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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import json
from .constants import *
class ElasticityError(Exception):
"""
Base exception for all elasticity related errors
"""
class ElasticityConfigError(ElasticityError):
"""
Elasticity configuration error
"""
class ElasticityIncompatibleWorldSize(ElasticityError):
"""
Attempting to run a world size that is incompatible with a given elastic config
"""
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)
if not isinstance(self.micro_batches, list):
raise ElasticityConfigError(
f"Elasticity expected value of {MICRO_BATCHES} to be a "
f"list of micro batches, instead is: {type(self.micro_batches)}, containing: {self.micro_batches}")
if not all(map(lambda m: isinstance(m, int), self.micro_batches)):
raise ElasticityConfigError(f"Elasticity expected {MICRO_BATCHES} to only contain a list of integers, "
f"instead contains: f{self.micro_batches}")
if not all(map(lambda m: m > 0, self.micro_batches)):
raise ElasticityConfigError(f"Elasticity expected {MICRO_BATCHES} to only contain positive integers, "
f"instead contains: f{self.micro_batches}")
self.min_gpus = param_dict.get(MIN_GPUS, MIN_GPUS_DEFAULT)
self.max_gpus = param_dict.get(MAX_GPUS, MAX_GPUS_DEFAULT)
if self.min_gpus < 1 or self.max_gpus < 1:
raise ElasticityConfigError("Elasticity min/max gpus must be > 0, "
f"given min_gpus: {self.min_gpus}, max_gpus: {self.max_gpus}")
if self.max_gpus < self.min_gpus:
raise ElasticityConfigError("Elasticity min_gpus cannot be greater than max_gpus, "
f"given min_gpus: {self.min_gpus}, max_gpus: {self.max_gpus}")
self.model_parallel_size = param_dict.get(MODEL_PARALLEL_SIZE, MODEL_PARALLEL_SIZE_DEFAULT)
if self.model_parallel_size < 1:
raise ElasticityConfigError("Model-Parallel size cannot be less than 1, "
f"given model-parallel size: {self.model_parallel_size}")
self.num_gpus_per_node = param_dict.get(NUM_GPUS_PER_NODE, NUM_GPUS_PER_NODE_DEFAULT)
if self.num_gpus_per_node < 1:
raise ElasticityConfigError("Number of GPUs per node cannot be less than 1, "
f"given number of GPUs per node: {self.num_gpus_per_node}")
self.min_time = param_dict.get(MIN_TIME, MIN_TIME_DEFAULT)
if self.min_time < 0:
raise ElasticityConfigError(f"Elasticity min time needs to be >= 0: given {self.min_time}")
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)
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