File size: 6,595 Bytes
8c38a63 |
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 |
# Copyright 2024 Garena Online Private Limited
#
# 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
#
# http://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.
"""Deep networks."""
from copy import deepcopy
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
def init_weights(m):
@torch.no_grad()
def truncated_normal_init(t, mean=0.0, std=0.01):
# torch.nn.init.normal_(t, mean=mean, std=std)
t.data.normal_(mean, std)
while True:
cond = torch.logical_or(t < mean - 2 * std, t > mean + 2 * std)
if not torch.sum(cond):
break
w = torch.empty(t.shape, device=t.device, dtype=t.dtype)
# torch.nn.init.normal_(w, mean=mean, std=std)
w.data.normal_(mean, std)
t = torch.where(cond, w, t)
return t
if type(m) is nn.Linear or isinstance(m, EnsembleFC):
truncated_normal_init(m.weight, std=1 / (2 * np.sqrt(m.in_features)))
if m.bias is not None:
m.bias.data.fill_(0.0)
def init_weights_uniform(m):
input_dim = m.in_features
torch.nn.init.uniform(m.weight, -1 / np.sqrt(input_dim), 1 / np.sqrt(input_dim))
if m.bias is not None:
m.bias.data.fill_(0.0)
class Swish(nn.Module):
def __init__(self):
super(Swish, self).__init__()
def forward(self, x):
x = x * F.sigmoid(x)
return x
class MLPModel(nn.Module):
def __init__(self, encoding_dim, hidden_dim=128, activation="relu") -> None:
super(MLPModel, self).__init__()
self.hidden_size = hidden_dim
self.output_dim = 1
self.nn1 = nn.Linear(encoding_dim, hidden_dim)
self.nn2 = nn.Linear(hidden_dim, hidden_dim)
self.nn_out = nn.Linear(hidden_dim, self.output_dim)
self.apply(init_weights)
if activation == "swish":
self.activation = Swish()
elif activation == "relu":
self.activation = nn.ReLU()
else:
raise ValueError(f"Unknown activation {activation}")
def get_params(self) -> torch.Tensor:
params = []
for pp in list(self.parameters()):
params.append(pp.view(-1))
return torch.cat(params)
def forward(self, encoding: torch.Tensor) -> torch.Tensor:
x = self.activation(self.nn1(encoding))
x = self.activation(self.nn2(x))
score = self.nn_out(x)
return score
def init(self):
self.init_params = self.get_params().data.clone()
if torch.cuda.is_available():
self.init_params = self.init_params.cuda()
def regularization(self):
"""Prior towards independent initialization."""
return ((self.get_params() - self.init_params) ** 2).mean()
class EnsembleFC(nn.Module):
__constants__ = ["in_features", "out_features"]
in_features: int
out_features: int
ensemble_size: int
weight: torch.Tensor
def __init__(
self,
in_features: int,
out_features: int,
ensemble_size: int,
bias: bool = True,
dtype=torch.float32,
) -> None:
super(EnsembleFC, self).__init__()
self.in_features = in_features
self.out_features = out_features
self.ensemble_size = ensemble_size
# init immediately to avoid error
self.weight = nn.Parameter(torch.empty(ensemble_size, in_features, out_features, dtype=dtype))
if bias:
self.bias = nn.Parameter(torch.empty(ensemble_size, out_features, dtype=dtype))
else:
self.register_parameter("bias", None)
def forward(self, input: torch.Tensor) -> torch.Tensor:
input = input.to(self.weight.dtype)
wx = torch.einsum("eblh,ehm->eblm", input, self.weight)
return torch.add(wx, self.bias[:, None, None, :]) # w times x + b
def get_params(model):
return torch.cat([p.view(-1) for p in model.parameters()])
class _EnsembleModel(nn.Module):
def __init__(self, encoding_dim, num_ensemble, hidden_dim=128, activation="relu", dtype=torch.float32) -> None:
# super().__init__(encoding_dim, hidden_dim, activation)
super(_EnsembleModel, self).__init__()
self.num_ensemble = num_ensemble
self.hidden_dim = hidden_dim
self.output_dim = 1
self.nn1 = EnsembleFC(encoding_dim, hidden_dim, num_ensemble, dtype=dtype)
self.nn2 = EnsembleFC(hidden_dim, hidden_dim, num_ensemble, dtype=dtype)
self.nn_out = EnsembleFC(hidden_dim, self.output_dim, num_ensemble, dtype=dtype)
self.apply(init_weights)
if activation == "swish":
self.activation = Swish()
elif activation == "relu":
self.activation = nn.ReLU()
else:
raise ValueError(f"Unknown activation {activation}")
def forward(self, encoding: torch.Tensor) -> torch.Tensor:
x = self.activation(self.nn1(encoding))
x = self.activation(self.nn2(x))
score = self.nn_out(x)
return score
def regularization(self):
"""Prior towards independent initialization."""
return ((self.get_params() - self.init_params) ** 2).mean()
class EnsembleModel(nn.Module):
def __init__(self, encoding_dim, num_ensemble, hidden_dim=128, activation="relu", dtype=torch.float32) -> None:
super(EnsembleModel, self).__init__()
self.encoding_dim = encoding_dim
self.num_ensemble = num_ensemble
self.hidden_dim = hidden_dim
self.model = _EnsembleModel(encoding_dim, num_ensemble, hidden_dim, activation, dtype)
self.reg_model = deepcopy(self.model) # only used for regularization
# freeze the reg model
for param in self.reg_model.parameters():
param.requires_grad = False
def forward(self, encoding: torch.Tensor) -> torch.Tensor:
return self.model(encoding)
def regularization(self):
"""Prior towards independent initialization."""
model_params = get_params(self.model)
reg_params = get_params(self.reg_model).detach()
return ((model_params - reg_params) ** 2).mean()
|