File size: 5,929 Bytes
422ae83 6f5f900 7bd22e4 422ae83 7bd22e4 422ae83 7bd22e4 1e0c4e0 ee89d66 5e70df1 ee89d66 c9ead17 1e0c4e0 d1fc27c b1fcba0 d1fc27c b1fcba0 d1fc27c b1fcba0 ee89d66 1e0c4e0 b1fcba0 1e0c4e0 b1fcba0 1e0c4e0 b1fcba0 1e0c4e0 b1fcba0 1e0c4e0 ee89d66 1e0c4e0 ee89d66 1e0c4e0 399daf2 b1fcba0 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class UltimateMOE(nn.Module):
def __init__(self, experts):
super(UltimateMOE, self).__init__()
self.threshold = 0.27
self.experts = nn.ModuleList(experts)
num_experts = len(experts)
self.lrelu = nn.LeakyReLU()
self.bn = nn.BatchNorm1d(32)
self.fc1 = nn.Linear(64, 32)
self.fc2 = nn.Linear(64, 32)
self.fc3 = nn.Linear(64, 32)
self.fc4 = nn.Linear(64, 32)
self.pooling = nn.Parameter(torch.ones(32))
self.gating_network = nn.Sequential(
nn.Linear(32 * (num_experts + 1), 64),
nn.Dropout(0.2),
nn.BatchNorm1d(64),
nn.LeakyReLU(),
nn.Linear(64, num_experts),
)
self.softmax = nn.Softmax(dim=1)
def forward(self, x):
outputs = [expert(x)[0] for expert in self.experts]
embeddings = [expert(x)[1] for expert in self.experts]
emb_1 = self.lrelu(self.bn(self.fc1(embeddings[0])))
emb_2 = self.lrelu(self.bn(self.fc2(embeddings[1])))
emb_3 = self.lrelu(self.bn(self.fc3(embeddings[2])))
emb_4 = self.lrelu(self.bn(self.fc4(embeddings[3])))
combined = emb_1 * emb_2 * emb_3 * emb_4
weighted_combined = combined * self.pooling.unsqueeze(0)
concatenated_embeddings = torch.cat((emb_1, emb_2, emb_3, emb_4, weighted_combined), dim=1)
gating_weights = self.gating_network(concatenated_embeddings)
gating_weights = F.softmax(gating_weights, dim=-1)
weighted_logits = torch.stack(outputs, dim=-1)
weighted_logits = torch.einsum('bn,bcn->bc', gating_weights, weighted_logits)
score = self.softmax(weighted_logits)
return score
class MOE_attention(nn.Module):
def __init__(self, experts, device, input_dim=128, freezing=False):
super(MOE_attention, self).__init__()
self.threshold = 0.1
self.temperature = 1.2
self.device = device
self.experts = nn.ModuleList(experts)
self.num_experts = len(experts)
# self.proc_emb = nn.ModuleList([
# nn.Sequential(
# nn.Linear(input_dim, 128),
# nn.BatchNorm1d(128),
# nn.GLU(),
# nn.Linear(64, 32)
# ) for _ in range(self.num_experts)
# ])
self.proc_emb = nn.ModuleList([
nn.Sequential(
nn.Linear(128, 128),
nn.BatchNorm1d(128),
nn.GLU(),
nn.Linear(64, 32)
),
nn.Sequential(
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.GLU(),
nn.Linear(64, 32)
),
nn.Sequential(
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.GLU(),
nn.Linear(64, 32)
)
])
self.TransfEnc = nn.Sequential(
nn.TransformerEncoderLayer(d_model=32, nhead=4, dropout=0.1, dim_feedforward=512),
nn.TransformerEncoderLayer(d_model=32, nhead=4, dropout=0.1, dim_feedforward=512)
)
self.linear_out = nn.Linear(32, 1)
self.softmax = nn.Softmax(dim=1)
if freezing:
for expert in self.experts:
for param in expert.parameters():
param.requires_grad = False
def forward(self, x):
results = [expert(x) for expert in self.experts]
outputs = [res[0] for res in results]
embeddings = [res[1] for res in results]
processed_embs = torch.stack([proc_emb(emb) for proc_emb, emb in zip(self.proc_emb, embeddings)], dim=1)
transf_out = self.TransfEnc(processed_embs)
gating_weights = self.linear_out(transf_out)
gating_weights = self.softmax(gating_weights / self.temperature)
expert_outputs = torch.stack(outputs, dim=1)
combined_output = torch.sum(gating_weights * expert_outputs, dim=1)
return combined_output
class MOE_attention_FS(nn.Module):
def __init__(self, experts, device, input_dim=128, freezing=False):
super(MOE_attention_FS, self).__init__()
self.threshold = 0.5
self.temperature = 1.2
self.device = device
self.experts = nn.ModuleList(experts)
self.num_experts = len(experts)
self.proc_emb = nn.ModuleList([
nn.Sequential(
nn.Linear(128, 128),
nn.BatchNorm1d(128),
nn.GLU(),
nn.Linear(64, 32)
) for _ in range(self.num_experts)
])
self.TransfEnc = nn.Sequential(
nn.TransformerEncoderLayer(d_model=32, nhead=4, dropout=0.1, dim_feedforward=512),
nn.TransformerEncoderLayer(d_model=32, nhead=4, dropout=0.1, dim_feedforward=512)
)
self.linear_out = nn.Linear(32, 1)
self.softmax = nn.Softmax(dim=1)
if freezing:
for expert in self.experts:
for param in expert.parameters():
param.requires_grad = False
def forward(self, x_16, x_22, x_24):
results = [self.experts[0](x_16), self.experts[1](x_22), self.experts[2](x_24)]
# results = [expert(x) for expert in self.experts]
outputs = [res[0] for res in results]
embeddings = [res[1] for res in results]
processed_embs = torch.stack([proc_emb(emb) for proc_emb, emb in zip(self.proc_emb, embeddings)], dim=1)
transf_out = self.TransfEnc(processed_embs)
gating_weights = self.linear_out(transf_out)
gating_weights = self.softmax(gating_weights / self.temperature)
expert_outputs = torch.stack(outputs, dim=1)
combined_output = torch.sum(gating_weights * expert_outputs, dim=1)
return combined_output |