File size: 2,225 Bytes
e12111a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | """Training state and checkpoints.
https://github.com/google/flax/blob/main/examples/imagenet/train.py
"""
from __future__ import annotations
from typing import Callable
import chex
import flax.linen as nn
import jax
import jax.numpy as jnp
from absl import logging
from flax.training import dynamic_scale as dynamic_scale_lib
from omegaconf import DictConfig
from imgx.train_state import TrainState as BaseTrainState
from imgx.train_state import init_optimizer
class TrainState(BaseTrainState):
"""Train state.
If using nn.BatchNorm, batch_stats needs to be tracked.
https://flax.readthedocs.io/en/latest/guides/batch_norm.html
https://github.com/google/flax/blob/main/examples/imagenet/train.py
"""
loss_count_hist: jnp.ndarray # mutable
loss_sq_hist: jnp.ndarray # mutable
def create_train_state(
key: jax.Array,
batch: dict[str, jnp.ndarray],
model: nn.Module,
config: DictConfig,
initialized: Callable[[jax.Array, chex.ArrayTree, nn.Module], chex.ArrayTree],
) -> TrainState:
"""Create initial training state.
Args:
key: random key.
batch: batch data for determining input shapes.
model: model.
config: entire configuration.
initialized: function to get initialized model parameters.
Returns:
initial training state.
"""
dynamic_scale = None
platform = jax.local_devices()[0].platform
if config.half_precision and platform == "gpu":
dynamic_scale = dynamic_scale_lib.DynamicScale()
params = initialized(key, batch, model)
# count params
params_count = sum(x.size for x in jax.tree_util.tree_leaves(params))
logging.info(f"The model has {params_count:,} parameters.")
# diffusion related
num_timesteps = config.task.diffusion.num_timesteps
loss_count_hist = jnp.zeros((num_timesteps,), dtype=jnp.int32)
loss_sq_hist = jnp.zeros((num_timesteps,), dtype=jnp.float32)
tx = init_optimizer(config=config)
state = TrainState.create(
apply_fn=model.apply,
params=params,
tx=tx,
dynamic_scale=dynamic_scale,
loss_count_hist=loss_count_hist,
loss_sq_hist=loss_sq_hist,
)
return state
|