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feat(train): shared MetricAccumulator for disp_avg intervals - #6065
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Generalize training display aggregation into a weighted MetricAccumulator used by both PT and pt_expt. Training steps keep unit weight; validation batches use atom weights. NaN optional-label metrics are excluded from per-metric means instead of poisoning the interval. Distributed runs all-reduce packed sums, weights, and observation counts only at display boundaries. Refs deepmodeling#5819 deepmodeling#5755
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main change: introducing a shared MetricAccumulator for disp_avg interval reporting across training backends. Linked Issues check Check skipped because no linked issues were found for this pull request. Out of Scope Changes check Check skipped because no linked issues were found for this pull request.
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Review comments at @deepmd/dpmodel/train/metrics.py:
- Line 38: Update the NaN check used by MetricAccumulator.add to use a
backend-aware tensor operation instead of converting each value with
float(value), keeping CUDA checks on-device during training steps while
preserving NaN exclusion behavior.
Review comments at @deepmd/pt_expt/train/metrics.py:
- Around line 54-59: Update _state_device to select a device compatible with the
active process group and rank, rather than falling back to CPU when packed
contains no tensors. Ensure ranks with all-NaN windows use the same
collective-compatible device as ranks with finite values.
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📒 Files selected for processing (9)
deepmd/dpmodel/train/__init__.pydeepmd/dpmodel/train/metrics.pydeepmd/dpmodel/train/trainer.pydeepmd/pt/train/training.pydeepmd/pt_expt/train/metrics.pydeepmd/pt_expt/train/training.pydeepmd/utils/argcheck.pysource/tests/common/dpmodel/test_train_metrics.pysource/tests/pt_expt/train/test_metric_reduce.py
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NaN filtering on add only inspects host scalars (None/float/int/0-d NumPy). Present device metrics stay on-device until average() or a display-boundary all-reduce; optional-label gaps already arrive as host nan from display_if_exist. Refs deepmodeling#5819
Host-only windows left _state_device on CPU while other ranks used CUDA, which breaks NCCL collectives. Choose the device from the group backend and this rank's current CUDA device instead.
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## master #6065 +/- ##
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Files 1170 1171 +1
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Move Mapping/Sequence under TYPE_CHECKING and mark flat_state zip strict.
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Summary
Implements
training.disp_avginterval aggregation in the shared training stack (#5819).MetricAccumulatorunderdeepmd/dpmodel/train(aliasTrainingMetricAccumulator).deepmd/pt_expt/train/metrics.py), hooked fromAbstractTrainer.synchronize_metric_accumulatorand the legacy PT loop.disp_avg=falseinstantaneous reporting and fix(disp): div 0 error when using disp_avg under multitask #5809 unsampled-task safeguards.Tests
pytest source/tests/common/dpmodel/test_train_metrics.py \ source/tests/common/dpmodel/test_train_abstract_trainer.py \ source/tests/pt_expt/train/test_metric_reduce.py \ source/tests/common/test_disp_avg.py -q34 passed locally.
Refs #5819 #5755
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