docstr: Batch size ramp up.
  Over 
    steps = (global-batch-size - start-batch-size) / batch_size_increment
  increment batch size from start-batch-size to global-batch-size using
    rampup-samples / steps
  samples.
  Arguments:
      start_batch_size: global batch size to start with
      batch_size_increment: global batch size increments
      ramup_samples: number of samples to use ramp up global
         batch size from `start_batch_size` to `global_batch_size`
      global_batch_size: global batch size post rampup
      micro_batch_size: micro batch size
      data_parallel_size: data parallel size.
docstr_tok: ["Batch","size","ramp","up",".","Over","steps","=","(","global-batch-size","-","start-batch-size",")","\/","batch_size_increment","increment","batch","size","from","start-batch-size","to","global-batch-size","using","rampup-samples","\/","steps","samples",".","Arguments",":","start_batch_size",":","global","batch","size","to","start","with","batch_size_increment",":","global","batch","size","increments","ramup_samples",":","number","of","samples","to","use","ramp","up","global","batch","size","from","`","start_batch_size","`","to","`","global_batch_size","`","global_batch_size",":","global","batch","size","post","rampup","micro_batch_size",":","micro","batch","size","data_parallel_size",":","data","parallel","size","."]
code: def __init__(
      self,
      start_batch_size,
      batch_size_increment,
      ramup_samples,
      global_batch_size,
      micro_batch_size,
      data_parallel_size,
  ):
      """Batch size ramp up.
      Over
        steps = (global-batch-size - start-batch-size) / batch_size_increment
      increment batch size from start-batch-size to global-batch-size using
        rampup-samples / steps
      samples.
      Arguments:
          start_batch_size: global batch size to start with
          batch_size_increment: global batch size increments
          ramup_samples: number of samples to use ramp up global
             batch size from `start_batch_size` to `global_batch_size`
          global_batch_size: global batch size post rampup
          micro_batch_size: micro batch size
          data_parallel_size: data parallel size.
      """
  
      self.micro_batch_size = micro_batch_size
      self.data_parallel_size = data_parallel_size
      self.micro_batch_times_data_parallel_size = (
          self.micro_batch_size * self.data_parallel_size
      )
      assert self.micro_batch_times_data_parallel_size > 0
  
      assert start_batch_size > 0
      self.start_batch_size = start_batch_size
  
      assert global_batch_size > 0
      self.global_batch_size = global_batch_size
      diff_batch_size = self.global_batch_size - self.start_batch_size
      assert diff_batch_size >= 0
      assert batch_size_increment > 0
      self.batch_size_increment = batch_size_increment
      assert diff_batch_size % batch_size_increment == 0, (
          "expected "
          "global batch size interval ({}) to be divisible by global batch "
          "size increment ({})".format(
              diff_batch_size, batch_size_increment
          )
      )
  
      num_increments = diff_batch_size // self.batch_size_increment
      self.ramup_samples = ramup_samples
      assert self.ramup_samples >= 0
      self.rampup_samples_per_increment = (
          self.ramup_samples / num_increments
      )
  
      # Initialize number of microbatches.
      self.update(0, False)
  
file_path: megatron-lm/megatron/microbatches.py
imports: from abc import ABC
  from abc import abstractmethod
label_desc: Use image node_id 1 to create a new RampupBatchsizeNumMicroBatches object from inherited base classes: NumMicroBatchesCalculator with example: obj = RampupBatchsizeNumMicroBatches(start_batch_size, batch_size_increment, ramup_samples, global_batch_size, micro_batch_size, data_parallel_size)