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Scheduler with Warmup #1184
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Scheduler with Warmup #1184
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Summary: Pull Request resolved: facebookresearch#1184 Current implementations of warmup in pytext either involve doing warmup and optionally inverse square root decay (TODO) or using polynomial decay (TODO). However, through my experiments, I notice for large batch training a warmup period is helpful on other schedulers as well, especially when trying to mimic results of small batch training on large batches. This diff adds support for `SchedulerWithWarmup`, underneath it holds two schedulers, WarmupScheduler and any other scheduler. After `warmup_steps`, the scheduler will switch from warmup to the specified scheduler. This allows something like Warmup with Expontential Decay. Since the scheduler is built on top of the existing warmup scheduler, any new features that come to that scheduler, will directly be applicable here. Sample Config ``` "SchedulerWithWarmup": { "warmup_scheduler": { "warmup_steps": 500 }, "scheduler": { "ExponentialLR": { "gamma": 0.95 } } } ``` Differential Revision: D18838272 fbshipit-source-id: 8bdb4616987d9030e8b09c073a8ba6d753d0fd8c
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Differential Revision: D18725798 fbshipit-source-id: 131cd0dc983f6a8f5d7ef0a90451238681aef821
Summary: Pull Request resolved: facebookresearch#1184 Current implementations of warmup in pytext either involve doing warmup and optionally inverse square root decay (TODO) or using polynomial decay (TODO). However, through my experiments, I notice for large batch training a warmup period is helpful on other schedulers as well, especially when trying to mimic results of small batch training on large batches. This diff adds support for `SchedulerWithWarmup`, underneath it holds two schedulers, WarmupScheduler and any other scheduler. After `warmup_steps`, the scheduler will switch from warmup to the specified scheduler. This allows something like Warmup with Expontential Decay. Since the scheduler is built on top of the existing warmup scheduler, any new features that come to that scheduler, will directly be applicable here. Sample Config ``` "SchedulerWithWarmup": { "warmup_scheduler": { "warmup_steps": 500 }, "scheduler": { "ExponentialLR": { "gamma": 0.95 } } } ``` Differential Revision: D18838272 fbshipit-source-id: e5aea7434d0563e14f357b8647781ec1ff0b0868
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Summary:
Current implementations of warmup in pytext either involve doing warmup and optionally inverse square root decay (TODO) or using polynomial decay (TODO). However, through my experiments, I notice for large batch training a warmup period is helpful on other schedulers as well, especially when trying to mimic results of small batch training on large batches.
This diff adds support for
SchedulerWithWarmup, underneath it holds two schedulers, WarmupScheduler and any other scheduler. Afterwarmup_steps, the scheduler will switch from warmup to the specified scheduler.This allows something like Warmup with Expontential Decay.
Since the scheduler is built on top of the existing warmup scheduler, any new features that come to that scheduler, will directly be applicable here.
Sample Config
Differential Revision: D18838272