fix(metrics): accumulate half-precision meter updates in float32 - #6580

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Review comments at @changelog.d/+average-meter-precision.fixed.md:
- Line 1: Rename the changelog fragment from its temporary +name form to the
PR-numbered fixed-fragment name 6580.fixed.md, keeping its existing changelog
text unchanged.

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• changelog.d/+average-meter-precision.fixed.md
• kornia/metrics/average_meter.py
• tests/metrics/test_average_meter.py

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The fix is right. On main a bfloat16 meter updated with 1.0 a thousand times stops at sum == 256 and reports avg == 0.2559, so a loss logged from a bfloat16 training loop reads wrong. With float16, 1000.0 * n=100 overflows before it reaches the accumulator.

On this head merged with current main (torch 2.15.0), tests/metrics/test_average_meter.py gives 18 passed at CPU float16/bfloat16/float64, and 14 passed and 1 skipped at CPU and MPS float32. Seven cases fail with main's average_meter.py at CPU float16/bfloat16/float32. val stays the raw tensor, and float32 inputs keep a float32 sum until a float64 value widens it.

One thing before we approve: the description has no "Which part was done by a human, if any?" section from the pull request template. Please add it, and say plainly what you did and what an AI tool did. We will approve once that is in.

Posted on behalf of @ducha-aiki by Claude (Opus 5.5).

The fix holds on CUDA as well. A bfloat16 meter updated with 1.0 a thousand times now has sum == 1000 where main stops at 256, and avg is 1.0 where main reports 0.2559. On main, float16 1000.0 with n=100 gives inf; here it gives 100000. Python numbers mixed with half-precision tensors, the case that keeps this in scope, are fixed too: update(1000.0, 100) followed by a float16 1000.0 with n=100 gives sum == 200000 where main gives inf.

val stays the raw value and dtype. Float32 and float64 inputs keep their dtype. The human-involvement section you added reads as a clear statement of who did what.

Calls that changed against main, all of them justified:

• sum is float32 instead of float16 or bfloat16 for half-precision inputs, as the changelog says.
• A float32 meter that receives a float64 value now widens to float64 instead of staying float32.
• Some calls that raised now work, because the update is no longer in place: an integer or bool tensor followed by a float tensor, a CPU meter followed by a CUDA value, and an inference-mode meter updated outside inference mode.
• A reference taken earlier with s = meter.sum is no longer changed by later updates.
• Compiled and eager results now agree. On main, 6 of 22 compiled cases differed from eager, for example bfloat16 with n=100 gave sum 100352 compiled and 99840 eager. Here all 22 match.

Speed

An idle machine, both trees in one process, minimum over alternating rounds, three runs for anything above 1.03. One update plus avg, in microseconds:

The half-precision rows, about 1.2x, are the conversion the fix needs. A meter normally updates once per batch, so none of this matters in practice.

Checked on an RTX 4090

torch 2.15.0+cu132. Counts are this PR's merged tree, then main.

• tests/metrics/test_average_meter.py: CUDA float32/float64 14, 9. The same under KORNIA_TEST_OPTIMIZER=inductor: 14, 9; this file has no compile tests, so the direct checks below cover compile. CPU float32/float64 14, 9. CPU float16 10, 7. CPU bfloat16 10, 7. CUDA half precision, isolated: 14, 9.
• With main's average_meter.py under your tests: 4 failures at float32/float64/bfloat16, as your description says, and 7 at float16/bfloat16/float32.
• torch.compile(fullgraph=True) with a meter local to the function, 11 dtype pairs on CPU and CUDA: compiled equals eager in all 22 cases. A long-lived meter updated inside a compiled function accumulates correctly over 3 calls, with and without fullgraph, at all four dtypes.
• torch.jit.trace of a module whose parameter feeds update, with the meter created in forward: trace equals eager on new inputs and after a parameter update, at all four dtypes on CPU and CUDA.
• Mixed dtypes: 22 sequences on CPU and CUDA, plus Python numbers and parameter-backed values. Backward reaches a half-precision parameter with the right value and dtype.
• Mutants, all 10 caught: dropping either dtype from the tuple, .double(), weighting before promotion, in-place add, storing the promoted value in val, .detach(), removing the isinstance guard, promoting every float dtype, and not weighting by n.
• Conformance: --compile-conformance=metrics 42 passed and --onnx-conformance=metrics 63 passed, on both trees, on CPU. AverageMeter itself is listed as not a tensor op there, so these only show its neighbours are unaffected.

Not verified here

MPS and Windows. The earlier review ran this head on MPS float32: 14 passed and 1 skipped. torch.jit.script does not apply: AverageMeter cannot be scripted on either tree.

Written by Claude (Opus 5.5) on behalf of @ducha-aiki