Refactor vllm sm endpoint test to use pytest (#5521)

* Refactor vllm sm endpoint test to use pytest

Signed-off-by: Sally Seok <sallyseo@amazon.com>

* empty commit for test

Signed-off-by: Sally Seok <sallyseo@amazon.com>

* fix import

Signed-off-by: Sally Seok <sallyseo@amazon.com>

* empty commit for test

Signed-off-by: Sally Seok <sallyseo@amazon.com>

* revert sglang

Signed-off-by: Sally Seok <sallyseo@amazon.com>

* skip audio test

Signed-off-by: Sally Seok <sallyseo@amazon.com>

* add docs link comment

Signed-off-by: Sally Seok <sallyseo@amazon.com>

---------

Signed-off-by: Sally Seok <sallyseo@amazon.com>
Co-authored-by: Sirut Buasai <73297481+sirutBuasai@users.noreply.github.com>

Author: sallyseok

.github/workflows/pr-vllm.yml

@@ -939,4 +939,5 @@ jobs:
       - name: Run sagemaker endpoint test
         run: |
           source .venv/bin/activate
-          python test/vllm/sagemaker/test_sm_endpoint.py --image-uri ${{ needs.set-sagemaker-test-environment.outputs.image-uri }} --endpoint-name test-sm-vllm-endpoint-${{ github.sha }}
+          cd test/
+          python3 -m pytest -vs -rA --image-uri ${{ needs.set-sagemaker-test-environment.outputs.image-uri }} vllm/sagemaker

test/sglang/sagemaker/test_sm_endpoint.py

@@ -17,11 +17,11 @@
 from pprint import pformat
 
 import pytest
-from botocore.exceptions import ClientError
 from sagemaker.model import Model
 from sagemaker.predictor import Predictor
 from sagemaker.serializers import JSONSerializer
-from test_utils import clean_string, random_suffix_name, wait_for_status
+from test_utils import clean_string, get_hf_token, random_suffix_name, wait_for_status
+from test_utils.constants import INFERENCE_AMI_VERSION, SAGEMAKER_ROLE
 
 # To enable debugging, change logging.INFO to logging.DEBUG
 LOGGER = logging.getLogger(__name__)
@@ -38,23 +38,6 @@ def get_endpoint_status(sagemaker_client, endpoint_name):
     return response["EndpointStatus"]
 
 
-def get_hf_token(aws_session):
-    LOGGER.info("Retrieving HuggingFace token from AWS Secrets Manager...")
-    token_path = "test/hf_token"
-
-    try:
-        get_secret_value_response = aws_session.secretsmanager.get_secret_value(SecretId=token_path)
-        LOGGER.info("Successfully retrieved HuggingFace token")
-    except ClientError as e:
-        LOGGER.error(f"Failed to retrieve HuggingFace token: {e}")
-        raise e
-
-    # Do not print secrets token in logs
-    response = json.loads(get_secret_value_response["SecretString"])
-    token = response.get("HF_TOKEN")
-    return token
-
-
 @pytest.fixture(scope="function")
 def model_id(request):
     # Return the model_id given by the test parameter
@@ -63,14 +46,13 @@ def model_id(request):
 
 @pytest.fixture(scope="function")
 def instance_type(request):
-    # Return the model_id given by the test parameter
+    # Return the instance_type given by the test parameter
     return request.param
 
 
 @pytest.fixture(scope="function")
 def model_package(aws_session, image_uri, model_id):
     sagemaker_client = aws_session.sagemaker
-    sagemaker_role = aws_session.iam_resource.Role("SageMakerRole").arn
     cleaned_id = clean_string(model_id.split("/")[1], "_./")
     model_name = random_suffix_name(f"sglang-{cleaned_id}-model-package", 50)
 
@@ -82,7 +64,7 @@ def model_package(aws_session, image_uri, model_id):
     model = Model(
         name=model_name,
         image_uri=image_uri,
-        role=sagemaker_role,
+        role=SAGEMAKER_ROLE,
         predictor_cls=Predictor,
         env={
             "SM_SGLANG_MODEL_PATH": model_id,
@@ -111,7 +93,7 @@ def model_endpoint(aws_session, model_package, instance_type):
         instance_type=instance_type,
         initial_instance_count=1,
         endpoint_name=endpoint_name,
-        inference_ami_version="al2-ami-sagemaker-inference-gpu-3-1",
+        inference_ami_version=INFERENCE_AMI_VERSION,
         serializer=JSONSerializer(),
         wait=True,
     )

test/test_utils/__init__.py

@@ -16,13 +16,18 @@
 When necessary, use docstrings to explain the functions' mechanisms.
 """
 
+import json
 import logging
 import random
 import string
 import time
 from collections.abc import Callable
 from typing import Any
 
+from botocore.exceptions import ClientError
+
+from .aws import AWSSessionManager
+
 LOGGER = logging.getLogger(__name__)
 LOGGER.setLevel(logging.INFO)
 
@@ -58,3 +63,20 @@ def wait_for_status(
 
     LOGGER.error(f"Wait for status: {expected_status} timed out. Actual status: {actual_status}")
     return False
+
+
+def get_hf_token(aws_session: AWSSessionManager) -> str:
+    LOGGER.info("Retrieving HuggingFace token from AWS Secrets Manager...")
+    token_path = "test/hf_token"
+
+    try:
+        get_secret_value_response = aws_session.secretsmanager.get_secret_value(SecretId=token_path)
+        LOGGER.info("Successfully retrieved HuggingFace token")
+    except ClientError as e:
+        LOGGER.error(f"Failed to retrieve HuggingFace token: {e}")
+        raise e
+
+    # Do not print secrets token in logs
+    response = json.loads(get_secret_value_response["SecretString"])
+    token = response.get("HF_TOKEN")
+    return token

test/test_utils/constants.py

@@ -1 +1,4 @@
 DEFAULT_REGION = "us-west-2"
+SAGEMAKER_ROLE = "SageMakerRole"
+# https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ProductionVariant.html
+INFERENCE_AMI_VERSION = "al2-ami-sagemaker-inference-gpu-3-1"