Almost-exact graph recognizes equivalence in the split-split pattern

csrc/id_model/id_model.cpp

@@ -12,7 +12,6 @@
 #include <utility>
 
 #include "device_lower/analysis/circular_buffer.h"
-#include "device_lower/analysis/trivial_broadcast.h"
 #include "device_lower/lower2device.h"
 #include "device_lower/utils.h"
 #include "disjoint_set.h"
@@ -25,7 +24,6 @@
 #include "iter_visitor.h"
 #include "logical_domain_map.h"
 #include "transform_iter.h"
-#include "val_graph_visitor.h"
 
 namespace nvfuser {
 
@@ -495,6 +493,61 @@ std::vector<std::vector<Val*>> getTriviallyMappedIds(Expr* expr) {
   return mapped_ids;
 }
 
+void mapSplitOfSplit(ValGraph& graph) {
+  // The following is a subpattern of
+  // https://github.com/NVIDIA/Fuser/blob/main/doc/reading/iterdomain.md#2-properties-of-iterdomain-transformations
+  //
+  // outer, _ = split(root)
+  // outermost_grand, _ = split(outer)
+  // outer', _ = split(root)
+  //
+  // If outermost_grand and outer' have the same extent, map them.
+  std::vector<std::pair<Val*, Val*>> ids_to_map;
+  for (const ValGroup& root : graph.disjointValSets().disjointSets()) {
+    const ExprGroups& uses_of_root = graph.getUses(root);
+    std::vector<ValGroup> outermost_grands;
+    for (const ExprGroup& use_of_root : uses_of_root) {
+      auto* split0 = dynamic_cast<Split*>(use_of_root->front());
+      if (split0 == nullptr) {
+        continue;
+      }
+      // Only follow the outer output of the first split; outer and inner
+      // must not be conflated.
+      const ValGroup& outer = graph.toGroup(split0->outer());
+      for (const ExprGroup& use_of_outer : graph.getUses(outer)) {
+        auto* split1 = dynamic_cast<Split*>(use_of_outer->front());
+        if (split1 == nullptr) {
+          continue;
+        }
+        const ValGroup& outermost_grand = graph.toGroup(split1->outer());
+        outermost_grands.push_back(outermost_grand);
+      }
+    }
+
+    for (const ValGroup& outermost_grand : outermost_grands) {
+      Val* extent_of_grand =
+          outermost_grand->front()->as<IterDomain>()->extent();
+
+      for (const ExprGroup& use_of_root : uses_of_root) {
+        auto* split = dynamic_cast<Split*>(use_of_root->front());
+        if (split == nullptr) {
+          continue;
+        }
+
+        const ValGroup& outer = graph.toGroup(split->outer());
+        if (outer->front()->as<IterDomain>()->extent()->sameAs(
+                extent_of_grand)) {
+          ids_to_map.emplace_back(outermost_grand->front(), outer->front());
+        }
+      }
+    }
+  }
+
+  for (const auto& [id1, id2] : ids_to_map) {
+    graph.mapVals(id1, id2);
+  }
+}
+
 } // namespace
 
 ValGraph& IdModel::buildAlmostExactGraph() {
@@ -554,6 +607,8 @@ ValGraph& IdModel::buildAlmostExactGraph() {
     almost_exact_graph.mapVals(id1, id2);
   }
 
+  mapSplitOfSplit(almost_exact_graph);
+
   almost_exact_graph.validateConsistency();
 
   if (!allow_self_mapping_) {

csrc/val_graph.cpp

@@ -397,11 +397,12 @@ const ExprGroups& ValGraph::getDefinitions(const ValGroup& val_group) const {
 
 const ExprGroups& ValGraph::getUses(const ValGroup& val_group) const {
   NVF_ERROR(val_group, "Nullptr not allowed");
+
+  static ExprGroups empty_expr_groups;
   const auto it = unique_uses_.find(val_group);
-  NVF_ERROR(
-      it != unique_uses_.end(),
-      "Use group not found for ",
-      nvfuser::toString(val_group));
+  if (it == unique_uses_.end()) {
+    return empty_expr_groups;
+  }
   return it->second;
 }
 

tests/cpp/test_id_model.cpp

@@ -6,8 +6,6 @@
  */
 // clang-format on
 
-#include <fstream>
-
 #include <gmock/gmock-matchers.h>
 #include <gtest/gtest.h>
 
@@ -17,7 +15,6 @@
 #include "id_model/loop_promotion.h"
 #include "id_model/schedule.h"
 #include "id_model/to_string.h"
-#include "ir/graphviz.h"
 #include "ops/all_ops.h"
 #include "scheduler/tools/inlining.h"
 #include "scheduler/tools/resize_utils.h"
@@ -235,8 +232,7 @@ void validateIELResolution(
     auto promotion_id = iel_promotion_map_it->second;
     ASSERT_TRUE(
         exact_graph.disjointValSets().strictAreMapped(promotion_id, ref_id))
-        << "Unexpected promotion. "
-        << "Expected: " << ref_id->toString()
+        << "Unexpected promotion. Expected: " << ref_id->toString()
         << ". Actual: " << promotion_id->toString();
     ASSERT_TRUE(loop_graph.disjointValSets().strictAreMapped(id, promotion_id))
         << "Promotion of " << id->toString()
@@ -376,9 +372,9 @@ void checkStep4Results(
   const auto& iel_promotion_map = tester.s4_iel_promotion_map;
 
   EXPECT_EQ(iel_promotion_map.size(), ref_promotion_map.size())
-      << "Mismatched Step-4 result map. "
-      << "Expected to have " << ref_promotion_map.size()
-      << " mappings but found " << iel_promotion_map.size();
+      << "Mismatched Step-4 result map. Expected to have "
+      << ref_promotion_map.size() << " mappings but found "
+      << iel_promotion_map.size();
 
   for (const auto& ref_promotion_pair : ref_promotion_map) {
     const auto& ref_promotion_group = ref_promotion_pair.first;
@@ -2937,9 +2933,8 @@ TEST_F(IdModelTest, LoopPromotionCyclicGraphWar) {
 // Test to verify the split-aware covered group analysis. See
 // also https://github.com/NVIDIA/Fuser/pull/3877.
 TEST_F(IdModelTest, CoveredGroups) {
-  auto fusion_ptr = std::make_unique<Fusion>();
-  auto& fusion = *fusion_ptr;
-  FusionGuard fg(fusion_ptr.get());
+  Fusion fusion;
+  FusionGuard fg(&fusion);
 
   auto tv0 = makeContigConcreteTensor({-1, 1});
   fusion.addInput(tv0);
@@ -3000,7 +2995,7 @@ TEST_F(IdModelTest, CoveredGroups) {
 TEST_F(IdModelTest, InvalidLoopPromotion) {
   auto fusion_ptr = std::make_unique<Fusion>();
   auto& fusion = *fusion_ptr;
-  FusionGuard fg(fusion_ptr.get());
+  FusionGuard fg(&fusion);
 
   auto T0 = makeContigConcreteTensor({1, 32, 6});
   fusion.addInput(T0);
@@ -3086,9 +3081,8 @@ TEST_F(IdModelTest, InvalidLoopPromotion) {
 // When a loop group only includes broadcast IDs, the group should not
 // need to be promoted
 TEST_F(IdModelTest, BroadcastOnlyNoLoopPromotion) {
-  auto fusion_ptr = std::make_unique<Fusion>();
-  auto& fusion = *fusion_ptr;
-  FusionGuard fg(fusion_ptr.get());
+  Fusion fusion;
+  FusionGuard fg(&fusion);
 
   auto tv0 = makeContigConcreteTensor({-1, 1});
   fusion.addInput(tv0);
@@ -3130,9 +3124,8 @@ TEST_F(IdModelTest, BroadcastOnlyNoLoopPromotion) {
 
 // Scatter output uses unique mapping schemes
 TEST_F(IdModelTest, ScatterLoopMapping) {
-  auto fusion_ptr = std::make_unique<Fusion>();
-  auto& fusion = *fusion_ptr;
-  FusionGuard fg(fusion_ptr.get());
+  Fusion fusion;
+  FusionGuard fg(&fusion);
 
   auto tv0 = makeContigTensor(1);
   fusion.addInput(tv0);
@@ -3185,8 +3178,7 @@ TEST_F(IdModelTest, ScatterLoopMapping) {
 // required but is a WAR for special ops like
 // PreprocessGroupedMatmulInputSf. See also issue #5391.
 TEST_F(IdModelTest, LoopPromotionIncludeOnlyLoopIds) {
-  auto fusion_ptr = std::make_unique<Fusion>();
-  Fusion& fusion = *fusion_ptr.get();
+  Fusion fusion;
   FusionGuard fg(&fusion);
 
   auto tv0 = makeSymbolicTensor(2);
@@ -3219,8 +3211,7 @@ TEST_F(IdModelTest, LoopPromotionIncludeOnlyLoopIds) {
 }
 
 TEST_F(IdModelTest, PermissiveResizeGraph) {
-  auto fusion_ptr = std::make_unique<Fusion>();
-  Fusion& fusion = *fusion_ptr.get();
+  Fusion fusion;
   FusionGuard fg(&fusion);
 
   auto tv0 = makeConcreteTensor({36});
@@ -3270,8 +3261,7 @@ TEST_F(IdModelTest, PermissiveResizeGraph) {
 // This is the failing segment of the reproducer of
 // https://github.com/NVIDIA/Fuser/issues/5803.
 TEST_F(IdModelTest, ReproIssue5803) {
-  auto fusion_ptr = std::make_unique<Fusion>();
-  Fusion& fusion = *fusion_ptr.get();
+  Fusion fusion;
   FusionGuard fg(&fusion);
 
   auto tv2 = makeContigConcreteTensor({4}, DataType::Int);
@@ -3312,8 +3302,7 @@ TEST_F(IdModelTest, ReproIssue5803) {
 // This is a minimal fusion pattern to trigger the loop promotion
 // issue as reported in https://github.com/NVIDIA/Fuser/issues/5803
 TEST_F(IdModelTest, ReproIssue5803Minimal) {
-  auto fusion_ptr = std::make_unique<Fusion>();
-  Fusion& fusion = *fusion_ptr.get();
+  Fusion fusion;
   FusionGuard fg(&fusion);
 
   auto tv0 = makeConcreteTensor({4, 8});
@@ -3337,4 +3326,44 @@ TEST_F(IdModelTest, ReproIssue5803Minimal) {
   IdModel id_model(&fusion, true);
 }
 
+TEST_F(IdModelTest, SplitingReshape) {
+  Fusion fusion;
+  FusionGuard fg(&fusion);
+
+  TensorView* in = makeContigConcreteTensor({2 * 2 * 2});
+  fusion.addInput(in);
+  TensorView* out = reshape(in, {2 * 2 * 2}, {2 * 2, 2});
+  fusion.addOutput(out);
+
+  in->outer_split(0, 2);
+  out->outer_split(0, 2);
+
+  IdModel id_model(&fusion);
+  const ValGraph& almost_exact_graph = id_model.buildAlmostExactGraph();
+  EXPECT_TRUE(almost_exact_graph.disjointValSets().strictAreMapped(
+      in->axis(0), out->axis(0)));
+  EXPECT_FALSE(almost_exact_graph.disjointValSets().strictAreMapped(
+      in->axis(0), out->axis(1)));
+  EXPECT_FALSE(almost_exact_graph.disjointValSets().strictAreMapped(
+      in->axis(1), out->axis(2)));
+}
+
+TEST_F(IdModelTest, SplitingReshape_DifferentExtents) {
+  Fusion fusion;
+  FusionGuard fg(&fusion);
+
+  TensorView* in = makeContigConcreteTensor({12});
+  fusion.addInput(in);
+  TensorView* out = reshape(in, {12}, {6, 2});
+  fusion.addOutput(out);
+
+  in->outer_split(0, 2);
+  out->outer_split(0, 3);
+
+  IdModel id_model(&fusion);
+  const ValGraph& almost_exact_graph = id_model.buildAlmostExactGraph();
+  EXPECT_FALSE(almost_exact_graph.disjointValSets().strictAreMapped(
+      in->axis(0), out->axis(0)));
+}
+
 } // namespace nvfuser