ViperEkura
a74e5b91a3
- Store gains fetch_record/num_records alongside stream fetch/__len__ - save_bin/load_bin support per-record offsets via record_keys param - H5Store/MmapStore/JsonlStore all support dual stream+record access - DPODataset/GRPODataset use fetch_record, no cross-record concat - dpo_collate_fn + collate_fn wired through TrainConfig - fixes attention context leakage in DPO from windowed concatenation
219 lines
7.4 KiB
Python
219 lines
7.4 KiB
Python
from dataclasses import dataclass, field
|
|
from pathlib import Path
|
|
from typing import Any, Dict, Optional, Self
|
|
|
|
import torch
|
|
import torch.nn as nn
|
|
from torch.utils.data import DataLoader, random_split
|
|
|
|
from astrai.config.train_config import TrainConfig
|
|
from astrai.dataset import RDSampler
|
|
from astrai.model.components.lora import inject_lora
|
|
from astrai.parallel.executor import BaseExecutor, ExecutorFactory
|
|
from astrai.parallel.setup import get_current_device, get_rank, get_world_size
|
|
from astrai.protocols import OptimizerProtocol, SchedulerProtocol
|
|
from astrai.serialization import Checkpoint, load_json
|
|
from astrai.trainer.strategy import BaseStrategy, StrategyFactory, create_ref_model
|
|
|
|
|
|
@dataclass
|
|
class TrainContext:
|
|
model: nn.Module = field(default=None)
|
|
strategy: BaseStrategy = field(default=None)
|
|
dataloader: DataLoader = field(default=None)
|
|
optimizer: OptimizerProtocol = field(default=None)
|
|
scheduler: SchedulerProtocol = field(default=None)
|
|
checkpoint: Checkpoint = field(default=None)
|
|
config: TrainConfig = field(default=None)
|
|
model_config: dict = field(default_factory=dict)
|
|
executor: BaseExecutor = field(default=None)
|
|
|
|
epoch: int = field(default=0)
|
|
consumed_samples: int = field(default=0)
|
|
loss: float = field(default=0.0)
|
|
grad_norm: Optional[float] = field(default=None)
|
|
val_dataloader: Optional[DataLoader] = field(default=None)
|
|
val_loss: Optional[float] = field(default=None)
|
|
|
|
world_size: int = field(default=1)
|
|
rank: int = field(default=0)
|
|
kwargs: Dict[str, Any] = field(default_factory=dict)
|
|
|
|
@property
|
|
def optimizer_step(self) -> int:
|
|
return self.consumed_samples // (
|
|
self.config.batch_per_device
|
|
* self.world_size
|
|
* self.config.grad_accum_steps
|
|
)
|
|
|
|
|
|
class TrainContextBuilder:
|
|
def __init__(
|
|
self,
|
|
config: TrainConfig,
|
|
):
|
|
self.config = config
|
|
self._param_path: Optional[str] = None
|
|
self._resume: bool = False
|
|
|
|
def with_param_path(self, param_path: Optional[str], resume: bool = False) -> Self:
|
|
self._param_path = param_path
|
|
self._resume = resume
|
|
return self
|
|
|
|
def build(self) -> TrainContext:
|
|
cfg = self.config
|
|
device = get_current_device()
|
|
|
|
executor = ExecutorFactory.create(
|
|
cfg.parallel_mode,
|
|
grad_accum_steps=cfg.grad_accum_steps,
|
|
**cfg.executor_kwargs,
|
|
)
|
|
|
|
model = cfg.model_fn()
|
|
model = model.to(device=device)
|
|
|
|
model_config = {}
|
|
if self._param_path:
|
|
config_path = Path(self._param_path) / "config.json"
|
|
if config_path.exists():
|
|
model_config = load_json(config_path)
|
|
|
|
if not model_config and hasattr(model, "config"):
|
|
model_config = model.config.to_dict()
|
|
|
|
context = TrainContext(
|
|
model=model,
|
|
world_size=get_world_size(),
|
|
rank=get_rank(),
|
|
config=cfg,
|
|
model_config=model_config,
|
|
executor=executor,
|
|
)
|
|
|
|
if self._param_path:
|
|
checkpoint = Checkpoint.load_any(self._param_path)
|
|
if checkpoint is not None:
|
|
model.load_state_dict(checkpoint.state_dict, strict=False)
|
|
if checkpoint.config:
|
|
context.model_config = checkpoint.config
|
|
|
|
if self._resume:
|
|
context.epoch = checkpoint.epoch or cfg.start_epoch
|
|
if checkpoint.consumed_samples > 0:
|
|
per_step = (
|
|
cfg.batch_per_device
|
|
* context.world_size
|
|
* cfg.grad_accum_steps
|
|
)
|
|
context.consumed_samples = (
|
|
checkpoint.consumed_samples // per_step
|
|
) * per_step
|
|
else:
|
|
context.consumed_samples = (
|
|
cfg.start_samples * context.world_size
|
|
)
|
|
context.checkpoint = checkpoint
|
|
|
|
if cfg.lora is not None:
|
|
inject_lora(
|
|
model,
|
|
r=cfg.lora.r,
|
|
alpha=cfg.lora.alpha,
|
|
target_modules=set(cfg.lora.target_modules),
|
|
)
|
|
|
|
context.optimizer = cfg.optimizer_fn(model)
|
|
context.scheduler = cfg.scheduler_fn(context.optimizer)
|
|
|
|
train_dataset = cfg.dataset
|
|
val_dataset = cfg.val_dataset
|
|
|
|
if val_dataset is None and cfg.val_split is not None:
|
|
n_total = len(cfg.dataset)
|
|
n_val = max(1, int(n_total * cfg.val_split))
|
|
n_train = n_total - n_val
|
|
generator = torch.Generator().manual_seed(cfg.random_seed)
|
|
train_dataset, val_dataset = random_split(
|
|
cfg.dataset, [n_train, n_val], generator=generator
|
|
)
|
|
|
|
sampler_offset = context.consumed_samples // context.world_size
|
|
sampler = RDSampler(
|
|
data_source=train_dataset,
|
|
start_epoch=context.epoch,
|
|
start_iter=sampler_offset,
|
|
seed=cfg.random_seed,
|
|
)
|
|
context.dataloader = DataLoader(
|
|
train_dataset,
|
|
batch_size=cfg.batch_per_device,
|
|
sampler=sampler,
|
|
num_workers=cfg.num_workers,
|
|
pin_memory=cfg.pin_memory,
|
|
prefetch_factor=cfg.prefetch_factor,
|
|
collate_fn=cfg.collate_fn,
|
|
)
|
|
|
|
if val_dataset is not None:
|
|
val_sampler = RDSampler(
|
|
data_source=val_dataset,
|
|
start_epoch=0,
|
|
start_iter=0,
|
|
seed=cfg.random_seed,
|
|
shuffle=False,
|
|
)
|
|
context.val_dataloader = DataLoader(
|
|
val_dataset,
|
|
batch_size=cfg.batch_per_device,
|
|
sampler=val_sampler,
|
|
num_workers=cfg.num_workers,
|
|
pin_memory=cfg.pin_memory,
|
|
prefetch_factor=cfg.prefetch_factor,
|
|
collate_fn=cfg.collate_fn,
|
|
)
|
|
|
|
context.model, context.optimizer, context.dataloader, context.scheduler = (
|
|
executor.prepare(
|
|
model,
|
|
context.optimizer,
|
|
context.dataloader,
|
|
context.scheduler,
|
|
)
|
|
)
|
|
|
|
if context.checkpoint and context.checkpoint.extra:
|
|
extra = context.checkpoint.extra
|
|
for name in ("optimizer", "scheduler"):
|
|
if name in extra:
|
|
obj = getattr(context, name, None)
|
|
if obj is not None:
|
|
obj.load_state_dict(extra[name])
|
|
|
|
strategy_kwargs = dict(cfg.extra_kwargs)
|
|
|
|
if cfg.strategy in ("dpo", "grpo"):
|
|
ref_model = create_ref_model(
|
|
cfg.model_fn, executor.unwrap_model(context.model)
|
|
).to(device=device)
|
|
strategy_kwargs["ref_model"] = ref_model
|
|
|
|
if cfg.strategy == "grpo":
|
|
old_model = create_ref_model(
|
|
cfg.model_fn, executor.unwrap_model(context.model)
|
|
).to(device=device)
|
|
strategy_kwargs["old_model"] = old_model
|
|
|
|
context.strategy = StrategyFactory.create(
|
|
cfg.strategy,
|
|
model=context.model,
|
|
device=device,
|
|
executor=executor,
|
|
model_fn=cfg.model_fn,
|
|
**strategy_kwargs,
|
|
)
|
|
|
|
return context
|