From 4cdb7ee51db6ec544382b75cda3685ae39c4ec8f Mon Sep 17 00:00:00 2001
From: yujun <50394665+JunnYu@users.noreply.github.com>
Date: Wed, 14 Jul 2021 05:18:54 +0800
Subject: [PATCH] fix #11724 (#11897)

---
 src/transformers/models/marian/modeling_tf_marian.py   | 7 ++++---
 src/transformers/models/pegasus/modeling_tf_pegasus.py | 7 ++++---
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/src/transformers/models/marian/modeling_tf_marian.py b/src/transformers/models/marian/modeling_tf_marian.py
index f0bfea8c549..8dddcfa6331 100644
--- a/src/transformers/models/marian/modeling_tf_marian.py
+++ b/src/transformers/models/marian/modeling_tf_marian.py
@@ -151,11 +151,12 @@ class TFMarianSinusoidalPositionalEmbedding(tf.keras.layers.Layer):
         position_enc = np.array(
             [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
         )
+        table = np.zeros_like(position_enc)
         # index 0 is all zero
-        position_enc[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
-        position_enc[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
+        table[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
+        table[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
         # convert to tensor
-        table = tf.convert_to_tensor(position_enc)
+        table = tf.convert_to_tensor(table)
         tf.stop_gradient(table)
         return table
 
diff --git a/src/transformers/models/pegasus/modeling_tf_pegasus.py b/src/transformers/models/pegasus/modeling_tf_pegasus.py
index f71878ecaee..ced69871f6f 100644
--- a/src/transformers/models/pegasus/modeling_tf_pegasus.py
+++ b/src/transformers/models/pegasus/modeling_tf_pegasus.py
@@ -152,11 +152,12 @@ class TFPegasusSinusoidalPositionalEmbedding(tf.keras.layers.Layer):
         position_enc = np.array(
             [[pos / np.power(10000, 2 * (j // 2) / dim) for j in range(dim)] for pos in range(n_pos)]
         )
+        table = np.zeros_like(position_enc)
         # index 0 is all zero
-        position_enc[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
-        position_enc[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
+        table[:, 0 : dim // 2] = np.sin(position_enc[:, 0::2])
+        table[:, dim // 2 :] = np.cos(position_enc[:, 1::2])
         # convert to tensor
-        table = tf.convert_to_tensor(position_enc)
+        table = tf.convert_to_tensor(table)
         tf.stop_gradient(table)
         return table