[python] remove deepspeed related AOT code (deepjavalibrary#2692)

Author: sindhuvahinis

serving/docker/partition/partition.py

@@ -20,7 +20,6 @@
 
 from pathlib import Path
 
-import utils
 from properties_manager import PropertiesManager
 from huggingface_hub import snapshot_download
 from datasets import load_dataset
@@ -196,12 +195,10 @@ def run_partition(self) -> str:
         logging.info(proc)
         if proc.returncode == 0:
             logging.info("Partitioning done.")
-            self.properties_manager.validate_and_correct_checkpoints_json()
             self.properties_manager.generate_properties_file()
             if not self.properties_manager.skip_copy:
                 logging.info("Copying config files...")
                 self.copy_config_files()
-            self.load_the_generated_checkpoints()
             self.upload_checkpoints_to_s3()
             self.cleanup()
             return partition_stdout
@@ -212,36 +209,6 @@ def run_partition(self) -> str:
                 f"Partitioning exited with return code: {proc.returncode}. Details: {partition_stderr}"
             )
 
-    def load_the_generated_checkpoints(self):
-        if self.properties['engine'] == 'DeepSpeed':
-            saved_checkpoints_dir = self.properties[
-                "option.save_mp_checkpoint_path"]
-            properties = utils.load_properties(saved_checkpoints_dir)
-            if not self.properties_manager.skip_copy:
-                properties['model_dir'] = saved_checkpoints_dir
-            properties['option.entryPoint'] = self.properties[
-                'option.entryPoint']
-            properties['partition_handler'] = 'handle'
-
-            entry_point_file = None
-            if properties['option.entryPoint'] == 'model.py':
-                entry_point_file = os.path.join(
-                    self.properties_manager.properties_dir, 'model.py')
-                shutil.copy(entry_point_file, saved_checkpoints_dir)
-
-            commands = get_partition_cmd(True, properties)
-            self.set_environmental_vars()
-            result = subprocess.run(commands)
-            logging.info(result)
-            if result.returncode == 0:
-                logging.info(
-                    "Successfully loaded the partitioned checkpoints.")
-            else:
-                raise Exception("DeepSpeed does not support partitioning. "
-                                "Please use a different engine")
-            if entry_point_file:
-                os.remove(os.path.join(saved_checkpoints_dir, 'model.py'))
-
     def run_quantization(self):
         quant_method = self.properties['option.quantize']
         if quant_method == 'awq':

serving/docker/partition/properties_manager.py

@@ -12,13 +12,12 @@
 import logging
 import os
 import glob
-import json
 import torch
 import requests
 
 # Properties to exclude while generating serving.properties
-from utils import (is_engine_mpi_mode, get_engine_configs, get_download_dir,
-                   load_properties, update_kwargs_with_env_vars)
+from utils import (is_engine_mpi_mode, get_download_dir, load_properties,
+                   update_kwargs_with_env_vars)
 
 EXCLUDE_PROPERTIES = [
     'option.model_id', 'option.save_mp_checkpoint_path', 'model_dir',
@@ -88,34 +87,6 @@ def set_and_validate_model_dir(self):
                     f'No .bin or .safetensors files found in the dir: {self.properties_dir}'
                     '\nPlease specify the model_dir or model_id')
 
-    def validate_and_correct_checkpoints_json(self):
-        """
-        Removes base_dir from ds_inference_checkpoints.json file.
-
-        DeepSpeed writes base_dir directory, which is the path of checkpoints saved to the file.
-        Removing the base_dir since the user's deployment environment could be different from partition environment.
-        User can specify base_dir argument in deepspeed.init_inference while using this file.
-
-        :return:
-        """
-        if self.properties.get('engine') == 'DeepSpeed':
-            config_file = os.path.join(
-                self.properties['option.save_mp_checkpoint_path'],
-                'ds_inference_config.json')
-            if not os.path.exists(config_file):
-                raise ValueError("Checkpoints json file was not generated."
-                                 "Partition was not successful.")
-
-            with open(config_file) as f:
-                configs = json.load(f)
-
-            if not configs.get('base_dir'):
-                return
-
-            configs.pop('base_dir')
-            with open(config_file, "w") as f:
-                json.dump(configs, f)
-
     def generate_properties_file(self):
         checkpoint_path = self.properties.get('option.save_mp_checkpoint_path')
         configs = get_engine_configs(self.properties)
@@ -172,8 +143,6 @@ def set_and_validate_entry_point(self):
                         pass
                     elif engine is None:
                         raise ValueError("Please specify engine")
-                    elif engine.lower() == "deepspeed":
-                        entry_point = "djl_python.deepspeed"
                     elif engine.lower() == "python":
                         entry_point = "djl_python.transformers_neuronx"
                     else:

serving/docker/partition/utils.py

@@ -42,16 +42,6 @@ def get_partition_cmd(is_mpi_mode, properties):
         ]
 
 
-def get_engine_configs(properties):
-    engine = properties.get('engine')
-    configs = {}
-    if engine == 'DeepSpeed':
-        configs['option.checkpoint'] = 'ds_inference_config.json'
-        configs['option.parallel_loading'] = True
-
-    return configs
-
-
 def extract_python_jar(target_dir):
     os.makedirs(target_dir, exist_ok=True)
     jar_files = glob.glob('/usr/local/djl-serving-*/lib/python-*.jar')
@@ -72,7 +62,7 @@ def get_djl_version_from_lib():
 
 
 def is_engine_mpi_mode(engine):
-    if engine == 'DeepSpeed':
+    if engine == 'MPI':
         return True
     else:
         return False