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# Copyright 2024 EPFL and Apple Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------
# Based on timm code base
# https://github.com/rwightman/pytorch-image-models/tree/master/timm
# --------------------------------------------------------
import torch
class NativeScalerWithGradNormCount:
state_dict_key = "amp_scaler"
def __init__(self, enabled=True):
self._scaler = torch.cuda.amp.GradScaler(enabled=enabled)
def __call__(self, loss, optimizer, clip_grad=None, skip_grad=None, parameters=None, create_graph=False, update_grad=True, compute_grad_norm=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)
elif skip_grad is not None:
self._scaler.unscale_(optimizer)
norm = get_grad_norm_(parameters)
if norm >= skip_grad:
self._scaler.update()
return norm
else:
self._scaler.unscale_(optimizer)
norm = get_grad_norm_(parameters) if compute_grad_norm else None
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)
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
total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
return total_norm |