Update Inference.py

Inference.py

@@ -178,11 +178,7 @@ class LoU(layers.Layer):
         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.norm = layers.LayerNormalization(epsilon=1e-5, dtype='float32')
+        self.mha = layers.MultiHeadAttention(8, 20)
         self.norm1 = layers.LayerNormalization(epsilon=1e-5, dtype='float32')
         
         self.glu = SwiGLU(d_model, 320)
@@ -193,31 +189,7 @@ class LoU(layers.Layer):
         residual = x_f32
         x_f32 = self.norm1(x)
 
-        q = self.Q(x_f32)
-        k = self.K(x_f32)
-        V = self.V(x_f32)
-        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
-
-        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
-        
-        # 정규화 분모 설정
-        denom = tf.maximum(score_mean, self.eps)
-        score_norm = score / denom
-        # -----------------------------------------------
-
-        score_clipped = tf.clip_by_value(score_norm, -self.clip_value, self.clip_value)
-        x_comb = score_clipped * V
+        x_comb = self.mha(x, x, x, use_causal_mask=True)
         
         out = self.norm(x_comb + residual)
         out = self.cross(out, z)