Spaces:
Running
Running
""" CUDA / AMP utils | |
Hacked together by / Copyright 2020 Ross Wightman | |
""" | |
import torch | |
try: | |
from apex import amp | |
has_apex = True | |
except ImportError: | |
amp = None | |
has_apex = False | |
from .clip_grad import dispatch_clip_grad | |
class ApexScaler: | |
state_dict_key = "amp" | |
def __call__(self, loss, optimizer, clip_grad=None, clip_mode='norm', parameters=None, create_graph=False): | |
with amp.scale_loss(loss, optimizer) as scaled_loss: | |
scaled_loss.backward(create_graph=create_graph) | |
if clip_grad is not None: | |
dispatch_clip_grad(amp.master_params(optimizer), clip_grad, mode=clip_mode) | |
optimizer.step() | |
def state_dict(self): | |
if 'state_dict' in amp.__dict__: | |
return amp.state_dict() | |
def load_state_dict(self, state_dict): | |
if 'load_state_dict' in amp.__dict__: | |
amp.load_state_dict(state_dict) | |
class NativeScaler: | |
state_dict_key = "amp_scaler" | |
def __init__(self): | |
self._scaler = torch.cuda.amp.GradScaler() | |
def __call__(self, loss, optimizer, clip_grad=None, clip_mode='norm', parameters=None, create_graph=False): | |
self._scaler.scale(loss).backward(create_graph=create_graph) | |
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 | |
dispatch_clip_grad(parameters, clip_grad, mode=clip_mode) | |
self._scaler.step(optimizer) | |
self._scaler.update() | |
def state_dict(self): | |
return self._scaler.state_dict() | |
def load_state_dict(self, state_dict): | |
self._scaler.load_state_dict(state_dict) | |