vasilito
cb424d7448
verify-patch-sanity.py validates every active recipe .patch has internally- consistent hunk line counts — catching the 'malformed patch at line N' failure at commit/CI/preflight time instead of hours into a cook. This cycle hit that class three times (qtwaylandscanner, sddm, xwayland), each only discovered when cookbook tried to apply the patch. Running it across the repo found 29 latent malformed patches (validated against GNU patch: e.g. relibc/P3-sysv-ipc reproduces 'malformed patch at line 22'). They were harmless only because they sit in vendored recipes (baked, not re- applied) — but would fail on any version-bump re-derivation. --fix recounts the hunk headers (body untouched) and repaired all 29. Wired into build-preflight.sh (Phase 1.0D) and redbear-ci.yml, with a unit test (test-patch-sanity.sh). Skips archived/legacy trees and unvalidatable formats (empty placeholders, bare-@@ git hunks).
90 lines
2.6 KiB
C++
90 lines
2.6 KiB
C++
//===- TrainingLogger.cpp - mlgo feature/reward logging -------------------===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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//
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// This file implements logging infrastructure for extracting features and
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// rewards for mlgo policy training.
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//
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//===----------------------------------------------------------------------===//
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#include "llvm/Analysis/TensorSpec.h"
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#include "llvm/Config/config.h"
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#include "llvm/ADT/Twine.h"
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#include "llvm/Analysis/Utils/TrainingLogger.h"
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#include "llvm/Support/CommandLine.h"
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#include "llvm/Support/Debug.h"
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#include "llvm/Support/JSON.h"
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#include "llvm/Support/MemoryBuffer.h"
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#include "llvm/Support/Path.h"
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#include "llvm/Support/raw_ostream.h"
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#include <cassert>
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#include <numeric>
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using namespace llvm;
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void Logger::writeHeader(std::optional<TensorSpec> AdviceSpec) {
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json::OStream JOS(*OS);
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JOS.object([&]() {
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JOS.attributeArray("features", [&]() {
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for (const auto &TS : FeatureSpecs)
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TS.toJSON(JOS);
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});
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if (IncludeReward) {
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JOS.attributeBegin("score");
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RewardSpec.toJSON(JOS);
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JOS.attributeEnd();
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}
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if (AdviceSpec.has_value()) {
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JOS.attributeBegin("advice");
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AdviceSpec->toJSON(JOS);
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JOS.attributeEnd();
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}
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});
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*OS << "\n";
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}
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void Logger::switchContext(StringRef Name) {
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CurrentContext = Name.str();
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json::OStream JOS(*OS);
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JOS.object([&]() { JOS.attribute("context", Name); });
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*OS << "\n";
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}
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void Logger::startObservation() {
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auto I = ObservationIDs.insert({CurrentContext, 0});
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size_t NewObservationID = I.second ? 0 : ++I.first->second;
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json::OStream JOS(*OS);
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JOS.object([&]() {
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JOS.attribute("observation", static_cast<int64_t>(NewObservationID));
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});
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*OS << "\n";
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}
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void Logger::endObservation() { *OS << "\n"; }
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void Logger::logRewardImpl(const char *RawData) {
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assert(IncludeReward);
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json::OStream JOS(*OS);
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JOS.object([&]() {
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JOS.attribute("outcome", static_cast<int64_t>(
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ObservationIDs.find(CurrentContext)->second));
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});
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*OS << "\n";
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writeTensor(RewardSpec, RawData);
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*OS << "\n";
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}
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Logger::Logger(std::unique_ptr<raw_ostream> OS,
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const std::vector<TensorSpec> &FeatureSpecs,
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const TensorSpec &RewardSpec, bool IncludeReward,
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std::optional<TensorSpec> AdviceSpec)
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: OS(std::move(OS)), FeatureSpecs(FeatureSpecs), RewardSpec(RewardSpec),
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IncludeReward(IncludeReward) {
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writeHeader(AdviceSpec);
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}
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