Elastic training support (#602)

Co-authored-by: Samyam Rajbhandari <samyamr@microsoft.com>

.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

+#!/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

+from .elasticity import compute_elastic_config, elasticity_enabled, ensure_immutable_elastic_config

deepspeed/elasticity/config.py

+"""
+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

+"""
+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"

deepspeed/elasticity/elasticity.py

+"""
+Copyright 2020 The Microsoft DeepSpeed Team
+"""
+import os
+import re
+import json
+import numpy as np
+
+from .config import ElasticityConfig, ElasticityConfigError, ElasticityError, \
+    ElasticityIncompatibleWorldSize
+from .constants import ELASTICITY, ENABLED, ENABLED_DEFAULT, LATEST_ELASTICITY_VERSION, \
+    MINIMUM_DEEPSPEED_VERSION, IGNORE_NON_ELASTIC_BATCH_INFO, \
+    IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT, DEEPSPEED_ELASTICITY_CONFIG
+from ..git_version_info import version as __version__
+from ..utils import logger
+
+# Thirty eight smallest highly composite numbers. The list should
+# be enough to support up to 720K batch size.
+HCN_LIST = [
+    1,
+    2,
+    4,
+    6,
+    12,
+    24,
+    36,
+    48,
+    60,
+    120,
+    180,
+    240,
+    360,
+    720,
+    840,
+    1260,
+    1680,
+    2520,
+    5040,
+    7560,
+    10080,
+    15120,
+    20160,
+    25200,
+    27720,
+    45360,
+    50400,
+    55440,
+    83160,
+    110880,
+    166320,
+    221760,
+    277200,
+    332640,
+    498960,
+    554400,
+    665280,
+    720720
+]
+
+
+def get_candidate_batch_sizes(base_list, max_acceptable_batch_size):
+    candidate_batch_size = []
+
+    #brute force is fine here. We are working with very small lists
+    for base in base_list:
+        batch_size = base
+        for hcn in HCN_LIST:
+            new_batch_size = base * hcn
+            if new_batch_size > max_acceptable_batch_size:
+                break
+            batch_size = new_batch_size
+        candidate_batch_size.append(batch_size)
+    return list(set(candidate_batch_size))
+
+
+def get_valid_gpus(batch_size, micro_batches, min_valid_gpus, max_valid_gpus):
+    valid_gpus = []
+    for micro_batch in micro_batches:
+        if batch_size % micro_batch == 0:
+
+            max_gpus = batch_size // micro_batch
+            if max_gpus >= min_valid_gpus and max_gpus <= max_valid_gpus:
+                valid_gpus.append(max_gpus)
+
+            for i in range(1, max_gpus // 2 + 1):
+                if max_gpus % i == 0:
+                    if i >= min_valid_gpus and i <= max_valid_gpus:
+                        valid_gpus.append(i)
+    valid_gpus = set(valid_gpus)
+    valid_gpus = sorted(list(valid_gpus))
+    return valid_gpus
+
+
+def get_best_candidates(candidate_batch_sizes,
+                        micro_batches,
+                        min_gpus,
+                        max_gpus,
+                        prefer_larger):
+
+    max_valid_gpus = 0
+    valid_gpus = None
+    final_batch_size = int(min(micro_batches))
+
+    for batch_size in candidate_batch_sizes:
+
+        current_valid_gpus = get_valid_gpus(batch_size,
+                                            micro_batches,
+                                            min_gpus,
+                                            max_gpus)
+
+        if (len(current_valid_gpus) > max_valid_gpus
+                or (len(current_valid_gpus) == max_valid_gpus and
+                    ((prefer_larger and batch_size > final_batch_size) or
+                     (not prefer_larger and batch_size < final_batch_size)))):
+            max_valid_gpus = len(current_valid_gpus)
+            valid_gpus = current_valid_gpus
+            final_batch_size = batch_size
+
+    return final_batch_size, valid_gpus
+
+
+def _get_compatible_gpus_v01(micro_batches,
+                             max_acceptable_batch_size,
+                             min_gpus=None,
+                             max_gpus=None,
+                             prefer_larger=True):
+    '''We use two heuristics to compute the batch size
+        1. We use the Lowest Common Multiple of the micro-batches
+    as the base batch size and scale it by a HCN such that the result is
+    the largest batch size less than the max_acceptable batch size
+        2. We use each of the micro batches as a base and scale it
+    by a HCN such that the result is the largest batch size less than the
+    max_acceptable batch size.
+
+    We then use brute force to count the number of compatible GPU count for
+    each of the aforementioned cases, and return the batch size with the most number of
+    compatible GPU counts in the min-max GPU range if provided, other wise
+    we return the batch size with the most number of total compatible GPU counts.
+
+    Returns:
+        final_batch_size
+        valid_gpus
+    '''
+
+    if min_gpus is None:
+        min_gpus = int(1)
+
+    if max_gpus is None:
+        max_gpus = int(max_acceptable_batch_size / min(micro_batches))
+
+    assert all(mb <= max_acceptable_batch_size for mb in micro_batches ), \
+            f"All micro batches must be less than \
+            or equal to max_acceptable_batch_size: {max_acceptable_batch_size}"
+
+    lcm = np.lcm.reduce(micro_batches)
+
+    base_list = []
+    base_list.extend(micro_batches)
+    base_list.append(lcm)
+
+    candidate_batch_sizes = get_candidate_batch_sizes(base_list,
+                                                      max_acceptable_batch_size)
+
+    final_batch_size, valid_gpus = get_best_candidates(
+        candidate_batch_sizes,
+        micro_batches,
+        min_gpus,
+        max_gpus,
+        prefer_larger)
+
+    return final_batch_size, valid_gpus
+
+
+def _parse_version(version_str):
+    '''Parse a version string and extract the major and minor versions (and possibly patch version).'''
+    matched = re.search('^(\d+)\.(\d+)\.(\d+)', version_str)
+    if matched:
+        return int(matched.group(1)), int(matched.group(2)), int(matched.group(3))
+    else:
+        matched = re.search('^(\d+)\.(\d+)', version_str)
+        assert matched != None, "Unable to parse version number, expecting" \
+            f"major.minor[.patch] format but received {version_str}"
+        return int(matched.group(1)), int(matched.group(2)), 0
+
+
+def _compatible_ds_version_check(target_deepspeed_version: str):
+    min_major, min_minor, min_patch = _parse_version(MINIMUM_DEEPSPEED_VERSION)
+    trg_major, trg_minor, trg_patch = _parse_version(target_deepspeed_version)
+
+    err_str = f"Target deepspeed version of {target_deepspeed_version} is not compatible " \
+        f"with minimum version {MINIMUM_DEEPSPEED_VERSION} supporting elasticity."
+    if trg_major < min_major:
+        raise ElasticityError(err_str)
+    if trg_minor < min_minor:
+        raise ElasticityError(err_str)
+    if trg_patch < min_patch:
+        raise ElasticityError(err_str)
+    return True
+
+
+def elasticity_enabled(ds_config: dict):
+    if ELASTICITY not in ds_config:
+        return False
+    return ds_config[ELASTICITY].get(ENABLED, ENABLED_DEFAULT)
+
+
+def ensure_immutable_elastic_config(runtime_elastic_config_dict: dict):
+    """
+    Ensure the resource scheduler saw the same elastic config we are using at runtime
+    """
+    if DEEPSPEED_ELASTICITY_CONFIG in os.environ:
+        scheduler_elastic_config_dict = json.loads(
+            os.environ[DEEPSPEED_ELASTICITY_CONFIG])
+        scheduler_elastic_config = ElasticityConfig(scheduler_elastic_config_dict)
+        runtime_elastic_config = ElasticityConfig(runtime_elastic_config_dict)
+        err_str = "Elastic config '{}={}' seen by resource scheduler does not match config passed to runtime {}={}"
+        if runtime_elastic_config.max_acceptable_batch_size != scheduler_elastic_config.max_acceptable_batch_size:
+            raise ElasticityConfigError(
+                err_str.format('max_acceptable_batch_size',
+                               scheduler_elastic_config.max_acceptable_batch_size,
+                               'max_acceptable_batch_size',
+                               runtime_elastic_config.max_acceptable_batch_size))
+        if runtime_elastic_config.micro_batches != scheduler_elastic_config.micro_batches:
+            raise ElasticityConfigError(
+                err_str.format('micro_batches',
+                               scheduler_elastic_config.micro_batches,
+                               'micro_batches',
+                               runtime_elastic_config.micro_batches))
+        if runtime_elastic_config.version != scheduler_elastic_config.version:
+            raise ElasticityConfigError(
+                err_str.format('version',
+                               scheduler_elastic_config.version,
+                               'version',
+                               runtime_elastic_config.version))
+    else:
+        logger.warning("Unable to find DEEPSPEED_ELASTICITY_CONFIG environment variable, cannot " \
+            "guarantee resource scheduler will scale this job using compatible GPU counts.")
+
+
+def compute_elastic_config(ds_config: dict, target_deepspeed_version: str, world_size=0):
+    """Core deepspeed elasticity API. Given an elastic config (similar to the example below)
+    DeepSpeed will compute a total train batch size corresponding valid GPU count list that
+    provides a high level of elasticity. Elasticity in this case means we are safe to scale
+    the training job up/down across the GPU count list *without* any negative impacts on
+    training convergence. This is achievable primarily due to DeepSpeed's gradient accumulation
+    feature which allows us to decompose a global training batch size into:
+    micro-batch-size * gradient-accumulation-steps * world-size.
+
+    "elasticity": {
+        "enabled": true,
+        "max_train_batch_size": 2000,
+        "micro_batch_sizes": [2,4,6],
+        "min_gpus": 1,
+        "max_gpus" : 10000
+        "min_time": 20
+        "version": 0.1
+    }
+
+    Intended to be called both by scheduling infrastructure and deepspeed runtime.
+    For the same `ds_config` we should return deterministic results.
+
+    Args:
+        ds_config (dict): DeepSpeed config dictionary/json
+        target_deepspeed_version (str): When called from scheduling
+            infrastructure we want to ensure that the target deepspeed version is
+            compatible with the elasticity version used in the backend.
+        world_size (int, optional): Intended/current world size, will do some sanity
+            checks to ensure world size is actually valid with the config.
+
+    Raises:
+        ElasticityConfigError: Missing required elasticity config or elasticity disabled
+        ElasticityError: If target deepspeed version is not compatible with current version
+
+    Returns:
+        final_batch_size (int): total batch size used for training
+        valid_gpus (list(int)): list of valid GPU counts with this config
+        micro_batch_size (int, optional): if world_size is provided will return
+            specific micro batch size
+    """
+    if not isinstance(ds_config, dict):
+        raise ValueError("Expected ds_config to be a dictionary but received " \
+            f"a {type(ds_config)}, containing: {ds_config}")
+
+    if ELASTICITY not in ds_config:
+        raise ElasticityConfigError(f"'{ELASTICITY}' is missing from config json," \
+            " please add it if running an elastic training job.")
+
+    elastic_config_dict = ds_config[ELASTICITY]
+    if not elastic_config_dict.get(ENABLED, ENABLED_DEFAULT):
+        raise ElasticityConfigError("Elasticity is disabled, please enable it " \
+            "('enabled':true) if running an elastic training job.")
+
+    elastic_config = ElasticityConfig(elastic_config_dict)
+
+    if float(elastic_config.version) > LATEST_ELASTICITY_VERSION:
+        raise ElasticityConfigError("Attempting to run elasticity version " \
+            f"{elastic_config.version} but runtime only supports up " \
+            f"to {LATEST_ELASTICITY_VERSION}")
+
+    # Ensure target deepspeed version works with intended elasticity version
+    if not _compatible_ds_version_check(target_deepspeed_version):
+        raise ElasticityError("Unable to run elasticity on target deepspeed version of" \
+            f" {target_deepspeed_version}, currently {__version__}")
+
+    if float(elastic_config.version) == 0.1:
+        final_batch_size, valid_gpus = _get_compatible_gpus_v01(
+            micro_batches=elastic_config.micro_batches,
+            max_acceptable_batch_size=elastic_config.max_acceptable_batch_size,
+            min_gpus=elastic_config.min_gpus,
+            max_gpus=elastic_config.max_gpus,
+            prefer_larger=elastic_config.prefer_larger_batch_size)
+        # ensure batch size is int dtype
+        final_batch_size = int(final_batch_size)
+    else:
+        raise NotImplementedError(
+            f"Unable to find elastic logic for version: {elastic_config.version}")
+
+    if world_size > 0:
+        if world_size not in valid_gpus:
+            raise ElasticityIncompatibleWorldSize(f"World size ({world_size}) is not valid " \
+        f"with the current list of valid GPU counts: {valid_gpus}")
+
+        # Pick largest valid micro batch size
+        micro_batch_size = None
+        for mbsz in sorted(list(set(elastic_config.micro_batches)), reverse=True):
+            if final_batch_size // world_size % mbsz == 0:
+                micro_batch_size = mbsz
+                break
+        assert micro_batch_size is not None, "Unable to find divisible micro batch size" \
+            f" world_size={world_size}, final_batch_size={final_batch_size}, and " \
+            f" micro_batches={elastic_config.micro_batches}."
+        return final_batch_size, valid_gpus, micro_batch_size
+
+    return final_batch_size, valid_gpus

deepspeed/runtime/config.py

@@ -6,13 +6,21 @@ Licensed under the MIT license.
 import torch
 import json
 import copy
-from deepspeed.runtime.constants import *
-from deepspeed.runtime.fp16.loss_scaler import INITIAL_LOSS_SCALE, SCALE_WINDOW, DELAYED_SHIFT, MIN_LOSS_SCALE
-from deepspeed.runtime.config_utils import get_scalar_param, dict_raise_error_on_duplicate_keys
-from deepspeed.runtime.zero.config import DeepSpeedZeroConfig
-from deepspeed.runtime.zero.constants import *
-from deepspeed.runtime.activation_checkpointing.config import DeepSpeedActivationCheckpointingConfig
-from deepspeed.utils import logger
+
+from .constants import *
+from .fp16.loss_scaler import INITIAL_LOSS_SCALE, SCALE_WINDOW, DELAYED_SHIFT, MIN_LOSS_SCALE
+from .config_utils import get_scalar_param, dict_raise_error_on_duplicate_keys
+from .zero.config import DeepSpeedZeroConfig
+from .zero.constants import *
+from .activation_checkpointing.config import DeepSpeedActivationCheckpointingConfig
+
+from ..git_version_info import version as __version__
+from ..utils import logger
+
+from ..elasticity import elasticity_enabled, compute_elastic_config, ensure_immutable_elastic_config
+from ..elasticity.config import ElasticityConfigError
+from ..elasticity.constants import ELASTICITY, IGNORE_NON_ELASTIC_BATCH_INFO, \
+    IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT
 
 TENSOR_CORE_ALIGN_SIZE = 8
 
@@ -504,6 +512,59 @@ class DeepSpeedConfig(object):
             self.global_rank = 0
             self.world_size = 1
 
+        # If elastic-mode enabled, update compute + update _param_dict
+        self.elasticity_enabled = elasticity_enabled(self._param_dict)
+        if self.elasticity_enabled:
+            logger.info("DeepSpeed elasticity support enabled")
+            final_batch_size, valid_gpus, micro_batch_size = compute_elastic_config(
+                ds_config=self._param_dict,
+                target_deepspeed_version=__version__,
+                world_size=self.world_size)
+
+            elastic_dict = self._param_dict[ELASTICITY]
+
+            # Ensure the resource scheduler saw the same elastic config we are using at runtime
+            ensure_immutable_elastic_config(runtime_elastic_config_dict=elastic_dict)
+
+            ignore_non_elastic_batch_info = elastic_dict.get(
+                IGNORE_NON_ELASTIC_BATCH_INFO,
+                IGNORE_NON_ELASTIC_BATCH_INFO_DEFAULT)
+
+            if not ignore_non_elastic_batch_info:
+                batch_params = [
+                    TRAIN_BATCH_SIZE,
+                    TRAIN_MICRO_BATCH_SIZE_PER_GPU,
+                    GRADIENT_ACCUMULATION_STEPS
+                ]
+                if any(map(lambda t: t in self._param_dict, batch_params)):
+                    raise ElasticityConfigError("One or more batch related parameters were found in your " \
+                        f"ds_config ({TRAIN_BATCH_SIZE}, {TRAIN_MICRO_BATCH_SIZE_PER_GPU}, and/or " \
+                        f"{GRADIENT_ACCUMULATION_STEPS}). These parameters *will not be used* since " \
+                        "elastic training is enabled, which takes control of these parameters. " \
+                        "If you want to supress this error (the parameters will be silently ignored) " \
+                        f"please set {IGNORE_NON_ELASTIC_BATCH_INFO}':true in your elasticity config.")
+
+            # micro_bsz * world_size * gas = total_batch_size
+            # gas = total_batch_size // (micro_bsz * world_size)
+            gradient_accu_steps = final_batch_size // (micro_batch_size *
+                                                       self.world_size)
+
+            if TRAIN_BATCH_SIZE in self._param_dict:
+                logger.warning("[Elasticity] overriding training_batch_size: " \
+                    f"{self._param_dict[TRAIN_BATCH_SIZE]} -> {final_batch_size}")
+            if TRAIN_MICRO_BATCH_SIZE_PER_GPU in self._param_dict:
+                logger.warning("[Elasticity] overriding train_micro_batch_size_per_gpu: " \
+                    f"{self._param_dict[TRAIN_MICRO_BATCH_SIZE_PER_GPU]} -> {micro_batch_size}")
+            if GRADIENT_ACCUMULATION_STEPS in self._param_dict:
+                logger.warning("[Elasticity] overriding gradient_accumulation_steps: "\
+                    f"{self._param_dict[GRADIENT_ACCUMULATION_STEPS]} -> {gradient_accu_steps}")
+
+            logger.info(f"[Elasticity] valid GPU counts: {valid_gpus}")
+
+            self._param_dict[TRAIN_BATCH_SIZE] = final_batch_size
+            self._param_dict[TRAIN_MICRO_BATCH_SIZE_PER_GPU] = micro_batch_size
+            self._param_dict[GRADIENT_ACCUMULATION_STEPS] = gradient_accu_steps
+
         self._initialize_params(self._param_dict)
         self._configure_train_batch_size()
         self._do_sanity_check()

deepspeed/runtime/config_utils.py

@@ -13,6 +13,10 @@ def get_scalar_param(param_dict, param_name, param_default_value):
     return param_dict.get(param_name, param_default_value)
 
 
+def get_list_param(param_dict, param_name, param_default_value):
+    return param_dict.get(param_name, param_default_value)
+
+
 def dict_raise_error_on_duplicate_keys(ordered_pairs):
     """Reject duplicate keys."""
     d = dict((k, v) for k, v in ordered_pairs)

deepspeed/runtime/engine.py

@@ -137,6 +137,10 @@ class DeepSpeedEngine(Module):
         self._configure_with_arguments(args, mpu)
         self._do_sanity_check()
 
+        if mpu is not None:
+            assert not self.elasticity_enabled(), "Elasticity is not currently supported" \
+                " with model parallelism."
+
         self._set_distributed_vars()
 
         if self.tensorboard_enabled() and self.global_rank == 0:
@@ -194,6 +198,22 @@ class DeepSpeedEngine(Module):
         self.flatten = util_ops.flatten
         self.unflatten = util_ops.unflatten
 
+    def get_batch_info(self):
+        """ Get all training batch related settings.
+
+        Returns:
+            train_batch_size (int): The effective training batch size. This is the amount of data
+                samples that leads to one step of model update.
+            train_micro_batch_size_per_gpu (int): Batch size to be processed by one GPU in one
+                step (without gradient accumulation).
+            gradient_accumulation_steps (int): Number of training steps to accumulate gradients
+                before averaging and applying them.
+        """
+        return self.train_batch_size, self.train_micro_batch_size_per_gpu, self.gradient_accumulation_steps
+
+    def elasticity_enabled(self):
+        return self._config.elasticity_enabled
+
     def pld_enabled(self):
         return self._config.pld_enabled
 
@@ -1224,10 +1244,13 @@ class DeepSpeedEngine(Module):
 
         if tag is None:
             latest_path = os.path.join(load_dir, 'latest')
-            assert os.path.isfile(latest_path), f"Unable to find latest file at {latest_path}, if trying to load latest " \
-                "checkpoint please ensure this file exists or pass an explicit checkpoint tag when loading a checkpoint."
+            if os.path.isfile(latest_path):
                 with open(latest_path, 'r') as fd:
                     tag = fd.read().strip()
+            else:
+                logger.warning(f"Unable to find latest file at {latest_path}, if trying to load latest " \
+                "checkpoint please ensure this file exists or pass an explicit checkpoint tag when loading a checkpoint.")
+                return None, None
 
         load_path, client_states = self._load_checkpoint(load_dir,
                                                          tag,

deepspeed/runtime/pipe/engine.py

@@ -54,6 +54,8 @@ class PipelineEngine(DeepSpeedEngine):
 
         # We schedule the all-reduces, so disable it in super().backward()
         self.enable_backward_allreduce = False
+        assert not self.elasticity_enabled(), "Elasticity is not currently supported" \
+            " with pipeline parallelism."
 
         # pipeline step for logging
         self.log_batch_step_id = -1

op_builder/builder.py

@@ -33,7 +33,9 @@ def installed_cuda_version():
 
 def get_default_compute_capatabilities():
     compute_caps = DEFAULT_COMPUTE_CAPABILITIES
-    if installed_cuda_version()[0] >= 11:
+    import torch.utils.cpp_extension
+    if torch.utils.cpp_extension.CUDA_HOME is not None and installed_cuda_version(
+    )[0] >= 11:
         compute_caps += ";8.0;8.6"
     return compute_caps
 

requirements/requirements.txt

@@ -3,3 +3,4 @@ torchvision>=0.4.0
 tqdm
 tensorboardX==1.8
 ninja
+numpy

setup.py

@@ -184,7 +184,8 @@ setup(name='deepspeed',
           'bin/deepspeed.pt',
           'bin/ds',
           'bin/ds_ssh',
-          'bin/ds_report'
+          'bin/ds_report',
+          'bin/ds_elastic'
       ],
       classifiers=[
           'Programming Language :: Python :: 3.6',

tests/unit/test_checkpointing.py

@@ -757,7 +757,7 @@ def test_checkpoint_missing_latest(tmpdir):
         model, _, _,_ = deepspeed.initialize(args=args,
                                              model=model,
                                              model_parameters=model.parameters())
-        with pytest.raises(AssertionError):
+        # should be no-op, since latest doesn't exist
         model.load_checkpoint(tmpdir)
 
     _helper(args=args, model=model, hidden_dim=hidden_dim)

tests/unit/test_elastic.py

+import pytest
+import deepspeed
+from common import distributed_test
+from deepspeed.git_version_info import version as ds_version
+from simple_model import SimpleModel, SimpleOptimizer, random_dataloader, args_from_dict
+
+base_ds_config = {
+    "elasticity": {
+        "enabled": True,
+        "max_train_batch_size": 10000,
+        "micro_batch_sizes": [8,
+                              12,
+                              16,
+                              17],
+        "min_gpus": 32,
+        "max_gpus": 1500,
+        "min_time": 20,
+        "version": 0.1
+    }
+}
+
+
+def test_basic_10k():
+    ds_config = base_ds_config.copy()
+    final_batch_size, valid_gpus = deepspeed.elasticity.compute_elastic_config(
+        ds_config=ds_config,
+        target_deepspeed_version=ds_version)
+
+    for gpu_num in valid_gpus:
+        assert final_batch_size % gpu_num == 0, f"Batch {final_batch_size} is not divisible by GPU count {gpu_num}"
+        batch_per_gpu = final_batch_size // gpu_num
+        found_valid_mbsize = False
+
+        for mb in ds_config['elasticity']['micro_batch_sizes']:
+            if batch_per_gpu % mb == 0:
+                found_valid_mb = True
+                break
+        assert found_valid_mb, "No valid mb found"
+
+    assert len(valid_gpus) == 23
+    assert final_batch_size == 9792
+
+
+def test_old_version():
+    ds_config = base_ds_config.copy()
+    with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+        final_batch_size, valid_gpus = deepspeed.elasticity.compute_elastic_config(
+            ds_config=ds_config,
+            target_deepspeed_version="0.2")
+
+
+def test_disabled():
+    ds_config = base_ds_config.copy()
+    ds_config['elasticity']['enabled'] = False
+    with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+        final_batch_size, valid_gpus = deepspeed.elasticity.compute_elastic_config(
+            ds_config=ds_config,
+            target_deepspeed_version=ds_version)
+
+
+def test_valid_world_size():
+    ds_config = base_ds_config.copy()
+    final_batch_size, valid_gpus, mbsize = deepspeed.elasticity.compute_elastic_config(
+            ds_config=ds_config,
+            target_deepspeed_version=ds_version,
+            world_size=64)
+    assert mbsize == 17
+
+
+def test_invalid_world_size():
+    ds_config = base_ds_config.copy()
+    with pytest.raises(deepspeed.elasticity.config.ElasticityIncompatibleWorldSize):
+        final_batch_size, valid_gpus, mbsize = deepspeed.elasticity.compute_elastic_config(
+            ds_config=ds_config,
+            target_deepspeed_version=ds_version,
+            world_size=128)
+
+
+def test_future_elastic_version():
+    ds_config = base_ds_config.copy()
+    ds_config['elasticity']['version'] = '0.2'
+    with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+        deepspeed.elasticity.compute_elastic_config(ds_config=ds_config,
+                                                    target_deepspeed_version=ds_version)
+
+
+def test_missing_max_batch():
+    ds_config = base_ds_config.copy()
+    del ds_config['elasticity']['max_train_batch_size']
+    with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+        deepspeed.elasticity.compute_elastic_config(ds_config=ds_config,
+                                                    target_deepspeed_version=ds_version)
+
+
+def test_missing_micro_batch():
+    ds_config = base_ds_config.copy()
+    del ds_config['elasticity']['micro_batch_sizes']
+    with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+        deepspeed.elasticity.compute_elastic_config(ds_config=ds_config,
+                                                    target_deepspeed_version=ds_version)
+
+
+def test_empty_config():
+    ds_config = {"elasticity": {"enabled": True}}
+    with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+        deepspeed.elasticity.compute_elastic_config(ds_config=ds_config,
+                                                    target_deepspeed_version=ds_version)
+
+
+def test_proper_mbsz():
+    ds_config = base_ds_config.copy()
+    ds_config["elasticity"]["max_train_batch_size"] = 32
+    ds_config["elasticity"]["micro_batch_sizes"] = [1, 2, 3, 7]
+    ds_config["elasticity"]["min_gpus"] = 1
+    final_batch_size, valid_gpus, mbsize = deepspeed.elasticity.compute_elastic_config(
+        ds_config=ds_config,
+        target_deepspeed_version=ds_version,
+        world_size=7)
+    assert mbsize == 3
+
+
+def test_non_elastic_batch_params(tmpdir):
+    config_dict = {
+        "train_batch_size": 2,
+        "steps_per_print": 1,
+        "optimizer": {
+            "type": "Lamb",
+            "params": {
+                "lr": 0.00015
+            }
+        },
+        "gradient_clipping": 1.0,
+        "elasticity": {
+            "enabled": True,
+            "max_train_batch_size": 4,
+            "micro_batch_sizes": [1,
+                                  2,
+                                  3,
+                                  4],
+            "min_gpus": 1,
+            "max_gpus": 4,
+            "min_time": 20,
+            "version": 0.1
+        }
+    }
+    args = args_from_dict(tmpdir, config_dict)
+    hidden_dim = 10
+
+    model = SimpleModel(hidden_dim, empty_grad=False)
+
+    @distributed_test(world_size=[1, 2])
+    def _test_elastic(args, model, hidden_dim):
+        with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+            model, _, _,_ = deepspeed.initialize(args=args,
+                                                 model=model,
+                                                 model_parameters=model.parameters())
+
+    _test_elastic(args=args, model=model, hidden_dim=hidden_dim)
+
+
+def test_non_elastic_batch_params_w_override(tmpdir):
+    config_dict = {
+        "train_batch_size": 2,
+        "steps_per_print": 1,
+        "optimizer": {
+            "type": "Lamb",
+            "params": {
+                "lr": 0.00015
+            }
+        },
+        "gradient_clipping": 1.0,
+        "elasticity": {
+            "enabled": True,
+            "max_train_batch_size": 4,
+            "micro_batch_sizes": [1,
+                                  2,
+                                  3,
+                                  4],
+            "min_gpus": 1,
+            "max_gpus": 4,
+            "min_time": 20,
+            "version": 0.1,
+            "ignore_non_elastic_batch_info": True
+        }
+    }
+    args = args_from_dict(tmpdir, config_dict)
+    hidden_dim = 10
+
+    model = SimpleModel(hidden_dim, empty_grad=False)
+
+    @distributed_test(world_size=[1, 2])
+    def _test_elastic(args, model, hidden_dim):
+        model, _, _,_ = deepspeed.initialize(args=args,
+                                             model=model,
+                                             model_parameters=model.parameters())
+
+    _test_elastic(args=args, model=model, hidden_dim=hidden_dim)
+
+
+def test_elastic_config_changed(tmpdir):
+    config_dict = {
+        "train_batch_size": 2,
+        "steps_per_print": 1,
+        "optimizer": {
+            "type": "Lamb",
+            "params": {
+                "lr": 0.00015
+            }
+        },
+        "gradient_clipping": 1.0,
+        "elasticity": {
+            "enabled": True,
+            "max_train_batch_size": 4,
+            "micro_batch_sizes": [1,
+                                  2,
+                                  3,
+                                  4],
+            "min_gpus": 1,
+            "max_gpus": 4,
+            "min_time": 20,
+            "version": 0.1,
+            "ignore_non_elastic_batch_info": True
+        }
+    }
+    import json, os
+    scheduler_elastic_config = config_dict.copy()
+    scheduler_elastic_config["elasticity"]["max_train_batch_size"] = 27
+    os.environ['DEEPSPEED_ELASTICITY_CONFIG'] = json.dumps(scheduler_elastic_config)
+    args = args_from_dict(tmpdir, config_dict)
+    hidden_dim = 10
+
+    model = SimpleModel(hidden_dim, empty_grad=False)
+
+    @distributed_test(world_size=[1, 2])
+    def _test_elastic(args, model, hidden_dim):
+        with pytest.raises(deepspeed.elasticity.config.ElasticityError):
+            model, _, _,_ = deepspeed.initialize(args=args,
+                                                 model=model,
+                                                 model_parameters=model.parameters())
+
+    _test_elastic(args=args, model=model, hidden_dim=hidden_dim)

version.txt

-0.3.8
+0.3.9