From d23a57555dbb0022b7fb3190cd5a0d0f6c54c434 Mon Sep 17 00:00:00 2001 From: William Zijie Zhang Date: Wed, 14 Jan 2026 23:49:15 -0500 Subject: [PATCH 1/2] Move CVXPY converters to cvxpy repo, simplify to pure C bindings - Remove C_problem class and ATOM_CONVERTERS from this package - Now only re-exports low-level C functions from _core extension - Remove cvxpy/scipy from core dependencies (only numpy needed) - Add cvxpy/scipy to test dependencies for integration tests - Update tests to import C_problem from cvxpy.reductions.solvers.nlp_solvers.diff_engine - Update CLAUDE.md with improved documentation This makes dnlp-diff-engine a pure C autodiff library with no circular dependencies, ready for PyPI publishing. Co-Authored-By: Claude Opus 4.5 --- CLAUDE.md | 32 ++- pyproject.toml | 6 +- python/tests/test_constrained.py | 2 +- python/tests/test_unconstrained.py | 2 +- src/dnlp_diff_engine/__init__.py | 417 ++++------------------------- uv.lock | 345 ++++++++++++++++++++++++ 6 files changed, 418 insertions(+), 386 deletions(-) create mode 100644 uv.lock diff --git a/CLAUDE.md b/CLAUDE.md index 43b2bfd..c44e1aa 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -58,12 +58,12 @@ The core abstraction is the `expr` struct (in `include/expr.h`) representing a n Atoms are organized by mathematical properties in `src/`: -- **`affine/`** - Linear operations: `variable`, `constant`, `add`, `neg`, `sum`, `promote`, `hstack`, `trace`, `linear_op`, `index` -- **`elementwise_univariate/`** - Scalar functions applied elementwise: `log`, `exp`, `entr`, `power`, `logistic`, `xexp`, trigonometric (`sin`, `cos`, `tan`), hyperbolic (`sinh`, `tanh`, `asinh`, `atanh`) -- **`bivariate/`** - Two-argument operations: `multiply`, `quad_over_lin`, `rel_entr`, `const_scalar_mult`, `const_vector_mult`, `left_matmul`, `right_matmul` -- **`other/`** - Special atoms: `quad_form`, `prod` +- **`affine/`** - Linear operations: `variable`, `constant`, `add`, `neg`, `sum`, `promote`, `hstack`, `trace`, `linear_op`, `index`, `reshape` +- **`elementwise_univariate/`** - Scalar functions applied elementwise: `log`, `exp`, `entr`, `power`, `logistic`, `xexp`, trigonometric (`sin`, `cos`, `tan`), hyperbolic (`sinh`, `tanh`, `asinh`, `atanh`). Uses `common.c` for shared chain-rule patterns. +- **`bivariate/`** - Two-argument operations: `multiply`, `quad_over_lin`, `rel_entr`, `const_scalar_mult`, `const_vector_mult`, `left_matmul` (A @ f(x)), `right_matmul` (f(x) @ A) +- **`other/`** - Special atoms: `quad_form` (x'Px), `prod` (product of elements) -Each atom implements its own `forward`, `jacobian_init`, `eval_jacobian`, and `eval_wsum_hess` functions following a consistent pattern. +Each atom implements: `forward`, `jacobian_init`, `eval_jacobian`, and optionally `eval_wsum_hess` (defaults to zero for affine atoms). ### Problem Struct @@ -95,12 +95,14 @@ The Python package `dnlp_diff_engine` (in `src/dnlp_diff_engine/`) provides: ### Derivative Computation Flow -1. Call `problem_init_derivatives()` to allocate Jacobian/Hessian storage and compute sparsity patterns -2. Call forward pass (`objective_forward` / `constraint_forward`) to propagate values through tree -3. Call derivative functions (`gradient`, `jacobian`, `hessian`) which traverse tree computing derivatives +1. **Initialization**: `problem_init_derivatives()` allocates storage and computes sparsity patterns for all Jacobians and Hessians. This is done once per problem. +2. **Forward pass**: `objective_forward(u)` / `constraint_forward(u)` propagate values through expression tree, storing results in each node's `value` field. +3. **Derivative computation**: `gradient()`, `jacobian()`, `hessian()` traverse tree computing derivatives via chain rule. -Jacobian uses chain rule: each node computes local Jacobian, combined via sparse matrix operations. -Hessian computes weighted sum: `obj_w * H_obj + sum(lambda_i * H_constraint_i)` +**Key invariant**: Forward pass must be called before corresponding derivative functions. The derivatives are computed using values cached during forward pass. + +Jacobian uses chain rule: `J_composite = J_outer * J_inner` via sparse matrix operations. +Hessian computes weighted sum: `obj_w * H_obj + sum(lambda_i * H_constraint_i)`, returning lower triangular. ### Sparse Matrix Utilities @@ -110,11 +112,11 @@ Hessian computes weighted sum: `obj_w * H_obj + sum(lambda_i * H_constraint_i)` - `include/` - Header files defining public API (`expr.h`, `problem.h`, atom headers) - `src/` - C implementation files organized by atom category -- `src/dnlp_diff_engine/` - Python package with high-level API +- `src/dnlp_diff_engine/` - Python package with high-level API (`__init__.py` contains `C_problem` class and `ATOM_CONVERTERS`) - `python/` - Python bindings C code (`bindings.c`) - `python/atoms/` - Python binding headers for each atom type - `python/problem/` - Python binding headers for problem interface -- `python/tests/` - Python integration tests (run via pytest) +- `python/tests/` - Python integration tests (run via pytest): `test_unconstrained.py`, `test_constrained.py`, `test_problem_native.py` - `tests/` - C tests using minunit framework - `tests/forward_pass/` - Forward evaluation tests (C) - `tests/jacobian_tests/` - Jacobian correctness tests (C) @@ -131,6 +133,12 @@ Hessian computes weighted sum: `obj_w * H_obj + sum(lambda_i * H_constraint_i)` 7. Rebuild: `pip install -e .` 8. Add tests in `tests/` (C) and `tests/python/` (Python) +## Known Limitations + +- **Bivariate matmul not supported**: `f(x) @ g(x)` where both sides depend on variables is not implemented. Only `A @ f(x)` and `f(x) @ A` with constant A work. +- **Reshape order**: Only Fortran order (`order='F'`) is supported. C order would require permutation logic. +- **Hessian sparsity**: Some atoms (`hstack`, `trace`) don't compute hessian sparsity patterns during initialization (see TODO.md). + ## License Header ```c diff --git a/pyproject.toml b/pyproject.toml index fc4c8ae..37c735f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,18 +5,18 @@ build-backend = "scikit_build_core.build" [project] name = "dnlp-diff-engine" version = "0.1.0" -description = "Automatic differentiation engine for DNLP optimization problems" +description = "Low-level C autodiff engine for nonlinear optimization" readme = "README.md" requires-python = ">=3.10" dependencies = [ "numpy", - "scipy", - "cvxpy", ] [project.optional-dependencies] test = [ "pytest>=7.0", + "cvxpy", # For integration tests + "scipy", # Required by cvxpy integration ] dev = [ "pytest>=7.0", diff --git a/python/tests/test_constrained.py b/python/tests/test_constrained.py index f7a933b..3b21897 100644 --- a/python/tests/test_constrained.py +++ b/python/tests/test_constrained.py @@ -3,7 +3,7 @@ import cvxpy as cp import numpy as np -from dnlp_diff_engine import C_problem +from cvxpy.reductions.solvers.nlp_solvers.diff_engine import C_problem # Note: CVXPY converts constraints A >= B to B - A <= 0 # So constr.expr for "log(x) >= 0" is "0 - log(x)" = -log(x) diff --git a/python/tests/test_unconstrained.py b/python/tests/test_unconstrained.py index bcb0c54..c50e41c 100644 --- a/python/tests/test_unconstrained.py +++ b/python/tests/test_unconstrained.py @@ -3,7 +3,7 @@ import cvxpy as cp import numpy as np -from dnlp_diff_engine import C_problem +from cvxpy.reductions.solvers.nlp_solvers.diff_engine import C_problem def test_sum_log(): diff --git a/src/dnlp_diff_engine/__init__.py b/src/dnlp_diff_engine/__init__.py index 4b8efb1..0e6db19 100644 --- a/src/dnlp_diff_engine/__init__.py +++ b/src/dnlp_diff_engine/__init__.py @@ -1,373 +1,52 @@ """ -DNLP Diff Engine - Automatic differentiation for nonlinear optimization. +DNLP Diff Engine - Low-level C bindings for automatic differentiation. -This package provides automatic differentiation capabilities for CVXPY problems, -computing gradients, Jacobians, and Hessians needed by NLP solvers. +This package provides the raw C extension for building expression trees +and computing derivatives. For CVXPY integration, use: + from cvxpy.reductions.solvers.nlp_solvers.diff_engine import C_problem """ -import cvxpy as cp -import numpy as np -from cvxpy.reductions.inverse_data import InverseData -from scipy import sparse - -from . import _core as _diffengine - -__all__ = ["C_problem", "convert_problem", "build_variable_dict"] - - -def _chain_add(children): - """Chain multiple children with binary adds: a + b + c -> add(add(a, b), c).""" - result = children[0] - for child in children[1:]: - result = _diffengine.make_add(result, child) - return result - - -def _convert_matmul(expr, children): - """Convert matrix multiplication A @ f(x) or f(x) @ A.""" - # MulExpression has args: [left, right] - # One of them should be a Constant, the other a variable expression - left_arg, right_arg = expr.args - - if left_arg.is_constant(): - # A @ f(x) -> left_matmul - # TODO: why is this always dense? What's going on here? - # we later convert it to csr.... - A = np.asarray(left_arg.value, dtype=np.float64) - if A.ndim == 1: - A = A.reshape(1, -1) # Convert 1D to row vector - A_csr = sparse.csr_matrix(A) - m, n = A_csr.shape - return _diffengine.make_left_matmul( - children[1], # right child is the variable expression - A_csr.data.astype(np.float64), - A_csr.indices.astype(np.int32), - A_csr.indptr.astype(np.int32), - m, - n, - ) - elif right_arg.is_constant(): - # f(x) @ A -> right_matmul - - # TODO: why is this always dense? What's going on here? - # we later convert it to csr.... - A = np.asarray(right_arg.value, dtype=np.float64) - if A.ndim == 1: - A = A.reshape(-1, 1) # Convert 1D to column vector - A_csr = sparse.csr_matrix(A) - m, n = A_csr.shape - return _diffengine.make_right_matmul( - children[0], # left child is the variable expression - A_csr.data.astype(np.float64), - A_csr.indices.astype(np.int32), - A_csr.indptr.astype(np.int32), - m, - n, - ) - else: - raise NotImplementedError("MulExpression with two non-constant args not supported") - - -def _convert_multiply(expr, children): - """Convert multiplication based on argument types.""" - # multiply has args: [left, right] - left_arg, right_arg = expr.args - - # Check if left is a constant - if left_arg.is_constant(): - value = np.asarray(left_arg.value, dtype=np.float64) - - # Scalar constant - if value.size == 1: - scalar = float(value.flat[0]) - return _diffengine.make_const_scalar_mult(children[1], scalar) - - # Vector constant - if value.ndim == 1 or (value.ndim == 2 and min(value.shape) == 1): - vector = value.flatten() - return _diffengine.make_const_vector_mult(children[1], vector) - - # Check if right is a constant - elif right_arg.is_constant(): - value = np.asarray(right_arg.value, dtype=np.float64) - - # Scalar constant - if value.size == 1: - scalar = float(value.flat[0]) - return _diffengine.make_const_scalar_mult(children[0], scalar) - - # Vector constant - if value.ndim == 1 or (value.ndim == 2 and min(value.shape) == 1): - vector = value.flatten() - return _diffengine.make_const_vector_mult(children[0], vector) - - # Neither is constant, use general multiply - return _diffengine.make_multiply(children[0], children[1]) - - -def _extract_flat_indices_from_index(expr): - """Extract flattened indices from CVXPY index expression.""" - parent_shape = expr.args[0].shape - indices_per_dim = [np.arange(s.start, s.stop, s.step) for s in expr.key] - - if len(indices_per_dim) == 1: - return indices_per_dim[0].astype(np.int32) - elif len(indices_per_dim) == 2: - # Fortran order: idx = row + col * n_rows - return ( - np.add.outer(indices_per_dim[0], indices_per_dim[1] * parent_shape[0]) - .flatten(order="F") - .astype(np.int32) - ) - else: - raise NotImplementedError("index with >2 dimensions not supported") - - -def _extract_flat_indices_from_special_index(expr): - """Extract flattened indices from CVXPY special_index expression.""" - return np.reshape(expr._select_mat, expr._select_mat.size, order="F").astype(np.int32) - - -def _convert_rel_entr(_expr, children): - """Convert rel_entr(x, y) = x * log(x/y) elementwise.""" - return _diffengine.make_rel_entr(children[0], children[1]) - - -def _convert_quad_form(expr, children): - """Convert quadratic form x.T @ P @ x.""" - - P_arg = expr.args[1] - - if not isinstance(P_arg, cp.Constant): - raise NotImplementedError("quad_form requires P to be a constant matrix") - - P = np.asarray(P_arg.value, dtype=np.float64) - if P.ndim == 1: - P = P.reshape(-1, 1) - - P_csr = sparse.csr_matrix(P) - m, n = P_csr.shape - - return _diffengine.make_quad_form( - children[0], # x expression - P_csr.data.astype(np.float64), - P_csr.indices.astype(np.int32), - P_csr.indptr.astype(np.int32), - m, - n, - ) - - -def _convert_reshape(expr, children): - """Convert reshape - only Fortran order is supported. - - For Fortran order, reshape is a no-op since the underlying data layout - is unchanged. We create a reshape node that updates dimension metadata. - - Note: Only order='F' (Fortran/column-major) is supported. This is the - default and most common case in CVXPY. C order would require permutation. - """ - if expr.order != "F": - raise NotImplementedError( - f"reshape with order='{expr.order}' not supported. " - "Only order='F' (Fortran) is currently supported." - ) - - d1, d2 = expr.shape - return _diffengine.make_reshape(children[0], d1, d2) - - -# Mapping from CVXPY atom names to C diff engine functions -# Converters receive (expr, children) where expr is the CVXPY expression -ATOM_CONVERTERS = { - # Elementwise unary - "log": lambda _expr, children: _diffengine.make_log(children[0]), - "exp": lambda _expr, children: _diffengine.make_exp(children[0]), - # Affine unary - "NegExpression": lambda _expr, children: _diffengine.make_neg(children[0]), - "Promote": lambda expr, children: _diffengine.make_promote( - children[0], - expr.shape[0] if len(expr.shape) >= 1 else 1, - expr.shape[1] if len(expr.shape) >= 2 else 1, - ), - # N-ary (handles 2+ args) - "AddExpression": lambda _expr, children: _chain_add(children), - # Reductions - "Sum": lambda _expr, children: _diffengine.make_sum(children[0], -1), - # Bivariate - "multiply": _convert_multiply, - "QuadForm": _convert_quad_form, - "quad_over_lin": lambda _expr, children: _diffengine.make_quad_over_lin( - children[0], children[1] - ), - "rel_entr": _convert_rel_entr, - # Matrix multiplication - "MulExpression": _convert_matmul, - # Elementwise univariate with parameter - "power": lambda expr, children: _diffengine.make_power(children[0], float(expr.p.value)), - # Trigonometric - "sin": lambda _expr, children: _diffengine.make_sin(children[0]), - "cos": lambda _expr, children: _diffengine.make_cos(children[0]), - "tan": lambda _expr, children: _diffengine.make_tan(children[0]), - # Hyperbolic - "sinh": lambda _expr, children: _diffengine.make_sinh(children[0]), - "tanh": lambda _expr, children: _diffengine.make_tanh(children[0]), - "asinh": lambda _expr, children: _diffengine.make_asinh(children[0]), - "atanh": lambda _expr, children: _diffengine.make_atanh(children[0]), - # Other elementwise - "entr": lambda _expr, children: _diffengine.make_entr(children[0]), - "logistic": lambda _expr, children: _diffengine.make_logistic(children[0]), - "xexp": lambda _expr, children: _diffengine.make_xexp(children[0]), - # Indexing/slicing - "index": lambda expr, children: _diffengine.make_index( - children[0], _extract_flat_indices_from_index(expr) - ), - "special_index": lambda expr, children: _diffengine.make_index( - children[0], _extract_flat_indices_from_special_index(expr) - ), - "reshape": _convert_reshape, - # Reductions returning scalar - "Prod": lambda _expr, children: _diffengine.make_prod(children[0]), -} - - -def build_variable_dict(variables: list) -> tuple[dict, int]: - """ - Build dictionary mapping CVXPY variable ids to C variables. - - Args: - variables: list of CVXPY Variable objects - - Returns: - var_dict: {var.id: c_variable} mapping - n_vars: total number of scalar variables - """ - id_map, _, n_vars, var_shapes = InverseData.get_var_offsets(variables) - - var_dict = {} - for var in variables: - offset, _ = id_map[var.id] - shape = var_shapes[var.id] - if len(shape) == 2: - d1, d2 = shape[0], shape[1] - elif len(shape) == 1: - d1, d2 = shape[0], 1 - else: # scalar - d1, d2 = 1, 1 - c_var = _diffengine.make_variable(d1, d2, offset, n_vars) - var_dict[var.id] = c_var - - return var_dict, n_vars - - -def _convert_expr(expr, var_dict: dict, n_vars: int): - """Convert CVXPY expression using pre-built variable dictionary.""" - # Base case: variable lookup - if isinstance(expr, cp.Variable): - return var_dict[expr.id] - - # Base case: constant - if isinstance(expr, cp.Constant): - value = np.asarray(expr.value, dtype=np.float64).flatten() - d1 = expr.shape[0] if len(expr.shape) >= 1 else 1 - d2 = expr.shape[1] if len(expr.shape) >= 2 else 1 - return _diffengine.make_constant(d1, d2, n_vars, value) - - # Recursive case: atoms - atom_name = type(expr).__name__ - - if atom_name in ATOM_CONVERTERS: - children = [_convert_expr(arg, var_dict, n_vars) for arg in expr.args] - return ATOM_CONVERTERS[atom_name](expr, children) - - raise NotImplementedError(f"Atom '{atom_name}' not supported") - - -def convert_expressions(problem: cp.Problem) -> tuple: - """ - Convert CVXPY Problem to C expressions (low-level). - - Args: - problem: CVXPY Problem object - - Returns: - c_objective: C expression for objective - c_constraints: list of C expressions for constraints - """ - var_dict, n_vars = build_variable_dict(problem.variables()) - - # Convert objective - c_objective = _convert_expr(problem.objective.expr, var_dict, n_vars) - - # Convert constraints (expression part only for now) - c_constraints = [] - for constr in problem.constraints: - c_expr = _convert_expr(constr.expr, var_dict, n_vars) - c_constraints.append(c_expr) - - return c_objective, c_constraints - - -def convert_problem(problem: cp.Problem) -> "C_problem": - """ - Convert CVXPY Problem to C problem struct. - - Args: - problem: CVXPY Problem object - - Returns: - C_Problem wrapper around C problem struct - """ - return C_problem(problem) - - -class C_problem: - """Wrapper around C problem struct for CVXPY problems.""" - - def __init__(self, cvxpy_problem: cp.Problem): - 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) - 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 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) +# Re-export all C functions directly from the _core extension +from ._core import * # noqa: F401, F403 + +__all__ = [ + # Atom constructors + "make_variable", + "make_constant", + "make_add", + "make_neg", + "make_sum", + "make_promote", + "make_index", + "make_reshape", + "make_log", + "make_exp", + "make_power", + "make_entr", + "make_logistic", + "make_xexp", + "make_sin", + "make_cos", + "make_tan", + "make_sinh", + "make_tanh", + "make_asinh", + "make_atanh", + "make_multiply", + "make_const_scalar_mult", + "make_const_vector_mult", + "make_left_matmul", + "make_right_matmul", + "make_quad_form", + "make_quad_over_lin", + "make_rel_entr", + "make_prod", + # Problem interface + "make_problem", + "problem_init_derivatives", + "problem_objective_forward", + "problem_constraint_forward", + "problem_gradient", + "problem_jacobian", + "problem_hessian", +] diff --git a/uv.lock b/uv.lock new file mode 100644 index 0000000..484730c --- /dev/null +++ b/uv.lock @@ -0,0 +1,345 @@ +version = 1 +revision = 2 +requires-python = ">=3.10" +resolution-markers = [ + "python_full_version >= '3.11'", + "python_full_version < '3.11'", +] + +[[package]] +name = "colorama" +version = "0.4.6" +source = { 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