class_docstr: Profile alpha and beta value for a given device list.
  
  Usage:
      # Note: the environment of execution is supposed to be
      # multi-process with multi-gpu in mpi style.
      >>> physical_devices = [0, 1, 4, 5]
      >>> ab_profiler = AlphaBetaProfiler(physical_devices)
      >>> ab_dict = profiler.alpha_beta_dict
      >>> print(ab_dict)
      {(0, 1): (1.9641406834125518e-05, 4.74049549614719e-12), (0, 4): (1.9506998360157013e-05, 6.97421973297474e-11), (0, 5): (2.293858677148819e-05, 7.129930361393644e-11),
       (1, 4): (1.9010603427886962e-05, 7.077968863788975e-11), (1, 5): (1.9807778298854827e-05, 6.928845708992215e-11), (4, 5): (1.8681809306144713e-05, 4.7522367291330524e-12),
       (1, 0): (1.9641406834125518e-05, 4.74049549614719e-12), (4, 0): (1.9506998360157013e-05, 6.97421973297474e-11), (5, 0): (2.293858677148819e-05, 7.129930361393644e-11),
       (4, 1): (1.9010603427886962e-05, 7.077968863788975e-11), (5, 1): (1.9807778298854827e-05, 6.928845708992215e-11), (5, 4): (1.8681809306144713e-05, 4.7522367291330524e-12)}
code: def _profile(self, process_group, pg_handler, nbytes):
      logger = get_dist_logger()
      rank = dist.get_rank()
      src_device_num = process_group[0]
      world_size = len(process_group)
  
      device = torch.cuda.current_device()
      buf = torch.randn(nbytes // 4).to(device)
  
      torch.cuda.synchronize()
      # warmup
      for _ in range(self.warmup):
          if self.ctype == "a":
              dist.all_reduce(
                  buf, op=dist.ReduceOp.SUM, group=pg_handler
              )
          elif self.ctype == "b":
              dist.broadcast(buf, src=src_device_num, group=pg_handler)
      torch.cuda.synchronize()
  
      dist.barrier(group=pg_handler)
      begin = time.perf_counter()
      for _ in range(self.repeat):
          if self.ctype == "a":
              dist.all_reduce(
                  buf, op=dist.ReduceOp.SUM, group=pg_handler
              )
          elif self.ctype == "b":
              dist.broadcast(buf, src=src_device_num, group=pg_handler)
      torch.cuda.synchronize()
      end = time.perf_counter()
      dist.barrier(group=pg_handler)
  
      if rank == src_device_num:
          avg_time_s = (end - begin) / self.repeat - FRAMEWORK_LATENCY
          alg_band = nbytes / avg_time_s
          if self.ctype == "a":
              # convert the bandwidth of all-reduce algorithm to the bandwidth of the hardware.
              bus_band = 2 * (world_size - 1) / world_size * alg_band
              bus_band = alg_band
          elif self.ctype == "b":
              bus_band = alg_band
  
          logger.info(
              f"GPU:{rank}, Bytes: {nbytes} B,Time: {round(avg_time_s * 1e6,2)} us, Bus bandwidth: {round(bus_band / GB,2)} GB/s"
          )
          return (avg_time_s, alg_band)
      else:
          # Just a placeholder
          return (None, None)
  
file_path: ColossalAI/colossalai/device/alpha_beta_profiler.py
imports: import math
  import time
  from typing import Dict, List, Tuple
  import torch
  import torch.distributed
  from colossalai.logging import get_dist_logger
label_desc: Use image node_id 3 for calling the AlphaBetaProfiler obj's underlying member method code with example usage: obj._profile(process_group, pg_handler, nbytes) and returns: avg_time_s,  alg_band, None,  None