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""" 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
class ApexScaler:
state_dict_key = "amp"
def __call__(self, loss, optimizer, clip_grad=None, 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:
torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), clip_grad)
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, 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
torch.nn.utils.clip_grad_norm_(parameters, clip_grad)
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)
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