Performance improvements for symbolic derivatives

benchmark/CMakeLists.txt

@@ -43,6 +43,7 @@ ADD_HEYOKA_BENCHMARK(taylor_ANN)
 ADD_HEYOKA_BENCHMARK(taylor_ANN_2)
 ADD_HEYOKA_BENCHMARK(apophis)
 ADD_HEYOKA_BENCHMARK(stiff_equation)
+ADD_HEYOKA_BENCHMARK(nn_diff)
 ADD_HEYOKA_BENCHMARK(mascon_models)
 ADD_HEYOKA_BENCHMARK(outer_ss_jet_benchmark)
 ADD_HEYOKA_BENCHMARK(outer_ss_long_term)

benchmark/nn_diff.cpp

@@ -0,0 +1,59 @@
+// Copyright 2020, 2021, 2022, 2023 Francesco Biscani ([email protected]), Dario Izzo ([email protected])
+//
+// This file is part of the heyoka library.
+//
+// This Source Code Form is subject to the terms of the Mozilla
+// Public License v. 2.0. If a copy of the MPL was not distributed
+// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
+
+#include <iostream>
+#include <stdexcept>
+
+#include <boost/program_options.hpp>
+
+#include <spdlog/spdlog.h>
+
+#include <heyoka/expression.hpp>
+#include <heyoka/kw.hpp>
+#include <heyoka/logging.hpp>
+#include <heyoka/math/tanh.hpp>
+#include <heyoka/model/ffnn.hpp>
+
+using namespace heyoka;
+
+int main(int argc, char *argv[])
+{
+    namespace po = boost::program_options;
+
+    create_logger();
+    set_logger_level_trace();
+
+    auto logger = spdlog::get("heyoka");
+
+    unsigned nn_layer{};
+
+    po::options_description desc("Options");
+
+    desc.add_options()("help", "produce help message")("nn_layer", po::value<unsigned>(&nn_layer)->default_value(10u),
+                                                       "number of neurons per layer");
+
+    po::variables_map vm;
+    po::store(po::parse_command_line(argc, argv, desc), vm);
+    po::notify(vm);
+
+    if (vm.count("help") != 0u) {
+        std::cout << desc << "\n";
+        return 0;
+    }
+
+    if (nn_layer == 0u) {
+        throw std::invalid_argument("The number of neurons per layer cannot be zero");
+    }
+
+    auto [x, y] = make_vars("x", "y");
+    auto ffnn = model::ffnn(kw::inputs = {x, y}, kw::nn_hidden = {nn_layer, nn_layer, nn_layer}, kw::n_out = 2,
+                            kw::activations = {heyoka::tanh, heyoka::tanh, heyoka::tanh, heyoka::tanh});
+
+    auto dt = diff_tensors(ffnn, kw::diff_args = diff_args::params);
+    auto dNdtheta = dt.get_jacobian();
+}

doc/advanced_tutorials.rst

@@ -5,7 +5,7 @@ Advanced tutorials
 
 .. important::
 
-  More :ref:`tutorials <hypy:adv_tutorials>` and :ref:`examples <hypy:examples>` are available in the documentation
+  More tutorials and examples are available in the documentation
   of heyoka's `Python bindings <https://bluescarni.github.io/heyoka.py>`__.
 
 In this section we will show some of heyoka's more advanced functionalities,

doc/basic_tutorials.rst

@@ -5,7 +5,7 @@ Basic tutorials
 
 .. important::
 
-   More :ref:`tutorials <hypy:basic_tutorials>` and :ref:`examples <hypy:examples>` are available in the documentation
+   More tutorials and examples are available in the documentation
    of heyoka's `Python bindings <https://bluescarni.github.io/heyoka.py>`__.
 
 The code snippets in these tutorials assume the inclusion of the

doc/changelog.rst

@@ -21,6 +21,9 @@ New
 Changes
 ~~~~~~~
 
+- Substantial speedups in the computation of first-order derivatives
+  with respect to many variables/parameters
+  (`#358 <https://github.com/bluescarni/heyoka/pull/358>`__).
 - Substantial performance improvements in the computation of
   derivative tensors of large expressions with a high degree
   of internal redundancy
@@ -154,7 +157,7 @@ Changes
 ~~~~~~~
 
 - The step callbacks are now copied in :ref:`ensemble propagations <tut_ensemble>`
-  rather then being shared among threads. The aim of this change
+  rather than being shared among threads. The aim of this change
   is to reduce the likelihood of data races
   (`#334 <https://github.com/bluescarni/heyoka/pull/334>`__).
 - Comprehensive overhaul of the expression system, including: