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test_pointwise.cpp
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test_pointwise.cpp
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// clang-format off
/*
* SPDX-FileCopyrightText: Copyright (c) 2023-present NVIDIA CORPORATION & AFFILIATES.
* All rights reserved.
* SPDX-License-Identifier: BSD-3-Clause
*/
// clang-format on
#include <gmock/gmock-matchers.h>
#include <gtest/gtest.h>
#include <fusion.h>
#include <ir/interface_nodes.h>
#include <ops/all_ops.h>
#include <preseg_passes/mark_aliases_prepare.h>
#include <preseg_passes/optimization_pass.h>
#include <runtime/fusion_executor_cache.h>
#include <scheduler/pointwise_utils.h>
#include <tests/cpp/utils.h>
#include <tests/cpp/validator.h>
namespace nvfuser {
using PointwiseTest = NVFuserTest;
namespace {
int64_t getVecSizeForPointwise(const FusionExecutorCache& executor_cache) {
FusionKernelRuntime* runtime = executor_cache.getMostRecentKernelRuntime();
NVF_CHECK(!runtime->isSegmented());
const PointwiseParams* params = runtime->schedulerHeuristics()
->heuristicsList()
.at(0)
->as<PointwiseParams>();
return params->vectorization_factor;
}
bool hasVectorizationCache(TensorView* tv) {
NVF_CHECK(tv->isFusionInput());
NVF_CHECK(tv->uses().size() == 1);
auto set_expr = dynamic_cast<LoadStoreOp*>(tv->uses().at(0));
NVF_CHECK(set_expr != nullptr && set_expr->opType() == LoadStoreOpType::Set);
auto cached_input = set_expr->out()->as<TensorView>();
NVF_CHECK(cached_input, "expects input to be cached");
for (const auto* id : cached_input->getLoopDomain()) {
if (id->getParallelType() == ParallelType::Vectorize) {
return true;
}
}
return false;
}
class DomainMapUnitTest : public pointwise_utils::DomainMap {
public:
DomainMapUnitTest(Fusion* fusion) : pointwise_utils::DomainMap(fusion) {};
bool testTargetCoverage(TensorView* target_tv, TensorView* reference_tv)
const {
return areAllTargetIdsCoveredBy(target_tv, reference_tv);
}
};
} // namespace
TEST_F(PointwiseTest, VectorizeStrideContiguity2D) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 =
TensorViewBuilder().ndims(2).contiguity({false, true}).build();
fusion->addInput(tv0);
auto tv1 = add(tv0, tv0);
fusion->addOutput(tv1);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
std::vector<std::pair<int, int>> size_and_vec{{17, 1}, {18, 2}, {32, 4}};
for (auto pair : size_and_vec) {
auto size = pair.first;
auto vec = pair.second;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::randn({1000000, size}, options).narrow(1, 0, 16);
auto cg_outputs = executor_cache.runFusionWithInputs({input0});
EXPECT_EQ(getVecSizeForPointwise(executor_cache), vec);
testValidate(fusion, cg_outputs, {input0}, __LINE__, __FILE__);
}
}
TEST_F(PointwiseTest, VectorizeStrideContiguity3D) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 =
TensorViewBuilder().ndims(3).contiguity({false, true, true}).build();
fusion->addInput(tv0);
auto tv1 = add(tv0, tv0);
fusion->addOutput(tv1);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
std::vector<std::pair<int, int>> size_and_vec{{17, 1}, {10, 2}, {16, 4}};
for (auto pair : size_and_vec) {
auto size = pair.first;
auto vec = pair.second;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::randn({1000000, size, 3}, options).narrow(1, 0, 8);
auto cg_outputs = executor_cache.runFusionWithInputs({input0});
EXPECT_EQ(getVecSizeForPointwise(executor_cache), vec);
testValidate(fusion, cg_outputs, {input0}, __LINE__, __FILE__);
}
}
TEST_F(PointwiseTest, VectorizeStrideContiguity5D) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(5)
.contiguity({false, true, false, true, true})
.build();
fusion->addInput(tv0);
auto tv1 = add(tv0, tv0);
fusion->addOutput(tv1);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
std::vector<std::tuple<int, int, int>> sizes_and_vec{
{9, 17, 1}, {9, 10, 2}, {9, 16, 4}};
for (auto tup : sizes_and_vec) {
auto size1 = std::get<0>(tup);
auto size2 = std::get<1>(tup);
auto vec = std::get<2>(tup);
at::Tensor input0 = at::randn({4, size1, 12345, size2, 3}, options)
.narrow(1, 0, 8)
.narrow(3, 0, 4);
auto cg_outputs = executor_cache.runFusionWithInputs({input0});
EXPECT_EQ(getVecSizeForPointwise(executor_cache), vec);
testValidate(fusion, cg_outputs, {input0}, __LINE__, __FILE__);
}
}
// Test that vectorization is properly computed when base pointer is not aligned
// at 16 bytes. This can happen if a tensor is sliced then passed as input.
// See https://github.com/NVIDIA/Fuser/pull/2118
TEST_F(PointwiseTest, VectorizeStrideMisalignedBase) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(5)
.contiguity({false, true, false, true, true})
.build();
fusion->addInput(tv0);
auto tv1 = add(tv0, tv0);
fusion->addOutput(tv1);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
std::vector<std::tuple<int, int, int, int, int>> sizes_strides_align_and_vec{
{4, 4, 4, 4, 4},
{4, 4, 4, 2, 2},
{4, 4, 4, 1, 1},
{4, 4, 2, 4, 2},
{4, 4, 2, 1, 1},
{4, 2, 4, 4, 2},
{4, 2, 4, 1, 1},
{2, 4, 4, 4, 2},
{2, 4, 4, 1, 1},
{2, 2, 2, 4, 2},
{2, 2, 2, 1, 1}};
for (auto tup : sizes_strides_align_and_vec) {
auto size = std::get<0>(tup);
auto stride1 = std::get<1>(tup);
auto stride2 = std::get<2>(tup);
auto align = std::get<3>(tup);
auto vec = std::get<4>(tup);
std::vector<int64_t> shape = {4, 4, 12345, size, 3};
std::vector<int64_t> stride = {
stride1, (int64_t)stride2 * 12345, (int64_t)stride2, 3, 1};
// Create a strided input that is misaligned by "align" elements
// First, find required size of align=0 tensor. Allocate this much plus
// align elements. Then slice and view as aligned tensor.
int64_t alloc_size = 1l;
for (auto i : c10::irange(shape.size())) {
alloc_size += (shape.at(i) - 1) * stride.at(i);
}
alloc_size += align;
at::Tensor flat = at::randn({alloc_size}, options);
at::Tensor input0 =
flat.as_strided(shape, stride, /*storage_offset=*/align);
auto cg_outputs = executor_cache.runFusionWithInputs({input0});
EXPECT_EQ(getVecSizeForPointwise(executor_cache), vec);
testValidate(fusion, cg_outputs, {input0}, __LINE__, __FILE__);
}
}
TEST_F(PointwiseTest, VectorizeStrideContiguitySelfOverlapping) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(5)
.contiguity({false, true, false, true, true})
.build();
fusion->addInput(tv0);
auto tv1 = add(tv0, tv0);
fusion->addOutput(tv1);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
std::vector<std::tuple<int, int, int, int>> sizes_strides_and_vec{
{4, 4, 4, 4},
{4, 4, 2, 2},
{4, 2, 4, 2},
{2, 4, 4, 2},
{4, 4, 1, 1},
{4, 1, 4, 1},
{1, 4, 4, 1},
{2, 2, 2, 2},
{2, 2, 1, 1},
{2, 1, 2, 1},
{1, 2, 2, 1}};
for (auto tup : sizes_strides_and_vec) {
auto size = std::get<0>(tup);
auto stride1 = std::get<1>(tup);
auto stride2 = std::get<2>(tup);
auto vec = std::get<3>(tup);
std::vector<int64_t> shape = {4, 4, 12345, size, 3};
std::vector<int64_t> stride = {
stride1, (int64_t)stride2 * 12345, (int64_t)stride2, 3, 1};
at::Tensor input0 = at::empty_strided(shape, stride, options);
input0.random_();
auto cg_outputs = executor_cache.runFusionWithInputs({input0});
EXPECT_EQ(getVecSizeForPointwise(executor_cache), vec);
testValidate(fusion, cg_outputs, {input0}, __LINE__, __FILE__);
}
}
TEST_F(PointwiseTest, VectorizeAllocationDomain) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(3)
.contiguity({true, true, true})
.strideOrder({2, 0, 1})
.build();
fusion->addInput(tv0);
auto tv1 = add(tv0, IrBuilder::create<Val>(1.0, DataType::Float));
tv1->setAllocationDomain({tv1->axis(0), tv1->axis(2), tv1->axis(1)}, true);
fusion->addOutput(tv1);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 =
at::empty_strided({1024, 128, 25}, {128 * 25, 1, 128}, options);
auto cg_outputs = executor_cache.runFusionWithInputs({input0});
EXPECT_EQ(getVecSizeForPointwise(executor_cache), 4);
testValidate(fusion, cg_outputs, {input0}, __LINE__, __FILE__);
}
// All inputs & outputs share the same allocation domain permutation from root
// domain, but intermediate tv2 isn't specified a stride order. There's also a
// broadcast IterDomain on tv1, which is tricky for vectorization analysis to
// figure out which axes should be excluded from the computation of
// vectorization factor.
TEST_F(PointwiseTest, Issue1567VectorizeAllocationDomain) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(3)
.contiguity({true, true, true})
.strideOrder({2, 0, 1})
.build();
TensorView* tv1 = TensorViewBuilder()
.ndims(3)
.shape({1, -1, 1})
.contiguity({std::nullopt, std::nullopt, true})
.strideOrder({2, 0, 1})
.build();
fusion->addInput(tv0);
fusion->addInput(tv1);
auto tv2 = add(tv0, tv1);
auto tv3 = add(tv2, IrBuilder::create<Val>(1.0, DataType::Float));
tv3->setAllocationDomain({tv3->axis(0), tv3->axis(2), tv3->axis(1)}, true);
fusion->addOutput(tv3);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 =
at::empty_strided({1024, 128, 25}, {128 * 25, 1, 128}, options);
at::Tensor input1 = at::empty_strided({1, 128, 1}, {128, 1, 128}, options);
std::vector<c10::IValue> aten_inputs = {input0, input1};
// NOTE: force pointwise scheduler here just for testing purpose
auto cg_results =
scheduleAndRun(fusion, SchedulerType::PointWise, aten_inputs);
auto pparams = cg_results.heuristic_params->as<PointwiseParams>();
EXPECT_EQ(pparams->vectorization_factor, 4);
EXPECT_TRUE(hasVectorizationCache(tv0));
EXPECT_TRUE(hasVectorizationCache(tv1));
testValidate(fusion, cg_results.outputs, aten_inputs, __LINE__, __FILE__);
}
TEST_F(PointwiseTest, Issue1567VectorizationFactorAnalysisCase0) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(3)
.contiguity({true, true, std::nullopt})
.shape({-1, -1, 1})
.build();
TensorView* tv1 =
TensorViewBuilder().ndims(3).contiguity({true, true, true}).build();
fusion->addInput(tv0);
fusion->addInput(tv1);
auto tv2 = add(tv0, tv1);
fusion->addOutput(tv2);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::randn({1024, 2, 1}, options);
at::Tensor input1 = at::randn({1024, 2, 512}, options);
std::vector<c10::IValue> aten_inputs = {input0, input1};
// NOTE: force pointwise scheduler here just for testing purpose
auto cg_results =
scheduleAndRun(fusion, SchedulerType::PointWise, aten_inputs, false);
auto pparams = cg_results.heuristic_params->as<PointwiseParams>();
EXPECT_EQ(pparams->vectorization_factor, 4);
EXPECT_FALSE(hasVectorizationCache(tv0));
EXPECT_TRUE(hasVectorizationCache(tv1));
testValidate(fusion, cg_results.outputs, aten_inputs, __LINE__, __FILE__);
}
TEST_F(PointwiseTest, Issue1567VectorizationFactorAnalysisCase1) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(3)
.contiguity({true, std::nullopt, true})
.shape({-1, 1, -1})
.build();
TensorView* tv1 =
TensorViewBuilder().ndims(3).contiguity({true, true, true}).build();
fusion->addInput(tv0);
fusion->addInput(tv1);
auto tv2 = add(tv0, tv1);
fusion->addOutput(tv2);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::randn({1024, 1, 2}, options);
at::Tensor input1 = at::randn({1024, 512, 2}, options);
std::vector<c10::IValue> aten_inputs = {input0, input1};
// NOTE: force pointwise scheduler here just for testing purpose
auto cg_results =
scheduleAndRun(fusion, SchedulerType::PointWise, aten_inputs);
auto pparams = cg_results.heuristic_params->as<PointwiseParams>();
EXPECT_EQ(pparams->vectorization_factor, 2);
EXPECT_TRUE(hasVectorizationCache(tv0));
EXPECT_TRUE(hasVectorizationCache(tv1));
testValidate(fusion, cg_results.outputs, aten_inputs, __LINE__, __FILE__);
}
TEST_F(PointwiseTest, Issue1567VectorizationFactorAnalysisCase2) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(3)
.contiguity({true, std::nullopt, true})
.shape({-1, 1, -1})
.build();
TensorView* tv1 = TensorViewBuilder()
.ndims(3)
.contiguity({true, true, true})
.strideOrder({1, 2, 0})
.build();
fusion->addInput(tv0);
fusion->addInput(tv1);
auto tv2 = add(tv0, tv1);
auto tv3 = transpose(tv2, 0, 1);
fusion->addOutput(tv3);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::randn({1024, 1, 2}, options);
at::Tensor input1 = at::empty_strided({1024, 512, 2}, {2, 2048, 1}, options);
std::vector<c10::IValue> aten_inputs = {input0, input1};
// NOTE: force pointwise scheduler here just for testing purpose
auto cg_results =
scheduleAndRun(fusion, SchedulerType::PointWise, aten_inputs);
auto pparams = cg_results.heuristic_params->as<PointwiseParams>();
EXPECT_EQ(pparams->vectorization_factor, 4);
EXPECT_TRUE(hasVectorizationCache(tv0));
EXPECT_TRUE(hasVectorizationCache(tv1));
testValidate(fusion, cg_results.outputs, aten_inputs, __LINE__, __FILE__);
}
TEST_F(PointwiseTest, VIssue1567ectorizationFactorAnalysisCase3) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
TensorView* tv0 = TensorViewBuilder()
.ndims(3)
.contiguity({std::nullopt, true, true})
.shape({1, -1, -1})
.build();
TensorView* tv1 =
TensorViewBuilder().ndims(3).contiguity({true, true, true}).build();
fusion->addInput(tv0);
fusion->addInput(tv1);
auto tv2 = add(tv0, tv1);
auto tv3 = transpose(tv2, 0, 1);
fusion->addOutput(tv3);
FusionExecutorCache executor_cache(std::move(fusion_ptr));
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::randn({1, 1024, 2}, options);
at::Tensor input1 = at::randn({512, 1024, 2}, options);
std::vector<c10::IValue> aten_inputs = {input0, input1};
// NOTE: force pointwise scheduler here just for testing purpose
auto cg_results =
scheduleAndRun(fusion, SchedulerType::PointWise, aten_inputs);
auto pparams = cg_results.heuristic_params->as<PointwiseParams>();
EXPECT_EQ(pparams->vectorization_factor, 2);
EXPECT_TRUE(hasVectorizationCache(tv0));
EXPECT_TRUE(hasVectorizationCache(tv1));
testValidate(fusion, cg_results.outputs, aten_inputs, __LINE__, __FILE__);
}
namespace {
Fusion createPointwiseFusion(bool shard, int sharded_dim = -1) {
Fusion fusion;
FusionGuard fg(&fusion);
// Sharded fusion needs to add an additional sharded axis.
TensorView* tv0 = makeContigTensor(shard ? 4 : 3);
TensorView* tv1 = makeContigTensor(2);
auto tv2 = add(tv0, tv0);
std::vector<bool> bcast_mask;
if (shard) {
bcast_mask = {false, true, false, false};
bcast_mask[sharded_dim] = true;
} else {
bcast_mask = {true, false, false};
}
TensorView* tv3 = broadcast(tv1, bcast_mask);
TensorView* tv4 = add(tv2, tv3);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv4);
if (shard) {
DeviceMesh mesh = DeviceMesh::createForNumDevices(4);
for (TensorView* tv : {tv0, tv2, tv3, tv4}) {
tv->setDeviceMesh(mesh);
tv->axis(sharded_dim)->parallelize(ParallelType::DIDx);
}
tv1->setDeviceMesh(mesh);
}
return fusion;
}
} // namespace
// Check that (1) a sharded pointwise fusion returns the same
// pointwise scheduling parameters as its equivalent
// unsharded fusion and (2) the output is correct.
TEST_F(PointwiseTest, ShardedPointwise) {
int64_t sharded_dim = 0;
std::vector<std::vector<int64_t>> input_sizes = {
{16, 8, 48},
{2, 512, 4096},
{2048, 512, 16},
{65536, 512, 16},
{512, 3, 65536},
};
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
for (auto input_size : input_sizes) {
at::Tensor input0 = at::randn(input_size, options);
at::Tensor input1 = at::randn({input_size[1], input_size[2]}, options);
std::vector<c10::IValue> sharded_inputs = {
input0.unsqueeze(sharded_dim), input1};
auto pwise_scheduler =
SchedulerEntry::makeSchedulerInstance(SchedulerType::PointWise);
Fusion sharded_fusion = createPointwiseFusion(true, sharded_dim);
SchedulerRuntimeInfo sharded_runtime_info(&sharded_fusion, sharded_inputs);
auto sharded_params = pwise_scheduler->computeHeuristics(
&sharded_fusion, sharded_runtime_info);
auto sharded_pparams = sharded_params->as<PointwiseParams>();
Fusion unsharded_fusion = createPointwiseFusion(false);
SchedulerRuntimeInfo unsharded_runtime_info(
&unsharded_fusion, {input0, input1});
auto unsharded_params = pwise_scheduler->computeHeuristics(
&unsharded_fusion, unsharded_runtime_info);
auto unsharded_pparams = unsharded_params->as<PointwiseParams>();
// Note: occasionally one of the compile parameter index types is int64_t
// instead of int which causes PointwiseParams::sameAs to return false,
// despite the pointwise specific parameters being identical, so we just
// explicitly check pointwise schedule params.
EXPECT_EQ(sharded_pparams->break_point, unsharded_pparams->break_point);
EXPECT_EQ(sharded_pparams->split_block, unsharded_pparams->split_block);
EXPECT_EQ(
sharded_pparams->split_grid_y_dim, unsharded_pparams->split_grid_y_dim);
EXPECT_EQ(
sharded_pparams->vectorization_factor,
unsharded_pparams->vectorization_factor);
EXPECT_EQ(
sharded_pparams->flip_grid_binding,
unsharded_pparams->flip_grid_binding);
pwise_scheduler->schedule(&sharded_fusion, sharded_params.get());
KernelExecutor ke;
ke.compile(&sharded_fusion, sharded_inputs, sharded_params->lparams);
auto cg_outputs = ke.run(sharded_inputs, sharded_params->lparams);
testValidate(
&sharded_fusion, cg_outputs, sharded_inputs, __LINE__, __FILE__);
}
}
// Repro of issue #657
TEST_F(PointwiseTest, VectorizeWithBroadcastAndReshape1) {
preseg_passes::OptimizationPassGuard<preseg_passes::MarkAliasesPreparePass>
optimization_guard(false);
auto fusion = std::make_unique<Fusion>();
FusionGuard fg(fusion.get());
// Sizes don't matter as long as they are large enough to trigger
// vectorization
std::vector<int64_t> shape1{1024, 1024};
std::vector<int64_t> shape2{1024, 1024, 4};
std::vector<int64_t> shape3{1024 * 1024 * 4};
auto tv0 = makeContigConcreteTensor(shape1);
fusion->addInput(tv0);
auto tv1 = makeContigConcreteTensor(shape2);
fusion->addInput(tv1);
auto tv2 = broadcast(tv0, {false, false, true});
fusion->addOutput(tv2);
auto tv3 = add(tv1, tv2);
auto tv4 = reshape(tv3, shape2, shape3);
fusion->addOutput(tv4);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
auto input0 = at::randn(shape1, options);
auto input1 = at::randn(shape2, options);
std::vector<c10::IValue> aten_inputs({input0, input1});
FusionExecutorCache executor_cache(std::move(fusion));
auto cg_outputs = executor_cache.runFusionWithInputs(aten_inputs);
EXPECT_EQ(getVecSizeForPointwise(executor_cache), 4);
}
// Repro of issue #657
TEST_F(PointwiseTest, VectorizeWithBroadcastAndReshape2) {
preseg_passes::OptimizationPassGuard<preseg_passes::MarkAliasesPreparePass>
optimization_guard(false);
auto fusion = std::make_unique<Fusion>();
FusionGuard fg(fusion.get());
// Sizes don't matter as long as they are large enough to trigger
// vectorization
std::vector<int64_t> shape1{1024, 1024};
std::vector<int64_t> shape2{1024, 1024, 4};
std::vector<int64_t> shape3{1024 * 1024 * 4};
auto tv0 = makeContigConcreteTensor(shape1);
fusion->addInput(tv0);
auto tv1 = makeContigConcreteTensor(shape1);
fusion->addInput(tv1);
auto tv2 = makeContigConcreteTensor(shape2);
fusion->addInput(tv2);
auto tv3 = broadcast(tv0, {false, false, true});
fusion->addOutput(tv3);
auto tv4 = add(tv3, tv2);
auto tv5 = broadcast(tv1, {false, false, true});
auto tv6 = add(tv4, tv5);
auto tv7 = reshape(tv6, shape2, shape3);
fusion->addOutput(tv7);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
auto input0 = at::randn(shape1, options);
auto input1 = at::randn(shape1, options);
auto input2 = at::randn(shape2, options);
std::vector<c10::IValue> aten_inputs({input0, input1, input2});
FusionExecutorCache executor_cache(std::move(fusion));
auto cg_outputs = executor_cache.runFusionWithInputs(aten_inputs);
EXPECT_EQ(getVecSizeForPointwise(executor_cache), 4);
}
TEST_F(PointwiseTest, VectorizeWithExpandedBroadcast) {
auto fusion = std::make_unique<Fusion>();
FusionGuard fg(fusion.get());
constexpr int64_t kTensorSize = 65536;
TensorView* in = TensorViewBuilder()
.dtype(DataType::Half)
.shape({2, kTensorSize})
.expanded({true, false})
.build();
in->setAllocationDomain({in->axis(1), in->axis(0)}, true);
TensorView* out = add(in, in);
fusion->addInput(in);
fusion->addOutput(out);
auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0);
auto in_tensor =
at::randn({kTensorSize}, options).as_strided({2, kTensorSize}, {0, 1});
FusionExecutorCache executor_cache(std::move(fusion));
auto out_tensors = executor_cache.runFusionWithInputs({in_tensor});
testValidate(
executor_cache.fusion(), out_tensors, {in_tensor}, __LINE__, __FILE__);
EXPECT_GT(getVecSizeForPointwise(executor_cache), 1);
}
using VectUnrollFactors = std::tuple<int64_t, int64_t, int64_t>;
using PointwiseParamsTest = NVFuserFixtureParamTest<VectUnrollFactors>;
TEST_P(PointwiseParamsTest, UnrollOnTopOfVectorize) {
auto fusion = std::make_unique<Fusion>();
FusionGuard fg(fusion.get());
auto tv0 = makeContigTensor(2);
auto tv1 = makeContigTensor(1);
fusion->addInput(tv0);
fusion->addInput(tv1);
auto tv2 = broadcast(tv1, {true, false});
auto tv3 = add(tv0, tv2);
fusion->addOutput(tv3);
int dim0 = 1024;
int dim1 = 2048;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
auto t0 = at::randn({dim0, dim1}, options);
auto t1 = at::randn({dim1}, options);
std::vector<c10::IValue> runtime_inputs{t0, t1};
// Generate heuristics
SchedulerRuntimeInfo runtime_info(fusion.get(), runtime_inputs);
auto scheduler_instance =
SchedulerEntry::makeSchedulerInstance(SchedulerType::PointWise);
auto heuristic_params =
scheduler_instance->computeHeuristics(fusion.get(), runtime_info);
auto pparams = heuristic_params->as<PointwiseParams>();
// Modify heuristics to enforce unroll on top of vectorization
// Set unroll factors from test parameters
auto [vect_factor, unroll_inner, unroll_outer] = GetParam();
pparams->unroll_factor_inner = unroll_inner;
pparams->unroll_factor_outer = unroll_outer;
pparams->vectorization_factor = vect_factor;
// Schedule, compile, run, validate
scheduler_instance->schedule(fusion.get(), pparams);
KernelExecutor ke;
ke.compile(fusion.get(), runtime_inputs, pparams->lparams);
auto cg_outputs = ke.run(runtime_inputs, pparams->lparams);
const auto& lparams = ke.lastLaunchParams();
ASSERT_EQ(lparams.gdimy(), dim0 / unroll_outer);
ASSERT_EQ(
lparams.gdimx(), dim1 / vect_factor / lparams.bdimx() / unroll_inner);
testValidate(fusion.get(), cg_outputs, runtime_inputs, __LINE__, __FILE__);
}
INSTANTIATE_TEST_SUITE_P(
,
PointwiseParamsTest,
::testing::Combine(
testing::Values(1, 4), // vectorization factors
testing::Values(1, 2), // inner unroll factors
testing::Values(1, 2) // outer unroll factors
),
[](const testing::TestParamInfo<VectUnrollFactors>& info) -> std::string {
std::stringstream ss;
ss << "vect_" << std::get<0>(info.param);
ss << "_inner_unroll_" << std::get<1>(info.param);
ss << "_outer_unroll_" << std::get<2>(info.param);
return sanitizeTestName(ss.str());
});
TEST_F(PointwiseTest, VectorizePadLoweringPermuted) {
// Pointwise scheduler applies permutation to restore contiguous memory access
// on reference TV. Vectorization validation requires vectorized operations to
// preserve the allocation domain of their inputs. This test checks that PadOp
// propagates the allocation domain properly.
auto fusion_ptr = std::make_unique<Fusion>();
auto& fusion = *fusion_ptr;
FusionGuard fg(fusion_ptr.get());
// input is permuted
auto tv0 = TensorViewBuilder()
.shape({1024, 1024})
.dtype(DataType::Float)
.contiguity(true)
.strideOrder({0, 1})
.build();
fusion.addInput(tv0);
auto tv1 = pad(tv0, {IrBuilder::create<Val>(4L), IrBuilder::create<Val>(4L)});
auto tv2 = relu(tv1);
fusion.addOutput(tv2);
// output is permuted
tv2->setAllocationDomain({tv2->axis(1), tv2->axis(0)}, true);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
auto t0 =
at::randn({1024 * 1024}, options).as_strided({1024, 1024}, {1, 1024});
std::vector<c10::IValue> aten_inputs({t0});
auto cg_outputs =
scheduleAndRun(&fusion, SchedulerType::PointWise, aten_inputs).outputs;
// check that we vectorize 4
bool found_vectorize = false;
for (auto id : fusion.outputs().at(0)->as<TensorView>()->getLoopDomain()) {
if (id->getParallelType() == ParallelType::Vectorize) {
EXPECT_EQ(id->extent()->evaluate(), 4);
found_vectorize = true;
break;
}
}
EXPECT_TRUE(found_vectorize);
testValidate(&fusion, cg_outputs, aten_inputs, __LINE__, __FILE__);
}
TEST_F(PointwiseTest, DomainMapTestEg0) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
// tv0 {i0, i1}
TensorView* tv0 = makeContigTensor(2);
fusion->addInput(tv0);
// tv1 {i0, i1}
auto tv1 = relu(tv0);
fusion->addOutput(tv1);
// tv2 {i0, b2, i1}
auto tv2 = broadcast(tv1, {false, true, false});
// tv3 {i0, b3{1 ex 4}, i1}
auto tv3 = expand(
tv2,
{tv2->axis(0)->extent(),
IrBuilder::create<Val>(4),
tv2->axis(2)->extent()});
// Note that currently expand doesn't introduce an iter domain operation, so
// we don't see that i4 is produced by realizing the expanded extent of b3{1
// ex 4} tv4 {i0, i4*i1}
auto tv4 = reshape(tv3, {2, 4, 3}, {2, 12});
fusion->addOutput(tv4);
DomainMapUnitTest domain_map(fusion);
// tv4 is not covered by tv1, because the expanded ID i4 participates in
// transformation
EXPECT_FALSE(domain_map.testTargetCoverage(tv4, tv1));
// tv3 is covered by tv1, because the missing ID b3{1 ex 4} is broadcast
EXPECT_TRUE(domain_map.testTargetCoverage(tv3, tv1));
// tv1 is covered by tv4
EXPECT_TRUE(domain_map.testTargetCoverage(tv1, tv4));
// tv1 is not a valid reference
EXPECT_FALSE(domain_map.isValidReference(tv1));
// tv4 is a valid reference
EXPECT_TRUE(domain_map.isValidReference(tv4));
// validate generated kernel
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({4, 7}, options);
std::vector<c10::IValue> aten_inputs = {t0};
// NOTE: force pointwise scheduler here for unit test
auto cg_results =
scheduleAndRun(fusion, SchedulerType::PointWise, aten_inputs);
testValidate(fusion, cg_results.outputs, aten_inputs, __LINE__, __FILE__);
}
TEST_F(PointwiseTest, DomainMapTestEg1) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
// tv0 {i0, i1}
TensorView* tv0 = makeContigTensor(2);
fusion->addInput(tv0);
// tv1 {i2, i0, i1}
TensorView* tv1 = makeContigTensor(3);
fusion->addInput(tv1);
// tv2 {i0*i1}
auto tv2 = reshape(tv0, {2, 4}, {8});
fusion->addOutput(tv2);
// tv3 {b3, i0, i1}
auto tv3 = broadcast(tv0, {true, false, false});
// tv4 {i2, i0, i1}
auto tv4 = add(tv1, tv3);
fusion->addOutput(tv4);
DomainMapUnitTest domain_map(fusion);
// tv4 is not covered by tv2, because it misses i2
EXPECT_FALSE(domain_map.testTargetCoverage(tv4, tv2));
// tv2 is covered by tv4
EXPECT_TRUE(domain_map.testTargetCoverage(tv2, tv4));
// tv2 is not a valid reference
EXPECT_FALSE(domain_map.isValidReference(tv2));
// tv4 is a valid reference
EXPECT_TRUE(domain_map.isValidReference(tv4));
// validate generated kernel
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({2, 4}, options);
at::Tensor t1 = at::randn({3, 2, 4}, options);
std::vector<c10::IValue> aten_inputs = {t0, t1};
// NOTE: force pointwise scheduler here for unit test
auto cg_results =
scheduleAndRun(fusion, SchedulerType::PointWise, aten_inputs);
testValidate(fusion, cg_results.outputs, aten_inputs, __LINE__, __FILE__);
}
TEST_F(PointwiseTest, DomainMapFactory) {
auto fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);
// tv1 {i1}
TensorView* tv0 = makeContigTensor(1);
fusion->addInput(tv0);
// tv1 {i0, i1}
TensorView* tv1 = makeContigTensor(2);
fusion->addInput(tv1);
// tv2 {b2, b3, i1}
auto tv2 = broadcast(tv0, {true, true, false});
// Note: tv1 will be broadcasted to {b2, i0, i1} before the add.
// tv3 {b2, i0, i1}
auto tv3 = add(tv2, tv1);
fusion->addOutput(tv3);
auto size_val = IrBuilder::create<Val>(4.0, DataType::Int);
auto one_val = IrBuilder::create<Val>(1, DataType::Int);
// factory method creates an iter domain out of thin air
// tv4 {i4{4}, b4, i1}
auto tv4 = ones({size_val, one_val, tv0->axis(0)->extent()}, DataType::Float);
// tv5 {i4{4}, i0, i1}
auto tv5 = mul(tv2, tv4);
fusion->addOutput(tv5);
DomainMapUnitTest domain_map(fusion);
// tv4 is not covered by tv3, because it's missing i4{4}
EXPECT_FALSE(domain_map.testTargetCoverage(tv4, tv3));
// tv1 is not covered by tv4, since it's missing i0
EXPECT_FALSE(domain_map.testTargetCoverage(tv1, tv4));
EXPECT_FALSE(domain_map.isValidReference(tv3));
// tv5 has the same IDs as tv4, and is not a valid reference.
EXPECT_FALSE(domain_map.isValidReference(tv5));
FusionExecutorCache executor_cache(std::move(fusion_ptr));
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::empty_strided({25}, {1}, options);
at::Tensor input1 = at::empty_strided({7, 25}, {25, 1}, options);
auto cg_outputs = executor_cache.runFusionWithInputs({input0, input1});
FusionKernelRuntime* runtime = executor_cache.getMostRecentKernelRuntime();
SegmentedFusion* segmented_fusion = runtime->fusionSegments();
// This fusion currently cannot be scheduled as a single kernel. It is
// expected to be segmented as: g{(pointwise)
// inputs: tv0, tv1
// outputs: tv2, tv3
// tv2 = broadcast(tv0)
// tv3 = add (tv2, broadcast(tv1))
// }
//
// g{(pointwise)
// inputs: tv2
// outputs: tv5
// tv4 = full({4, 1, i0})
// tv5 = mul(tv2, tv4)
// }
EXPECT_EQ(segmented_fusion->groups().size(), 2);
for (SegmentedGroup* group : segmented_fusion->groups()) {
const std::vector<Expr*>& exprs = group->exprs();
size_t num_full = std::count_if(exprs.begin(), exprs.end(), [](Expr* expr) {
return expr->isA<FullOp>();
});
if (num_full != 0) {
// this is the segment contains the factory op.
EXPECT_EQ(exprs.size(), 2);
EXPECT_EQ(num_full, 1);
auto binary_op_iter =
std::find_if(exprs.begin(), exprs.end(), [](Expr* expr) {
return expr->isA<BinaryOp>();
});
EXPECT_EQ(
(*binary_op_iter)->as<BinaryOp>()->getBinaryOpType(),
BinaryOpType::Mul);
Fusion* group_fusion = group->getFusion();
// validate that we have a valid reference in the segmented fusion
DomainMapUnitTest group_dm(group_fusion);
EXPECT_EQ(group_fusion->outputs().size(), 1);
EXPECT_TRUE(group_dm.isValidReference(
group_fusion->outputs()[0]->as<TensorView>()));
} else {
// validate segmentation has the correct ops
EXPECT_EQ(exprs.size(), 3);
EXPECT_EQ(
std::count_if(
exprs.begin(),
exprs.end(),
[](Expr* expr) { return expr->isA<BroadcastOp>(); }),
2);
EXPECT_EQ(
std::count_if(
exprs.begin(),
exprs.end(),
[](Expr* expr) { return expr->isA<BinaryOp>(); }),
1);
Fusion* group_fusion = group->getFusion();
auto output_add = std::find_if(
group_fusion->outputs().begin(),
group_fusion->outputs().end(),
[](Val* val) { return val->definition()->isA<BinaryOp>(); });
EXPECT_TRUE(output_add != group_fusion->outputs().end());
DomainMapUnitTest group_dm(group_fusion);
// validate that the segmented fusion choose the add output as the
// reference
EXPECT_TRUE(group_dm.isValidReference((*output_add)->as<TensorView>()));
}
}
testValidate(fusion, cg_outputs, {input0, input1}, __LINE__, __FILE__);
}