Elastic training support

.github/workflows/main.yml

@@ -4,14 +4,12 @@ name: Build
 
 # Controls when the action will run.
 on:
-  # Triggers the workflow on push or pull request events but only for the master branch
   push:
-    branches: [ master ]
+    paths-ignore:
+    - 'docs/**'
   pull_request:
-    branches: [ master ]
-
-  # Allows you to run this workflow manually from the Actions tab
-  workflow_dispatch:
+    paths-ignore:
+    - 'docs/**'
 
 # A workflow run is made up of one or more jobs that can run sequentially or in parallel
 jobs:

bin/ds_elastic

@@ -0,0 +1,39 @@
+#!/usr/bin/env python
+
+import argparse
+import json
+
+import deepspeed
+from deepspeed.elasticity import compute_elastic_config
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', type=str, help="DeepSpeed config json")
+    parser.add_argument('-w', '--world-size', type=int, default=0, help="Intended/current world size")
+    args = parser.parse_args()
+    ds_config = json.load(open(args.config, 'r'))
+
+    ds_version = deepspeed.__version__
+
+    elastic_config = ds_config['elasticity']
+    print('------------------------------------------')
+    print("Elasticity config:")
+    print('------------------------------------------')
+    print(json.dumps(elastic_config, indent=4, sort_keys=True))
+
+    if args.world_size > 0:
+        final_batch_size, valid_gpus, micro_batch_size = compute_elastic_config(ds_config=ds_config, target_deepspeed_version=ds_version, world_size=args.world_size)
+        print('------------------------------------------')
+        print(f"Calculated results for world size {args.world_size}:")
+        print('------------------------------------------')
+        print(f'final_batch_size .... {final_batch_size}')
+        print(f'valid_gpus .......... {valid_gpus}')
+        print(f'micro_batch_size .... {micro_batch_size}')
+    else:
+        final_batch_size, valid_gpus = compute_elastic_config(ds_config=ds_config, target_deepspeed_version=ds_version)
+        print('------------------------------------------')
+        print("Calculated results:")
+        print('------------------------------------------')
+        print(f'final_batch_size .... {final_batch_size}')
+        print(f'valid_gpus .......... {valid_gpus}')

deepspeed/elasticity/__init__.py

@@ -0,0 +1 @@
+from .elasticity import compute_elastic_config, elasticity_enabled, ensure_immutable_elastic_config

deepspeed/elasticity/config.py

@@ -0,0 +1,80 @@
+"""
+Copyright 2020 The Microsoft DeepSpeed Team
+"""
+
+import json
+from .constants import *
+
+
+class ElasticityError(Exception):
+    """
+    Base exception for all elasticity related errors
+    """
+    pass
+
+
+class ElasticityConfigError(ElasticityError):
+    """
+    Elasticity configuration error
+    """
+    pass
+
+
+class ElasticityIncompatibleWorldSize(ElasticityError):
+    """
+    Attempting to run a world size that is incompatible with a given elastic config
+    """
+    pass
+
+
+class ElasticityConfig:
+    """
+    Elastic config object, constructed from a param dictionary that only contains elastic
+    config parameters, example below:
+
+    If elasticity is enabled, user must specify (at least) max_train_batch_size
+    and micro_batch_sizes.
+
+    {
+        "enabled": true,
+        "max_train_batch_size": 2000,
+        "micro_batch_sizes": [2,4,6],
+        "min_gpus": 1,
+        "max_gpus" : 10000
+        "min_time": 20
+        "ignore_non_elastic_batch_info": false
+        "version": 0.1
+    }
+    """
+    def __init__(self, param_dict):
+        self.enabled = param_dict.get(ENABLED, ENABLED_DEFAULT)
+        if self.enabled:
+            if MAX_ACCEPTABLE_BATCH_SIZE in param_dict:
+                self.max_acceptable_batch_size = param_dict[MAX_ACCEPTABLE_BATCH_SIZE]
+            else:
+                raise ElasticityConfigError(
+                    f"Elasticity config missing {MAX_ACCEPTABLE_BATCH_SIZE}")
+            if MICRO_BATCHES in param_dict:
+                self.micro_batches = param_dict[MICRO_BATCHES]
+            else:
+                raise ElasticityConfigError(f"Elasticity config missing {MICRO_BATCHES}")
+        else:
+            self.max_acceptable_batch_size = param_dict.get(
+                MAX_ACCEPTABLE_BATCH_SIZE,
+                MAX_ACCEPTABLE_BATCH_SIZE_DEFAULT)
+            self.micro_batches = param_dict.get(MICRO_BATCHES, MICRO_BATCHES_DEFAULT)
+        self.min_gpus = param_dict.get(MIN_GPUS, MIN_GPUS_DEFAULT)
+        self.max_gpus = param_dict.get(MAX_GPUS, MAX_GPUS_DEFAULT)
+        self.min_time = param_dict.get(MIN_TIME, MIN_TIME_DEFAULT)
+        self.version = param_dict.get(VERSION, VERSION_DEFAULT)
+        self.prefer_larger_batch_size = param_dict.get(PREFER_LARGER_BATCH,
+                                                       PREFER_LARGER_BATCH_DEFAULT)
+        self.ignore_non_elastic_batch_info = param_dict.get(
+            IGNORE_NON_ELASTIC_BATCH_INFO,
+            IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT)
+
+    def repr(self):
+        return self.__dict__
+
+    def __repr__(self):
+        return json.dumps(self.__dict__, sort_keys=True, indent=4)

deepspeed/elasticity/constants.py

@@ -0,0 +1,74 @@
+"""
+Copyright 2020 The Microsoft DeepSpeed Team
+"""
+
+#########################################
+# Elasticity
+#########################################
+''' Elasticity Utility in DeepSpeed can be used to create highly elastic jobs compatible
+with a large number of GPUs. For elastic jobs, DeepSpeed will provide a batch size that
+can support a large number of GPUs based on the user specified parameters
+'''
+FORMAT = '''
+Elasticity should be enabled as:
+"elasticity": {
+  "enabled": true,
+  "max_train_batch_size": 2000,
+  "micro_batch_sizes": [2,4,6],
+  "min_gpus": 1,
+  "max_gpus" : 10000
+  "min_time": 20,
+  "prefer_larger_batch": true,
+  "ignore_non_elastic_batch_info": false,
+  "version": 0.1
+}
+'''
+
+ELASTICITY = 'elasticity'
+
+# Current elasticity version
+LATEST_ELASTICITY_VERSION = 0.1
+
+ENABLED = 'enabled'
+ENABLED_DEFAULT = False
+
+# Max acceptable train_batch_size
+MAX_ACCEPTABLE_BATCH_SIZE = 'max_train_batch_size'
+MAX_ACCEPTABLE_BATCH_SIZE_DEFAULT = 2000
+
+# Acceptable micro batch sizes, same as train_micro_batch_size_per_gpu
+MICRO_BATCHES = 'micro_batch_sizes'
+MICRO_BATCHES_DEFAULT = [2, 4, 6]
+
+# Min/max of GPUs to search over
+MIN_GPUS = 'min_gpus'
+MIN_GPUS_DEFAULT = 1
+MAX_GPUS = 'max_gpus'
+MAX_GPUS_DEFAULT = 10000
+
+# Minimum running time (minutes) before the scheduler will scale us
+MIN_TIME = "min_time"
+MIN_TIME_DEFAULT = "20"
+
+# When finding a suitable batch size, attempt to find one that is closest
+# to the max train batch size given.
+PREFER_LARGER_BATCH = 'prefer_larger_batch'
+PREFER_LARGER_BATCH_DEFAULT = True
+
+# In order to reduce confusion, if elastic mode is enabled we
+# require (via assert) that no batch info is set outside of the
+# elastic config. You can turn off this assert via this config
+# but keep in mind that all batch info defined outside the
+# elastic mode *will be ignored*.
+IGNORE_NON_ELASTIC_BATCH_INFO = 'ignore_non_elastic_batch_info'
+IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT = False
+
+# Version of elastic logic to use
+VERSION = "version"
+VERSION_DEFAULT = LATEST_ELASTICITY_VERSION
+
+# Minimum deepspeed version to use elasticity
+MINIMUM_DEEPSPEED_VERSION = "0.3.8"
+
+# Environment variable storing elastic config from resource scheduler
+DEEPSPEED_ELASTICITY_CONFIG = "DEEPSPEED_ELASTICITY_CONFIG"