mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-12 23:48:57 +00:00
Michael Chung
79 lines
2.4 KiB
Python
79 lines
2.4 KiB
Python
#!/usr/bin/env python3
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# Copyright 2023 (authors: Feiteng Li)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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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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import torch
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from modules.optim import Eden
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def calc_lr(step, dim_embed, warmup_steps):
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return dim_embed ** (-0.5) * min(
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step ** (-0.5), step * warmup_steps ** (-1.5)
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)
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class NoamScheduler(torch.optim.lr_scheduler._LRScheduler):
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def __init__(
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self,
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base_lr: float,
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optimizer: torch.optim.Optimizer,
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dim_embed: int,
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warmup_steps: int,
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last_epoch: int = -1,
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verbose: bool = False,
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) -> None:
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self.dim_embed = dim_embed
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self.base_lr = base_lr
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self.warmup_steps = warmup_steps
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self.num_param_groups = len(optimizer.param_groups)
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super().__init__(optimizer, last_epoch, verbose)
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def get_lr(self) -> float:
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lr = self.base_lr * calc_lr(
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self._step_count, self.dim_embed, self.warmup_steps
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)
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return [lr] * self.num_param_groups
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def set_step(self, step: int):
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self._step_count = step
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def get_scheduler(params, optimizer):
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if params.scheduler_name.lower() == "eden":
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scheduler = Eden(optimizer, 5000, 4, warmup_batches=params.warmup_steps)
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elif params.scheduler_name.lower() == "noam":
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scheduler = NoamScheduler(
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params.base_lr,
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optimizer,
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params.decoder_dim,
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warmup_steps=params.warmup_steps,
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)
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# scheduler.set_step(params.start_batch or params.batch_idx_train)
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elif params.scheduler_name.lower() == "cosine":
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scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
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params.warmup_steps,
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optimizer,
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eta_min=params.base_lr,
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)
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else:
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raise NotImplementedError(f"{params.scheduler_name}")
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return scheduler
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