| # Copyright (c) ModelScope Contributors. All rights reserved. | |
| import torch | |
| from megatron.core import mpu | |
| def reduce_max_stat_across_model_parallel_group(stat: float) -> float: | |
| """ | |
| Ranks without an optimizer will have no grad_norm or num_zeros_in_grad stats. | |
| We need to ensure the logging and writer rank has those values. | |
| This function reduces a stat tensor across the model parallel group. | |
| We use an all_reduce max since the values have already been summed across optimizer ranks where possible | |
| """ | |
| stat = torch.tensor([stat], dtype=torch.float32, device=torch.cuda.current_device()) | |
| torch.distributed.all_reduce(stat, op=torch.distributed.ReduceOp.MAX, group=mpu.get_model_parallel_group()) | |
| return stat.item() | |
| def logical_and_across_model_parallel_group(input: bool) -> bool: | |
| """ | |
| This function gathers a bool value across the model parallel group | |
| """ | |
| input = int(bool(input)) | |
| input = torch.tensor([input], dtype=torch.int, device=torch.cuda.current_device()) | |
| torch.distributed.all_reduce(input, op=torch.distributed.ReduceOp.MIN, group=mpu.get_model_parallel_group()) | |
| return bool(input.item()) | |