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Avoid redundancy with resize ops #3713

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Avoid redundancy with resize ops #3713

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naoyam
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@naoyam naoyam commented Jan 15, 2025

Closes #3710

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naoyam commented Jan 15, 2025

!test

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PR Reviewer Guide 🔍

Here are some key observations to aid the review process:

⏱️ Estimated effort to review: 4 🔵🔵🔵🔵⚪
🧪 PR contains tests
⚡ Recommended focus areas for review

Logic Change

The getPredicates function has been modified to predicate the input ID of a resize operation. This change may have unintended consequences and should be thoroughly reviewed.

std::vector<IterDomain*> predicate_domains = getPredicateDomains(tv, expr);

const IndexingInfo& index_info =
    computeIndex(expr, predicate_domains, for_loops);

const auto& index_map = index_info.index_map;

const std::unordered_map<Val*, Val*> replacement_map_start =
    getPredicateIndexReplacementMap(
        tv,
        for_loops,
        index_map,
        traversalGraph(),
        index_info.traversal_path,
        id_model_,
        /*is_start_predicate=*/true,
        /*unswitched_loop=*/unswitched_loop);

const std::unordered_map<Val*, Val*> replacement_map_stop =
    getPredicateIndexReplacementMap(
        tv,
        for_loops,
        index_map,
        traversalGraph(),
        index_info.traversal_path,
        id_model_,
        /*is_start_predicate=*/false,
        /*unswitched_loop=*/unswitched_loop);

// When resize is involved, predicate its input ID as well to avoid
// redudancy. This is only necessary if a predicated resize is
// preceded by a split, however, for now it's always predicated
// with an exception of static resize. See
// PredicateIndexingTest.SplitThenPad for a concrete example.
for (const auto& [eg, direction] : index_info.traversal_path) {
  auto resize = dynamic_cast<Resize*>(eg->front());
  if (resize == nullptr) {
    continue;
  }

  // TODO: It seems this shouldn't be predicated when the direction is
  // Forward, i.e., when resize ops are propagated from
  // producers to consumers. For example, ResizeTest.SliceThenPadLeftHalf
  // would fail with this. Revisit for the Forward case if necessary.
  if (direction == Direction::Forward) {
    continue;
  }

  // If the input ID is guaranteed to cover the output ID, then
  // the input index should never exceed its boundary.
  if (resize->leftExpand()->isConstInt() &&
      resize->rightExpand()->isConstInt()) {
    auto left_int = resize->leftExpand()->evaluate().as<int64_t>();
    auto right_int = resize->rightExpand()->evaluate().as<int64_t>();
    // If the traversal direction is forward, the predicate is not
    // necessary if both of the left and right factors are
    // non-negative as the ouput ID is guaranteed to cover the
    // input ID. Similarly, if it's backward, it is not necessary
    // if they are non-positive.
    if ((direction == Direction::Forward && left_int >= 0 &&
         right_int >= 0) ||
        (direction == Direction::Backward && left_int <= 0 &&
         right_int <= 0)) {
      continue;
    }
  }

  IterDomain* id_to_predicate =
      direction == Direction::Forward ? resize->out() : resize->in();

  predicate_domains.push_back(id_to_predicate);
}
Test Coverage

New test cases have been added to cover the changes made to the getPredicates function. These tests should be reviewed to ensure they are comprehensive and accurate.

// Check if resize input IDs are predicated. Repro of issue
// https://github.com/NVIDIA/Fuser/issues/3710.
TEST_F(PredicateIndexingTest, SplitThenPad) {
  Fusion fusion;
  FusionGuard fg(&fusion);

  const int64_t i0 = 4;
  const int64_t i1 = 32;

  auto zero = fusion.zeroVal();

  auto tv0 = makeContigConcreteTensor({i0 * i1});
  fusion.addInput(tv0);

  auto tv1 = set(tv0);
  auto tv2 =
      reshape(tv1, {IrBuilder::create<Val>(i0), IrBuilder::create<Val>(i1)});
  auto tv3 = pad(tv2, {zero, IrBuilder::create<Val>(i1)});
  auto tv4 = set(tv3);
  fusion.addOutput(tv4);

  scheduler_tools::propagateResizeToInputs(tv3->definition());

  inlineMost();

  // tv1 should be scheduled as:
  //
  // T1_l_float[iS11{4}, iS13{64}]
  //  logical domain : (iS1{128})
  //  contiguity: t
  //   Outer split: iS1{128} by factor 4 -> iS11{4}, iS12{32}
  //   Resize: iS12{32} by 0 and 32 -> iS13{64}
  // loop domain : (iS11{4}, iS13{64})
  //
  // In addition to its logical ID, the resize input ID should be
  // predicated.

  struct GetReference : AbstractGetReference {
    GetReference(const TensorIndexer& indexer, const IdModel& id_model)
        : AbstractGetReference(indexer, id_model) {}

    Val* getInlinePredicate(TensorView* tv) const override {
      if (tv->name() != 1) {
        return nullptr;
      }

      // Without index hoist and expr simplification, the predicate
      // should look like:
      //
      // (((((((i0 * 32LL) + i1) >= 0LL) &&
      // (((i0 * 32LL) + i1) < 128LL)) &&
      // (i1 >= 0LL)) &&
      // (i1 < 32LL)))

      std::vector<Val*> loop_indices = getLoopIndices(tv, indexer_, for_loops_);

      Val* zero = tv->fusion()->zeroVal();

      auto resize = dynamic_cast<Resize*>(tv->axis(1)->definition());
      NVF_ERROR(resize != nullptr);

      auto logical_idx = addExpr(
          mulExpr(loop_indices.at(0), createInt(i1)), loop_indices.at(1));

      auto resize_idx = loop_indices.at(1);

      return andExpr(
          andExpr(
              andExpr(
                  geExpr(logical_idx, zero),
                  ltExpr(logical_idx, createInt(i0 * i1))),
              geExpr(resize_idx, zero)),
          ltExpr(resize_idx, createInt(i1)));
    }
  };

  PredicateIndexValidator<GetReference>::validate(&fusion, false);

  auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
  auto t0 = at::randn({i0 * i1}, options);
  std::vector<c10::IValue> inputs{t0};

  KernelExecutor ke;
  ke.compile(&fusion, inputs);
  auto outputs = ke.run(inputs);

  testValidate(&fusion, outputs, inputs, __LINE__, __FILE__);
}

TEST_F(PredicateIndexingTest, SplitThenPadTwice) {
  Fusion fusion;
  FusionGuard fg(&fusion);

  const int64_t i0 = 4;
  const int64_t i1 = 32;

  auto zero = fusion.zeroVal();

  auto tv0 = makeContigConcreteTensor({i0 * i1});
  fusion.addInput(tv0);

  auto tv1 = set(tv0);
  auto tv2 =
      reshape(tv1, {IrBuilder::create<Val>(i0), IrBuilder::create<Val>(i1)});
  auto tv3 = pad(tv2, {zero, IrBuilder::create<Val>(1L)});
  auto tv4 = pad(tv3, {IrBuilder::create<Val>(1L), zero});
  auto tv5 = set(tv4);
  fusion.addOutput(tv5);

  scheduler_tools::propagateResizeToInputs(tv3->definition());
  scheduler_tools::propagateResizeToInputs(tv4->definition());

  inlineMost();

  // tv1 should be scheduled as:
  //
  // T1_l_float[iS14{4}, iS18{34}] ca_pos( 2 )
  //  logical domain : (iS1{128})
  //  contiguity: t
  //   Outer split: iS1{128} by factor 4 -> iS14{4}, iS15{32}
  //   Resize: iS15{32} by 0 and 1 -> iS16{33}
  //   Resize: iS16{33} by 1 and 0 -> iS18{34}
  //  loop domain : (iS14{4}, iS18{34})
  //
  // In addition to its logical ID, the two resize input IDs should be
  // predicated.

  struct GetReference : AbstractGetReference {
    GetReference(const TensorIndexer& indexer, const IdModel& id_model)
        : AbstractGetReference(indexer, id_model) {}

    Val* getInlinePredicate(TensorView* tv) const override {
      if (tv->name() != 1) {
        return nullptr;
      }

      // Without index hoist and expr simplification, the predicate
      // should look like:
      //
      // (((((((((i0 * 32LL) + (i1 - 1LL)) >= 0LL) &&
      // (((i0 * 32LL) + (i1 - 1LL)) < 128LL)) &&
      // ((i1 - 1LL) >= 0LL)) &&
      // ((i1 - 1LL) < 33LL)) &&
      // ((i1 - 1LL) >= 0LL)) &&
      // ((i1 - 1LL) < 32LL)))

      std::vector<Val*> loop_indices = getLoopIndices(tv, indexer_, for_loops_);

      Val* zero = tv->fusion()->zeroVal();
      Val* one = tv->fusion()->oneVal();

      auto resize = dynamic_cast<Resize*>(tv->axis(1)->definition());
      NVF_ERROR(resize != nullptr);

      auto logical_idx = addExpr(
          mulExpr(loop_indices.at(0), createInt(i1)),
          subExpr(loop_indices.at(1), one));

      auto resize_idx = subExpr(loop_indices.at(1), one);

      return andExpr(
          andExpr(
              andExpr(
                  andExpr(
                      andExpr(
                          geExpr(logical_idx, zero),
                          ltExpr(logical_idx, createInt(i0 * i1))),
                      geExpr(resize_idx, zero)),
                  ltExpr(resize_idx, createInt(i1 + 1))),
              geExpr(resize_idx, zero)),
          ltExpr(resize_idx, createInt(i1)));
    }
  };

  PredicateIndexValidator<GetReference>::validate(&fusion, false);

  auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
  auto t0 = at::randn({i0 * i1}, options);
  std::vector<c10::IValue> inputs{t0};

  KernelExecutor ke;
  ke.compile(&fusion, inputs);
  auto outputs = ke.run(inputs);

  testValidate(&fusion, outputs, inputs, __LINE__, __FILE__);
}

// Testing a split reshape followed by slice and pad, which is a
// common pattern in RoPE.
TEST_F(PredicateIndexingTest, SplitThenSliceAndPad) {
  Fusion fusion;
  FusionGuard fg(&fusion);

  const int64_t i0 = 4;
  const int64_t i1 = 32;

  auto zero = fusion.zeroVal();

  auto tv0 = makeContigConcreteTensor({i0 * i1});
  fusion.addInput(tv0);

  auto tv1 = set(tv0);
  auto tv2 =
      reshape(tv1, {IrBuilder::create<Val>(i0), IrBuilder::create<Val>(i1)});
  auto tv3 = slice(
      tv2,
      {{zero, IrBuilder::create<Val>(i0)},
       {IrBuilder::create<Val>(i1 / 2), IrBuilder::create<Val>(i1)}});
  auto tv4 = pad(tv3, {zero, IrBuilder::create<Val>(i1 / 2)});
  auto tv5 = set(tv4);
  fusion.addOutput(tv5);

  scheduler_tools::propagateResizeToInputs(tv3->definition());
  scheduler_tools::propagateResizeToInputs(tv4->definition());

  inlineMost();

  // tv1 should be scheduled as:
  //
  // T1_l_float[iS14{4}, iS18{32}] ca_pos( 2 )
  //  logical domain : (iS1{128})
  //  contiguity: t
  //   Outer split: iS1{128} by factor 4 -> iS14{4}, iS15{32}
  //   Resize: iS15{32} by -16 and 0 -> iS16{16}
  //   Resize: iS16{16} by 0 and 16 -> iS18{32}
  //  loop domain : (iS14{4}, iS18{32})
  //
  // In addition to its logical ID, the input of the second resize
  // should be predicated. The first resize should not be predicated
  // as its input can be known to cover the output since the expansion
  // factors are static, so as long as the index of the
  // output is within the boundary, its index should never need to be
  // predicated.

  struct GetReference : AbstractGetReference {
    GetReference(const TensorIndexer& indexer, const IdModel& id_model)
        : AbstractGetReference(indexer, id_model) {}

    Val* getInlinePredicate(TensorView* tv) const override {
      if (tv->name() != 1) {
        return nullptr;
      }

      // Without index hoist and expr simplification, the predicate
      // should look like:
      //
      // (((((((i0 * 32LL) + (i1 + 16LL)) >= 0LL) &&
      // (((i0 * 32LL) + (i1 + 16LL)) < 128LL)) &&
      // (i1 >= 0LL)) &&
      // (i1 < 16LL)))

      std::vector<Val*> loop_indices = getLoopIndices(tv, indexer_, for_loops_);

      Val* zero = tv->fusion()->zeroVal();

      auto resize = dynamic_cast<Resize*>(tv->axis(1)->definition());
      NVF_ERROR(resize != nullptr);

      auto logical_idx = addExpr(
          mulExpr(loop_indices.at(0), createInt(i1)),
          addExpr(loop_indices.at(1), createInt(i1 / 2)));

      auto resize_idx = loop_indices.at(1);

      return andExpr(
          andExpr(
              andExpr(
                  geExpr(logical_idx, zero),
                  ltExpr(logical_idx, createInt(i0 * i1))),
              geExpr(resize_idx, zero)),
          ltExpr(resize_idx, createInt(i1 / 2)));
    }
  };

  PredicateIndexValidator<GetReference>::validate(&fusion, false);

  auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
  auto t0 = at::randn({i0 * i1}, options);
  std::vector<c10::IValue> inputs{t0};

  KernelExecutor ke;
  ke.compile(&fusion, inputs);
  auto outputs = ke.run(inputs);

  testValidate(&fusion, outputs, inputs, __LINE__, __FILE__);
}

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Redundant accesses due to missing predicates in resize
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@naoyam