File size: 5,678 Bytes
7180154 | 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 | from __future__ import annotations
import json
import os
from pathlib import Path
from typing import Any
import numpy as np
import xarray
import yaml
PROJECT_ROOT = Path(__file__).resolve().parents[1]
def load_config(path: str | Path) -> dict[str, Any]:
with Path(path).open(encoding="utf-8") as source:
return yaml.safe_load(source)
def resolve_path(path: str | Path) -> Path:
candidate = Path(path).expanduser()
return candidate if candidate.is_absolute() else PROJECT_ROOT / candidate
def configure_jax(platform: str) -> None:
if platform != "auto" and "JAX_PLATFORM_NAME" not in os.environ:
os.environ["JAX_PLATFORM_NAME"] = platform
def load_stats(stats_dir: str | Path) -> dict[str, xarray.Dataset]:
directory = resolve_path(stats_dir)
names = (
"diffs_stddev_by_level",
"mean_by_level",
"stddev_by_level",
"min_by_level",
)
stats = {}
for name in names:
path = directory / f"{name}.nc"
if not path.exists():
raise FileNotFoundError(f"Missing GenCast statistic: {path}")
stats[name] = xarray.load_dataset(path).compute()
from model.graphcast import gencast, graphcast
inputs = set(gencast.TASK.input_variables) - set(graphcast.GENERATED_FORCING_VARS)
targets = set(gencast.TASK.target_variables)
required_by_stat = {
"mean_by_level": inputs | (targets - inputs),
"stddev_by_level": inputs | (targets - inputs),
"diffs_stddev_by_level": targets & inputs,
"min_by_level": {"sea_surface_temperature"},
}
for stat_name, dataset in stats.items():
missing = sorted(required_by_stat[stat_name] - set(dataset.data_vars))
if missing:
raise ValueError(f"{stat_name} is missing GenCast variables: {missing}")
for name, values in dataset.data_vars.items():
array = np.asarray(values)
if not np.all(np.isfinite(array)):
raise ValueError(f"{stat_name}.{name} contains non-finite values")
if "level" in values.dims and tuple(values.level.values) != tuple(
gencast.TASK.pressure_levels
):
raise ValueError(f"{stat_name}.{name} does not use GenCast WB13 order")
if stat_name in ("stddev_by_level", "diffs_stddev_by_level") and np.any(array <= 0):
raise ValueError(f"{stat_name}.{name} must be strictly positive")
return stats
def save_trainer_checkpoint(
path: str | Path,
*,
params: Any,
state: Any,
optimizer_state: Any,
step: int,
config: dict[str, Any],
) -> None:
import jax
destination = resolve_path(path)
destination.parent.mkdir(parents=True, exist_ok=True)
leaves, treedef = jax.tree_util.tree_flatten(
{"params": params, "state": state, "optimizer_state": optimizer_state}
)
arrays = {f"leaf_{i}": np.asarray(value) for i, value in enumerate(leaves)}
arrays["treedef"] = np.array([treedef], dtype=object)
arrays["step"] = np.asarray(step, dtype=np.int64)
arrays["config_json"] = np.asarray(json.dumps(config, sort_keys=True))
temporary = destination.with_suffix(destination.suffix + ".tmp")
with temporary.open("wb") as output:
np.savez(output, **arrays)
os.replace(temporary, destination)
def load_trainer_checkpoint(
path: str | Path,
) -> tuple[Any, Any, Any, int, dict[str, Any]]:
import jax
source_path = resolve_path(path)
with np.load(source_path, allow_pickle=True) as source:
treedef = source["treedef"].item()
leaves = [source[f"leaf_{i}"] for i in range(len(source.files) - 3)]
tree = jax.tree_util.tree_unflatten(treedef, leaves)
saved_config = json.loads(str(source["config_json"]))
return (
tree["params"], tree["state"], tree["optimizer_state"],
int(source["step"]), saved_config,
)
def validate_checkpoint_config(
current: dict[str, Any],
saved: dict[str, Any],
*,
scope: str = "resume",
) -> None:
"""Validate checkpoint compatibility for training resume or inference."""
if scope not in ("resume", "inference"):
raise ValueError("scope must be 'resume' or 'inference'")
inference_paths = (
("model",), ("sampler",), ("data", "stats_dir"),
("data", "static_dir"), ("data", "precipitation_interval_hours"),
)
resume_only_paths = (
("training", "learning_rate"),
("training", "betas"), ("training", "epsilon"),
("training", "seed"), ("data", "data_dir"),
("data", "train_years"), ("data", "train_stride"),
("parallel", "mode"), ("parallel", "num_devices"),
("parallel", "global_batch_size"), ("parallel", "axis_name"),
)
if scope == "resume":
if "parallel" not in saved:
saved = dict(saved)
saved["parallel"] = {
"mode": "single",
"num_devices": 1,
"global_batch_size": 1,
"axis_name": "devices",
}
paths = inference_paths + resume_only_paths
else:
paths = inference_paths
for path in paths:
current_value: Any = current
saved_value: Any = saved
for key in path:
current_value = current_value[key]
saved_value = saved_value[key]
if current_value != saved_value:
name = ".".join(path)
raise ValueError(
f"Trainer checkpoint configuration mismatch for {name} "
f"during {scope}: "
f"saved={saved_value!r}, current={current_value!r}"
)
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