Fixing support `batch_size` and `num_return_Sequences` in `text-generation` pipeline (#15318)

* Fixing support `batch_size` and `num_return_Sequences` in
`text-generation` pipeline

And `text2text-generation` too.

The bug was caused by the batch_size containing both the incoming batch
**and** the generated `num_sequences`.

The fix simply consists into splitting both of these again into
different dimensions.

* TF support.

* Odd backward compatibility script in the way.

src/transformers/pipelines/text2text_generation.py

@@ -136,7 +136,11 @@ class Text2TextGenerationPipeline(Pipeline):
         """
 
         result = super().__call__(*args, **kwargs)
-        if isinstance(args[0], list) and all(isinstance(el, str) for el in args[0]):
+        if (
+            isinstance(args[0], list)
+            and all(isinstance(el, str) for el in args[0])
+            and all(len(res) == 1 for res in result)
+        ):
             return [res[0] for res in result]
         return result
 
@@ -146,19 +150,24 @@ class Text2TextGenerationPipeline(Pipeline):
 
     def _forward(self, model_inputs, **generate_kwargs):
         if self.framework == "pt":
-            input_length = model_inputs["input_ids"].shape[-1]
+            in_b, input_length = model_inputs["input_ids"].shape
         elif self.framework == "tf":
-            input_length = tf.shape(model_inputs["input_ids"])[-1].numpy()
+            in_b, input_length = tf.shape(model_inputs["input_ids"]).numpy()
 
         generate_kwargs["min_length"] = generate_kwargs.get("min_length", self.model.config.min_length)
         generate_kwargs["max_length"] = generate_kwargs.get("max_length", self.model.config.max_length)
         self.check_inputs(input_length, generate_kwargs["min_length"], generate_kwargs["max_length"])
         output_ids = self.model.generate(**model_inputs, **generate_kwargs)
+        out_b = output_ids.shape[0]
+        if self.framework == "pt":
+            output_ids = output_ids.reshape(in_b, out_b // in_b, *output_ids.shape[1:])
+        elif self.framework == "tf":
+            output_ids = tf.reshape(output_ids, (in_b, out_b // in_b, *output_ids.shape[1:]))
         return {"output_ids": output_ids}
 
     def postprocess(self, model_outputs, return_type=ReturnType.TEXT, clean_up_tokenization_spaces=False):
         records = []
-        for output_ids in model_outputs["output_ids"]:
+        for output_ids in model_outputs["output_ids"][0]:
             if return_type == ReturnType.TENSORS:
                 record = {f"{self.return_name}_token_ids": model_outputs}
             elif return_type == ReturnType.TEXT:

src/transformers/pipelines/text_generation.py

@@ -2,10 +2,14 @@ import enum
 
 from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
 
-from ..file_utils import add_end_docstrings
+from ..file_utils import add_end_docstrings, is_tf_available
 from .base import PIPELINE_INIT_ARGS, Pipeline
 
 
+if is_tf_available():
+    import tensorflow as tf
+
+
 class ReturnType(enum.Enum):
     TENSORS = 0
     NEW_TEXT = 1
@@ -202,23 +206,29 @@ class TextGenerationPipeline(Pipeline):
         # Allow empty prompts
         if input_ids.shape[1] == 0:
             input_ids = None
+            in_b = 1
+        else:
+            in_b = input_ids.shape[0]
         prompt_text = model_inputs.pop("prompt_text")
         generated_sequence = self.model.generate(input_ids=input_ids, **generate_kwargs)  # BS x SL
+        out_b = generated_sequence.shape[0]
+        if self.framework == "pt":
+            generated_sequence = generated_sequence.reshape(in_b, out_b // in_b, *generated_sequence.shape[1:])
+        elif self.framework == "tf":
+            generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
         return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
 
     def postprocess(self, model_outputs, return_type=ReturnType.FULL_TEXT, clean_up_tokenization_spaces=True):
-        generated_sequence = model_outputs["generated_sequence"]
+        generated_sequence = model_outputs["generated_sequence"][0]
         input_ids = model_outputs["input_ids"]
         prompt_text = model_outputs["prompt_text"]
-        if self.framework == "pt" and generated_sequence is not None:
-            generated_sequence = generated_sequence.cpu()
         generated_sequence = generated_sequence.numpy().tolist()
+        records = []
+        for sequence in generated_sequence:
             if return_type == ReturnType.TENSORS:
                 record = {"generated_token_ids": generated_sequence}
             elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                 # Decode text
-            record = []
-            for sequence in generated_sequence:
                 text = self.tokenizer.decode(
                     sequence,
                     skip_special_tokens=True,
@@ -242,7 +252,7 @@ class TextGenerationPipeline(Pipeline):
                 else:
                     all_text = text[prompt_length:]
 
-                item = {"generated_text": all_text}
-                record.append(item)
+                record = {"generated_text": all_text}
+            records.append(record)
 
-        return record
+        return records

tests/test_pipelines_text2text_generation.py

@@ -40,6 +40,26 @@ class Text2TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTest
         # These are encoder decoder, they don't just append to incoming string
         self.assertFalse(outputs[0]["generated_text"].startswith("Something there"))
 
+        outputs = generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
+        self.assertEqual(
+            outputs,
+            [
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+            ],
+        )
+
+        outputs = generator(
+            ["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
+        )
+        self.assertEqual(
+            outputs,
+            [
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+            ],
+        )
+
         with self.assertRaises(ValueError):
             generator(4)
 

tests/test_pipelines_text_generation.py

@@ -113,6 +113,27 @@ class TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseM
         self.assertEqual(outputs, [{"generated_text": ANY(str)}])
         self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
 
+        outputs = text_generator(["This is great !", "Something else"], num_return_sequences=2, do_sample=True)
+        self.assertEqual(
+            outputs,
+            [
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+            ],
+        )
+
+        if text_generator.tokenizer.pad_token is not None:
+            outputs = text_generator(
+                ["This is great !", "Something else"], num_return_sequences=2, batch_size=2, do_sample=True
+            )
+            self.assertEqual(
+                outputs,
+                [
+                    [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                    [{"generated_text": ANY(str)}, {"generated_text": ANY(str)}],
+                ],
+            )
+
         # Empty prompt is slighly special
         # it requires BOS token to exist.
         # Special case for Pegasus which will always append EOS so will