Update inference widget from adapter-transformers to Adapters

docker_images/adapter_transformers/app/main.py

@@ -7,7 +7,9 @@
 from app.pipelines import (
     Pipeline,
     QuestionAnsweringPipeline,
+    SummarizationPipeline,
     TextClassificationPipeline,
+    TextGenerationPipeline,
     TokenClassificationPipeline,
 )
 from starlette.applications import Starlette
@@ -39,7 +41,9 @@
 # directories. Implement directly within the directories.
 ALLOWED_TASKS: Dict[str, Type[Pipeline]] = {
     "question-answering": QuestionAnsweringPipeline,
+    "summarization": SummarizationPipeline,
     "text-classification": TextClassificationPipeline,
+    "text-generation": TextGenerationPipeline,
     "token-classification": TokenClassificationPipeline,
     # IMPLEMENT_THIS: Add your implemented tasks here !
 }

docker_images/adapter_transformers/app/pipelines/__init__.py

@@ -1,5 +1,7 @@
 from app.pipelines.base import Pipeline, PipelineException  # isort:skip
 
 from app.pipelines.question_answering import QuestionAnsweringPipeline
+from app.pipelines.summarization import SummarizationPipeline
 from app.pipelines.text_classification import TextClassificationPipeline
+from app.pipelines.text_generation import TextGenerationPipeline
 from app.pipelines.token_classification import TokenClassificationPipeline

docker_images/adapter_transformers/app/pipelines/base.py

@@ -1,7 +1,9 @@
 from abc import ABC, abstractmethod
 from typing import Any
 
-from transformers import AutoModelWithHeads, AutoTokenizer, get_adapter_info
+from adapters import AutoAdapterModel, get_adapter_info
+from transformers import AutoTokenizer
+from transformers.pipelines.base import logger
 
 
 class Pipeline(ABC):
@@ -20,8 +22,16 @@ def _load_pipeline_instance(pipeline_class, adapter_id):
             raise ValueError(f"Adapter with id '{adapter_id}' not available.")
 
         tokenizer = AutoTokenizer.from_pretrained(adapter_info.model_name)
-        model = AutoModelWithHeads.from_pretrained(adapter_info.model_name)
+        model = AutoAdapterModel.from_pretrained(adapter_info.model_name)
         model.load_adapter(adapter_id, source="hf", set_active=True)
+
+        # Transformers incorrectly logs an error because class name is not known. Filter this out.
+        logger.addFilter(
+            lambda record: not record.getMessage().startswith(
+                f"The model '{model.__class__.__name__}' is not supported"
+            )
+        )
+
         return pipeline_class(model=model, tokenizer=tokenizer)
 
 

docker_images/adapter_transformers/app/pipelines/summarization.py

@@ -0,0 +1,20 @@
+from typing import Dict, List
+
+from app.pipelines import Pipeline
+from transformers import SummarizationPipeline as TransformersSummarizationPipeline
+
+
+class SummarizationPipeline(Pipeline):
+    def __init__(self, adapter_id: str):
+        self.pipeline = self._load_pipeline_instance(
+            TransformersSummarizationPipeline, adapter_id
+        )
+
+    def __call__(self, inputs: str) -> List[Dict[str, str]]:
+        """
+        Args:
+            inputs (:obj:`str`): a string to be summarized
+        Return:
+            A :obj:`list` of :obj:`dict` in the form of {"summary_text": "The string after summarization"}
+        """
+        return self.pipeline(inputs, truncation=True)

docker_images/adapter_transformers/app/pipelines/text_generation.py

@@ -0,0 +1,21 @@
+from typing import Dict, List
+
+from app.pipelines import Pipeline
+from transformers import TextGenerationPipeline as TransformersTextGenerationPipeline
+
+
+class TextGenerationPipeline(Pipeline):
+    def __init__(self, adapter_id: str):
+        self.pipeline = self._load_pipeline_instance(
+            TransformersTextGenerationPipeline, adapter_id
+        )
+
+    def __call__(self, inputs: str) -> List[Dict[str, str]]:
+        """
+        Args:
+            inputs (:obj:`str`):
+                The input text
+        Return:
+            A :obj:`list`:. The list contains a single item that is a dict {"text": the model output}
+        """
+        return self.pipeline(inputs, truncation=True)

docker_images/adapter_transformers/requirements.txt

@@ -1,5 +1,5 @@
-starlette==0.27.0
-api-inference-community==0.0.23
-torch==1.13.1
-adapter-transformers==2.2.0
-huggingface_hub==0.5.1
+starlette==0.37.2
+api-inference-community==0.0.32
+torch==2.3.0
+adapters==0.2.1
+huggingface_hub==0.23.0

docker_images/adapter_transformers/tests/test_api.py

@@ -9,8 +9,10 @@
 # Tests do not check the actual values of the model output, so small dummy
 # models are recommended for faster tests.
 TESTABLE_MODELS: Dict[str, str] = {
-    "question-answering": "calpt/adapter-bert-base-squad1",
+    "question-answering": "AdapterHub/roberta-base-pf-squad",
+    "summarization": "AdapterHub/facebook-bart-large_sum_xsum_pfeiffer",
     "text-classification": "AdapterHub/roberta-base-pf-sick",
+    "text-generation": "AdapterHub/gpt2_lm_poem_pfeiffer",
     "token-classification": "AdapterHub/roberta-base-pf-conll2003",
 }
 

docker_images/adapter_transformers/tests/test_api_summarization.py

@@ -0,0 +1,75 @@
+import json
+import os
+from unittest import TestCase, skipIf
+
+from app.main import ALLOWED_TASKS
+from starlette.testclient import TestClient
+from tests.test_api import TESTABLE_MODELS
+
+
+@skipIf(
+    "summarization" not in ALLOWED_TASKS,
+    "summarization not implemented",
+)
+class SummarizationTestCase(TestCase):
+    def setUp(self):
+        model_id = TESTABLE_MODELS["summarization"]
+        self.old_model_id = os.getenv("MODEL_ID")
+        self.old_task = os.getenv("TASK")
+        os.environ["MODEL_ID"] = model_id
+        os.environ["TASK"] = "summarization"
+        from app.main import app
+
+        self.app = app
+
+    @classmethod
+    def setUpClass(cls):
+        from app.main import get_pipeline
+
+        get_pipeline.cache_clear()
+
+    def tearDown(self):
+        if self.old_model_id is not None:
+            os.environ["MODEL_ID"] = self.old_model_id
+        else:
+            del os.environ["MODEL_ID"]
+        if self.old_task is not None:
+            os.environ["TASK"] = self.old_task
+        else:
+            del os.environ["TASK"]
+
+    def test_simple(self):
+        inputs = "The weather is nice today."
+
+        with TestClient(self.app) as client:
+            response = client.post("/", json={"inputs": inputs})
+
+        self.assertEqual(
+            response.status_code,
+            200,
+        )
+        content = json.loads(response.content)
+        self.assertEqual(type(content), list)
+        self.assertEqual(type(content[0]["summary_text"]), str)
+
+        with TestClient(self.app) as client:
+            response = client.post("/", json=inputs)
+
+        self.assertEqual(
+            response.status_code,
+            200,
+        )
+        content = json.loads(response.content)
+        self.assertEqual(type(content), list)
+        self.assertEqual(type(content[0]["summary_text"]), str)
+
+    def test_malformed_question(self):
+        with TestClient(self.app) as client:
+            response = client.post("/", data=b"\xc3\x28")
+
+        self.assertEqual(
+            response.status_code,
+            400,
+        )
+        content = json.loads(response.content)
+        self.assertEqual(set(content.keys()), {"error"})

docker_images/adapter_transformers/tests/test_api_text_generation.py

@@ -0,0 +1,74 @@
+import json
+import os
+from unittest import TestCase, skipIf
+
+from app.main import ALLOWED_TASKS
+from starlette.testclient import TestClient
+from tests.test_api import TESTABLE_MODELS
+
+
+@skipIf(
+    "text-generation" not in ALLOWED_TASKS,
+    "text-generation not implemented",
+)
+class TextGenerationTestCase(TestCase):
+    def setUp(self):
+        model_id = TESTABLE_MODELS["text-generation"]
+        self.old_model_id = os.getenv("MODEL_ID")
+        self.old_task = os.getenv("TASK")
+        os.environ["MODEL_ID"] = model_id
+        os.environ["TASK"] = "text-generation"
+        from app.main import app
+
+        self.app = app
+
+    @classmethod
+    def setUpClass(cls):
+        from app.main import get_pipeline
+
+        get_pipeline.cache_clear()
+
+    def tearDown(self):
+        if self.old_model_id is not None:
+            os.environ["MODEL_ID"] = self.old_model_id
+        else:
+            del os.environ["MODEL_ID"]
+        if self.old_task is not None:
+            os.environ["TASK"] = self.old_task
+        else:
+            del os.environ["TASK"]
+
+    def test_simple(self):
+        inputs = "The weather is nice today."
+
+        with TestClient(self.app) as client:
+            response = client.post("/", json={"inputs": inputs})
+
+        self.assertEqual(
+            response.status_code,
+            200,
+        )
+        content = json.loads(response.content)
+        self.assertEqual(type(content), list)
+
+        with TestClient(self.app) as client:
+            response = client.post("/", json=inputs)
+
+        self.assertEqual(
+            response.status_code,
+            200,
+        )
+        content = json.loads(response.content)
+        self.assertEqual(type(content), list)
+        self.assertEqual(type(content[0]["generated_text"]), str)
+
+    def test_malformed_question(self):
+        with TestClient(self.app) as client:
+            response = client.post("/", data=b"\xc3\x28")
+
+        self.assertEqual(
+            response.status_code,
+            400,
+        )
+        content = json.loads(response.content)
+        self.assertEqual(set(content.keys()), {"error"})

tests/test_dockers.py

@@ -136,7 +136,13 @@ def test_adapter_transformers(self):
         self.framework_docker_test(
             "adapter_transformers",
             "question-answering",
-            "calpt/adapter-bert-base-squad1",
+            "AdapterHub/roberta-base-pf-squad",
+        )
+
+        self.framework_docker_test(
+            "adapter_transformers",
+            "summarization",
+            "AdapterHub/facebook-bart-large_sum_xsum_pfeiffer",
         )
 
         self.framework_docker_test(
@@ -145,6 +151,12 @@ def test_adapter_transformers(self):
             "AdapterHub/roberta-base-pf-sick",
         )
 
+        self.framework_docker_test(
+            "adapter_transformers",
+            "text-generation",
+            "AdapterHub/gpt2_lm_poem_pfeiffer",
+        )
+
         self.framework_docker_test(
             "adapter_transformers",
             "token-classification",