fix conflicts

src/transformers/modeling_bart.py

@@ -16,6 +16,7 @@
 import logging
 import math
 import random
+import ipdb
 from typing import Dict, List, Optional, Tuple
 
 import torch
@@ -943,7 +944,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         return outputs
 
     @staticmethod
-    def prepare_inputs_for_generation(input_ids, past, decoder_input_ids, attention_mask):
+    def prepare_inputs_for_generation_1(input_ids, past, decoder_input_ids, attention_mask):
         if past is None:  # first step
             encoder_outputs, decoder_cached_states = None, None
         else:
@@ -954,7 +955,21 @@ class BartForConditionalGeneration(PretrainedBartModel):
             "decoder_input_ids": decoder_input_ids,
             "encoder_outputs": encoder_outputs,
             "attention_mask": attention_mask,
-            # "decoder_attention_mask": decoder_attention_mask,
+        }
+
+    @staticmethod
+    def prepare_inputs_for_generation(decoder_input_ids, past, encoder_inputs):
+        if past is None:  # first step
+            encoder_outputs, decoder_cached_states = None, None
+        else:
+            encoder_outputs, decoder_cached_states = past
+
+        input_ids = encoder_inputs
+        return {
+            "input_ids": input_ids,  # ignored after first pass
+            "encoder_outputs": encoder_outputs,
+            "decoder_cached_states": decoder_cached_states,
+            "decoder_input_ids": decoder_input_ids,
         }
 
     @staticmethod
@@ -979,7 +994,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         return self.lm_head
 
     @torch.no_grad()
-    def generate(
+    def generate_1(
         self,
         input_ids,
         attention_mask=None,
@@ -1099,7 +1114,7 @@ class BartForConditionalGeneration(PretrainedBartModel):
         self.model.decoder.generation_mode = True  # tells decoder not to use causal mask
         for step in range(max_length + 1):
             decoder_input_ids = prev_output_tokens.clone()
-            model_inputs = self.prepare_inputs_for_generation(
+            model_inputs = self.prepare_inputs_for_generation_1(
                 input_ids, decoder_cache, decoder_input_ids, attention_mask,
             )
             outputs = self(**model_inputs)