AlphaS2S.py

import tensorflow as tf
from tensorflow.keras import layers, Model

class SwiGLU(layers.Layer):
    def __init__(self, d_model, d_ff):
        super().__init__()
        self.proj = layers.Dense(d_ff*2)
        self.out = layers.Dense(d_model)
    def call(self, x):
        x_proj = self.proj(x)
        x_val, x_gate = tf.split(x_proj, 2, axis=-1)
        return self.out(x_val * tf.nn.silu(x_gate))

class EncoderBlock(layers.Layer):
    def __init__(self, d_model, num_heads, dff, dropout=0.1):
        super().__init__()
        self.mha = layers.MultiHeadAttention(num_heads=num_heads, key_dim=d_model)
        self.ffn = SwiGLU(d_model, dff)
        self.norm1 = layers.LayerNormalization(epsilon=1e-6)
        self.norm2 = layers.LayerNormalization(epsilon=1e-6)
        self.dropout1 = layers.Dropout(dropout)
        self.dropout2 = layers.Dropout(dropout)
    def call(self, x, mask=None, training=False):
        attn_out = self.dropout1(self.mha(x, x, x, attention_mask=mask), training=training)
        out1 = self.norm1(x + attn_out)
        ffn_out = self.dropout2(self.ffn(out1), training=training)
        return self.norm2(out1 + ffn_out)

class LoU(layers.Layer):
    def __init__(self, d_model, clip_value=5.0, eps=1e-6):
        super().__init__()
        self.d_model = d_model
        self.clip_value = float(clip_value)
        self.eps = float(eps)
        self.Q = layers.Dense(d_model, dtype='float32')
        self.K = layers.Dense(d_model, dtype='float32')
        self.V = layers.Dense(d_model, dtype='float32')
        self.proj = layers.Dense(d_model, use_bias=True, dtype='float32')
        self.norm = layers.LayerNormalization(epsilon=1e-5, dtype='float32')
        self.norm1 = layers.LayerNormalization(epsilon=1e-5, dtype='float32')
        
        self.alpha_linear = layers.Dense(1, activation='sigmoid', dtype='float32')

    def _ema_over_time(self, score, alpha_dynamic):
        seq = tf.transpose(score, perm=[1, 0, 2])
        alpha_seq = tf.transpose(alpha_dynamic, perm=[1, 0, 2])

        def step(prev_ema, inputs):
            x_t, alpha_t = inputs
            new = alpha_t * x_t + (1.0 - alpha_t) * prev_ema
            return new

        init = seq[0]
        first_alpha = alpha_seq[0]
        remaining_seq = seq[1:]
        remaining_alpha = alpha_seq[1:]
        elems = (remaining_seq, remaining_alpha)
        ema_seq = tf.scan(fn=step, elems=elems, initializer=init)
        ema_seq = tf.concat([tf.expand_dims(init, 0), ema_seq], axis=0)
        ema = tf.transpose(ema_seq, perm=[1, 0, 2])
        return ema

    def call(self, x):
        x_f32 = tf.cast(x, tf.float32)
        residual = x_f32
        x_f32 = self.norm1(x)

        q = self.Q(x_f32)
        k = self.K(x_f32)
        V = self.V(x_f32)
        # 기존 코드:
        # g_q = tf.nn.sigmoid(q)
        # g_k = tf.nn.sigmoid(k)

        g_q = (tf.nn.tanh(q) + 1.0) / 2.0
        g_k = (tf.nn.tanh(k) + 1.0) / 2.0
        score = g_q * g_k

        alpha_dynamic = self.alpha_linear(x_f32)
        score_ema = self._ema_over_time(score, alpha_dynamic)
        mean_last = tf.reduce_mean(score_ema, axis=-1, keepdims=True)
        denom = tf.maximum(mean_last, self.eps)
        score_norm = score_ema / denom
        score_clipped = tf.clip_by_value(score_norm, -self.clip_value, self.clip_value)
        x_comb = score_clipped * V
        out = self.proj(x_comb)
        out = self.norm(out + residual)
        return tf.cast(out, x.dtype)
        
class Transformer(tf.keras.Model):
    def __init__(self, num_layers, d_model, num_heads, dff, input_vocab_size, target_vocab_size, max_len=100, dropout=0.1):
        super().__init__()
        self.max_len = max_len
        self.d_model = d_model
        self.enc_embedding = layers.Embedding(input_vocab_size, d_model)
        self.enc_pos_embedding = layers.Embedding(max_len, d_model)
        self.dec_embedding = layers.Embedding(target_vocab_size, d_model)
        self.dec_pos_embedding = layers.Embedding(max_len, d_model)
        self.enc_layers = [EncoderBlock(d_model, num_heads, dff, dropout) for _ in range(num_layers)]
        self.dec_layers = [LoU(d_model) for _ in range(num_layers)]
        self.final_layer = layers.Dense(target_vocab_size)
    def call(self, inputs, training=False):
        enc_inputs = inputs["enc_inputs"]
        dec_inputs = inputs["dec_inputs"]
        enc_pos = tf.range(tf.shape(enc_inputs)[1])[tf.newaxis, :]
        dec_pos = tf.range(tf.shape(dec_inputs)[1])[tf.newaxis, :]
        x = self.enc_embedding(enc_inputs) + self.enc_pos_embedding(enc_pos)
        for layer in self.enc_layers: x = layer(x, training=training)
        enc_out = x
        y = self.dec_embedding(dec_inputs) + self.dec_pos_embedding(dec_pos)
        for layer in self.dec_layers: y = layer(y, enc_out, training=training)
        return self.final_layer(y)