[GitHub] [spark] mathewjacob1002 commented on a diff in pull request #41770: [WIP] Write a Deepspeed Distributed Learning Class DeepspeedTorchDistributor

["mathewjacob1002 (via GitHub)" <gi...@apache.org>]

mathewjacob1002 commented on code in PR #41770:
URL: https://github.com/apache/spark/pull/41770#discussion_r1255022006


##########
python/pyspark/ml/torch/distributor.py:
##########
@@ -1003,3 +1007,97 @@ def _get_spark_partition_data_loader(
         # if num_workers is zero, we cannot set `prefetch_factor` otherwise
         # torch will raise error.
         return DataLoader(dataset, batch_size, num_workers=num_workers)
+
+
+class DeepspeedTorchDistributor(TorchDistributor):
+    
+    def __init__(self, num_processes: int = 1, local_mode: bool = True, use_gpu: bool = True, deepspeed_config = None):
+        super().__init__(num_processes, local_mode, use_gpu)
+        self.deepspeed_config = deepspeed_config 
+        self.ssl_conf = "deepspeed.spark.distributor.ignoreSsl"
+        self._validate_input_params()
+        self.input_params = self._create_input_params()
+
+    @staticmethod
+    def _get_deepspeed_config_path(deepspeed_config):
+        if isinstance(deepspeed_config, dict):
+            with tempfile.NamedTemporaryFile(mode='w+', delete=False, suffix='.json') as fil:
+                json.dump(deepspeed_config, fil)
+                deepspeed_config_path = fil.name
+        else:
+            deepspeed_config_path = deepspeed_config
+        return deepspeed_config_path
+
+
+    @staticmethod 
+    def _get_torchrun_args(local_mode, num_processes):
+        if local_mode:
+            torchrun_args = ["--standalone", "--nnodes=1"]
+            processes_per_node = num_processes
+        else:
+            master_addr, master_port = (
+                os.environ["MASTER_ADDR"],
+                os.environ["MASTER_PORT"],
+            )
+            node_rank = os.environ["RANK"]
+            torchrun_args = [
+                f"--nnodes={num_processes}",
+                f"--node_rank={node_rank}",
+                f"--rdzv_endpoint={master_addr}:{master_port}",
+                "--rdzv_id=0",
+            ]
+            processes_per_node = 1
+        return torchrun_args, processes_per_node
+
+    @staticmethod
+    def _create_torchrun_command(
+            input_params: Dict[str, Any], train_path: str, *args: Any) -> List[str]:
+        local_mode = input_params["local_mode"]
+        num_processes = input_params["num_processes"]
+        deepspeed_config = input_params["deepspeed_config"]
+        
+        deepspeed_config_path = DeepspeedTorchDistributor._get_deepspeed_config_path(deepspeed_config)
+
+        torchrun_args, processes_per_node = DeepspeedTorchDistributor._get_torchrun_args(local_mode, num_processes)
+
+        args_string = list(map(str, args))
+        
+        command_to_run = [ 
+                          sys.executable,
+                          "-m",
+                          "torch.distributed.run",
+                          *torchrun_args,
+                          f"--nproc_per_node={processes_per_node}",
+                          train_path,
+                          *args_string,
+                          "-deepspeed",
+                          "--deepspeed_config",
+                          deepspeed_config_path
+                        ]
+        print(command_to_run)
+        return command_to_run
+
+
+    @staticmethod
+    def _run_training_on_pytorch_file(input_params: Dict[str, Any], train_path: str, *args: Any, **kwargs : Any) -> None :
+        if kwargs:
+            raise ValueError("DeepspeedTorchDistributor with pytorch file doesn't support key-word type arguments")
+
+        log_streaming_client = input_params.get("log_streaming_client", None)
+        training_command = DeepspeedTorchDistributor._create_torchrun_command(input_params, train_path, *args)
+        DeepspeedTorchDistributor._execute_command(training_command, log_streaming_client=log_streaming_client)
+
+    def run(self, train_object: Union[Callable, str], *args : Any, **kwargs: Any) -> Optional[Any]:
+        # if the "train_object" is a string, then we assume it's a filepath. Otherwise, we assume it's a function
+        if isinstance(train_object, str):
+            framework_wrapper_fn = DeepspeedTorchDistributor._run_training_on_pytorch_file
+        else:
+            framework_wrapper_fn = TorchDistributor._run_training_on_pytorch_file

Review Comment:
   For now, I'll put an error saying "Not yet supported" or something. We can make supporting this another PR



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