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105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections import defaultdict
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from paddle.amp import GradScaler
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from paddle import _C_ops
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import paddle
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class LSCGradScaler(GradScaler):
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def __init__(self,
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enable=True,
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init_loss_scaling=2.**15,
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incr_ratio=2.0,
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decr_ratio=0.5,
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incr_every_n_steps=1000,
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decr_every_n_nan_or_inf=2,
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use_dynamic_loss_scaling=True,
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max_loss_scaling=32768.0):
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super(LSCGradScaler, self).__init__(
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enable, init_loss_scaling, incr_ratio, decr_ratio,
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incr_every_n_steps, decr_every_n_nan_or_inf,
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use_dynamic_loss_scaling)
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self.max_loss_scaling = max_loss_scaling
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def step(self, optimizer, classifier=None):
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if not self._enable:
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if classifier is not None:
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classifier.step(optimizer)
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return optimizer.step()
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# if self._scale >= self.max_loss_scaling:
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# self._scale = paddle.to_tensor([self.max_loss_scaling], dtype='float32')
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# unscale the grad
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self._unscale(optimizer)
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if self._found_inf:
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self._cache_founf_inf = True
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else:
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optimizer.step()
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if classifier is not None:
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classifier.step(optimizer)
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self._cache_founf_inf = False
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if self._use_dynamic_loss_scaling:
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# update the scale
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self._update()
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def _unscale(self, optimizer):
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if not self._enable:
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return
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param_grads_dict = defaultdict(list)
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dist_param_grads_dict = defaultdict(list)
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if getattr(optimizer, '_param_groups', None) and isinstance(
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optimizer._param_groups[0], dict):
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for group in optimizer._param_groups:
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for param in group['params']:
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if not param.is_distributed:
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if param._grad_ivar() is not None:
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param_grads_dict[param._grad_ivar().dtype].append(
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param._grad_ivar())
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else:
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if param._grad_ivar() is not None:
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dist_param_grads_dict[param._grad_ivar(
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).dtype].append(param._grad_ivar())
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else:
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for param in optimizer._parameter_list:
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if not param.is_distributed:
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if param._grad_ivar() is not None:
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param_grads_dict[param._grad_ivar().dtype].append(
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param._grad_ivar())
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else:
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if param._grad_ivar() is not None:
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dist_param_grads_dict[param._grad_ivar().dtype].append(
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param._grad_ivar())
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for dtype in dist_param_grads_dict:
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for grad in dist_param_grads_dict[dtype]:
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self._found_inf = paddle.logical_not(
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paddle.all(paddle.isfinite(grad)))
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if self._found_inf:
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print('Found inf or nan in classifier, dtype is', dtype)
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return
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for dtype in param_grads_dict:
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param_grads = param_grads_dict[dtype]
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_C_ops.check_finite_and_unscale(param_grads, self._scale,
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param_grads, self._found_inf)
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if self._found_inf:
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print('Found inf or nan in backbone, dtype is', dtype)
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break
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