[Refactor] Replace std::tie with structured bindings (apache#12610)

* [Refactor] Replace std::tie with structured bindings

With C++17 enabled in apache#12337, using
structured bindings to replace cases where `std::tie` is used to
define local variables.

* Added missing header for <optional>

* Silenced unused variable warnings after structured bindings

This is a bug in gcc version 7, resolved in gcc 8.  While gcc version
7 is used for CI, we'll need to silence unused variable warnings
resulting from using only part of a structured binding.

More information: https://gcc.gnu.org/bugzilla/show_bug.cgi?id=81767

src/auto_scheduler/auto_schedule.cc

@@ -78,9 +78,7 @@ TVM_REGISTER_GLOBAL("auto_scheduler.TuningOptions")
 
 TVM_REGISTER_GLOBAL("auto_scheduler.AutoSchedule")
     .set_body_typed([](SearchPolicy search_policy, TuningOptions tuning_options) {
-      te::Schedule sch;
-      Array<te::Tensor> return_tensors;
-      std::tie(sch, return_tensors) = AutoSchedule(search_policy, tuning_options);
+      auto [sch, return_tensors] = AutoSchedule(search_policy, tuning_options);
       return Array<ObjectRef>{sch, return_tensors};
     });
 }  // namespace auto_scheduler

src/auto_scheduler/compute_dag.cc

@@ -1325,10 +1325,9 @@ State ComputeDAG::InferBound(const State& state) const {
 
   Array<te::Stage> stages;
   StageToAxesMap stage_to_axes;
-  te::Schedule sch;
-  Array<te::Tensor> tensors;
   // Replay steps to tvm::Schedule
-  std::tie(sch, tensors) = ApplySteps(pstate->transform_steps, &stages, &stage_to_axes);
+  auto [sch, tensors] = ApplySteps(pstate->transform_steps, &stages, &stage_to_axes);
+  (void)tensors;  // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=81767
   sch = sch.normalize_for_feature_extraction();
   // Get bound information from TVM schedule
   Map<IterVar, Range> bounds = te::InferBound(sch);
@@ -1382,9 +1381,8 @@ Array<State> ComputeDAG::InferBound(const Array<State>& states) const {
 }
 
 ComputeDAG ComputeDAG::ReplayAndGetDAG(const Array<Step>& transform_steps) const {
-  te::Schedule sch;
-  Array<te::Tensor> old_tensors;
-  std::tie(sch, old_tensors) = ApplySteps(transform_steps);
+  auto [sch, old_tensors] = ApplySteps(transform_steps);
+  (void)old_tensors;  // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=81767
   return ComputeDAG(sch);
 }
 
@@ -1481,11 +1479,8 @@ TVM_REGISTER_GLOBAL("auto_scheduler.ComputeDAG")
 
 TVM_REGISTER_GLOBAL("auto_scheduler.ComputeDAGApplyStepsFromState")
     .set_body_typed([](const ComputeDAG& dag, const State& state, int layout_rewrite) {
-      te::Schedule sch;
-      Array<te::Tensor> return_tensors;
-      std::tie(sch, return_tensors) =
-          dag.ApplySteps(state->transform_steps, nullptr, nullptr,
-                         static_cast<LayoutRewriteOption>(layout_rewrite));
+      auto [sch, return_tensors] = dag.ApplySteps(state->transform_steps, nullptr, nullptr,
+                                                  static_cast<LayoutRewriteOption>(layout_rewrite));
       return Array<ObjectRef>{sch, return_tensors};
     });
 

src/auto_scheduler/feature.cc

@@ -952,9 +952,7 @@ class PerStoreFeatureExtractor : public StmtExprVisitor {
         unique_lines = std::max(unique_lines, 1.0f);
       }
 
-      ReuseType reuse_type;
-      float reuse_dis_iter, reuse_dis_bytes, reuse_ct;
-      std::tie(reuse_type, reuse_dis_iter, reuse_dis_bytes, reuse_ct) =
+      auto [reuse_type, reuse_dis_iter, reuse_dis_bytes, reuse_ct] =
           ComputeReuse(t, acc.indices, for_loop_stack_, for_touch_regions_, ana_);
 
       acc_feas.emplace_back();
@@ -1356,10 +1354,7 @@ void GetPerStoreFeatureName(int max_n_bufs, std::vector<std::string>* ret) {
 
 void GetPerStoreFeaturesWorkerFunc(const SearchTask& task, const State& state, int max_n_bufs,
                                    std::vector<float>* feature, std::atomic<int>* error_ct) {
-  te::Schedule sch;
-  Array<te::Tensor> tensors;
-
-  std::tie(sch, tensors) = task->compute_dag.ApplySteps(state->transform_steps);
+  auto [sch, tensors] = task->compute_dag.ApplySteps(state->transform_steps);
 
   // When inlining, replace const matrices with const values.
   // Produces wrong IR, but good enough for feature extraction, and

src/auto_scheduler/search_policy/search_policy.cc

@@ -106,9 +106,7 @@ TVM_REGISTER_GLOBAL("auto_scheduler.SearchPolicyRunCallbacks")
 
 TVM_REGISTER_GLOBAL("auto_scheduler.SearchPolicyContinueSearchOneRound")
     .set_body_typed([](SearchPolicy policy, int num_measure, ProgramMeasurer measurer) {
-      Array<MeasureInput> inputs;
-      Array<MeasureResult> results;
-      std::tie(inputs, results) = policy->ContinueSearchOneRound(num_measure, measurer);
+      auto [inputs, results] = policy->ContinueSearchOneRound(num_measure, measurer);
       return Array<ObjectRef>{inputs, results};
     });
 

src/auto_scheduler/search_policy/sketch_policy_rules.cc

@@ -343,8 +343,7 @@ SketchGenerationRule::ConditionKind RuleCrossThreadReduction::MeetCondition(
   const auto& op = state->stages[stage_id]->op;
   if (op->IsInstance<te::ComputeOpNode>()) {
     // Compute the product of lengths of all space iters and all reduce iters
-    int cum_space_len, cum_reduce_len;
-    std::tie(cum_space_len, cum_reduce_len) =
+    auto [cum_space_len, cum_reduce_len] =
         GetCumulativeSpaceAndReductionLength(state->stages[stage_id]);
 
     if (NeedsMultilevelTiling(policy.search_task, state, stage_id)) {

src/ir/instrument.cc

@@ -288,10 +288,7 @@ String RenderPassProfiles() {
   os << std::fixed;
 
   while (profiles.size() > 0) {
-    size_t depth;
-    PassProfile::Duration parent_duration;
-    PassProfile* profile;
-    std::tie(depth, parent_duration, profile) = profiles.top();
+    auto [depth, parent_duration, profile] = profiles.top();
     profiles.pop();
 
     // indent depth

src/meta_schedule/database/json_database.cc

@@ -115,9 +115,7 @@ class JSONDatabaseNode : public DatabaseNode {
 
   Workload CommitWorkload(const IRModule& mod) {
     // Try to insert `mod` into `workloads_`
-    decltype(this->workloads2idx_)::iterator it;
-    bool inserted = false;
-    std::tie(it, inserted) =
+    auto [it, inserted] =
         this->workloads2idx_.emplace(Workload(mod, tvm::StructuralHash()(mod)), -1);
     Workload workload = it->first;
     // If `mod` is new in `workloads2idx_`, append it to the workload file

src/meta_schedule/mutator/mutate_compute_location.cc

@@ -86,9 +86,7 @@ std::vector<MutateComputeLocationNode::Candidate> MutateComputeLocationNode::Fin
       int old_decision = Downcast<Integer>(decision)->value;
 
       // Step 2. Collect all the compute_at locations.
-      Array<tir::StmtSRef> location_srefs;
-      std::vector<int> location_indices;
-      std::tie(location_srefs, location_indices) = CollectComputeLocation(sch->state(), block_sref);
+      auto [location_srefs, location_indices] = CollectComputeLocation(sch->state(), block_sref);
       // Step 3. Remove the old decision.
       auto it = std::find(location_indices.begin(), location_indices.end(), old_decision);
       if (it != location_indices.end()) {

src/meta_schedule/schedule_rule/cross_thread_reduction.cc

@@ -64,15 +64,11 @@ class CrossThreadReductionNode : public ScheduleRuleNode {
     // Step 2. Check the opportunity for block fusion. We say "fusible", if we can compute-at the
     // block to its consumers. We want to fuse as much as possible because it results in
     // significantly faster schedule.
-    bool fusible = false;
     // `target_loop` is the loop position where the input block will be computed at.
-    tir::LoopRV target_loop{nullptr};
     // `target_block` is the consumer block that we want to compute-at the input block to.
-    tir::BlockRV target_block{nullptr};
     // `tgt_block_innermost_loop` is the innermost loop outside the target block.
-    tir::LoopRV tgt_block_innermost_loop{nullptr};
 
-    std::tie(fusible, target_loop, target_block, tgt_block_innermost_loop) =
+    auto [fusible, target_loop, target_block, tgt_block_innermost_loop] =
         GetComputeTargetLoopAndBlock(tmp_sch, block_rv);
 
     // Step 3. Try block fusion.

src/meta_schedule/space_generator/post_order_apply.cc

@@ -140,9 +140,7 @@ class PostOrderApplyNode : public SpaceGeneratorNode {
       result.clear();
       while (!stack.empty()) {
         // get the stack.top()
-        tir::Schedule sch;
-        Array<tir::BlockRV> blocks;
-        std::tie(sch, blocks) = stack.back();
+        auto [sch, blocks] = stack.back();
         stack.pop_back();
         // if all blocks are visited
         if (blocks.empty()) {

src/relay/collage/partition_rule.cc

@@ -92,9 +92,7 @@ std::vector<CandidatePartition> DFPatternPartitionRuleNode::AllCandidates(
       continue;
     }
     IndexSet inside = MatcherToIndexSet(matcher);
-    OpPatternKind kind;
-    String label;
-    std::tie(kind, label) = SubGraphKindAndLabel(dataflow_graph, inside);
+    auto [kind, label] = SubGraphKindAndLabel(dataflow_graph, inside);
     SubGraph sub_graph(dataflow_graph, std::move(inside), kind, std::move(label));
     String rule_name = rule_name_.empty() ? sub_graph->label_ : rule_name_;
     CandidatePartition candidate(std::move(rule_name), std::move(sub_graph), spec);
@@ -256,9 +254,7 @@ std::vector<CandidatePartition> OpCallByKindPartitionRuleNode::AllCandidates(
     auto node = dataflow_graph.index_to_node(index);
     Expr sub_expr = node->ref();
     if (sub_expr->IsInstance<CallNode>()) {
-      OpPatternKind kind;
-      String label;
-      std::tie(kind, label) = SubExprKindAndLabel(sub_expr);
+      auto [kind, label] = SubExprKindAndLabel(sub_expr);
       if (kind <= kOutEWiseFusable) {
         IndexSet inside(dataflow_graph.size(), {index});
         SubGraph sub_graph(dataflow_graph, std::move(inside), kind, std::move(label));
@@ -404,9 +400,7 @@ std::vector<CandidatePartition> HostPartitionRuleNode::AllCandidates(
       continue;
     }
     IndexSet inside(dataflow_graph.size(), {index});
-    OpPatternKind kind;
-    String label;
-    std::tie(kind, label) = SubGraphKindAndLabel(dataflow_graph, inside);
+    auto [kind, label] = SubGraphKindAndLabel(dataflow_graph, inside);
     SubGraph sub_graph(dataflow_graph, std::move(inside), kind, label);
     String rule_name = NestLabels(rule_name_, sub_graph->label_);
     // We'll a zero cost for the candidate since we'll never want to actually estimate the cost

src/relay/collage/sub_graph.cc

@@ -439,9 +439,7 @@ std::pair<OpPatternKind, std::string> SubGraphKindAndLabel(const DataflowGraph&
   bool first = true;
   OpPatternKind max_kind = kElemWise;
   for (PostDfsIndex index : inside) {
-    OpPatternKind sub_kind;
-    std::string sub_label;
-    std::tie(sub_kind, sub_label) = SubExprKindAndLabel(dataflow_graph.index_to_node(index)->ref());
+    auto [sub_kind, sub_label] = SubExprKindAndLabel(dataflow_graph.index_to_node(index)->ref());
     if (!sub_label.empty()) {
       if (first) {
         first = false;
@@ -995,9 +993,7 @@ transform::Pass PartitionForTesting(Integer max_exits, Bool allow_taps, String c
     // Build the overall sub-graph, which will include any "Composite" functions as
     // well as any nodes without a label.
     IndexSet inside(dataflow_graph.size(), node_indexes);
-    OpPatternKind kind;
-    String label;
-    std::tie(kind, label) = SubGraphKindAndLabel(dataflow_graph, inside);
+    auto [kind, label] = SubGraphKindAndLabel(dataflow_graph, inside);
     SubGraph sub_graph(dataflow_graph, inside, kind, label, std::move(nested_sub_graphs));
 
     // Push the overall sub-graph into the final "Compiler" function.

src/relay/qnn/op/convolution.cc

@@ -722,9 +722,9 @@ Expr QnnConv2DCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
       << "qnn.conv2d supports only OIHW/HWIO/HWOI/OHWI kernel data layout.";
   ICHECK(param->kernel_size.defined()) << "qnn.conv2d requires kernel size to be specified.";
 
-  int batch_size, in_channels, out_channels, kernel_h, kernel_w, channel_multiplier;
-  std::tie(batch_size, in_channels, out_channels, kernel_h, kernel_w, channel_multiplier) =
+  auto [batch_size, in_channels, out_channels, kernel_h, kernel_w, channel_multiplier] =
       GetWorkload(arg_types, param);
+  (void)batch_size;  // https://gcc.gnu.org/bugzilla/show_bug.cgi?id=81767
 
   // zero points are allowed to be non-scalar. Let's check if that's the case.
   bool dynamic_zp = false;

src/relay/qnn/op/leaky_relu.cc

@@ -125,13 +125,11 @@ Expr QnnLeakyReluCanonicalize(const Attrs& attrs, const Array<Expr>& new_args,
                                              output_zero_point, input_shape);
 
   // alpha * Q_i'
-  int32_t fixed_point_multiplier, shift;
-  std::tie(fixed_point_multiplier, shift) = GetFixedPointMultiplierShift(alpha);
+  auto [fixed_point_multiplier, shift] = GetFixedPointMultiplierShift(alpha);
   auto prod = FixedPointMultiply(requantized_expr, fixed_point_multiplier, shift);
 
   // (1 - alpha) * zp_o
-  int32_t fixed_point_multiplier_z, shift_z;
-  std::tie(fixed_point_multiplier_z, shift_z) = GetFixedPointMultiplierShift(1 - alpha);
+  auto [fixed_point_multiplier_z, shift_z] = GetFixedPointMultiplierShift(1 - alpha);
   auto scaled_z = FixedPointMultiply(output_zero_point, fixed_point_multiplier_z, shift_z);
 
   // alpha * Q_i' + (1 - alpha) * zp_o

src/relay/qnn/op/requantize.cc

@@ -223,8 +223,7 @@ Expr RequantizeLowerInt(const Expr& input_tensor, const Expr& input_scale,
         static_cast<double>(input_scale_float) / static_cast<double>(output_scale_float);
     // Skip if input and output scales are same.
     if (!IsEqualScalar(input_scale, output_scale)) {
-      int32_t fixed_point_multiplier, shift;
-      std::tie(fixed_point_multiplier, shift) = GetFixedPointMultiplierShift(double_multiplier);
+      auto [fixed_point_multiplier, shift] = GetFixedPointMultiplierShift(double_multiplier);
 
       const bool is_upward_rounding = (param->rounding == "UPWARD");
 

src/relay/qnn/utils.cc

@@ -64,8 +64,7 @@ Expr FixedPointMultiplyToNearest(Expr tensor, double multiplier,
   tensor = Cast(tensor, hp_dtype);
 
   // 1) Calculating the integer multiplier and integer shift
-  int32_t fixed_point_multiplier, shift;
-  std::tie(fixed_point_multiplier, shift) = GetFixedPointMultiplierShift(multiplier);
+  auto [fixed_point_multiplier, shift] = GetFixedPointMultiplierShift(multiplier);
   int left_shift = shift > 0 ? shift : 0;
   int right_shift = shift > 0 ? 0 : -shift;
 
@@ -128,8 +127,7 @@ Expr FixedPointMultiplyPerChannel(Expr tensor, std::vector<double> multipliers,
   std::vector<int32_t> fixed_pt_multipliers, lshifts, rshifts;
   bool is_lshift_required = false;
   for (auto multiplier : multipliers) {
-    int32_t fixed_pt_multiplier, shift;
-    std::tie(fixed_pt_multiplier, shift) = GetFixedPointMultiplierShift(multiplier);
+    auto [fixed_pt_multiplier, shift] = GetFixedPointMultiplierShift(multiplier);
     int lshift = shift > 0 ? shift : 0;
     int rshift = shift > 0 ? 0 : -shift;
     fixed_pt_multipliers.push_back(fixed_pt_multiplier);

src/relay/quantize/realize.cc

@@ -77,8 +77,7 @@ inline Expr MulAndDiv(Expr data, float s1, float s2, DataType dtype,
     return Multiply(data, MakeConstantScalar(dtype, factor));
   } else {
     if (cfg->rounding == "UPWARD") {
-      int32_t fixed_point_multiplier, shift;
-      std::tie(fixed_point_multiplier, shift) = qnn::GetFixedPointMultiplierShift(factor);
+      auto [fixed_point_multiplier, shift] = qnn::GetFixedPointMultiplierShift(factor);
       data = relay::FixedPointMultiply(data, fixed_point_multiplier, shift);
     } else {
       data = qnn::FixedPointMultiplyToNearest(data, factor, data_shape);
@@ -135,8 +134,7 @@ Expr QuantizeRealize(const Call& ref_call, const Array<Expr>& new_args, const Ob
     } else {
       data = Cast(data, DataType::Int(64));
       if (cfg->rounding == "UPWARD") {
-        int32_t fixed_point_multiplier, shift;
-        std::tie(fixed_point_multiplier, shift) =
+        auto [fixed_point_multiplier, shift] =
             qnn::GetFixedPointMultiplierShift(idom_scale_imm / odom_scale_imm);
         data = relay::FixedPointMultiply(data, fixed_point_multiplier, shift);
       } else {

src/relay/transforms/combine_parallel_conv2d.cc

@@ -83,9 +83,7 @@ class ParallelConv2DCombiner : public ParallelOpCombiner {
   Call MakeCombinedOp(const Group& branches) {
     const Op& conv2d = Op::Get("nn.conv2d");
     Expr data = branches[0][0]->args[0];
-    Expr new_weight;
-    IndexExpr new_channels;
-    std::tie(new_weight, new_channels) = TransformWeight(branches);
+    auto [new_weight, new_channels] = TransformWeight(branches);
 
     const CallNode* group_root = branches[0][0];
     const auto* attrs = group_root->attrs.as<Conv2DAttrs>();

src/relay/transforms/combine_parallel_dense.cc

@@ -116,10 +116,8 @@ class ParallelDenseToDenseCombiner : public ParallelOpCombiner {
   Call MakeCombinedOp(const Group& branches) {
     const Op& dense_op = Op::Get("nn.dense");
     Expr input = branches[0][0]->args[0];
-    Expr new_weight;
-    IndexExpr new_output_dims;
     // concat all weights into one
-    std::tie(new_weight, new_output_dims) = TransformWeight(branches);
+    auto [new_weight, new_output_dims] = TransformWeight(branches);
     const auto* origin_attrs = branches[0][0]->attrs.as<DenseAttrs>();
     ICHECK(origin_attrs);
     const auto dense_attrs = make_object<DenseAttrs>();

src/runtime/graph_executor/graph_executor.cc

@@ -674,9 +674,7 @@ PackedFunc GraphExecutor::GetFunction(const std::string& name,
     });
   } else if (name == "get_input_info") {
     return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
-      GraphExecutor::ShapeInfo shape_info;
-      GraphExecutor::DtypeInfo dtype_info;
-      std::tie(shape_info, dtype_info) = this->GetInputInfo();
+      auto [shape_info, dtype_info] = this->GetInputInfo();
       Map<String, ObjectRef> input_info;
       input_info.Set("shape", shape_info);
       input_info.Set("dtype", dtype_info);

src/target/source/ptx.cc

@@ -403,8 +403,7 @@ class Replacer {
   }
   std::string rewrite(std::string str) {
     for (auto&& rule : _rules) {
-      std::string pattern, replacement;
-      std::tie(pattern, replacement) = rule;
+      auto [pattern, replacement] = rule;
       size_t len = pattern.size();
       size_t new_len = replacement.size();
       size_t pos = str.find(pattern);
@@ -532,8 +531,7 @@ std::string PrintMMAAssembly(const std::string& shape, const std::string& A_layo
                 dtype_c = ptx::DTypeFromString(C_dtype);
   ptx::LayoutType layout_a = ptx::LayoutTypeFromString(A_layout),
                   layout_b = ptx::LayoutTypeFromString(B_layout);
-  int m, n, k;
-  std::tie(m, n, k) = ptx::ParseMMAShape(shape);
+  auto [m, n, k] = ptx::ParseMMAShape(shape);
   CheckMMAConfigValidity(m, n, k, layout_a, layout_b, dtype_a, dtype_b, dtype_c, bit_op, sparse,
                          saturate);
   std::string asm_code = R"(
@@ -545,8 +543,7 @@ std::string PrintMMAAssembly(const std::string& shape, const std::string& A_layo
       : {inputs});
   }
 )";
-  std::string templates_str, inputs_str, outputs_str;
-  std::tie(templates_str, inputs_str, outputs_str) =
+  auto [templates_str, inputs_str, outputs_str] =
       GetMMAOperands(m, n, k, dtype_a, dtype_b, dtype_c, sparse);
 
   // replace patterns
@@ -622,8 +619,7 @@ std::string PrintLoadMatrixAssembly(bool trans, int num, const std::string& type
     );
   }
 )";
-  std::string templates_str, outputs_str;
-  std::tie(templates_str, outputs_str) = GetLoadMatrixOperands(num, local_ptr, local_elem_offset);
+  auto [templates_str, outputs_str] = GetLoadMatrixOperands(num, local_ptr, local_elem_offset);
 
   Replacer replacer;
   replacer.register_rule("{.shape}", ".m8n8");

src/te/autodiff/ad_simplify.cc

@@ -1183,21 +1183,19 @@ PrimExpr RemoveJacobianAndLiftNonzeroCondImpl(const PrimExpr& expr_orig, const A
         return RemoveJacobianAndLiftNonzeroCondImpl(new_red, axis, vranges);
       }
 
-      PrimExpr new_outer_cond, new_reduce_cond;
       Array<PrimExpr> new_source = red->source;
 
       // Partially lift conditions from the reduce condition
-      std::tie(new_outer_cond, new_reduce_cond) =
+      auto [new_outer_cond, new_reduce_cond] =
           LiftConditionsThroughReduction(red->condition, red->axis, axis);
 
       // If it's not sum then we haven't yet lifted nonzeroness cond from the source
       if (!is_sum) {
-        PrimExpr outer_nz_cond, nz_cond, nz_source;
         auto nz = NonzeronessCondition(red->source[red->value_index]);
         // Append conditions from the reduction
-        nz_cond = new_reduce_cond && nz.cond;
-        nz_source = nz.value;
-        std::tie(outer_nz_cond, nz_cond) = LiftConditionsThroughReduction(nz_cond, red->axis, axis);
+        PrimExpr nz_source = nz.value;
+        auto [outer_nz_cond, nz_cond] =
+            LiftConditionsThroughReduction(new_reduce_cond && nz.cond, red->axis, axis);
         new_outer_cond = new_outer_cond && outer_nz_cond;
         new_source.Set(red->value_index, Select(nz_cond, nz_source, make_zero(nz_source.dtype())));
       }