[MLGO] ML Regalloc Priority Advisor

[Flpha0830]

Summary

The bulk of the implementation is common between 'release' mode (==AOT-ed
model) and 'development' mode (for training), the main difference is
that in development mode, we may also log features (for training logs),
inject scoring information and then produce the log file.

[mtrofin]

I think we need to separate in a first patch the changes needed to reuse regallocscoring. For those, I think maybe a simple solution right now would be to have the base RegAllocEvictionAdvisorAnalysis and RegAllocPriorityAdvisorAnalysis (the base types) have a virtual void logRewardIfNeeded(float Reward) method. By default it does nothing. Just the Development variant does something. So that way you don't need the additional .h for the scoring, just moving it to a .cpp would be sufficient.

[Flpha0830]

Add logRewardIfNeeded method

[Flpha0830]

Add a model test

[mtrofin]

Overall fine, I think you should still split out the change that introduces the logRewardIfNeeded as a NFC.

For the main change (i.e. this after rebasing with the above NFC), you also need test.

llvm/lib/CodeGen/MLRegallocEvictAdvisor.cpp

424 ↗

(On Diff #461154)

in the overridden case, drop the virtual.

llvm/lib/CodeGen/MLRegallocPriorityAdvisor.cpp

122

AU.setPreservesAll(), too.

182

why is this virtual?

197

AU.setPreservesAll()

301

There's a loss here due to the ping-ponging between float and unsigned and then back to float for logging. I imagine we want to stay in float domain for training, though. One way to avoid unnecessary lossiness is to: factor getPriority in the ML case into a getPriorityImpl that returns a float, and then getPriority just casts.

The oddity is that here you'd have to keep track of the "float" priorty as well as the unisgned one, so you'd have a float TrainingPrio and a unsigned Prio; or have Prio be a double and cast it to float when logging and to unsigned when returning, but you still need the getPriorityImpl.

[Flpha0830]

add a getPriorityImpl and update the model test

[mtrofin]

added Yundi for the test model generator, please wait for her feedback before submitting.

On my side, lgtm, left some comments about not trying to enable release mode at this point.

(self-note: there's some boilerplate we can further simplify, I think like ctoring of the dev mode could be more streamlined - I'll take a look into that)

llvm/lib/CodeGen/RegAllocPriorityAdvisor.cpp

39

I'd remove this. We don't currently support constructing the release model - that also needs some stuff in CMakeLists.txt. All good, all we need is the development stuff anyway, and all the release stuff is common anyway.

84–86

check LLVM_HAVE_TF_AOT_REGALLOCPRIORITYMODEL here (which nothing sets, which is good right now). We need to do something similar for evictadvisor now, too (not in this patch)

[Flpha0830]

use LLVM_HAVE_TF_AOT_REGALLOCPRIORITYMODEL

llvm/lib/Analysis/models/gen-regalloc-priority-test-model.py

+"""Generate a mock model for LLVM tests for Register Allocation.
+The generated model is not a neural net - it is just a tf.function with the
+correct input and output parameters. 
+"""
+## By construction, the mock model will always output the first liverange that can be evicted.
+
+import os
+import sys
+import tensorflow as tf
+POLICY_DECISION_LABEL = 'priority'
+POLICY_OUTPUT_SPEC = """
+[
+    {
+        "logging_name": "priority", 
+        "tensor_spec": {
+            "name": "StatefulPartitionedCall", 
+            "port": 0, 
+            "type": "float", 
+            "shape": [
+                1
+            ]
+        }
+    }
+]
+"""
+PER_LIVEINTERVAL_INT64_FEATURE_LIST = [
+    'li_size', 'stage'
+]
+PER_LIVEINTERVAL_FLOAT32_FEATURE_LIST = ['weight'
+]
+PER_LIVEINTERVAL_FEATURE_LIST = PER_LIVEINTERVAL_FLOAT32_FEATURE_LIST + \
+    PER_LIVEINTERVAL_INT64_FEATURE_LIST
+CONTEXT_FEATURE_LIST =  ('discount', 'reward', 'step_type')
+
+
+def get_input_signature():
+   """Returns (time_step_spec, action_spec) for LLVM register allocation."""
+   inputs = dict(
+       (key, tf.TensorSpec(dtype=tf.int64, shape=(), name=key))
+       for key in PER_LIVEINTERVAL_INT64_FEATURE_LIST)
+   inputs.update(
+       dict((key,
+             tf.TensorSpec(dtype=tf.float32, shape=(), name=key))
+            for key in PER_LIVEINTERVAL_FLOAT32_FEATURE_LIST))
+   inputs.update(
+       dict((key, tf.TensorSpec(dtype=tf.float32, shape=(), name=key))
+            for key in ['discount', 'reward']))
+   inputs.update(
+       dict((key, tf.TensorSpec(dtype=tf.int32, shape=(), name=key))
+            for key in ['step_type']))
+   return inputs
+
+
+def get_output_spec_path(path):
+   return os.path.join(path, 'output_spec.json')
+
+
+def build_mock_model(path):
+   """Build and save the mock model with the given signature."""
+   module = tf.Module()
+   # We have to set this useless variable in order for the TF C API to correctly
+   # intake it
+   module.var = tf.Variable(0, dtype=tf.float32)
+
+   def action(*inputs):
+     s1 = tf.reduce_sum([
+         tf.cast(inputs[0][key], tf.float32) for key in PER_LIVEINTERVAL_FEATURE_LIST
+     ],
+         axis=0)
+     s2 = tf.reduce_sum(
+         [tf.cast(inputs[0][key], tf.float32) for key in CONTEXT_FEATURE_LIST])
+     # Add a large number so s won't be 0.
+     s = s1 + s2
+     result = s + module.var
+     return {POLICY_DECISION_LABEL: result}
+   module.action = tf.function()(action)
+   action = {
+       'action': module.action.get_concrete_function(get_input_signature())
+   }
+
+   tf.saved_model.save(module, path, signatures=action)
+   output_spec_path = get_output_spec_path(path)
+   with open(output_spec_path, 'w') as f:
+     print(f'Writing output spec to {output_spec_path}.')
+     f.write(POLICY_OUTPUT_SPEC)
+
+
+def main(argv):
+   assert len(argv) == 2
+   model_path = argv[1]
+   build_mock_model(model_path)
+
+
+if __name__ == '__main__':
+   main(sys.argv)

llvm/lib/CodeGen/CMakeLists.txt

add_llvm_component_library(LLVMCodeGen
   MachineSSAUpdater.cpp
   MachineStripDebug.cpp
   MachineTraceMetrics.cpp
   MachineVerifier.cpp
   MIRFSDiscriminator.cpp
   MIRSampleProfile.cpp
   MIRYamlMapping.cpp
   MLRegallocEvictAdvisor.cpp
+  MLRegallocPriorityAdvisor.cpp
   ModuloSchedule.cpp
   MultiHazardRecognizer.cpp
   PatchableFunction.cpp
   MBFIWrapper.cpp
   MIRPrinter.cpp
   MIRPrintingPass.cpp
   MacroFusion.cpp
   NonRelocatableStringpool.cpp

llvm/lib/CodeGen/MLRegallocPriorityAdvisor.cpp

+//===- MLRegAllocPriorityAdvisor.cpp - ML priority advisor-----------------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+//
+// Implementation of the ML priority advisor and reward injection pass
+//
+//===----------------------------------------------------------------------===//
+
+#include "AllocationOrder.h"
+#include "RegAllocGreedy.h"
+#include "RegAllocPriorityAdvisor.h"
+#include "llvm/Analysis/AliasAnalysis.h"
+#include "llvm/Analysis/MLModelRunner.h"
+#include "llvm/Analysis/ReleaseModeModelRunner.h"
+#include "llvm/Analysis/TensorSpec.h"
+#include "llvm/CodeGen/CalcSpillWeights.h"
+#include "llvm/CodeGen/LiveRegMatrix.h"
+#include "llvm/CodeGen/MachineBlockFrequencyInfo.h"
+#include "llvm/CodeGen/MachineFunction.h"
+#include "llvm/CodeGen/MachineLoopInfo.h"
+#include "llvm/CodeGen/MachineRegisterInfo.h"
+#include "llvm/CodeGen/Passes.h"
+#include "llvm/CodeGen/RegisterClassInfo.h"
+#include "llvm/CodeGen/SlotIndexes.h"
+#include "llvm/CodeGen/VirtRegMap.h"
+#include "llvm/InitializePasses.h"
+#include "llvm/Pass.h"
+#include "llvm/PassRegistry.h"
+#include "llvm/Support/CommandLine.h"
+
+#if defined(LLVM_HAVE_TF_API)
+#include "llvm/Analysis/ModelUnderTrainingRunner.h"
+#include "llvm/Analysis/NoInferenceModelRunner.h"
+#include "llvm/Analysis/Utils/TrainingLogger.h"
+#endif
+
+using namespace llvm;
+
+// Options that only make sense in development mode
+#ifdef LLVM_HAVE_TF_API
+#include "RegAllocScore.h"
+#include "llvm/Analysis/Utils/TFUtils.h"
+
+static cl::opt<std::string> TrainingLog(
+    "regalloc-priority-training-log", cl::Hidden,
+    cl::desc("Training log for the register allocator priority model"));
+
+static cl::opt<std::string> ModelUnderTraining(
+    "regalloc-priority-model", cl::Hidden,
+    cl::desc("The model being trained for register allocation priority"));
+
+#endif // #ifdef LLVM_HAVE_TF_API
+
+namespace llvm {
+
+static const std::vector<int64_t> PerLiveRangeShape{1};
+
+#define RA_PRIORITY_FEATURES_LIST(M)                                           \
+  M(int64_t, li_size, PerLiveRangeShape, "size")                               \
+  M(int64_t, stage, PerLiveRangeShape, "stage")                                \
+  M(float, weight, PerLiveRangeShape, "weight")
+
+#define DecisionName "priority"
+
+// Named features index.
+enum FeatureIDs {
+#define _FEATURE_IDX(_, name, __, ___) name,
+  RA_PRIORITY_FEATURES_LIST(_FEATURE_IDX)
+#undef _FEATURE_IDX
+      FeatureCount
+};
+
+class MLPriorityAdvisor : public RegAllocPriorityAdvisor {
+public:
+  MLPriorityAdvisor(const MachineFunction &MF, const RAGreedy &RA,
+                    SlotIndexes *const Indexes, MLModelRunner *Runner);
+
+protected:
+  const RegAllocPriorityAdvisor &getDefaultAdvisor() const {
+    return static_cast<const RegAllocPriorityAdvisor &>(DefaultAdvisor);
+  }
+
+  // The assumption is that if the Runner could not be constructed, we emit-ed
+  // error, and we shouldn't be asking for it here.
+  const MLModelRunner &getRunner() const { return *Runner; }
+  float getPriorityImpl(const LiveInterval &LI) const;
+  unsigned getPriority(const LiveInterval &LI) const override;
+
+private:
+  const DefaultPriorityAdvisor DefaultAdvisor;
+  MLModelRunner *const Runner;
+};
+
+#define _DECL_FEATURES(type, name, shape, _)                                   \
+  TensorSpec::createSpec<type>(#name, shape),
+
+static const std::vector<TensorSpec> InputFeatures{
+    {RA_PRIORITY_FEATURES_LIST(_DECL_FEATURES)},
+};
+#undef _DECL_FEATURES
+
+// ===================================
+// Release (AOT) - specifics
+// ===================================
+class ReleaseModePriorityAdvisorAnalysis final
+    : public RegAllocPriorityAdvisorAnalysis {
+public:
+  ReleaseModePriorityAdvisorAnalysis()
+      : RegAllocPriorityAdvisorAnalysis(AdvisorMode::Release) {}
+  // support for isa<> and dyn_cast.
+  static bool classof(const RegAllocPriorityAdvisorAnalysis *R) {
+    return R->getAdvisorMode() == AdvisorMode::Release;
+  }
+
+private:
+  void getAnalysisUsage(AnalysisUsage &AU) const override {
+    AU.setPreservesAll();
+    AU.addRequired<SlotIndexes>();
+    RegAllocPriorityAdvisorAnalysis::getAnalysisUsage(AU);
+  }
+
+  std::unique_ptr<RegAllocPriorityAdvisor>
+  getAdvisor(const MachineFunction &MF, const RAGreedy &RA) override {
+    if (!Runner)
+      Runner = std::make_unique<ReleaseModeModelRunner<NoopSavedModelImpl>>(
+          MF.getFunction().getContext(), InputFeatures, DecisionName);
+    return std::make_unique<MLPriorityAdvisor>(
+        MF, RA, &getAnalysis<SlotIndexes>(), Runner.get());
+  }
+  std::unique_ptr<ReleaseModeModelRunner<NoopSavedModelImpl>> Runner;
+};
+
+// ===================================
+// Development mode-specifics
+// ===================================
+//
+// Features we log
+#ifdef LLVM_HAVE_TF_API
+
+static const TensorSpec Output =
+    TensorSpec::createSpec<float>(DecisionName, {1});
+static const TensorSpec Reward = TensorSpec::createSpec<float>("reward", {1});
+
+#define _DECL_TRAIN_FEATURES(type, name, shape, _)                             \
+  TensorSpec::createSpec<type>(std::string("action_") + #name, shape),
+
+static const std::vector<TensorSpec> TrainingInputFeatures{
+    {RA_PRIORITY_FEATURES_LIST(_DECL_TRAIN_FEATURES)
+         TensorSpec::createSpec<float>("action_discount", {1}),
+     TensorSpec::createSpec<int32_t>("action_step_type", {1}),
+     TensorSpec::createSpec<float>("action_reward", {1})}};
+#undef _DECL_TRAIN_FEATURES
+
+class DevelopmentModePriorityAdvisor : public MLPriorityAdvisor {
+public:
+  DevelopmentModePriorityAdvisor(const MachineFunction &MF, const RAGreedy &RA,
+                                 SlotIndexes *const Indexes,
+                                 MLModelRunner *Runner, Logger *Log)
+      : MLPriorityAdvisor(MF, RA, Indexes, Runner), Log(Log) {}
+
+private:
+  unsigned getPriority(const LiveInterval &LI) const override;
+  Logger *const Log;
+};
+
+class DevelopmentModePriorityAdvisorAnalysis final
+    : public RegAllocPriorityAdvisorAnalysis {
+public:
+  DevelopmentModePriorityAdvisorAnalysis()
+      : RegAllocPriorityAdvisorAnalysis(AdvisorMode::Development) {}
+  // support for isa<> and dyn_cast.
+  static bool classof(const RegAllocPriorityAdvisorAnalysis *R) {
+    return R->getAdvisorMode() == AdvisorMode::Development;
+  }
+
+  /// get the logger for the given function, or nullptr if we didn't collect
+  /// one. This is used to inject the score by the RegAllocScoring pass.
+  Logger *getLogger(const MachineFunction &MF) const {
+    auto I = LogMap.find(MF.getName());
+    if (I == LogMap.end())
+      return nullptr;
+    return I->second.get();
+  }
+
+  void logRewardIfNeeded(const MachineFunction &MF,
+                         llvm::function_ref<float()> GetReward) override {
+    if (auto *Log = this->getLogger(MF))
+      Log->logFloatFinalReward(GetReward());
+  }
+
+private:
+  void getAnalysisUsage(AnalysisUsage &AU) const override {
+    AU.setPreservesAll();
+    AU.addRequired<SlotIndexes>();
+    RegAllocPriorityAdvisorAnalysis::getAnalysisUsage(AU);
+  }
+
+  // Save all the logs (when requested).
+  bool doFinalization(Module &M) override {
+    if (TrainingLog.empty())
+      return false;
+    std::error_code EC;
+    auto OS = std::make_unique<raw_fd_ostream>(TrainingLog, EC);
+    if (EC) {
+      M.getContext().emitError(EC.message() + ":" + TrainingLog);
+      return false;
+    }
+    Logger::flushLogs(*OS, LogMap);
+    return false;
+  }
+
+  std::unique_ptr<RegAllocPriorityAdvisor>
+  getAdvisor(const MachineFunction &MF, const RAGreedy &RA) override {
+
+    LLVMContext &Ctx = MF.getFunction().getContext();
+    if (ModelUnderTraining.empty() && TrainingLog.empty()) {
+      Ctx.emitError("Regalloc development mode should be requested with at "
+                    "least logging enabled and/or a training model");
+      return nullptr;
+    }
+    if (!Runner) {
+      if (ModelUnderTraining.empty())
+        Runner = std::make_unique<NoInferenceModelRunner>(Ctx, InputFeatures);
+      else
+        Runner = ModelUnderTrainingRunner::createAndEnsureValid(
+            Ctx, ModelUnderTraining, DecisionName, TrainingInputFeatures);
+      if (!Runner) {
+        Ctx.emitError("Regalloc: could not set up the model runner");
+        return nullptr;
+      }
+    }
+
+    Logger *Log = nullptr;
+    if (!TrainingLog.empty()) {
+      std::vector<LoggedFeatureSpec> LFS;
+      for (const auto &FS : InputFeatures)
+        LFS.push_back({FS, None});
+      if (auto *MUTR = dyn_cast<ModelUnderTrainingRunner>(Runner.get()))
+        if (MUTR->outputLoggedFeatureSpecs().size() > 1)
+          append_range(LFS, drop_begin(MUTR->outputLoggedFeatureSpecs()));
+      // We always log the output; in particular, if we're not evaluating, we
+      // don't have an output spec json file. That's why we handle the
+      // 'normal' output separately.
+      LFS.push_back({Output, None});
+      auto I = LogMap.insert(std::make_pair(
+          MF.getFunction().getName(),
+          std::make_unique<Logger>(LFS, Reward, /*IncludeReward*/ true)));
+      assert(I.second);
+      Log = I.first->second.get();
+    }
+
+    return std::make_unique<DevelopmentModePriorityAdvisor>(
+        MF, RA, &getAnalysis<SlotIndexes>(), Runner.get(), Log);
+  }
+
+  std::unique_ptr<MLModelRunner> Runner;
+  StringMap<std::unique_ptr<Logger>> LogMap;
+};
+#endif //#ifdef LLVM_HAVE_TF_API
+
+} // namespace llvm
+
+RegAllocPriorityAdvisorAnalysis *llvm::createReleaseModePriorityAdvisor() {
+  return new ReleaseModePriorityAdvisorAnalysis();
+}
+
+MLPriorityAdvisor::MLPriorityAdvisor(const MachineFunction &MF,
+                                     const RAGreedy &RA,
+                                     SlotIndexes *const Indexes,
+                                     MLModelRunner *Runner)
+    : RegAllocPriorityAdvisor(MF, RA, Indexes), DefaultAdvisor(MF, RA, Indexes),
+      Runner(std::move(Runner)) {
+  assert(this->Runner);
+}
+
+float MLPriorityAdvisor::getPriorityImpl(const LiveInterval &LI) const {
+  const unsigned Size = LI.getSize();
+  LiveRangeStage Stage = RA.getExtraInfo().getStage(LI);
+
+  *Runner->getTensor<int64_t>(0) = static_cast<int64_t>(Size);
+  *Runner->getTensor<int64_t>(1) = static_cast<int64_t>(Stage);
+  *Runner->getTensor<float>(2) = static_cast<float>(LI.weight());
+
+  return Runner->evaluate<float>();
+}
+
+unsigned MLPriorityAdvisor::getPriority(const LiveInterval &LI) const {
+  return static_cast<unsigned>(getPriorityImpl(LI));
+}
+
+#ifdef LLVM_HAVE_TF_API
+RegAllocPriorityAdvisorAnalysis *llvm::createDevelopmentModePriorityAdvisor() {
+  return new DevelopmentModePriorityAdvisorAnalysis();
+}
+
+unsigned
+DevelopmentModePriorityAdvisor::getPriority(const LiveInterval &LI) const {
+  double Prio = 0;
+
+  if (isa<ModelUnderTrainingRunner>(getRunner())) {
+    Prio = MLPriorityAdvisor::getPriorityImpl(LI);
+  } else {
+    Prio = getDefaultAdvisor().getPriority(LI);
+  }
+
+  if (TrainingLog.empty())
+    return Prio;
+
+  size_t CurrentFeature = 0;
+  for (; CurrentFeature < InputFeatures.size(); ++CurrentFeature) {
+    Log->logSpecifiedTensorValue(
+        CurrentFeature, reinterpret_cast<const char *>(
+                            getRunner().getTensorUntyped(CurrentFeature)));
+  }
+
+  if (auto *MUTR = dyn_cast<ModelUnderTrainingRunner>(&getRunner())) {
+    for (size_t I = 1; I < MUTR->outputLoggedFeatureSpecs().size();
+         ++I, ++CurrentFeature)
+      Log->logSpecifiedTensorValue(
+          CurrentFeature,
+          reinterpret_cast<const char *>(
+              MUTR->lastEvaluationResult()->getUntypedTensorValue(I)));
+  }
+
+  float Ret = static_cast<float>(Prio);
+  Log->logFloatValue(CurrentFeature, &Ret);
+
+  return static_cast<unsigned>(Prio);
+}
+
+#endif // #ifdef LLVM_HAVE_TF_API

llvm/lib/CodeGen/RegAllocPriorityAdvisor.cpp

cl::values(
                    "release", "precompiled"),
         clEnumValN(RegAllocPriorityAdvisorAnalysis::AdvisorMode::Development,
                    "development", "for training")));
 
 char RegAllocPriorityAdvisorAnalysis::ID = 0;
 INITIALIZE_PASS(RegAllocPriorityAdvisorAnalysis, "regalloc-priority",
                 "Regalloc priority policy", false, true)
 
 namespace {
 class DefaultPriorityAdvisorAnalysis final
     : public RegAllocPriorityAdvisorAnalysis {
 public:
   DefaultPriorityAdvisorAnalysis(bool NotAsRequested)
       : RegAllocPriorityAdvisorAnalysis(AdvisorMode::Default),
         NotAsRequested(NotAsRequested) {}
 
   // support for isa<> and dyn_cast.

 
 template <> Pass *llvm::callDefaultCtor<RegAllocPriorityAdvisorAnalysis>() {
   Pass *Ret = nullptr;
   switch (Mode) {
   case RegAllocPriorityAdvisorAnalysis::AdvisorMode::Default:
     Ret = new DefaultPriorityAdvisorAnalysis(/*NotAsRequested*/ false);
     break;
   case RegAllocPriorityAdvisorAnalysis::AdvisorMode::Development:
-    // TODO: add implementation
+#if defined(LLVM_HAVE_TF_API)
+    Ret = createDevelopmentModePriorityAdvisor();
+#endif
     break;
   case RegAllocPriorityAdvisorAnalysis::AdvisorMode::Release:
-    // TODO: add implementation
+#if defined(LLVM_HAVE_TF_AOT_REGALLOCPRIORITYMODEL)
+    Ret = createReleaseModePriorityAdvisor();
+#endif
     break;
   }
   if (Ret)
     return Ret;
   return new DefaultPriorityAdvisorAnalysis(/*NotAsRequested*/ true);
 }
 
 StringRef RegAllocPriorityAdvisorAnalysis::getPassName() const {

llvm/test/CodeGen/MLRegalloc/Inputs/reference-prio-log-noml.txt

+fields {
+ key: "SyFgets"
+ value {
+ string_value: "feature_lists {
+ feature_list {
+ key: \"li_size\"  value {
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
+ feature {  int64_list {  value: 0  }  }
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+ feature {  int64_list {  value: 0  }  }
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+ feature {  float_list {  value: 0  }  }  }  } } "
+ }
+}

llvm/test/CodeGen/MLRegalloc/dev-mode-prio-logging.ll

+; REQUIRES: have_tf_api
+; REQUIRES: x86_64-linux
+;
+; Check that we log correctly, both with a learned policy, and the default policy
+;
+; RUN: llc -mtriple=x86_64-linux-unknown -regalloc=greedy -regalloc-enable-priority-advisor=development \
+; RUN:   -regalloc-priority-training-log=%t1 -tfutils-text-log < %S/Inputs/input.ll
+; RUN: sed -i 's/ \+/ /g' %t1
+; RUN: sed -i 's/\\n key:/\n key:/g' %t1
+; RUN: sed -i 's/\\n feature/\n feature/g' %t1
+; RUN: sed -i 's/\\n/ /g' %t1
+; RUN: FileCheck --input-file %t1 %s --check-prefixes=CHECK,NOML
+; RUN: diff %t1 %S/Inputs/reference-prio-log-noml.txt
+
+; RUN: rm -rf %t && mkdir %t
+; RUN: %python %S/../../../lib/Analysis/models/gen-regalloc-priority-test-model.py %t
+; RUN: llc -mtriple=x86_64-linux-unknown -regalloc=greedy -regalloc-enable-priority-advisor=development \
+; RUN:   -regalloc-priority-training-log=%t2 -tfutils-text-log -regalloc-priority-model=%t < %S/Inputs/input.ll
+; RUN: sed -i 's/ \+/ /g' %t2
+; RUN: sed -i 's/\\n key:/\n key:/g' %t2
+; RUN: sed -i 's/\\n feature/\n feature/g' %t2
+; RUN: sed -i 's/\\n/ /g' %t2
+; RUN: FileCheck --input-file %t2 %s --check-prefixes=CHECK,ML
+
+; CHECK-NOT: nan
+; CHECK-LABEL: key: \"priority\"
+; NOML-NEXT: feature {  float_list {  value: 2.68435814e+09  }  }
+; ML-NEXT: feature {  float_list {  value: 3551  }  }
+; CHECK-LABEL: key: \"reward\"