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import tensorflow as tf
import numpy as np
from transformers import BertTokenizer
tokenizer_en = BertTokenizer.from_pretrained("bert-base-cased")
tokenizer_cn = BertTokenizer.from_pretrained("bert-base-chinese")
MAX_TOKENIZE_LENGTH = 128
EMBEDDING_DEPTH = 256
def positional_encoding(length, depth):
depth = depth/2
positions = np.arange(length)[:, np.newaxis] # (seq, 1)
depths = np.arange(depth)[np.newaxis, :]/depth # (1, depth)
angle_rates = 1 / (10000**depths) # (1, depth)
angle_rads = positions * angle_rates # (pos, depth)
pos_encoding = np.concatenate(
[np.sin(angle_rads), np.cos(angle_rads)],
axis=-1)
return tf.cast(pos_encoding, dtype=tf.float32)
class PositionalEmbedding(tf.keras.layers.Layer):
def __init__(self, vocab_size, d_model):
super().__init__()
self.d_model = d_model
self.embedding = tf.keras.layers.Embedding(input_dim=vocab_size, output_dim=d_model, mask_zero=True)
self.pos_encoding = positional_encoding(length=MAX_TOKENIZE_LENGTH, depth=d_model)
def compute_mask(self, *args, **kwargs):
return self.embedding.compute_mask(*args, **kwargs)
def call(self, x):
length = tf.shape(x)[1]
x = self.embedding(x)
# This factor sets the relative scale of the embedding and positonal_encoding.
x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))
x = x + self.pos_encoding[tf.newaxis, :length, :]
return x
class BaseAttention(tf.keras.layers.Layer):
def __init__(self, **kwargs):
super().__init__()
self.mha = tf.keras.layers.MultiHeadAttention(**kwargs)
self.layernorm = tf.keras.layers.LayerNormalization()
self.add = tf.keras.layers.Add()
class CrossAttention(BaseAttention):
def call(self, x, context): #x = query, content = key,value pairs
attn_output, attn_scores = self.mha(
query=x,
key=context,
value=context,
return_attention_scores=True)
# Cache the attention scores for plotting later.
self.last_attn_scores = attn_scores
x = self.add([x, attn_output])
x = self.layernorm(x)
return x
class GlobalSelfAttention(BaseAttention):
def call(self, x):
attn_output = self.mha(
query=x,
value=x,
key=x)
x = self.add([x, attn_output])
x = self.layernorm(x)
return x
class CausalSelfAttention(BaseAttention):
def call(self, x):
attn_output = self.mha(
query=x,
value=x,
key=x,
use_causal_mask = True)
x = self.add([x, attn_output])
x = self.layernorm(x)
return x
class FeedForward(tf.keras.layers.Layer):
def __init__(self, d_model, dff, dropout_rate=0.1):
super().__init__()
self.seq = tf.keras.Sequential([
tf.keras.layers.Dense(dff, activation='relu'),
tf.keras.layers.Dense(d_model),
tf.keras.layers.Dropout(dropout_rate)
])
self.add = tf.keras.layers.Add()
self.layer_norm = tf.keras.layers.LayerNormalization()
def call(self, x):
x = self.add([x, self.seq(x)])
x = self.layer_norm(x)
return x
class EncoderLayer(tf.keras.layers.Layer):
def __init__(self,*, d_model, num_heads, dff, dropout_rate=0.1):
super().__init__()
self.self_attention = GlobalSelfAttention(
num_heads=num_heads,
key_dim=d_model,
dropout=dropout_rate)
self.ffn = FeedForward(d_model, dff)
def call(self, x):
x = self.self_attention(x)
x = self.ffn(x)
return x
class DecoderLayer(tf.keras.layers.Layer):
def __init__(self,
*,
d_model,
num_heads,
dff,
dropout_rate=0.1):
super(DecoderLayer, self).__init__()
self.causal_self_attention = CausalSelfAttention(
num_heads=num_heads,
key_dim=d_model,
dropout=dropout_rate)
self.cross_attention = CrossAttention(
num_heads=num_heads,
key_dim=d_model,
dropout=dropout_rate)
self.ffn = FeedForward(d_model, dff)
def call(self, x, context):
x = self.causal_self_attention(x=x)
x = self.cross_attention(x=x, context=context)
# Cache the last attention scores for plotting later
self.last_attn_scores = self.cross_attention.last_attn_scores
x = self.ffn(x) # Shape `(batch_size, seq_len, d_model)`.
return x
class Encoder(tf.keras.layers.Layer):
def __init__(self, *, num_layers, d_model, num_heads,
dff, vocab_size, dropout_rate=0.1):
super().__init__()
self.d_model = d_model
self.num_layers = num_layers
self.pos_embedding = PositionalEmbedding(
vocab_size=vocab_size, d_model=d_model)
self.enc_layers = [
EncoderLayer(d_model=d_model,
num_heads=num_heads,
dff=dff,
dropout_rate=dropout_rate)
for _ in range(num_layers)]
self.dropout = tf.keras.layers.Dropout(dropout_rate)
def call(self, x):
# `x` is token-IDs shape: (batch, seq_len)
x = self.pos_embedding(x) # Shape `(batch_size, seq_len, d_model)`.
# Add dropout.
x = self.dropout(x)
for i in range(self.num_layers):
x = self.enc_layers[i](x)
return x # Shape `(batch_size, seq_len, d_model)`.
class Decoder(tf.keras.layers.Layer):
def __init__(self, *, num_layers, d_model, num_heads, dff, vocab_size,
dropout_rate=0.1):
super(Decoder, self).__init__()
self.d_model = d_model
self.num_layers = num_layers
self.pos_embedding = PositionalEmbedding(vocab_size=vocab_size,
d_model=d_model)
self.dropout = tf.keras.layers.Dropout(dropout_rate)
self.dec_layers = [
DecoderLayer(d_model=d_model, num_heads=num_heads,
dff=dff, dropout_rate=dropout_rate)
for _ in range(num_layers)]
self.last_attn_scores = None
def call(self, x, context):
# `x` is token-IDs shape (batch, target_seq_len)
x = self.pos_embedding(x) # (batch_size, target_seq_len, d_model)
x = self.dropout(x)
for i in range(self.num_layers):
x = self.dec_layers[i](x, context)
self.last_attn_scores = self.dec_layers[-1].last_attn_scores
# The shape of x is (batch_size, target_seq_len, d_model).
return x
# @tf.keras.saving.register_keras_serializable()
class Transformer(tf.keras.Model):
def __init__(self, *, num_layers, d_model, num_heads, dff,
input_vocab_size, target_vocab_size, dropout_rate=0.1):
super().__init__()
self.encoder = Encoder(num_layers=num_layers, d_model=d_model,
num_heads=num_heads, dff=dff,
vocab_size=input_vocab_size,
dropout_rate=dropout_rate)
self.decoder = Decoder(num_layers=num_layers, d_model=d_model,
num_heads=num_heads, dff=dff,
vocab_size=target_vocab_size,
dropout_rate=dropout_rate)
self.final_layer = tf.keras.layers.Dense(target_vocab_size)
def call(self, inputs):
# To use a Keras model with `.fit` you must pass all your inputs in the
# first argument.
context, x = inputs
context = self.encoder(context) # (batch_size, context_len, d_model)
x = self.decoder(x, context) # (batch_size, target_len, d_model)
# Final linear layer output.
logits = self.final_layer(x) # (batch_size, target_len, target_vocab_size)
try:
# Drop the keras mask, so it doesn't scale the losses/metrics.
# b/250038731
del logits._keras_mask
except AttributeError:
pass
# Return the final output and the attention weights.
return logits
# @tf.keras.saving.register_keras_serializable()
# class CustomSchedule(tf.keras.optimizers.schedules.LearningRateSchedule):
# def __init__(self, d_model, warmup_steps=4000):
# super().__init__()
# self.d_model = d_model
# self.d_model = tf.cast(self.d_model, tf.float32)
# self.warmup_steps = warmup_steps
# def __call__(self, step):
# step = tf.cast(step, dtype=tf.float32)
# arg1 = tf.math.rsqrt(step)
# arg2 = step * (self.warmup_steps ** -1.5)
# return tf.math.rsqrt(self.d_model) * tf.math.minimum(arg1, arg2)
# def get_config(self):
# return {
# 'd_model': int(self.d_model),
# 'warmup_steps': int(self.warmup_steps)
# }
# # learning_rate = CustomSchedule(EMBEDDING_DEPTH)
# # @tf.keras.saving.register_keras_serializable()
# class CustomAdam(tf.keras.optimizers.Adam):
# def __init__(self, custom_param, **kwargs):
# super(CustomAdam, self).__init__(**kwargs)
# self.custom_param = custom_param #this is the learning rate (custom schedule)
# def get_config(self):
# config = super(CustomAdam, self).get_config()
# config.update({
# 'custom_param': self.custom_param
# })
# return config
# # optimizer = CustomAdam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)
# # @tf.keras.saving.register_keras_serializable()
# def masked_loss(label, pred):
# mask = label != 0
# loss_object = tf.keras.losses.SparseCategoricalCrossentropy(
# from_logits=True, reduction='none')
# loss = loss_object(label, pred)
# mask = tf.cast(mask, dtype=loss.dtype)
# loss *= mask
# loss = tf.reduce_sum(loss)/tf.reduce_sum(mask)
# return loss
# # @tf.keras.saving.register_keras_serializable()
# def masked_accuracy(label, pred):
# pred = tf.argmax(pred, axis=2)
# label = tf.cast(label, pred.dtype)
# match = label == pred
# mask = label != 0
# match = match & mask
# match = tf.cast(match, dtype=tf.float32)
# mask = tf.cast(mask, dtype=tf.float32)
# return tf.reduce_sum(match)/tf.reduce_sum(mask) |