Add batch inferencing support for GPT2LMHeadModel

src/transformers/modeling_gpt2.py

@@ -701,10 +701,23 @@ def prepare_inputs_for_generation(self, input_ids, past=None, **kwargs):
         if past:
             input_ids = input_ids[:, -1].unsqueeze(-1)
 
+        attention_mask = kwargs.get("attention_mask", None)
+        position_ids = kwargs.get("position_ids", None)
+
+        if attention_mask is not None and position_ids is None:
+            # create postion_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:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+        else:
+            position_ids = None
         return {
             "input_ids": input_ids,
             "past_key_values": past,
             "use_cache": kwargs.get("use_cache"),
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
         }
 
     @add_start_docstrings_to_callable(GPT2_INPUTS_DOCSTRING)

tests/test_modeling_gpt2.py

@@ -32,6 +32,7 @@
         GPT2DoubleHeadsModel,
         GPT2LMHeadModel,
         GPT2Model,
+        GPT2Tokenizer,
     )
 
 
@@ -405,6 +406,50 @@ def test_gpt2_gradient_checkpointing(self):
         config_and_inputs = self.model_tester.prepare_config_and_inputs(gradient_checkpointing=True)
         self.model_tester.create_and_check_forward_and_backwards(*config_and_inputs)
 
+    @slow
+    def test_batch_generation(self):
+        model = GPT2LMHeadModel.from_pretrained("gpt2")
+        model.to(torch_device)
+        tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+        tokenizer.padding_side = "left"
+
+        # Define PAD Token = EOS Token = 50256
+        tokenizer.pad_token = tokenizer.eos_token
+        model.config.pad_token_id = model.config.eos_token_id
+
+        # use different length sentences to test batching
+        sentences = [
+            "Hello, my dog is a little",
+            "Today, I",
+        ]
+
+        inputs = tokenizer(sentences, return_tensors="pt", padding=True)
+
+        torch.manual_seed(0)
+        outputs = model.generate(
+            input_ids=inputs["input_ids"].to(torch_device),
+            attention_mask=inputs["attention_mask"].to(torch_device),
+        )
+
+        inputs_non_padded = tokenizer(sentences[0], return_tensors="pt").input_ids.to(torch_device)
+        output_non_padded = model.generate(input_ids=inputs_non_padded)
+
+        num_paddings = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
+        inputs_padded = tokenizer(sentences[1], return_tensors="pt").input_ids.to(torch_device)
+        output_padded = model.generate(input_ids=inputs_padded, max_length=model.config.max_length - num_paddings)
+
+        batch_out_sentence = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+        non_padded_sentence = tokenizer.decode(output_non_padded[0], skip_special_tokens=True)
+        padded_sentence = tokenizer.decode(output_padded[0], skip_special_tokens=True)
+
+        expected_output_sentence = [
+            "Hello, my dog is a little bit of a mess. I'm not sure if he's going",
+            "Today, I'm going to be doing a lot of research on this. I",
+        ]
+        self.assertListEqual(expected_output_sentence, batch_out_sentence)
+        self.assertListEqual(expected_output_sentence, [non_padded_sentence, padded_sentence])
+
     @slow
     def test_model_from_pretrained(self):
         for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: