File size: 7,246 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 | """项目内官方等价 GenCast JAX/Haiku 实现封装。"""
from __future__ import annotations
import dataclasses
from pathlib import Path
from typing import Any
import haiku as hk
import jax
import xarray
from model.graphcast import checkpoint
from model.graphcast import denoiser
from model.graphcast import gencast
from model.graphcast import nan_cleaning
from model.graphcast import normalization
from model.graphcast import xarray_jax
from model.graphcast import xarray_tree
def build_model_config(config: dict[str, Any]) -> tuple[
Any, denoiser.DenoiserArchitectureConfig, gencast.SamplerConfig,
gencast.NoiseConfig, denoiser.NoiseEncoderConfig
]:
"""Build a random-weight configuration without changing GenCast semantics."""
model_cfg = config["model"]
sampler_cfg = config["sampler"]
transformer = denoiser.SparseTransformerConfig(
attention_k_hop=int(model_cfg["attention_k_hop"]),
d_model=int(model_cfg["latent_size"]),
num_layers=int(model_cfg["num_layers"]),
num_heads=int(model_cfg["num_heads"]),
attention_type=str(model_cfg["attention_type"]),
mask_type=str(model_cfg.get("mask_type", "full")),
ffw_hidden=int(model_cfg["ffw_hidden"]),
)
architecture = denoiser.DenoiserArchitectureConfig(
sparse_transformer_config=transformer,
mesh_size=int(model_cfg["mesh_size"]),
latent_size=int(model_cfg["latent_size"]),
hidden_layers=int(model_cfg.get("hidden_layers", 1)),
radius_query_fraction_edge_length=float(
model_cfg.get("radius_query_fraction_edge_length", 0.6)
),
)
sampler = gencast.SamplerConfig(**sampler_cfg)
return (
gencast.TASK,
architecture,
sampler,
gencast.NoiseConfig(),
denoiser.NoiseEncoderConfig(),
)
def load_model_checkpoint(path: str | Path) -> gencast.CheckPoint:
"""Load the typed official GenCast NPZ checkpoint."""
with Path(path).open("rb") as source:
return checkpoint.load(source, gencast.CheckPoint)
class GenCastModel:
"""Owns official-equivalent GenCast loss and sampling Haiku transforms."""
def __init__(
self,
*,
task_config: Any,
architecture_config: denoiser.DenoiserArchitectureConfig,
sampler_config: gencast.SamplerConfig,
noise_config: gencast.NoiseConfig,
noise_encoder_config: denoiser.NoiseEncoderConfig,
diffs_stddev_by_level: xarray.Dataset,
mean_by_level: xarray.Dataset,
stddev_by_level: xarray.Dataset,
min_by_level: xarray.Dataset,
reintroduce_nans: bool = True,
) -> None:
self.task_config = task_config
self.architecture_config = architecture_config
self.sampler_config = sampler_config
self.noise_config = noise_config
self.noise_encoder_config = noise_encoder_config
self.diffs_stddev_by_level = diffs_stddev_by_level
self.mean_by_level = mean_by_level
self.stddev_by_level = stddev_by_level
self.min_by_level = min_by_level
self.reintroduce_nans = reintroduce_nans
def construct() -> Any:
predictor = gencast.GenCast(
task_config=self.task_config,
denoiser_architecture_config=self.architecture_config,
sampler_config=self.sampler_config,
noise_config=self.noise_config,
noise_encoder_config=self.noise_encoder_config,
)
predictor = normalization.InputsAndResiduals(
predictor,
diffs_stddev_by_level=self.diffs_stddev_by_level,
mean_by_level=self.mean_by_level,
stddev_by_level=self.stddev_by_level,
)
return nan_cleaning.NaNCleaner(
predictor,
var_to_clean="sea_surface_temperature",
fill_value=self.min_by_level,
reintroduce_nans=self.reintroduce_nans,
)
@hk.transform_with_state
def loss_fn(inputs, targets, forcings):
loss, diagnostics = construct().loss(inputs, targets, forcings)
return xarray_tree.map_structure(
lambda value: xarray_jax.unwrap_data(
value.mean(), require_jax=True
),
(loss, diagnostics),
)
@hk.transform_with_state
def forward_fn(inputs, targets_template, forcings):
return construct()(
inputs,
targets_template=targets_template,
forcings=forcings,
)
self.loss_fn = loss_fn
self.forward_fn = forward_fn
@classmethod
def from_config_and_stats(
cls, config: dict[str, Any], stats: dict[str, xarray.Dataset]
) -> "GenCastModel":
configs = build_model_config(config)
return cls(
task_config=configs[0],
architecture_config=configs[1],
sampler_config=configs[2],
noise_config=configs[3],
noise_encoder_config=configs[4],
diffs_stddev_by_level=stats["diffs_stddev_by_level"],
mean_by_level=stats["mean_by_level"],
stddev_by_level=stats["stddev_by_level"],
min_by_level=stats["min_by_level"],
reintroduce_nans=bool(config.get("data", {}).get("reintroduce_sst_nans", True)),
)
@classmethod
def from_checkpoint_and_stats(
cls,
model_checkpoint: gencast.CheckPoint,
stats: dict[str, xarray.Dataset],
*,
attention_type: str | None = None,
) -> "GenCastModel":
architecture = model_checkpoint.denoiser_architecture_config
if attention_type is not None:
architecture = dataclasses.replace(
architecture,
sparse_transformer_config=dataclasses.replace(
architecture.sparse_transformer_config,
attention_type=attention_type,
mask_type="full",
),
)
return cls(
task_config=model_checkpoint.task_config,
architecture_config=architecture,
sampler_config=model_checkpoint.sampler_config,
noise_config=model_checkpoint.noise_config,
noise_encoder_config=model_checkpoint.noise_encoder_config,
diffs_stddev_by_level=stats["diffs_stddev_by_level"],
mean_by_level=stats["mean_by_level"],
stddev_by_level=stats["stddev_by_level"],
min_by_level=stats["min_by_level"],
)
def init(self, rng, inputs, targets, forcings):
return self.loss_fn.init(rng, inputs, targets, forcings)
def loss(self, params, state, rng, inputs, targets, forcings):
return self.loss_fn.apply(params, state, rng, inputs, targets, forcings)
def predict(self, params, state, rng, inputs, targets_template, forcings):
return self.forward_fn.apply(
params, state, rng, inputs, targets_template, forcings
)
def parameter_count(params: Any) -> int:
return sum(int(value.size) for value in jax.tree_util.tree_leaves(params))
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