Spaces:
Running
Running
File size: 11,467 Bytes
36c5570 |
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 |
import tensorflow as tf
import time
import numpy as np
import matplotlib.pyplot as plt
def gelu(x):
return 0.5 * x * (1.0 + tf.math.erf(x / tf.sqrt(2.)))
def scaled_dot_product_attention(q, k, v, mask,adjoin_matrix):
"""Calculate the attention weights.
q, k, v must have matching leading dimensions.
k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.
The mask has different shapes depending on its type(padding or look ahead)
but it must be broadcastable for addition.
Args:
q: query shape == (..., seq_len_q, depth)
k: key shape == (..., seq_len_k, depth)
v: value shape == (..., seq_len_v, depth_v)
mask: Float tensor with shape broadcastable
to (..., seq_len_q, seq_len_k). Defaults to None.
Returns:
output, attention_weights
"""
matmul_qk = tf.matmul(q, k, transpose_b=True) # (..., seq_len_q, seq_len_k)
# scale matmul_qk
dk = tf.cast(tf.shape(k)[-1], tf.float32)
scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
# add the mask to the scaled tensor.
if mask is not None:
scaled_attention_logits += (mask * -1e9)
if adjoin_matrix is not None:
#adjoin_matrix1 =tf.where(adjoin_matrix>0,0.0,-1e9)
#scaled_attention_logits += adjoin_matrix1
#scaled_attention_logits = scaled_attention_logits * adjoin_matrix
scaled_attention_logits += adjoin_matrix
# softmax is normalized on the last axis (seq_len_k) so that the scores
# add up to 1.
attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1) # (..., seq_len_q, seq_len_k)
output = tf.matmul(attention_weights, v) # (..., seq_len_q, depth_v)
return output, attention_weights
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.d_model = d_model
assert d_model % self.num_heads == 0
self.depth = d_model // self.num_heads
self.wq = tf.keras.layers.Dense(d_model)
self.wk = tf.keras.layers.Dense(d_model)
self.wv = tf.keras.layers.Dense(d_model)
self.dense = tf.keras.layers.Dense(d_model)
def split_heads(self, x, batch_size):
"""Split the last dimension into (num_heads, depth).
Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)
"""
x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
return tf.transpose(x, perm=[0, 2, 1, 3])
def call(self, v, k, q, mask,adjoin_matrix):
batch_size = tf.shape(q)[0]
q = self.wq(q) # (batch_size, seq_len, d_model)
k = self.wk(k) # (batch_size, seq_len, d_model)
v = self.wv(v) # (batch_size, seq_len, d_model)
q = self.split_heads(q, batch_size) # (batch_size, num_heads, seq_len_q, depth)
k = self.split_heads(k, batch_size) # (batch_size, num_heads, seq_len_k, depth)
v = self.split_heads(v, batch_size) # (batch_size, num_heads, seq_len_v, depth)
# scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)
# attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)
scaled_attention, attention_weights = scaled_dot_product_attention(
q, k, v, mask,adjoin_matrix)
scaled_attention = tf.transpose(scaled_attention,
perm=[0, 2, 1, 3]) # (batch_size, seq_len_q, num_heads, depth)
concat_attention = tf.reshape(scaled_attention,
(batch_size, -1, self.d_model)) # (batch_size, seq_len_q, d_model)
output = self.dense(concat_attention) # (batch_size, seq_len_q, d_model)
return output, attention_weights
def point_wise_feed_forward_network(d_model, dff):
return tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation=gelu), # (batch_size, seq_len, dff)tf.keras.layers.LeakyReLU(0.01)
tf.keras.layers.Dense(d_model) # (batch_size, seq_len, d_model)
])
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self, d_model, num_heads, dff, rate=0.1):
super(EncoderLayer, self).__init__()
self.mha = MultiHeadAttention(d_model, num_heads)
self.ffn = point_wise_feed_forward_network(d_model, dff)
self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
self.dropout1 = tf.keras.layers.Dropout(rate)
self.dropout2 = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask,adjoin_matrix):
attn_output, attention_weights = self.mha(x, x, x, mask,adjoin_matrix) # (batch_size, input_seq_len, d_model)
attn_output = self.dropout1(attn_output, training=training)
out1 = self.layernorm1(x + attn_output) # (batch_size, input_seq_len, d_model)
ffn_output = self.ffn(out1) # (batch_size, input_seq_len, d_model)
ffn_output = self.dropout2(ffn_output, training=training)
out2 = self.layernorm2(out1 + ffn_output) # (batch_size, input_seq_len, d_model)
return out2,attention_weights
class Encoder(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Encoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
# self.pos_encoding = positional_encoding(maximum_position_encoding,
# self.d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask,adjoin_matrix):
seq_len = tf.shape(x)[1]
adjoin_matrix = adjoin_matrix[:,tf.newaxis,:,:]
# adding embedding and position encoding.
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x = self.dropout(x, training=training)
for i in range(self.num_layers):
x,attention_weights = self.enc_layers[i](x, training, mask,adjoin_matrix)
return x # (batch_size, input_seq_len, d_model)
class Encoder_test(tf.keras.Model):
def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size,
maximum_position_encoding, rate=0.1):
super(Encoder_test, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)
# self.pos_encoding = positional_encoding(maximum_position_encoding,
# self.d_model)
self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(rate)
def call(self, x, training, mask,adjoin_matrix):
seq_len = tf.shape(x)[1]
adjoin_matrix = adjoin_matrix[:,tf.newaxis,:,:]
# adding embedding and position encoding.
x = self.embedding(x) # (batch_size, input_seq_len, d_model)
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
# x += self.pos_encoding[:, :seq_len, :]
x = self.dropout(x, training=training)
attention_weights_list = []
xs = []
for i in range(self.num_layers):
x,attention_weights = self.enc_layers[i](x, training, mask,adjoin_matrix)
attention_weights_list.append(attention_weights)
xs.append(x)
return x,attention_weights_list,xs
class BertModel_test(tf.keras.Model):
def __init__(self,num_layers = 6,d_model = 256,dff = 512,num_heads = 8,vocab_size = 17,dropout_rate = 0.1):
super(BertModel_test, self).__init__()
self.encoder = Encoder_test(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(d_model, activation=gelu)
self.layernorm = tf.keras.layers.LayerNormalization(-1)
self.fc2 = tf.keras.layers.Dense(vocab_size)
def call(self,x,adjoin_matrix,mask,training=False):
x,att,xs = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = self.fc1(x)
x = self.layernorm(x)
x = self.fc2(x)
return x,att,xs
class BertModel(tf.keras.Model):
def __init__(self,num_layers = 6,d_model = 256,dff = 512,num_heads = 8,vocab_size = 17,dropout_rate = 0.1):
super(BertModel, self).__init__()
self.encoder = Encoder(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(d_model, activation=gelu)
self.layernorm = tf.keras.layers.LayerNormalization(-1)
self.fc2 = tf.keras.layers.Dense(vocab_size)
def call(self,x,adjoin_matrix,mask,training=False):
x = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = self.fc1(x)
x = self.layernorm(x)
x = self.fc2(x)
return x
class PredictModel(tf.keras.Model):
def __init__(self,num_layers = 8,d_model = 256,dff = 512,num_heads = 8,vocab_size =17,dropout_rate = 0.1,dense_dropout=0.1):
super(PredictModel, self).__init__()
self.encoder = Encoder(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(256,activation=tf.keras.layers.LeakyReLU(0.25))
self.fc2 = tf.keras.layers.Dense(256,activation=tf.keras.layers.LeakyReLU(0.25))
self.dropout = tf.keras.layers.Dropout(dense_dropout)
self.fc3 = tf.keras.layers.Dense(1)
def call(self,x,adjoin_matrix,mask,training=False):
x = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = x[:,0,:]
x = self.fc1(x)
x = self.dropout(x,training=training)
x = self.fc2(x)
x = self.fc3(x)
return x
class PredictModel_test(tf.keras.Model):
def __init__(self,num_layers = 6,d_model = 256,dff = 512,num_heads = 8,vocab_size =17,dropout_rate = 0.1,dense_dropout=0.5):
super(PredictModel_test, self).__init__()
self.encoder = Encoder_test(num_layers=num_layers,d_model=d_model,
num_heads=num_heads,dff=dff,input_vocab_size=vocab_size,maximum_position_encoding=200,rate=dropout_rate)
self.fc1 = tf.keras.layers.Dense(256, activation=tf.keras.layers.LeakyReLU(0.1))
self.dropout = tf.keras.layers.Dropout(dense_dropout)
self.fc2 = tf.keras.layers.Dense(1)
def call(self,x,adjoin_matrix,mask,training=False):
x,att,xs = self.encoder(x,training=training,mask=mask,adjoin_matrix=adjoin_matrix)
x = x[:, 0, :]
x = self.fc1(x)
x = self.dropout(x, training=training)
x = self.fc2(x)
return x,att,xs
|