Reuse input as output of reshape_op in Transformer

fluid/neural_machine_translation/transformer/model.py

@@ -80,7 +80,7 @@ def multi_head_attention(queries,
         # The value 0 in shape attr means copying the corresponding dimension
         # size of the input as the output dimension size.
         reshaped = layers.reshape(
-            x=x, shape=[0, 0, n_head, hidden_size // n_head])
+            x=x, shape=[0, 0, n_head, hidden_size // n_head], inplace=True)
 
         # permuate the dimensions into:
         # [batch_size, n_head, max_sequence_len, hidden_size_per_head]
@@ -99,7 +99,9 @@ def multi_head_attention(queries,
         # The value 0 in shape attr means copying the corresponding dimension
         # size of the input as the output dimension size.
         return layers.reshape(
-            x=trans_x, shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]])
+            x=trans_x,
+            shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
+            inplace=True)
 
     def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate):
         """
@@ -637,7 +639,8 @@ def wrap_decoder(trg_vocab_size,
         postprocess_cmd,
         caches=caches)
     # Reshape to 2D tensor to use GEMM instead of BatchedGEMM
-    dec_output = layers.reshape(dec_output, shape=[-1, dec_output.shape[-1]])
+    dec_output = layers.reshape(
+        dec_output, shape=[-1, dec_output.shape[-1]], inplace=True)
     if weight_sharing:
         predict = layers.matmul(
             x=dec_output,