Add ipopt autoload

Author: metab0t

src/pyoptinterface/_src/codegen_llvm.py

@@ -226,20 +226,21 @@ def create_indirect_load_store(module: ir.Module):
     graph_op.sinh: "sinh",
     graph_op.sqrt: "sqrt",
     graph_op.tan: "tan",
-    graph_op.tanh: "tanh"
+    graph_op.tanh: "tanh",
 }
 
 math_ops = set(op2name.keys())
 
 binary_ops = set([graph_op.pow])
 
+
 def create_llvmir_basic_functions(module: ir.Module):
     create_azmul(module)
     create_sign(module)
     create_direct_load_store(module)
     create_indirect_load_store(module)
 
-    for (op, op_name) in op2name.items():
+    for op, op_name in op2name.items():
         if op in binary_ops:
             func_type = ir.FunctionType(D, [D, D])
         else:

src/pyoptinterface/_src/ipopt.py

@@ -1,9 +1,11 @@
 from io import StringIO
 import types
+import logging
+import platform
 
 from llvmlite import ir
 
-from .ipopt_model_ext import RawModel, ApplicationReturnStatus
+from .ipopt_model_ext import RawModel, ApplicationReturnStatus, load_library
 from .codegen_c import generate_csrc_prelude, generate_csrc_from_graph
 from .jit_c import TCCJITCompiler
 from .codegen_llvm import create_llvmir_basic_functions, generate_llvmir_from_graph
@@ -29,6 +31,33 @@
 from .aml import make_nd_variable
 
 
+def detected_libraries():
+    libs = []
+
+    # default names
+    default_libnames = {
+        "Linux": ["libipopt.so"],
+        "Darwin": ["libpopt.dylib"],
+        "Windows": ["ipopt-3.dll", "ipopt.dll", "libipopt-3.dll", "libipopt.dll"],
+    }[platform.system()]
+    libs.extend(default_libnames)
+
+    return libs
+
+
+def autoload_library():
+    libs = detected_libraries()
+    for lib in libs:
+        ret = load_library(lib)
+        if ret:
+            logging.info(f"Loaded IPOPT library: {lib}")
+            return True
+    return False
+
+
+autoload_library()
+
+
 def compile_functions_c(backend: RawModel, jit_compiler: TCCJITCompiler):
     io = StringIO()
 
@@ -294,9 +323,10 @@ def get_constraint_primal(model, constraint):
         dim = constraint.dim
         values = [model.get_constraint_primal(index + i) for i in range(dim)]
         return values
-    
+
     raise ValueError(f"Unknown constraint type: {type(constraint)}")
 
+
 def get_constraint_dual(model, constraint):
     if isinstance(constraint, ConstraintIndex):
         return model.get_constraint_dual(constraint.index)
@@ -305,7 +335,7 @@ def get_constraint_dual(model, constraint):
         dim = constraint.dim
         values = [model.get_constraint_dual(index + i) for i in range(dim)]
         return values
-    
+
     raise ValueError(f"Unknown constraint type: {type(constraint)}")
 
 

tests/nlp.py renamed to tests/test_nlp.py

@@ -4,17 +4,15 @@
 import pyoptinterface as poi
 from pyoptinterface import ipopt
 
-IPOPT_PATH = r"D:\Ipopt\bin\ipopt-3.dll"
-
-if not ipopt.load_library(IPOPT_PATH):
+if not ipopt.is_library_loaded():
     exit(1)
 
 
 def test_ipopt():
     model = ipopt.Model()
 
-    x = model.add_variable(lb=0.0, ub=10.0, start=0.8)
-    y = model.add_variable(lb=0.0, ub=10.0, start=0.5)
+    x = model.add_variable(lb=0.1, ub=10.0, start=0.8)
+    y = model.add_variable(lb=0.1, ub=10.0, start=0.5)
 
     model.add_linear_constraint(x + y, poi.Eq, 1.0)
 
@@ -93,7 +91,7 @@ def all_nlfuncs(vars):
             if f == poi.acosh:
                 v = f(x + 1)
             elif f == poi.pow:
-                v = f(x, x)
+                v = f(x, 2)
             else:
                 v = f(x)
             values.append(v)
@@ -103,7 +101,9 @@ def all_nlfuncs(vars):
     all_nlfuncs_f = model.register_function(all_nlfuncs, var=["x"], name="all_nlfuncs")
     N = len(nl_funcs)
     B = 1e10
-    all_nlfuncs_con = model.add_nl_constraint(all_nlfuncs_f, [x], poi.In, lb=[-B] * N, ub=[B] * N)
+    all_nlfuncs_con = model.add_nl_constraint(
+        all_nlfuncs_f, [x], poi.In, lb=[-B] * N, ub=[B] * N
+    )
 
     model.optimize()
 
@@ -120,19 +120,17 @@ def all_nlfuncs(vars):
     obj_value = model.get_model_attribute(poi.ModelAttribute.ObjectiveValue)
     assert obj_value == pytest.approx(math.exp(x_value) + math.exp(y_value))
 
-    con_values = model.get_constraint_attribute(all_nlfuncs_con, poi.ConstraintAttribute.Primal)
-    
+    con_values = model.get_constraint_attribute(
+        all_nlfuncs_con, poi.ConstraintAttribute.Primal
+    )
+
     correct_con_values = []
     for f in py_funcs:
         if f == math.acosh:
             v = f(x_value + 1)
         elif f == math.pow:
-            v = f(x_value, x_value)
+            v = f(x_value, 2)
         else:
             v = f(x_value)
         correct_con_values.append(v)
     assert con_values == pytest.approx(correct_con_values)
-
-
-if __name__ == "__main__":
-    test_ipopt()