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model.py
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model.py
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import torch
import math
import torch.nn as nn
import numpy as np
import torch.nn.functional as F
def init_params(module, n_layers):
if isinstance(module, nn.Linear):
module.weight.data.normal_(mean=0.0, std=0.02 / math.sqrt(n_layers))
if module.bias is not None:
module.bias.data.zero_()
if isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=0.02)
def gelu(x):
"""
GELU activation
https://arxiv.org/abs/1606.08415
https://github.com/huggingface/pytorch-openai-transformer-lm/blob/master/model_pytorch.py#L14
https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/modeling.py
"""
# return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))
return 0.5 * x * (1.0 + torch.erf(x / math.sqrt(2.0)))
class TransformerModel(nn.Module):
def __init__(
self,
token_length,
n_class,
input_dim,
n_layers=6,
num_heads=8,
hidden_dim=64,
ffn_dim=64,
dropout_rate=0.0,
attention_dropout_rate=0.1
):
super().__init__()
self.seq_len = token_length+1
self.input_dim = input_dim
self.hidden_dim = hidden_dim
self.ffn_dim = 2 * hidden_dim
self.num_heads = num_heads
self.n_layers = n_layers
self.n_class = n_class
self.dropout_rate = dropout_rate
self.attention_dropout_rate = attention_dropout_rate
self.att_embeddings_nope = nn.Linear(self.input_dim, self.hidden_dim)
encoders = [EncoderLayer(self.hidden_dim, self.ffn_dim, self.dropout_rate, self.attention_dropout_rate, self.num_heads)
for _ in range(self.n_layers)]
self.layers = nn.ModuleList(encoders)
self.final_ln = nn.LayerNorm(hidden_dim)
self.out_proj = nn.Linear(self.hidden_dim, int(self.hidden_dim/2))
self.attn_layer = nn.Linear(2 * self.hidden_dim, 1)
self.Linear1 = nn.Linear(int(self.hidden_dim/2), self.n_class)
self.scaling = nn.Parameter(torch.ones(1) * 0.5)
self.apply(lambda module: init_params(module, n_layers=n_layers))
def forward(self, batched_data):
tensor = self.att_embeddings_nope(batched_data)
# transformer encoder
for enc_layer in self.layers:
tensor = enc_layer(tensor)
output = self.final_ln(tensor)
target = output[:,0,:].unsqueeze(1).repeat(1,self.seq_len-1,1)
split_tensor = torch.split(output, [1, self.seq_len-1], dim=1)
node_tensor = split_tensor[0]
neighbor_tensor = split_tensor[1]
layer_atten = self.attn_layer(torch.cat((target, neighbor_tensor), dim=2))
layer_atten = F.softmax(layer_atten, dim=1)
neighbor_tensor = neighbor_tensor * layer_atten
neighbor_tensor = torch.sum(neighbor_tensor, dim=1, keepdim=True)
output = (node_tensor + neighbor_tensor).squeeze()
output = self.Linear1(torch.relu(self.out_proj(output)))
return torch.log_softmax(output, dim=1)
class FeedForwardNetwork(nn.Module):
def __init__(self, hidden_size, ffn_size, dropout_rate):
super(FeedForwardNetwork, self).__init__()
self.layer1 = nn.Linear(hidden_size, ffn_size)
self.gelu = nn.GELU()
self.layer2 = nn.Linear(ffn_size, hidden_size)
def forward(self, x):
x = self.layer1(x)
x = self.gelu(x)
x = self.layer2(x)
return x
class MultiHeadAttention(nn.Module):
def __init__(self, hidden_size, attention_dropout_rate, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.att_size = att_size = hidden_size // num_heads
self.scale = att_size ** -0.5
self.linear_q = nn.Linear(hidden_size, num_heads * att_size)
self.linear_k = nn.Linear(hidden_size, num_heads * att_size)
self.linear_v = nn.Linear(hidden_size, num_heads * att_size)
self.att_dropout = nn.Dropout(attention_dropout_rate)
self.output_layer = nn.Linear(num_heads * att_size, hidden_size)
def forward(self, q, k, v, attn_bias=None):
orig_q_size = q.size()
d_k = self.att_size
d_v = self.att_size
batch_size = q.size(0)
# head_i = Attention(Q(W^Q)_i, K(W^K)_i, V(W^V)_i)
q = self.linear_q(q).view(batch_size, -1, self.num_heads, d_k)
k = self.linear_k(k).view(batch_size, -1, self.num_heads, d_k)
v = self.linear_v(v).view(batch_size, -1, self.num_heads, d_v)
q = q.transpose(1, 2) # [b, h, q_len, d_k]
v = v.transpose(1, 2) # [b, h, v_len, d_v]
k = k.transpose(1, 2).transpose(2, 3) # [b, h, d_k, k_len]
# Scaled Dot-Product Attention.
# Attention(Q, K, V) = softmax((QK^T)/sqrt(d_k))V
q = q * self.scale
x = torch.matmul(q, k) # [b, h, q_len, k_len]
if attn_bias is not None:
x = x + attn_bias
x = torch.softmax(x, dim=3)
x = self.att_dropout(x)
x = x.matmul(v) # [b, h, q_len, attn]
x = x.transpose(1, 2).contiguous() # [b, q_len, h, attn]
x = x.view(batch_size, -1, self.num_heads * d_v)
x = self.output_layer(x)
assert x.size() == orig_q_size
return x
class EncoderLayer(nn.Module):
def __init__(self, hidden_size, ffn_size, dropout_rate, attention_dropout_rate, num_heads):
super(EncoderLayer, self).__init__()
self.self_attention_norm = nn.LayerNorm(hidden_size)
self.self_attention = MultiHeadAttention(
hidden_size, attention_dropout_rate, num_heads)
self.self_attention_dropout = nn.Dropout(dropout_rate)
self.ffn_norm = nn.LayerNorm(hidden_size)
self.ffn = FeedForwardNetwork(hidden_size, ffn_size, dropout_rate)
self.ffn_dropout = nn.Dropout(dropout_rate)
def forward(self, x, attn_bias=None):
y = self.self_attention_norm(x)
y = self.self_attention(y, y, y, attn_bias)
y = self.self_attention_dropout(y)
x = x + y
y = self.ffn_norm(x)
y = self.ffn(y)
y = self.ffn_dropout(y)
x = x + y
return x