Wire equality constraints through generator and acquisition to BoTorch (#5177)#5177
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Wire equality constraints through generator and acquisition to BoTorch (#5177)#5177sdaulton wants to merge 5 commits intofacebook:mainfrom
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Pull Request resolved: facebook#5177 Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Pull Request resolved: facebook#5177 Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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Codecov Report❌ Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #5177 +/- ##
========================================
Coverage 96.41% 96.42%
========================================
Files 618 619 +1
Lines 68882 69177 +295
========================================
+ Hits 66410 66701 +291
- Misses 2472 2476 +4 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
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…ok#5173) Summary: Add support for linear equality constraints (`w^T x == b`) alongside existing inequality constraints (`w^T x <= b`) in Ax's `ParameterConstraint` class. This is the first diff in a stack that threads equality constraints all the way down to BoTorch's `optimize_acqf`. Changes: - Add `extract_coefficient_dict_from_equality` to `ax/utils/common/sympy.py` for parsing `"expr == bound"` strings (SymPy can't parse `==` directly since Python evaluates it as a boolean). - Extend `ParameterConstraint.__init__` to accept `equality=` kwarg alongside existing `inequality=` kwarg. Exactly one must be provided. - Add `is_equality` property. - Update `check()` to use `|w^T x - b| <= tol` for equality constraints. - Update `__repr__`, `clone()`, `clone_with_transformed_parameters()`. - Add comprehensive tests for equality constraints. Reviewed By: esantorella Differential Revision: D100256486
…k#5174) Summary: Add support for parsing equality constraint strings (e.g. `"x1 + x2 == 3"`) in `constraint_from_str`. This extends the existing `<=`/`>=` parsing to also accept `==` as a comparison operator. - Add `_process_equality_constraint` function (analogous to `_process_linear_constraint`) that constructs `ParameterConstraint(equality=...)`. - Detect `==` in `constraint_from_str` and route to the new function. - Reject equality order constraints (`"x1 == x2"`) with a clear error message. - Update `INVALID_CONSTRAINT_ERROR_MSG` to document `==` support. Reviewed By: bletham Differential Revision: D100256487
…ok#5175) Summary: Update `SearchSpace.check_membership_df` and `compute_chebyshev_center` to handle equality constraints alongside existing inequality constraints. - `check_membership_df`: branch on `constraint.is_equality` — use `|weighted_sum - bound| <= tol` for equality, `weighted_sum <= bound + tol` for inequality. - `compute_chebyshev_center`: separate equality and inequality constraints into `A_eq/b_eq` and `A_ub/b_ub` for `scipy.optimize.linprog`. Equality constraints don't get the `r * ||a_i||` augmentation since the inscribed ball center must lie on the hyperplane. - `check_membership` already works via the updated `constraint.check()`. Reviewed By: bletham Differential Revision: D100256478
…acebook#5176) Summary: Add equality constraint extraction and propagation through the adapter layer to TorchOptConfig, enabling downstream generators to receive equality constraints. - Add `extract_equality_constraints` in `adapter_utils.py` (filters for `is_equality=True` constraints, returns `(A, b)` matrices). - Update `extract_parameter_constraints` to filter out equality constraints. - Add `equality_constraints` parameter to `validate_and_apply_final_transform` (now returns a 7-tuple). - Add `equality_constraints: tuple[Tensor, Tensor] | None` field to `TorchOptConfig`. - Update `TorchAdapter._get_transformed_model_gen_args` to extract equality constraints and pass them through to `TorchOptConfig`. Reviewed By: bletham Differential Revision: D100256480
facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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facebook#5177) Summary: Thread equality constraints from TorchOptConfig through to BoTorch's optimize_acqf and related optimizers. This is the key diff that connects Ax's equality constraint representation to BoTorch's SLSQP-based optimizer. - Add `_to_equality_constraints` in `torch/utils.py` — converts (A, b) tensor format to BoTorch's `(indices, coefficients, rhs)` format. No sign negation needed (equality is symmetric). - Update `BoTorchGenerator.gen()` to pass equality constraints to `acqf.optimize()`. - Add `equality_constraints` parameter to `Acquisition.optimize()` and forward to `optimize_acqf`, `optimize_acqf_mixed`, `optimize_acqf_mixed_alternating`. - Raise `ValueError` for discrete optimizers and NSGA-II (unsupported). - Update `validate_candidates` to check equality constraints. - Update `_prune_irrelevant_parameters` and `_remove_infeasible_candidates` to handle equality constraints. - Update `Surrogate.best_point` to pass equality constraints. Reviewed By: bletham Differential Revision: D100256482
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Summary:
Thread equality constraints from TorchOptConfig through to BoTorch's
optimize_acqf and related optimizers. This is the key diff that connects
Ax's equality constraint representation to BoTorch's SLSQP-based optimizer.
_to_equality_constraintsintorch/utils.py— converts (A, b) tensorformat to BoTorch's
(indices, coefficients, rhs)format. No sign negationneeded (equality is symmetric).
BoTorchGenerator.gen()to pass equality constraints toacqf.optimize().equality_constraintsparameter toAcquisition.optimize()and forwardto
optimize_acqf,optimize_acqf_mixed,optimize_acqf_mixed_alternating.ValueErrorfor discrete optimizers and NSGA-II (unsupported).validate_candidatesto check equality constraints._prune_irrelevant_parametersand_remove_infeasible_candidatesto handle equality constraints.
Surrogate.best_pointto pass equality constraints.Reviewed By: bletham
Differential Revision: D100256482