Fixing batching pipelines on single items for ChunkPipeline (#21132)

* Fixing #20783

* Update src/transformers/pipelines/base.py

* Fixing some tests.

* Fixup.

* Remove ffmpeg dep + a bit more relaxed for bigbird QA precision.

* Better dataset.

* Prevent failing on TF.

* Better condition. We can't use `can_use_iterator` since we cannot use it
directly.

src/transformers/pipelines/base.py

@@ -1072,6 +1072,14 @@ class Pipeline(_ScikitCompat):
             )
         elif is_iterable:
             return self.iterate(inputs, preprocess_params, forward_params, postprocess_params)
+        elif self.framework == "pt" and isinstance(self, ChunkPipeline):
+            return next(
+                iter(
+                    self.get_iterator(
+                        [inputs], num_workers, batch_size, preprocess_params, forward_params, postprocess_params
+                    )
+                )
+            )
         else:
             return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)
 

tests/pipelines/test_pipelines_common.py

@@ -26,6 +26,7 @@ from functools import lru_cache
 from pathlib import Path
 from unittest import skipIf
 
+import datasets
 import numpy as np
 
 from huggingface_hub import HfFolder, Repository, delete_repo, set_access_token
@@ -965,6 +966,29 @@ class CustomPipelineTest(unittest.TestCase):
             self.assertEqual(counter.head_request_count, 1)
             self.assertEqual(counter.other_request_count, 0)
 
+    @require_torch
+    def test_chunk_pipeline_batching_single_file(self):
+        # Make sure we have cached the pipeline.
+        pipe = pipeline(model="hf-internal-testing/tiny-random-Wav2Vec2ForCTC")
+        ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        audio = ds[40]["audio"]["array"]
+
+        pipe = pipeline(model="hf-internal-testing/tiny-random-Wav2Vec2ForCTC")
+        # For some reason scoping doesn't work if not using `self.`
+        self.COUNT = 0
+        forward = pipe.model.forward
+
+        def new_forward(*args, **kwargs):
+            self.COUNT += 1
+            return forward(*args, **kwargs)
+
+        pipe.model.forward = new_forward
+
+        for out in pipe(audio, return_timestamps="char", chunk_length_s=3, stride_length_s=[1, 1], batch_size=1024):
+            pass
+
+        self.assertEqual(self.COUNT, 1)
+
 
 @require_torch
 @is_staging_test

tests/pipelines/test_pipelines_question_answering.py

@@ -106,11 +106,13 @@ class QAPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
         self.assertEqual(outputs, {"answer": ANY(str), "start": ANY(int), "end": ANY(int), "score": ANY(float)})
 
         # Using batch is OK
+        if question_answerer.tokenizer.pad_token_id is None:
+            question_answerer.tokenizer.pad_token_id = question_answerer.model.config.eos_token_id
         new_outputs = question_answerer(
             question="Where was HuggingFace founded ?", context="HuggingFace was founded in Paris." * 20, batch_size=2
         )
         self.assertEqual(new_outputs, {"answer": ANY(str), "start": ANY(int), "end": ANY(int), "score": ANY(float)})
-        self.assertEqual(outputs, new_outputs)
+        self.assertEqual(nested_simplify(outputs), nested_simplify(new_outputs))
 
     @require_torch
     def test_small_model_pt(self):