Update Mo.py

Mo.py

@@ -125,57 +125,51 @@ class SwiGLU(layers.Layer):
         return self.out(x_val * tf.nn.silu(x_gate))
 
 class LoU(layers.Layer):
-    def __init__(self, d_model, clip_value=5.0, eps=1e-6, dropout_rate=0.1):
+    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.dropout_rate = dropout_rate
-        
         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.norm = layers.LayerNormalization(epsilon=1e-5, dtype='float32')
-        self.norm1 = layers.LayerNormalization(epsilon=1e-5, dtype='float32')  
+        self.norm1 = layers.LayerNormalization(epsilon=1e-5, dtype='float32')
+        
         self.glu = SwiGLU(d_model, 320)
-        self.dropout = layers.Dropout(dropout_rate)
-
-    def call(self, x, training=False):
+    def call(self, x):
         x_f32 = tf.cast(x, tf.float32)
         residual = x_f32
-        
-        x_f32 = self.norm1(x_f32)
+        x_f32 = self.norm1(x)
+
         q = self.Q(x_f32)
         k = self.K(x_f32)
         V = self.V(x_f32)
-        
-        # gating
         g_q = (tf.nn.tanh(q) + 1.0) / 2.0
         g_k = (tf.nn.tanh(k) + 1.0) / 2.0
-        
-        # cumulative score
         score = g_q * g_k
-        score = tf.cumsum(score, axis=1)
+
+        score = tf.cumsum(score, axis=1) # (B, L, D)
         
+        # 💡 수정된 부분: 현재 토큰까지의 누적합 평균으로 정규화
         seq_len = tf.shape(score)[1]
+        # [1, 2, 3, ..., L]을 D_model 차원으로 확장
         count_for_mean = tf.cast(tf.range(seq_len) + 1, score.dtype)
         count_for_mean = tf.reshape(count_for_mean, (1, seq_len, 1))
+        
+        # 누적합을 현재까지의 토큰 개수로 나누어 평균 누적합 계산 (B, L, D)
         score_mean = score / count_for_mean
         
-        # normalization + softmax-ish
+        # 정규화 분모 설정
         denom = tf.maximum(score_mean, self.eps)
         score_norm = score / denom
-        score_norm = tf.nn.softmax(score_norm, axis=1)
-        
-        # clipping + dropout
+        # -----------------------------------------------
+
         score_clipped = tf.clip_by_value(score_norm, -self.clip_value, self.clip_value)
-        score_clipped = self.dropout(score_clipped, training=training)
-        
         x_comb = score_clipped * V
+        
         out = self.norm(x_comb + residual)
         out = self.glu(out)
-        
         return tf.cast(out, x.dtype)