text: """
  Copyright (c) Microsoft Corporation
  Licensed under the MIT license.
  """
  import os
  from typing import Union
  
  import torch
  import json
  import copy
  
  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, ScientificNotationEncoder
  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
  
  from ..profiling.config import DeepSpeedFlopsProfilerConfig
  
  from .swap_tensor.aio_config import get_aio_config
  
  TENSOR_CORE_ALIGN_SIZE = 8
  
  ADAM_OPTIMIZER = 'adam'
  ADAMW_OPTIMIZER = 'adamw'
  LAMB_OPTIMIZER = 'lamb'
  ONEBIT_ADAM_OPTIMIZER = 'onebitadam'
  ONEBIT_LAMB_OPTIMIZER = 'onebitlamb'
  DEEPSPEED_OPTIMIZERS = [
      ADAM_OPTIMIZER,
      ADAMW_OPTIMIZER,
      LAMB_OPTIMIZER,
      ONEBIT_ADAM_OPTIMIZER,
      ONEBIT_LAMB_OPTIMIZER,
  ]
  
  # extra optimizer parameters for adam/adamw
  TORCH_ADAM_PARAM = "torch_adam"
  
  # default to adamw logic for adam/adamw optimizers unless user explictly opts out
  ADAM_W_MODE = "adam_w_mode"
  ADAM_W_MODE_DEFAULT = True
  
  
  class DeepSpeedConfigError(Exception):
      pass
  
  
  def get_pld_enabled(param_dict):
      if PROGRESSIVE_LAYER_DROP in param_dict.keys():
          return get_scalar_param(param_dict[PROGRESSIVE_LAYER_DROP],
                                  PLD_ENABLED,
                                  PLD_ENABLED_DEFAULT)
      else:
          return False
  
  
  def get_pld_params(param_dict):
      if PROGRESSIVE_LAYER_DROP in param_dict.keys():
          pld_params = copy.copy(param_dict[PROGRESSIVE_LAYER_DROP])
          pld_params.pop(PLD_ENABLED)
          return pld_params
      else:
          return False
  
  
  def get_amp_enabled(param_dict):
      if AMP in param_dict.keys():
          return get_scalar_param(param_dict[AMP], AMP_ENABLED, AMP_ENABLED_DEFAULT)
      else:
          return False
  
  
  def get_amp_params(param_dict):
      if AMP in param_dict.keys():
          amp_params = copy.copy(param_dict[AMP])
          amp_params.pop(AMP_ENABLED)
          return amp_params
      else:
          return False
  
  
  def get_fp16_enabled(param_dict):
      if FP16 in param_dict.keys():
          return get_scalar_param(param_dict[FP16], FP16_ENABLED, FP16_ENABLED_DEFAULT)
      else:
          return False
  
  
  def get_loss_scale(param_dict):
      if get_fp16_enabled(param_dict):
          return get_scalar_param(param_dict[FP16],
                                  FP16_LOSS_SCALE,
                                  FP16_LOSS_SCALE_DEFAULT)
      else:
          return FP16_LOSS_SCALE_DEFAULT
  
  
  def get_initial_dynamic_scale(param_dict):
      if get_fp16_enabled(param_dict):
          initial_scale_power = get_scalar_param(param_dict[FP16],
                                                 FP16_INITIAL_SCALE_POWER,
                                                 FP16_INITIAL_SCALE_POWER_DEFAULT)
      else:
          initial_scale_power = FP16_INITIAL_SCALE_POWER_DEFAULT
  
      return 2**initial_scale_power
  
  
  def get_dynamic_loss_scale_args(param_dict):
      loss_scale_args = None
      if get_fp16_enabled(param_dict):
          fp16_dict = param_dict[FP16]
          dynamic_loss_args = [
              FP16_INITIAL_SCALE_POWER,
              FP16_LOSS_SCALE_WINDOW,
              FP16_MIN_LOSS_SCALE,
              FP16_HYSTERESIS
          ]
          if any(arg in list(fp16_dict.keys()) for arg in dynamic_loss_args):
              init_scale = get_scalar_param(fp16_dict,
                                            FP16_INITIAL_SCALE_POWER,
                                            FP16_INITIAL_SCALE_POWER_DEFAULT)
              scale_window = get_scalar_param(fp16_dict,
                                              FP16_LOSS_SCALE_WINDOW,
                                              FP16_LOSS_SCALE_WINDOW_DEFAULT)
              delayed_shift = get_scalar_param(fp16_dict,
                                               FP16_HYSTERESIS,
                                               FP16_HYSTERESIS_DEFAULT)
              min_loss_scale = get_scalar_param(fp16_dict,
                                                FP16_MIN_LOSS_SCALE,
                                                FP16_MIN_LOSS_SCALE_DEFAULT)
              loss_scale_args = {
                  INITIAL_LOSS_SCALE: 2**init_scale,
                  SCALE_WINDOW: scale_window,
                  DELAYED_SHIFT: delayed_shift,
                  MIN_LOSS_SCALE: min_loss_scale
              }
  
      return loss_scale_args
  
  
  def get_gradient_accumulation_steps(param_dict):
      return get_scalar_param(param_dict,
                              GRADIENT_ACCUMULATION_STEPS,
                              GRADIENT_ACCUMULATION_STEPS_DEFAULT)
  
  
  def get_sparse_gradients_enabled(param_dict):
      return get_scalar_param(param_dict, SPARSE_GRADIENTS, SPARSE_GRADIENTS_DEFAULT)
  
  
  def get_zero_optimization(param_dict):
      return get_scalar_param(param_dict, ZERO_OPTIMIZATION, ZERO_OPTIMIZATION_DEFAULT)
  
  
  def get_zero_reduce_scatter(param_dict):
      return get_scalar_param(param_dict,
                              ZERO_OPTIMIZATION_REDUCE_SCATTER,
                              ZERO_OPTIMIZATION_REDUCE_SCATTER_DEFAULT)
  
  
  def get_allreduce_always_fp32(param_dict):
      return get_scalar_param(param_dict, FP32_ALLREDUCE, FP32_ALLREDUCE_DEFAULT)
  
  
  def get_prescale_gradients(param_dict):
      return get_scalar_param(param_dict, PRESCALE_GRADIENTS, PRESCALE_GRADIENTS_DEFAULT)
  
  
  def get_gradient_predivide_factor(param_dict):
      return get_scalar_param(param_dict,
                              GRADIENT_PREDIVIDE_FACTOR,
                              GRADIENT_PREDIVIDE_FACTOR_DEFAULT)
  
  
  def get_steps_per_print(param_dict):
      return get_scalar_param(param_dict, STEPS_PER_PRINT, STEPS_PER_PRINT_DEFAULT)
  
  
  def get_disable_allgather(param_dict):
      return get_scalar_param(param_dict, DISABLE_ALLGATHER, DISABLE_ALLGATHER_DEFAULT)
  
  
  def get_dump_state(param_dict):
      return get_scalar_param(param_dict, DUMP_STATE, DUMP_STATE_DEFAULT)
  
  
  def get_gradient_clipping(param_dict):
      return get_scalar_param(param_dict, GRADIENT_CLIPPING, GRADIENT_CLIPPING_DEFAULT)
  
  
  def get_sparse_attention(param_dict):
      if SPARSE_ATTENTION in param_dict.keys():
          sparsity = param_dict[SPARSE_ATTENTION]
          mode = get_sparse_attention_mode(sparsity)
  
          if (mode == SPARSE_DENSE_MODE):
              return get_sparse_dense_config(sparsity)
          elif (mode == SPARSE_FIXED_MODE):
              return get_sparse_fixed_config(sparsity)
          elif (mode == SPARSE_VARIABLE_MODE):
              return get_sparse_variable_config(sparsity)
          elif (mode == SPARSE_BIGBIRD_MODE):
              return get_sparse_bigbird_config(sparsity)
          elif (mode == SPARSE_BSLONGFORMER_MODE):
              return get_sparse_bslongformer_config(sparsity)
          else:
              raise NotImplementedError(
                  f'Given sparsity mode, {mode}, has not been implemented yet!')
  
      else:
          return None
  
  
  def get_sparse_dense_config(sparsity):
      block = get_scalar_param(sparsity, SPARSE_BLOCK, SPARSE_BLOCK_DEFAULT)
      return {SPARSE_MODE: SPARSE_DENSE_MODE, SPARSE_BLOCK: block}
  
  
  def get_sparse_fixed_config(sparsity):
      block = get_scalar_param(sparsity, SPARSE_BLOCK, SPARSE_BLOCK_DEFAULT)
      different_layout_per_head = get_scalar_param(
          sparsity,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD_DEFAULT)
      num_local_blocks = get_scalar_param(sparsity,
                                          SPARSE_NUM_LOCAL_BLOCKS,
                                          SPARSE_NUM_LOCAL_BLOCKS_DEFAULT)
      num_global_blocks = get_scalar_param(sparsity,
                                           SPARSE_NUM_GLOBAL_BLOCKS,
                                           SPARSE_NUM_GLOBAL_BLOCKS_DEFAULT)
      attention = get_scalar_param(sparsity,
                                   SPARSE_ATTENTION_TYPE,
                                   SPARSE_ATTENTION_TYPE_DEFAULT)
      horizontal_global_attention = get_scalar_param(
          sparsity,
          SPARSE_HORIZONTAL_GLOBAL_ATTENTION,
          SPARSE_HORIZONTAL_GLOBAL_ATTENTION_DEFAULT)
      num_different_global_patterns = get_scalar_param(
          sparsity,
          SPARSE_NUM_DIFFERENT_GLOBAL_PATTERNS,
          SPARSE_NUM_DIFFERENT_GLOBAL_PATTERNS_DEFAULT)
  
      return {
          SPARSE_MODE: SPARSE_FIXED_MODE,
          SPARSE_BLOCK: block,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD: different_layout_per_head,
          SPARSE_NUM_LOCAL_BLOCKS: num_local_blocks,
          SPARSE_NUM_GLOBAL_BLOCKS: num_global_blocks,
          SPARSE_ATTENTION_TYPE: attention,
          SPARSE_HORIZONTAL_GLOBAL_ATTENTION: horizontal_global_attention,
          SPARSE_NUM_DIFFERENT_GLOBAL_PATTERNS: num_different_global_patterns
      }
  
  
  def get_sparse_variable_config(sparsity):
      block = get_scalar_param(sparsity, SPARSE_BLOCK, SPARSE_BLOCK_DEFAULT)
      different_layout_per_head = get_scalar_param(
          sparsity,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD_DEFAULT)
      num_random_blocks = get_scalar_param(sparsity,
                                           SPARSE_NUM_RANDOM_BLOCKS,
                                           SPARSE_NUM_RANDOM_BLOCKS_DEFAULT)
      local_window_blocks = get_scalar_param(sparsity,
                                             SPARSE_LOCAL_WINDOW_BLOCKS,
                                             SPARSE_LOCAL_WINDOW_BLOCKS_DEFAULT)
      global_block_indices = get_scalar_param(sparsity,
                                              SPARSE_GLOBAL_BLOCK_INDICES,
                                              SPARSE_GLOBAL_BLOCK_INDICES_DEFAULT)
      global_block_end_indices = get_scalar_param(sparsity,
                                                  SPARSE_GLOBAL_BLOCK_END_INDICES,
                                                  SPARSE_GLOBAL_BLOCK_END_INDICES_DEFAULT)
      attention = get_scalar_param(sparsity,
                                   SPARSE_ATTENTION_TYPE,
                                   SPARSE_ATTENTION_TYPE_DEFAULT)
      horizontal_global_attention = get_scalar_param(
          sparsity,
          SPARSE_HORIZONTAL_GLOBAL_ATTENTION,
          SPARSE_HORIZONTAL_GLOBAL_ATTENTION_DEFAULT)
  
      return {
          SPARSE_MODE: SPARSE_VARIABLE_MODE,
          SPARSE_BLOCK: block,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD: different_layout_per_head,
          SPARSE_NUM_RANDOM_BLOCKS: num_random_blocks,
          SPARSE_LOCAL_WINDOW_BLOCKS: local_window_blocks,
          SPARSE_GLOBAL_BLOCK_INDICES: global_block_indices,
          SPARSE_GLOBAL_BLOCK_END_INDICES: global_block_end_indices,
          SPARSE_ATTENTION_TYPE: attention,
          SPARSE_HORIZONTAL_GLOBAL_ATTENTION: horizontal_global_attention
      }
  
  
  def get_sparse_bigbird_config(sparsity):
      block = get_scalar_param(sparsity, SPARSE_BLOCK, SPARSE_BLOCK_DEFAULT)
      different_layout_per_head = get_scalar_param(
          sparsity,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD_DEFAULT)
      num_random_blocks = get_scalar_param(sparsity,
                                           SPARSE_NUM_RANDOM_BLOCKS,
                                           SPARSE_NUM_RANDOM_BLOCKS_DEFAULT)
      num_sliding_window_blocks = get_scalar_param(
          sparsity,
          SPARSE_NUM_SLIDING_WINDOW_BLOCKS,
          SPARSE_NUM_SLIDING_WINDOW_BLOCKS_DEFAULT)
      num_global_blocks = get_scalar_param(sparsity,
                                           SPARSE_NUM_GLOBAL_BLOCKS,
                                           SPARSE_NUM_GLOBAL_BLOCKS_DEFAULT)
  
      return {
          SPARSE_MODE: SPARSE_BIGBIRD_MODE,
          SPARSE_BLOCK: block,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD: different_layout_per_head,
          SPARSE_NUM_RANDOM_BLOCKS: num_random_blocks,
          SPARSE_NUM_SLIDING_WINDOW_BLOCKS: num_sliding_window_blocks,
          SPARSE_NUM_GLOBAL_BLOCKS: num_global_blocks
      }
  
  
  def get_sparse_bslongformer_config(sparsity):
      block = get_scalar_param(sparsity, SPARSE_BLOCK, SPARSE_BLOCK_DEFAULT)
      different_layout_per_head = get_scalar_param(
          sparsity,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD_DEFAULT)
      num_sliding_window_blocks = get_scalar_param(
          sparsity,
          SPARSE_NUM_SLIDING_WINDOW_BLOCKS,
          SPARSE_NUM_SLIDING_WINDOW_BLOCKS_DEFAULT)
      global_block_indices = get_scalar_param(sparsity,
                                              SPARSE_GLOBAL_BLOCK_INDICES,
                                              SPARSE_GLOBAL_BLOCK_INDICES_DEFAULT)
      global_block_end_indices = get_scalar_param(sparsity,
                                                  SPARSE_GLOBAL_BLOCK_END_INDICES,
                                                  SPARSE_GLOBAL_BLOCK_END_INDICES_DEFAULT)
  
      return {
          SPARSE_MODE: SPARSE_BSLONGFORMER_MODE,
          SPARSE_BLOCK: block,
          SPARSE_DIFFERENT_LAYOUT_PER_HEAD: different_layout_per_head,
          SPARSE_NUM_SLIDING_WINDOW_BLOCKS: num_sliding_window_blocks,
          SPARSE_GLOBAL_BLOCK_INDICES: global_block_indices,
          SPARSE_GLOBAL_BLOCK_END_INDICES: global_block_end_indices
      }
  
  
  def get_sparse_attention_mode(param_dict):
      if SPARSE_MODE in param_dict.keys():
          return param_dict[SPARSE_MODE]
      else:
          return SPARSE_MODE_DEFAULT
  
  
  def get_sparse_attention_type(param_dict):
      if SPARSE_ATTENTION_TYPE in param_dict.keys():
          return param_dict[SPARSE_ATTENTION_TYPE]
      else:
          return SPARSE_ATTENTION_TYPE_DEFAULT
  
  
  def get_pipeline_config(param_dict):
      '''Parses pipeline engine configuration. '''
      default_pipeline = {
          'stages': 'auto',
          'partition': 'best',
          'seed_layers': False,
          'activation_checkpoint_interval': 0
      }
      config = default_pipeline
      for key, val in param_dict.get('pipeline', {}).items():
          config[key] = val
      return config
  
  
  def get_optimizer_name(param_dict):
      if OPTIMIZER in param_dict.keys() and \
              TYPE in param_dict[OPTIMIZER].keys():
          return param_dict[OPTIMIZER][TYPE]
      else:
          return OPTIMIZER_TYPE_DEFAULT
  
  
  def get_optimizer_params(param_dict):
      if get_optimizer_name(param_dict) is not None and \
              OPTIMIZER_PARAMS in param_dict[OPTIMIZER].keys():
          return param_dict[OPTIMIZER][OPTIMIZER_PARAMS]
      else:
          return None
  
  
  def get_optimizer_gradient_clipping(param_dict):
      optimizer_params = get_optimizer_params(param_dict)
      if optimizer_params is not None and \
              MAX_GRAD_NORM in optimizer_params.keys():
          return optimizer_params[MAX_GRAD_NORM]
      else:
          return None
  
  
  def get_optimizer_legacy_fusion(param_dict):
      if OPTIMIZER in param_dict.keys() and \
          LEGACY_FUSION in param_dict[OPTIMIZER].keys():
          return param_dict[OPTIMIZER][LEGACY_FUSION]
      else:
          return LEGACY_FUSION_DEFAULT
  
  
  def get_zero_allow_untested_optimizer(param_dict):
      return get_scalar_param(param_dict,
                              ZERO_ALLOW_UNTESTED_OPTIMIZER,
                              ZERO_ALLOW_UNTESTED_OPTIMIZER_DEFAULT)
  
  
  def get_scheduler_name(param_dict):
      if SCHEDULER in param_dict.keys() and \
              TYPE in param_dict[SCHEDULER].keys():
          return param_dict[SCHEDULER][TYPE]
      else:
          return SCHEDULER_TYPE_DEFAULT
  
  
  def get_scheduler_params(param_dict):
      if get_scheduler_name(param_dict) is not None and \
              SCHEDULER_PARAMS in param_dict[SCHEDULER].keys():
          return param_dict[SCHEDULER][SCHEDULER_PARAMS]
      else:
          return None
  
  
  def get_train_batch_size(param_dict):
      return get_scalar_param(param_dict, TRAIN_BATCH_SIZE, TRAIN_BATCH_SIZE_DEFAULT)
  
  
  def get_train_micro_batch_size_per_gpu(param_dict):
      return get_scalar_param(param_dict,
                              TRAIN_MICRO_BATCH_SIZE_PER_GPU,
                              TRAIN_MICRO_BATCH_SIZE_PER_GPU_DEFAULT)
  
  
  def get_wall_clock_breakdown(param_dict):
      return get_scalar_param(param_dict,
                              WALL_CLOCK_BREAKDOWN,
                              WALL_CLOCK_BREAKDOWN_DEFAULT)
  
  
  def get_memory_breakdown(param_dict):
      return get_scalar_param(param_dict, MEMORY_BREAKDOWN, MEMORY_BREAKDOWN_DEFAULT)
  
  
  def get_tensorboard_enabled(param_dict):
      if TENSORBOARD in param_dict.keys():
          return get_scalar_param(param_dict[TENSORBOARD],
                                  TENSORBOARD_ENABLED,
                                  TENSORBOARD_ENABLED_DEFAULT)
      else:
          return False
  
  
  def get_tensorboard_output_path(param_dict):
      if get_tensorboard_enabled(param_dict):
          return get_scalar_param(param_dict[TENSORBOARD],
                                  TENSORBOARD_OUTPUT_PATH,
                                  TENSORBOARD_OUTPUT_PATH_DEFAULT)
      else:
          return TENSORBOARD_OUTPUT_PATH_DEFAULT
  
  
  def get_tensorboard_job_name(param_dict):
      if get_tensorboard_enabled(param_dict):
          return get_scalar_param(param_dict[TENSORBOARD],
                                  TENSORBOARD_JOB_NAME,
                                  TENSORBOARD_JOB_NAME_DEFAULT)
      else:
          return TENSORBOARD_JOB_NAME_DEFAULT
  
  
  def get_checkpoint_params(param_dict):
      return param_dict.get(CHECKPOINT, {})
  
  
  def get_checkpoint_tag_validation_mode(checkpoint_params):
      tag_validation_mode = checkpoint_params.get(CHECKPOINT_TAG_VALIDATION,
                                                  CHECKPOINT_TAG_VALIDATION_DEFAULT)
      tag_validation_mode = tag_validation_mode.upper()
      if tag_validation_mode in CHECKPOINT_TAG_VALIDATION_MODES:
          return tag_validation_mode
      else:
          raise DeepSpeedConfigError("Checkpoint config contains invalid tag_validation " \
              f"value of {tag_validation_mode}, expecting one of {CHECKPOINT_TAG_VALIDATION_MODES}")
  
  
  '''Write deepspeed config files by modifying basic templates.
  Can be used for quicly changing parameters via command line parameters.'''
  
  
  class DeepSpeedConfigWriter:
      def __init__(self, data=None):
          self.data = data if data is not None else {}
  
      def add_config(self, key, value):
          self.data[key] = value
  
      def load_config(self, filename):
          self.data = json.load(open(filename,
                                     'r'),
                                object_pairs_hook=dict_raise_error_on_duplicate_keys)
  
      def write_config(self, filename):
          with open(filename, 'w') as outfile:
              json.dump(self.data, outfile)
  
  
  class DeepSpeedConfig(object):
      def __init__(self, config: Union[str, dict], mpu=None):
          super(DeepSpeedConfig, self).__init__()
          if isinstance(config, dict):
              self._param_dict = config
          elif os.path.exists(config):
              self._param_dict = json.load(
                  open(config,
                       'r'),
                  object_pairs_hook=dict_raise_error_on_duplicate_keys)
          else:
              raise ValueError(
                  f"Expected a string path to an existing deepspeed config, or a dictionary. Received: {config}"
              )
          try:
              self.global_rank = torch.distributed.get_rank()
              if mpu is None:
                  self.world_size = torch.distributed.get_world_size()
              else:
                  self.world_size = mpu.get_data_parallel_world_size()
          except:
              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()
  
      def _initialize_params(self, param_dict):
          self.train_batch_size = get_train_batch_size(param_dict)
          self.train_micro_batch_size_per_gpu = get_train_micro_batch_size_per_gpu(
              param_dict)
          self.gradient_accumulation_steps = get_gradient_accumulation_steps(param_dict)
          self.steps_per_print = get_steps_per_print(param_dict)
          self.dump_state = get_dump_state(param_dict)
  
          self.disable_allgather = get_disable_allgather(param_dict)
          self.allreduce_always_fp32 = get_allreduce_always_fp32(param_dict)
          self.prescale_gradients = get_prescale_gradients(param_dict)
          self.gradient_predivide_factor = get_gradient_predivide_factor(param_dict)
          self.sparse_gradients_enabled = get_sparse_gradients_enabled(param_dict)
  
          self.zero_config = DeepSpeedZeroConfig(param_dict)
          self.zero_optimization_stage = self.zero_config.stage
          self.zero_enabled = self.zero_optimization_stage > 0
  
          self.activation_checkpointing_config = DeepSpeedActivationCheckpointingConfig(
              param_dict)
  
          self.gradient_clipping = get_gradient_clipping(param_dict)
          self.fp16_enabled = get_fp16_enabled(param_dict)
          self.amp_enabled = get_amp_enabled(param_dict)
          self.amp_params = get_amp_params(param_dict)
          self.loss_scale = get_loss_scale(param_dict)
          self.initial_dynamic_scale = get_initial_dynamic_scale(param_dict)
          self.dynamic_loss_scale_args = get_dynamic_loss_scale_args(param_dict)
  
          self.optimizer_name = get_optimizer_name(param_dict)
          if self.optimizer_name is not None and \
              self.optimizer_name.lower() in DEEPSPEED_OPTIMIZERS:
              self.optimizer_name = self.optimizer_name.lower()
  
          self.optimizer_params = get_optimizer_params(param_dict)
          self.optimizer_legacy_fusion = get_optimizer_legacy_fusion(param_dict)
  
          self.zero_allow_untested_optimizer = get_zero_allow_untested_optimizer(
              param_dict)
  
          self.scheduler_name = get_scheduler_name(param_dict)
          self.scheduler_params = get_scheduler_params(param_dict)
  
          self.flops_profiler_config = DeepSpeedFlopsProfilerConfig(param_dict)
          self.wall_clock_breakdown = get_wall_clock_breakdown(
              param_dict) | self.flops_profiler_config.enabled
          self.memory_breakdown = get_memory_breakdown(param_dict)
          self.tensorboard_enabled = get_tensorboard_enabled(param_dict)
          self.tensorboard_output_path = get_tensorboard_output_path(param_dict)
          self.tensorboard_job_name = get_tensorboard_job_name(param_dict)
  
          self.sparse_attention = get_sparse_attention(param_dict)
          self.pipeline = get_pipeline_config(param_dict)
  
          self.pld_enabled = get_pld_enabled(param_dict)
          self.pld_params = get_pld_params(param_dict)
  
          checkpoint_params = get_checkpoint_params(param_dict)
          validation_mode = get_checkpoint_tag_validation_mode(checkpoint_params)
          self.checkpoint_tag_validation_enabled = validation_mode != ValidationMode.IGNORE
          self.checkpoint_tag_validation_fail = validation_mode == ValidationMode.FAIL
  
          self.aio_config = get_aio_config(param_dict)
  
      def _batch_assertion(self):
  
          train_batch = self.train_batch_size
          micro_batch = self.train_micro_batch_size_per_gpu
          grad_acc = self.gradient_accumulation_steps
  
          assert train_batch > 0, \
              f'Train batch size: {train_batch} has to be greater than 0'
  
          assert micro_batch > 0, \
              f'Micro batch size per gpu: {micro_batch} has to be greater than 0'
  
          assert grad_acc > 0, \
              f'Gradient accumulation steps: {grad_acc} has to be greater than 0'
  
          assert train_batch == micro_batch * grad_acc * self.world_size, \
                  (f'Check batch related parameters. train_batch_size is not equal'
                  ' to micro_batch_per_gpu * gradient_acc_step * world_size'
                  f'{train_batch} != {micro_batch} * {grad_acc} * {self.world_size}')
  
      def _set_batch_related_parameters(self):
  
          train_batch = self.train_batch_size
          micro_batch = self.train_micro_batch_size_per_gpu
          grad_acc = self.gradient_accumulation_steps
  
          #all values are provided nothing needs to be set
          if train_batch is not None and \
              micro_batch is not None and \
              grad_acc is not None:
              return
  
          #global_accumulation_steps needs to be set
          elif train_batch is not None and \
              micro_batch is not None:
              grad_acc = train_batch // micro_batch
              grad_acc //= self.world_size
              self.gradient_accumulation_steps = grad_acc
  
          #micro_batch_per_gpu needs to be set
          elif train_batch is not None and \
              grad_acc is not None:
              micro_batch = train_batch // self.world_size
              micro_batch //= grad_acc
              self.train_micro_batch_size_per_gpu = micro_batch
  
          #train_batch_size needs to be set
          elif micro_batch is not None and \
              grad_acc is not None:
              train_batch_size = micro_batch * grad_acc
              train_batch_size *= self.world_size
              self.train_batch_size = train_batch_size
  
          #gradient_accumulation_steps and micro_batch_per_gpus is set
          elif train_batch is not None:
              self.gradient_accumulation_steps = 1
              self.train_micro_batch_size_per_gpu = train_batch // self.world_size
  
          #train_batch_size and gradient_accumulation_step is set
          elif micro_batch is not None:
              self.train_batch_size = micro_batch * self.world_size
              self.gradient_accumulation_steps = 1
  
          #either none of the three parameters are provided or just gradient_accumulation_step is provided
          else:
              assert False, \
                  'Either train_batch_size or micro_batch_per_gpu needs to be provided'
  
      def _configure_train_batch_size(self):
          self._set_batch_related_parameters()
          self._batch_assertion()
  
      def _do_sanity_check(self):
          self._do_error_check()
  
          self._do_warning_check()
  
      def print(self, name):
          logger.info('{}:'.format(name))
          for arg in sorted(vars(self)):
              if arg != '_param_dict':
                  dots = '.' * (29 - len(arg))
                  logger.info('  {} {} {}'.format(arg, dots, getattr(self, arg)))
  
          logger.info('  json = {}'.format(
              json.dumps(self._param_dict,
                         sort_keys=True,
                         indent=4,
                         cls=ScientificNotationEncoder,
                         separators=(',',
                                     ':'))))
  
      def _do_error_check(self):
          assert self.train_micro_batch_size_per_gpu, "DeepSpeedConfig: {} is not defined".format(TRAIN_MICRO_BATCH_SIZE_PER_GPU)
  
          assert self.gradient_accumulation_steps, "DeepSpeedConfig: {} is not defined".format(
              GRADIENT_ACCUMULATION_STEPS)
  
          if self.zero_enabled:
              assert self.zero_optimization_stage <= MAX_STAGE_ZERO_OPTIMIZATION, "DeepSpeedConfig: Maximum supported ZeRO stage is {}".format(MAX_STAGE_ZERO_OPTIMIZATION)
  
      def _do_warning_check(self):
          fp16_enabled = self.fp16_enabled or self.zero_enabled
  
          vocabulary_size = self._param_dict.get(VOCABULARY_SIZE, VOCABULARY_SIZE_DEFAULT)
          if vocabulary_size and vocabulary_size % TENSOR_CORE_ALIGN_SIZE != 0:
              logger.warning(
                  "DeepSpeedConfig: vocabulary size {} is not aligned to {}, may import tensor core utilization."
                  .format(vocabulary_size,
                          TENSOR_CORE_ALIGN_SIZE))
  
          if self.optimizer_params is not None and \
              MAX_GRAD_NORM in self.optimizer_params.keys() and \
                  self.optimizer_params[MAX_GRAD_NORM] > 0:
              if fp16_enabled:
                  if self.global_rank == 0:
                      logger.warning(
                          'DeepSpeedConfig: In FP16 mode, DeepSpeed will pass {}:{} to FP16 wrapper'
                          .format(MAX_GRAD_NORM,
                                  self.optimizer_params[MAX_GRAD_NORM]))
              else:
                  if self.global_rank == 0:
                      logger.warning(
                          'DeepSpeedConfig: In FP32 mode, DeepSpeed does not permit MAX_GRAD_NORM ({}) > 0, setting to zero'
                          .format(self.optimizer_params[MAX_GRAD_NORM]))
                  self.optimizer_params[MAX_GRAD_NORM] = 0.0
  