mirror of
https://github.com/twitter/the-algorithm-ml.git
synced 2024-12-23 23:01:48 +01:00
100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
"""
|
|
Contains aggregation metrics.
|
|
"""
|
|
from typing import Tuple, Union
|
|
|
|
import torch
|
|
import torchmetrics
|
|
|
|
|
|
def update_mean(
|
|
current_mean: torch.Tensor,
|
|
current_weight_sum: torch.Tensor,
|
|
value: torch.Tensor,
|
|
weight: torch.Tensor,
|
|
) -> Tuple[torch.Tensor, torch.Tensor]:
|
|
"""
|
|
Update the mean according to Welford formula:
|
|
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Weighted_batched_version.
|
|
See also https://nullbuffer.com/articles/welford_algorithm.html for more information.
|
|
Args:
|
|
current_mean: The value of the current accumulated mean.
|
|
current_weight_sum: The current weighted sum.
|
|
value: The new value that needs to be added to get a new mean.
|
|
weight: The weights for the new value.
|
|
|
|
Returns: The updated mean and updated weighted sum.
|
|
|
|
"""
|
|
weight = torch.broadcast_to(weight, value.shape)
|
|
|
|
# Avoiding (on purpose) in-place operation when using += in case
|
|
# current_mean and current_weight_sum share the same storage
|
|
current_weight_sum = current_weight_sum + torch.sum(weight)
|
|
current_mean = current_mean + torch.sum((weight / current_weight_sum) * (value - current_mean))
|
|
return current_mean, current_weight_sum
|
|
|
|
|
|
def stable_mean_dist_reduce_fn(state: torch.Tensor) -> torch.Tensor:
|
|
"""
|
|
Merge the state from multiple workers.
|
|
Args:
|
|
state: A tensor with the first dimension indicating workers.
|
|
|
|
Returns: The accumulated mean from all workers.
|
|
|
|
"""
|
|
mean, weight_sum = update_mean(
|
|
current_mean=torch.as_tensor(0.0, dtype=state.dtype, device=state.device),
|
|
current_weight_sum=torch.as_tensor(0.0, dtype=state.dtype, device=state.device),
|
|
value=state[:, 0],
|
|
weight=state[:, 1],
|
|
)
|
|
merged_accumulated_mean = torch.stack([mean, weight_sum])
|
|
return merged_accumulated_mean
|
|
|
|
|
|
class StableMean(torchmetrics.Metric):
|
|
"""
|
|
This implements a numerical stable mean metrics computation using Welford algorithm according to
|
|
https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Weighted_batched_version.
|
|
For example when using float32, the algorithm will give a valid output even if the "sum" is larger
|
|
than the maximum float32 as far as the mean is within the limit of float32.
|
|
See also https://nullbuffer.com/articles/welford_algorithm.html for more information.
|
|
"""
|
|
|
|
def __init__(self, **kwargs):
|
|
"""
|
|
Args:
|
|
**kwargs: Additional parameters supported by all torchmetrics.Metric.
|
|
"""
|
|
super().__init__(**kwargs)
|
|
self.add_state(
|
|
"mean_and_weight_sum",
|
|
default=torch.zeros(2),
|
|
dist_reduce_fx=stable_mean_dist_reduce_fn,
|
|
)
|
|
|
|
def update(self, value: torch.Tensor, weight: Union[float, torch.Tensor] = 1.0) -> None:
|
|
"""
|
|
Update the current mean.
|
|
Args:
|
|
value: Value to update the mean with.
|
|
weight: weight to use. Shape should be broadcastable to that of value.
|
|
"""
|
|
mean, weight_sum = self.mean_and_weight_sum[0], self.mean_and_weight_sum[1]
|
|
|
|
if not isinstance(weight, torch.Tensor):
|
|
weight = torch.as_tensor(weight, dtype=value.dtype, device=value.device)
|
|
|
|
self.mean_and_weight_sum[0], self.mean_and_weight_sum[1] = update_mean(
|
|
mean, weight_sum, value, torch.as_tensor(weight)
|
|
)
|
|
|
|
def compute(self) -> torch.Tensor:
|
|
"""
|
|
Compute and return the accumulated mean.
|
|
"""
|
|
accumulated_mean = self.mean_and_weight_sum[0]
|
|
return accumulated_mean
|