mirror of
https://github.com/wassname/Clover-Edition.git
synced 2026-09-19 12:10:28 +08:00
138 lines
4.4 KiB
Python
138 lines
4.4 KiB
Python
import tensorflow as tf
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import numpy as np
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def angle_defn(pos, i, d_model_size):
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angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model_size))
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return pos * angle_rates
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def positional_encoding(position, d_model_size):
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# create the sinusoidal pattern for the positional encoding
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angle_rads = angle_defn(np.arange(position)[:, np.newaxis], np.arange(d_model_size)[np.newaxis, :], d_model_size)
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sines = np.sin(angle_rads[:, 0::2])
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cosines = np.cos(angle_rads[:, 1::2])
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pos_encoding = tf.cast(np.concatenate([sines, cosines], axis=-1)[np.newaxis, ...], dtype=tf.float32)
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return pos_encoding
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def scaled_dot_product_attention(q, k, v, mask):
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# calculate attention
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matmul_qk = tf.matmul(q, k, transpose_b=True)
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dk = tf.cast(tf.shape(k)[-1], tf.float32)
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scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)
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if mask is not None:
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scaled_attention_logits += (mask * -1e9)
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attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)
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output = tf.matmul(attention_weights, v)
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return output
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class MultiHeadAttention(tf.keras.layers.Layer):
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def __init__(self, d_model_size, num_heads):
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super(MultiHeadAttention, self).__init__()
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self.num_heads = num_heads
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self.d_model_size = d_model_size
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self.depth = int(d_model_size / self.num_heads)
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self.Wq = tf.keras.layers.Dense(d_model_size)
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self.Wk = tf.keras.layers.Dense(d_model_size)
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self.Wv = tf.keras.layers.Dense(d_model_size)
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self.dense = tf.keras.layers.Dense(d_model_size)
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def split_into_heads(self, x, batch_size):
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x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))
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return tf.transpose(x, perm=[0, 2, 1, 3])
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def call(self, v, k, q, mask):
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batch_size = tf.shape(q)[0]
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q = self.Wq(q)
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k = self.Wk(k)
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v = self.Wv(v)
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q = self.split_into_heads(q, batch_size)
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k = self.split_into_heads(k, batch_size)
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v = self.split_into_heads(v, batch_size)
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scaled_attention = tf.transpose(scaled_dot_product_attention(q, k, v, mask), perm=[0, 2, 1, 3])
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original_size_attention = tf.reshape(scaled_attention, (batch_size, -1, self.d_model_size))
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output = self.dense(original_size_attention)
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return output
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def point_wise_feed_forward_network(d_model_size, dff):
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return tf.keras.Sequential([tf.keras.layers.Dense(dff, activation='relu'),
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tf.keras.layers.Dense(d_model_size)])
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class EncoderLayer(tf.keras.layers.Layer):
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def __init__(self, d_model_size, num_heads, dff, rate=0.1):
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super(EncoderLayer, self).__init__()
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self.multi_head_attention = MultiHeadAttention(d_model_size, num_heads)
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self.ffn = point_wise_feed_forward_network(d_model_size, dff)
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self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
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self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)
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self.dropout1 = tf.keras.layers.Dropout(rate)
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self.dropout2 = tf.keras.layers.Dropout(rate)
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def call(self, x, training, mask):
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normed = self.layernorm1(x)
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attn_output = self.multi_head_attention(normed, normed, normed, mask)
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attn_output = self.dropout1(attn_output, training=training)
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out1 = x + attn_output
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out2 = self.layernorm2(out1)
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ffn_output = self.ffn(out2)
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ffn_output = self.dropout2(ffn_output, training=training)
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out2 = out1 + ffn_output
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return out2
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class Encoder(tf.keras.layers.Layer):
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def __init__(self, num_layers=48, d_model_size=1280, num_heads=16, dff=8192, input_vocab_size=50000,
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rate=0.1, **kwargs):
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super(Encoder, self).__init__()
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self.d_model_size = d_model_size
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self.num_layers = num_layers
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self.pos_encoding = positional_encoding(input_vocab_size, self.d_model_size)
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for i in range(num_layers):
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setattr(self, "layer%i" % i, EncoderLayer(d_model_size, num_heads, dff, rate))
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self.layernorm = tf.keras.layers.LayerNormalization(epsilon=1e-6)
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self.dropout = tf.keras.layers.Dropout(rate)
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def get_config(self):
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base_config = super(Encoder, self).get_config()
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return base_config
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def call(self, x, training):
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seq_len = tf.shape(x)[1]
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mask = 1 - tf.linalg.band_part(tf.ones((seq_len, seq_len)), -1, 0)
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x *= tf.math.sqrt(tf.cast(self.d_model_size, tf.float32))
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x += self.pos_encoding[:, :seq_len, :]
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x = self.dropout(x, training=training)
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for i in range(self.num_layers):
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x = getattr(self, "layer%i" % i)(x, training, mask)
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return self.layernorm(x)
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