Add support for continuous relaxation within optimize_acqf_mixed_alternating #2635
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…rnating (pytorch#2635) Summary: `optimize_acqf_mixed_alternating` utilizes local search to optimize discrete dimensions. This works well when there are a small number of values for the discrete dimensions but it does not scale well as the number of values increases. To address this, we have been transforming the high-cardinality dimensions in Ax and only passing in the low-cardinality dimensions as part of `discrete_dims`. This diff adds support for using continuous relaxation for discrete dimensions that have more than `max_discrete_values` (configurable via `options`). Also updates the optimizer to fall back to `optimize_acqf` if there are no discrete dimensions left. This is more user friendly than erroring out (particularly when used through Ax). Differential Revision: D66239005
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Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## main #2635 +/- ##
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Coverage 99.98% 99.98%
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Files 196 196
Lines 17347 17362 +15
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+ Hits 17345 17360 +15
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…rnating (pytorch#2635) Summary: `optimize_acqf_mixed_alternating` utilizes local search to optimize discrete dimensions. This works well when there are a small number of values for the discrete dimensions but it does not scale well as the number of values increases. To address this, we have been transforming the high-cardinality dimensions in Ax and only passing in the low-cardinality dimensions as part of `discrete_dims`. This diff adds support for using continuous relaxation for discrete dimensions that have more than `max_discrete_values` (configurable via `options`). Also updates the optimizer to fall back to `optimize_acqf` if there are no discrete dimensions left. This is more user friendly than erroring out (particularly when used through Ax). Differential Revision: D66239005
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…rnating (pytorch#2635) Summary: `optimize_acqf_mixed_alternating` utilizes local search to optimize discrete dimensions. This works well when there are a small number of values for the discrete dimensions but it does not scale well as the number of values increases. To address this, we have been transforming the high-cardinality dimensions in Ax and only passing in the low-cardinality dimensions as part of `discrete_dims`. This diff adds support for using continuous relaxation for discrete dimensions that have more than `max_discrete_values` (configurable via `options`). Also updates the optimizer to fall back to `optimize_acqf` if there are no discrete dimensions left. This is more user friendly than erroring out (particularly when used through Ax). Differential Revision: D66239005
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Summary:
optimize_acqf_mixed_alternatingutilizes local search to optimize discrete dimensions. This works well when there are a small number of values for the discrete dimensions but it does not scale well as the number of values increases. To address this, we have been transforming the high-cardinality dimensions in Ax and only passing in the low-cardinality dimensions as part ofdiscrete_dims.This diff adds support for using continuous relaxation for discrete dimensions that have more than
max_discrete_values(configurable viaoptions).Differential Revision: D66239005