modify qa-trainer (#11872)

* modify qa-trainer

* fix flax model

examples/pytorch/question-answering/run_qa_no_trainer.py

@@ -692,7 +692,11 @@ def main():
             if completed_steps >= args.max_train_steps:
                 break
 
-    # Validation
+    # Evaluation
+    logger.info("***** Running Evaluation *****")
+    logger.info(f"  Num examples = {len(eval_dataset)}")
+    logger.info(f"  Batch size = {args.per_device_eval_batch_size}")
+
     all_start_logits = []
     all_end_logits = []
     for step, batch in enumerate(eval_dataloader):
@@ -725,6 +729,10 @@ def main():
 
     # Prediction
     if args.do_predict:
+        logger.info("***** Running Prediction *****")
+        logger.info(f"  Num examples = {len(predict_dataset)}")
+        logger.info(f"  Batch size = {args.per_device_eval_batch_size}")
+
         all_start_logits = []
         all_end_logits = []
         for step, batch in enumerate(predict_dataloader):

src/transformers/models/albert/modeling_albert.py

@@ -1218,8 +1218,8 @@ class AlbertForQuestionAnswering(AlbertPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/bart/modeling_bart.py

@@ -1556,8 +1556,8 @@ class BartForQuestionAnswering(BartPretrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/bert/modeling_bert.py

@@ -1801,8 +1801,8 @@ class BertForQuestionAnswering(BertPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/big_bird/modeling_big_bird.py

@@ -2983,8 +2983,8 @@ class BigBirdForQuestionAnswering(BigBirdPreTrainedModel):
             logits = logits - logits_mask * 1e6
 
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/bigbird_pegasus/modeling_bigbird_pegasus.py

@@ -2761,8 +2761,8 @@ class BigBirdPegasusForQuestionAnswering(BigBirdPegasusPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/convbert/modeling_convbert.py

@@ -1293,8 +1293,8 @@ class ConvBertForQuestionAnswering(ConvBertPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/deberta/modeling_deberta.py

@@ -1364,8 +1364,8 @@ class DebertaForQuestionAnswering(DebertaPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/deberta_v2/modeling_deberta_v2.py

@@ -1488,8 +1488,8 @@ class DebertaV2ForQuestionAnswering(DebertaV2PreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/distilbert/modeling_distilbert.py

@@ -728,8 +728,8 @@ class DistilBertForQuestionAnswering(DistilBertPreTrainedModel):
         hidden_states = self.dropout(hidden_states)  # (bs, max_query_len, dim)
         logits = self.qa_outputs(hidden_states)  # (bs, max_query_len, 2)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)  # (bs, max_query_len)
+        start_logits = start_logits.squeeze(-1).contiguous()  # (bs, max_query_len)
-        end_logits = end_logits.squeeze(-1)  # (bs, max_query_len)
+        end_logits = end_logits.squeeze(-1).contiguous()  # (bs, max_query_len)
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/dpr/modeling_dpr.py

@@ -241,8 +241,8 @@ class DPRSpanPredictor(PreTrainedModel):
         # compute logits
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
         relevance_logits = self.qa_classifier(sequence_output[:, 0, :])
 
         # resize

src/transformers/models/electra/modeling_electra.py

@@ -1318,8 +1318,8 @@ class ElectraForQuestionAnswering(ElectraPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/funnel/modeling_funnel.py

@@ -1549,8 +1549,8 @@ class FunnelForQuestionAnswering(FunnelPreTrainedModel):
 
         logits = self.qa_outputs(last_hidden_state)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/ibert/modeling_ibert.py

@@ -1319,8 +1319,8 @@ class IBertForQuestionAnswering(IBertPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/led/modeling_led.py

@@ -2585,8 +2585,8 @@ class LEDForQuestionAnswering(LEDPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/longformer/modeling_longformer.py

@@ -2017,8 +2017,8 @@ class LongformerForQuestionAnswering(LongformerPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/mbart/modeling_mbart.py

@@ -1563,8 +1563,8 @@ class MBartForQuestionAnswering(MBartPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/megatron_bert/modeling_megatron_bert.py

@@ -1794,8 +1794,8 @@ class MegatronBertForQuestionAnswering(MegatronBertPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/mobilebert/modeling_mobilebert.py

@@ -1371,8 +1371,8 @@ class MobileBertForQuestionAnswering(MobileBertPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/mpnet/modeling_mpnet.py

@@ -1023,8 +1023,8 @@ class MPNetForQuestionAnswering(MPNetPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/reformer/modeling_reformer.py

@@ -2555,8 +2555,8 @@ class ReformerForQuestionAnswering(ReformerPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/roberta/modeling_roberta.py

@@ -1472,8 +1472,8 @@ class RobertaForQuestionAnswering(RobertaPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/squeezebert/modeling_squeezebert.py

@@ -1068,8 +1068,8 @@ class SqueezeBertForQuestionAnswering(SqueezeBertPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/xlm/modeling_xlm.py

@@ -941,8 +941,8 @@ class XLMForQuestionAnsweringSimple(XLMPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None:

src/transformers/models/xlnet/modeling_xlnet.py

@@ -1862,8 +1862,8 @@ class XLNetForQuestionAnsweringSimple(XLNetPreTrainedModel):
 
         logits = self.qa_outputs(sequence_output)
         start_logits, end_logits = logits.split(1, dim=-1)
-        start_logits = start_logits.squeeze(-1)
+        start_logits = start_logits.squeeze(-1).contiguous()
-        end_logits = end_logits.squeeze(-1)
+        end_logits = end_logits.squeeze(-1).contiguous()
 
         total_loss = None
         if start_positions is not None and end_positions is not None: