oops
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1
setup.py
1
setup.py
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@ -48,6 +48,7 @@ setup(
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"numpy>=1.23.3",
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"omegaconf==2.0.6",
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"tqdm>=4.64.1",
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"humanize>=4.4.0",
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"pandas>=1.5.0",
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"torch>=1.13.0",
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@ -2,252 +2,254 @@
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# https://github.com/enhuiz/pytorch-training-utilities
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"""
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# todo: replace this
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import humanize
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import json
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import logging
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import time
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from typing import Any, Protocol
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import numpy as np
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import random
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import selectors
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import sys
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import torch
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import torch.distributed
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from deepspeed import DeepSpeedEngine
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from torch import Tensor
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from torch.distributed import all_reduce
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from .config import Config
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from .distributed import fix_unset_envs
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from .utils import dispatch_attribute, flatten_dict, gather_attribute, do_gc, to_device
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from functools import cache
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from torch.distributed import broadcast_object_list
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from torch.utils.data import DataLoader
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from tqdm import tqdm
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from typing import Protocol
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Stats = dict[str, float]
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from ..config import Config
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from .distributed import (
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global_leader_only,
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global_rank,
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is_global_leader,
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is_local_leader,
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local_leader_only,
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)
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from .engines import Engine, Engines, TrainFeeder
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from .utils import to_device, do_gc
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_logger = logging.getLogger(__name__)
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_engines: Engines
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_command: str
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def get_global_step():
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try:
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return _engines.global_step
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except:
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return None
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def get_micro_step():
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try:
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return _engines.micro_step
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except:
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return None
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class Engine(DeepSpeedEngine):
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def __init__(self, *args, **kwargs):
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fix_unset_envs()
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super().__init__(None, *args, **kwargs)
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self._frozen_params = set()
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def freeze(self):
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for p in self.module.parameters():
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if p.requires_grad:
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p.requires_grad_(False)
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self._frozen_params.add(p)
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def unfreeze(self):
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for p in self._frozen_params:
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p.requires_grad_(True)
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self._frozen_params.clear()
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@property
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def global_step(self):
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return self.global_steps
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def gather_attribute(self, *args, **kwargs):
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return gather_attribute(self.module, *args, **kwargs)
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def dispatch_attribute(self, *args, **kwargs):
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return dispatch_attribute(self.module, *args, **kwargs)
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def get_cfg():
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try:
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return _engines.cfg
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except:
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raise RuntimeError("Trainer has not been setup. Have you called trainer.train?")
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class TrainFeeder(Protocol):
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def __call__(
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self, *, engines: "Engines", batch: Any, name: str
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) -> None | tuple[Tensor, Stats]:
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def get_cmd():
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try:
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return _command
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except:
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raise RuntimeError("Trainer has not been setup. Have you called trainer.train?")
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get_iteration = get_global_step
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class EnginesLoader(Protocol):
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def __call__(self) -> Engines:
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...
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class Engines(dict[str, Engine]):
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def setup(self, cfg: Config):
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self._cfg = cfg
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self._global_step = 0
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def load_engines(engines: dict[str, Engine], config: Config):
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engines = Engines(engines)
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engines.setup(config)
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if not engines.cfg.trainer.load_state_dict:
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engines.load_checkpoint()
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return engines
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@property
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def cfg(self) -> Config:
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return self._cfg
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@property
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def config(self):
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return self._cfg
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class EvalFn(Protocol):
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def __call__(self, *, engines: Engines):
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...
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@property
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def global_step(self):
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return self._global_step
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def gather_attribute(self, *args, **kwargs):
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ret = {}
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for engine in self.values():
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ret |= engine.gather_attribute(*args, **kwargs)
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return ret
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class Logger(Protocol):
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def __call__(self, *, data: dict):
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...
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def dispatch_attribute(self, *args, **kwargs):
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for engine in self.values():
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engine.dispatch_attribute(*args, **kwargs)
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def save_checkpoint(self, tag=None):
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if not tag:
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tag = self.cfg.trainer.save_tag
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tag = tag.lower()
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if tag[:2] == "it" or tag[:4] == "step":
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tag = self.global_step
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@cache
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def _get_stdin_selector():
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selector = selectors.DefaultSelector()
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selector.register(fileobj=sys.stdin, events=selectors.EVENT_READ)
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return selector
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self.cfg.ckpt_dir.mkdir(parents=True, exist_ok=True)
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for name, engine in self.items():
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engine.save_checkpoint(self.cfg.ckpt_dir / name, tag=tag)
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def load_checkpoint(self, tag=None):
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if not tag:
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tag = self.cfg.trainer.load_tag
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def _non_blocking_input():
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global _command
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l = [""]
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if is_global_leader():
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s = ""
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selector = _get_stdin_selector()
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events = selector.select(timeout=0)
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for key, _ in events:
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s: str = key.fileobj.readline().strip()
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_logger.info(f'Get stdin "{s}".')
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l[0] = s
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broadcast_object_list(l, src=0)
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_command = l[0]
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return _command
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for name, engine in self.items():
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load_dir = self.cfg.ckpt_dir / name
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engine.load_checkpoint(
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tag=tag,
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load_dir=load_dir,
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load_module_strict=self.cfg.trainer.strict_loading,
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load_optimizer_states=self.cfg.trainer.load_states,
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load_lr_scheduler_states=self.cfg.trainer.load_states,
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load_module_only=False, # not self.cfg.trainer.load_states,
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)
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if self.cfg.trainer.restart_step_count:
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engine.global_steps = 0
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# update the LR because for some god awful reason it gets overwritten when loading from a checkpoint but only when it's not using a scheduler
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if self.cfg.hyperparameters.scheduler_type == "":
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self.set_lr(self.cfg.hyperparameters.learning_rate)
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def _make_infinite_epochs(dl):
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while True:
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_logger.info("New epoch starts.")
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yield from tqdm(dl, "Epoch progress", dynamic_ncols=True)
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self._update_global_step()
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def set_lr(self, lr):
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try:
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for engine in self.values():
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if hasattr(engine.optimizer, 'param_groups'):
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print(engine.optimizer.param_groups)
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for param_group in engine.optimizer.param_groups:
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param_group['lr'] = lr
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else:
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engine.optimizer.set_lr(lr)
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except Exception as e:
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print(str(e))
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@local_leader_only(default=None)
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def logger(data):
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return _logger.info(json.dumps(data, default=str))
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def _update_global_step(self):
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for engine in self.values():
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self._global_step = max(self._global_step, engine.global_step)
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def eval(self):
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for engine in self.values():
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engine.eval()
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def seed(seed):
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# Set up random seeds, after fork()
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random.seed(seed + global_rank())
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np.random.seed(seed + global_rank())
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torch.manual_seed(seed + global_rank())
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def train(self):
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for engine in self.values():
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engine.train()
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def step(self, feeder: TrainFeeder, batch):
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total_elapsed_time = 0
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def train(
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engines_loader: EnginesLoader,
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train_dl: DataLoader,
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train_feeder: TrainFeeder,
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eval_fn: EvalFn,
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logger: Logger = logger,
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):
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engines = engines_loader()
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cfg = engines.cfg
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stats: Any = dict()
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"""
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if is_local_leader():
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cfg.dump()
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_logger.info(cfg)
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"""
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if self.cfg.trainer.gc_mode == 'step':
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do_gc()
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# Setup global engines
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global _engines
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_engines = engines
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batch = to_device(batch, torch.cuda.current_device())
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events = []
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for name, engine in self.items():
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torch.cuda.synchronize()
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if self.cfg.trainer.gc_mode == 'substep':
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do_gc()
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eval_fn = global_leader_only(eval_fn)
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start_time = time.time()
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# Pre-loop command
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command = _non_blocking_input()
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if command in ["eval", "eval_quit"]:
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engines.eval()
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eval_fn(engines=engines)
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engines.train()
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if command in ["quit", "eval_quit"]:
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return
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tries = 4
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n_ooms = torch.zeros([], device=self.cfg.device)
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if self.cfg.trainer.aggressive_optimizations:
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batch = to_device(batch, torch.cuda.current_device())
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# engine = engine.to(torch.cuda.current_device())
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last_save_step = engines.global_step
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last_eval_step = 0
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while tries >= 0:
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try:
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maybe_loss_and_engine_stats = feeder( engines=self, batch=batch, name=name )
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# Training loop
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for batch in _make_infinite_epochs(train_dl):
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if engines.global_step >= cfg.trainer.iterations:
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break
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except RuntimeError as e:
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print("Forward", str(e))
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if "out of memory" not in str(e):
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self.save_checkpoint()
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raise e
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#batch = to_device(batch, torch.cuda.current_device())
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stats = engines.step(feeder=train_feeder, batch=batch)
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# shrink batch size until it's happy
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for k in batch:
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batch[k] = batch[k][:-1]
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iteration = stats['global_step'] # * cfg.hyperparameters.gradient_accumulation_steps
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stats['it'] = iteration
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stats['epoch'] = iteration * cfg.hyperparameters.gradient_accumulation_steps / len(train_dl)
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if tries <= 0:
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# trigger OOM
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n_ooms += 1
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else:
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# also do GC
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do_gc()
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continue
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all_reduce(n_ooms)
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if n_ooms.item() > 0:
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self.save_checkpoint()
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raise RuntimeError("Out of memory during forward pass!")
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# Here we allow skip optimizers. It's useful when, for example,
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# skipping discriminators in the begining of GAN training.
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if maybe_loss_and_engine_stats is None:
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continue
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loss, engine_stats = maybe_loss_and_engine_stats
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n_ooms = torch.zeros([], device=self.cfg.device)
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if self.cfg.trainer.aggressive_optimizations:
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batch = to_device(batch, 'cpu')
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try:
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engine.backward(loss)
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except RuntimeError as e:
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print("Backwards:", str(e))
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if "out of memory" not in str(e):
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self.save_checkpoint()
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raise e
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n_ooms += 1
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all_reduce(n_ooms)
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if n_ooms.item() > 0:
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self.save_checkpoint()
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raise RuntimeError("Out of memory during backwards pass!")
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engine.step()
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torch.cuda.synchronize()
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elapsed_time = time.time() - start_time
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total_elapsed_time += elapsed_time
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stats.update(
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flatten_dict(
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{
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name.split("-")[0]: dict(
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loss=loss.item(),
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lr=engine.get_lr()[0],
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grad_norm=engine.get_global_grad_norm(), # This norm is delayed but global and avoids extra computation
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elapsed_time=elapsed_time,
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engine_step=engine.global_step,
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**engine_stats,
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)
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stats['batch'] = {
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'size': stats['batch_size'],
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'id': batch['spkr_id'],
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'index': [ index for index in batch['index'] ],
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'text_len': [ text.shape[0] for text in batch['text'] ],
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'prom_len': [ prom.shape[0] for prom in batch['proms'] ],
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'resp_len': [ resp.shape[0] for resp in batch['resps'] ],
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}
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),
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)
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del loss
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# engine = engine.to('cpu')
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self._update_global_step()
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stats["batch_size"] = len(batch["text"])
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stats["elapsed_time"] = total_elapsed_time
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stats["wall_time"] = time.time()
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stats["global_step"] = self.global_step
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del stats['batch_size']
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del stats['wall_time']
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del stats['global_step']
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return stats
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elapsed_time = stats.get("elapsed_time", 0)
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_logger.info(f"Training Metrics: {json.dumps(stats)}.")
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command = _non_blocking_input()
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if "@" in command:
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what, when = command.split("@")
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try:
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events.append((what, int(when)))
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_logger.info(f"Event {command} registered.")
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except Exception as e:
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_logger.error(e)
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command = ""
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# Commands are the current command plus the triggered (i.e. iteration >= trigger point) events
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events = [e for e in events if e[1] >= engines.global_step]
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commands = [command] + [e[0] for e in events if e[1] == engines.global_step]
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for command in commands:
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if command in ["event show", "event"]:
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msg = "Events:\n" + "\n".join(["@".join(map(str, e)) for e in events])
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_logger.info(msg)
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if command == "event clear":
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events.clear()
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if "time" in command:
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target_iter = cfg.trainer.iterations
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if " to " in command:
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try:
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target_iter = int(command.split(" to ")[-1])
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except Exception as e:
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_logger.error(e)
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remaining_iters = target_iter - engines.global_step + 1
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remaining_time = int(remaining_iters * elapsed_time)
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_logger.info(humanize.precisedelta(remaining_time))
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if "lr" in command:
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rate = float(command.split(" ")[-1])
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engines.set_lr(rate)
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print("Updating LR to:", rate)
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save_ckpt_every = cfg.trainer.save_frequency or cfg.evaluation.frequency
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saving_commands = ["save"]
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if cfg.trainer.save_on_quit:
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saving_commands.append("quit")
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if engines.global_step != last_save_step:
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if engines.global_step % save_ckpt_every == 0 or command in saving_commands:
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engines.save_checkpoint()
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last_save_step = engines.global_step
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if engines.global_step != last_eval_step:
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if engines.global_step % cfg.evaluation.frequency == 0 or command in ["eval"]:
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do_gc()
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engines.eval()
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eval_fn(engines=engines)
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engines.train()
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last_eval_step = engines.global_step
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if command in ["quit"]:
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return
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