change nlp_solver Oracles class to use C diffengine (and adds setup for everything) #131

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Merged

dance858 merged 55 commits into master from diffengine-setup

Jan 31, 2026

.github/workflows/build.yml

-Original file line number
+Diff line change
@@ Expand Up / @@ -54,6 +54,8 @@ jobs: @@
         steps:
           - uses: actions/checkout@v5
+            with:
+              submodules: recursive
           - name: Set Additional Envs
             run: |
               echo "PYTHON_SUBVERSION=$(echo $PYTHON_VERSION | cut -c 3-)" >> $GITHUB_ENV
@@ Expand Down Expand Up / @@ -114,6 +116,8 @@ jobs: @@
         steps:
           - uses: actions/checkout@v5
+            with:
+              submodules: recursive
           - uses: actions/setup-python@v6
             with:
               python-version: ${{ matrix.python-version }}
@@ Expand Down @@

.github/workflows/test_backends.yml

-Original file line number
+Diff line change
@@ Expand Up / @@ -16,6 +16,8 @@ jobs: @@
             with:
               python-version: "3.12"
           - uses: actions/checkout@v5
+            with:
+              submodules: recursive
           - name: Install cvxpy dependencies
             run: |
               pip install -e .
@@ Expand Down @@

.github/workflows/test_nlp_solvers.yml

-Original file line number
+Diff line change
@@ Expand Up / @@ -22,6 +22,8 @@ jobs: @@
             with:
               python-version: "3.12"
           - uses: actions/checkout@v5
+            with:
+              submodules: recursive
           - name: Set up Conda
             uses: conda-incubator/setup-miniconda@v3
             with:
@@ Expand All / @@ -35,13 +37,10 @@ jobs: @@
           - name: Install cyipopt via conda
             run: |
               conda install -y -c conda-forge cyipopt
-          - name: Install uno via pip
-            run: |
-              pip install unopy
           - name: Install test dependencies
             run: |
               pip install -e .
               pip install pytest hypothesis
           - name: Run nlp tests
             run: |
-              pytest cvxpy/tests/nlp_tests/.
+              pytest -s -v cvxpy/tests/nlp_tests/.

.github/workflows/test_optional_solvers.yml

-Original file line number
+Diff line change
@@ Expand Up / @@ -31,7 +31,6 @@ jobs: @@
             with:
               python-version: 3.12
               enable-cache: true
-          - uses: actions/checkout@v5
           - name: Setup Julia
             uses: julia-actions/setup-julia@v2
             with:
@@ Expand Down @@

.gitmodules

-Original file line number
+Diff line change
@@ -0,0 +1,3 @@
+    [submodule "diff_engine_core"]
+    	path = diff_engine_core
+    	url = https://github.com/dance858/DNLP-diff-engine

cvxpy/atoms/elementwise/power.py

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -386,16 +386,16 @@ def _grad(self, values):
  
        def _verify_hess_vec_args(self):

            # we can't compute the hessian if p is not constant and specified

            if (self.p_rational is None and self.p.value is None) or \

            if (self.p_used is None and self.p.value is None) or \

                not isinstance(self.args[0], Variable):

                return False

            return True

        def _hess_vec(self, vec):

            """ See the docstring of the hess_vec method of the atom class. """

            if self.p_rational is not None:

                p = self.p_rational

            if self.p_used is not None:

                p = self.p_used

            elif self.p.value is not None:

                p = self.p.value

    @@ -409,7 +409,7 @@ def _hess_vec(self, vec):
  
            return {(x, x): (idxs, idxs, vals)}

        def _verify_jacobian_args(self):

            if (self.p_rational is None and self.p.value is None):

            if (self.p_used is None and self.p.value is None):

                return False

            if not isinstance(self.args[0], Variable):

    @@ -423,8 +423,8 @@ def _jacobian(self):
  
            entries p * x_i^(p-1). We vectorize matrix expressions, so we flatten the

            values in column-major (Fortran) order.

            """

            if self.p_rational is not None:

                p = self.p_rational

            if self.p_used is not None:

                p = self.p_used

            elif self.p.value is not None:

                p = self.p.value

cvxpy/problems/problem.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -1285,10 +1285,21 @@ def _solve(self, @@
                         canon_problem, inverse_data = nlp_chain.apply(problem=self)
                         solution = nlp_chain.solver.solve_via_data(canon_problem, warm_start,
                                                                     verbose, solver_opts=kwargs)
+                        # This gives the objective value of the C problem
+                        # which can be slightly different from the original NLP
+                        # so we use the below approach with unpacking. Preferably
+                        # we would have a way to do this without unpacking.
+                        #obj_value = canon_problem['objective'](solution['x'])
+                        # set cvxpy variable
+                        self.unpack_results(solution, nlp_chain, inverse_data)
                         obj_value = self.objective.value
                         all_objs[run] = obj_value
                         if obj_value < best_obj:
                             best_obj = obj_value
+                            print("best_obj: ", best_obj)
                             best_solution = solution
                     # unpack best solution
@@ Expand Down Expand Up / @@ -1610,7 +1621,7 @@ def unpack(self, solution) -> None: @@
         def unpack_results(self, solution, chain: SolvingChain, inverse_data) -> None:
             """Updates the problem state given the solver results.
-            Updates problem.status, problem.value and value of
+            Updates problem.status, problem.value and value ofro
             primal and dual variables.
             Arguments
@@ Expand Down @@

cvxpy/reductions/dnlp2smooth/canonicalizers/__init__.py

            
                      Original file line number
                      Diff line number
                      Diff line change
                  
    @@ -13,7 +13,7 @@
  
    See the License for the specific language governing permissions and

    limitations under the License.

    """

    from cvxpy.atoms.geo_mean import geo_mean

    from cvxpy.atoms.geo_mean import GeoMean, GeoMeanApprox

    from cvxpy.atoms.prod import Prod

    from cvxpy.atoms.quad_over_lin import quad_over_lin

    from cvxpy.atoms.elementwise.exp import exp

    @@ -24,13 +24,13 @@
  
    from cvxpy.atoms.elementwise.kl_div import kl_div

    from cvxpy.atoms.elementwise.minimum import minimum

    from cvxpy.atoms.elementwise.maximum import maximum

    from cvxpy.atoms.elementwise.power import power

    from cvxpy.atoms.elementwise.power import Power, PowerApprox

    from cvxpy.atoms.elementwise.trig import cos, sin, tan

    from cvxpy.atoms.elementwise.hyperbolic import sinh, asinh, tanh, atanh

    from cvxpy.atoms.elementwise.huber import huber

    from cvxpy.atoms.norm1 import norm1

    from cvxpy.atoms.norm_inf import norm_inf

    from cvxpy.atoms.pnorm import Pnorm

    from cvxpy.atoms.pnorm import Pnorm, PnormApprox

    from cvxpy.atoms.sum_largest import sum_largest

    from cvxpy.atoms.elementwise.abs import abs

    from cvxpy.atoms.max import max

    @@ -77,15 +77,18 @@
  
        tanh: tanh_canon,

        atanh: atanh_canon,

        quad_over_lin: quad_over_lin_canon,

        power: power_canon,

        Pnorm : pnorm_canon,

        Power: power_canon,

        PowerApprox: power_canon,

        Pnorm: pnorm_canon,

        PnormApprox: pnorm_canon,

        DivExpression: div_canon,

        entr: entr_canon,

        rel_entr: rel_entr_canon,

        kl_div: kl_div_canon,

        multiply: multiply_canon,

        MulExpression: matmul_canon,

        geo_mean: geo_mean_canon,

        GeoMean: geo_mean_canon,

        GeoMeanApprox: geo_mean_canon,

        log_sum_exp: log_sum_exp_canon,

        Prod: prod_canon,

cvxpy/reductions/dnlp2smooth/canonicalizers/log_sum_exp_canon.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -31,7 +31,7 @@ def log_sum_exp_canon(expr, args): @@
         if x.value is not None:
             t.value = expr.numeric(x.value)
-            v.value = x.value - t.value
+            v.value = np.minimum(x.value - t.value, -1)
         else:
             t.value = np.ones(expr.shape)
             v.value = -np.ones(x.shape)
@@ Expand Down @@

cvxpy/reductions/dnlp2smooth/canonicalizers/power_canon.py

-Original file line number
+Diff line change
@@ Expand Up / @@ -24,7 +24,7 @@ @@
     def power_canon(expr, args):
         x = args[0]
-        p = expr.p_rational
+        p = expr.p_used
         shape = expr.shape
         ones = Constant(np.ones(shape))
         if p == 0:
@@ Expand Down @@

cvxpy/reductions/solvers/nlp_solvers/copt_nlpif.py

-Original file line number
+Diff line change
@@ Expand Up @@
             """
             import coptpy as copt
+            from cvxpy.reductions.solvers.nlp_solvers.nlp_solver import Oracles
+            # Create oracles object (deferred from apply() so we have access to verbose)
+            bounds = data["_bounds"]
+            oracles = Oracles(bounds.new_problem, bounds.x0, len(bounds.cl), verbose=verbose)
             class COPTNlpCallbackCVXPY(copt.NlpCallbackBase):
                 def __init__(self, oracles, m):
                     super().__init__()
@@ Expand Down Expand Up / @@ -184,9 +190,6 @@ def EvalHess(self, xdata, sigma, lambdata, outdata): @@
             # Pass through verbosity
             model.setParam(copt.COPT.Param.Logging, verbose)
-            # Get oracles for function evaluation
-            oracles = data['oracles']
             # Get the NLP problem data
             x0 = data['x0']
             lb, ub = data['lb'].copy(), data['ub'].copy()
@@ Expand Down @@

cvxpy/reductions/solvers/nlp_solvers/diff_engine/__init__.py

-Original file line number
+Diff line change
@@ -0,0 +1,38 @@
+    """
+    Copyright 2025, the CVXPY developers
+    Licensed under the Apache License, Version 2.0 (the "License");
+    you may not use this file except in compliance with the License.
+    You may obtain a copy of the License at
+        http://www.apache.org/licenses/LICENSE-2.0
+    Unless required by applicable law or agreed to in writing, software
+    distributed under the License is distributed on an "AS IS" BASIS,
+    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+    See the License for the specific language governing permissions and
+    limitations under the License.
+    """
+    """
+    CVXPY integration layer for the DNLP diff engine.
+    This module converts CVXPY expressions to C expression trees
+    for automatic differentiation.
+    """
+    from cvxpy.reductions.solvers.nlp_solvers.diff_engine.c_problem import C_problem
+    from cvxpy.reductions.solvers.nlp_solvers.diff_engine.converters import (
+        ATOM_CONVERTERS,
+        build_variable_dict,
+        convert_expr,
+        convert_expressions,
+    )
+    __all__ = [
+        "C_problem",
+        "ATOM_CONVERTERS",
+        "build_variable_dict",
+        "convert_expr",
+        "convert_expressions",
+    ]

cvxpy/reductions/solvers/nlp_solvers/diff_engine/c_problem.py

-Original file line number
+Diff line change
@@ -0,0 +1,96 @@
+    """Wrapper around C problem struct for CVXPY problems.
+    Copyright 2025, the CVXPY developers
+    Licensed under the Apache License, Version 2.0 (the "License");
+    you may not use this file except in compliance with the License.
+    You may obtain a copy of the License at
+        http://www.apache.org/licenses/LICENSE-2.0
+    Unless required by applicable law or agreed to in writing, software
+    distributed under the License is distributed on an "AS IS" BASIS,
+    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+    See the License for the specific language governing permissions and
+    limitations under the License.
+    """
+    import numpy as np
+    from scipy import sparse
+    import cvxpy as cp
+    # Import the low-level C bindings
+    try:
+        import _diffengine
+    except ImportError as e:
+        raise ImportError(
+            "NLP support requires diff-engine. Rebuild with: pip install -e ."
+        ) from e
+    from cvxpy.reductions.solvers.nlp_solvers.diff_engine.converters import (
+        build_variable_dict,
+        convert_expr,
+    )
+    class C_problem:
+        """Wrapper around C problem struct for CVXPY problems."""
+        def __init__(self, cvxpy_problem: cp.Problem, verbose: bool = True):
+            var_dict, n_vars = build_variable_dict(cvxpy_problem.variables())
+            c_obj = convert_expr(cvxpy_problem.objective.expr, var_dict, n_vars)
+            c_constraints = [convert_expr(c.expr, var_dict, n_vars) for c in cvxpy_problem.constraints]
+            self._capsule = _diffengine.make_problem(c_obj, c_constraints, verbose)
+            self._allocated = False
+        def init_derivatives(self):
+            """Initialize derivative structures. Must be called before forward/gradient/jacobian."""
+            _diffengine.problem_init_derivatives(self._capsule)
+            self._allocated = True
+        def objective_forward(self, u: np.ndarray) -> float:
+            """Evaluate objective. Returns obj_value float."""
+            return _diffengine.problem_objective_forward(self._capsule, u)
+        def constraint_forward(self, u: np.ndarray) -> np.ndarray:
+            """Evaluate constraints only. Returns constraint_values array."""
+            return _diffengine.problem_constraint_forward(self._capsule, u)
+        def gradient(self) -> np.ndarray:
+            """Compute gradient of objective. Call objective_forward first. Returns gradient array."""
+            return _diffengine.problem_gradient(self._capsule)
+        def jacobian(self) -> sparse.csr_matrix:
+            """Compute constraint Jacobian. Call constraint_forward first."""
+            data, indices, indptr, shape = _diffengine.problem_jacobian(self._capsule)
+            return sparse.csr_matrix((data, indices, indptr), shape=shape)
+        def get_jacobian(self) -> sparse.csr_matrix:
+            """Get constraint Jacobian. This function does not evaluate the jacobian. """
+            data, indices, indptr, shape = _diffengine.get_jacobian(self._capsule)
+            return sparse.csr_matrix((data, indices, indptr), shape=shape)
+        def hessian(self, obj_factor: float, lagrange: np.ndarray) -> sparse.csr_matrix:
+            """Compute Lagrangian Hessian.
+            Computes: obj_factor * H_obj + sum(lagrange_i * H_constraint_i)
+            Call objective_forward and constraint_forward before this.
+            Args:
+                obj_factor: Weight for objective Hessian
+                lagrange: Array of Lagrange multipliers (length = total_constraint_size)
+            Returns:
+                scipy CSR matrix of shape (n_vars, n_vars)
+            """
+            data, indices, indptr, shape = _diffengine.problem_hessian(
+                self._capsule, obj_factor, lagrange
+            )
+            return sparse.csr_matrix((data, indices, indptr), shape=shape)
+        def get_hessian(self) -> sparse.csr_matrix:
+            """Get Lagrangian Hessian. This function does not evaluate the hessian."""
+            data, indices, indptr, shape = _diffengine.get_hessian(self._capsule)
+            return sparse.csr_matrix((data, indices, indptr), shape=shape)

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change nlp_solver Oracles class to use C diffengine (and adds setup for everything) #131

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