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fcb1768 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | import datetime
import torch
import builtins
import torch.distributed as dist
from torch import inf
def is_dist_avail_and_initialized():
if not dist.is_available():
return False
if not dist.is_initialized():
return False
return True
def get_world_size():
if not is_dist_avail_and_initialized():
return 1
return dist.get_world_size()
def get_rank():
if not is_dist_avail_and_initialized():
return 0
return dist.get_rank()
def is_main_process():
return get_rank() == 0
def save_on_master(*args, **kwargs):
if is_main_process():
torch.save(*args, **kwargs)
def setup_for_distributed(is_master):
"""
This function disables printing when not in master process
"""
builtin_print = builtins.print
def print(*args, **kwargs):
force = kwargs.pop('force', False)
force = force or (get_world_size() > 8)
if is_master or force:
now = datetime.datetime.now().time()
builtin_print('[{}] '.format(now), end='') # print with time stamp
builtin_print(*args, **kwargs)
builtins.print = print
def all_reduce_mean(x):
world_size = get_world_size()
if world_size > 1:
x_reduce = torch.tensor(x).cuda()
# x_reduce = x.clone()
dist.all_reduce(x_reduce)
x_reduce /= world_size
return x_reduce.item()
else:
return x
def gather_tensors(tensor):
# Gather tensors across all processes
gather_list = [torch.zeros_like(tensor) for _ in range(dist.get_world_size())]
dist.all_gather(gather_list, tensor)
# return torch.cat(gather_list, dim=0)
# Stack tensors along a new dimension (each GPU result along dimension 0)
tensor_gather = torch.stack(gather_list, dim=0)
# Transpose to get them in the correct order (swap dimensions 0 and 1)
tensor_gather = tensor_gather.transpose(0, 1)
# Reshape back into a 2D tensor with the correct order
tensor_gather = tensor_gather.reshape(-1, tensor.size(-1))
return tensor_gather
def get_grad_norm_(parameters, norm_type: float = 2.0) -> torch.Tensor:
if isinstance(parameters, torch.Tensor):
parameters = [parameters]
parameters = [p for p in parameters if p.grad is not None]
norm_type = float(norm_type)
if len(parameters) == 0:
return torch.tensor(0.)
device = parameters[0].grad.device
if norm_type == inf:
total_norm = max(p.grad.detach().abs().max().to(device) for p in parameters)
else:
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
return total_norm
class NativeScalerWithGradNormCount:
state_dict_key = "amp_scaler"
def __init__(self):
self._scaler = torch.cuda.amp.GradScaler()
def __call__(self, loss, optimizer, clip_grad=None, parameters=None, create_graph=False, update_grad=True):
self._scaler.scale(loss).backward(create_graph=create_graph)
if update_grad:
if clip_grad is not None:
assert parameters is not None
self._scaler.unscale_(optimizer) # unscale the gradients of optimizer's assigned params in-place
norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
else:
self._scaler.unscale_(optimizer)
norm = get_grad_norm_(parameters)
self._scaler.step(optimizer)
self._scaler.update()
else:
norm = None
return norm
def state_dict(self):
return self._scaler.state_dict()
def load_state_dict(self, state_dict):
self._scaler.load_state_dict(state_dict) |