Moving text-generation pipeline to new testing framework. (#13285)

* Moving `text-generation` pipeline to new testing framework.

* Keep check_model_type but log instead of raise Exception.

* warning -> error.

src/transformers/pipelines/base.py

@@ -735,10 +735,8 @@ class Pipeline(_ScikitCompat):
                     supported_models_names.append(model.__name__)
             supported_models = supported_models_names
         if self.model.__class__.__name__ not in supported_models:
-            raise PipelineException(
-                self.task,
-                self.model.base_model_prefix,
-                f"The model '{self.model.__class__.__name__}' is not supported for {self.task}. Supported models are {supported_models}",
+            logger.error(
+                f"The model '{self.model.__class__.__name__}' is not supported for {self.task}. Supported models are {supported_models}."
             )
 
     def _parse_and_tokenize(

src/transformers/pipelines/text_generation.py

@@ -1,3 +1,5 @@
+from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
+
 from ..file_utils import add_end_docstrings
 from .base import PIPELINE_INIT_ARGS, Pipeline
 
@@ -30,25 +32,12 @@ class TextGenerationPipeline(Pipeline):
     begging for his blessing. <eod> </s> <eos>
     """
 
-    ALLOWED_MODELS = [
-        "XLNetLMHeadModel",
-        "TransfoXLLMHeadModel",
-        "ReformerModelWithLMHead",
-        "GPT2LMHeadModel",
-        "GPTNeoForCausalLM",
-        "OpenAIGPTLMHeadModel",
-        "CTRLLMHeadModel",
-        "TFXLNetLMHeadModel",
-        "TFTransfoXLLMHeadModel",
-        "TFGPT2LMHeadModel",
-        "TFOpenAIGPTLMHeadModel",
-        "TFCTRLLMHeadModel",
-    ]
-
     def __init__(self, *args, return_full_text=True, **kwargs):
         super().__init__(*args, **kwargs)
+        self.check_model_type(
+            TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING
+        )
 
-        self.check_model_type(self.ALLOWED_MODELS)
         self.return_full_text = return_full_text
 
     # overriding _parse_and_tokenize to allow for unusual language-modeling tokenizer arguments
@@ -124,6 +113,9 @@ class TextGenerationPipeline(Pipeline):
                     prefix_length = prefix_inputs["input_ids"].shape[-1]
                     if generate_kwargs.get("max_length", None) is not None:
                         generate_kwargs["max_length"] += prefix_length
+                    else:
+                        generate_kwargs["max_length"] = self.model.config.max_length + prefix_length
+
                     if generate_kwargs.get("min_length", None) is not None:
                         generate_kwargs["min_length"] += prefix_length
 

tests/test_pipelines_text_generation.py

@@ -14,49 +14,95 @@
 
 import unittest
 
-from transformers import pipeline
-from transformers.testing_utils import require_torch
+from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TextGenerationPipeline, pipeline
+from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
 
-from .test_pipelines_common import MonoInputPipelineCommonMixin
+from .test_pipelines_common import ANY, PipelineTestCaseMeta
 
 
-class TextGenerationPipelineTests(MonoInputPipelineCommonMixin, unittest.TestCase):
-    pipeline_task = "text-generation"
-    pipeline_running_kwargs = {"prefix": "This is "}
-    small_models = ["sshleifer/tiny-ctrl"]  # Models tested without the @slow decorator
-    large_models = []  # Models tested with the @slow decorator
-
-    def test_simple_generation(self):
-        text_generator = pipeline(task="text-generation", model=self.small_models[0])
-        # text-generation is non-deterministic by nature, we can't fully test the output
-
-        outputs = text_generator("This is a test")
-
-        self.assertEqual(len(outputs), 1)
-        self.assertEqual(list(outputs[0].keys()), ["generated_text"])
-        self.assertEqual(type(outputs[0]["generated_text"]), str)
-
-        outputs = text_generator(["This is a test", "This is a second test"])
-        self.assertEqual(len(outputs[0]), 1)
-        self.assertEqual(list(outputs[0][0].keys()), ["generated_text"])
-        self.assertEqual(type(outputs[0][0]["generated_text"]), str)
-        self.assertEqual(list(outputs[1][0].keys()), ["generated_text"])
-        self.assertEqual(type(outputs[1][0]["generated_text"]), str)
+@is_pipeline_test
+class TextGenerationPipelineTests(unittest.TestCase, metaclass=PipelineTestCaseMeta):
+    model_mapping = MODEL_FOR_CAUSAL_LM_MAPPING
+    tf_model_mapping = TF_MODEL_FOR_CAUSAL_LM_MAPPING
 
     @require_torch
-    def test_generation_output_style(self):
-        text_generator = pipeline(task="text-generation", model=self.small_models[0])
-        # text-generation is non-deterministic by nature, we can't fully test the output
+    def test_small_model_pt(self):
+        text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="pt")
+        # Using `do_sample=False` to force deterministic output
+        outputs = text_generator("This is a test", do_sample=False)
+        self.assertEqual(
+            outputs,
+            [
+                {
+                    "generated_text": "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope. oscope. FiliFili@@"
+                }
+            ],
+        )
 
+        outputs = text_generator(["This is a test", "This is a second test"])
+        self.assertEqual(
+            outputs,
+            [
+                [
+                    {
+                        "generated_text": "This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope. oscope. FiliFili@@"
+                    }
+                ],
+                [
+                    {
+                        "generated_text": "This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope. oscope. FiliFili@@"
+                    }
+                ],
+            ],
+        )
+
+    @require_tf
+    def test_small_model_tf(self):
+        text_generator = pipeline(task="text-generation", model="sshleifer/tiny-ctrl", framework="tf")
+
+        # Using `do_sample=False` to force deterministic output
+        outputs = text_generator("This is a test", do_sample=False)
+        self.assertEqual(
+            outputs,
+            [
+                {
+                    "generated_text": "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵 please,"
+                }
+            ],
+        )
+
+        outputs = text_generator(["This is a test", "This is a second test"], do_sample=False)
+        self.assertEqual(
+            outputs,
+            [
+                [
+                    {
+                        "generated_text": "This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵 please,"
+                    }
+                ],
+                [
+                    {
+                        "generated_text": "This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵 please,"
+                    }
+                ],
+            ],
+        )
+
+    def run_pipeline_test(self, model, tokenizer, feature_extractor):
+        text_generator = TextGenerationPipeline(model=model, tokenizer=tokenizer)
         outputs = text_generator("This is a test")
-        self.assertIn("This is a test", outputs[0]["generated_text"])
+        self.assertEqual(outputs, [{"generated_text": ANY(str)}])
+        self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))
 
         outputs = text_generator("This is a test", return_full_text=False)
+        self.assertEqual(outputs, [{"generated_text": ANY(str)}])
         self.assertNotIn("This is a test", outputs[0]["generated_text"])
 
-        text_generator = pipeline(task="text-generation", model=self.small_models[0], return_full_text=False)
+        text_generator = pipeline(task="text-generation", model=model, tokenizer=tokenizer, return_full_text=False)
         outputs = text_generator("This is a test")
+        self.assertEqual(outputs, [{"generated_text": ANY(str)}])
         self.assertNotIn("This is a test", outputs[0]["generated_text"])
 
         outputs = text_generator("This is a test", return_full_text=True)
-        self.assertIn("This is a test", outputs[0]["generated_text"])
+        self.assertEqual(outputs, [{"generated_text": ANY(str)}])
+        self.assertTrue(outputs[0]["generated_text"].startswith("This is a test"))