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f4a39ee | 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 | # Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Basic neural network towers for whirl/gcm codebase.
A tower is a neural network that operates identically over the last two
dimensions, i.e. (longitude, latitude).
"""
from collections import abc
from typing import Callable, Optional, Tuple
from dinosaur import typing
import gin
import haiku as hk
import jax
import jax.numpy as jnp
from model.legacy import layers
Array = typing.Array
TowerFactory = typing.TowerFactory
LayerFactory = typing.LayerFactory
MLP = gin.external_configurable(hk.nets.MLP)
@gin.register(denylist=['output_size'])
class ColumnTower(hk.Module):
"""Column tower module parameterized by column_net_factory."""
def __init__(
self,
output_size: int,
column_net_factory: LayerFactory = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
checkpoint_tower: bool = False,
name: Optional[str] = None,
):
"""Tower that maps a column_net over two spatial dimensions."""
super().__init__(name=name)
column_net = column_net_factory(output_size)
vmap_last = lambda fn: hk.vmap(fn, in_axes=-1, out_axes=-1, split_rng=False)
column_tower = vmap_last(vmap_last(column_net))
if checkpoint_tower:
column_tower = hk.remat(column_tower)
self.column_tower = column_tower
def __call__(self, inputs: Array) -> Array:
"""Applies Column tower to inputs."""
return self.column_tower(inputs)
@gin.register(denylist=['output_size'])
class ColumnTransformerTower(ColumnTower):
"""Same as ColumnTower, but passes additional transformer inputs."""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __call__(
self,
inputs: Array,
latents: Optional[Array] = None,
positional_encoding: Optional[Array] = None,
) -> Array:
"""Applies Column tower to inputs."""
return self.column_tower(inputs, latents, positional_encoding)
@gin.register(denylist=['output_size'])
class VerticalConvTower(hk.Module):
"""Tower that stacks up layers of Conv1D.
input shape: [in_channel, level, lon, lat],
output shape: [output_size, level, lon, lat].
"""
def __init__(
self,
output_size: int, # The number of channels in the last layer
channels: abc.Sequence[int] = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
kernel_shape: int = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
with_bias: bool = True,
activation: Callable[[jnp.ndarray], jnp.ndarray] = jax.nn.relu,
activate_final: bool = False,
checkpoint_tower: bool = False,
name: Optional[str] = None,
):
super().__init__(name=name)
self.activation = activation
self.output_size = output_size
self.activate_final = activate_final
self.checkpoint_tower = checkpoint_tower
self.layers = []
channels = list(channels) + [self.output_size]
for channels_i in channels:
self.layers.append(layers.ConvLevel(
output_channels=channels_i,
kernel_shape=kernel_shape,
with_bias=with_bias))
def net(self, inputs: Array) -> Array:
out = inputs
num_layers = len(self.layers)
for i, layer in enumerate(self.layers):
out = layer(out)
if i < (num_layers - 1) or self.activate_final:
out = self.activation(out)
return out
def __call__(self, inputs: Array) -> Array:
vmap_last = lambda fn: hk.vmap(fn, in_axes=-1, out_axes=-1, split_rng=False)
tower_fn = vmap_last(vmap_last(self.net))
if self.checkpoint_tower:
tower_fn = hk.remat(tower_fn)
return tower_fn(inputs)
@gin.register(denylist=['output_size'])
class Conv2DTower(hk.Module):
"""Two dimensional ConvNet tower module."""
def __init__(
self,
output_size: int,
num_hidden_units: int = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
num_hidden_layers: int = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
kernel_shape: Tuple[int, int] = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
with_bias: bool = True,
activation: Callable[[jnp.ndarray], jnp.ndarray] = jax.nn.relu,
activate_final: bool = False,
name: Optional[str] = None,
):
"""Tower that stacks up layers of ConvLonLat."""
super().__init__(name=name)
self.activation = activation
self.activate_final = activate_final
output_sizes = [num_hidden_units] * num_hidden_layers + [output_size]
self.layers = []
for output_size in output_sizes:
self.layers.append(layers.ConvLonLat(
output_size=output_size,
kernel_shape=kernel_shape,
with_bias=with_bias))
def __call__(self, inputs: Array) -> Array:
"""Applies ConvNet tower to inputs."""
num_layers = len(self.layers)
out = inputs
for i, layer in enumerate(self.layers):
out = layer(out)
if i < (num_layers - 1) or self.activate_final:
out = self.activation(out)
return out
@gin.register(denylist=['output_size'])
class EpdTower(hk.Module):
"""EPD tower module parameterized by encode/process/decode factories."""
def __init__(
self,
output_size: int,
latent_size: int = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
num_process_blocks: int = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
encode_tower_factory: TowerFactory = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
process_tower_factory: TowerFactory = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
decode_tower_factory: TowerFactory = gin.REQUIRED, # pyrefly: ignore[bad-function-definition]
post_encode_activation: Optional[Callable[[Array], Array]] = None,
pre_decode_activation: Optional[Callable[[Array], Array]] = None,
final_activation: Optional[Callable[[jnp.ndarray], jnp.ndarray]] = None,
name: Optional[str] = None,
):
super().__init__(name=name)
self.output_size = output_size
self.latent_size = latent_size
self.num_process_blocks = num_process_blocks
self.encode_tower_factory = encode_tower_factory
self.process_tower_factory = process_tower_factory
self.decode_tower_factory = decode_tower_factory
self.post_encode_activation = post_encode_activation
self.pre_decode_activation = pre_decode_activation
self.final_activation = final_activation
def __call__(self, inputs: Array) -> Array:
"""Applies EPD tower to inputs."""
encoded = self.encode_tower_factory(self.latent_size)(inputs)
if self.post_encode_activation is not None:
encoded = self.post_encode_activation(encoded)
current = encoded
for _ in range(self.num_process_blocks):
current = current + self.process_tower_factory(self.latent_size)(current)
if self.pre_decode_activation is not None:
current = self.pre_decode_activation(current)
out = self.decode_tower_factory(self.output_size)(current)
if self.final_activation is not None:
return self.final_activation(out)
return out
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