code: '''
  Copyright 2019 The Microsoft DeepSpeed Team
  '''
  
  import os
  import stat
  import torch
  import warnings
  import hashlib
  import torch.distributed as dist
  from collections import OrderedDict
  from shutil import copyfile
  
  from torch.nn.modules import Module
  from torch.distributed.distributed_c10d import _get_global_rank
  from tensorboardX import SummaryWriter
  
  from deepspeed.runtime.utils import see_memory_usage
  from deepspeed.runtime.zero.stage2 import FP16_DeepSpeedZeroOptimizer
  from deepspeed.runtime.zero.stage1 import FP16_DeepSpeedZeroOptimizer_Stage1
  from deepspeed.runtime.zero.partition_parameters import ZeroParamStatus
  from deepspeed.runtime.zero.utils import is_zero_supported_optimizer
  from deepspeed.runtime.activation_checkpointing import checkpointing as activation_checkpointing
  from deepspeed.runtime.fp16.fused_optimizer import FP16_Optimizer
  from deepspeed.runtime.fp16.unfused_optimizer import FP16_UnfusedOptimizer
  from deepspeed.runtime.config import DeepSpeedConfig, DEEPSPEED_OPTIMIZERS, \
      ADAM_OPTIMIZER, ADAMW_OPTIMIZER, LAMB_OPTIMIZER, ONEBIT_ADAM_OPTIMIZER, \
      TORCH_ADAM_PARAM, ADAM_W_MODE, ADAM_W_MODE_DEFAULT
  
  from deepspeed.runtime.dataloader import DeepSpeedDataLoader
  from deepspeed.runtime.constants import \
      ROUTE_TRAIN, ROUTE_PREDICT, ROUTE_EVAL, \
      PLD_THETA, PLD_GAMMA
  from deepspeed.runtime.zero.constants import \
      ZERO_OPTIMIZATION_OPTIMIZER_STATES, ZERO_OPTIMIZATION_GRADIENTS, ZERO_OPTIMIZATION_WEIGHTS
  from deepspeed.runtime.csr_tensor import CSRTensor
  import deepspeed.runtime.lr_schedules as lr_schedules
  from deepspeed.utils import logger, log_dist, init_distributed
  from deepspeed.utils.timer import ThroughputTimer, SynchronizedWallClockTimer
  from deepspeed.runtime.progressive_layer_drop import ProgressiveLayerDrop
  
  from .pipe.module import PipelineModule
  from .utils import ensure_directory_exists
  from ..ops.op_builder import UtilsBuilder
  from ..ops.adam import DeepSpeedCPUAdam
  from ..ops.adam import FusedAdam
  
  from deepspeed.profiling.flops_profiler.profiler import FlopsProfiler
  
  MEMORY_OPT_ALLREDUCE_SIZE = 500000000
  
  try:
      from apex import amp
  except ImportError:
      # Fail silently so we don't spam logs unnecessarily if user isn't using amp
      pass
  
  
  def split_half_float_double_csr(tensors):
      dtypes = [
          "torch.cuda.HalfTensor",
          "torch.cuda.FloatTensor",
          "torch.cuda.DoubleTensor",
          CSRTensor.type()
      ]
      buckets = []
      for i, dtype in enumerate(dtypes):
          bucket = [t for t in tensors if t.type() == dtype]
          if bucket:
              buckets.append((dtype, bucket))
      return buckets
  
  
  def _initialize_parameter_parallel_groups(parameter_parallel_size=None):
      data_parallel_size = int(dist.get_world_size())
      if parameter_parallel_size is None:
          parameter_parallel_size = int(data_parallel_size)
      logger.info("data_parallel_size: %s, parameter_parallel_size: %s",
                  data_parallel_size,
                  parameter_parallel_size)
      assert data_parallel_size % parameter_parallel_size == 0, \
          'world size should be divisible by parameter parallel size'
      rank = dist.get_rank()
      my_group = None
      for i in range(dist.get_world_size() // parameter_parallel_size):
          ranks = range(i * parameter_parallel_size, (i + 1) * parameter_parallel_size)
          group = torch.distributed.new_group(ranks)
          if rank in ranks:
              my_group = group
      return my_group
  
  
  def print_configuration(args, name):
      logger.info('{}:'.format(name))
      for arg in sorted(vars(args)):
          dots = '.' * (29 - len(arg))
          logger.info('  {} {} {}'.format(arg, dots, getattr(args, arg)))
  
  
  class DeepSpeedEngine(Module):
      r"""DeepSpeed engine for training.
      """
      def __init__(self,
                   args,
                   model,
                   optimizer=None,
                   model_parameters=None,
                   training_data=None,
                   lr_scheduler=None,
                   mpu=None,
                   dist_init_required=None,
                   collate_fn=None,
                   config_params=None,
                   dont_change_device=False):
          super(DeepSpeedEngine, self).__init__()
          self.dont_change_device = dont_change_device
          self.client_optimizer = optimizer
          self.client_model_parameters = model_parameters
          self.client_lr_scheduler = lr_scheduler
          self.training_data = training_data
          self.collate_fn = collate_fn
          self.mpu = mpu
          self.data_parallel_group = None
          self.global_steps = 0
          self.global_samples = 0
          self.micro_steps = 0
          self.skipped_steps = 0
          self.gradient_average = True
          self.warn_unscaled_loss = True
          self.config_params = config_params
          self.loaded_checkpoint_mp_world_size = None
          self.loaded_checkpoint_dp_world_size = None
          self.enable_backward_allreduce = True
          self.progressive_layer_drop = None
          self.dist_backend = "nccl"
  
          if dist_init_required is None:
              dist_init_required = not dist.is_initialized()
  
          if dist_init_required is False:
              assert dist.is_initialized() is True, "Torch distributed not initialized. Please set dist_init_required to True or initialize before calling deepspeed.initialize()"
          else:
              # Initialize torch distributed if needed
              init_distributed(dist_backend=self.dist_backend)
  
          see_memory_usage(f"DeepSpeed Engine: Before args sanity test")
          self._do_args_sanity_check(args)
          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:
              self.summary_writer = self.get_summary_writer()
  
          see_memory_usage(f"DeepSpeed Engine: Before configure distributed model")
  
          # Configure distributed model
          self._configure_distributed_model(model)
  
          see_memory_usage(f"DeepSpeed Engine: After configure distributed model")
  
          # Configure wall clock timer
          self.timers = SynchronizedWallClockTimer()
  
          # Throughput timer
          self.tput_timer = ThroughputTimer(
              batch_size=self.train_micro_batch_size_per_gpu(),
              num_workers=self.dp_world_size,
              steps_per_output=self.steps_per_print(),
              monitor_memory=False)
  
          if training_data:
              self.training_dataloader = self.deepspeed_io(training_data)
          else:
              self.training_dataloader = None
  
          # Configure optimizer and scheduler
          self.optimizer = None
          self.lr_scheduler = None
          if model_parameters or optimizer:
              self._configure_optimizer(optimizer, model_parameters)
              self._configure_lr_scheduler(lr_scheduler)
              self._report_progress(0)
  
          # Bookkeeping for csr support
          self.csr_tensor_module_names = set()
          if self.sparse_gradients_enabled():
              for name, module in self.module.named_modules():
                  if isinstance(module, torch.nn.Embedding):
                      self.csr_tensor_module_names.add(name + ".weight")
                      logger.info("Will convert {} to sparse (csr) "
                                  "tensor during training".format(name))
  
          self.save_non_zero_checkpoint = False
          self.save_zero_checkpoint = False
          self._configure_checkpointing(dist_init_required)
  
          if self.pld_enabled():
              self.progressive_layer_drop = self._configure_progressive_layer_drop()
  
          if self.global_rank == 0:
              self._config.print('DeepSpeedEngine configuration')
              if self.dump_state():
                  print_configuration(self, 'DeepSpeedEngine')
  
          # Load pre-installed or JIT compile (un)flatten ops
          util_ops = UtilsBuilder().load()
          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 checkpoint_tag_validation_enabled(self):
          return self._config.checkpoint_tag_validation_enabled
  
      def checkpoint_tag_validation_fail(self):
          return self._config.checkpoint_tag_validation_fail
  
      def elasticity_enabled(self):
          return self._config.elasticity_enabled
  
      def pld_enabled(self):
          return self._config.pld_enabled
  
      def pld_params(self):
          return self._config.pld_params
  
      def pld_theta(self):
          return self.pld_params()[PLD_THETA]
  
      def pld_gamma(self):
          return self.pld_params()[PLD_GAMMA]
  
      def tensorboard_enabled(self):
          return self._config.tensorboard_enabled
  
      def tensorboard_output_path(self):
          return self._config.tensorboard_output_path
  
      def tensorboard_job_name(self):
          return self._config.tensorboard_job_name
  
      def get_summary_writer(self,
                             name="DeepSpeedJobName",
                             base=os.path.join(os.path.expanduser("~"),
                                               "tensorboard")):
          if self.tensorboard_output_path():
              base_dir = self.tensorboard_output_path()
              job_name = self.tensorboard_job_name()
              log_dir = os.path.join(base_dir, job_name)
          else:
              if self.tensorboard_job_name():
                  name = self.tensorboard_job_name()
  
              # Infrastructure-specific job-id
              if 'DLWS_JOB_ID' in os.environ:
                  infra_job_id = os.environ['DLWS_JOB_ID']
              elif 'DLTS_JOB_ID' in os.environ:
                  infra_job_id = os.environ['DLTS_JOB_ID']
              else:
                  infra_job_id = 'unknown-job-id'
  
              summary_writer_dir_name = os.path.join(infra_job_id, "logs")
              log_dir = os.path.join(base, summary_writer_dir_name, name)
  
          os.makedirs(log_dir, exist_ok=True)
  
          return SummaryWriter(log_dir=log_dir)
  
      def wall_clock_breakdown(self):
          return self._config.wall_clock_breakdown
  
      def flops_profiler_enabled(self):
          return self._config.flops_profiler_config.enabled
  
      def flops_profiler_profile_step(self):
          return self._config.flops_profiler_config.profile_step
  
      def flops_profiler_module_depth(self):
          return self._config.flops_profiler_config.module_depth
  
      def flops_profiler_top_modules(self):
          return self._config.flops_profiler_config.top_modules
  
      def flops_profiler_detailed(self):
          return self._config.flops_profiler_config.detailed
  
      def memory_breakdown(self):
          return self._config.memory_breakdown
  
      def sparse_gradients_enabled(self):
          return self._config.sparse_gradients_enabled
  
      def train_batch_size(self):
          return self._config.train_batch_size
  
      def train_micro_batch_size_per_gpu(self):
          return self._config.train_micro_batch_size_per_gpu
  
      def optimizer_name(self):
          return self.client_optimizer.__class__.__name__ if self.client_optimizer else self._config.optimizer_name
  
      def optimizer_params(self):
          return self._config.optimizer_params
  
      def optimizer_legacy_fusion(self):
          return self._config.optimizer_legacy_fusion
  
      def scheduler_name(self):
          return self._config.scheduler_name
  
      def scheduler_params(self):
          return self._config.scheduler_params
  
      def zero_optimization(self):
          return self._config.zero_enabled
  
      def zero_allow_untested_optimizer(self):
          return self._config.zero_allow_untested_optimizer
  
      def zero_reduce_scatter(self):
          return self._config.zero_config.reduce_scatter
  
      def zero_overlap_comm(self):
          return self._config.zero_config.overlap_comm
  
      def zero_offload_optimizer(self):
          return self._config.zero_config.offload_optimizer
  
      def zero_offload_param(self):
          return self._config.zero_config.offload_param
  
      def zero_cpu_offload(self):
          return self._config.zero_config.offload_optimizer is not None
  
      def zero_sub_group_size(self):
          return self._config.zero_config.sub_group_size
  
      def zero_optimization_stage(self):
          return self._config.zero_optimization_stage
  
      def zero_reduce_bucket_size(self):
          return self._config.zero_config.reduce_bucket_size
  
      def zero_allgather_bucket_size(self):
          return self._config.zero_config.allgather_bucket_size
  
      def zero_optimization_partition_gradients(self):
          return self.zero_optimization_stage() >= ZERO_OPTIMIZATION_GRADIENTS
  
      def zero_optimization_partition_weights(self):
          return self.zero_optimization_stage() >= ZERO_OPTIMIZATION_WEIGHTS
  
      def zero_contiguous_gradients(self):
          return self._config.zero_config.contiguous_gradients
  
      def zero_load_from_fp32_weights(self):
          return self._config.zero_config.load_from_fp32_weights
  
      def zero_elastic_checkpoint(self):
          return self._config.zero_config.elastic_checkpoint
  
      def zero_max_live_parameters(self):
          return self._config.zero_config.max_live_parameters
  
      def zero_max_reuse_distance(self):
          return self._config.zero_config.max_reuse_distance
  
      def zero_prefetch_bucket_size(self):
          return self._config.zero_config.prefetch_bucket_size
  
      def zero_param_persistence_threshold(self):
          return self._config.zero_config.param_persistence_threshold
  
      def zero_gather_fp16_weights_on_model_save(self):
          return self._config.zero_config.gather_fp16_weights_on_model_save
  
      def fp16_enabled(self):
          return self._config.fp16_enabled
  
      def amp_enabled(self):
          return self._config.amp_enabled
  
      def amp_params(self):
          return self._config.amp_params
  
      def loss_scale(self):
          return self._config.loss_scale
  
      def gradient_accumulation_steps(self):
          return self._config.gradient_accumulation_steps
  
      def allreduce_always_fp32(self):
          return self._config.allreduce_always_fp32
  
      def postscale_gradients(self):
          return not self._config.prescale_gradients
  
      def gradient_predivide_factor(self):
          return self._config.gradient_predivide_factor
  
      def steps_per_print(self):
          return self._config.steps_per_print
  
      def zero_allgather_partitions(self):
          return self._config.zero_config.allgather_partitions
  
      def dump_state(self):
          return self._config.dump_state
  
      def gradient_clipping(self):
          return self._config.gradient_clipping
  
      def dynamic_loss_scale(self):
          return self._config.loss_scale == 0
  
      def initial_dynamic_scale(self):
          return self._config.initial_dynamic_scale
  
      def dynamic_loss_scale_args(self):
          return self._config.dynamic_loss_scale_args
  
      def swap_tensor_config(self):
          return self._config.swap_tensor_config
  
      def aio_config(self):
          return self._config.aio_config
  
      def _configure_lr_scheduler(self, client_lr_scheduler):
          # First check for scheduler in json configuration
          lr_scheduler = self._scheduler_from_config(self.optimizer)
          if lr_scheduler:
              if self.global_rank == 0:
                  logger.info(
                      f'DeepSpeed using configured LR scheduler = {self.scheduler_name()}')
              self.lr_scheduler = lr_scheduler
          else:
              if self.global_rank == 0:
                  logger.info('DeepSpeed using client LR scheduler')
              self.lr_scheduler = client_lr_scheduler
          log_dist(f'DeepSpeed LR Scheduler = {self.lr_scheduler}', ranks=[0])
  
      def _configure_checkpointing(self, dist_init_required):
  
          dp_rank = self.global_rank
          if self.mpu:
              dp_rank = self.mpu.get_data_parallel_rank()
  
          # only the first data parallel process needs to store the model checkpoint
          self.save_non_zero_checkpoint = (
              dp_rank == 0) or self.zero_optimization_partition_weights()
  
          if self.zero_optimization():
              param_rank = torch.distributed.get_rank(
                  group=self.optimizer.dp_process_group)
  
              # Only the first parameter parallel process needs to store the
              # optimizer state checkpoints for zero
              self.save_zero_checkpoint = (param_rank == dp_rank)
  
      def _scheduler_from_config(self, optimizer):
          scheduler_name = self.scheduler_name()
          if scheduler_name is not None:
              if hasattr(lr_schedules, scheduler_name):
                  scheduler = getattr(lr_schedules, scheduler_name)
              else:
                  assert hasattr(torch.optim.lr_scheduler, scheduler_name), \
                      f"DeepSpeed does not recognize LR scheduler {scheduler_name}"
  
                  scheduler = getattr(torch.optim.lr_scheduler, scheduler_name)
  
              scheduler_params = self.scheduler_params()
              instantiated_scheduler = scheduler(optimizer, **scheduler_params)
              return instantiated_scheduler
          else:
              return None
  
      def _set_distributed_vars(self):
          if self.local_rank >= 0:
              torch.cuda.set_device(self.local_rank)
              self.device = torch.device("cuda", self.local_rank)
              self.world_size = dist.get_world_size()
              self.global_rank = dist.get_rank()
          else:
              self.world_size = 1
              self.global_rank = 0
              self.device = torch.device("cuda")
  
      # Configure based on command line arguments
      def _configure_with_arguments(self, args, mpu):
          # After the distributed backend is initialized we are guaranteed the LOCAL_RANK
          # environment variable is set. We must align args.local_rank to this value for
          # backwards compatability with scripts relying on [args|self].local_rank containing
          # the correct local rank info. _do_args_sanity_check will ensure this is the case.
          self.local_rank = int(os.environ['LOCAL_RANK'])
          if hasattr(args, 'local_rank'):
              args.local_rank = self.local_rank
  
          config_file = args.deepspeed_config if hasattr(args,
                                                         'deepspeed_config') else None
          self._config = DeepSpeedConfig(config_file, mpu, param_dict=self.config_params)
  
      # Validate command line arguments
      def _do_args_sanity_check(self, args):
          if hasattr(args, 'deepscale_config') and args.deepscale_config is not None:
              logger.warning(
                  "************ --deepscale_config is deprecated, please use --deepspeed_config ************"
              )
              if hasattr(args, 'deepspeed_config'):
                  assert args.deepspeed_config is None, "Not sure how to proceed, we were given both a deepscale_config and deepspeed_config"
              args.deepspeed_config = args.deepscale_config
  
          assert "LOCAL_RANK" in os.environ, "DeepSpeed requires the LOCAL_RANK environment variable, it is set by the deepspeed launcher, " \
              "deepspeed.init_distributed, or the torch.distributed launcher. If using a different launcher please ensure LOCAL_RANK is set prior to initializing deepspeed."
          if hasattr(args, 'local_rank') and args.local_rank != None:
              assert isinstance(args.local_rank, int), f"args.local_rank of {args.local_rank} is an unknown type {type(args.local_rank)}"
              if args.local_rank >= 0:
                  env_local_rank = int(os.environ.get("LOCAL_RANK"))
                  assert env_local_rank == args.local_rank, \
                      f"Mismatch in local rank setting, args.local_rank={args.local_rank} but env['LOCAL_RANK']={env_local_rank}."
  
          if self.config_params is None:
              assert hasattr(args, 'deepspeed_config') and args.deepspeed_config is not None, \
                  'DeepSpeed requires --deepspeed_config to specify configuration file'
  
              assert os.path.isfile(args.deepspeed_config), \
                  'DeepSpeed configuration file: {} is not an existing file'.format(args.deepspeed_config)
  
      def _is_supported_optimizer(self, optimizer_name):
          return optimizer_name in DEEPSPEED_OPTIMIZERS or \
              getattr(torch.optim, optimizer_name, None) is not None
  
      # Validate configuration based on command line arguments
      def _do_sanity_check(self):
          if not self.client_optimizer:
              if self.optimizer_name() is not None:
                  assert self._is_supported_optimizer(self.optimizer_name()), \
                      '{} is not a supported DeepSpeed Optimizer'.format(self.optimizer_name())
  
          if self.optimizer_name() == LAMB_OPTIMIZER:
              assert self.dynamic_loss_scale(), \
                  'DeepSpeed {} optimizer requires dynamic loss scaling'.format(self.optimizer_name())
  
      def _broadcast_model(self):
          def is_replicated(p):
              if hasattr(p, 'ds_status') and p.ds_status is not ZeroParamStatus.AVAILABLE:
                  return False
              return True
  
          for p in self.module.parameters():
              if torch.is_tensor(p) and is_replicated(p):
                  dist.broadcast(p,
                                 self.broadcast_src_rank,
                                 group=self.data_parallel_group)
  
      def _configure_distributed_model(self, model):
          self.module = model
          if self.fp16_enabled():
              self.module.half()
  
          if not self.dont_change_device:
              self.module.to(self.device)
  
          if self.mpu is None:
              self.data_parallel_group = _initialize_parameter_parallel_groups()
              self.dp_world_size = dist.get_world_size()
              self.mp_world_size = 1
              self.broadcast_src_rank = 0
          else:
              self.data_parallel_group = self.mpu.get_data_parallel_group()
              self.dp_world_size = self.mpu.get_data_parallel_world_size()
              self.mp_world_size = self.mpu.get_model_parallel_world_size()
              self.broadcast_src_rank = _get_global_rank(
                  self.mpu.get_data_parallel_group(),
                  0)
  
          if not self.amp_enabled():
              self._broadcast_model()
  
      # Configure optimizer
      def _configure_optimizer(self, client_optimizer, model_parameters):
  
          if client_optimizer is not None:
              client_optimizer.param_groups[:] = [
                  pg for pg in client_optimizer.param_groups if len(pg["params"]) != 0
              ]
              if self.global_rank == 0:
                  logger.info(
                      "Removing param_group that has no 'params' in the client Optimizer")
  
              basic_optimizer = client_optimizer
              if self.global_rank == 0:
                  logger.info('Using client Optimizer as basic optimizer')
          else:
              basic_optimizer = self._configure_basic_optimizer(model_parameters)
              if self.global_rank == 0:
                  logger.info(
                      'Using DeepSpeed Optimizer param name {} as basic optimizer'.format(
                          self.optimizer_name()))
  
          if self.global_rank == 0:
              logger.info('DeepSpeed Basic Optimizer = {}'.format(
                  basic_optimizer.__class__.__name__))
  
          if self.zero_optimization():
              assert not self.amp_enabled(), "Amp and ZeRO are not currently compatible, please use (legacy) fp16 mode which performs similar to amp opt_mode=O2"
              if not is_zero_supported_optimizer(basic_optimizer):
                  assert self.zero_allow_untested_optimizer(), \
                      'You are using an untested ZeRO Optimizer. Please add <"zero_allow_untested_optimizer": true> in the configuration file to use it.'
  
                  if self.global_rank == 0:
                      logger.warning(
                          "**** You are using ZeRO with an untested optimizer, proceed with caution *****"
                      )
              self.optimizer = self._configure_zero_optimizer(basic_optimizer)
          elif self.amp_enabled():
              assert not self.fp16_enabled(), "Cannot enable both amp with (legacy) fp16 mode"
              amp_params = self.amp_params()
              if self.global_rank == 0:
                  logger.info(f"Initializing AMP with these params: {amp_params}")
              try:
                  logger.info("Initializing Apex amp from: {}".format(amp.__path__))
              except NameError:
                  # If apex/amp is available it will be imported above
                  raise RuntimeError(
                      "Unable to import apex/amp, please make sure it is installed")
              self.module, self.optimizer = amp.initialize(self.module, basic_optimizer, **amp_params)
              self._broadcast_model()
          elif self.fp16_enabled():
              self.optimizer = self._configure_fp16_optimizer(basic_optimizer)
          else:
              self.optimizer = basic_optimizer
          log_dist('DeepSpeed Final Optimizer = {}'.format(self.optimizer_name()),
                   ranks=[0])
  
      def _configure_basic_optimizer(self, model_parameters):
          optimizer_parameters = self.optimizer_params()
          # print(optimizer_parameters.keys())
          if 'max_grad_norm' in optimizer_parameters.keys():
              raise ValueError(
                  "'max_grad_norm' is not supported as an optimizer parameter, please switch to using the deepspeed parameter 'gradient_clipping' see: https://www.deepspeed.ai/docs/config-json/#gradient-clipping for more details"
              )
  
          if self.optimizer_name() in [ADAM_OPTIMIZER, ADAMW_OPTIMIZER]:
              torch_adam = optimizer_parameters.pop(TORCH_ADAM_PARAM, False)
              adam_w_mode = optimizer_parameters.pop(ADAM_W_MODE, ADAM_W_MODE_DEFAULT)
  
              # Optimizer name of Adam forces AdamW logic unless adam_w_mode is explictly set
              effective_adam_w_mode = self.optimizer_name(
              ) == ADAMW_OPTIMIZER or adam_w_mode
  
              if torch_adam:
                  if not effective_adam_w_mode:
                      optimizer = torch.optim.Adam(model_parameters,
                                                   **optimizer_parameters)
                  else:
                      optimizer = torch.optim.AdamW(model_parameters,
                                                    **optimizer_parameters)
              else:
                  if self.zero_cpu_offload():
                      from deepspeed.ops.adam import DeepSpeedCPUAdam
                      optimizer = DeepSpeedCPUAdam(model_parameters,
                                                   **optimizer_parameters,
                                                   adamw_mode=effective_adam_w_mode)
                  else:
                      from deepspeed.ops.adam import FusedAdam
                      optimizer = FusedAdam(model_parameters,
                                            **optimizer_parameters,
                                            adam_w_mode=effective_adam_w_mode)
  
          elif self.optimizer_name() == LAMB_OPTIMIZER:
              from deepspeed.ops.lamb import FusedLamb
              optimizer = FusedLamb(model_parameters, **optimizer_parameters)
          elif self.optimizer_name() == ONEBIT_ADAM_OPTIMIZER:
              from deepspeed.runtime.fp16.onebit.adam import OnebitAdam
              optimizer = OnebitAdam(model_parameters, self, **optimizer_parameters)
              if not self.fp16_enabled():
                  logger.warning(
                      f'Currently the convergence of 1-bit Adam is only verified under FP16'
                  )
          else:
              torch_optimizer = getattr(torch.optim, self.optimizer_name())
              optimizer = torch_optimizer(model_parameters, **optimizer_parameters)
          return optimizer
  
      def _configure_fp16_optimizer(self, optimizer):
          initial_dynamic_scale = self.initial_dynamic_scale()
          dynamic_loss_args = self.dynamic_loss_scale_args()
          clip_grad = self.gradient_clipping()
          if isinstance(optimizer,
                        FusedAdam) or self.optimizer_name() == ONEBIT_ADAM_OPTIMIZER:
              if self.dynamic_loss_scale():
                  log_dist('Creating fp16 optimizer with dynamic loss scale', ranks=[0])
                  timers = self.timers if self.wall_clock_breakdown() else None
                  optimizer = FP16_Optimizer(
                      optimizer,
                      dynamic_loss_scale=True,
                      initial_dynamic_scale=initial_dynamic_scale,
                      dynamic_loss_args=dynamic_loss_args,
                      mpu=self.mpu,
                      clip_grad=clip_grad,
                      fused_adam_legacy=self.optimizer_legacy_fusion(),
                      timers=timers)
              else:
                  log_dist('Creating fp16 optimizer with static loss scale: {}'.format(
                      self.loss_scale()),
                           ranks=[0])
                  optimizer = FP16_Optimizer(
                      optimizer,
                      static_loss_scale=self.loss_scale(),
                      mpu=self.mpu,
                      clip_grad=clip_grad,
                      fused_adam_legacy=self.optimizer_legacy_fusion())
          else:
              log_dist('Creating fp16 unfused optimizer with dynamic loss scale',
                       ranks=[0])
              optimizer = FP16_UnfusedOptimizer(
                  optimizer,
                  static_loss_scale=self.loss_scale(),
                  dynamic_loss_scale=self.dynamic_loss_scale(),
                  dynamic_loss_args=dynamic_loss_args,
                  mpu=self.mpu,
                  clip_grad=clip_grad,
                  fused_lamb_legacy=self.optimizer_name() == LAMB_OPTIMIZER)
  
          return optimizer
  
      def _configure_zero_optimizer(self, optimizer):
          zero_stage = self.zero_optimization_stage()
          log_dist('Creating fp16 ZeRO stage {} optimizer'.format(zero_stage), ranks=[0])
          assert not self.allreduce_always_fp32(), "ZeRO does not support 'fp32_allreduce': true"
          timers = self.timers if self.wall_clock_breakdown() else None
  
          if zero_stage == ZERO_OPTIMIZATION_OPTIMIZER_STATES:
              assert self.zero_reduce_scatter(), 'Stage 1 only supports reduce scatter mode'
              optimizer = FP16_DeepSpeedZeroOptimizer_Stage1(
                  optimizer,
                  static_loss_scale=self.loss_scale(),
                  dynamic_loss_scale=self.dynamic_loss_scale(),
                  dynamic_loss_args=self.dynamic_loss_scale_args(),
                  clip_grad=self.gradient_clipping(),
                  all_gather_partitions=self.zero_allgather_partitions(),
                  allgather_size=self.zero_allgather_bucket_size(),
                  max_elements_per_comm=self.zero_reduce_bucket_size(),
                  dp_process_group=self.data_parallel_group,
                  elastic_checkpoint=self.zero_elastic_checkpoint(),
                  mpu=self.mpu)
          elif zero_stage == ZERO_OPTIMIZATION_GRADIENTS:
              optimizer = FP16_DeepSpeedZeroOptimizer(
                  optimizer,
                  timers=timers,
                  static_loss_scale=self.loss_scale(),
                  dynamic_loss_scale=self.dynamic_loss_scale(),
                  dynamic_loss_args=self.dynamic_loss_scale_args(),
                  clip_grad=self.gradient_clipping(),
                  contiguous_gradients=self.zero_contiguous_gradients(),
                  reduce_bucket_size=self.zero_reduce_bucket_size(),
                  allgather_bucket_size=self.zero_allgather_bucket_size(),
                  dp_process_group=self.data_parallel_group,
                  reduce_scatter=self.zero_reduce_scatter(),
                  overlap_comm=self.zero_overlap_comm(),
                  cpu_offload=self.zero_cpu_offload(),
                  mpu=self.mpu,
                  postscale_gradients=self.postscale_gradients(),
                  gradient_predivide_factor=self.gradient_predivide_factor(),
                  gradient_accumulation_steps=self.gradient_accumulation_steps())
          elif zero_stage == ZERO_OPTIMIZATION_WEIGHTS:
              print("Initializing ZeRO Stage 3") if dist.get_rank() == 0 else None
              from deepspeed.runtime.zero.stage3 import FP16_DeepSpeedZeroOptimizer_Stage3
              optimizer = FP16_DeepSpeedZeroOptimizer_Stage3(
                  self.module,
                  optimizer,
                  timers=timers,
                  static_loss_scale=self.loss_scale(),
                  dynamic_loss_scale=self.dynamic_loss_scale(),
                  dynamic_loss_args=self.dynamic_loss_scale_args(),
                  clip_grad=self.gradient_clipping(),
                  contiguous_gradients=self.zero_contiguous_gradients(),
                  reduce_bucket_size=self.zero_reduce_bucket_size(),
                  prefetch_bucket_size=self.zero_prefetch_bucket_size(),
                  max_reuse_distance=self.zero_max_reuse_distance(),
                  max_live_parameters=self.zero_max_live_parameters(),
                  param_persistence_threshold=self.zero_param_persistence_threshold(),
                  dp_process_group=self.data_parallel_group,
                  reduce_scatter=self.zero_reduce_scatter(),
                  overlap_comm=self.zero_overlap_comm(),
                  offload_optimizer_config=self.zero_offload_optimizer(),
                  offload_param_config=self.zero_offload_param(),
                  sub_group_size=self.zero_sub_group_size(),
                  mpu=self.mpu,
                  postscale_gradients=self.postscale_gradients(),
                  gradient_predivide_factor=self.gradient_predivide_factor(),
                  gradient_accumulation_steps=self.gradient_accumulation_steps(),
                  aio_config=self.aio_config())
  
          else:
              raise NotImplementedError("ZeRO stage {} not implemented".format(zero_stage))
  
          return optimizer
  
      def _configure_progressive_layer_drop(self):
          pld = ProgressiveLayerDrop(theta=self.pld_theta(), gamma=self.pld_gamma())
  
          return pld
  
      def deepspeed_io(self,
                       dataset,
                       batch_size=None,
                       route=ROUTE_TRAIN,
                       pin_memory=True,
                       data_sampler=None,
                       collate_fn=None,
                       num_local_io_workers=None):
          if not isinstance(dataset, torch.utils.data.Dataset):
              raise ValueError("Training data must be a torch Dataset")
  
          if data_sampler is None and (route == ROUTE_PREDICT or route == ROUTE_EVAL):
              data_sampler = torch.utils.data.SequentialSampler(dataset)
  
          if batch_size is None:
              batch_size = self.train_micro_batch_size_per_gpu()
  
          if collate_fn is None:
              collate_fn = self.collate_fn
  
          # Currently we only use timer in train route
          deepspeed_io_timer = None
          if route == ROUTE_TRAIN:
              deepspeed_io_timer = self.tput_timer
  
          # If mpu is provied, forward world size and parallel rank to sampler.
          data_parallel_world_size = None
          data_parallel_rank = None
          if self.mpu is not None:
              data_parallel_world_size = self.mpu.get_data_parallel_world_size()
              data_parallel_rank = self.mpu.get_data_parallel_rank()
  
          return DeepSpeedDataLoader(dataset=dataset,
                                     batch_size=batch_size,
                                     pin_memory=pin_memory,
                                     collate_fn=collate_fn,
                                     local_rank=self.local_rank,
                                     tput_timer=deepspeed_io_timer,
                                     num_local_io_workers=num_local_io_workers,
                                     data_sampler=data_sampler,
                                     data_parallel_world_size=data_parallel_world_size,
                                     data_parallel_rank=data_parallel_rank)
  
      def train(self, mode=True):
          r"""
          """
  
          self.warn_unscaled_loss = True
          self.module.train(mode)
  
      def eval(self):
          r"""
          """
  
          self.warn_unscaled_loss = True
          self.module.train(False)
  
      def _scale_loss(self, prescaled_loss):
          if isinstance(prescaled_loss, torch.Tensor):
              scaled_loss = prescaled_loss / self.gradient_accumulation_steps()
          elif isinstance(prescaled_loss, tuple) or isinstance(prescaled_loss, list):
              scaled_loss = []
              for l in prescaled_loss:
                  if isinstance(l, torch.Tensor):
                      scaled_loss.append(l / self.gradient_accumulation_steps())
                  else:
                      scaled_loss.append(l)
          else:
              scaled_loss = prescaled_loss
              if self.warn_unscaled_loss:
                  logger.warning(
                      f'DeepSpeed unable to scale loss because of type: {type(prescaled_loss)}'
                  )
                  self.warn_unscaled_loss = False
  
          return scaled_loss
  
      def forward(self, *inputs, **kwargs):
          r"""Execute forward propagation
  
          Arguments:
              *inputs: Variable length input list
              **kwargs: variable length keyword arguments
          """
          if self.flops_profiler_enabled(
          ) and self.global_steps == self.flops_profiler_profile_step(
          ) and self.global_rank == 0:
              self.flops_profiler = FlopsProfiler(self.module)
              self.flops_profiler.start_profile(ignore_list=None)
  
          if self.module.training and self.progressive_layer_drop:
              kwargs.update(self.progressive_layer_drop.get_state())
  
          if self.zero_optimization_partition_weights():
              # Enable automated discovery of external parameters by indicating that
              # we are in a forward pass.
              for module in self.module.modules():
                  module._parameters._in_forward = True
                  pass
  
          if self.wall_clock_breakdown():
              self.timers('forward_microstep').start()
              self.timers('forward').start()
  
          if self.training_dataloader is None:
              self.tput_timer.start()
          loss = self.module(*inputs, **kwargs)
  
          if self.zero_optimization_partition_weights():
              # Reset the ZeRO-3 state if we are only doing forward-passes (ie evaluation).
              if not torch._C.is_grad_enabled():
                  self.optimizer.param_coordinator.reset_step()
  
              # Disable automated discovery of external parameters
              for module in self.module.modules():
                  module._parameters._in_forward = False
  
          if self.wall_clock_breakdown():
              self.timers('forward').stop()
              self.timers('forward_microstep').stop()
  
          if self.flops_profiler_enabled(
          ) and self.global_steps == self.flops_profiler_profile_step(
          ) and self.global_rank == 0:
              self.flops_profiler.print_model_profile(
                  profile_step=self.global_steps,
                  module_depth=self.flops_profiler_module_depth(),
                  top_modules=self.flops_profiler_top_modules(),
                  detailed=self.flops_profiler_detailed())
              self.flops_profiler.end_profile()
  
          return loss
  
      def allreduce_gradients(self, bucket_size=MEMORY_OPT_ALLREDUCE_SIZE):
          #Zero stage 2 communicates during non gradient accumulation boundaries as well
          if self.zero_optimization_partition_gradients():
              self.optimizer.overlapping_partition_gradients_reduce_epilogue()
  
          #Communicate only at gradient accumulation boundaries
          elif self.is_gradient_accumulation_boundary():
              if self.zero_optimization_stage() == ZERO_OPTIMIZATION_OPTIMIZER_STATES:
                  assert self.zero_reduce_scatter()
                  self.optimizer.reduce_scatter_gradients(
                      postscale_gradients=self.postscale_gradients(),
                      gradient_predivide_factor=self.gradient_predivide_factor(),
                      gradient_average=self.gradient_average)
              else:
                  self.buffered_allreduce_fallback(elements_per_buffer=bucket_size)
  
      def backward(self, loss, allreduce_gradients=True, release_loss=False):
          r"""Execute backward pass on the loss
  
          Arguments:
              loss: Torch tensor on which to execute backward propagation
              allreduce_gradients: is deprecated, ignored, and will soon be removed'
          """
  
          if not allreduce_gradients:
              logger.warning(
                  f'Argument `allreduce_gradients` is deprecated, ignored, and will soon be removed'
              )
  
          # scale loss w.r.t. gradient accumulation if needed
          if self.gradient_accumulation_steps() > 1:
              loss = self._scale_loss(loss.float())
  
          # Log training Loss
          if self.tensorboard_enabled():
              if self.is_gradient_accumulation_boundary():
                  if self.global_rank == 0:
                      self.summary_events = [
                          (f'Train/Samples/train_loss',
                           loss.mean().item() * self.gradient_accumulation_steps(),
                           self.global_samples)
                      ]
                      for event in self.summary_events:  # write_summary_events
                          self.summary_writer.add_scalar(event[0], event[1], event[2])
                      self.summary_writer.flush()
  
          if self.wall_clock_breakdown():
              self.timers('backward_microstep').start()
              self.timers('backward').start()
  
          assert self.optimizer is not None, "must provide optimizer during " \
                                             "init in order to use backward"
  
          if self.wall_clock_breakdown():
              self.timers('backward_inner_microstep').start()
              self.timers('backward_inner').start()
  
          if self.zero_optimization():
              self.optimizer.is_gradient_accumulation_boundary = self.is_gradient_accumulation_boundary(
              )
              self.optimizer.backward(loss)
          elif self.amp_enabled():
              # AMP requires delaying unscale when inside gradient accumulation boundaries
              # https://nvidia.github.io/apex/advanced.html#gradient-accumulation-across-iterations
              delay_unscale = not self.is_gradient_accumulation_boundary()
              with amp.scale_loss(loss,
                                  self.optimizer,
                                  delay_unscale=delay_unscale) as scaled_loss:
                  scaled_loss.backward()
          elif self.fp16_enabled():
              self.optimizer.backward(loss)
          else:
              loss.backward()
  
          if self.wall_clock_breakdown():
              self.timers('backward_inner').stop()
              self.timers('backward_inner_microstep').stop()
  
          if self.wall_clock_breakdown():
              self.timers('backward_allreduce_microstep').start()
              self.timers('backward_allreduce').start()
  
          if self.enable_backward_allreduce:
              self.allreduce_gradients()
  
          if self.wall_clock_breakdown():
              self.timers('backward_allreduce').stop()
              self.timers('backward_allreduce_microstep').stop()
              self.timers('backward').stop()
              self.timers('backward_microstep').stop()
  
          if release_loss:
              # loss.data = None
              pass
  
          return loss
  
      def is_gradient_accumulation_boundary(self):
          """Query whether the current micro-batch is at the boundary of
          gradient accumulation, and thus will trigger gradient reductions and
          an optimizer step.
  
          Returns:
              bool: if the current step is a gradient accumulation boundary.
          """
          return (self.micro_steps + 1) % \
              self.gradient_accumulation_steps() == 0
  
      def zero_grad(self):
          """
          Zero parameter grads.
          """
          for param_name, param in self.module.named_parameters():
              param.grad = None
  
      def clip_fp32_gradients(self):
          torch.nn.utils.clip_grad_norm_(parameters=self.module.parameters(),
                                         max_norm=self.gradient_clipping())
  
      def _take_model_step(self, lr_kwargs):
          if self.gradient_clipping() > 0.0:
              if not self.fp16_enabled() and not self.amp_enabled():
                  self.clip_fp32_gradients()
              elif self.amp_enabled():
                  # AMP's recommended way of doing clipping
                  # https://nvidia.github.io/apex/advanced.html#gradient-clipping
                  master_params = amp.master_params(self.optimizer)
                  torch.nn.utils.clip_grad_norm_(parameters=master_params,
                                                 max_norm=self.gradient_clipping())
          self.optimizer.step()
  
          #zero grad in basic optimizer could be unreliable and may not exhibit
          #the behaviour that we want
          if not self.zero_optimization() and not self.fp16_enabled(
          ) and not self.amp_enabled():
              self.zero_grad()
          else:
              self.optimizer.zero_grad()
  
          report_progress = self.global_rank == 0 if self.global_rank else True
  
          # Check overlow here since in DS fp16 optimizer, the overflow is updated in above step() function.
          overflow = False
          if hasattr(self.optimizer, 'overflow'):
              overflow = self.optimizer.overflow
  
          if overflow:
              self.skipped_steps += 1
          else:
              if self.lr_scheduler is not None:
                  self.lr_scheduler.step(**(lr_kwargs or {}))
  
          if report_progress and (self.global_steps + 1) % self.steps_per_print() == 0:
              self._report_progress(self.global_steps + 1)
  
          self.global_steps += 1
          self.global_samples += self.train_batch_size()
  
      def step(self, lr_kwargs=None):
          r"""Execute the weight update step after forward and backward propagation
          on effective_train_batch.
          """
          if self.wall_clock_breakdown():
              self.timers('step_microstep').start()
              self.timers('step').start()
  
          assert self.optimizer is not None, "must provide optimizer during " \
                                             "init in order to use step"
          report_progress = self.global_rank == 0 if self.global_rank else True
  
          # Update the model when we reach gradient accumulation boundaries
          if self.is_gradient_accumulation_boundary():
              if self.progressive_layer_drop:
                  self.progressive_layer_drop.update_state(self.global_steps)
  
              self._take_model_step(lr_kwargs)
  
          self.tput_timer.stop(report_progress)
  
          # Log learning rate
          if self.tensorboard_enabled():
              if self.is_gradient_accumulation_boundary():
                  if self.global_rank == 0:
                      self.summary_events = [(f'Train/Samples/lr',
                                              self.get_lr()[0],
                                              self.global_samples)]
                      for event in self.summary_events:  # write_summary_events
                          self.summary_writer.add_scalar(event[0], event[1], event[2])
                      if self.fp16_enabled() and hasattr(self.optimizer, 'cur_scale'):
                          self.summary_events.append((f'Train/Samples/loss_scale',
                                                      self.optimizer.cur_scale,
                                                      self.global_samples))
                      for event in self.summary_events:  # write_summary_events
                          self.summary_writer.add_scalar(event[0], event[1], event[2])
                      self.summary_writer.flush()
  
          if self.wall_clock_breakdown():
              self.timers('step').stop()
              self.timers('step_microstep').stop()
              timer_names = [
                  'forward_microstep',
                  'backward_microstep',
                  'backward_inner_microstep',
                  'backward_allreduce_microstep',
                  'step_microstep'
              ]
              self.timers.log(names=timer_names, memory_breakdown=self.memory_breakdown())
  
              # Log timing
              if self.is_gradient_accumulation_boundary():
                  if self.tensorboard_enabled():
                      if self.global_rank == 0:
                          self.summary_events = [
                              (f'Train/Samples/elapsed_time_ms_forward',
                               self.timers('forward').elapsed(reset=False) * 1000.0,
                               self.global_samples),
                              (f'Train/Samples/elapsed_time_ms_backward',
                               self.timers('backward').elapsed(reset=False) * 1000.0,
                               self.global_samples),
                              (f'Train/Samples/elapsed_time_ms_backward_inner',
                               self.timers('backward_inner').elapsed(reset=False) * 1000.0,
                               self.global_samples),
                              (f'Train/Samples/elapsed_time_ms_backward_allreduce',
                               self.timers('backward_allreduce').elapsed(reset=False) *
                               1000.0,
                               self.global_samples),
                              (f'Train/Samples/elapsed_time_ms_step',
                               self.timers('step').elapsed(reset=False) * 1000.0,
                               self.global_samples)
                          ]
                          for event in self.summary_events:  # write_summary_events
                              self.summary_writer.add_scalar(event[0], event[1], event[2])
                          self.summary_writer.flush()
  
              if self.wall_clock_breakdown():
                  self.timers.log([
                      'forward',
                      'backward',
                      'backward_inner',
                      'backward_allreduce',
                      'step'
                  ])
  
          self.micro_steps += 1
  
      def _get_optimizer_param(self, param_name):
          result = []
          if not self.optimizer:
              return result
          for group in self.optimizer.param_groups:
              if param_name in group:
                  result.append(group[param_name])
              else:
                  result.append(0.0)
          return result
  
      def get_lr(self):
          return self._get_optimizer_param('lr')
  
      def get_type(self):
          return self._get_optimizer_param('type')
  
      def get_mom(self):
          if self.optimizer_name() in ['SGD', 'RMSprop']:
              return self._get_optimizer_param('momentum')
          else:
              return self._get_optimizer_param('betas')
  
      def get_pld_theta(self):
          if self.progressive_layer_drop:
              return self.progressive_layer_drop.get_theta()
          else:
              return None
  
      def _report_progress(self, step):
          lr = self.get_lr()
          mom = self.get_mom()
          log_dist(f'step={step}, skipped={self.skipped_steps}, lr={lr}, mom={mom}',
                   ranks=[0])
  
      def allreduce_bucket(self, bucket):
          tensor = self.flatten(bucket)
  
          tensor_to_allreduce = tensor
  
          if self.allreduce_always_fp32():
              tensor_to_allreduce = tensor.float()
  
          if self.postscale_gradients():
              if self.gradient_predivide_factor() != 1.0:
                  tensor_to_allreduce.mul_(1. / self.gradient_predivide_factor())
  
              dist.all_reduce(tensor_to_allreduce, group=self.data_parallel_group)
  
              if self.gradient_average:
                  if self.gradient_predivide_factor() != self.dp_world_size:
                      tensor_to_allreduce.mul_(self.gradient_predivide_factor() /
                                               self.dp_world_size)
          else:
              tensor_to_allreduce.div_(self.dp_world_size)
              dist.all_reduce(tensor_to_allreduce, group=self.data_parallel_group)
  
          if self.allreduce_always_fp32() and tensor is not tensor_to_allreduce:
              tensor.copy_(tensor_to_allreduce)
  
          return tensor
  
      def allreduce_and_copy(self, small_bucket):
          allreduced = self.allreduce_bucket(small_bucket)
          for buf, synced in zip(small_bucket, self.unflatten(allreduced, small_bucket)):
              buf.copy_(synced)
  
      def allreduce_no_retain(self, bucket, numel_per_bucket=500000000):
          small_bucket = []
          numel = 0
          for tensor in bucket:
              small_bucket.append(tensor)
              numel = numel + tensor.numel()
              if numel > numel_per_bucket:
                  self.allreduce_and_copy(small_bucket)
                  small_bucket = []
                  numel = 0
          if len(small_bucket) > 0:
              self.allreduce_and_copy(small_bucket)
  
      def buffered_allreduce_fallback(self, grads=None, elements_per_buffer=500000000):
          grads = []
          for param_name, param in self.module.named_parameters():
              if param.grad is None:
                  # In cases where there is an imbalance of empty grads across
                  # ranks we must create empty grads, this will ensure that every
                  # rank is reducing the same size. In some cases it may make
                  # sense in the future to support the ability to average not
                  # w.r.t. world size but with a different value.
                  param.grad = torch.zeros(param.size(),
                                           dtype=param.dtype,
                                           device=param.device)
                  grads.append(param.grad.data)
              else:
                  grad_data = param.grad.data
                  if self.sparse_gradients_enabled(
                  ) and param_name in self.csr_tensor_module_names:
                      grads.append(CSRTensor(grad_data))
                  else:
                      grads.append(grad_data)
  
          split_buckets = split_half_float_double_csr(grads)
  
          for i, bucket_tuple in enumerate(split_buckets):
              bucket_type, bucket = bucket_tuple
              if bucket_type == CSRTensor.type():
                  self.csr_allreduce_no_retain(bucket)
              else:
                  self.allreduce_no_retain(bucket, numel_per_bucket=elements_per_buffer)
  
      def csr_allreduce_no_retain(self, bucket):
          allreduced_csrs = self.csr_allreduce_bucket(bucket)
          # Densify csr tensor and copy back to original location
          for csr in allreduced_csrs:
              dense_tensor = csr.to_dense()
              csr.orig_dense_tensor.copy_(dense_tensor)
  
      def csr_allreduce_bucket(self, bucket):
          csr_list = []
          for csr in bucket:
              csr_list.append(self.csr_allreduce(csr))
          return csr_list
  
      def csr_allreduce(self, csr):
          # Pre-divide for fp16 stability
          csr.values.div_(self.dp_world_size)
  
          indices_device_list = self.csr_all_gather(csr.indices)
          values_device_list = self.csr_all_gather(csr.values)
  
          csr.indices = torch.cat(indices_device_list)
          csr.values = torch.cat(values_device_list)
          return csr
  
      def csr_all_gather(self, value):
          my_size = torch.LongTensor([value.size()[0]]).to(self.device)
          all_sizes = self.all_gather_scalar(my_size)
          max_size = torch.cat(all_sizes).max()
          fill_size = (max_size - my_size)
  
          assert value.dim() in [1, 2]
          if value.dim() == 1:
              if fill_size > 0:
                  value = torch.cat([value, value.new_zeros(fill_size)])
              tensor_list = [value.new_zeros(max_size) for _ in range(self.dp_world_size)]
          else:
              if fill_size > 0:
                  value = torch.cat([value, value.new_zeros(fill_size, value.size()[1])])
              tensor_list = [
                  value.new_zeros(max_size,
                                  value.size()[1]) for _ in range(self.dp_world_size)
              ]
  
          dist.all_gather(tensor_list, value, group=self.data_parallel_group)
          tensors = []
          for dev_idx, t in enumerate(tensor_list):
              size = all_sizes[dev_idx][0]
              tensors.append(
                  t.index_select(0,
                                 torch.LongTensor(range(size)).to(self.device)))
  
          return tensors
  
      def all_gather_scalar(self, value):
          tensor_list = [value.new_zeros(value.size()) for _ in range(self.dp_world_size)]
          dist.all_gather(tensor_list, value, group=self.data_parallel_group)
          return tensor_list
  
      def module_state_dict(self, destination=None, prefix='', keep_vars=False):
          sd = self.module.state_dict(destination, prefix, keep_vars)
          return sd
  
      def load_module_state_dict(self, state_dict, strict=True):
          self.module.load_state_dict(state_dict, strict=strict)
  
      def _get_rank_zero_ckpt_name(self, checkpoints_path, tag, mp_rank, dp_rank):
          filename = 'zero_pp_rank_{}'.format(dp_rank)
          zero_ckpt_name = os.path.join(
              checkpoints_path,
              str(tag),
              filename + '_mp_rank_{:02d}'.format(mp_rank) + '_optim_states.pt')
          return zero_ckpt_name
  
      def _get_zero_ckpt_name(self, checkpoints_path, tag):
          mp_rank = 0 if self.mpu is None else self.mpu.get_model_parallel_rank()
          pp_rank = torch.distributed.get_rank(group=self.optimizer.dp_process_group)
          return self._get_rank_zero_ckpt_name(checkpoints_path, tag, mp_rank, pp_rank)
  
      def _get_ckpt_name(self, checkpoints_path, tag):
          mp_rank = 0 if self.mpu is None else self.mpu.get_model_parallel_rank()
          if self.zero_optimization_partition_weights():
              filename = 'zero_pp_rank_{}'.format(
                  torch.distributed.get_rank(group=self.optimizer.dp_process_group))
              ckpt_name = os.path.join(
                  checkpoints_path,
                  str(tag),
                  filename + '_mp_rank_{:02d}'.format(mp_rank) + '_model_states.pt')
          else:
              ckpt_name = os.path.join(
                  checkpoints_path,
                  str(tag),
                  'mp_rank_{:02d}'.format(mp_rank) + '_model_states.pt')
          return ckpt_name
  
      def load_checkpoint(self,
                          load_dir,
                          tag=None,
                          load_module_strict=True,
                          load_optimizer_states=True,
                          load_lr_scheduler_states=True):
          """Load training checkpoint
  
          Arguments:
              load_dir: Required. Directory to load the checkpoint from
              tag: Checkpoint tag used as a unique identifier for checkpoint, if not provided will attempt to load tag in 'latest' file
              load_module_strict: Optional. Boolean to strictly enforce that the keys in state_dict of module and checkpoint match.
              load_optimizer_states: Optional. Boolean to load the training optimizer states from Checkpoint. Ex. ADAM's momentum and variance
              load_lr_scheduler_states: Optional. Boolean to add the learning rate scheduler states from Checkpoint.
          Returns:
              A tuple of ``load_path`` and ``client_state``.
  
              *``load_path``: Path of the loaded checkpoint. ``None`` if loading the checkpoint failed.
  
              *``client_state``: State dictionary used for loading required training states in the client code.
          """
  
          if tag is None:
              latest_path = os.path.join(load_dir, 'latest')
              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,
                                                           load_module_strict=load_module_strict,
                                                           load_optimizer_states=load_optimizer_states,
                                                           load_lr_scheduler_states=load_lr_scheduler_states)
  
          if self.zero_optimization() and load_path is not None:
              self._load_zero_checkpoint(load_dir,
                                         tag,
                                         load_optimizer_states=load_optimizer_states)
  
          return load_path, client_states
  
      def _load_checkpoint(self,
                           load_dir,
                           tag,
                           load_module_strict=True,
                           load_optimizer_states=True,
                           load_lr_scheduler_states=True):
  
          load_path = self._get_ckpt_name(load_dir, tag)
  
          if not os.path.exists(load_path):
              logger.warn(
                  'Client provided checkpoint load path: {} does not exist ... skip checkpoint load'
                  .format(load_path))
              return None, None
  
          logger.info(f'rank: {self.global_rank} loading checkpoint: {load_path}')
          checkpoint = torch.load(load_path, map_location=lambda storage, loc: storage)
  
          if isinstance(self.module, PipelineModule):
              # Pipeline parallelism uses this to load its own checkpoint files.
              self._curr_ckpt_path = os.path.join(load_dir, tag)
  
          self.load_module_state_dict(state_dict=checkpoint['module'],
                                      strict=load_module_strict)
          if self.optimizer is not None and not self.zero_optimization():
              if self.fp16_enabled():
                  self.optimizer.load_state_dict(
                      checkpoint['optimizer'],
                      load_optimizer_states=load_optimizer_states)
              elif load_optimizer_states:
                  self.optimizer.load_state_dict(checkpoint['optimizer'])
  
          if load_lr_scheduler_states and self.lr_scheduler is not None:
              self.lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
  
          self.csr_tensor_module_names = checkpoint['csr_tensor_module_names']
          self.global_steps = checkpoint['global_steps']
          self.global_samples = checkpoint.get('global_samples',
                                               self.global_steps * self.train_batch_size())
          self.skipped_steps = checkpoint['skipped_steps']
          self.loaded_checkpoint_mp_world_size = checkpoint['mp_world_size']
          self.loaded_checkpoint_dp_world_size = checkpoint['dp_world_size']
          deepspeed_states = [
              'module',
              'optimizer',
              'lr_scheduler',
              'csr_tensor_module_names',
              'skipped_steps',
              'global_steps',
              'dp_world_size',
              'mp_world_size'
          ]
          client_state = {
              key: value
              for key,
              value in checkpoint.items() if not key in deepspeed_states
          }
  
          return load_path, client_state
  
      def _load_zero_checkpoint(self, load_dir, tag, load_optimizer_states=True):
          zero_sd_list = self._get_all_zero_checkpoints(load_dir, tag)
          if zero_sd_list is None:
              return
  
          self.optimizer.load_state_dict(
              state_dict_list=zero_sd_list,
              load_optimizer_states=load_optimizer_states,
              load_from_fp32_weights=self.zero_load_from_fp32_weights())
          print(
              f'loading {len(zero_sd_list)} zero partition checkpoints for rank {self.global_rank}'
          )
  
      def _get_mp_rank_zero_checkpoint_names(self, load_dir, tag, mp_rank, dp_world_size):
          zero_ckpt_names = []
          for dp_rank in range(dp_world_size):
              ckpt_name = self._get_rank_zero_ckpt_name(checkpoints_path=load_dir,
                                                        tag=tag,
                                                        mp_rank=mp_rank,
                                                        dp_rank=dp_rank)
              zero_ckpt_names.append(ckpt_name)
  
          return zero_ckpt_names
  
      def _get_all_zero_checkpoint_names(self,
                                         load_dir,
                                         tag,
                                         mp_world_size,
                                         dp_world_size):
          zero_ckpt_names = []
          for mp_rank in range(mp_world_size):
              mp_rank_ckpt_names = self._get_mp_rank_zero_checkpoint_names(
                  load_dir=load_dir,
                  tag=tag,
                  mp_rank=mp_rank,
                  dp_world_size=dp_world_size)
              zero_ckpt_names += mp_rank_ckpt_names
  
          return zero_ckpt_names
  
      def _get_all_zero_checkpoints(self, load_dir, tag):
          mp_rank = 0 if self.mpu is None else self.mpu.get_model_parallel_rank()
          zero_ckpt_names = self._get_mp_rank_zero_checkpoint_names(
              load_dir=load_dir,
              tag=tag,
              mp_rank=mp_rank,
              dp_world_size=self.loaded_checkpoint_dp_world_size)
          invalid_zero_ckpt_paths = []
          for i, ckpt_name in enumerate(zero_ckpt_names):
              if not os.path.exists(ckpt_name):
                  # transparently handle the old file pattern for optim_states
                  if 'optim_states.pt' in ckpt_name:
                      ckpt_name_try = ckpt_name.replace("_optim_states.pt",
                                                        "optim_states.pt")
                      if os.path.exists(ckpt_name_try):
                          zero_ckpt_names[i] = ckpt_name_try
                          continue
                  invalid_zero_ckpt_paths.append(ckpt_name)
  
          if len(invalid_zero_ckpt_paths) > 0:
              logger.warn(
                  f"The following zero checkpoints paths are missing: {invalid_zero_ckpt_paths}"
              )
              return None
  
          zero_sd_list = []
          for ckpt_name in zero_ckpt_names:
              zero_sd_list.append(torch.load(ckpt_name, map_location='cpu'))
  
          zero_optimizer_sd = [sd['optimizer_state_dict'] for sd in zero_sd_list]
          print(
              f"successfully loaded {len(zero_optimizer_sd)} ZeRO state_dicts for rank {self.global_rank}"
          )
          return zero_optimizer_sd
  
      def _checkpoint_tag_validation(self, tag):
          if self.checkpoint_tag_validation_enabled():
              s_hash = hashlib.sha1(tag.encode())
              bhash = torch.ByteTensor([s_hash.digest()]).flatten().to(self.device)
              max_bhash = bhash.clone()
              min_bhash = bhash.clone()
              dist.all_reduce(max_bhash, op=torch.distributed.ReduceOp.MAX)
              dist.all_reduce(min_bhash, op=torch.distributed.ReduceOp.MIN)
              valid = all(min_bhash == bhash) and all(max_bhash == bhash)
              msg = f"[rank={dist.get_rank()}] The checkpoint tag name '{tag}' is not consistent across " \
                  "all ranks. Including rank unique information in checkpoint tag could cause issues when " \
                  "restoring with different world sizes."
              if self.checkpoint_tag_validation_fail():
                  assert valid, msg
              elif not valid:
                  logger.warning(msg)
  
      def save_checkpoint(self, save_dir, tag=None, client_state={}, save_latest=True):
          r"""Save training checkpoint
  
          Arguments:
              save_dir: Required. Directory for saving the checkpoint
              tag: Optional. Checkpoint tag used as a unique identifier for the checkpoint, global step is
                  used if not provided. Tag name must be the same across all ranks.
              client_state: Optional. State dictionary used for saving required training states in the client code.
              save_latest: Optional. Save a file 'latest' pointing to the latest saved checkpoint.
  
          Important: all processes must call this method and not just the process with rank 0. It is
          because each process needs to save its master weights and scheduler+optimizer states. This
          method will hang waiting to synchronize with other processes if it's called just for the
          process with rank 0.
          """
  
          if self.zero_optimization_partition_weights():
              # Prepare for state_dict() by ensuring all parameters are partitioned
              self.optimizer.save_checkpoint_prologue()
  
          # This is to make sure the checkpoint names are created without collision
          # There seems to be issue creating them in parallel
  
          # Ensure save_dir directory exists
          os.makedirs(save_dir, exist_ok=True)
  
          if tag is None:
              tag = f"global_step{self.global_steps}"
  
          # Ensure tag is a string
          tag = str(tag)
  
          # Ensure checkpoint tag is consistent across ranks
          self._checkpoint_tag_validation(tag)
  
          if self.save_non_zero_checkpoint:
              self._create_checkpoint_file(save_dir, tag, False)
              self._save_checkpoint(save_dir, tag, client_state=client_state)
  
          if self.save_zero_checkpoint:
              self._create_zero_checkpoint_files(save_dir, tag)
              self._save_zero_checkpoint(save_dir, tag)
  
          # Save latest checkpoint tag
          if save_latest:
              with open(os.path.join(save_dir, 'latest'), 'w') as fd:
                  fd.write(tag)
  
          if self.zero_optimization_partition_weights():
              self.optimizer.save_checkpoint_epilogue()
  
          return True
  
      def _create_checkpoint_file(self, save_dir, tag, zero_checkpoint):
          name_function = self._get_zero_ckpt_name if zero_checkpoint else self._get_ckpt_name
          try:
              checkpoint_name = name_function(save_dir, tag)
              ensure_directory_exists(checkpoint_name)
          except:
              logger.error(f'Failed saving model checkpoint to {save_dir} with tag {tag}')
              return False
  
          return True
  
      def _create_zero_checkpoint_files(self, save_dir, tag):
          success = True
          # zero checkpoint files are created sequentially
          for rank in range(self.world_size):
              if rank == self.global_rank:
                  success = self._create_checkpoint_file(save_dir, tag, True)
  
              dist.barrier()
  
          return success
  
      def _save_checkpoint(self, save_dir, tag, client_state={}):
  
          save_path = self._get_ckpt_name(save_dir, tag)
          # A hack to save the checkpointing directory. Pipeline parallelism overrides
          # module_state_dict() and uses this path to save the model. module_state_dict()
          # then instead just returns None.
          self._curr_ckpt_path = os.path.join(save_dir, tag)
  
          state = dict(
              module=self.module_state_dict(),
              optimizer=self.optimizer.state_dict()
              if self.optimizer and not self.zero_optimization() else None,
              lr_scheduler=self.lr_scheduler.state_dict()
              if self.lr_scheduler is not None else None,
              csr_tensor_module_names=self.csr_tensor_module_names,
              skipped_steps=self.skipped_steps,
              global_steps=self.global_steps,
              global_samples=self.global_samples,
              dp_world_size=self.dp_world_size,
              mp_world_size=self.mp_world_size,
          )
          state.update(client_state)
  
          log_dist(message=f'Saving model checkpoint: {save_path}', ranks=[0])
          #logger.info('Saving model checkpoint: {}'.format(save_path))
          torch.save(state, save_path)
          self._curr_save_path = None
  
      def _get_param_shapes(self):
          param_shapes = OrderedDict()
          for name, param in self.module.named_parameters():
              param_shapes[name] = param.ds_shape if hasattr(param,
                                                             "ds_shape") else param.shape
              # print(f"saving param {name} {param_shapes[name]}")
          return param_shapes
  
      def _copy_recovery_script(self, save_path):
          base_dir = os.path.dirname(os.path.dirname(__file__))
          script = "zero_to_fp32.py"
          src = os.path.join(base_dir, "utils", script)
          dst = os.path.join(save_path, script)
          logger.info(f"creating recovery script {dst}")
          copyfile(src, dst)
          # make executable
          os.chmod(dst, os.stat(dst).st_mode | stat.S_IEXEC)
  
      def _save_zero_checkpoint(self, save_path, tag):
          zero_checkpoint_name = self._get_zero_ckpt_name(save_path, tag)
          zero_sd = dict(
              optimizer_state_dict=self.optimizer.state_dict(),
              param_shapes=self._get_param_shapes(),
          )
          torch.save(zero_sd, zero_checkpoint_name)
          self._copy_recovery_script(save_path)
          logger.info('zero checkpoint saved {}'.format(zero_checkpoint_name))
  
      def _zero3_consolidated_fp16_state_dict(self):
          """
  
          Get a full non-partitioned state_dict with fp16 weights on cpu.
  
          Important: this function must be called on all ranks and not just rank 0.
  
          This is similar to nn.Module.state_dict (modelled after _save_to_state_dict), but:
  
          1. consolidates the weights from different partitions on gpu0
          2. works on one layer at a time to require as little gpu0 memory as possible, by
          moving the already consolidated weights to cpu
          3. takes care to keep the shared params shared when gradually copying the params to cpu
  
          Returns:
              a consolidated fp16 ``state_dict`` on cpu on rank 0, ``None`` on other ranks
  
          """
          import deepspeed
  
          if not self.zero_optimization_partition_weights():
              raise ValueError("this function requires ZeRO-3 mode")
  
          state_dict = OrderedDict() if torch.distributed.get_rank() == 0 else None
          shared_weights = {}
  
          def get_layer_state_dict(module, prefix=""):
              # gather one layer at a time to be memory-efficient
              with deepspeed.zero.GatheredParameters(list(
                      module.parameters(recurse=False))):
                  if torch.distributed.get_rank() == 0:
                      for name, param in module.named_parameters(recurse=False):
                          if param is None:
                              continue
                          key = prefix + name
                          # for shared weights we want to make sure not to unshare them when copying to cpu
                          data_ptr_id = param.storage().data_ptr()
                          if data_ptr_id in shared_weights:
                              # shared weights
                              # print(f"`{key}` is shared with `{shared_weights[data_ptr_id]}`")
                              state_dict[key] = state_dict[shared_weights[data_ptr_id]]
                          else:
                              state_dict[key] = param.detach().cpu()
                              shared_weights[data_ptr_id] = key
                          #print(f"param {name} {param.shape}")
                          #print(f"param {key} {param.shape} {state_dict[key].storage().data_ptr()}")
  
                      # now buffers - not sure if need to take care of potentially shared weights here
                      for name, buf in module.named_buffers(recurse=False):
                          if buf is not None and name not in module._non_persistent_buffers_set:
                              state_dict[prefix + name] = buf.detach().cpu()
  
              for name, child in module.named_children():
                  if child is not None:
                      get_layer_state_dict(child, prefix + name + ".")
  
          see_memory_usage("before get_layer_state_dict", force=False)
          get_layer_state_dict(self.module, prefix="")
          see_memory_usage("after get_layer_state_dict", force=False)
  
          return state_dict
  
      def save_fp16_model(self, save_dir, save_filename="pytorch_model.bin"):
          r"""Save fp16 model weights
  
          This method saves the fp16 model weights at the desired destination.
  
          Arguments:
              save_dir: Required. Directory for saving the model
              save_filename: Optional. Filename to save to. Defaults to ``pytorch_model.bin``
  
          Important: all processes must call this method and not just the process with rank 0. It is
          because the processes need to work in sync to gather the weights. This method will hang
          waiting to synchronize with other processes if it's called just for the process with rank 0.
  
          """
  
          path = os.path.join(save_dir, save_filename)
  
          if self.zero_optimization_partition_weights():
              if self.zero_gather_fp16_weights_on_model_save():
                  # consolidation is expensive in time and memory and therefore isn't a default
                  state_dict = self._zero3_consolidated_fp16_state_dict()
              else:
                  # the model will be bogus if not consolidated so don't confuse the user by saving it
                  logger.info(
                      f"Did not save the model {path} because `stage3_gather_fp16_weights_on_model_save` is False"
                  )
                  return
          else:
              state_dict = self.module.state_dict()
  
          if torch.distributed.get_rank() == 0:
              os.makedirs(save_dir, exist_ok=True)
              logger.info(f"Saving model weights to {path}")
              torch.save(state_dict, path)
  
apis: torch.distributed.all_gather torch.distributed.get_world_size torch.distributed.get_rank torch.load torch.distributed.broadcast torch.save torch.utils.data.SequentialSampler torch.device torch.distributed.is_initialized torch.distributed.new_group torch.distributed.barrier torch.optim.Adam torch.distributed.all_reduce torch.is_tensor torch.optim.AdamW torch.cat torch._C.is_grad_enabled torch.cuda.set_device