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import os
import torch
try:
from torch._six import inf
except:
from torch import inf
class NativeScalerWithGradNormCount:
state_dict_key = "amp_scaler"
def __init__(self):
#self._scaler = torch.cuda.amp.GradScaler(init_scale=16384) #init_scale=4096.0
self._scaler = torch.cuda.amp.GradScaler(init_scale=1)
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
try:
norm = torch.nn.utils.clip_grad_norm_(parameters, clip_grad, error_if_nonfinite=True)
except:
print('NAN gradient ....')
else:
raise NotImplementedError
self._scaler.unscale_(optimizer)
norm = get_grad_norm_(parameters)
self._scaler.step(optimizer)
self._scaler.update()
else:
norm = None
return True
#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)
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
def is_bf16_supported():
"""Returns a bool indicating if the current CUDA device supports dtype bfloat16"""
cu_vers = torch.version.cuda
if cu_vers is not None:
cuda_maj_decide = int(cu_vers.split('.')[0]) >= 11
else:
cuda_maj_decide = False
return torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8 and cuda_maj_decide |