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Update layer integration documentations (#108)

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Update the documentations of layer integration

Update _log_hook.py

Update _operation.py
This commit is contained in:
BoxiangW
2022-01-10 18:05:58 +08:00
committed by GitHub
parent 3a61d785b5
commit 4a3d3446b0
25 changed files with 1020 additions and 98 deletions
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37
colossalai/trainer/hooks/_metric_hook.py
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@@ -133,6 +133,8 @@ class LearningRateMetric(Metric):
:param epoch_only: Whether the metric only read for the full epoch
:type epoch_only: bool
:param initial_lr: initial learning rate, defaults to 0.0
:type initial_lr: float, optional
"""
def __init__(self, epoch_only: bool, initial_lr: float = 0.):
@@ -161,6 +163,8 @@ class AccuracyMetric(Metric):
:param epoch_only: Whether the metric only read for the full epoch
:type epoch_only: bool
:param accuracy_func: accuracy function for the classification task
:type accuracy_func: typing.Callable
"""
def __init__(self, epoch_only: bool, accuracy_func: Callable):
@@ -182,7 +186,8 @@ class AccuracyMetric(Metric):
and labels. It expects the output has logits and labels.
:param logits: The logits output of the model
:param label: The labels of the input data
:param targets: real labels of the dataset
:param batch_size: batch size of the task
"""
if isinstance(logits, (list, tuple)):
logits = logits[0]
@@ -216,10 +221,10 @@ class MetricHook(BaseHook):
update their states. Others are used to display and
record the metric.
:param trainer: Trainer attached with current hook
:param priority: Priority in the printing, hooks with small priority will be printed in front
:type trainer: Trainer
:type priority: int
:param trainer: Trainer attached with current hook
:type trainer: Trainer
"""
def __init__(
@@ -238,10 +243,10 @@ class MetricHook(BaseHook):
class LossHook(MetricHook):
"""Specialized hook class for :class:`Loss`.
:param trainer: Trainer attached with current hook
:param priority: Priority in the printing, hooks with small priority will be printed in front
:type trainer: Trainer
:param priority: Priority in the printing, hooks with small priority will be printed in front, defaults to 0
:type priority: int, optional
:param trainer: Trainer attached with current hook
:type trainer: Trainer
"""
def __init__(self, priority: int = 0):
@@ -279,10 +284,12 @@ class LossHook(MetricHook):
class AccuracyHook(MetricHook):
"""Specialized hook class for :class:`Accuracy`.
:param accuracy_func: Priority in the printing, hooks with small priority will be printed in front
:type accuracy_func: typing.Callable
:param priority: Priority in the printing, hooks with small priority will be printed in front, defaults to 0
:type priority: int, optional
:param trainer: Trainer attached with current hook
:param priority: Priority in the printing, hooks with small priority will be printed in front
:type trainer: Trainer
:type priority: int
"""
def __init__(self, accuracy_func: Callable, priority: int = 0):
@@ -308,6 +315,13 @@ class AccuracyHook(MetricHook):
class ThroughputMetric(Metric):
"""Metric for :class:`Throughput`.
:param epoch_only: epoch only
:type epoch_only: bool
:param num_samples: number of samples
:param time: time
"""
def __init__(self, epoch_only: bool):
super().__init__(epoch_only=epoch_only)
self.accumulated_num_samples = torch.zeros(1, device=get_current_device())
@@ -345,6 +359,13 @@ class ThroughputMetric(Metric):
@HOOKS.register_module
class ThroughputHook(MetricHook):
"""Specialized hook class for :class:`Throughput`.
:param priority: priority of throughput hook, defaults to 10
:type priority: int, optional
:param trainer: Trainer attached with current hook
:type trainer: Trainer
"""
def __init__(self, priority: int = 10):
super().__init__(priority)