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mindformers.core.LinearWithWarmUpLR

class mindformers.core.LinearWithWarmUpLR(learning_rate: float, total_steps: int, warmup_steps: int = None, warmup_lr_init: float = 0., warmup_ratio: float = None, **kwargs)

Linear with Warm Up Learning Rate.

The LinearWithWarmUpLR scheduler uses a linear warm-up strategy to gradually increase the learning rate for each parameter group, followed by a linear adjustment of the learning rate after the warm-up phase ends.

During the warm-up phase, the learning rate increases linearly from a smaller initial value to the base learning rate, as described by the following formula:

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

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

After the warm-up phase, the learning rate is adjusted according to the following linear schedule:

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

where ${\eta }_{\text{end}}$ is the minimum learning rate at the end of training, $\text{total\_steps}$ is the total number of training steps, and $\text{warmup\_steps}$ is the number of steps in the warm-up phase.

This method allows for a smooth increase in learning rate through linear warm-up, followed by a gradual decrease during the remainder of the training, enhancing the stability and effectiveness of the training process.

Parameters

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

• total_steps (int) – The number of total steps.

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

• 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.

Inputs:

• global_step (int) - The global step.

Outputs:

Learning rate.

Examples

>>> import mindspore as ms
>>> from mindformers.core import LinearWithWarmUpLR
>>>
>>> ms.set_context(mode=ms.GRAPH_MODE)
>>> total_steps = 20
>>> warmup_steps = 10
>>> learning_rate = 0.005
>>>
>>> linear_warmup = LinearWithWarmUpLR(learning_rate=learning_rate,
...                                    warmup_steps=warmup_steps,
...                                    total_steps=total_steps)
>>> print(linear_warmup(1))
0.0005
>>> print(linear_warmup(15))
0.0025