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Fixed docstring in colossalai (#171)

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HELSON
2022-01-21 10:44:30 +08:00
committed by GitHub
parent e2089c5c15
commit 0f8c7f9804
77 changed files with 983 additions and 603 deletions
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19
colossalai/nn/lr_scheduler/cosine.py
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@@ -38,7 +38,7 @@ class CosineAnnealingLR(_CosineAnnealingLR):
:param optimizer: Wrapped optimizer
:type optimizer: torch.optim.Optimizer
:param total_steps: number of total training steps
:param total_steps: Number of total training steps
:type total_steps: int
:param eta_min: Minimum learning rate, defaults to 0
:type eta_min: int, optional
@@ -56,9 +56,9 @@ class CosineAnnealingWarmupLR(WarmupScheduler):
:param optimizer: Wrapped optimizer
:type optimizer: torch.optim.Optimizer
:param total_steps: number of total training steps
:param total_steps: Number of total training steps
:type total_steps: int
:param warmup_steps: number of warmup steps, defaults to 0
:param warmup_steps: Number of warmup steps, defaults to 0
:type warmup_steps: int, optional
:param eta_min: Minimum learning rate, defaults to 0
:type eta_min: int, optional
@@ -78,9 +78,9 @@ class FlatAnnealingLR(DelayerScheduler):
:param optimizer: Wrapped optimizer
:type optimizer: torch.optim.Optimizer
:param total_steps: number of total training steps
:param total_steps: Number of total training steps
:type total_steps: int
:param pct_start: percent of steps before starting learning rate decay
:param pct_start: Percent of steps before starting learning rate decay
:type pct_start: float
:param last_epoch: The index of last epoch, defaults to -1
:type last_epoch: int, optional
@@ -99,15 +99,16 @@ class FlatAnnealingLR(DelayerScheduler):
@LR_SCHEDULERS.register_module
class FlatAnnealingWarmupLR(WarmupDelayerScheduler):
"""Flat and cosine annealing learning rate scheduler with learning rate warmup. A linear warmup schedule will be applied, and then the learning rate will be a fixed value before starting decay.
"""Flat and cosine annealing learning rate scheduler with learning rate warmup. A linear warmup schedule will be
applied, and then the learning rate will be a fixed value before starting decay.
:param optimizer: Wrapped optimizer
:type optimizer: torch.optim.Optimizer
:param total_steps: number of total training steps
:param total_steps: Number of total training steps
:type total_steps: int
:param warmup_steps: number of warmup steps, defaults to 0
:param warmup_steps: Number of warmup steps, defaults to 0
:type warmup_steps: int, optional
:param pct_start: percent of steps before starting learning rate decay
:param pct_start: Percent of steps before starting learning rate decay
:type pct_start: float
:param eta_min: Minimum learning rate, defaults to 0
:type eta_min: int, optional