| import time |
| import torch |
| import os |
| import yaml |
| from types import SimpleNamespace |
|
|
| def dict_to_namespace(d): |
| if isinstance(d, dict): |
| return SimpleNamespace(**{k: dict_to_namespace(v) for k, v in d.items()}) |
| elif isinstance(d, list): |
| return [dict_to_namespace(i) for i in d] |
| else: |
| return d |
|
|
| def load_config(path): |
| with open(f'/opt/tiger/Abbie/trainer/template/{path}', 'r') as f: |
| cfg_dict = yaml.safe_load(f) |
| return dict_to_namespace(cfg_dict) |
|
|
|
|
| def make_handler(rank, local_dir): |
| def handler_fn(p): |
| |
| fname = "profileStep" + str(p.step_num) + "_globalStep" + str(0) + "_rank" + str(rank) + "." + \ |
| str(int(time.time())) + ".pt.trace.json.gz" |
| local_file = os.path.join(local_dir, fname) |
| if not os.path.exists(local_dir): |
| print("mkdir ", local_dir) |
| os.makedirs(local_dir) |
| print("Save profile results to {}".format(local_file)) |
| p.export_chrome_trace(local_file) |
| print("Local profile file saved") |
|
|
| return handler_fn |
|
|
|
|
| def collect_scalars_across_data_parallel_group(scalars, dp_group): |
| """Reduce a tensor of losses across all GPUs.""" |
| scalars = torch.cat( |
| [loss.clone().detach().view(1) for loss in scalars]) |
| group_size = torch.distributed.get_world_size(group=dp_group) |
| out_scalars = [torch.ones_like(scalars) for i in range(group_size)] |
| torch.distributed.all_gather(out_scalars, scalars, |
| group=dp_group) |
| return out_scalars, group_size |
|
|
