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mindformers.core.CosineWithWarmUpLR

class mindformers.core.CosineWithWarmUpLR(learning_rate: float, warmup_steps: int = 0, total_steps: int = None, num_cycles: float = 0.5, lr_end: float = 0., warmup_lr_init: float = 0., warmup_ratio: float = None, decay_steps: int = None, **kwargs)

Cosine with Warm Up Learning Rate.

The CosineWithWarmUpLR learning rate scheduler applies a cosine annealing schedule with warm-up steps to set the learning rate for each parameter group. Initially, the learning rate increases linearly during the warm-up phase, after which it follows a cosine function to decay.

During the warm-up phase, the learning rate increases from a small initial value to the base learning rate as follows:

$${\eta }_t={\eta }_{\text{warmup}}+t\times \frac{{\eta }_{\text{base}}-{\eta }_{\text{warmup}}}{\text{warmup\_steps}}$$

where ${\eta }_{\text{warmup}}$ is the initial learning rate, and ${\eta }_{\text{base}}$ is the learning rate after the warm-up phase.

once the warm-up phase is completed, the learning rate follows a cosine decay schedule:

$${\eta }_t={\eta }_{\text{end}}+\frac{1}{2}({\eta }_{\text{base}}-{\eta }_{\text{end}})(1+\cos \left(\frac{t_{cur}}{t_{max}}\pi \right))$$

where $t_{cur}$ is the number of epochs since the end of the warm-up phase, and $t_{max}$ is the total number of epochs until the next restart.

Parameters

• learning_rate (float) – Initial value of learning rate.

• warmup_steps (int) – The number of warm up steps. Default: None.

• total_steps (int) – The number of total steps. Default: None.

• num_cycles (float) – The number of waves in the cosine schedule (the defaults is to just decrease from the max value to 0 following a half-cosine). Default: 0.5.

• lr_end (float) – Final value of learning rate. Default: 0.

• warmup_lr_init (float) – Initial learning rate in warm up steps. Default: 0.

• warmup_ratio (float) – Ratio of total training steps used for warmup. Default: None.

• decay_steps (int) – The number of decay steps. Default: None.

Inputs:

• global_step (int) - The global step.

Outputs:

Learning rate.

Examples

>>> import mindspore as ms
>>> from mindformers.core import CosineWithWarmUpLR
>>>
>>> ms.set_context(mode=ms.GRAPH_MODE)
>>> total_steps = 20
>>> warmup_steps = 10
>>> learning_rate = 0.005
>>>
>>> cosine_warmup = CosineWithWarmUpLR(learning_rate=learning_rate,
...                                    warmup_steps=warmup_steps,
...                                    total_steps=total_steps)
>>> print(cosine_warmup(1))
0.0005
>>> print(cosine_warmup(15))
0.0024999997