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Merge branch 'main' into feat/plugin-feature

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SoftFever
2026-07-15 17:19:14 +08:00
parent 8cb0cb7693 d500ba4e9e
commit bf932e8797
7 changed files with 107 additions and 45 deletions
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6
.github/workflows/build_all.yml vendored
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@@ -172,17 +172,17 @@ jobs:
# binary); this covers src/engine PRs with the PR-built binary. Runs in
# parallel off build_linux's artifact so it doesn't lengthen the build leg.
slice_check_linux:
name: Slice check (Linux x86_64)
name: Slice check (Linux aarch64)
needs: build_linux
if: ${{ !cancelled() && success() }}
runs-on: ${{ vars.SELF_HOSTED && 'orca-lnx-server' || 'ubuntu-24.04' }}
runs-on: ubuntu-24.04-arm
steps:
- name: Checkout repository
uses: actions/checkout@v7
- name: Download profile validator
uses: actions/download-artifact@v8
with:
name: ${{ github.sha }}-profile-validator-linux-x86_64
name: ${{ github.sha }}-profile-validator-linux-aarch64
path: validator-bin
- name: Validate slice (expand custom g-code)
timeout-minutes: 60

8
.github/workflows/build_orca.yml vendored
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@@ -515,13 +515,15 @@ jobs:
# Ship the freshly-built validator so the parallel slice_check_linux job
# (build_all.yml) can slice-sweep the shipped profiles with this PR's
# engine. Stable sha-based name mirrors the tests artifact above so the
# engine. Taken from the aarch64 leg so the sweep runs on arm64 (its
# GitHub-hosted runner is free, and it also exercises the arm build).
# Stable sha-based name mirrors the tests artifact above so the
# downstream job downloads it by exact name.
- name: Upload profile validator (for slice check)
if: runner.os == 'Linux' && inputs.arch != 'aarch64'
if: runner.os == 'Linux' && inputs.arch == 'aarch64'
uses: actions/upload-artifact@v7
with:
name: ${{ github.sha }}-profile-validator-linux-x86_64
name: ${{ github.sha }}-profile-validator-linux-aarch64
overwrite: true
path: ./build/src/Release/OrcaSlicer_profile_validator
retention-days: 5

30
src/dev-utils/OrcaSlicer_profile_validator.cpp
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@@ -212,14 +212,26 @@ void install_slice_context_log_sink()
// -v). Unlike the static reference/placeholder checks, this expands every custom *_gcode - including
// change_filament_gcode at the one filament change - against the printer's fully-resolved config, so
// undefined-placeholder / invalid-flow bugs surface here. Reports every offending printer and returns 1
// if any failed, 0 otherwise. The sweep is SEQUENTIAL by necessity: Print::process() keeps
// process-global state, so slicing printers concurrently in one process races even with per-slice
// Model+Print. Load in validation mode so the vendors are read straight from the -p profiles dir
// (no data_dir/system tree) and -v scoping is honoured for free.
int slice_all_printers(const std::string &vendor)
// if any failed, 0 otherwise. When outdir is non-empty, each printer's g-code is also written there as
// "<vendor>__<printer>.gcode" for manual inspection. The sweep is SEQUENTIAL by necessity:
// Print::process() keeps process-global state, so slicing printers concurrently in one process races
// even with per-slice Model+Print. Load in validation mode so the vendors are read straight from the -p
// profiles dir (no data_dir/system tree) and -v scoping is honoured for free.
int slice_all_printers(const std::string &vendor, const std::string &outdir)
{
install_slice_context_log_sink();
if (!outdir.empty()) {
boost::system::error_code ec;
fs::create_directories(outdir, ec);
if (ec) {
BOOST_LOG_TRIVIAL(error) << "Could not create output directory \"" << outdir << "\": " << ec.message();
std::cout << "Validation failed" << std::endl;
return 1;
}
std::cout << "Saving sliced g-code to " << outdir << std::endl;
}
PresetBundle bundle;
bundle.set_is_validation_mode(true);
bundle.set_vendor_to_validate(vendor); // empty == all vendors
@@ -323,6 +335,10 @@ int slice_all_printers(const std::string &vendor)
try {
const std::string out = slice_two_color_cube_and_export(cfg, bundle.is_bbl_vendor());
if (!outdir.empty() && !out.empty()) {
const fs::path f = fs::path(outdir) / (sanitize_filename(vendor_name) + "__" + sanitize_filename(printer) + ".gcode");
save_string_file(f, out);
}
if (out.empty() || out.find("G1") == std::string::npos) {
BOOST_LOG_TRIVIAL(error) << "Printer \"" << printer << "\" produced no g-code";
++failures;
@@ -364,6 +380,7 @@ int main(int argc, char* argv[])
("vendor,v", po::value<std::string>()->default_value(""), "Vendor name. Optional, all profiles present in the folder will be validated if not specified")
("generate_presets,g", po::value<bool>()->default_value(false), "Generate user presets for mock test")
("slice,s", po::bool_switch()->default_value(false), "Slice a two-colour cube through every printer to expand all custom g-code (catches placeholder/flow errors that static checks miss). Off unless this flag is present.")
("outdir,o", po::value<std::string>()->default_value(""), "With -s, also save each printer's g-code to this folder (as <vendor>__<printer>.gcode) for manual inspection. Optional.")
("check_filament_subtypes,f", po::bool_switch()->default_value(false), "Also flag printers with duplicate (ambiguous) filament subtypes. Off unless this flag is present.")
("log_level,l", po::value<int>()->default_value(2), "Log level. Optional, default is 2 (warning). Higher values produce more detailed logs.");
// clang-format on
@@ -389,6 +406,7 @@ int main(int argc, char* argv[])
int log_level = vm["log_level"].as<int>();
bool generate_user_preset = vm["generate_presets"].as<bool>();
bool slice_mode = vm["slice"].as<bool>();
std::string slice_outdir = vm["outdir"].as<std::string>();
bool check_filament_subtypes = vm["check_filament_subtypes"].as<bool>();
// check if path is valid, and return error if not
@@ -419,7 +437,7 @@ int main(int argc, char* argv[])
// Slice mode expands every printer's custom g-code by actually slicing (see slice_all_printers).
// A distinct opt-in mode so the default static checks stay fast for every profile PR.
if (slice_mode)
return slice_all_printers(vendor);
return slice_all_printers(vendor, slice_outdir);
auto preset_bundle = new PresetBundle();
// preset_bundle->setup_directories();

17
src/libslic3r/FilamentGroup.cpp
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@@ -352,8 +352,7 @@ namespace Slic3r
int k,
const std::vector<unsigned int>& used_filaments,
const std::unordered_map<int, std::vector<int>>& unplaceable_limits,
int* cost,
int timeout_ms)
int* cost)
{
auto distance_evaluator = std::make_shared<FlushDistanceEvaluator>(ctx.model_info.flush_matrix, used_filaments, ctx.model_info.layer_filaments);
KMediods PAM(k, (int)used_filaments.size(), distance_evaluator, ctx.machine_info.master_extruder_id);
@@ -369,7 +368,7 @@ namespace Slic3r
}
PAM.set_cluster_group_size(cluster_size_limit);
PAM.do_clustering(ctx, timeout_ms, 30);
PAM.do_clustering(ctx, m_clustering_budget);
m_memoryed_heap = PAM.get_memoryed_groups();
@@ -793,7 +792,7 @@ namespace Slic3r
2.1 In each cluster, make the point that minimizes the sum of distances within the cluster the medoid
2.2 Reassign each point to the cluster defined by the closest medoid determined in the previous step
*/
void KMediods::do_clustering(const FilamentGroupContext &context, int timeout_ms, int retry)
void KMediods::do_clustering(const FilamentGroupContext& context, const ClusteringBudget& budget)
{
FlushTimeMachine T;
T.time_machine_start();
@@ -817,7 +816,11 @@ namespace Slic3r
double best_cluster_cost = std::numeric_limits<double>::max();
int retry_count = 0;
while (retry_count < retry && T.time_machine_end() < timeout_ms) {
// Run at least one restart; otherwise every filament would stay in the default group.
const int retry = std::max(1, budget.max_restarts);
auto within_budget = [&]() { return budget.timeout_ms <= 0 || T.time_machine_end() < budget.timeout_ms; };
while (retry_count < retry && within_budget()) {
std::vector<int> curr_cluster_centers = init_cluster_center(m_placeable_limits, m_unplaceable_limits, m_max_cluster_size, m_cluster_group_size, retry_count);
std::vector<int> curr_cluster_labels = assign_cluster_label(curr_cluster_centers, m_placeable_limits, m_unplaceable_limits, m_max_cluster_size, m_cluster_group_size);
double curr_cluster_cost = evaluate_labels(curr_cluster_labels);
@@ -826,7 +829,7 @@ namespace Slic3r
update_memoryed_groups(g, memory_threshold, memoryed_groups);
bool mediods_changed = true;
while (mediods_changed && T.time_machine_end() < timeout_ms) {
while (mediods_changed && within_budget()) {
mediods_changed = false;
double best_swap_cost = curr_cluster_cost;
int best_swap_cluster = -1;
@@ -889,7 +892,7 @@ namespace Slic3r
if (estimated < ENUM_THRESHOLD)
result = calc_group_by_enum(k, used_filaments, unplaceable_limits, cost);
else
result = calc_group_by_kmedoids(k, used_filaments, unplaceable_limits, cost, 3000);
result = calc_group_by_kmedoids(k, used_filaments, unplaceable_limits, cost);
change_memoryed_heaps_to_arrays(m_memoryed_heap, ctx.group_info.total_filament_num, used_filaments, m_memoryed_groups);

17
src/libslic3r/FilamentGroup.hpp
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@@ -142,6 +142,16 @@ namespace Slic3r
FilamentGroupContext::SpeedInfo m_speed_info;
};
// Search budget for the k-medoids clustering, an anytime search. Each restart is seeded from its
// own index, so what it returns depends on how many restarts complete before the clock expires,
// and therefore on the speed of the machine. A timeout_ms <= 0 removes the clock and bounds the
// search by max_restarts alone.
struct ClusteringBudget
{
int timeout_ms = 3000;
int max_restarts = 30;
};
class FilamentGroup
{
using MemoryedGroup = FilamentGroupUtils::MemoryedGroup;
@@ -149,6 +159,8 @@ namespace Slic3r
public:
explicit FilamentGroup(const FilamentGroupContext& ctx_) :ctx(ctx_) {}
public:
void set_clustering_budget(const ClusteringBudget& budget) { m_clustering_budget = budget; }
std::vector<int> calc_filament_group(int * cost = nullptr);
std::vector<std::vector<int>> get_memoryed_groups()const { return m_memoryed_groups; }
@@ -162,7 +174,7 @@ namespace Slic3r
std::vector<int> calc_group_by_enum(int k, const std::vector<unsigned int>& used_filaments,
const std::unordered_map<int, std::vector<int>>& unplaceable_limits, int* cost = nullptr);
std::vector<int> calc_group_by_kmedoids(int k, const std::vector<unsigned int>& used_filaments,
const std::unordered_map<int, std::vector<int>>& unplaceable_limits, int* cost = nullptr, int timeout_ms = 500);
const std::unordered_map<int, std::vector<int>>& unplaceable_limits, int* cost = nullptr);
std::map<int, int> rebuild_unprintables(const std::vector<unsigned int>& used_filaments, const std::map<int,int>& extruder_unprintables);
std::unordered_map<int, std::vector<int>> rebuild_nozzle_unprintables(const std::vector<unsigned int>& used_filaments, const std::unordered_map<int, std::vector<int>>& extruder_unprintables, const std::vector<int>& filament_volume_map);
@@ -175,6 +187,7 @@ namespace Slic3r
FilamentGroupContext ctx;
MemoryedGroupHeap m_memoryed_heap;
std::vector<std::vector<int>> m_memoryed_groups;
ClusteringBudget m_clustering_budget;
public:
std::optional<std::function<bool(int, std::vector<int>&)>> get_custom_seq;
};
@@ -220,7 +233,7 @@ namespace Slic3r
void set_memory_threshold(double threshold) { memory_threshold = threshold; }
MemoryedGroupHeap get_memoryed_groups()const { return memoryed_groups; }
void do_clustering(const FilamentGroupContext& context, int timeout_ms = 100, int retry = 10);
void do_clustering(const FilamentGroupContext& context, const ClusteringBudget& budget);
std::vector<int> get_cluster_labels()const { return m_cluster_labels; }
protected:

4
tests/filament_group/fg_test_evaluator.hpp
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@@ -210,13 +210,15 @@ inline FullEvalResult full_evaluate_map(const FilamentGroupContext& ctx,
return result;
}
inline TestResult run_and_evaluate(const FilamentGroupContext& ctx) {
inline TestResult run_and_evaluate(const FilamentGroupContext& ctx,
const ClusteringBudget& budget = {}) {
TestResult result;
auto start = std::chrono::high_resolution_clock::now();
int algo_cost = 0;
FilamentGroup fg(ctx);
fg.set_clustering_budget(budget);
result.filament_map = fg.calc_filament_group(&algo_cost);
auto end = std::chrono::high_resolution_clock::now();

70
tests/filament_group/filament_group_regression_main.cpp
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@@ -211,19 +211,23 @@ static std::vector<PropertySpec>& get_property_specs() {
return specs;
}
// Orca: a small number of config_c "stress" goldens run the nozzle-centric kmedoids clustering path,
// which is bounded by a 3000 ms wall-clock budget (FilamentGroup.cpp calc_group_by_kmedoids). On
// slower hardware the clustering explores fewer restarts and lands on a deterministically-worse-but-
// valid grouping than the stored golden. We regression-lock those against Orca's own deterministic
// score (bit-stable across runs on this machine — verified twice) so the gate stays green while the
// divergence is documented; every other golden is a true parity gate at 3% tolerance.
static std::optional<double> orca_locked_base_score(const std::string& stem) {
if (stem == "stress_66") return 125103.0; // config_c 15-filament kmedoids case; golden 117843
return std::nullopt;
}
// Under the default wall clock the result depends on how fast the machine is (see ClusteringBudget),
// so the goldens are graded under a fixed budget instead. Two restarts is the fewest that reaches
// parity with the reference on every golden, stress_79 being the last to get there. Four leaves
// margin, since the search follows a different path on each standard library (see below).
static constexpr ClusteringBudget FIXED_SEARCH_BUDGET{
/*timeout_ms*/ 0, // no wall clock
/*max_restarts*/ 4};
// ============ Layer 1: Golden Regression (all configs) ============
// Graded against the BambuStudio golden the harness was ported from, one-directional at 3%.
//
// The tolerance is a parity allowance, and it also covers a small spread across standard libraries.
// The k-medoids search seeds each restart with std::shuffle, whose algorithm the C++ standard leaves
// unspecified, so libstdc++, libc++ and the MSVC STL permute the same seed differently, start from
// different medoids, and settle on slightly different groupings, about 3e-4 apart on either side of
// the reference, and only on the goldens heavy enough to reach the k-medoids search.
TEST_CASE("FilamentGroup golden regression", "[filament_group][golden]") {
auto files = get_golden_files();
if (files.empty()) {
@@ -238,29 +242,49 @@ TEST_CASE("FilamentGroup golden regression", "[filament_group][golden]") {
auto tc = load_test_case(file_path);
REQUIRE(tc.base_result.has_value());
auto result = run_and_evaluate(tc.context);
auto result = run_and_evaluate(tc.context, FIXED_SEARCH_BUDGET);
auto eval = full_evaluate_map(tc.context, result.filament_map);
auto& base = *tc.base_result;
// Reference score: the stored golden by default; Orca's deterministic score for the
// documented heuristic-divergent config_c stress golden (see orca_locked_base_score).
std::string stem = fs::path(file_path).stem().string();
double base_score = base.full_score;
if (auto locked = orca_locked_base_score(stem))
base_score = *locked;
INFO("Case: " << tc.metadata.id);
INFO("Reference score: " << base_score << " (BBS golden " << base.full_score << ")");
INFO("Golden score: " << base.full_score);
INFO("Actual score: " << eval.full_score);
INFO("Flush cost: " << eval.flush_cost << " (BBS golden " << base.flush_cost << ")");
INFO("Flush cost: " << eval.flush_cost << " (golden " << base.flush_cost << ")");
INFO("Elapsed: " << result.elapsed_ms << " ms");
int tolerance = std::max(50, (int)(base_score * 0.03));
int tolerance = std::max(50, (int)(base.full_score * 0.03));
REQUIRE(result.constraints_ok);
REQUIRE(eval.full_score <= base_score + tolerance);
// RelWithDebInfo runaway guard; the Release-calibrated 20 s limit is raised for the slower build.
REQUIRE(eval.full_score <= base.full_score + tolerance);
// A slower search still scores the same above, since it searches just as far, but in slicing
// it would mean fewer restarts fit in the wall clock and so worse groupings. Loose on
// purpose, so it never becomes a proxy for how loaded the runner is.
const double throughput_ceiling_ms = 10.0 * ClusteringBudget{}.timeout_ms;
REQUIRE(result.elapsed_ms < throughput_ceiling_ms);
}
}
// Covers the path real slicing takes, under the default wall clock. The score there depends on the
// runner rather than on the code (see FIXED_SEARCH_BUDGET), so the only things worth asserting are
// that the grouping comes back valid and that the search terminates.
TEST_CASE("FilamentGroup returns a valid grouping under the default budget", "[filament_group][budget]") {
auto files = get_golden_files();
REQUIRE(!files.empty());
auto file_path = GENERATE_REF(from_range(files));
DYNAMIC_SECTION("Golden: " << fs::path(file_path).stem().string()) {
auto tc = load_test_case(file_path);
auto result = run_and_evaluate(tc.context); // the default budget, as real slicing runs it
INFO("Case: " << tc.metadata.id);
INFO("Elapsed: " << result.elapsed_ms << " ms");
REQUIRE(result.constraints_ok);
// A hang guard. The clock is only checked between swaps, so a sweep can overshoot.
REQUIRE(result.elapsed_ms < 40000.0);
}
}