_grad_norm(self, model: nn.Module, max_norm: float) -> float:
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if isinstance(model, FSDPModule) and self.use_distributed:
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for module in model.modules():
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if isinstance(module, FSDPModule):
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module.unshard()
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total_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm)
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if isinstance(total_norm, torch.Tensor):
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return total_norm.item()
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return total_norm
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return super().clip_grad_norm(model, max_norm)
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def unwrap_model(self, model: nn.Module):
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if not self.use_distributed or not isinstance(model, FSDPModule):
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return model.state_dict()
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if get_rank() != 0:
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return None
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for module in model.modules():
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if isinstance(module, FSDPModule):
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module.unshard()
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state_dict = model.state_dict()
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return {
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k: (v.full_tensor() if isinstance(v, DTensor) else v)
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for k, v in state_dict.items()
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}
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