File size: 7,917 Bytes
d891407 |
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 |
import numpy as np
import pandas as pd
import tensorflow as tf
import math
from tqdm import tqdm
def scaled_dot_product_attention(q, k, v):
# calculate the dot product of query and key
dot_product = tf.matmul(q, k, transpose_b=True)
# scale the dot product
scaled_dot_product = dot_product / tf.math.sqrt(tf.cast(tf.shape(k)[-1], dtype=tf.float32))
# apply softmax activation to obtain attention weights
attention_weights = tf.nn.softmax(scaled_dot_product, axis=-1)
# compute the weighted sum of the value vectors with attention weights
output = tf.matmul(attention_weights, v)
return output
class LinearLayer(tf.keras.layers.Layer):
def __init__(self, ix, ox):
super().__init__()
self.ix = ix
self.ox = ox
def build(self, input_shapes):
self.w1 = self.add_weight(shape=(self.ix, self.ox))
self.b1 = self.add_weight(shape=(1, self.ox))
def call(self, inputs):
bz, key = tf.shape(inputs)[0], tf.shape(inputs)[1]
inputs = tf.reshape(inputs, (-1, self.ix))
inputs = tf.matmul(inputs, self.w1) + self.b1
inputs = tf.reshape(inputs, (bz, key, self.ox))
return inputs
class split_heads(tf.keras.layers.Layer):
def __init__(self, num_heads = 10):
super().__init__()
self.num_heads = num_heads
def call(self, inputs):
bz, key = tf.shape(inputs)[0], tf.shape(inputs)[1]
inputs = tf.reshape(inputs, (bz, key, self.num_heads, -1))
inputs = tf.transpose(inputs, (0, 2, 1, 3))
return inputs
class merge_heads(tf.keras.layers.Layer):
def __init__(self):
super().__init__()
def call(self, inputs):
bz, key = tf.shape(inputs)[0], tf.shape(inputs)[2]
inputs = tf.transpose(inputs, (0, 2, 1, 3))
inputs = tf.reshape(inputs, (bz, key, -1))
return inputs
class GPT_Attention(tf.keras.layers.Layer):
def __init__(self, ix, ox, num_heads):
super().__init__()
self.ix = ix
self.ox = ox
self.num_heads = num_heads
self.linear1 = LinearLayer(self.ix, self.ox * 3)
self.split = split_heads(num_heads = self.num_heads)
self.merge = merge_heads()
self.linear2 = LinearLayer(self.ox, self.ix)
if self.ox % self.num_heads != 0:
raise ValueError('The value ox = '+ str(self.ox) +' SHOULD be divisible by number of heads provided')
def call(self, inputs):
if len(inputs) > 0:
inputs = inputs[0]
inputs = self.linear1(inputs)
k, q, v = tf.split(inputs, 3, axis = -1)
k = self.split(k)
q = self.split(q)
v = self.split(v)
#k, q, v = tf.split(inputs, 3, axis = -1)
inputs = scaled_dot_product_attention(k, q, v)
inputs = self.merge(inputs)
inputs = self.linear2(inputs)
return inputs
class MultiHeadAttention(tf.keras.layers.Layer):
def __init__(self, num_heads = 8, key_dim = 64, key_embedding = 512):
super(MultiHeadAttention, self).__init__()
self.num_heads = num_heads
self.key_dim = key_dim
self.key_embedding = key_embedding
self.head_vectors = []
def build(self, input_shape):
#print(input_shape)
self.W_k = self.add_weight(shape=(self.num_heads, self.key_dim, self.key_embedding), name='key')
self.W_q = self.add_weight(shape=(self.num_heads, self.key_dim, self.key_embedding), name='query')
self.W_v = self.add_weight(shape=(self.num_heads, self.key_dim, self.key_embedding), name='value')
self.W_o = self.add_weight(shape=(self.key_dim, self.key_embedding))
def call(self, inputs):
query, key, value = inputs
self.head_vectors = []
head_concat = None
for i in range(self.num_heads):
q = tf.einsum('bij, ij -> bij', query, self.W_q[i])
k = tf.einsum('bij, ij -> bij', key, self.W_k[i])
v = tf.einsum('bij, ij -> bij', value, self.W_v[i])
self.head_vectors += [scaled_dot_product_attention(q, k, v)]
head_concat = tf.concat(self.head_vectors, -2)
#print(tf.shape(head_concat))
output =tf.einsum('bij, kj -> bkj', head_concat, self.W_o)
return output
class Decoder(tf.keras.layers.Layer):
def __init__(self, num_heads = 8, key_dim = 64, key_embedding = 512, GPT_attention = False):
super(Decoder, self).__init__()
self.num_heads = num_heads
self.key_dim = key_dim
self.key_embedding = key_embedding
if GPT_attention:
self.attention = GPT_Attention(key_embedding, key_embedding, num_heads)
else:
self.attention = MultiHeadAttention(num_heads = num_heads, key_dim = key_dim, key_embedding = key_embedding)
self.normalize1 = tf.keras.layers.LayerNormalization(axis = -2)
self.normalize2 = tf.keras.layers.LayerNormalization(axis = -2)
def build(self, input_shape):
#print(input_shape)
self.x1 = self.add_weight(shape=(self.key_dim, self.key_embedding), name='vec1')
self.x2 = self.add_weight(shape=(self.key_dim, self.key_embedding), name='vec2')
self.y1 = self.add_weight(shape=(self.key_dim, self.key_embedding), name='bias1')
self.y2 = self.add_weight(shape=(self.key_dim, self.key_embedding), name='bias2')
def call(self, inputs):
first_sublayer_output = self.attention((inputs, inputs, inputs))
first_sublayer_output = self.normalize1(first_sublayer_output + inputs)
first_nn = tf.einsum('bij, ij -> bij', first_sublayer_output, self.x1) + self.y1
first_nn = tf.keras.activations.relu(first_nn, alpha=0.0, max_value=None, threshold=0.0)
second_nn = tf.einsum('bij, ij -> bij', first_nn, self.x2) + self.y2
second_sublayer_output = self.normalize2(second_nn + first_sublayer_output)
return second_sublayer_output
def positional_function(words, embedding):
pos = np.zeros((words, embedding))
for i in range(words):
for j in range(embedding):
if j%2 == 0:
pos[i, j] = math.sin(i/pow(10000, 2*j/(512)))
else:
pos[i, j] = math.cos(i/pow(10000, 2*j/(512)))
return pos
class PositionalEmbedding(tf.keras.layers.Layer):
def __init__(self, positional_function = positional_function, embedding_size = 512, words = 64):
super(PositionalEmbedding, self).__init__()
self.embedding_size = embedding_size
self.words = words
self.pos_mat = tf.cast(tf.convert_to_tensor(positional_function(self.words, self.embedding_size)), tf.float32)
def build(self, input_sizes):
print(input_sizes)
def call(self, inputs):
embed = tf.einsum("bij, ij -> bij", inputs, self.pos_mat)
return embed
def generate_output(model, vectorizer, text_size = 70, gpt_input = 64, input_sequence = []):
if input_sequence == []:
input_sequence = tf.zeros((1, gpt_input)).numpy()
text = tf.zeros((1, text_size)).numpy()
text[0][: gpt_input] = input_sequence[0][: gpt_input]
GPT = model
for i in tqdm(range(gpt_input, text_size)):
#print("Iteration number:" + str(i))
output = tf.argmax(GPT(input_sequence), -1).numpy()
text[0][i - 1] = output
input_sequence = text[0][i - gpt_input : i].reshape(1, gpt_input)
op = [vectorizer.get_vocabulary()[int(text[0][i])] for i in range(len(text[0]))]
return ' '.join(op) |