| |
| |
| |
| |
| import random |
| import numpy as np |
| import torch |
|
|
| from .shardedtensor import * |
| from .load_config import * |
|
|
|
|
| def set_seed(seed=43211): |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
| torch.cuda.manual_seed_all(seed) |
| if torch.backends.cudnn.enabled: |
| torch.backends.cudnn.benchmark = False |
| torch.backends.cudnn.deterministic = True |
|
|
|
|
| def get_world_size(): |
| if torch.distributed.is_initialized(): |
| world_size = torch.distributed.get_world_size() |
| else: |
| world_size = 1 |
| return world_size |
|
|
|
|
| def get_local_rank(): |
| return torch.distributed.get_rank() \ |
| if torch.distributed.is_initialized() else 0 |
|
|
|
|
| def print_on_rank0(func): |
| local_rank = get_local_rank() |
| if local_rank == 0: |
| print("[INFO]", func) |
|
|
|
|
| class RetriMeter(object): |
| """ |
| Statistics on whether retrieval yields a better pair. |
| """ |
| def __init__(self, freq=1024): |
| self.freq = freq |
| self.total = 0 |
| self.replace = 0 |
| self.updates = 0 |
|
|
| def __call__(self, data): |
| if isinstance(data, np.ndarray): |
| self.replace += data.shape[0] - int((data[:, 0] == -1).sum()) |
| self.total += data.shape[0] |
| elif torch.is_tensor(data): |
| self.replace += int(data.sum()) |
| self.total += data.size(0) |
| else: |
| raise ValueError("unsupported RetriMeter data type.", type(data)) |
|
|
| self.updates += 1 |
| if get_local_rank() == 0 and self.updates % self.freq == 0: |
| print("[INFO]", self) |
|
|
| def __repr__(self): |
| return "RetriMeter (" + str(self.replace / self.total) \ |
| + "/" + str(self.replace) + "/" + str(self.total) + ")" |
|
|
