Document feedback

Question document fragment

When a question document fragment contains a formula, it is displayed as a space.

Submission type

issue

It's a little complicated...

I'd like to ask someone.

PR

Just a small problem.

I can fix it online!

Please select the submission type

Problem type

Specifications and Common Mistakes

- Specifications and Common Mistakes:

- Misspellings or punctuation mistakes,incorrect formulas, abnormal display.

- Incorrect links, empty cells, or wrong formats.

- Chinese characters in English context.

- Minor inconsistencies between the UI and descriptions.

- Low writing fluency that does not affect understanding.

- Incorrect version numbers, including software package names and version numbers on the UI.

Usability

- Usability:

- Incorrect or missing key steps.

- Missing main function descriptions, keyword explanation, necessary prerequisites, or precautions.

- Ambiguous descriptions, unclear reference, or contradictory context.

- Unclear logic, such as missing classifications, items, and steps.

Correctness

- Correctness:

- Technical principles, function descriptions, supported platforms, parameter types, or exceptions inconsistent with that of software implementation.

- Incorrect schematic or architecture diagrams.

- Incorrect commands or command parameters.

- Incorrect code.

- Commands inconsistent with the functions.

- Wrong screenshots.

- Sample code running error, or running results inconsistent with the expectation.

Risk Warnings

- Risk Warnings:

- Lack of risk warnings for operations that may damage the system or important data.

Content Compliance

- Content Compliance:

- Contents that may violate applicable laws and regulations or geo-cultural context-sensitive words and expressions.

- Copyright infringement.

Please select the type of question

Problem description

Describe the bug so that we can quickly locate the problem.

Submit

Document feedback

mindformers.core.CosineAnnealingWarmRestarts

class mindformers.core.CosineAnnealingWarmRestarts(base_lr: float, t_0: int, t_mult: int = 1, eta_min: float = 0., **kwargs)

Set the learning rate of each parameter group using a cosine annealing schedule, where ${\eta }_{max}$ is set to the initial lr, $T_{cur}$ is the number of epochs since the last restart and $T_i$ is the number of epochs between two warm restarts in SGDR:

$${\eta }_t={\eta }_{min}+\frac{1}{2}({\eta }_{max}-{\eta }_{min})(1+\cos \left(\frac{T_{cur}}{T_i}\pi \right))$$

When $T_{cur}=T_i$, set ${\eta }_t={\eta }_{min}$. When $T_{cur}=0$ after restart, set ${\eta }_t={\eta }_{max}$.

It has been proposed in SGDR: Stochastic Gradient Descent with Warm Restarts .

Parameters

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

• t_0 (int) – Number of iterations for the first restart.

• t_mult (int, optional) – A factor increases $T_i$ after a restart. Default: 1.

• eta_min (float, optional) – Minimum learning rate. Default: 0.

Inputs:

• global_step (int) - The global step.

Outputs:

Learning rate.

Examples

>>> import mindspore as ms
>>> from mindformers.core import CosineAnnealingWarmRestarts
>>>
>>> ms.set_context(mode=ms.GRAPH_MODE)
>>> base_lr = 0.005
>>> t_0 = 10
>>> t_mult = 2
>>> eta_min = 0.0000001
>>>
>>> cosine_annealing_restart = CosineAnnealingWarmRestarts(base_lr=base_lr,
...                                                        t_0=t_0,
...                                                        t_mult=t_mult,
...                                                        eta_min=eta_min)
>>> print(cosine_annealing_restart(1))
0.0048776437
>>> print(cosine_annealing_restart(15))
0.0042677815