add unit test

tests/unit/test_pytorch.py

@@ -23,7 +23,10 @@
 from sagemaker import image_uris
 from sagemaker.pytorch import defaults
 from sagemaker.pytorch import PyTorch, PyTorchPredictor, PyTorchModel
-from sagemaker.pytorch.estimator import _get_training_recipe_image_uri
+from sagemaker.pytorch.estimator import (
+    _get_training_recipe_image_uri,
+    _get_training_recipe_gpu_script,
+)
 from sagemaker.instance_group import InstanceGroup
 from sagemaker.session_settings import SessionSettings
 
@@ -1049,6 +1052,52 @@ def test_training_recipe_for_trainium(sagemaker_session):
     assert pytorch.distribution == expected_distribution
 
 
+@pytest.mark.parametrize(
+    "test_case",
+    [
+        {
+            "script": "llama_pretrain.py",
+            "recipe": {
+                "model": {
+                    "model_type": "llama_v3",
+                },
+            },
+        },
+        {
+            "script": "mistral_pretrain.py",
+            "recipe": {
+                "model": {
+                    "model_type": "mistral",
+                },
+            },
+        },
+        {
+            "script": "deepseek_pretrain.py",
+            "recipe": {
+                "model": {
+                    "model_type": "deepseek_llamav3",
+                },
+            },
+        },
+        {
+            "script": "deepseek_pretrain.py",
+            "recipe": {
+                "model": {
+                    "model_type": "deepseek_qwenv2",
+                },
+            },
+        },
+    ],
+)
+@patch("shutil.copyfile")
+def test_get_training_recipe_gpu_script(mock_copyfile, test_case):
+    script = test_case["script"]
+    recipe = test_case["recipe"]
+    mock_copyfile.return_value = None
+
+    assert _get_training_recipe_gpu_script("code_dir", recipe, "source_dir") == script
+
+
 def test_training_recipe_for_trainium_custom_source_dir(sagemaker_session):
     container_log_level = '"logging.INFO"'