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Avoid redundancy with resize ops #3713

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Avoid redundancy with resize ops #3713

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47 changes: 45 additions & 2 deletions csrc/id_model/indexing.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1063,8 +1063,7 @@ std::vector<PredicateInfo> TensorIndexer::getPredicates(
ForLoop* unswitched_loop) const {
const auto& zero_val = tv->fusion()->zeroVal();

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

const IndexingInfo& index_info =
computeIndex(expr, predicate_domains, for_loops);
Expand Down Expand Up @@ -1093,6 +1092,50 @@ std::vector<PredicateInfo> TensorIndexer::getPredicates(
/*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);
}

const std::unordered_map<IterDomain*, ValGroup> contig_domains =
isContigIndexingEnabled()
? getContigDomains(
Expand Down
288 changes: 288 additions & 0 deletions tests/cpp/test_indexing.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -23,6 +23,7 @@
#include <ops/all_ops.h>
#include <scheduler/tools/abstract_tensor.h>
#include <scheduler/tools/inlining.h>
#include <scheduler/tools/loop_domain_scheduler.h>
#include <scheduler/tools/resize_utils.h>
#include <scheduler/utils.h>

Expand Down Expand Up @@ -5497,6 +5498,293 @@ TEST_F(PredicateIndexingTest, VectorizedResizeRotation) {
testValidate(&fusion, outputs, inputs, __LINE__, __FILE__);
}

// 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__);
}

// Repro of issue #3505. The indexing WAR for resize triggered an
// assertion due to loop promotion.
TEST_F(IndexingTest, Issue3505Repro1) {
Expand Down
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