Add chat templating support for KeyDataset in text-generation pipeline (#30558)

* added chat templating support for keydataset in generation pipeline

* fixed and improved test

* fix formatting test failures

* Fix tests

* Fix tests

src/transformers/pipelines/text_generation.py

@@ -8,6 +8,7 @@ from .base import Pipeline, build_pipeline_init_args
 
 if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
+    from .pt_utils import KeyDataset
 
 if is_tf_available():
     import tensorflow as tf
@@ -243,7 +244,9 @@ class TextGenerationPipeline(Pipeline):
             - **generated_token_ids** (`torch.Tensor` or `tf.Tensor`, present when `return_tensors=True`) -- The token
               ids of the generated text.
         """
-        if isinstance(text_inputs, (list, tuple)) and isinstance(text_inputs[0], (list, tuple, dict)):
+        if isinstance(
+            text_inputs, (list, tuple, KeyDataset) if is_torch_available() else (list, tuple)
+        ) and isinstance(text_inputs[0], (list, tuple, dict)):
             # We have one or more prompts in list-of-dicts format, so this is chat mode
             if isinstance(text_inputs[0], dict):
                 return super().__call__(Chat(text_inputs), **kwargs)
@@ -380,7 +383,8 @@ class TextGenerationPipeline(Pipeline):
                     if isinstance(prompt_text, str):
                         all_text = prompt_text + all_text
                     elif isinstance(prompt_text, Chat):
-                        all_text = prompt_text.messages + [{"role": "assistant", "content": all_text}]
+                        # Explicit list parsing is necessary for parsing chat datasets
+                        all_text = list(prompt_text.messages) + [{"role": "assistant", "content": all_text}]
 
                 record = {"generated_text": all_text}
             records.append(record)

tests/pipelines/test_pipelines_text_generation.py

@@ -177,6 +177,48 @@ class TextGenerationPipelineTests(unittest.TestCase):
             ],
         )
 
+    @require_torch
+    def test_small_chat_model_with_dataset_pt(self):
+        from torch.utils.data import Dataset
+
+        from transformers.pipelines.pt_utils import KeyDataset
+
+        class MyDataset(Dataset):
+            data = [
+                [
+                    {"role": "system", "content": "This is a system message."},
+                    {"role": "user", "content": "This is a test"},
+                    {"role": "assistant", "content": "This is a reply"},
+                ],
+            ]
+
+            def __len__(self):
+                return 1
+
+            def __getitem__(self, i):
+                return {"text": self.data[i]}
+
+        text_generator = pipeline(
+            task="text-generation", model="rocketknight1/tiny-gpt2-with-chatml-template", framework="pt"
+        )
+
+        dataset = MyDataset()
+        key_dataset = KeyDataset(dataset, "text")
+
+        for outputs in text_generator(key_dataset, do_sample=False, max_new_tokens=10):
+            expected_chat = dataset.data[0] + [
+                {
+                    "role": "assistant",
+                    "content": " factors factors factors factors factors factors factors factors factors factors",
+                }
+            ]
+            self.assertEqual(
+                outputs,
+                [
+                    {"generated_text": expected_chat},
+                ],
+            )
+
     @require_tf
     def test_small_model_tf(self):
         text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="tf")