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Merge pull request #15 from danclaudino/scipy_optimizer
Scipy optimizer
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add_subdirectory(nlopt-optimizers) | ||
add_subdirectory(mlpack) | ||
add_subdirectory(mlpack) | ||
if(XACC_BUILD_SCIPY) | ||
execute_process(COMMAND ${Python_EXECUTABLE} -c "import scipy" RESULT_VARIABLE SCIPY_EXISTS) | ||
if(SCIPY_EXISTS EQUAL "1") | ||
# if not, check we have pip | ||
execute_process(COMMAND ${Python_EXECUTABLE} -c "import pip" RESULT_VARIABLE PIP_EXISTS) | ||
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if(PIP_EXISTS EQUAL "0") | ||
# we have pip, so just install scipy | ||
message(STATUS "${BoldGreen}Installing Scipy.${ColorReset}") | ||
execute_process(COMMAND ${Python_EXECUTABLE} -m pip install scipy) | ||
else() | ||
# we dont have pip, so warn the user | ||
message(STATUS "${BoldYellow}Scipy not found, but can't install via pip. Ensure you install scipy module if you would like to use the Scipy optimizer.${ColorReset}") | ||
endif() | ||
else() | ||
message(STATUS "${BoldGreen}Found Scipy.${ColorReset}") | ||
endif() | ||
add_subdirectory(scipy) | ||
else() | ||
message(STATUS "${BoldYellow}XACC will not build the Scipy optimizer. You can turn it on with -DXACC_BUILD_SCIPY=ON${ColorReset}") | ||
endif() |
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message(STATUS "${BoldGreen}Building Scipy Optimizer.${ColorReset}") | ||
set(LIBRARY_NAME xacc-scipy-optimizer) | ||
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file(GLOB | ||
SRC | ||
scipy_optimizer.cpp) | ||
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usfunctiongetresourcesource(TARGET | ||
${LIBRARY_NAME} | ||
OUT | ||
SRC) | ||
usfunctiongeneratebundleinit(TARGET | ||
${LIBRARY_NAME} | ||
OUT | ||
SRC) | ||
#find_package(pybind11 REQUIRED) | ||
find_package(Python COMPONENTS Interpreter Development) | ||
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add_library(${LIBRARY_NAME} SHARED ${SRC}) | ||
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target_include_directories(${LIBRARY_NAME} | ||
PUBLIC . ${CMAKE_SOURCE_DIR}/tpls/pybind11/include ${Python_INCLUDE_DIRS}) | ||
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target_link_libraries(${LIBRARY_NAME} PUBLIC xacc Python::Python) | ||
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set(_bundle_name xacc_optimizer_scipy) | ||
set_target_properties(${LIBRARY_NAME} | ||
PROPERTIES COMPILE_DEFINITIONS | ||
US_BUNDLE_NAME=${_bundle_name} | ||
US_BUNDLE_NAME | ||
${_bundle_name}) | ||
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usfunctionembedresources(TARGET | ||
${LIBRARY_NAME} | ||
WORKING_DIRECTORY | ||
${CMAKE_CURRENT_SOURCE_DIR} | ||
FILES | ||
manifest.json) | ||
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if(APPLE) | ||
set_target_properties(${LIBRARY_NAME} | ||
PROPERTIES INSTALL_RPATH "@loader_path/../lib") | ||
set_target_properties(${LIBRARY_NAME} | ||
PROPERTIES LINK_FLAGS "-undefined dynamic_lookup") | ||
else() | ||
set_target_properties(${LIBRARY_NAME} | ||
PROPERTIES INSTALL_RPATH "$ORIGIN/../lib") | ||
set_target_properties(${LIBRARY_NAME} PROPERTIES LINK_FLAGS "-shared") | ||
endif() | ||
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if(XACC_BUILD_TESTS) | ||
add_subdirectory(tests) | ||
endif() | ||
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install(TARGETS ${LIBRARY_NAME} DESTINATION ${CMAKE_INSTALL_PREFIX}/plugins) |
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{ | ||
"bundle.symbolic_name" : "xacc_optimizer_scipy", | ||
"bundle.activator" : true, | ||
"bundle.name" : "XACC Scipy Optimizer", | ||
"bundle.description" : "" | ||
} |
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#include "scipy_optimizer.hpp" | ||
#include "Optimizer.hpp" | ||
#include "pybind11/stl.h" | ||
#include "pybind11/numpy.h" | ||
#include "xacc.hpp" | ||
#include "xacc_plugin.hpp" | ||
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namespace py = pybind11; | ||
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namespace xacc { | ||
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const std::string ScipyOptimizer::get_algorithm() const { | ||
std::string optimizerAlgo = "COBYLA"; | ||
if (options.stringExists("algorithm")) { | ||
optimizerAlgo = options.getString("algorithm"); | ||
} | ||
if (options.stringExists("scipy-optimizer")) { | ||
optimizerAlgo = options.getString("scipy-optimizer"); | ||
} | ||
return optimizerAlgo; | ||
} | ||
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const bool ScipyOptimizer::isGradientBased() const { | ||
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std::string optimizerAlgo = "cobyla"; | ||
if (options.stringExists("algorithm")) { | ||
optimizerAlgo = options.getString("algorithm"); | ||
} | ||
if (options.stringExists("scipy-optimizer")) { | ||
optimizerAlgo = options.getString("scipy-optimizer"); | ||
} | ||
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if (options.stringExists("optimizer")) { | ||
optimizerAlgo = options.getString("optimizer"); | ||
} | ||
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if (optimizerAlgo == "bfgs") { | ||
return true; | ||
} else { | ||
return false; | ||
} | ||
} | ||
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OptResult ScipyOptimizer::optimize(OptFunction &function) { | ||
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bool maximize = false; | ||
if (options.keyExists<bool>("maximize")) { | ||
xacc::info("Turning on maximize!"); | ||
maximize = options.get<bool>("maximize"); | ||
} | ||
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std::string algo = "COBYLA"; | ||
if (options.stringExists("algorithm")) { | ||
algo = options.getString("algorithm"); | ||
} | ||
if (options.stringExists("scipy-optimizer")) { | ||
algo = options.getString("scipy-optimizer"); | ||
} | ||
if (options.stringExists("optimizer")) { | ||
algo = options.getString("optimizer"); | ||
} | ||
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if (algo == "cobyla" || algo == "COBYLA") { | ||
algo = "COBYLA"; | ||
} else if (algo == "nelder-mead" || algo == "Nelder-Mead") { | ||
algo = "Nelder-Mead"; | ||
} else if (algo == "bfgs" || algo == "BFGS" || algo == "l-bfgs") { | ||
algo = "BFGS"; | ||
} else { | ||
xacc::XACCLogger::instance()->error("Invalid optimizer at this time: " + | ||
algo); | ||
} | ||
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double tol = 1e-6; | ||
if (options.keyExists<double>("ftol")) { | ||
tol = options.get<double>("ftol"); | ||
xacc::info("[Scipy] function tolerance set to " + std::to_string(tol)); | ||
} | ||
if (options.keyExists<double>("scipy-ftol")) { | ||
tol = options.get<double>("ftol"); | ||
xacc::info("[Scipy] function tolerance set to " + std::to_string(tol)); | ||
} | ||
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int maxeval = 1000; | ||
if (options.keyExists<int>("maxeval")) { | ||
maxeval = options.get<int>("maxeval"); | ||
xacc::info("[Scipy] max function evaluations set to " + | ||
std::to_string(maxeval)); | ||
} | ||
if (options.keyExists<int>("scipy-maxeval")) { | ||
maxeval = options.get<int>("maxeval"); | ||
xacc::info("[Scipy] max function evaluations set to " + | ||
std::to_string(maxeval)); | ||
} | ||
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std::vector<double> x(function.dimensions()); | ||
if (options.keyExists<std::vector<double>>("initial-parameters")) { | ||
x = options.get_with_throw<std::vector<double>>("initial-parameters"); | ||
} else if (options.keyExists<std::vector<int>>("initial-parameters")) { | ||
auto tmpx = options.get<std::vector<int>>("initial-parameters"); | ||
x = std::vector<double>(tmpx.begin(), tmpx.end()); | ||
} | ||
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// here the python stuff starts | ||
py::list pyInitialParams; | ||
for (const auto ¶m : x) { | ||
pyInitialParams.append(param); | ||
} | ||
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// wrap the objective function in this lambda | ||
// scipy passes a numpy array to this function, hence the py::array_t type | ||
py::object pyObjFunction = | ||
py::cpp_function([&function](const py::array_t<double> &pyParams) { | ||
std::vector<double> params(pyParams.size()); | ||
std::memcpy(params.data(), pyParams.data(), | ||
pyParams.size() * sizeof(double)); | ||
return function(std::move(params)); | ||
}); | ||
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// call this for gradient-based optimization | ||
py::object pyObjFunctionWithGrad = | ||
py::cpp_function([&function](const py::array_t<double> &pyParams) { | ||
std::vector<double> params(pyParams.size()); | ||
std::memcpy(params.data(), pyParams.data(), | ||
pyParams.size() * sizeof(double)); | ||
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std::vector<double> grad(params.size()); | ||
double result = function(params, grad); | ||
py::array_t<double> pyGrad(grad.size()); | ||
std::memcpy(pyGrad.mutable_data(), grad.data(), | ||
grad.size() * sizeof(double)); | ||
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return py::make_tuple(result, pyGrad); | ||
}); | ||
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py::module scipy_optimize = py::module::import("scipy.optimize"); | ||
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// error handling helps here to see if it's coming from C++ or python | ||
try { | ||
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py::object result = scipy_optimize.attr("minimize")( | ||
isGradientBased() ? pyObjFunctionWithGrad : pyObjFunction, | ||
pyInitialParams, | ||
py::arg("args") = py::tuple(), | ||
py::arg("method") = algo, | ||
py::arg("tol") = tol, | ||
py::arg("jac") = (isGradientBased() ? true : false)); | ||
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std::vector<double> optimizedParams = | ||
result.attr("x").cast<std::vector<double>>(); | ||
double optimalValue = result.attr("fun").cast<double>(); | ||
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return {optimalValue, optimizedParams}; | ||
} catch (const py::error_already_set &e) { | ||
std::cerr << "Python error: " << e.what() << std::endl; | ||
throw; | ||
} catch (const std::exception &e) { | ||
std::cerr << "Error: " << e.what() << std::endl; | ||
throw; | ||
} | ||
} | ||
} // namespace xacc | ||
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// Register the plugin with XACC | ||
REGISTER_OPTIMIZER(xacc::ScipyOptimizer) |
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#ifndef SCIPYOPTIMIZER_HPP | ||
#define SCIPYOPTIMIZER_HPP | ||
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#include <xacc.hpp> | ||
#include <xacc_service.hpp> | ||
#include <Optimizer.hpp> | ||
#include <pybind11/embed.h> | ||
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namespace xacc { | ||
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class ScipyOptimizer : public xacc::Optimizer { | ||
public: | ||
ScipyOptimizer() = default; | ||
~ScipyOptimizer() = default; | ||
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const std::string name() const override { return "scipy"; } | ||
const std::string description() const override { return ""; } | ||
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OptResult optimize(OptFunction &function) override; | ||
const bool isGradientBased() const override; | ||
virtual const std::string get_algorithm() const override; | ||
}; | ||
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} // namespace xacc | ||
#endif // SCIPYOPTIMIZER_HPP |
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include_directories(${CMAKE_SOURCE_DIR}/tpls/pybind11/include ${Python_INCLUDE_DIRS}) | ||
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add_xacc_test(ScipyOptimizer) | ||
target_link_libraries(ScipyOptimizerTester xacc Python::Python) |
98 changes: 98 additions & 0 deletions
98
quantum/plugins/optimizers/scipy/tests/ScipyOptimizerTester.cpp
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#include <gtest/gtest.h> | ||
#include "xacc.hpp" | ||
#include "xacc_service.hpp" | ||
#include <dlfcn.h> | ||
#include <pybind11/embed.h> | ||
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using namespace xacc; | ||
namespace py = pybind11; | ||
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TEST(ScipyOptimizerTester, checkSimple) { | ||
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auto optimizer = | ||
xacc::getService<Optimizer>("scipy"); | ||
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OptFunction f([](const std::vector<double> &x, | ||
std::vector<double> &g) { return x[0] * x[0] + 5; }, | ||
1); | ||
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EXPECT_EQ(optimizer->name(), "scipy"); | ||
EXPECT_EQ(1, f.dimensions()); | ||
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optimizer->setOptions(HeterogeneousMap{std::make_pair("maxeval", 20)}); | ||
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auto result = optimizer->optimize(f); | ||
EXPECT_NEAR(5.0, result.first, 1.0e-6); | ||
EXPECT_NEAR(result.second[0], 0.0, 1.0e-6); | ||
} | ||
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TEST(ScipyOptimizerTester, checkGradient) { | ||
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auto optimizer = xacc::getService<Optimizer>("scipy"); | ||
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OptFunction f( | ||
[](const std::vector<double> &x, std::vector<double> &grad) { | ||
if (!grad.empty()) { | ||
std::cout << "GRAD\n"; | ||
grad[0] = 2. * x[0]; | ||
} | ||
auto xx = x[0] * x[0] + 5; | ||
std::cout << xx << "\n"; | ||
return xx; | ||
}, | ||
1); | ||
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EXPECT_EQ(1, f.dimensions()); | ||
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optimizer->setOptions(HeterogeneousMap{ | ||
std::make_pair("maxeval", 20), | ||
std::make_pair("initial-parameters", std::vector<double>{1.0}), | ||
std::make_pair("optimizer", "bfgs")}); | ||
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auto result = optimizer->optimize(f); | ||
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EXPECT_NEAR(result.first, 5.0, 1e-4); | ||
EXPECT_NEAR(result.second[0], 0.0, 1e-4); | ||
} | ||
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TEST(ScipyOptimizerTester, checkGradientRosenbrock) { | ||
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auto optimizer = xacc::getService<Optimizer>("scipy"); | ||
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OptFunction f( | ||
[](const std::vector<double> &x, std::vector<double> &grad) { | ||
if (!grad.empty()) { | ||
// std::cout << "GRAD\n"; | ||
grad[0] = -2 * (1 - x[0]) + 400 * (std::pow(x[0], 3) - x[1] * x[0]); | ||
grad[1] = 200 * (x[1] - std::pow(x[0], 2)); | ||
} | ||
auto xx = | ||
100 * std::pow(x[1] - std::pow(x[0], 2), 2) + std::pow(1 - x[0], 2); | ||
std::cout << xx << ", " << x << ", " << grad << "\n"; | ||
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return xx; | ||
}, | ||
2); | ||
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EXPECT_EQ(2, f.dimensions()); | ||
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optimizer->setOptions(HeterogeneousMap{std::make_pair("maxeval", 200), | ||
std::make_pair("optimizer", "bfgs")}); | ||
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auto result = optimizer->optimize(f); | ||
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EXPECT_NEAR(result.first, 0.0, 1e-4); | ||
EXPECT_NEAR(result.second[0], 1.0, 1e-4); | ||
EXPECT_NEAR(result.second[1], 1.0, 1e-4); | ||
} | ||
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int main(int argc, char **argv) { | ||
dlopen("libpython3.8.so", RTLD_LAZY | RTLD_GLOBAL); | ||
xacc::Initialize(argc, argv); | ||
py::initialize_interpreter(); | ||
::testing::InitGoogleTest(&argc, argv); | ||
auto ret = RUN_ALL_TESTS(); | ||
py::finalize_interpreter(); | ||
xacc::Finalize(); | ||
return ret; | ||
} |