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import json
import logging
import time
from dataclasses import KW_ONLY, dataclass
from pathlib import Path
from typing import Protocol
import torch
from torch import Tensor
from torch.utils.data import DataLoader
from .control import non_blocking_input
from .distributed import is_global_leader
from .engine import Engine
from .utils import tree_map
logger = logging.getLogger(__name__)
class EvalFn(Protocol):
def __call__(self, engine: Engine, eval_dir: Path) -> None:
...
class EngineLoader(Protocol):
def __call__(self, run_dir: Path) -> Engine:
...
class GenFeeder(Protocol):
def __call__(self, engine: Engine, batch: dict[str, Tensor]) -> tuple[Tensor, dict[str, Tensor]]:
...
class DisFeeder(Protocol):
def __call__(self, engine: Engine, batch: dict[str, Tensor] | None, fake: Tensor) -> dict[str, Tensor]:
...
@dataclass
class TrainLoop:
_ = KW_ONLY
run_dir: Path
train_dl: DataLoader
load_G: EngineLoader
feed_G: GenFeeder
load_D: EngineLoader | None = None
feed_D: DisFeeder | None = None
update_every: int = 5_000
eval_every: int = 5_000
backup_steps: tuple[int, ...] = (5_000, 100_000, 500_000)
device: str = "cuda"
eval_fn: EvalFn | None = None
gan_training_start_step: int | None = None
@property
def global_step(self):
return self.engine_G.global_step # How many steps have been completed?
@property
def eval_dir(self) -> Path | None:
if self.eval_every != 0:
eval_dir = self.run_dir.joinpath("eval")
eval_dir.mkdir(exist_ok=True)
else:
eval_dir = None
return eval_dir
@property
def viz_dir(self) -> Path:
return Path(self.run_dir / "viz")
def make_current_step_viz_path(self, name: str, suffix: str) -> Path:
path = (self.viz_dir / name / f"{self.global_step}").with_suffix(suffix)
path.parent.mkdir(exist_ok=True, parents=True)
return path
def __post_init__(self):
engine_G = self.load_G(self.run_dir)
if self.load_D is None:
engine_D = None
else:
engine_D = self.load_D(self.run_dir)
self.engine_G = engine_G
self.engine_D = engine_D
@property
def model_G(self):
return self.engine_G.module
@property
def model_D(self):
if self.engine_D is None:
return None
return self.engine_D.module
def save_checkpoint(self, tag="default"):
engine_G = self.engine_G
engine_D = self.engine_D
engine_G.save_checkpoint(tag=tag)
if engine_D is not None:
engine_D.save_checkpoint(tag=tag)
def run(self, max_steps: int = -1):
self.set_running_loop_(self)
train_dl = self.train_dl
update_every = self.update_every
eval_every = self.eval_every
device = self.device
eval_fn = self.eval_fn
engine_G = self.engine_G
engine_D = self.engine_D
eval_dir = self.eval_dir
init_step = self.global_step
logger.info(f"\nTraining from step {init_step} to step {max_steps}")
warmup_steps = {init_step + x for x in [50, 100, 500]}
engine_G.train()
if engine_D is not None:
engine_D.train()
gan_start_step = self.gan_training_start_step
while True:
loss_G = loss_D = 0
for batch in train_dl:
torch.cuda.synchronize()
start_time = time.time()
# What's the step after this batch?
step = self.global_step + 1
# Send data to the GPU
batch = tree_map(lambda x: x.to(device) if isinstance(x, Tensor) else x, batch)
stats = {"step": step}
# Include step == 1 for sanity check
gan_started = gan_start_step is not None and (step >= gan_start_step or step == 1)
gan_started &= engine_D is not None
# Generator step
fake, losses = self.feed_G(engine=engine_G, batch=batch)
# Train generator
if gan_started:
assert engine_D is not None
assert self.feed_D is not None
# Freeze the discriminator to let gradient go through fake
engine_D.freeze_()
losses |= self.feed_D(engine=engine_D, batch=None, fake=fake)
loss_G = sum(losses.values())
stats |= {f"G/{k}": v.item() for k, v in losses.items()}
stats |= {f"G/{k}": v for k, v in engine_G.gather_attribute("stats").items()}
del losses
assert isinstance(loss_G, Tensor)
stats["G/loss"] = loss_G.item()
stats["G/lr"] = engine_G.get_lr()[0]
stats["G/grad_norm"] = engine_G.get_grad_norm() or 0
if loss_G.isnan().item():
logger.error("Generator loss is NaN, skipping step")
continue
engine_G.backward(loss_G)
engine_G.step()
# Discriminator step
if gan_started:
assert engine_D is not None
assert self.feed_D is not None
engine_D.unfreeze_()
losses = self.feed_D(engine=engine_D, batch=batch, fake=fake.detach())
del fake
assert isinstance(losses, dict)
loss_D = sum(losses.values())
assert isinstance(loss_D, Tensor)
stats |= {f"D/{k}": v.item() for k, v in losses.items()}
stats |= {f"D/{k}": v for k, v in engine_D.gather_attribute("stats").items()}
del losses
if loss_D.isnan().item():
logger.error("Discriminator loss is NaN, skipping step")
continue
engine_D.backward(loss_D)
engine_D.step()
stats["D/loss"] = loss_D.item()
stats["D/lr"] = engine_D.get_lr()[0]
stats["D/grad_norm"] = engine_D.get_grad_norm() or 0
torch.cuda.synchronize()
stats["elapsed_time"] = time.time() - start_time
stats = tree_map(lambda x: float(f"{x:.4g}") if isinstance(x, float) else x, stats)
logger.info(json.dumps(stats, indent=0))
command = non_blocking_input()
evaling = step % eval_every == 0 or step in warmup_steps or command.strip() == "eval"
if eval_fn is not None and is_global_leader() and eval_dir is not None and evaling:
engine_G.eval()
eval_fn(engine_G, eval_dir=eval_dir)
engine_G.train()
if command.strip() == "quit":
logger.info("Training paused")
self.save_checkpoint("default")
return
if command.strip() == "backup" or step in self.backup_steps:
logger.info("Backing up")
self.save_checkpoint(tag=f"backup_{step:07d}")
if step % update_every == 0 or command.strip() == "save":
self.save_checkpoint(tag="default")
if step == max_steps:
logger.info("Training finished")
self.save_checkpoint(tag="default")
return
@classmethod
def set_running_loop_(cls, loop):
assert isinstance(loop, cls), f"Expected {cls}, got {type(loop)}"
cls._running_loop: cls = loop
@classmethod
def get_running_loop(cls) -> "TrainLoop | None":
if hasattr(cls, "_running_loop"):
assert isinstance(cls._running_loop, cls)
return cls._running_loop
return None
@classmethod
def get_running_loop_global_step(cls) -> int | None:
if loop := cls.get_running_loop():
return loop.global_step
return None
@classmethod
def get_running_loop_viz_path(cls, name: str, suffix: str) -> Path | None:
if loop := cls.get_running_loop():
return loop.make_current_step_viz_path(name, suffix)
return None
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