mirror of https://github.com/hpcaitech/ColossalAI
149 lines
5.1 KiB
Python
149 lines
5.1 KiB
Python
# coding=utf-8
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# Copyright (c) 2020, NVIDIA CORPORATION. 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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"""Learning rate decay functions."""
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import math
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class AnnealingLR(object):
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"""Anneals the learning rate."""
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def __init__(
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self,
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optimizer,
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max_lr,
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min_lr,
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warmup_steps,
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decay_steps,
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decay_style,
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use_checkpoint_lr_scheduler=True,
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override_lr_scheduler=False,
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):
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# Class values.
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self.optimizer = optimizer
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self.max_lr = float(max_lr)
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self.min_lr = min_lr
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assert self.min_lr >= 0.0
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assert self.max_lr >= self.min_lr
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self.warmup_steps = warmup_steps
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self.num_steps = 0
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self.decay_steps = decay_steps
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assert self.decay_steps > 0
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assert self.warmup_steps < self.decay_steps
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self.decay_style = decay_style
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self.override_lr_scheduler = override_lr_scheduler
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self.use_checkpoint_lr_scheduler = use_checkpoint_lr_scheduler
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if self.override_lr_scheduler:
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assert not self.use_checkpoint_lr_scheduler, "both override and " "use-checkpoint are set."
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# Set the learning rate
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self.step(0)
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def get_lr(self):
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"""Learning rate decay functions from:
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https://openreview.net/pdf?id=BJYwwY9ll pg. 4"""
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# Use linear warmup for the initial part.
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if self.warmup_steps > 0 and self.num_steps <= self.warmup_steps:
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return self.max_lr * float(self.num_steps) / float(self.warmup_steps)
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# If the learning rate is constant, just return the initial value.
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if self.decay_style == "constant":
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return self.max_lr
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# For any steps larger than `self.decay_steps`, use `self.min_lr`.
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if self.num_steps > self.decay_steps:
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return self.min_lr
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# If we are done with the warmup period, use the decay style.
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num_steps_ = self.num_steps - self.warmup_steps
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decay_steps_ = self.decay_steps - self.warmup_steps
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decay_ratio = float(num_steps_) / float(decay_steps_)
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assert decay_ratio >= 0.0
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assert decay_ratio <= 1.0
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delta_lr = self.max_lr - self.min_lr
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if self.decay_style == "linear":
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coeff = 1.0 - decay_ratio
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elif self.decay_style == "cosine":
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coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
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else:
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raise Exception("{} decay style is not supported.".format(self.decay_style))
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return self.min_lr + coeff * delta_lr
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def step(self, increment=1):
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"""Set lr for all parameters groups."""
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self.num_steps += increment
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new_lr = self.get_lr()
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for group in self.optimizer.param_groups:
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group["lr"] = new_lr
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def state_dict(self):
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state_dict = {
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"max_lr": self.max_lr,
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"warmup_steps": self.warmup_steps,
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"num_steps": self.num_steps,
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"decay_style": self.decay_style,
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"decay_steps": self.decay_steps,
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"min_lr": self.min_lr,
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}
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return state_dict
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def _check_and_set(self, cls_value, sd_value, name):
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"""Auxiliary function for checking the values in the checkpoint and
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setting them."""
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if self.override_lr_scheduler:
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return cls_value
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if not self.use_checkpoint_lr_scheduler:
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assert cls_value == sd_value, (
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f"AnnealingLR: class input value {cls_value} and checkpoint" f"value {sd_value} for {name} do not match"
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)
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return sd_value
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def load_state_dict(self, sd):
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if "start_lr" in sd:
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max_lr_ = sd["start_lr"]
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else:
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max_lr_ = sd["max_lr"]
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self.max_lr = self._check_and_set(self.max_lr, max_lr_, "learning rate")
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self.min_lr = self._check_and_set(self.min_lr, sd["min_lr"], "minimum learning rate")
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if "warmup_iter" in sd:
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warmup_steps_ = sd["warmup_iter"]
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else:
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warmup_steps_ = sd["warmup_steps"]
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self.warmup_steps = self._check_and_set(self.warmup_steps, warmup_steps_, "warmup iterations")
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if "end_iter" in sd:
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decay_steps_ = sd["end_iter"]
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else:
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decay_steps_ = sd["decay_steps"]
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self.decay_steps = self._check_and_set(self.decay_steps, decay_steps_, "total number of iterations")
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self.decay_style = self._check_and_set(self.decay_style, sd["decay_style"], "decay style")
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if "num_iters" in sd:
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num_steps = sd["num_iters"]
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else:
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num_steps = sd["num_steps"]
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self.step(increment=num_steps)
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