File size: 14,744 Bytes
87b732d | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 | """Copyright (c) Microsoft Corporation. Licensed under the MIT license.
The cumbersome checkpoint wrangling in this file is for making the published checkpoints compatible
with the published versions of the model. You can safely ignore all of this.
"""
import torch
from .aurora_normalisation import level_to_str
__all__ = [
"_adapt_checkpoint_pretrained",
"_adapt_checkpoint_air_pollution",
"_adapt_checkpoint_wave",
"_adapt_checkpoint_v1p5",
]
def _adapt_checkpoint_pretrained(
patch_size: int,
d: dict[str, torch.Tensor],
) -> dict[str, torch.Tensor]:
# Remove possibly prefix from the keys.
for k, v in list(d.items()):
if k.startswith("net."):
del d[k]
d[k[4:]] = v
# Convert the ID-based parametrization to a name-based parametrization.
if "encoder.surf_token_embeds.weight" in d:
weight = d["encoder.surf_token_embeds.weight"]
del d["encoder.surf_token_embeds.weight"]
assert weight.shape[1] == 4 + 3
for i, name in enumerate(("2t", "10u", "10v", "msl", "lsm", "z", "slt")):
d[f"encoder.surf_token_embeds.weights.{name}"] = weight[:, [i]]
if "encoder.atmos_token_embeds.weight" in d:
weight = d["encoder.atmos_token_embeds.weight"]
del d["encoder.atmos_token_embeds.weight"]
assert weight.shape[1] == 5
for i, name in enumerate(("z", "u", "v", "t", "q")):
d[f"encoder.atmos_token_embeds.weights.{name}"] = weight[:, [i]]
if "decoder.surf_head.weight" in d:
weight = d["decoder.surf_head.weight"]
bias = d["decoder.surf_head.bias"]
del d["decoder.surf_head.weight"]
del d["decoder.surf_head.bias"]
assert weight.shape[0] == 4 * patch_size**2
assert bias.shape[0] == 4 * patch_size**2
weight = weight.reshape(patch_size**2, 4, -1)
bias = bias.reshape(patch_size**2, 4)
for i, name in enumerate(("2t", "10u", "10v", "msl")):
d[f"decoder.surf_heads.{name}.weight"] = weight[:, i]
d[f"decoder.surf_heads.{name}.bias"] = bias[:, i]
if "decoder.atmos_head.weight" in d:
weight = d["decoder.atmos_head.weight"]
bias = d["decoder.atmos_head.bias"]
del d["decoder.atmos_head.weight"]
del d["decoder.atmos_head.bias"]
assert weight.shape[0] == 5 * patch_size**2
assert bias.shape[0] == 5 * patch_size**2
weight = weight.reshape(patch_size**2, 5, -1)
bias = bias.reshape(patch_size**2, 5)
for i, name in enumerate(("z", "u", "v", "t", "q")):
d[f"decoder.atmos_heads.{name}.weight"] = weight[:, i]
d[f"decoder.atmos_heads.{name}.bias"] = bias[:, i]
return d
def _adapt_checkpoint_air_pollution(
patch_size: int,
d: dict[str, torch.Tensor],
) -> dict[str, torch.Tensor]:
if "encoder.surf_token_embeds.weight_new" in d:
weight = d["encoder.surf_token_embeds.weight_new"]
del d["encoder.surf_token_embeds.weight_new"]
assert weight.shape[1] == (3 + 5) + 4 * 2 + 3 * 2
for i, name in enumerate(
("pm1", "pm2p5", "pm10", "tcco", "tc_no", "tcno2", "gtco3", "tcso2")
+ ("static_ammonia", "static_ammonia_log", "static_co", "static_co_log")
+ ("static_nox", "static_nox_log", "static_so2", "static_so2_log")
+ ("tod_cos", "tod_sin", "dow_cos")
+ ("dow_sin", "doy_cos", "doy_sin")
):
d[f"encoder.surf_token_embeds.weights.{name}"] = weight[:, [i]]
# Now fix the patch embeddings for the atmospheric variables. These are more complicated.
if (
"encoder.atmos_token_embeds.weights.z" in d
and "encoder.atmos_token_embeds_new.layers.50.weight" in d
):
bias = d["encoder.atmos_token_embeds.bias"]
del d["encoder.atmos_token_embeds.bias"]
for name in ("z", "u", "v", "t", "q"):
weight = d[f"encoder.atmos_token_embeds.weights.{name}"]
del d[f"encoder.atmos_token_embeds.weights.{name}"]
for level in (50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000):
# Clone them here to prevent mutation from doing something weird!
d[f"encoder.atmos_token_embeds.layers.{level}.weights.{name}"] = weight.clone()
d[f"encoder.atmos_token_embeds.layers.{level}.bias"] = bias.clone()
n1 = "encoder.atmos_token_embeds.weight_new2"
if n1 in d:
weight = d[n1]
del d[n1]
for level in (50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000):
n2 = f"encoder.atmos_token_embeds.layers.{level_to_str(level)}.weights.{{}}"
assert weight.shape[1] == 17
# These are all taken from `atmos_token_embeds`!
for i, name in enumerate(
("static_lsm", "static_z", "static_slt")
# For the atmospheric variables, there is _another_ prefix `static_`.
+ ("static_static_ammonia", "static_static_ammonia_log")
+ ("static_static_co", "static_static_co_log")
+ ("static_static_nox", "static_static_nox_log")
+ ("static_static_so2", "static_static_so2_log")
+ ("static_tod_cos", "static_tod_sin", "static_dow_cos")
+ ("static_dow_sin", "static_doy_cos", "static_doy_sin")
):
d[n2.format(name)] = weight[:, [i]]
if "encoder.atmos_token_embeds.weight_new" in d:
del d["encoder.atmos_token_embeds.weight_new"]
if "encoder.atmos_token_embeds.weight_new2" in d:
del d["encoder.atmos_token_embeds.weight_new2"]
for level in (50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000):
# The patch embedding is doubly specified. Select the right one.
n1 = f"encoder.atmos_token_embeds_new.layers.{level_to_str(level)}.weight"
if n1 in d:
del d[n1]
n1 = f"encoder.atmos_token_embeds_new.layers.{level_to_str(level)}.weight_new"
n2 = f"encoder.atmos_token_embeds.layers.{level_to_str(level)}.weights.{{}}"
if n1 in d:
weight = d[n1]
del d[n1]
assert weight.shape[1] == 5
for i, name in enumerate(("co", "no", "no2", "go3", "so2")):
d[n2.format(name)] = weight[:, [i]]
# This simulates an indexing bug where `z` also uses the patch embedding for `static_z`.
d[f"encoder.atmos_token_embeds.layers.{level_to_str(level)}.weights.z"] = d[
f"encoder.atmos_token_embeds.layers.{level_to_str(level)}.weights.static_z"
]
n1 = f"encoder.atmos_token_embeds_new.layers.{level_to_str(level)}.bias"
n2 = f"encoder.atmos_token_embeds.layers.{level_to_str(level)}.bias"
if n1 in d:
assert n2 in d # The bias is already defined!
# Because the original implementation had two separate patch embedding instances, we
# need to add the biases.
d[n2] += d[n1]
del d[n1]
if f"encoder.atmos_token_embeds_new.layers.{level_to_str(level)}.weight_new2" in d:
del d[f"encoder.atmos_token_embeds_new.layers.{level_to_str(level)}.weight_new2"]
# Remove the feature combiners for the non-positive variables.
for name in ("2t", "10u", "10v", "msl"):
if f"surf_feature_combiner.{name}.weight" in d:
del d[f"surf_feature_combiner.{name}.weight"]
del d[f"surf_feature_combiner.{name}.bias"]
pass
for name in ("z", "u", "v", "t", "q"):
if f"atmos_feature_combiner.{name}.weight" in d:
del d[f"atmos_feature_combiner.{name}.weight"]
del d[f"atmos_feature_combiner.{name}.bias"]
pass
# Rename the second Perceiver in the decoder.
for k in list(d):
p1 = "decoder.level_decoder_new"
p2 = "decoder.level_decoder_alternate"
if k.startswith(p1):
d[p2 + k.removeprefix(p1)] = d[k]
del d[k]
# Do the same thing that we did for the encoder now for the decoder.
if "decoder.surf_head_new.weight" in d:
weight = d["decoder.surf_head_new.weight"]
bias = d["decoder.surf_head_new.bias"]
del d["decoder.surf_head_new.weight"]
del d["decoder.surf_head_new.bias"]
n = 8
assert weight.shape[0] == n * patch_size**2
assert bias.shape[0] == n * patch_size**2
weight = weight.reshape(patch_size**2, n, -1)
bias = bias.reshape(patch_size**2, n)
for i, name in enumerate(
("pm1", "pm2p5", "pm10", "tcco", "tc_no", "tcno2", "gtco3", "tcso2")
):
d[f"decoder.surf_heads.{name}.weight"] = weight[:, i]
d[f"decoder.surf_heads.{name}.bias"] = bias[:, i]
if "decoder.surf_head_mod.weight" in d:
weight = d["decoder.surf_head_mod.weight"]
bias = d["decoder.surf_head_mod.bias"]
del d["decoder.surf_head_mod.weight"]
del d["decoder.surf_head_mod.bias"]
n = 4 + 8
assert weight.shape[0] == n * patch_size**2
assert bias.shape[0] == n * patch_size**2
weight = weight.reshape(patch_size**2, n, -1)
bias = bias.reshape(patch_size**2, n)
for i, name in enumerate(
("2t", "10u", "10v", "msl")
+ ("pm1", "pm2p5", "pm10", "tcco", "tc_no", "tcno2", "gtco3", "tcso2"),
):
# Modulation heads should only be present for the pollution variables.
if name in ("pm1", "pm2p5", "pm10", "tcco", "tc_no", "tcno2", "gtco3", "tcso2"):
d[f"decoder.surf_heads.{name}_mod.weight"] = weight[:, i]
d[f"decoder.surf_heads.{name}_mod.bias"] = bias[:, i]
for suffix in ("", "_mod"):
for level in (50, 100, 150, 200, 250, 300, 400, 500, 600, 700, 850, 925, 1000):
if f"decoder.atmos_head{suffix}.layers.{level}.weight" in d:
weight = d[f"decoder.atmos_head{suffix}.layers.{level}.weight"]
bias = d[f"decoder.atmos_head{suffix}.layers.{level}.bias"]
del d[f"decoder.atmos_head{suffix}.layers.{level}.weight"]
del d[f"decoder.atmos_head{suffix}.layers.{level}.bias"]
# Modulation heads should only be present for the pollution variables.
if suffix != "_mod":
n = 5
assert weight.shape[0] == n * patch_size**2
assert bias.shape[0] == n * patch_size**2
weight = weight.reshape(patch_size**2, n, -1)
bias = bias.reshape(patch_size**2, n)
for i, v in enumerate(("z", "u", "v", "t", "q")):
d[f"decoder.atmos_heads.{v}{suffix}.layers.{level}.weight"] = weight[:, i]
d[f"decoder.atmos_heads.{v}{suffix}.layers.{level}.bias"] = bias[:, i]
if f"decoder.atmos_head{suffix}_new.layers.{level}.weight" in d:
weight = d[f"decoder.atmos_head{suffix}_new.layers.{level}.weight"]
bias = d[f"decoder.atmos_head{suffix}_new.layers.{level}.bias"]
del d[f"decoder.atmos_head{suffix}_new.layers.{level}.weight"]
del d[f"decoder.atmos_head{suffix}_new.layers.{level}.bias"]
n = 5
assert weight.shape[0] == n * patch_size**2
assert bias.shape[0] == n * patch_size**2
weight = weight.reshape(patch_size**2, n, -1)
bias = bias.reshape(patch_size**2, n)
for i, v in enumerate(("co", "no", "no2", "go3", "so2")):
d[f"decoder.atmos_heads.{v}{suffix}.layers.{level}.weight"] = weight[:, i]
d[f"decoder.atmos_heads.{v}{suffix}.layers.{level}.bias"] = bias[:, i]
return d
def _adapt_checkpoint_wave(
patch_size: int,
d: dict[str, torch.Tensor],
) -> dict[str, torch.Tensor]:
# The layer norm layers in the level aggregation are named differently.
for n1, n2 in [(".k_ln.", ".ln_k."), (".q_ln.", ".ln_q.")]:
for k in list(d):
if n1 in k:
d[k.replace(n1, n2)] = d[k]
del d[k]
return d
def _adapt_checkpoint_v1p5(
patch_size: int,
surf_vars: tuple[str, ...],
static_vars: tuple[str, ...],
atmos_vars: tuple[str, ...],
d: dict[str, torch.Tensor],
) -> dict[str, torch.Tensor]:
# Remove possible prefix from the keys.
for k, v in list(d.items()):
if k.startswith("net."):
del d[k]
d[k[4:]] = v
# The encoder combines surf_vars and static_vars into one embedding.
encoder_surf_names = surf_vars + static_vars
# Convert the ID-based parametrization to a name-based parametrization.
if "encoder.surf_token_embeds.weight" in d:
weight = d["encoder.surf_token_embeds.weight"]
del d["encoder.surf_token_embeds.weight"]
assert weight.shape[1] == len(encoder_surf_names)
for i, name in enumerate(encoder_surf_names):
d[f"encoder.surf_token_embeds.weights.{name}"] = weight[:, [i]]
if "encoder.atmos_token_embeds.weight" in d:
weight = d["encoder.atmos_token_embeds.weight"]
del d["encoder.atmos_token_embeds.weight"]
assert weight.shape[1] == len(atmos_vars)
for i, name in enumerate(atmos_vars):
d[f"encoder.atmos_token_embeds.weights.{name}"] = weight[:, [i]]
if "decoder.surf_head.weight" in d:
weight = d["decoder.surf_head.weight"]
bias = d["decoder.surf_head.bias"]
del d["decoder.surf_head.weight"]
del d["decoder.surf_head.bias"]
n = len(surf_vars)
assert weight.shape[0] == n * patch_size**2
assert bias.shape[0] == n * patch_size**2
weight = weight.reshape(patch_size**2, n, -1)
bias = bias.reshape(patch_size**2, n)
for i, name in enumerate(surf_vars):
d[f"decoder.surf_heads.{name}.weight"] = weight[:, i]
d[f"decoder.surf_heads.{name}.bias"] = bias[:, i]
if "decoder.atmos_head.weight" in d:
weight = d["decoder.atmos_head.weight"]
bias = d["decoder.atmos_head.bias"]
del d["decoder.atmos_head.weight"]
del d["decoder.atmos_head.bias"]
n = len(atmos_vars)
assert weight.shape[0] == n * patch_size**2
assert bias.shape[0] == n * patch_size**2
weight = weight.reshape(patch_size**2, n, -1)
bias = bias.reshape(patch_size**2, n)
for i, name in enumerate(atmos_vars):
d[f"decoder.atmos_heads.{name}.weight"] = weight[:, i]
d[f"decoder.atmos_heads.{name}.bias"] = bias[:, i]
return d
|