change: allow smdistributed to be enabled with torch_distributed.

src/sagemaker/estimator.py

@@ -101,6 +101,7 @@
 )
 from sagemaker.workflow import is_pipeline_variable
 from sagemaker.workflow.entities import PipelineVariable
+from sagemaker.workflow.parameters import ParameterString
 from sagemaker.workflow.pipeline_context import PipelineSession, runnable_by_pipeline
 
 logger = logging.getLogger(__name__)
@@ -3198,6 +3199,7 @@ class Framework(EstimatorBase):
     """
 
     _framework_name = None
+    UNSUPPORTED_DLC_IMAGE_FOR_SM_PARALLELISM = ("2.0.1-gpu-py310-cu121", "2.0-gpu-py310-cu121")
 
     def __init__(
         self,
@@ -3843,16 +3845,43 @@ def _distribution_configuration(self, distribution):
                 "custom_mpi_options", ""
             )
 
-            if get_mp_parameters(distribution):
-                distribution_config["mp_parameters"] = get_mp_parameters(distribution)
-
-        elif "modelparallel" in distribution.get("smdistributed", {}):
-            raise ValueError("Cannot use Model Parallelism without MPI enabled!")
-
         if "smdistributed" in distribution:
             # smdistributed strategy selected
+            if get_mp_parameters(distribution):
+                distribution_config["mp_parameters"] = get_mp_parameters(distribution)
+            # first make sure torch_distributed is enabled if instance type is p5
+            torch_distributed_enabled = False
+            if "torch_distributed" in distribution:
+                torch_distributed_enabled = distribution.get("torch_distributed").get(
+                    "enabled", False
+                )
             smdistributed = distribution["smdistributed"]
             smdataparallel_enabled = smdistributed.get("dataparallel", {}).get("enabled", False)
+            if isinstance(self.instance_type, ParameterString):
+                p5_enabled = "p5.48xlarge" in self.instance_type.default_value
+            elif isinstance(self.instance_type, str):
+                p5_enabled = "p5.48xlarge" in self.instance_type
+            else:
+                raise ValueError(
+                    "Invalid object type for instance_type argument. Expected "
+                    f"{type(str)} or {type(ParameterString)} but got {type(self.instance_type)}."
+                )
+            img_uri = "" if self.image_uri is None else self.image_uri
+            for unsupported_image in Framework.UNSUPPORTED_DLC_IMAGE_FOR_SM_PARALLELISM:
+                if (
+                    unsupported_image in img_uri and not torch_distributed_enabled
+                ):  # disabling DLC images with CUDA12
+                    raise ValueError(
+                        f"SMDistributed is currently incompatible with DLC image: {img_uri}. "
+                        "(Could be due to CUDA version being greater than 11.)"
+                    )
+            if (
+                not torch_distributed_enabled and p5_enabled
+            ):  # disabling p5 when torch distributed is disabled
+                raise ValueError(
+                    "SMModelParallel and SMDataParallel currently do not support p5 instances."
+                )
+            # smdistributed strategy selected with supported instance type
             distribution_config[self.LAUNCH_SM_DDP_ENV_NAME] = smdataparallel_enabled
             distribution_config[self.INSTANCE_TYPE] = self.instance_type
             if smdataparallel_enabled:

src/sagemaker/pytorch/estimator.py

@@ -326,6 +326,9 @@ def _pytorch_distribution_configuration(self, distribution):
             if self.instance_type is not None:
                 distribution_config[self.INSTANCE_TYPE_ENV_NAME] = self.instance_type
         elif torch_distributed_enabled:
+            if "smdistributed" in distribution:
+                # Enable torch_distributed for smdistributed.
+                distribution_config = self._distribution_configuration(distribution=distribution)
             distribution_config[self.LAUNCH_TORCH_DISTRIBUTED_ENV_NAME] = torch_distributed_enabled
             if self.instance_type is not None:
                 distribution_config[self.INSTANCE_TYPE_ENV_NAME] = self.instance_type

tests/unit/test_estimator.py

@@ -169,7 +169,20 @@
     "mpi": {"enabled": True, "custom_mpi_options": "options", "processes_per_host": 2}
 }
 DISTRIBUTION_SM_DDP_ENABLED = {
-    "smdistributed": {"dataparallel": {"enabled": True, "custom_mpi_options": "options"}}
+    "smdistributed": {"dataparallel": {"enabled": True, "custom_mpi_options": "options"}},
+    "torch_distributed": {"enabled": False},
+}
+DISTRIBUTION_SM_DDP_DISABLED = {
+    "smdistributed": {"enabled": True},
+    "torch_distributed": {"enabled": False},
+}
+DISTRIBUTION_SM_TORCH_DIST_AND_DDP_ENABLED = {
+    "smdistributed": {"dataparallel": {"enabled": True, "custom_mpi_options": "options"}},
+    "torch_distributed": {"enabled": True},
+}
+DISTRIBUTION_SM_TORCH_DIST_AND_DDP_DISABLED = {
+    "smdistributed": {"enabled": True},
+    "torch_distributed": {"enabled": True},
 }
 MOCKED_S3_URI = "s3://mocked_s3_uri_from_source_dir"
 _DEFINITION_CONFIG = PipelineDefinitionConfig(use_custom_job_prefix=False)
@@ -310,6 +323,85 @@ def training_job_description(sagemaker_session):
     return returned_job_description
 
 
+def test_validate_smdistributed_unsupported_image_raises(sagemaker_session):
+    # Test unsupported image raises error.
+    for unsupported_image in DummyFramework.UNSUPPORTED_DLC_IMAGE_FOR_SM_PARALLELISM:
+        # Fail due to unsupported CUDA12 DLC image.
+        f = DummyFramework(
+            "some_script.py",
+            role="DummyRole",
+            instance_type="ml.p4d.24xlarge",
+            sagemaker_session=sagemaker_session,
+            output_path="outputpath",
+            image_uri=unsupported_image,
+        )
+        with pytest.raises(ValueError):
+            f._distribution_configuration(DISTRIBUTION_SM_DDP_ENABLED)
+        with pytest.raises(ValueError):
+            f._distribution_configuration(DISTRIBUTION_SM_DDP_DISABLED)
+
+    # Test unsupported image with suffix raises error.
+    for unsupported_image in DummyFramework.UNSUPPORTED_DLC_IMAGE_FOR_SM_PARALLELISM:
+        # Fail due to unsupported CUDA12 DLC image.
+        f = DummyFramework(
+            "some_script.py",
+            role="DummyRole",
+            instance_type="ml.p4d.24xlarge",
+            sagemaker_session=sagemaker_session,
+            output_path="outputpath",
+            image_uri=unsupported_image + "-ubuntu20.04-sagemaker-pr-3303",
+        )
+        with pytest.raises(ValueError):
+            f._distribution_configuration(DISTRIBUTION_SM_DDP_ENABLED)
+        with pytest.raises(ValueError):
+            f._distribution_configuration(DISTRIBUTION_SM_DDP_DISABLED)
+
+
+def test_validate_smdistributed_p5_raises(sagemaker_session):
+    # Supported DLC image.
+    f = DummyFramework(
+        "some_script.py",
+        role="DummyRole",
+        instance_type="ml.p5.48xlarge",
+        sagemaker_session=sagemaker_session,
+        output_path="outputpath",
+        image_uri="some_acceptable_image",
+    )
+    # Both fail because instance type is p5 and torch_distributed is off.
+    with pytest.raises(ValueError):
+        f._distribution_configuration(DISTRIBUTION_SM_DDP_ENABLED)
+    with pytest.raises(ValueError):
+        f._distribution_configuration(DISTRIBUTION_SM_DDP_DISABLED)
+
+
+def test_validate_smdistributed_p5_not_raises(sagemaker_session):
+    f = DummyFramework(
+        "some_script.py",
+        role="DummyRole",
+        instance_type="ml.p5.48xlarge",
+        sagemaker_session=sagemaker_session,
+        output_path="outputpath",
+        image_uri="ecr-url/2.0.1-gpu-py310-cu121-ubuntu20.04-sagemaker-pr-3303",
+    )
+    # Testing with p5 instance and torch_distributed enabled.
+    f._distribution_configuration(DISTRIBUTION_SM_TORCH_DIST_AND_DDP_ENABLED)
+    f._distribution_configuration(DISTRIBUTION_SM_TORCH_DIST_AND_DDP_DISABLED)
+
+
+def test_validate_smdistributed_backward_compat_p4_not_raises(sagemaker_session):
+    f = DummyFramework(
+        "some_script.py",
+        role="DummyRole",
+        instance_type="ml.p4d.24xlarge",
+        sagemaker_session=sagemaker_session,
+        output_path="outputpath",
+        image_uri="some_acceptable_image",
+    )
+    # Testing backwards compatability with p4d instances.
+    f._distribution_configuration(DISTRIBUTION_SM_TORCH_DIST_AND_DDP_ENABLED)
+    f._distribution_configuration(DISTRIBUTION_SM_TORCH_DIST_AND_DDP_DISABLED)
+
+
 def test_framework_all_init_args(sagemaker_session):
     f = DummyFramework(
         "my_script.py",