wehub-resource-sync
65 lines
2.4 KiB
Python
65 lines
2.4 KiB
Python
from typing import Any, Union
|
|
|
|
import numpy as np
|
|
|
|
from ray.rllib.utils.framework import try_import_tf, try_import_torch
|
|
from ray.rllib.utils.metrics.stats.series import SeriesStats
|
|
from ray.util.annotations import DeveloperAPI
|
|
|
|
torch, _ = try_import_torch()
|
|
_, tf, _ = try_import_tf()
|
|
|
|
|
|
@DeveloperAPI
|
|
class MeanStats(SeriesStats):
|
|
"""A Stats object that tracks the mean of a series of singular values (not vectors).
|
|
|
|
Note the following limitation: When merging multiple MeanStats objects, the resulting mean is not the true mean of all values.
|
|
Instead, it is the mean of the means of the incoming MeanStats objects.
|
|
This is because we calculate the mean in parallel components and potentially merge them multiple times in one reduce cycle.
|
|
The resulting mean of means may differ significantly from the true mean, especially if some incoming means are the result of few outliers.
|
|
|
|
Example to illustrate this limitation:
|
|
First incoming mean: [1, 2, 3, 4, 5] -> 3
|
|
Second incoming mean: [15] -> 15
|
|
Mean of both merged means: [3, 15] -> 9
|
|
True mean of all values: [1, 2, 3, 4, 5, 15] -> 5
|
|
"""
|
|
|
|
stats_cls_identifier = "mean"
|
|
|
|
def _np_reduce_fn(self, values: np.ndarray) -> float:
|
|
return np.nanmean(values)
|
|
|
|
def _torch_reduce_fn(self, values: "torch.Tensor"):
|
|
"""Reduce function for torch tensors (stays on GPU)."""
|
|
return torch.nanmean(values.float())
|
|
|
|
def push(self, value: Any) -> None:
|
|
"""Pushes a value into this Stats object.
|
|
|
|
Args:
|
|
value: The value to be pushed. Can be of any type.
|
|
PyTorch GPU tensors are kept on GPU until reduce() or peek().
|
|
TensorFlow tensors are moved to CPU immediately.
|
|
"""
|
|
# Convert TensorFlow tensors to CPU immediately, keep PyTorch tensors as-is
|
|
if tf and tf.is_tensor(value):
|
|
value = value.numpy()
|
|
|
|
self.values.append(value)
|
|
|
|
def reduce(self, compile: bool = True) -> Union[Any, "MeanStats"]:
|
|
reduced_values = self.window_reduce() # Values are on CPU already after this
|
|
self._set_values([])
|
|
|
|
if compile:
|
|
return reduced_values[0]
|
|
|
|
return_stats = self.clone()
|
|
return_stats.values = reduced_values
|
|
return return_stats
|
|
|
|
def __repr__(self) -> str:
|
|
return f"MeanStats({self.peek()}; window={self._window}; len={len(self)})"
|