feat: Add recipe validation integ test for HP-ModelCustomization-RecipeValidator pipeline

sagemaker-train/recipe_tests/__init__.py

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+

sagemaker-train/recipe_tests/test_recipe_validation.py

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+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file is
+# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
+# ANY KIND, either express or implied. See the License for the specific
+# language governing permissions and limitations under the License.
+"""Recipe validation integ test for the HP-ModelCustomization-RecipeValidator pipeline.
+
+Iterates through every model in the private hub referenced by the ``SAGEMAKER_HUB_NAME``
+env var and validates that each fine-tuning recipe can be used to instantiate the
+appropriate ``sagemaker.train`` Trainer class (SFT/DPO/RLAIF/RLVR).
+"""
+from __future__ import absolute_import
+
+import json
+import os
+
+import boto3
+
+from sagemaker.train.common import TrainingType
+from sagemaker.train.dpo_trainer import DPOTrainer
+from sagemaker.train.rlaif_trainer import RLAIFTrainer
+from sagemaker.train.rlvr_trainer import RLVRTrainer
+from sagemaker.train.sft_trainer import SFTTrainer
+
+TRAINER_MAPPING = {
+    "sft": SFTTrainer,
+    "dpo": DPOTrainer,
+    "rlaif": RLAIFTrainer,
+    "rlvr": RLVRTrainer,
+}
+
+DUMMY_DATASET = "s3://placeholder/validation-data"
+DUMMY_MODEL_PACKAGE_GROUP = "recipe-validation-test"
+
+
+def detect_training_type(recipe_path: str) -> str:
+    """Detect SFT/DPO/RLAIF/RLVR from the recipe name; default to SFT."""
+    if not recipe_path:
+        return "sft"
+    lower = recipe_path.lower()
+    if "rlvr" in lower:
+        return "rlvr"
+    if "rlaif" in lower:
+        return "rlaif"
+    if "dpo" in lower:
+        return "dpo"
+    return "sft"
+
+
+def detect_lora_or_full(recipe_path: str) -> TrainingType:
+    """Detect LoRA vs full fine-tuning from the recipe name; default to LoRA."""
+    if not recipe_path:
+        return TrainingType.LORA
+    lower = recipe_path.lower()
+    if "_fft" in lower or "full_fine_tuning" in lower:
+        return TrainingType.FULL
+    return TrainingType.LORA
+
+
+def test_new_recipes_create_valid_trainers():
+    """Validate every new/modified recipe in the private hub yields a valid Trainer."""
+    hub_name = os.environ.get("SAGEMAKER_HUB_NAME")
+    assert hub_name, "SAGEMAKER_HUB_NAME environment variable must be set"
+
+    sm = boto3.client("sagemaker", region_name="us-west-2")
+
+    models = []
+    kwargs = {"HubName": hub_name, "HubContentType": "Model"}
+    while True:
+        response = sm.list_hub_contents(**kwargs)
+        models.extend([item["HubContentName"] for item in response["HubContentSummaries"]])
+        next_token = response.get("NextToken")
+        if not next_token:
+            break
+        kwargs["NextToken"] = next_token
+
+    if not models:
+        return
+
+    errors = []
+    for model_name in models:
+        try:
+            response = sm.describe_hub_content(
+                HubName=hub_name,
+                HubContentType="Model",
+                HubContentName=model_name,
+            )
+            doc = json.loads(response.get("HubContentDocument", "{}"))
+            recipes = doc.get("RecipeCollection", [])
+
+            ft_recipes = [r for r in recipes if r.get("Type") == "FineTuning"]
+            for i, recipe in enumerate(ft_recipes):
+                recipe_name = recipe.get("Name", f"recipe-{i}")
+                training_type = detect_training_type(recipe_name)
+                training_type_enum = detect_lora_or_full(recipe_name)
+                trainer_class = TRAINER_MAPPING[training_type]
+
+                trainer = trainer_class(
+                    model=model_name,
+                    training_type=training_type_enum,
+                    training_dataset=DUMMY_DATASET,
+                    model_package_group=DUMMY_MODEL_PACKAGE_GROUP,
+                    accept_eula=True,
+                )
+                assert trainer is not None, (
+                    f"{model_name}: {trainer_class.__name__} returned None"
+                )
+        except Exception as e:  # noqa: BLE001 - collect all errors across all models
+            errors.append(f"{model_name}: {e}")
+
+    assert not errors, "Recipe validation failures:\n" + "\n".join(errors)