[GPT2] Propose fix for #21080 (#21853)

* Make sure position ids are masked

* test that padded input produce the same results

* fix failing tests

* fixup

* fix batch test

src/transformers/models/decision_transformer/modeling_decision_transformer.py

@@ -553,7 +553,14 @@ class DecisionTransformerGPT2Model(DecisionTransformerGPT2PreTrainedModel):
             past_key_values = tuple([None] * len(self.h))
         else:
             past_length = past_key_values[0][0].size(-2)
-        if position_ids is None:
+
+        if attention_mask is not None and len(attention_mask.shape) == 2 and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_length > 0:
+                position_ids = position_ids[:, past_length : input_shape[-1] + past_length :]
+        elif position_ids is None:
             position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
             position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
 

src/transformers/models/gpt2/modeling_gpt2.py

@@ -797,7 +797,14 @@ class GPT2Model(GPT2PreTrainedModel):
             past_key_values = tuple([None] * len(self.h))
         else:
             past_length = past_key_values[0][0].size(-2)
-        if position_ids is None:
+
+        if attention_mask is not None and len(attention_mask.shape) == 2 and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_length > 0:
+                position_ids = position_ids[:, past_length : input_shape[-1] + past_length :]
+        elif position_ids is None:
             position_ids = torch.arange(past_length, input_shape[-1] + past_length, dtype=torch.long, device=device)
             position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
 

tests/models/gpt2/test_modeling_gpt2.py

@@ -590,6 +590,27 @@ class GPT2ModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
         self.assertTrue(batch_out_sentence_tt != batch_out_sentence)  # token_type_ids should change output
         self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
 
+    @slow
+    def test_batch_forward(self):
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+        tokenizer.padding_side = "left"
+
+        # This tokenizer has no pad token, so we have to set it in some way
+        # Define PAD Token = EOS Token = 50256
+        tokenizer.pad_token = tokenizer.eos_token
+
+        model = GPT2LMHeadModel.from_pretrained("gpt2", pad_token_id=tokenizer.eos_token_id)
+        sentences = ["Hello, my dog is a little bit of a mess. I'm not sure if he's"]
+        inputs = tokenizer(sentences, padding=True, return_tensors="pt")
+        logits = model(**inputs).logits[:, -1, :]
+        indexes = torch.argmax(logits).item()
+
+        inputs_padded = tokenizer(sentences, padding="max_length", max_length=30, return_tensors="pt")
+        logits_padded = model(**inputs_padded).logits[:, -1, :]
+        indexes_padded = torch.argmax(logits_padded).item()
+
+        self.assertTrue(indexes == indexes_padded)
+
     @slow
     def test_batch_generation_2heads(self):
         model = GPT2DoubleHeadsModel.from_pretrained("gpt2")