Yuval Tassa
4358a102cd
Also add MJTOL_SCALE to fixture to allow tests to be run with zero tolerance. This is useful when assesing the impact of code changes (A/B comparison of failure values) PiperOrigin-RevId: 924219083 Change-Id: Ifdd09ac850904ca8dd79179930ce738a4b37d284
134 lines
4.1 KiB
C++
134 lines
4.1 KiB
C++
// Copyright 2021 DeepMind Technologies Limited
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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// Tests of the entire pipeline that are not easily associated with one file.
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#include <string>
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#include <vector>
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#include <gmock/gmock.h>
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#include <gtest/gtest.h>
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#include <mujoco/mjmodel.h>
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#include <mujoco/mujoco.h>
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#include "src/engine/engine_io.h"
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#include "test/fixture.h"
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namespace mujoco {
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namespace {
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static const char* const kDefaultModel = "testdata/model.xml";
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using ::testing::Pointwise;
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using ::testing::NotNull;
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using PipelineTest = MujocoTest;
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// sparse and dense pipelines should produce the same results, for all solvers
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TEST_F(PipelineTest, SparseDenseEquivalent) {
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const std::string xml_path = GetTestDataFilePath(kDefaultModel);
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char error[1024];
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mjModel* model = mj_loadXML(xml_path.c_str(), nullptr, error, sizeof(error));
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ASSERT_THAT(model, NotNull()) << error;
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mjData* data = mj_makeData(model);
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const mjtNum tol = MjTol(1e-11, 1e-4);
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const char* sname[4] = {"NEWTON", "PGS", "CG", "NOSLIP"};
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mjtSolver solver[4] = {mjSOL_NEWTON, mjSOL_PGS, mjSOL_CG, mjSOL_NEWTON};
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#ifdef mjUSESINGLE
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// CG and NOSLIP sparse-dense equivalence breaks at float32 precision.
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int nsolvers = 2;
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#else
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int nsolvers = 4;
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#endif
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for (int i = 0; i < nsolvers; i++) {
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model->opt.solver = solver[i];
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if (i == 3) {
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model->opt.noslip_iterations = 2;
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}
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// set dense jacobian, call mj_step, save qacc and new qpos
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model->opt.jacobian = mjJAC_DENSE;
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mj_resetDataKeyframe(model, data, 0);
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mj_step(model, data);
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std::vector<mjtNum> qacc_dense = AsVector(data->qacc, model->nv);
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std::vector<mjtNum> qpos_dense = AsVector(data->qpos, model->nq);
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// set sparse jacobian, call mj_step, save qacc and new qpos
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model->opt.jacobian = mjJAC_SPARSE;
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mj_resetDataKeyframe(model, data, 0);
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mj_step(model, data);
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std::vector<mjtNum> qacc_sparse = AsVector(data->qacc, model->nv);
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std::vector<mjtNum> qpos_sparse = AsVector(data->qpos, model->nq);
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// expect accelerations to be insignificantly different
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EXPECT_THAT(qacc_dense, Pointwise(MjNear(tol, tol), qacc_sparse))
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<< "failed qacc equivalence for solver=" << sname[i];
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// expect positions to be insignificantly different
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EXPECT_THAT(qpos_dense, Pointwise(MjNear(tol, tol), qpos_sparse))
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<< "failed qpos equivalence for solver=" << sname[i];
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}
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mj_deleteData(data);
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mj_deleteModel(model);
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}
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// mj_forward should be idempotent when warm starts are disabled
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TEST_F(PipelineTest, DeterministicNoWarmstart) {
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const std::string xml_path = GetTestDataFilePath(kDefaultModel);
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mjModel* model = mj_loadXML(xml_path.c_str(), nullptr, nullptr, 0);
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mjData* data = mj_makeData(model);
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mjData* data2 = mj_makeData(model);
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// disable warmstarts
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model->opt.disableflags |= mjDSBL_WARMSTART;
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int nv = model->nv;
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int kNumSteps = 50;
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for (mjtSolver solver : {mjSOL_NEWTON, mjSOL_PGS, mjSOL_CG}) {
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model->opt.solver = solver;
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mj_resetData(model, data);
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mj_resetData(model, data2);
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for (int step = 0; step < kNumSteps; step++) {
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mj_step(model, data);
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mj_forward(model, data);
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mj_step(model, data2);
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mj_forward(model, data2);
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// test determinism: both models steps did the same thing
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EXPECT_EQ(AsVector(data->qacc, nv), AsVector(data2->qacc, nv));
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// one more mj_forward call on data2
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mj_forward(model, data2);
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// expect that the extra mj_forward call didn't change anything
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EXPECT_EQ(AsVector(data->qacc, nv), AsVector(data2->qacc, nv));
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}
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}
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mj_deleteData(data2);
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mj_deleteData(data);
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mj_deleteModel(model);
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}
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} // namespace
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} // namespace mujoco
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