CompositeLossMetrics now performs a weighted sum of losses.
#1251
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Currently, `CompositeLossMetrics` sums the losses without considering their weights (i.e., the number of live targets). To make this a weighted sum, downstream code has been implementing `CompositeLossWeights` to inject the number of live targets into `loss_weights`. This is essentially patching a surprising logic (initail loss sum) with complex logic (CompositeLossWeights) into a straightforward one (weighted sum). Therefore, we’re changing the default loss aggregation logic to be straightforward from the beginning. From now on, our standarized loss aggregation logic is ``` loss = sum(each_loss_weight * each_loss * num_each_samples) / sum(each_loss_weight * num_each_samples) ``` Historically, the complex logic was introduced because the weights of losses returned by child metrics were unknown. But now that child metrics return losses as `WeightedScalar`, we can adopt a simpler, cleaner aggregation logic. Note: alternative formulation could be ``` loss = sum(each_loss_weight * each_loss * num_each_samples) / sum(num_each_samples) ``` However, when num_each_samples is large and each_loss_weight is small, the denominator can become disproportionately large. So we discard this option.
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@markblee Could you take a look? From 1399 |
markblee
reviewed
Jun 10, 2025
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Currently,
CompositeLossMetricssums the losses without considering their weights (i.e., the number of live targets). To make this a weighted sum, downstream code has been implementingCompositeLossWeightsto inject the number of live targets intoloss_weights. This is essentially patching a surprising logic (initail loss sum) with complex logic (CompositeLossWeights) into a straightforward one (weighted sum).Therefore, we’re changing the default loss aggregation logic to be straightforward from the beginning.
From now on, our standarized loss aggregation logic is
Historically, the complex logic was introduced because the weights of losses returned by child metrics were unknown. But now that child metrics return losses as
WeightedScalar, we can adopt a simpler, cleaner aggregation logic.Note: alternative formulation could be
However, when num_each_samples is large and each_loss_weight is small, the denominator can become disproportionately large. So we discard this option.