[SCHEDULE] Fix inline with multiple outputs (#507)

src/op/compute_op.cc

@@ -98,7 +98,7 @@ Array<Tensor> compute(Array<Expr> shape,
   return outputs;
 }
 
-bool ReduceEqual(const ir::Reduce* a, const ir::Reduce* b) {
+inline bool ReduceEqual(const ir::Reduce* a, const ir::Reduce* b) {
   return (a->combiner.same_as(b->combiner)) &&
          (a->source.same_as(b->source)) &&
          (a->axis.same_as(b->axis)) &&

src/schedule/schedule_dataflow_rewrite.cc

@@ -275,10 +275,17 @@ void RebaseNonZeroMinLoop(const Schedule& sch) {
   }
 }
 
+inline bool ReduceEqual(const ir::Reduce* a, const ir::Reduce* b) {
+  return (a->combiner.same_as(b->combiner)) &&
+         (a->source.same_as(b->source)) &&
+         (a->axis.same_as(b->axis)) &&
+         (a->condition.same_as(b->condition));
+}
+
 void InjectInline(ScheduleNode* sch) {
   sch->InvalidateCache();
 
-  std::vector<Array<Expr>> new_body(sch->stages.size());
+  std::vector<Array<Expr> > new_body(sch->stages.size());
   std::vector<bool> changed(sch->stages.size(), false);
   // inline all the ops
   for (size_t i = sch->stages.size(); i != 0; --i) {
@@ -286,7 +293,7 @@ void InjectInline(ScheduleNode* sch) {
     if (stage->attach_type == kInline) {
       stage->attach_type = kInlinedAlready;
       Array<Var> args;
-      Array<Expr> body;
+      Expr body;
       {
         // setup args
         const ComputeOpNode* compute = stage->op.as<ComputeOpNode>();
@@ -295,7 +302,9 @@ void InjectInline(ScheduleNode* sch) {
         for (auto iv : compute->axis) {
           args.push_back(iv->var);
         }
-        body = compute->body;
+        CHECK_EQ(compute->body.size(), 1U)
+            << "can only inline compute op with 1 output";
+        body = compute->body[0];
       }
       for (size_t j = i; j < sch->stages.size(); ++j) {
         Stage s = sch->stages[j];
@@ -304,10 +313,39 @@ void InjectInline(ScheduleNode* sch) {
           if (!new_body[j].size()) {
             new_body[j] = s->op.as<ComputeOpNode>()->body;
           }
-          for (size_t k = 0; k < body.size(); ++k) {
-            changed[j] = true;
-            new_body[j].Set(k, ir::Inline(ir::Evaluate::make(new_body[j][k]),
-                            stage->op, args, body[k]).as<ir::Evaluate>()->value);
+          if (new_body[j][0]->is_type<ir::Reduce>()) {
+            // specially handle reduction inline for multiplre reductions.
+            const ir::Reduce* reduce = new_body[j][0].as<ir::Reduce>();
+            for (size_t k = 1; k < new_body[j].size(); ++k) {
+              const ir::Reduce* reduce_ = new_body[j][k].as<ir::Reduce>();
+              CHECK(reduce_);
+              CHECK(ReduceEqual(reduce_, reduce))
+                  << "The Reduce inputs of ComputeOp should "
+                  << "have the same attribute except value_index";
+            }
+            Expr new_value = ir::Inline(ir::Evaluate::make(new_body[j][0]),
+                                        stage->op, args, body).as<ir::Evaluate>()->value;
+            if (!new_value.same_as(new_body[j][0])) {
+              changed[j] = true;
+              const ir::Reduce* r = new_value.as<ir::Reduce>();
+              CHECK_EQ(new_body[j].size(), r->source.size());
+              CHECK(r != nullptr);
+              for (size_t k = 0; k < new_body[j].size(); ++k) {
+                std::shared_ptr<ir::Reduce> n = std::make_shared<ir::Reduce>(*r);
+                n->value_index = static_cast<int>(k);
+                n->type = r->source[k].type();
+                new_body[j].Set(k, Expr(n));
+              }
+            }
+          } else {
+            for (size_t k = 0; k < new_body[j].size(); ++k) {
+              Expr new_value = ir::Inline(ir::Evaluate::make(new_body[j][k]),
+                                          stage->op, args, body).as<ir::Evaluate>()->value;
+              if (!new_value.same_as(new_body[j][k])) {
+                new_body[j].Set(k, new_value);
+                changed[j] = true;
+              }
+            }
           }
         }
       }

tests/python/unittest/test_schedule_schedule_ops.py

@@ -56,6 +56,28 @@ def test_schedule_scan():
     assert(bounds[res.op.scan_axis].min.value == 1)
     stmt = tvm.schedule.ScheduleOps(s, bounds)
 
+def test_inline_multi_reduce():
+    def argmax_comp(x, y):
+        idx = tvm.select((x[1] >= y[1]), x[0], y[0])
+        val = tvm.select((x[1] >= y[1]), x[1], y[1])
+        return idx, val
+    def argmax_init(idx_typ, val_typ):
+        return tvm.const(-1, idx_typ), tvm.min_value(val_typ)
+
+    argmax = tvm.comm_reducer(argmax_comp, argmax_init, name='argmax')
+    m = tvm.var('m')
+    n = tvm.var('n')
+    val = tvm.placeholder((m, n), name='val', dtype='float32')
+    val2 = tvm.compute((m, n), lambda i, j: tvm.exp(val[i, j]), name='val2')
+    k = tvm.reduce_axis((0, n), 'k')
+    T_idx, T_val = tvm.compute((m, ), lambda i: argmax((k.var, val2[i, k]), axis=k), name='T')
+    s = tvm.create_schedule(T_idx.op)
+    s[val2].compute_inline()
+    s = s.normalize()
+    bounds = tvm.schedule.InferBound(s)
+    stmt = tvm.schedule.ScheduleOps(s, bounds)
+
+
 def test_auto_inline():
     m = tvm.var('m')
     n = tvm.var('n')
@@ -207,6 +229,7 @@ def test_schedule_cache_relayout3():
 
 
 if __name__ == "__main__":
+    test_inline_multi_reduce()
     test_schedule_cache_relayout3()
     test_schedule_cache_relayout2()
     test_schedule_cache_relayout1()