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22401b850b
pytorch/test/cpp/jit/test_gpu.cpp
Michael Suo 22401b850b port all JIT tests to gtest (#45264) 
Summary:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/45264

Context for why we are porting to gtest in: https://github.com/pytorch/pytorch/pull/45018.

This PR completes the process of porting and removes unused files/macros.

Test Plan: Imported from OSS

Reviewed By: ZolotukhinM

Differential Revision: D23901392

Pulled By: suo

fbshipit-source-id: 89526890e1a49462f3f77718f4ee273c5bc578ba
2020-09-25 11:37:43 -07:00

7055 lines
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#if defined(USE_CUDA)
#include <gtest/gtest.h>
#include <torch/csrc/jit/codegen/cuda/arith.h>
#include <torch/csrc/jit/codegen/cuda/codegen.h>
#include <torch/csrc/jit/codegen/cuda/executor.h>
#include <torch/csrc/jit/codegen/cuda/executor_launch_params.h>
#include <torch/csrc/jit/codegen/cuda/expr_evaluator.h>
#include <torch/csrc/jit/codegen/cuda/fusion.h>
#include <torch/csrc/jit/codegen/cuda/ir_all_nodes.h>
#include <torch/csrc/jit/codegen/cuda/ir_graphviz.h>
#include <torch/csrc/jit/codegen/cuda/ir_iostream.h>
#include <torch/csrc/jit/codegen/cuda/ir_utils.h>
#include <torch/csrc/jit/codegen/cuda/kernel_cache.h>
#include <torch/csrc/jit/codegen/cuda/lower2device.h>
#include <torch/csrc/jit/codegen/cuda/mutator.h>
#include <torch/csrc/jit/codegen/cuda/scheduler.h>
#include <torch/csrc/jit/codegen/cuda/transform_replay.h>
#include <torch/csrc/jit/codegen/cuda/transform_rfactor.h>
// fuser and IR parser
#include <torch/csrc/jit/codegen/cuda/parser.h>
#include "torch/csrc/jit/ir/irparser.h"
#include <ATen/cuda/Exceptions.h>
#include <c10/cuda/CUDAStream.h>
#include <iostream>
// Tests go in torch::jit
namespace torch {
namespace jit {
using namespace torch::jit::fuser;
namespace {
TensorView* makeContigTensor(int nDims, DataType dtype = DataType::Float) {
std::vector<IterDomain*> dom;
for (int i = 0; i < nDims; i++)
dom.push_back(new IterDomain(new Int(0), new Int()));
std::vector<bool> contig(dom.size(), true);
return new TensorView(new TensorDomain(dom, contig), dtype);
}
TensorView* makeDummyTensor(int nDims, DataType dtype = DataType::Float) {
// We can uncomment the below statement to test all tests with contiguous
// tensors. return makeContigTensor(nDims, dtype);
std::vector<IterDomain*> dom;
for (int i = 0; i < nDims; i++)
dom.push_back(new IterDomain(new Int(0), new Int()));
return new TensorView(new TensorDomain(dom), dtype);
}
TensorView* makeConcreteTensor(
std::vector<int> sizes,
DataType dtype = DataType::Float) {
// We can uncomment the below statement to test all tests with contiguous
// tensors. return makeContigTensor(nDims, dtype);
std::vector<IterDomain*> dom;
for (size_t i = 0; i < sizes.size(); i++)
dom.push_back(new IterDomain(new Int(0), new Int(sizes[i])));
return new TensorView(new TensorDomain(dom), dtype);
}
TensorView* makeTensorWithContig(
int nDims,
std::vector<bool> contig_info,
DataType dtype = DataType::Float) {
std::vector<IterDomain*> dom;
for (int i = 0; i < nDims; i++)
dom.push_back(new IterDomain(new Int(0), new Int()));
return new TensorView(new TensorDomain(dom, contig_info), dtype);
}
void checkIntValue(
StatefulExpressionEvaluator& evaluator,
Val* val,
Int::ScalarType expected_value) {
TORCH_CHECK(val->isAnInt());
const auto actual_value = evaluator.inferValue(val);
TORCH_CHECK(actual_value.has_value());
TORCH_CHECK(actual_value.value() == expected_value);
}
} // namespace
// 1. Test cases are void() functions.
// 2. They start with the prefix `test`
// A few smoke tests for IrGraphGenerator
// (These tests exercise IrGraphGenerator through a non-trivial IR,
// to make sure that it runs w/o crashing. The actual output is not
// validated)
TEST(NVFuserTest, IrGraphGenerator_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Make sure we can handle empty IRs
TORCH_CHECK(!IrGraphGenerator::toGraphviz(
&fusion, IrGraphGenerator::DetailLevel::Basic)
.empty());
// Construct an interesting IR
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv2 = add(tv0, new Float(3.141));
TensorView* tv3 = broadcast(tv0, {false, true, false, true});
TensorView* tv4 = reductionOp(BinaryOpType::Add, {2}, new Float(0), tv3);
TensorView* tv5 = clamp(tv4, new Float(0.f), new Float(1.f));
TensorView* tv6 = add(tv2, tv2);
// Another checkpoint before adding outputs
TORCH_CHECK(!IrGraphGenerator::toGraphviz(
&fusion, IrGraphGenerator::DetailLevel::Explicit)
.empty());
fusion.addOutput(tv6);
tv4->axis(2)->parallelize(ParallelType::BIDy);
tv6->merge(0);
tv6->split(0, 4);
tv6->axis(0)->parallelize(ParallelType::BIDx);
tv5->reorder({{-1, 0}});
tv2->computeAt(tv6, 1);
// Another checkpoint with more node types
TORCH_CHECK(!IrGraphGenerator::toGraphviz(
&fusion, IrGraphGenerator::DetailLevel::ComputeOnly)
.empty());
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = static_cast<TensorView*>(val);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
// Final IR graph
TORCH_CHECK(!IrGraphGenerator::toGraphviz(
&fusion, IrGraphGenerator::DetailLevel::Verbose)
.empty());
}
TEST(NVFuserTest, FusionDispatch_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Float* f = new Float{2.f};
std::stringstream ss1, ss2, ss3;
ss1 << f;
ss2 << static_cast<Val*>(f);
ss3 << static_cast<Statement*>(f);
TORCH_CHECK(
ss1.str().compare(ss2.str()) == 0 && ss1.str().compare(ss3.str()) == 0,
"Error with dispatch system where results differ by passing Float* vs Val* vs Statement*.");
}
// Evaluate basic scalar operations with constant values
TEST(NVFuserTest, FusionExprEvalConstants_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
StatefulExpressionEvaluator evaluator(&fusion);
auto* a = new Int(7);
auto* b = new Int(3);
checkIntValue(evaluator, neg(a), -7);
checkIntValue(evaluator, add(a, b), 10);
checkIntValue(evaluator, neg(mul(sub(a, b), div(a, b))), -8);
checkIntValue(evaluator, mod(a, b), 1);
checkIntValue(evaluator, ceilDiv(a, b), 3);
}
// Evaluate basic scalar operations with bound values
TEST(NVFuserTest, FusionExprEvalBindings_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
StatefulExpressionEvaluator evaluator(&fusion);
auto* a = new Int();
auto* b = new Int();
auto* c = add(a, b);
auto* d = neg(ceilDiv(c, b));
auto* e = new Int(0);
// trying to evaluate before binding should give empty results
TORCH_CHECK(!evaluator.inferValue(a).has_value());
TORCH_CHECK(!evaluator.inferValue(d).has_value());
evaluator.safeBind(a, 7);
evaluator.safeBind(b, 3);
// can't bind to the results of expressions
ASSERT_ANY_THROW(evaluator.safeBind(c, 100));
// can't bind to concrete values
ASSERT_ANY_THROW(evaluator.safeBind(e, 100));
checkIntValue(evaluator, c, 10);
checkIntValue(evaluator, sub(a, b), 4);
checkIntValue(evaluator, mod(a, b), 1);
checkIntValue(evaluator, ceilDiv(a, b), 3);
checkIntValue(evaluator, d, -4);
// Reset evaluation context
evaluator = StatefulExpressionEvaluator(&fusion);
evaluator.safeBind(a, 2);
evaluator.safeBind(b, 5);
checkIntValue(evaluator, c, 7);
checkIntValue(evaluator, sub(a, b), -3);
checkIntValue(evaluator, mod(a, b), 2);
checkIntValue(evaluator, ceilDiv(a, b), 1);
checkIntValue(evaluator, d, -2);
}
// Evaluate expressions in a simple IR
TEST(NVFuserTest, FusionExprEvalBasic_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Create a non-trivial IR
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
fusion.addInput(tv0);
fusion.addInput(tv1);
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
fusion.addOutput(tv3);
tv3->split(0, 4);
tv0->computeAt(tv3, 1);
tv1->computeAt(tv3, 1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(1)->parallelize(ParallelType::Unroll);
tv3->axis(1)->parallelize(ParallelType::Unroll);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
// 1. Create an evaluator
StatefulExpressionEvaluator evaluator(&fusion);
// 2. Bind values
//
// IMPORTANT:
// a. The bindings are only as stable as the Vals are in the fusion graph
// b. You must use the original (rootDomain) extents
// (ex. `tv0->getRootDomain()[0]->extent()`
// instead of `tv0->axis(0)->extent()`)
//
evaluator.safeBind(tv0->getRootDomain()[0]->extent(), 6);
evaluator.safeBind(tv0->getRootDomain()[1]->extent(), 128);
evaluator.safeBind(tv1->getRootDomain()[0]->extent(), 6);
evaluator.safeBind(tv1->getRootDomain()[1]->extent(), 128);
// 3. Evaluate and check result values
TORCH_CHECK(tv2->domain()->nDims() == 3);
checkIntValue(evaluator, tv2->axis(0)->rawExtent(), 2);
checkIntValue(evaluator, tv2->axis(1)->rawExtent(), 4);
checkIntValue(evaluator, tv2->axis(2)->rawExtent(), 128);
TORCH_CHECK(tv3->domain()->nDims() == 3);
checkIntValue(evaluator, tv3->axis(0)->rawExtent(), 2);
checkIntValue(evaluator, tv3->axis(1)->rawExtent(), 4);
checkIntValue(evaluator, tv3->axis(2)->rawExtent(), 128);
}
// Evaluate expressions in a more complex IR
TEST(NVFuserTest, FusionExprEvalComplex_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(-1.0));
TensorView* tv2 = add(tv0, new Float(3.0));
TensorView* tv3 = mul(tv0, new Float(2.0));
TensorView* tv4 = add(tv2, tv1);
TensorView* tv5 = add(tv4, tv3);
TensorView* tv6 = add(tv0, tv3);
fusion.addOutput(tv5);
fusion.addOutput(tv6);
tv5->reorder({{-1, 0}});
tv6->split(0, 5);
tv5->merge(0);
// 1. Create an evaluator
StatefulExpressionEvaluator evaluator(&fusion);
// 2. Bind values
evaluator.safeBind(tv0->getRootDomain()[0]->extent(), 129);
evaluator.safeBind(tv0->getRootDomain()[1]->extent(), 127);
// Evaluate and check extent values
TORCH_CHECK(tv0->domain()->nDims() == 2);
checkIntValue(evaluator, tv0->axis(0)->rawExtent(), 129);
checkIntValue(evaluator, tv0->axis(1)->rawExtent(), 127);
TORCH_CHECK(tv3->domain()->nDims() == 2);
checkIntValue(evaluator, tv3->axis(0)->rawExtent(), 129);
checkIntValue(evaluator, tv3->axis(1)->rawExtent(), 127);
TORCH_CHECK(tv4->domain()->nDims() == 2);
checkIntValue(evaluator, tv4->axis(0)->rawExtent(), 129);
checkIntValue(evaluator, tv4->axis(1)->rawExtent(), 127);
TORCH_CHECK(tv5->domain()->nDims() == 1);
checkIntValue(evaluator, tv5->axis(0)->rawExtent(), 16383);
TORCH_CHECK(tv6->domain()->nDims() == 3);
checkIntValue(evaluator, tv6->axis(0)->rawExtent(), 26);
checkIntValue(evaluator, tv6->axis(1)->rawExtent(), 5);
checkIntValue(evaluator, tv6->axis(2)->rawExtent(), 127);
}
// Evaluate expressions post lowering
TEST(NVFuserTest, FusionExprEvalPostLower_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Create a non-trivial IR
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
fusion.addInput(tv0);
fusion.addInput(tv1);
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
fusion.addOutput(tv3);
tv3->split(0, 4);
tv0->computeAt(tv3, 1);
tv1->computeAt(tv3, 1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(1)->parallelize(ParallelType::Unroll);
tv3->axis(1)->parallelize(ParallelType::Unroll);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
auto* bid_x = add(tv3->axis(0)->rawExtent(), new Int(0));
auto* tid_x = add(tv3->axis(-1)->rawExtent(), new Int(0));
// Lower
GpuLower gpulw(&fusion);
// 1. Create an evaluation context
StatefulExpressionEvaluator evaluator(&fusion);
// 2. Bind values
evaluator.safeBind(tv0->getRootDomain()[0]->extent(), 6);
evaluator.safeBind(tv0->getRootDomain()[1]->extent(), 128);
evaluator.safeBind(tv1->getRootDomain()[0]->extent(), 6);
evaluator.safeBind(tv1->getRootDomain()[1]->extent(), 128);
// 3. Evaluate and check result values
TORCH_CHECK(tv2->domain()->nDims() == 3);
checkIntValue(evaluator, tv2->axis(0)->rawExtent(), 2);
checkIntValue(evaluator, tv2->axis(1)->rawExtent(), 4);
checkIntValue(evaluator, tv2->axis(2)->rawExtent(), 128);
TORCH_CHECK(tv3->domain()->nDims() == 3);
checkIntValue(evaluator, tv3->axis(0)->rawExtent(), 2);
checkIntValue(evaluator, tv3->axis(1)->rawExtent(), 4);
checkIntValue(evaluator, tv3->axis(2)->rawExtent(), 128);
checkIntValue(evaluator, bid_x, 2);
checkIntValue(evaluator, tid_x, 128);
}
TEST(NVFuserTest, FusionClear_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// 1. Create a dummy IR
{
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
fusion.addInput(tv0);
fusion.addInput(tv1);
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
fusion.addOutput(tv3);
tv3->split(0, 4);
tv0->computeAt(tv3, 1);
tv1->computeAt(tv3, 1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(1)->parallelize(ParallelType::Unroll);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
}
// 2. Clear the IR
fusion.clear();
TORCH_CHECK(fusion.exprs().empty());
TORCH_CHECK(fusion.vals().empty());
TORCH_CHECK(fusion.inputs().empty());
TORCH_CHECK(fusion.outputs().empty());
TORCH_CHECK(!fusion.hasReduction());
TORCH_CHECK(!fusion.hasBlockReduction());
TORCH_CHECK(!fusion.hasGridReduction());
// 3. Rebuild the IR
{
TensorView* tv0 = makeDummyTensor(3);
TensorView* tv1 = makeDummyTensor(3);
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv3);
// tv3 [i0, i1, i2]
tv3->reorder({{0, 2}, {2, 0}});
// tv3 [i2, i1, i0]
tv3->split(-1, 4);
// tv3 [i2, i1, i0outer, i0inner{4}]
tv3->reorder({{2, 0}, {3, 1}, {0, 3}});
// tv3 [i0outer, i0inner{4}, i1, i2]
tv0->computeAt(tv3, -1);
tv1->computeAt(tv3, -1);
tv3->axis(1)->parallelize(ParallelType::BIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input1 = at::randn({16, 8, 8}, options);
at::Tensor input2 = at::randn_like(input1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input1, input2});
at::Tensor tv2_ref = input2 + 2.0;
at::Tensor output_ref = input1 + tv2_ref;
TORCH_CHECK(output_ref.equal(outputs[0]));
}
TEST(NVFuserTest, FusionCopy_CUDA) {
Fusion original_fusion;
// Create the test IR
{
FusionGuard fg(&original_fusion);
auto tv0 = makeDummyTensor(3);
auto tv1 = makeDummyTensor(3);
auto tv2 = add(tv1, new Float(2.0));
auto tv3 = sub(add(tv0, mul(tv2, tv2)), tv2);
original_fusion.addInput(tv0);
original_fusion.addInput(tv1);
original_fusion.addOutput(tv3);
tv3->reorder({{0, 2}, {2, 0}});
tv3->split(-1, 4);
tv3->reorder({{2, 0}, {3, 1}, {0, 3}});
tv0->computeAt(tv3, -1);
tv1->computeAt(tv3, -1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
}
// Test copy before lowering
Fusion clone = original_fusion;
// Compare IR dumps
std::stringstream original_ir;
std::stringstream clone_ir;
original_ir << original_fusion;
clone_ir << clone;
ASSERT_EQ(original_ir.str(), clone_ir.str());
// Lower original fusion
std::string original_kernel;
{
// TODO(kir): remove this guard once we implement the cuda codegen visitor
FusionGuard fg(&original_fusion);
original_kernel =
codegen::generateCudaKernel(GpuLower(&original_fusion).kernel());
}
// Make sure the "before lowering" clone was not mutated
// while lowering the original fusion IR
std::stringstream before_lowering_ir;
before_lowering_ir << clone;
ASSERT_EQ(original_ir.str(), before_lowering_ir.str());
// Test copy after lowering (including assignment operator)
Fusion before_lowering = clone;
clone = original_fusion;
// Compare IR dumps
std::stringstream original_lowered_ir;
std::stringstream clone_lowered_ir;
original_lowered_ir << original_fusion;
clone_lowered_ir << clone;
ASSERT_EQ(original_lowered_ir.str(), clone_lowered_ir.str());
// Lower the "before lowering" and compare kernels
std::string clone_kernel;
{
// TODO(kir): remove this guard once we implement the cuda codegen visitor
FusionGuard fg(&before_lowering);
clone_kernel =
codegen::generateCudaKernel(GpuLower(&before_lowering).kernel());
}
ASSERT_EQ(original_kernel, clone_kernel);
}
TEST(NVFuserTest, FusionMove_CUDA) {
Fusion fusion;
// Create the test IR
{
FusionGuard fg(&fusion);
auto tv0 = makeDummyTensor(3);
auto tv1 = makeDummyTensor(3);
auto tv2 = add(tv1, new Float(2.0));
auto tv3 = sub(add(tv0, mul(tv2, tv2)), tv2);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv3);
tv3->reorder({{0, 2}, {2, 0}});
tv3->split(-1, 4);
tv3->reorder({{2, 0}, {3, 1}, {0, 3}});
tv0->computeAt(tv3, -1);
tv1->computeAt(tv3, -1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
}
std::stringstream original_ir;
original_ir << fusion;
// Test move before lowering
Fusion another_fusion = std::move(fusion);
// Check that the original fusion is "empty"
//
// IMPORTANT: these checks assume knowledge of the internal
// implementation of the move operations. General uses
// should only assume that the moved-from object is in
// a valid, but unspecified state. This is similar to the
// standard library containers:
// https://en.cppreference.com/w/cpp/utility/move
//
TORCH_CHECK(fusion.exprs().empty());
TORCH_CHECK(fusion.vals().empty());
TORCH_CHECK(fusion.inputs().empty());
TORCH_CHECK(fusion.outputs().empty());
// clear() has no pre-conditions so it's valid to call on a moved-from object
fusion.clear();
// Compare IR dumps
std::stringstream another_ir;
another_ir << another_fusion;
ASSERT_EQ(original_ir.str(), another_ir.str());
// Lower the fusion IR
GpuLower lower(&another_fusion);
std::stringstream lowered_ir;
lowered_ir << another_fusion;
// Test move assignment after lowering
fusion = std::move(another_fusion);
// Compare IR dumps
std::stringstream moved_lowered_ir;
moved_lowered_ir << fusion;
ASSERT_EQ(lowered_ir.str(), moved_lowered_ir.str());
}
TEST(NVFuserTest, FusionSimpleArith_CUDA) {
std::stringstream ss1, ss2;
Fusion fusion;
FusionGuard fg(&fusion);
Float* f1 = new Float(1.f);
Float* f2 = new Float{2.f};
Float* f3 = new Float();
// Disrupt the fusion to make sure guard works well
{
Fusion fusion2;
FusionGuard fg(&fusion2);
Float* f1 = new Float(1.f);
Float* f2 = new Float(2.f);
add(f1, f2);
ss2 << fusion2;
}
new BinaryOp(BinaryOpType::Add, f3, f1, f2);
ss1 << fusion;
TORCH_CHECK(
ss1.str().compare(ss2.str()) == 0,
"Error where explicit add nodes don't match implicit add nodes.");
}
TEST(NVFuserTest, FusionSimpleTypePromote_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Float* f4 = new Float{4.f};
Int* i1 = new Int{3};
auto f5 = add(f4, i1);
TORCH_CHECK(f5->getDataType() == DataType::Float);
}
class ZeroMutator : public OptOutMutator {
public:
Statement* mutate(Float* f) {
if (f->isConst() && *(f->value()) == 1.0)
return new Float(0.0);
return f;
}
void mutate(Fusion* f) {
OptOutMutator::mutate(f);
}
};
TEST(NVFuserTest, FusionMutator_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Float* f4 = new Float{1.f};
Int* i1 = new Int{3};
Val* f5 = add(f4, i1);
ZeroMutator mutator;
mutator.mutate(&fusion);
Val* lhs = static_cast<BinaryOp*>(fusion.origin(f5))->lhs();
TORCH_CHECK(
lhs->getValType().value() == ValType::Scalar &&
lhs->getDataType().value() == DataType::Float);
Float* flhs = static_cast<Float*>(lhs);
TORCH_CHECK(flhs->value().value() == 0.f);
}
TEST(NVFuserTest, FusionRegister_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Float* v1 = new Float{1.f};
Float* v2 = new Float{2.f};
Val* v3 = binaryOp(BinaryOpType::Add, v1, v2);
Val* v4 = binaryOp(BinaryOpType::Add, v1, v2);
TORCH_CHECK(v1->name() + 1 == v2->name());
TORCH_CHECK(v2->name() + 1 == v3->name());
TORCH_CHECK(v3->name() + 1 == v4->name());
TORCH_CHECK(fusion.origin(v3)->name() + 1 == fusion.origin(v4)->name());
}
// dummy expr with 2 outputs only for toposort test.
struct DummyExpr : public Expr {
~DummyExpr() = default;
DummyExpr(Val* _outlhs, Val* _outrhs, Val* _lhs, Val* _rhs)
: Expr(ExprType::UnaryOp) // Not terribly safe...
{
addOutput(_outlhs);
addOutput(_outrhs);
addInput(_lhs);
addInput(_rhs);
this->name_ = FusionGuard::getCurFusion()->registerExpr(this);
}
DummyExpr(const DummyExpr& other) = delete;
DummyExpr& operator=(const DummyExpr& other) = delete;
DummyExpr(DummyExpr&& other) = delete;
DummyExpr& operator=(DummyExpr&& other) = delete;
};
TEST(NVFuserTest, FusionTopoSort_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// e0: v3, v2 = dummy(v1, v0)
// e1: v4 = add(v3, v2)
// e2: v5 = add(v2, v4)
// e3: v6 = add(v5, v5)
Float* v0 = new Float{1.f};
Float* v1 = new Float{2.f};
Float* v2 = new Float();
Float* v3 = new Float();
Float* v4 = new Float();
Float* v5 = new Float();
Float* v6 = new Float();
Expr* e0 = new DummyExpr(v3, v2, v1, v0);
Expr* e1 = new BinaryOp(BinaryOpType::Add, v4, v3, v2);
Expr* e2 = new BinaryOp(BinaryOpType::Add, v5, v2, v4);
Expr* e3 = new BinaryOp(BinaryOpType::Add, v6, v5, v5);
std::vector<Expr*> exprs = fusion.exprs();
TORCH_CHECK(exprs.size() == 4);
TORCH_CHECK(exprs[0] == e0);
TORCH_CHECK(exprs[1] == e1);
TORCH_CHECK(exprs[2] == e2);
TORCH_CHECK(exprs[3] == e3);
fusion.addOutput(v2);
exprs = fusion.exprs(true);
TORCH_CHECK(exprs.size() == 1);
TORCH_CHECK(exprs[0] == e0);
fusion.addOutput(v5);
exprs = fusion.exprs(true);
TORCH_CHECK(exprs[0] == e0);
TORCH_CHECK(exprs[1] == e1);
TORCH_CHECK(exprs[2] == e2);
fusion.addOutput(v4);
exprs = fusion.exprs(true);
TORCH_CHECK(exprs[0] == e0);
TORCH_CHECK(exprs[1] == e1);
TORCH_CHECK(exprs[2] == e2);
fusion.addOutput(v3);
exprs = fusion.exprs(true);
TORCH_CHECK(exprs[0] == e0);
TORCH_CHECK(exprs[1] == e1);
TORCH_CHECK(exprs[2] == e2);
fusion.addOutput(v6);
exprs = fusion.exprs(true);
TORCH_CHECK(exprs.size() == 4);
TORCH_CHECK(exprs[0] == e0);
TORCH_CHECK(exprs[1] == e1);
TORCH_CHECK(exprs[2] == e2);
TORCH_CHECK(exprs[3] == e3);
TORCH_CHECK(fusion.origin(v2)->name() == 0);
TORCH_CHECK(fusion.origin(v3)->name() == 0);
TORCH_CHECK(fusion.origin(v4)->name() == 1);
TORCH_CHECK(fusion.origin(v5)->name() == 2);
TORCH_CHECK(fusion.origin(v6)->name() == 3);
}
TEST(NVFuserTest, FusionTensor_CUDA) {
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
Fusion fusion;
FusionGuard fg(&fusion);
{
auto tensor = at::randn({2, 3, 4, 5}, options);
auto tensor_type = TensorType::create(tensor);
auto fuser_tensor = new TensorView(tensor_type);
TORCH_CHECK((int64_t)fuser_tensor->nDims() == tensor.dim());
TORCH_CHECK(fuser_tensor->getDataType().value() == DataType::Float);
TORCH_CHECK(fuser_tensor->domain() != nullptr);
for (int i = 0; i < static_cast<int>(fuser_tensor->nDims()); i++) {
// size 1 dimension are makred as broadcast
TORCH_CHECK(
fuser_tensor->axis(i)->isBroadcast() == (tensor.sizes()[i] == 1));
// check contiguity information;
TORCH_CHECK(fuser_tensor->domain()->contiguity()[i]);
}
}
// TensorType::create fills stride_properties, which helps us to mark
// IterDomain properly
// Note: implementation could change, depending on how much we want to invest
// in our home-brew contiguity coalescing. For now let's make sure that we
// properly test what we are using.
{
auto tensor = at::randn({4, 4, 4}, options);
auto sliced_tensor = tensor.slice(1, 0, -1, 2);
auto tensor_type = TensorType::create(sliced_tensor);
auto fuser_tensor = new TensorView(tensor_type);
TORCH_CHECK((int64_t)fuser_tensor->nDims() == tensor.dim());
TORCH_CHECK(fuser_tensor->getDataType().value() == DataType::Float);
TORCH_CHECK(fuser_tensor->domain() != nullptr);
for (int i = 0; i < static_cast<int>(fuser_tensor->nDims()); i++) {
// size 1 dimension are makred as broadcast
TORCH_CHECK(fuser_tensor->axis(i)->isBroadcast() == false);
}
TORCH_CHECK(fuser_tensor->domain()->contiguity()[0]);
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[1]);
TORCH_CHECK(fuser_tensor->domain()->contiguity()[2]);
}
{
auto tensor = at::randn({2, 3, 4, 5}, options);
auto permuted_tensor = tensor.permute({0, 3, 1, 2});
auto tensor_type = TensorType::create(permuted_tensor);
auto fuser_tensor = new TensorView(tensor_type);
TORCH_CHECK((int64_t)fuser_tensor->nDims() == tensor.dim());
TORCH_CHECK(fuser_tensor->getDataType().value() == DataType::Float);
TORCH_CHECK(fuser_tensor->domain() != nullptr);
for (int i = 0; i < static_cast<int>(fuser_tensor->nDims()); i++) {
// size 1 dimension are makred as broadcast
TORCH_CHECK(fuser_tensor->axis(i)->isBroadcast() == false);
}
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[0]);
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[1]);
TORCH_CHECK(fuser_tensor->domain()->contiguity()[2]);
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[3]);
}
}
TEST(NVFuserTest, FusionFilterVals_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
auto tv0 = makeDummyTensor(1);
auto tv1 = makeDummyTensor(1);
auto scalar0 = new Float(0);
auto scalar1 = new Int(0);
auto scalar2 = new Int(1);
const std::vector<Val*> vals = {tv0, scalar0, tv1, scalar1, scalar2};
std::vector<TensorView*> tvs(
ir_utils::filterByType<TensorView>(vals).begin(),
ir_utils::filterByType<TensorView>(vals).end());
TORCH_CHECK(tvs.size() == 2);
TORCH_CHECK(tvs[0] == tv0);
TORCH_CHECK(tvs[1] == tv1);
std::vector<Float*> floats(
ir_utils::filterByType<Float>(vals).begin(),
ir_utils::filterByType<Float>(vals).end());
TORCH_CHECK(floats.size() == 1);
TORCH_CHECK(floats[0] == scalar0);
std::vector<Int*> ints(
ir_utils::filterByType<Int>(vals).begin(),
ir_utils::filterByType<Int>(vals).end());
TORCH_CHECK(ints.size() == 2);
TORCH_CHECK(ints[0] == scalar1);
TORCH_CHECK(ints[1] == scalar2);
TORCH_CHECK(
ir_utils::filterByType<Expr>(vals).begin() ==
ir_utils::filterByType<Expr>(vals).end(),
"Not expecting any results");
}
TEST(NVFuserTest, FusionTVSplit_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv = makeDummyTensor(3);
tv = tv->split(2, 2);
TORCH_CHECK(tv->nDims() == 4);
Expr* outer = tv->axis(2)->extent()->getOrigin();
TORCH_CHECK(
outer->getExprType().value() == ExprType::BinaryOp &&
static_cast<BinaryOp*>(outer)->getBinaryOpType() ==
BinaryOpType::CeilDiv &&
static_cast<BinaryOp*>(outer)->lhs()->sameAs(
tv->getRootDomain()[2]->extent()) &&
static_cast<Int*>(static_cast<BinaryOp*>(outer)->rhs())
->sameAs(new Int(2)));
IterDomain* inner = static_cast<IterDomain*>(tv->axis(3));
TORCH_CHECK(
inner->extent()->isScalar() &&
static_cast<Int*>(inner->extent())->isConst() &&
static_cast<Int*>(inner->extent())->value().value() == 2);
}
TEST(NVFuserTest, FusionTVMerge_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv = makeDummyTensor(3);
tv = tv->merge(1);
Expr* axisOp = tv->axis(1)->extent()->getOrigin();
TORCH_CHECK(
tv->nDims() == 2 && axisOp->getExprType() == ExprType::BinaryOp &&
static_cast<BinaryOp*>(axisOp)->getBinaryOpType() == BinaryOpType::Mul &&
static_cast<BinaryOp*>(axisOp)->lhs() ==
tv->getRootDomain()[1]->extent() &&
static_cast<BinaryOp*>(axisOp)->rhs() ==
tv->getRootDomain()[2]->extent());
}
TEST(NVFuserTest, FusionTVReorder_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
std::unordered_map<int, int> shift_right{{-1, 0}};
std::unordered_map<int, int> shift_left{{0, -1}};
std::unordered_map<int, int> shift_left_2{{0, -1}, {1, 0}, {2, 1}};
std::unordered_map<int, int> swap{{0, 2}, {2, 0}};
auto tv = makeDummyTensor(3);
std::vector<IterDomain*> ref;
ref = std::vector<IterDomain*>(
tv->domain()->domain().begin(), tv->domain()->domain().end());
tv->reorder(shift_left);
for (int i = 0; i < (int)tv->nDims(); i++)
TORCH_CHECK(ref[i]->sameAs(tv->axis(i - 1)));
tv = makeDummyTensor(3);
ref = std::vector<IterDomain*>(
tv->domain()->domain().begin(), tv->domain()->domain().end());
tv->reorder(shift_left);
for (int i = 0; i < (int)tv->nDims(); i++)
TORCH_CHECK(ref[i]->sameAs(tv->axis(i - 1)));
tv = makeDummyTensor(3);
ref = std::vector<IterDomain*>(
tv->domain()->domain().begin(), tv->domain()->domain().end());
tv->reorder(shift_right);
TORCH_CHECK(ref[ref.size() - 1]->sameAs(tv->axis(0)));
for (int i = 1; i < (int)tv->nDims(); i++)
TORCH_CHECK(ref[i - 1]->sameAs(tv->axis(i)));
tv = makeDummyTensor(3);
ref = std::vector<IterDomain*>(
tv->domain()->domain().begin(), tv->domain()->domain().end());
tv->reorder(swap);
TORCH_CHECK(ref[0]->sameAs(tv->axis(2)));
TORCH_CHECK(ref[2]->sameAs(tv->axis(0)));
TORCH_CHECK(ref[1]->sameAs(tv->axis(1)));
}
TEST(NVFuserTest, FusionEquality_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Float* fval1 = new Float();
Float* fval1_copy = fval1;
Float* fval2 = new Float();
Float* fone = new Float(1.0);
TORCH_CHECK(fval1->sameAs(fval1_copy));
TORCH_CHECK(!fval1->sameAs(fval2));
TORCH_CHECK(!fone->sameAs(fval1));
TORCH_CHECK(fone->sameAs(new Float(1.0)));
Int* ival1 = new Int();
Int* ival1_copy = ival1;
Int* ival2 = new Int();
Int* ione = new Int(1);
TORCH_CHECK(ival1->sameAs(ival1_copy));
TORCH_CHECK(!ival1->sameAs(ival2));
TORCH_CHECK(!ione->sameAs(ival1));
TORCH_CHECK(ione->sameAs(new Int(1)));
BinaryOp* add1 = new BinaryOp(BinaryOpType::Add, new Float(), fval1, ival1);
BinaryOp* add1_copy =
new BinaryOp(BinaryOpType::Add, new Float(), fval1, ival1);
BinaryOp* sub1 = new BinaryOp(BinaryOpType::Sub, new Float(), fval1, ival1);
UnaryOp* neg1 = new UnaryOp(UnaryOpType::Neg, new Float(), fval1);
UnaryOp* neg2 = new UnaryOp(UnaryOpType::Neg, new Float(), fval2);
UnaryOp* neg1_copy = new UnaryOp(UnaryOpType::Neg, new Float(), fval1);
TORCH_CHECK(add1->sameAs(add1_copy));
TORCH_CHECK(!add1->sameAs(sub1));
TORCH_CHECK(neg1->sameAs(neg1_copy));
TORCH_CHECK(!static_cast<Expr*>(neg1)->sameAs(add1));
TORCH_CHECK(!neg1->sameAs(neg2));
}
TEST(NVFuserTest, FusionDependency_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Float* f0 = new Float(0.f);
Float* f1 = new Float(1.f);
auto f2 = add(f0, f1);
auto f3 = add(f2, f2);
Float* f4 = new Float(4.f);
Float* f5 = new Float(5.f);
auto f6 = add(f4, f5);
Float* f7 = new Float(7.f);
Float* f8 = new Float(8.f);
auto f9 = add(f7, f8);
auto f10 = add(f6, f9);
auto f11 = add(f3, f10);
TORCH_CHECK(DependencyCheck::isDependencyOf(f0, f11));
TORCH_CHECK(DependencyCheck::isDependencyOf(f1, f11));
TORCH_CHECK(DependencyCheck::isDependencyOf(f2, f11));
TORCH_CHECK(DependencyCheck::isDependencyOf(f3, f11));
TORCH_CHECK(DependencyCheck::isDependencyOf(f6, f11));
TORCH_CHECK(DependencyCheck::isDependencyOf(f9, f11));
TORCH_CHECK(DependencyCheck::isDependencyOf(f0, f2));
TORCH_CHECK(DependencyCheck::isDependencyOf(f2, f3));
TORCH_CHECK(DependencyCheck::isDependencyOf(f4, f6));
TORCH_CHECK(DependencyCheck::isDependencyOf(f8, f10));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f11, f0));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f11, f1));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f11, f2));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f11, f3));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f11, f4));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f11, f5));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f2, f0));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f3, f2));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f6, f4));
TORCH_CHECK(!DependencyCheck::isDependencyOf(f10, f8));
auto dep_chain = DependencyCheck::getSingleDependencyChain(f0, f11);
TORCH_CHECK(dep_chain.back() == f11);
dep_chain.pop_back();
TORCH_CHECK(dep_chain.back() == f3);
dep_chain.pop_back();
TORCH_CHECK(dep_chain.back() == f2);
dep_chain.pop_back();
dep_chain = DependencyCheck::getSingleDependencyChain(f6, f11);
TORCH_CHECK(dep_chain.back() == f11);
dep_chain.pop_back();
TORCH_CHECK(dep_chain.back() == f10);
dep_chain.pop_back();
dep_chain = DependencyCheck::getSingleDependencyChain(f4, f11);
TORCH_CHECK(dep_chain.back() == f11);
dep_chain.pop_back();
TORCH_CHECK(dep_chain.back() == f10);
dep_chain.pop_back();
TORCH_CHECK(dep_chain.back() == f6);
dep_chain.pop_back();
dep_chain = DependencyCheck::getSingleDependencyChain(f11, f2);
TORCH_CHECK(dep_chain.empty());
}
TEST(NVFuserTest, FusionParser_CUDA) {
auto g = std::make_shared<Graph>();
const auto graph0_string = R"IR(
graph(%0 : Float(2:1),
%1 : Float(2:1)):
%c0 : Float(2:1) = aten::mul(%0, %1)
%d0 : Float(2:1) = aten::mul(%c0, %0)
return (%d0))IR";
torch::jit::parseIR(graph0_string, g.get());
// strides are not yet supported in the irparser.
for (auto val : g->block()->inputs()) {
if (val->isCompleteTensor())
val->setType(val->type()->cast<TensorType>()->contiguous());
}
for (auto node : g->block()->nodes()) {
for (auto val : node->outputs()) {
if (val->isCompleteTensor())
val->setType(val->type()->cast<TensorType>()->contiguous());
}
}
auto fusion = fuser::cuda::parseJitIR(g);
FusionGuard fg(fusion.get());
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input1 = at::randn({16}, options);
at::Tensor input2 = at::randn({16}, options);
fuser::cuda::scheduleFusion(fusion.get(), {input1, input2});
// CONSIDER:
// 1. this can be moved to a dedicated "golden" file
// 2. use a fuzzy compare (ignore non-significant whitespaces for example)
const std::string expected_kernel = R"(
__global__ void CUDAGeneratedKernel(Tensor<float, 1> T0, Tensor<float, 1> T1, Tensor<float, 1> T3) {
float T2[1];
if ((((((blockIdx.x * 1) + (1 - 1)) * 128) + threadIdx.x) < T0.size[0])) {
for(size_t i6 = 0; i6 < 1; ++i6) {
T2[i6]
= T0[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)]
* T1[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)];
T3[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)]
= T2[i6]
* T0[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)];
}
} else {
for(size_t i6 = 0; i6 < 1; ++i6) {
if ((((((blockIdx.x * 1) + i6) * 128) + threadIdx.x) < T0.size[0])) {
T2[i6]
= T0[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)]
* T1[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)];
}
if ((((((blockIdx.x * 1) + i6) * 128) + threadIdx.x) < T0.size[0])) {
T3[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)]
= T2[i6]
* T0[((((blockIdx.x * 1) + i6) * 128) + threadIdx.x)];
}
}
}
}
)";
const std::string actual_kernel =
"\n" + codegen::generateCudaKernel(GpuLower(fusion.get()).kernel());
if (expected_kernel.size() != actual_kernel.size() ||
expected_kernel.compare(actual_kernel) != 0) {
std::cerr
<< " Codegen mismatch, codegen possibly changed, or is incorrect. "
<< " \n ========= EXPECTED ========= \n"
<< expected_kernel << "\n========= ACTUAL ========== \n"
<< actual_kernel << "\n=================" << std::endl;
TORCH_CHECK(false);
}
cuda::FusionExecutor fe;
fe.compileFusion(fusion.get());
auto outputs = fe.runFusion({input1, input2});
at::Tensor output_ref = input1 * input2 * input1;
TORCH_CHECK(output_ref.equal(outputs[0]));
}
TEST(NVFuserTest, FusionForLoop_CUDA) {
// TODO(kir): re-enable this test
// due to the current "GpuLower guard" approach, we can only create
// kernel IR during GpuLower::lower()
#if 0
Fusion fusion;
FusionGuard fg(&fusion);
const auto TV0 = new TensorView(
new TensorDomain({new IterDomain(new Int(0), new Int(16))}),
DataType::Float);
const auto TV1 = new TensorView(
new TensorDomain({new IterDomain(new Int(0), new Int(16))}),
DataType::Float);
fusion.addInput(TV0);
fusion.addInput(TV1);
auto ID0 = new kir::IterDomain(new IterDomain(new Int(0), new Int(8)));
TensorView* TV2 = add(TV0, TV1);
BinaryOp* op = static_cast<BinaryOp*>(TV2->getOrigin());
fusion.addOutput(TV2);
auto fl = new kir::ForLoop(new kir::Int(c10::nullopt), ID0, {op});
std::stringstream result;
std::stringstream ref;
result << fl;
ref << "for(size_t i3{0}; i3 < iS{8}; ++i3 ) {\nT2[ iS{16} ] = T0[ iS{16} ] + T1[ iS{16} ]\n}";
if (result.str().compare(ref.str()) == 0) {
std::stringstream err_msg;
err_msg << "ForLoop printing has changed or something has gone wrong. "
<< result.str() << "\n does not match reference: " << ref.str()
<< std::endl;
TORCH_CHECK(false, err_msg.str());
}
#endif
}
TEST(NVFuserTest, FusionCodeGen_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(3);
new BinaryOp(BinaryOpType::Add, tv0, new Float(0.0), new Float(1.0));
TensorView* tv1 = add(tv0, new Float(2.0));
TensorView* tv2 = add(tv1, new Float(3.0));
fusion.addOutput(tv2);
//[I0, I1, I2]
tv2 = tv2->split(0, 4);
//[I0o, I0i{4}, I1, I2]
tv2 = tv2->merge(1);
//[I0o, I0i{4}*I1, I2]
tv2 = tv2->split(-1, 2);
//[I0o, I0i{4}*I1, I2o, I2i{2}]
tv2 = tv2->reorder({{0, 1}, {1, 0}, {3, 2}});
//[I0i{4}*I1, I0o, I2i{2}, I2o]
tv0->computeAt(tv2, -1);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor output = at::empty({16, 8, 8}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({}, {output});
at::Tensor output_ref = at::zeros_like(output, options);
output_ref = output_ref + 0.0 + 1.0 + 2.0 + 3.0;
TORCH_CHECK(output_ref.equal(output));
}
TEST(NVFuserTest, FusionCodeGen2_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(3);
TensorView* tv1 = makeDummyTensor(3);
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv3);
//[I0, I1, I2]
tv3->reorder({{0, 2}, {2, 0}});
//[I2, I1, I0]
tv3->split(-1, 4);
//[I2, I1, I0o, I0i{4}]
tv3->reorder({{2, 0}, {3, 1}, {0, 3}});
// I0o, I0i{4}, I1, I2]
tv0->computeAt(tv3, -1);
tv1->computeAt(tv3, -1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input1 = at::randn({16, 8, 8}, options);
at::Tensor input2 = at::randn_like(input1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input1, input2});
at::Tensor tv2_ref = input2 + 2.0;
at::Tensor output_ref = input1 + tv2_ref;
TORCH_CHECK(output_ref.equal(outputs[0]));
}
TEST(NVFuserTest, FusionSimplePWise_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// dimensionality of the problem
int nDims = 3;
// Set up your input tensor views
TensorView* tv0 = makeContigTensor(nDims);
TensorView* tv1 = makeContigTensor(nDims);
// Register your inputs
fusion.addInput(tv0);
fusion.addInput(tv1);
// Do math with it, it returns a `Val*` but can be static_casted back to
// TensorView
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
// Register your outputs
fusion.addOutput(tv3);
// Do transformations, remember, transformations are outputs to inputs
// This doesn't have to be in this order
tv3->merge(1);
tv3->merge(0);
// Split by n_threads
tv3->split(0, 128);
tv3->split(0, 4);
// For all inputs, computeAt the output inline, temporaries should be squeezed
// between them
tv0->computeAt(tv3, -1);
tv1->computeAt(tv3, -1);
// Parallelize TV3
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv3->axis(-2)->parallelize(ParallelType::Unroll);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input1 = at::randn({64, 2, 128}, options);
at::Tensor input2 = at::rand_like(input1);
at::Tensor output = at::empty_like(input1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input1, input2}, {output});
at::Tensor tv2_ref = input2 + 2.0;
at::Tensor output_ref = input1 + tv2_ref;
TORCH_CHECK(output_ref.equal(output));
}
TEST(NVFuserTest, FusionExecKernel_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
// Register your inputs
fusion.addInput(tv0);
fusion.addInput(tv1);
// Do math with it, it returns a `Val*` but can be static_casted back to
// TensorView
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
// Register your outputs
fusion.addOutput(tv3);
tv3->merge(0);
tv3->split(0, 128);
tv3->split(0, 4);
// For all inputs, computeAt the output inline, temporaries should be squeezed
// between them
tv0->computeAt(tv3, 1);
tv1->computeAt(tv3, 1);
// Parallelize TV3
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(1)->parallelize(ParallelType::Unroll);
tv3->axis(1)->parallelize(ParallelType::Unroll);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input1 = at::ones({1, 128}, options);
at::Tensor input2 = at::ones_like(input1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input1, input2});
at::Tensor check = at::full({1, 128}, 4, options);
;
TORCH_CHECK(outputs[0].equal(check));
}
int ceilDiv_(int a, int b) {
return (a + b - 1) / b;
}
TEST(NVFuserTest, FusionAdvancedComputeAt_CUDA) {
// Case 1
// tv1 = tv0 * 0.5
// tv2 = tv1 * -1
// tv3 = tv1 + 3
// tv4 = tv1 * 2
// tv5 = tv3 + tv2
// tv6 = tv5 + tv4
// tv7 = tv1 + tv4
{
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(0.5));
TensorView* tv2 = mul(tv1, new Float(-1.0));
TensorView* tv3 = add(tv1, new Float(3.0));
TensorView* tv4 = mul(tv1, new Float(2.0));
TensorView* tv5 = add(tv3, tv2);
TensorView* tv6 = add(tv5, tv4);
TensorView* tv7 = add(tv1, tv4);
fusion.addOutput(tv6);
fusion.addOutput(tv7);
// Lets setup to actually run
tv7->merge(0);
tv7->split(0, 128);
tv7->split(0, 4);
tv7->axis(0)->parallelize(ParallelType::BIDx);
tv0->computeAt(tv7, 1);
TORCH_CHECK(tv1->hasComputeAt() && tv1->nDims() == 3);
TORCH_CHECK(tv2->getComputeAtView() == tv5 && tv2->nDims() == 3);
TORCH_CHECK(tv3->getComputeAtView() == tv5 && tv3->nDims() == 3);
TORCH_CHECK(tv4->hasComputeAt() && tv4->nDims() == 3);
TORCH_CHECK(tv5->getComputeAtView() == tv6 && tv5->nDims() == 3);
TORCH_CHECK(tv6->getComputeAtView() == tv7 && tv6->nDims() == 3);
TORCH_CHECK(!tv7->hasComputeAt());
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = static_cast<TensorView*>(val);
tv->axis(1)->parallelize(ParallelType::Unroll);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({129, 127}, options);
auto t1 = t0.mul({0.5});
auto t2 = t1.mul({-1.0});
auto t3 = t1.add({3.0});
auto t4 = t1.mul({2.0});
auto t5 = t3.add(t2);
auto t6 = t5.add(t4);
auto t7 = t1.add(t4);
at::Tensor kernel_tv6 = at::empty_like(t0, options);
at::Tensor kernel_tv7 = at::empty_like(t0, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {kernel_tv6, kernel_tv7});
TORCH_CHECK(at::allclose(kernel_tv6, t6));
TORCH_CHECK(at::allclose(kernel_tv7, t7));
}
// Case 2
// tv1 = tv0 * -1
// tv2 = tv0 + 3
// tv3 = tv0 * 2
// tv4 = tv2 + tv1
// tv5 = tv4 + tv3
// tv6 = tv5 + tv3
{
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(-1.0));
TensorView* tv2 = add(tv0, new Float(3.0));
TensorView* tv3 = mul(tv0, new Float(2.0));
TensorView* tv4 = add(tv2, tv1);
TensorView* tv5 = add(tv4, tv3);
TensorView* tv6 = add(tv5, tv3);
fusion.addOutput(tv5);
fusion.addOutput(tv6);
// Lets setup to actually run
tv6->merge(0);
tv6->split(0, 128);
tv6->split(0, 4);
tv6->axis(0)->parallelize(ParallelType::BIDx);
tv0->computeAt(tv6, 1);
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = static_cast<TensorView*>(val);
tv->axis(1)->parallelize(ParallelType::Unroll);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({129, 127}, options);
auto t1 = t0.mul({-1.0});
auto t2 = t0.add({3.0});
auto t3 = t0.mul({2.0});
auto t4 = t2.add(t1);
auto t5 = t4.add(t3);
auto t6 = t5.add(t3);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0});
TORCH_CHECK(at::allclose(outputs[0], t5));
TORCH_CHECK(at::allclose(outputs[1], t6));
}
// Case 3
// T2 = T1 * 0.979361
// T3 = T2 * T0
{
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(4);
fusion.addInput(tv0);
TensorView* tv1 = makeDummyTensor(4);
fusion.addInput(tv1);
TensorView* tv2 = mul(tv1, new Float(.979361));
TensorView* tv3 = mul(tv2, tv0);
fusion.addOutput(tv3);
// Lets setup to actually run
while (tv3->nDims() > 1)
tv3->merge(0);
tv3->split(0, 128);
tv3->split(0, 4);
tv0->computeAt(tv3, 1);
tv1->computeAt(tv3, 1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = static_cast<TensorView*>(val);
tv->axis(1)->parallelize(ParallelType::Unroll);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({129, 127, 63, 65}, options);
at::Tensor t1 = at::rand_like(t0, options);
auto t2 = t1.mul({0.979361});
auto t3 = t2.mul(t0);
at::Tensor kernel_tv3 = at::empty_like(t0, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0, t1}, {kernel_tv3});
TORCH_CHECK(at::allclose(kernel_tv3, t3));
}
// Case 4
// T4 = T2 - T3
// T5 = T1 + T4
// T6 = T5 - T0
{
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(4);
fusion.addInput(tv0);
TensorView* tv1 = makeDummyTensor(4);
fusion.addInput(tv1);
TensorView* tv2 = makeDummyTensor(4);
fusion.addInput(tv2);
TensorView* tv3 = makeDummyTensor(4);
fusion.addInput(tv3);
TensorView* tv4 = sub(tv2, tv3);
TensorView* tv5 = add(tv1, tv4);
TensorView* tv6 = sub(tv5, tv0);
fusion.addOutput(tv6);
// Lets setup to actually run
while (tv6->nDims() > 1)
tv6->merge(0);
tv6->split(0, 128);
tv6->split(0, 4);
tv0->computeAt(tv6, 1);
tv1->computeAt(tv6, 1);
tv2->computeAt(tv6, 1);
tv3->computeAt(tv6, 1);
tv6->axis(0)->parallelize(ParallelType::BIDx);
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = static_cast<TensorView*>(val);
tv->axis(1)->parallelize(ParallelType::Unroll);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({129, 127, 63, 65}, options);
at::Tensor t1 = at::rand_like(t0, options);
at::Tensor t2 = at::rand_like(t0, options);
at::Tensor t3 = at::rand_like(t0, options);
auto t4 = t2.sub(t3);
auto t5 = t1.add(t4);
auto t6 = t5.sub(t0);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1, t2, t3});
TORCH_CHECK(at::allclose(outputs[0], t6));
}
// Case 5
// tv2 = tv0 + 2.0
// tv3 = tv1 * tv2
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = makeDummyTensor(2);
fusion.addInput(tv1);
TensorView* tv2 = add(tv0, new Float(2.0));
TensorView* tv3 = mul(tv1, tv2);
fusion.addOutput(tv3);
tv3->merge(0);
tv3->split(-1, 8);
tv3->split(-1, 4);
tv2->computeAt(tv3, 1);
tv2->split(-1, 4); // Kernel will break without this split
tv3->axis(0)->parallelize(ParallelType::BIDx);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({63, 65}, options);
at::Tensor t1 = at::rand_like(t0, options);
auto t2 = t0.add(2.0);
auto t3 = t1.mul(t2);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
TORCH_CHECK(at::allclose(outputs[0], t3));
}
}
TEST(NVFuserTest, FusionComputeAtMultiConsumers_CUDA) {
// tv1 = tv0 * 0.5
// tv2 = tv1 * -1
// tv3 = tv2 * -2
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(0.5));
TensorView* tv2 = mul(tv1, new Float(-1.0));
TensorView* tv3 = mul(tv1, new Float(-2.0));
fusion.addOutput(tv2);
fusion.addOutput(tv3);
// This computeAt will affect tv2 as well, even though tv2 is not in
// the data-flow path between tv1 and tv3. The reason is that tv1 is
// now computed at tv3, so tv2 must also be computed at the same
// location. Overall, what will happen is basically we merge
// expressions of all tensors and compute them in a single loop
// nest.
TensorView* computeAtTarget = tv3;
computeAtTarget->split(0, 128);
tv1->computeAt(computeAtTarget, 1);
TensorView* affected_tensors[] = {tv1, tv2, tv3};
for (auto tv : affected_tensors) {
TORCH_CHECK(tv->nDims() == computeAtTarget->nDims());
}
// Note that tv2 is also computed at tv3.
TORCH_CHECK(tv1->getComputeAtView() == computeAtTarget);
TORCH_CHECK(tv2->getComputeAtView() == tv3);
TORCH_CHECK(!tv3->hasComputeAt());
computeAtTarget->axis(0)->parallelize(ParallelType::BIDx);
for (auto tv : affected_tensors) {
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({1000}, options);
auto t1 = t0 * 0.5;
auto t2 = t1 * -1.0;
auto t3 = t1 * -2.0;
at::Tensor kernel_tv2 = at::empty_like(t0, options);
at::Tensor kernel_tv3 = at::empty_like(t0, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {kernel_tv2, kernel_tv3});
TORCH_CHECK(at::allclose(kernel_tv2, t2));
TORCH_CHECK(at::allclose(kernel_tv3, t3));
}
// Similar to ComputeAtMultiConsumers, but with a common consumer.
TEST(NVFuserTest, FusionComputeAtCommonConsumer1_CUDA) {
// tv1 = tv0 * 0.5
// tv2 = tv1 * -1
// tv3 = tv2 * -2
// tv4 = tv2 + tv3
// tv5 = tv4 * 5
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(0.5));
TensorView* tv2 = mul(tv1, new Float(-1.0));
TensorView* tv3 = mul(tv1, new Float(-2.0));
TensorView* tv4 = add(tv2, tv3);
TensorView* tv5 = mul(tv4, new Float(5.0));
fusion.addOutput(tv3);
fusion.addOutput(tv4);
fusion.addOutput(tv5);
// Computing tv1 at tv3. This will affect tv2 as discussed in
// ComplexComputeAt1. Additionally, in this case, notice that tv4 is
// the common consumer of tv2 and tv3, so they are computed at
// tv4. The indirect propagation of the computeAt should stop at the
// common consumer, and no further change should occur. More
// specifically, tv4 and tv5 should not have a computeAt tensor.
TensorView* computeAtTarget = tv3;
computeAtTarget->split(0, 128);
tv1->computeAt(computeAtTarget, 1);
TensorView* affected_tensors[] = {tv1, tv2, tv3, tv4};
for (auto tv : affected_tensors) {
TORCH_CHECK(tv->nDims() == computeAtTarget->nDims());
}
TORCH_CHECK(tv1->getComputeAtView() == computeAtTarget);
TORCH_CHECK(tv2->getComputeAtView() == tv4);
TORCH_CHECK(tv3->getComputeAtView() == tv4);
TORCH_CHECK(!tv4->hasComputeAt());
TORCH_CHECK(!tv5->hasComputeAt());
computeAtTarget->axis(0)->parallelize(ParallelType::BIDx);
for (auto tv : affected_tensors) {
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({1000}, options);
auto t1 = t0 * 0.5;
auto t2 = t1 * -1.0;
auto t3 = t1 * -2.0;
auto t4 = t2 + t3;
auto t5 = t4 * 5.0;
at::Tensor kernel_tv3 = at::empty_like(t0, options);
at::Tensor kernel_tv4 = at::empty_like(t0, options);
at::Tensor kernel_tv5 = at::empty_like(t0, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {kernel_tv3, kernel_tv4, kernel_tv5});
TORCH_CHECK(at::allclose(kernel_tv3, t3));
TORCH_CHECK(at::allclose(kernel_tv4, t4));
TORCH_CHECK(at::allclose(kernel_tv5, t5));
}
TEST(NVFuserTest, FusionComputeAtCommonConsumer2_CUDA) {
// tv1 = tv0 * 0.5
// tv2 = tv1 * -1
// tv3 = tv2 * -1
// tv4 = tv1 + 4
// tv5 = tv3 + tv4
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(0.5));
TensorView* tv2 = mul(tv1, new Float(-1.0));
TensorView* tv3 = mul(tv2, new Float(-1.0));
TensorView* tv4 = add(tv1, new Float(4.0));
TensorView* tv5 = add(tv3, tv4);
fusion.addOutput(tv5);
TensorView* computeAtTarget = tv3;
computeAtTarget->merge(0);
computeAtTarget->split(0, 128);
computeAtTarget->split(0, 4);
computeAtTarget->axis(0)->parallelize(ParallelType::BIDx);
// This computeAt will affect all tensors including tv3, tv4 and
// tv5, even though it appears to impact only tv1 and tv2. The
// reason is that tv1 is now computed at tv3, so tv4 must also be
// computed at the same location. Similarly, the consumer of tv4,
// tv5, must also be computed at the same location. Overall, what
// will happen is basically we merge expressions of all tensors and
// compute them in a single loop nest. Internally, this will be
// realized by making all tensors, except for those in the path
// between tv1 and tv3, computed at tv5, which we call the common
// consumer.
tv1->computeAt(computeAtTarget, 1);
// All tensors should have the same dimenionality as the target
for (Val* val : fusion.vals()) {
if (fusion.hasInput(val) ||
val->getValType().value() != ValType::TensorView) {
continue;
}
TensorView* tv = val->as<TensorView>();
TORCH_CHECK(tv->nDims() == computeAtTarget->nDims());
}
TORCH_CHECK(tv1->getComputeAtView() == tv2);
TORCH_CHECK(tv2->getComputeAtView() == tv3);
// tv3 and tv4 are computed at tv5
TORCH_CHECK(tv3->getComputeAtView() == tv5);
TORCH_CHECK(tv4->getComputeAtView() == tv5);
TORCH_CHECK(!tv5->hasComputeAt());
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = val->as<TensorView>();
tv->axis(1)->parallelize(ParallelType::Unroll);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({129, 127}, options);
auto t1 = t0.mul({0.5});
auto t2 = t1.mul({-1.0});
auto t3 = t2.mul({-1.0});
auto t4 = t1.add({4.0});
auto t5 = t3 + t4;
at::Tensor kernel_tv5 = at::empty_like(t0, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {kernel_tv5});
TORCH_CHECK(at::allclose(kernel_tv5, t5));
}
// Similar to the above common consumer test but adds an additional
// tensor that has no common consumer with the other tensors.
TEST(NVFuserTest, FusionComputeAtCommonConsumer3_CUDA) {
// tv1 = tv0 * 0.5
// tv2 = tv1 * -1
// tv3 = tv2 * -1
// tv4 = tv1 + 4
// tv5 = tv2 + tv3
// tv6 = tv1 + 6
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(0.5));
TensorView* tv2 = mul(tv1, new Float(-1.0));
TensorView* tv3 = mul(tv2, new Float(-1.0));
TensorView* tv4 = add(tv1, new Float(4.0));
TensorView* tv5 = add(tv3, tv4);
TensorView* tv6 = add(tv1, new Float(6.0));
fusion.addOutput(tv5);
fusion.addOutput(tv6);
TensorView* computeAtTarget = tv3;
computeAtTarget->merge(0);
computeAtTarget->split(0, 128);
computeAtTarget->split(0, 4);
computeAtTarget->axis(0)->parallelize(ParallelType::BIDx);
// This will have the same impact on the tensors except for tv5 and
// tv6. tv6 does not have any common consumer with the computeAt
// target, but since it uses tv1, it must be also computed at the
// same location as the other impacted tensors. We can either make
// tv5 computed at tv6 or tv6 computed at tv5. In this case, tv5
// should be computed at tv6 just because the current implementation
// orders the computeAt relationship based on the order in which
// tensors are specified as outputs.
tv1->computeAt(computeAtTarget, 1);
// All tensors should have the same dimenionality as the target
for (Val* val : fusion.vals()) {
if (fusion.hasInput(val) ||
val->getValType().value() != ValType::TensorView) {
continue;
}
TensorView* tv = val->as<TensorView>();
TORCH_CHECK(tv->nDims() == computeAtTarget->nDims());
}
TORCH_CHECK(tv1->getComputeAtView() == tv2);
TORCH_CHECK(tv2->getComputeAtView() == tv3);
// tv3 and tv4 are computed at tv5
TORCH_CHECK(tv3->getComputeAtView() == tv5);
TORCH_CHECK(tv4->getComputeAtView() == tv5);
// tv5 should be computed at tv6 since tv5 is added as an output
// before tv6. If we call fusion.addOutput(tv6) first, tv6 should be
// computed at tv5.
TORCH_CHECK(tv5->getComputeAtView() == tv6);
TORCH_CHECK(!tv6->hasComputeAt());
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = val->as<TensorView>();
tv->axis(1)->parallelize(ParallelType::Unroll);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({129, 127}, options);
auto t1 = t0.mul({0.5});
auto t2 = t1.mul({-1.0});
auto t3 = t2.mul({-1.0});
auto t4 = t1.add({4.0});
auto t5 = t3 + t4;
auto t6 = t1.add({6.0});
at::Tensor kernel_tv5 = at::empty_like(t0, options);
at::Tensor kernel_tv6 = at::empty_like(t0, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {kernel_tv5, kernel_tv6});
TORCH_CHECK(at::allclose(kernel_tv5, t5));
TORCH_CHECK(at::allclose(kernel_tv6, t6));
}
// Similar to ComputeAtCommonConsumer1 but with an addtiona ltensor
// that does not have data dependency with the consumer.
TEST(NVFuserTest, FusionComputeAtNoCommonConsumer_CUDA) {
// tv1 = tv0 * 0.5
// tv2 = tv1 * -1
// tv3 = tv1 * -2
// tv4 = tv2 + tv3
// tv5 = tv4 * 5
// tv6 = tv1 * 6
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(0.5));
TensorView* tv2 = mul(tv1, new Float(-1.0));
TensorView* tv3 = mul(tv1, new Float(-2.0));
TensorView* tv4 = add(tv2, tv3);
TensorView* tv5 = mul(tv4, new Float(5.0));
// Notice that tv6 is not a consumer of tv4.
TensorView* tv6 = mul(tv1, new Float(6.0));
fusion.addOutput(tv3);
fusion.addOutput(tv4);
fusion.addOutput(tv5);
fusion.addOutput(tv6);
TensorView* computeAtTarget = tv3;
computeAtTarget->split(0, 128);
tv1->computeAt(computeAtTarget, 1);
TensorView* affected_tensors[] = {tv1, tv2, tv3, tv4, tv6};
for (auto tv : affected_tensors) {
TORCH_CHECK(tv->nDims() == computeAtTarget->nDims());
}
TORCH_CHECK(tv1->getComputeAtView() == computeAtTarget);
TORCH_CHECK(tv2->getComputeAtView() == tv4);
TORCH_CHECK(tv3->getComputeAtView() == tv4);
TORCH_CHECK(tv4->getComputeAtView() == tv5);
TORCH_CHECK(tv5->getComputeAtView() == tv6);
TORCH_CHECK(!tv6->hasComputeAt());
computeAtTarget->axis(0)->parallelize(ParallelType::BIDx);
for (auto tv : affected_tensors) {
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({1000}, options);
auto t1 = t0 * 0.5;
auto t2 = t1 * -1.0;
auto t3 = t1 * -2.0;
auto t4 = t2 + t3;
auto t5 = t4 * 5.0;
auto t6 = t1 * 6.0;
at::Tensor kernel_tv3 = at::empty_like(t0, options);
at::Tensor kernel_tv4 = at::empty_like(t0, options);
at::Tensor kernel_tv5 = at::empty_like(t0, options);
at::Tensor kernel_tv6 = at::empty_like(t0, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {kernel_tv3, kernel_tv4, kernel_tv5, kernel_tv6});
TORCH_CHECK(at::allclose(kernel_tv3, t3));
TORCH_CHECK(at::allclose(kernel_tv4, t4));
TORCH_CHECK(at::allclose(kernel_tv5, t5));
TORCH_CHECK(at::allclose(kernel_tv6, t6));
}
namespace {
void checkConcretized(
TensorView* v0,
int a0,
TensorView* v1,
int a1,
bool should_concretize) {
if (should_concretize) {
TORCH_CHECK(
IterDomain::concretizeDomain(v0->axis(a0))->sameAs(v1->axis(a1)));
} else {
TORCH_CHECK(
!IterDomain::concretizeDomain(v0->axis(a0))->sameAs(v1->axis(a1)));
}
}
} // namespace
TEST(NVFuserTest, FusionBCastConcretizeBasic_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// tv0: [I I]
TensorView* tv0 = makeDummyTensor(2);
// tv1: [I I I]
TensorView* tv1 = makeDummyTensor(3);
fusion.addInput(tv0);
fusion.addInput(tv1);
// tv2*: [B I I]
auto tv2_0 = broadcast(tv0, {true, false, false});
auto tv2_1 = broadcast(tv0, {true, false, false});
auto tv2 = add(tv2_0, tv2_1);
// tv3: [I I I]
auto tv3 = add(tv2, tv1);
fusion.addOutput(tv3);
checkConcretized(tv2, 0, tv1, 0, true);
checkConcretized(tv2_0, 0, tv1, 0, true);
checkConcretized(tv2_1, 0, tv1, 0, true);
checkConcretized(tv2_0, 1, tv1, 0, false);
checkConcretized(tv2_0, 0, tv1, 1, false);
}
TEST(NVFuserTest, FusionBCastConcretizeRfactor_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// both tv0 and tv1 = [I, I]
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
//[B,I,I]
auto tv2 = broadcast(tv1, {true, false, false});
//[B,I,R]
auto tv3 = sum(tv2, {2});
auto tv5 = add(tv3, tv1);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv5);
// scheduling:
//[B,I,R0,R1=128], root = [B,I,R]
tv3->split(2, 128);
// root=[B,I,Irf], rfactor=[B,I,Irf,Rrf]
auto tv4 = tv3->rFactor({3});
checkConcretized(tv2, 0, tv5, 0, true);
checkConcretized(tv4, 0, tv5, 0, true);
checkConcretized(tv3, 0, tv5, 0, true);
}
namespace {
void checkIdProvedEquivalent(
TensorView* v0,
int a0,
TensorView* v1,
int a1,
bool should_prove) {
if (should_prove) {
TORCH_CHECK(IterDomain::proveEquivalent(v0->axis(a0), v1->axis(a1)));
} else {
TORCH_CHECK(!IterDomain::proveEquivalent(v0->axis(a0), v1->axis(a1)));
}
}
} // namespace
TEST(NVFuserTest, FusionProveIdEqBasic_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
TensorView* tv2 = makeDummyTensor(3);
fusion.addInput(tv0);
fusion.addInput(tv1);
auto tv3 = broadcast(tv0, {true, false, false});
auto tv4 = broadcast(tv1, {false, true, false});
auto tv5 = add(tv3, tv4);
fusion.addOutput(tv5);
checkIdProvedEquivalent(tv0, 0, tv4, 1, true);
checkIdProvedEquivalent(tv1, 0, tv4, 0, true);
checkIdProvedEquivalent(tv1, 1, tv0, 1, true);
checkIdProvedEquivalent(tv0, 0, tv5, 1, true);
checkIdProvedEquivalent(tv1, 1, tv5, 2, true);
checkIdProvedEquivalent(tv0, 0, tv1, 0, false);
checkIdProvedEquivalent(tv0, 1, tv1, 0, false);
checkIdProvedEquivalent(tv0, 0, tv1, 1, false);
}
TEST(NVFuserTest, FusionProveIdEqRfactor_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// [I,I]
TensorView* tv0 = makeDummyTensor(2);
// [I,I,I]
TensorView* tv1 = makeDummyTensor(3);
//[I,I,R]
auto tv2 = sum(tv1, {2});
auto tv5 = add(tv2, tv0);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv5);
// scheduling:
//[B,I,R0,R1=128], root = [B,I,R]
tv2->split(2, 128);
// root=[B,I,Irf], rfactor=[B,I,Irf,Rrf]
auto tv3 = tv2->rFactor({3});
checkIdProvedEquivalent(tv1, 0, tv0, 0, true);
checkIdProvedEquivalent(tv2, 0, tv0, 0, true);
checkIdProvedEquivalent(tv3, 0, tv0, 0, true);
}
TEST(NVFuserTest, FusionScalarInputs_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = makeDummyTensor(2);
fusion.addInput(tv1);
Float* f0 = new Float();
fusion.addInput(f0);
Float* f1 = new Float();
fusion.addInput(f1);
Float* f2 = new Float();
fusion.addInput(f2);
Float* f3 = new Float();
fusion.addInput(f3);
Val* f4 = mul(f0, f1);
Val* f5 = sub(f2, f3);
TensorView* tv2 = sub(tv1, f4);
TensorView* tv3 = add(tv0, f5);
TensorView* tv4 = mul(tv3, tv2);
fusion.addOutput(tv4);
// Lets setup to actually run
while (tv4->nDims() > 1)
tv4->merge(0);
tv4->split(0, 128);
tv4->split(0, 4);
tv0->computeAt(tv4, 1);
tv1->computeAt(tv4, 1);
tv4->axis(0)->parallelize(ParallelType::BIDx);
for (Val* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
TensorView* tv = static_cast<TensorView*>(val);
tv->axis(1)->parallelize(ParallelType::Unroll);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
}
// f4 = f0 * f1
// f5 = f2 - f3
// t2 = t1 - f4
// t3 = t0 + f5
// t4 = t3 * t2
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
float fl0 = 0.1;
float fl1 = -0.2;
float fl2 = 0.3;
float fl3 = -0.4;
float fl4 = fl0 * fl1;
float fl5 = fl2 - fl3;
at::Tensor t0 = at::randn({129, 127}, options);
at::Tensor t1 = at::rand_like(t0, options);
auto t2 = t1.sub(fl4);
auto t3 = t0.add(fl5);
auto t4 = t3.mul(t2);
at::Tensor kernel_tv4 = at::empty_like(t0, options);
at::Scalar test(fl0);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion(
{t0,
t1,
at::Scalar(fl0),
at::Scalar(fl1),
at::Scalar(fl2),
at::Scalar(fl3)},
{kernel_tv4});
TORCH_CHECK(at::allclose(kernel_tv4, t4));
}
TEST(NVFuserTest, FusionLoopUnroll_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(3);
TensorView* tv1 = makeDummyTensor(3);
// Register your inputs
fusion.addInput(tv0);
fusion.addInput(tv1);
// Do math with it, it returns a `Val*` but can be static_casted back to
// TensorView
TensorView* tv2 = add(tv1, new Float(2.0));
TensorView* tv3 = add(tv0, tv2);
// Register your outputs
fusion.addOutput(tv3);
int block_size = 16;
tv3->merge(0, 1);
tv3->merge(0, 1);
tv3->split(0, block_size);
tv3->split(0, 4);
// For all inputs, computeAt the output inline, temporaries should be squeezed
// between them
tv0->computeAt(tv3, 1);
tv1->computeAt(tv3, 1);
// Parallelize
tv2->axis(1)->parallelize(ParallelType::Unroll);
tv3->axis(1)->parallelize(ParallelType::Unroll);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(0)->parallelize(ParallelType::BIDx);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input0 = at::rand({129, 13, 3}, options);
at::Tensor input1 = at::rand({129, 13, 3}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input0, input1});
TORCH_CHECK(outputs[0].equal(input0.add(input1.add(2.0))));
}
/*
* Helper function for single op testing that generates a codegen operand
*/
Val* gen_jit_operand(std::pair<ValType, DataType> desc) {
if (desc.first == ValType::TensorView) {
return makeDummyTensor(2, desc.second);
} else if (desc.first == ValType::Scalar) {
if (desc.second == DataType::Float)
return new Float();
else if (desc.second == DataType::Int)
return new Int();
else
TORCH_CHECK("Not currently supported type", desc.first);
} else {
TORCH_CHECK("Not currently supported type", desc.first);
}
return nullptr;
}
/*
* Helper function for single op testing that generates an ATen operand
*/
IValue gen_aten_operand(
std::pair<ValType, DataType> desc,
int blocks,
int threads,
bool rand) {
if (desc.first == ValType::TensorView) {
if (desc.second == DataType::Float) {
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
if (rand)
return IValue(at::rand({blocks, threads}, options));
else
return IValue(at::empty({blocks, threads}, options));
} else if (desc.second == DataType::Half) {
auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0);
if (rand)
return IValue(at::rand({blocks, threads}, options));
else
return IValue(at::empty({blocks, threads}, options));
} else if (desc.second == DataType::Bool) {
if (rand) {
auto options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
return IValue(at::rand({blocks, threads}, options).to(at::kBool));
} else {
auto options =
at::TensorOptions().dtype(at::kBool).device(at::kCUDA, 0);
return IValue(at::empty({blocks, threads}, options));
}
} else {
TORCH_CHECK("Not currently supported type", desc.second)
}
} else if (desc.first == ValType::Scalar) {
if (desc.second == DataType::Float)
return IValue(at::Scalar(1.f));
else if (desc.second == DataType::Int)
return IValue(at::Scalar(1));
else
TORCH_CHECK("Not currently supported type", desc.first);
} else {
TORCH_CHECK("Not currently supported type", desc.first);
}
return nullptr;
}
/*
* Templatized Helper Function To generate single Op comparison between the
* JIT codegen for Cuda and the ATen Library.
*/
using OutputPair = std::pair<ValType, DataType>;
template <
typename AtenFunc,
typename JitFunc,
typename InputTuple,
size_t... NumInputs>
void test_op(
int blocks,
int threads,
std::string op_str,
AtenFunc af,
JitFunc jf,
OutputPair op,
InputTuple it,
std::index_sequence<NumInputs...>) {
Fusion fusion;
FusionGuard fg(&fusion);
// Generate Input JIT function Inputs and add them as Inputs to the Fusion
// Graph
std::array<Val*, sizeof...(NumInputs)> jit_inputs = {
gen_jit_operand(std::get<NumInputs>(it))...};
std::for_each(jit_inputs.begin(), jit_inputs.end(), [&fusion](Val* v) {
fusion.addInput(v);
});
TensorView* out =
static_cast<TensorView*>(jf(std::get<NumInputs>(jit_inputs)...));
fusion.addOutput(out);
std::for_each(jit_inputs.begin(), jit_inputs.end(), [out](Val* v) {
if (v->getValType() == ValType::TensorView)
static_cast<TensorView*>(v)->computeAt(out, -1);
});
out->axis(0)->parallelize(ParallelType::BIDx);
out->axis(-1)->parallelize(ParallelType::TIDx);
std::array<IValue, sizeof...(NumInputs)> aten_inputs = {gen_aten_operand(
std::get<NumInputs>(it), blocks, threads, /*rand*/ true)...};
const at::ArrayRef<IValue> aten_inputs_ivalues(aten_inputs);
at::Tensor output =
gen_aten_operand(op, blocks, threads, /*rand*/ false).toTensor();
std::vector<at::Tensor> output_vect = {output};
cudaDeviceSynchronize();
if (fusion.isStochastic())
at::manual_seed(0);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion(aten_inputs_ivalues, output_vect);
cudaDeviceSynchronize();
if (fusion.isStochastic())
at::manual_seed(0);
at::Tensor ref_output = af(aten_inputs);
cudaDeviceSynchronize(); // This sync shouldn't be necessary;
std::function<std::string()> aten_inputs_to_str =
[&aten_inputs]() -> std::string {
int input_cnt = 1;
std::stringstream ss;
std::for_each(
aten_inputs.begin(), aten_inputs.end(), [&input_cnt, &ss](IValue& iv) {
ss << "\nINPUT" << input_cnt++ << ": " << iv.toTensor();
});
return ss.str();
};
at::Tensor diff;
if (output.scalar_type() == at::kBool) {
diff = at::eq(output, ref_output);
} else {
diff = at::sub(output, ref_output);
}
TORCH_CHECK(
(output.scalar_type() == at::kBool
? output.equal(ref_output)
:
// The absolute Tolerance was raised to 1e-07 from 1e-08 to allow
// allow for the remainder function to pass.
output.allclose(ref_output, /*rtol*/ 1e-05, /*atol*/ 1e-07)),
"\nOp Type: -- ",
op_str,
" -- had a mismatch.",
aten_inputs_to_str(),
"\nABS MAX DIFF: ",
output.sub(ref_output).abs().max(),
"\n");
}
/*
* Templatized Helper Function that uses variadic templates to
* process a variable length Input Tuple of different Operand Type.
*/
template <typename AtenFunc, typename JitFunc, typename InputTuple>
void test_op(
int blocks,
int threads,
std::string op_str,
AtenFunc af,
JitFunc jf,
OutputPair op,
InputTuple it) {
static constexpr auto size = std::tuple_size<InputTuple>::value;
test_op(
blocks,
threads,
op_str,
af,
jf,
op,
it,
std::make_index_sequence<size>{});
}
TEST(NVFuserTest, FusionUnaryOps_CUDA) {
using OpTuple =
std::tuple<at::Tensor (*)(const at::Tensor&), UnaryOpType, std::string>;
// [Note: explicit tuple type for uniform initialization list]
// Tuple type must be explicitly specified for each uniform initialization
// list within the vector to make this code compatible with some old env
// which we still need to support. eg. gcc 5.4 + cuda 9.2.
std::vector<OpTuple> ops{
OpTuple{at::abs, UnaryOpType::Abs, "abs"},
OpTuple{at::acos, UnaryOpType::Acos, "acos"},
OpTuple{at::asin, UnaryOpType::Asin, "asin"},
OpTuple{at::atan, UnaryOpType::Atan, "atan"},
// There does not appear to be an appropriate ATen function for atanh
// OpTuple{at::atanh, UnaryOpType::Atanh, "atanh" },
OpTuple{at::ceil, UnaryOpType::Ceil, "ceil"},
OpTuple{at::cos, UnaryOpType::Cos, "cos"},
OpTuple{at::cosh, UnaryOpType::Cosh, "cosh"},
OpTuple{at::erf, UnaryOpType::Erf, "erf"},
OpTuple{at::erfc, UnaryOpType::Erfc, "erfc"},
OpTuple{at::exp, UnaryOpType::Exp, "exp"},
OpTuple{at::expm1, UnaryOpType::Expm1, "expm1"},
OpTuple{at::floor, UnaryOpType::Floor, "floor"},
OpTuple{at::frac, UnaryOpType::Frac, "frac"},
OpTuple{at::gelu, UnaryOpType::Gelu, "gelu"},
OpTuple{at::lgamma, UnaryOpType::Lgamma, "lgamma"},
OpTuple{at::log, UnaryOpType::Log, "log"},
OpTuple{at::log10, UnaryOpType::Log10, "log10"},
OpTuple{at::log1p, UnaryOpType::Log1p, "log1p"},
OpTuple{at::log2, UnaryOpType::Log2, "log2"},
OpTuple{at::neg, UnaryOpType::Neg, "neg"},
OpTuple{at::reciprocal, UnaryOpType::Reciprocal, "reciprocal"},
OpTuple{at::relu, UnaryOpType::Relu, "relu"},
OpTuple{at::round, UnaryOpType::Round, "round"},
OpTuple{at::rsqrt, UnaryOpType::Rsqrt, "rsqrt"},
OpTuple{at::sigmoid, UnaryOpType::Sigmoid, "sigmoid"},
OpTuple{at::sin, UnaryOpType::Sin, "sin"},
OpTuple{at::sinh, UnaryOpType::Sinh, "sinh"},
OpTuple{at::sqrt, UnaryOpType::Sqrt, "sqrt"},
OpTuple{at::tan, UnaryOpType::Tan, "tan"},
OpTuple{at::tanh, UnaryOpType::Tanh, "tanh"},
OpTuple{at::trunc, UnaryOpType::Trunc, "trunc"}};
std::for_each(ops.begin(), ops.end(), [](OpTuple& op) {
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ std::get<2>(op),
/*Aten Func */
[&op](std::array<IValue, 1>& vals) {
return std::get<0>(op)(vals[0].toTensor());
},
/*JIT Func */
[&op](Val* in1) -> Val* { return unaryOp(std::get<1>(op), in1); },
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(std::make_pair(ValType::TensorView, DataType::Float)));
});
test_op(
/*blocks*/ 128,
/*threads*/ 64,
/*name*/ "rand_like",
/*Aten Func */
[](std::array<IValue, 1>& vals) {
return at::rand_like(vals[0].toTensor());
},
/*JIT Func */
[](Val* in1) -> Val* { return unaryOp(UnaryOpType::RandLike, in1); },
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(std::make_pair(ValType::TensorView, DataType::Float)));
}
TEST(NVFuserTest, FusionBinaryOps_CUDA) {
using AtenFuncSig = at::Tensor (*)(const at::Tensor&, const at::Tensor&);
using OpTuple = std::tuple<AtenFuncSig, BinaryOpType, std::string>;
// see [Note: explicit tuple type for uniform initialization list]
std::vector<OpTuple> logic_ops{OpTuple{at::eq, BinaryOpType::Eq, "eq"},
OpTuple{at::ge, BinaryOpType::GE, "ge"},
OpTuple{at::gt, BinaryOpType::GT, "gt"},
OpTuple{at::le, BinaryOpType::LE, "le"},
OpTuple{at::lt, BinaryOpType::LT, "lt"},
OpTuple{at::ne, BinaryOpType::NE, "ne"}};
std::for_each(logic_ops.begin(), logic_ops.end(), [](OpTuple& op) {
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ std::get<2>(op),
/*Aten Func */
[&op](std::array<IValue, 2>& vals) {
return std::get<0>(op)(vals[0].toTensor(), vals[1].toTensor());
},
/*JIT Func */
[&op](Val* in1, Val* in2) -> Val* {
return binaryOp(std::get<1>(op), in1, in2);
},
/*Output */ std::make_pair(ValType::TensorView, DataType::Bool),
/*Inputs Tuple*/
std::make_tuple(
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float)));
});
// see [Note: explicit tuple type for uniform initialization list]
std::vector<OpTuple> math_ops{
OpTuple{at::atan2, BinaryOpType::Atan2, "atan2"},
OpTuple{at::div, BinaryOpType::Div, "div"},
OpTuple{at::fmod, BinaryOpType::Fmod, "fmod"},
OpTuple{at::max, BinaryOpType::Max, "max"},
OpTuple{at::min, BinaryOpType::Min, "min"},
OpTuple{at::mul, BinaryOpType::Mul, "mul"},
OpTuple{at::pow, BinaryOpType::Pow, "pow"},
// NOTE: Remainder does not match the Aten impl exactly
// despite using an identical function.
OpTuple{at::remainder, BinaryOpType::Remainder, "remainder"},
};
std::for_each(math_ops.begin(), math_ops.end(), [](OpTuple& op) {
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ std::get<2>(op),
/*Aten Func */
[&op](std::array<IValue, 2>& vals) {
return std::get<0>(op)(vals[0].toTensor(), vals[1].toTensor());
},
/*JIT Func */
[&op](Val* in1, Val* in2) -> Val* {
return binaryOp(std::get<1>(op), in1, in2);
},
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float)));
});
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ "add_alpha",
/*Aten Func */
[](std::array<IValue, 3>& vals) {
return at::add(
vals[0].toTensor(), vals[1].toTensor(), vals[2].toScalar());
},
/*JIT Func */ static_cast<Val* (*)(Val*, Val*, Val*)>(&add_alpha),
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::Scalar, DataType::Float)));
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ "sub_alpha",
/*Aten Func */
[](std::array<IValue, 3>& vals) {
return at::sub(
vals[0].toTensor(), vals[1].toTensor(), vals[2].toScalar());
},
/*JIT Func */ static_cast<Val* (*)(Val*, Val*, Val*)>(&sub_alpha),
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::Scalar, DataType::Float)));
}
TEST(NVFuserTest, FusionTernaryOps_CUDA) {
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ "clamp",
/*Aten Func */
[](std::array<IValue, 1>& vals) {
return at::clamp(vals[0].toTensor(), 0.f, 1.f);
},
/*JIT Func */
[](Val* in1) -> Val* {
return clamp(in1, new Float(0.f), new Float(1.f));
},
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(std::make_pair(ValType::TensorView, DataType::Float)));
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ "threshold",
/*Aten Func */
[](std::array<IValue, 1>& vals) {
return at::threshold(vals[0].toTensor(), 0.f, 1.f);
},
/*JIT Func */
[](Val* in1) -> Val* {
return threshold(in1, new Float(0.f), new Float(1.f));
},
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(std::make_pair(ValType::TensorView, DataType::Float)));
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ "where",
/*Aten Func */
[](std::array<IValue, 3>& vals) {
return at::where(
vals[0].toTensor(), vals[1].toTensor(), vals[2].toTensor());
},
/*JIT Func */ static_cast<Val* (*)(Val*, Val*, Val*)>(&where),
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(
std::make_pair(ValType::TensorView, DataType::Bool),
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float)));
}
TEST(NVFuserTest, FusionCompoundOps_CUDA) {
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ "lerp",
/*Aten Func */
[](std::array<IValue, 3>& vals) {
return at::lerp(
vals[0].toTensor(), vals[1].toTensor(), vals[2].toTensor());
},
/*JIT Func */ static_cast<Val* (*)(Val*, Val*, Val*)>(&lerp),
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float)));
test_op(
/*blocks*/ 640,
/*threads*/ 64,
/*name*/ "addcmul",
/*Aten Func */
[](std::array<IValue, 4>& vals) {
return at::addcmul(
vals[0].toTensor(),
vals[1].toTensor(),
vals[2].toTensor(),
vals[3].toScalar());
},
/*JIT Func */ static_cast<Val* (*)(Val*, Val*, Val*, Val*)>(&addcmul),
/*Output */ std::make_pair(ValType::TensorView, DataType::Float),
/*Inputs Tuple*/
std::make_tuple(
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::TensorView, DataType::Float),
std::make_pair(ValType::Scalar, DataType::Float)));
}
TEST(NVFuserTest, FusionCastOps_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2, DataType::Half);
TensorView* intrm1 = castOp(DataType::Float, tv0);
TensorView* out = castOp(DataType::Half, intrm1);
fusion.addInput(tv0);
fusion.addOutput(out);
tv0->computeAt(out, -1);
out->axis(0)->parallelize(ParallelType::BIDx);
out->axis(-1)->parallelize(ParallelType::TIDx);
auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0);
at::Tensor input1 = at::rand({1, 4}, options);
at::Tensor ref_output = at::empty_like(input1);
std::array<IValue, 1> inputs = {input1};
const at::ArrayRef<IValue> input_ivalues(inputs);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion(input_ivalues);
ref_output = at::_cast_Half(at::_cast_Float(input1));
TORCH_CHECK(
outputs[0].equal(ref_output),
"\nOp Type: -- ",
"cast FP16->FP32->FP16",
" -- had a mismatch.\n",
"\nABS MAX DIFF: ",
outputs[0].sub(ref_output).abs().max(),
"\n");
}
// We want split/merge/reorder all tested both on and off rfactor domains, also
// want compute at into the rfactor domain, and into its consumer
TEST(NVFuserTest, FusionRFactorReplay_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
// Register your inputs
fusion.addInput(tv0);
// Do math with it, it returns a `Val*` but can be static_casted back to
// TensorView
TensorView* tv1 = sum(tv0, {1});
// tv1[I0, R1]
tv1->split(0, 32);
// tv1[I0o, I0i{32}, R1]
tv1->split(0, 16);
// tv1[I0oo, I0oi{16}, I0i{32}, R1]
tv1->split(-1, 8);
// tv1[I0oo, I0oi{16}, I0i{32}, R1o, R1i{8}]
tv1->split(-2, 4);
// tv1[I0oo, I0oi{16}, I0i{32}, R1oo, R1oi{4}, R1i{8}]
tv1->reorder({{0, -2}, {2, -1}, {-3, 0}, {-1, 1}});
// tv1[R1oo, R1i{8}, I0oi{16}, R1oi{4}, I0oo, I0i{32}]
tv1->merge(0);
tv1->merge(-2);
// tv1[R1oo*R1i{8}, I0oi{16}, R1oi{4}, I0oo*I0i{32}]
TensorDomain* new_domain = TransformRFactor::runReplay(tv1->domain(), {0});
// new_domain[r(R1oo*R1i{8})rf, I0oi{16}, ir1oi{4}rf, I0oo*I0i{32}]
TensorDomain* new_domain2 = TransformRFactor::runReplay2(tv1->domain(), {0});
// new_domain2[ I0oi{16}, , I0oo*I0i{32}, R1oi{4}]
// Move rfactor axis to end, keep iter rfactor axis
new_domain->reorder({{0, -1}, {2, 2}});
// Replay casp, replay new_domain2 as new_domain
// reordered_new_domain[I0oi{16}, I0oo*I0i{32}, ir1oi{4}rf, R(R1oo*R1i{8})rf]
auto replay_casp = TransformReplay::replayCasP(new_domain2, new_domain, 2);
TensorDomain* casp = replay_casp.first;
// new_domain[I0oi{16}, I0oo*I0i{32}, ir1oi{4}rf, R(R1oo*R1i{8})rf]
// casp[I0oi{16}, I0oo*I0i{32}, R1oi{4}]
casp->split(1, new Int(2));
// casp [I0oi{16}, (I0oo*I0i{32})o, I(Ioo*I0i)i{2}, ir1oi{4} ]
// new_domain[I0oi{16}, I0oo*I0i{32} , ir1oi{4}rf,
// R(R1oo*R1i{8})rf]
auto replay_pasc = TransformReplay::replayPasC(new_domain, casp, 2);
TensorDomain* pasc = replay_pasc.first;
// pasc [I0oi{16}, (I0oo*I0i{32})o, I(Ioo*I0i)i{2}, ir1oi{4}rf,
// R(R1oo*R1i{8})rf]
TORCH_CHECK(
new_domain->nDims() - 1 == new_domain2->nDims(),
casp->nDims() == new_domain2->nDims() + 1,
pasc->nDims() == new_domain->nDims() + 1,
"Error in rfactor, number of dimensions is not correct.");
TORCH_CHECK(
!casp->sameAs(new_domain2) && !pasc->sameAs(new_domain) &&
!new_domain->sameAs(new_domain2) &&
!tv1->domain()->sameAs(new_domain) &&
!tv1->domain()->sameAs(new_domain2),
"Error in rfactor, number of dimensions is not correct.");
auto dom = new_domain->getRootDomain();
TORCH_CHECK(
!dom[0]->isReduction() &&
std::any_of(
dom.begin(),
dom.end(),
[](IterDomain* id) { return id->isReduction(); }) &&
std::any_of(
dom.begin(),
dom.end(),
[](IterDomain* id) { return id->isRFactorProduct(); }),
"Error in rFactor, there seems to be something wrong in root domain.");
auto dom2 = new_domain2->getRootDomain();
TORCH_CHECK(
!dom2[0]->isReduction() &&
std::any_of(
dom2.begin(),
dom2.end(),
[](IterDomain* id) { return id->isReduction(); }),
"Error in rFactor, there seems to be something wrong in root domain.");
}
// Start off simple, block on the outer dim
// block stride + thread all reduce + unrolling on inner dim
TEST(NVFuserTest, FusionReduction_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(1, 128);
// tv1[I0, R1o, R1i{128}] = tv0[I0, I1]
tv1->split(1, 4);
// tv1[I0, R1oo, R1oi{4}, R1i{128}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{4}, Ir1i{128}] = tv0[I0, I1]
// tv1[I0, R1oi{4}, R1i{128}] = tv2[I0, R1oo, Ir1oi{4}, Ir1i{128}]
TensorView* tv3 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{4}, Ir1i{128}] = tv0[I0, I1]
// tv3[I0, R1oi{4}, Ir1i{128}] = tv2[I0, R1oo, Ir1oi{4}, Ir1i{128}]
// tv1[I0, R1i{128}] = tv3[I0, R1oi{4}, Ir1i{128}]
// Incrementally, can print in between for debugging
tv0->computeAt(tv2, 1);
tv2->computeAt(tv3, 1);
tv3->computeAt(tv1, 1);
// Re do it all at once, because why not.
tv0->computeAt(tv1, 1);
tv2->axis(2)->parallelize(ParallelType::Unroll);
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
int numel_x = 65000;
int numel_y = 1025;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
at::Tensor cg_output = at::empty({numel_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(cg_output));
}
TEST(NVFuserTest, FusionReduction2_CUDA) {
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
// switches to try some different scenarios. maybe we should iterate on all
// permutations.
bool bind_bidx = true;
bool bind_tidx = true;
bool bind_tidy = true;
bool bind_unroll = true;
int numel_x = 1025; // Cannot exceed block dim max size / tidy
int numel_y = 129;
int tidx = 16;
int tidy = 8;
int unroll_factor = 4;
tv1->split(1, tidx);
// tv1[I0, R1o, R1i{tidx}] = tv0[I0, I1]
tv1->split(1, unroll_factor);
// tv1[I0, R1oo, R1oi{unroll}, R1i{tidx}] = tv0[I0, I1]
tv1->split(0, tidy);
TensorView* tv2 = tv1->rFactor({-3});
// tv2[I0, >R1oo<, Ir1oi{unroll}, Ir1i{tidx}]
// tv1[I0o, I0i{tidy}, R1oi{unroll}, R1i{tidx}]
TensorView* tv3 = tv1->rFactor({-2});
// tv2[I0, >R1oo<, Ir1oi{unroll}, Ir1i{tidx}]
// tv3[I0, R1oi{unroll}, Ir1i{tidx}]
// tv1[I0o, I0i{tidy}, R1i{tidx}]
tv0->computeAt(tv1, -2);
if (bind_unroll)
tv2->axis(-2)->parallelize(ParallelType::Unroll);
if (bind_bidx)
tv1->axis(0)->parallelize(ParallelType::BIDx);
if (bind_tidy)
tv1->axis(1)->parallelize(ParallelType::TIDy);
if (bind_tidx) {
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream();
AT_CUDA_CHECK(cudaStreamSynchronize(stream));
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(outputs[0]));
}
{
// What if Z participates in the reduction with X?
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
int numel_x = 1025; // Cannot exceed block dim max size / tidy
int numel_y = 129;
int tidx = 16;
int tidz = 8;
tv1->split(1, tidz);
// tv1[I0, R1o, R1i{tidz}] = tv0[I0, I1]
tv1->split(1, tidx);
// tv1[I0, R1oo, R1oi{tidx}, R1i{tidz}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({-3});
// tv2[I0, >R1oo<, Ir1oi{tidx}, Ir1i{tidz}]
// tv1[I0o, R1oi{tidx}, R1i{tidz}]
tv0->computeAt(tv1, -3);
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(-2)->parallelize(ParallelType::TIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDz);
tv2->axis(-2)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDz);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
at::Tensor cg_output = at::empty({numel_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream();
AT_CUDA_CHECK(cudaStreamSynchronize(stream));
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(cg_output));
}
}
TEST(NVFuserTest, FusionReduction3_CUDA) {
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
TensorView* tv2 = add(tv0, tv1);
// tv2[I0, I1] = tv0[I0, I1] + tv1[I0, I1]
fusion.addInput(tv0);
fusion.addInput(tv1);
TensorView* tv3 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv2);
// tv3[I0, R1] = tv2[I0, I1]
TensorView* tv4 = makeDummyTensor(1);
fusion.addInput(tv4);
// tv5[I0] = tv3[I0, R1] * tv4[I0]
TensorView* tv5 = mul(tv3, tv4);
fusion.addOutput(tv5);
int tidx = 16;
// RFactor the reduction
tv3->split(1, tidx);
// tv3[I0, R1o, R1i{tidx}] = tv2[I0, I1]
TensorView* tv6 = tv3->rFactor({-2});
// tv6[I0, R1o, iR1i{tidx}] = tv2[I0, I1]
// tv3[I0, R1i{tidx}] = tv3[I0, I1]
tv2->computeAt(tv6, 2);
// Compute at inline with tv5 (only 1D)
tv6->computeAt(tv3, 1);
tv3->computeAt(tv5, 1);
tv5->axis(0)->parallelize(ParallelType::BIDx);
// Intermediate tensors only need this, but doesn't hurt to do on inputs
// tv0, 1, 4
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv6->axis(-1)->parallelize(ParallelType::TIDx);
int numel_x = 1025;
int numel_y = 129;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::rand({numel_x, numel_y}, options);
at::Tensor t1 = at::rand({numel_x, numel_y}, options);
auto t2 = t0.add(t1);
auto t3 = t2.sum({1});
at::Tensor t4 = at::rand({numel_x}, options);
auto t5 = t3.mul(t4);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1, t4});
c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream();
AT_CUDA_CHECK(cudaStreamSynchronize(stream));
TORCH_CHECK(
t5.allclose(outputs[0]), "Error of: ", t5.sub(outputs[0]).abs().max());
}
}
TEST(NVFuserTest, FusionReduction4_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(3);
fusion.addInput(tv0);
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
int bidy = 2;
int tidy = 4;
int tidx = 5;
int dim1 = 11;
tv1->split(-2, tidy);
TensorView* tv2 = tv1->rFactor({-3});
tv0->computeAt(tv1, 1);
tv1->axis(0)->parallelize(ParallelType::BIDy);
for (auto* val : fusion.vals()) {
if (!fusion.hasInput(val) &&
val->getValType().value() == ValType::TensorView) {
val->as<TensorView>()->axis(-1)->parallelize(ParallelType::TIDx);
}
}
tv2->axis(-2)->parallelize(ParallelType::TIDy);
tv1->axis(-2)->parallelize(ParallelType::TIDy);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::randn({bidy, dim1, tidx}, options);
at::Tensor cg_output = at::empty({bidy, tidx}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
auto aten_output = input.sum({1});
TORCH_CHECK(
aten_output.allclose(cg_output, 1e-5, 1e-7),
"Error of: ",
aten_output.sub(cg_output).abs().max());
}
TEST(NVFuserTest, FusionReduction5_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int bdimx = 64;
const int bdimy = 8;
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(3);
fusion.addInput(tv0);
// tv1[I0, R1, R2] = tv0[I0, I1, I2]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1, 2}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(2, bdimx);
// tv1[I0, R1, R2o, R2i{128}] = tv0[I0, I1, I2]
tv1->split(1, bdimy);
// tv1[I0, R1o, R1i{8}, R2o, R2i{128}] = tv0[I0, I1, I2]
TensorView* tv2 = tv1->rFactor({3});
// tv2[I0, I1o, I1i{8}, R2o, I2i{128}] = tv0[I0, I1, I2]
// tv1[I0, R1o, R1i{8}, R2i{128}] = tv2[I0, I1o, I1i{8}, R2o, I2i{128}]
TensorView* tv3 = tv1->rFactor({1});
// tv2[I0, I1o, I1i{8}, R2o, I2i{128}] = tv0[I0, I1, I2]
// tv3[I0, R1o, I1i{8}, I2i{128}] = tv2[I0, I1o, I1i{8}, R2o, I2i{128}]
// tv1[I0, R1i{8}, R2i{128}] = tv3[I0, R1o, I1i{8}, I2i{128}]
tv3->computeAt(tv1, 1);
tv2->computeAt(tv3, 2);
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(0)->parallelize(ParallelType::BIDx);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(-2)->parallelize(ParallelType::TIDy);
tv3->axis(-2)->parallelize(ParallelType::TIDy);
tv2->axis(-3)->parallelize(ParallelType::TIDy);
int numel_x = 650;
int numel_y = 1000;
int numel_z = 4;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y, numel_z}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = input.sum({1, 2});
TORCH_CHECK(aten_output.allclose(outputs[0]));
}
TEST(NVFuserTest, FusionReductionTFT_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
int numel_x = 1025;
int numel_y = 129;
int tidx = 16;
int tidy = 8;
int tidz = 8;
tv1->split(1, tidx);
// tv1[I0, R1o, R1i{tidx}]
tv1->split(1, tidz);
// tv1[I0, R1oo, R1Oi{tidz}, R1R1i{tidx}]
tv1->split(0, tidy);
// tv1[I0o, I0i, R1oo, R1Oi{tidz}, R1R1i{tidx}]
TensorView* tv2 = tv1->rFactor({2});
// tv2[I0o, I0i, R1oo, I1Oi{tidz}, I11i{tidx}]
// tv1[I0o, I0i, R1Oi{tidz}, R1R1i{tidx}]
tv2->computeAt(tv1, 2);
tv1->axis(1)->parallelize(ParallelType::TIDy);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(-2)->parallelize(ParallelType::TIDz);
tv2->axis(-2)->parallelize(ParallelType::TIDz);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
at::Tensor cg_output = at::empty({numel_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
c10::cuda::CUDAStream stream = c10::cuda::getCurrentCUDAStream();
AT_CUDA_CHECK(cudaStreamSynchronize(stream));
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(cg_output));
}
TEST(NVFuserTest, FusionBranches_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
TensorView* tv2 = makeDummyTensor(2);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addInput(tv2);
auto tv3 = add(tv0, new Float(1.0));
auto tv4 = add(tv3, tv1);
auto tv5 = add(tv3, tv2);
auto tv6 = add(tv4, tv5);
fusion.addOutput(tv6);
constexpr int x = 63, y = 33;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({x, y}, options);
at::Tensor t1 = at::randn({x, y}, options);
at::Tensor t2 = at::randn({x, y}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
tv6->merge(0);
tv6->split(0, 128);
tv6->split(0, 4);
tv6->axis(0)->parallelize(ParallelType::BIDx);
tv0->computeAt(tv6, 1);
tv1->computeAt(tv6, 1);
tv2->computeAt(tv6, 1);
tv3->axis(-2)->parallelize(ParallelType::Unroll);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv4->axis(-2)->parallelize(ParallelType::Unroll);
tv4->axis(-1)->parallelize(ParallelType::TIDx);
tv5->axis(-2)->parallelize(ParallelType::Unroll);
tv5->axis(-1)->parallelize(ParallelType::TIDx);
tv6->axis(-1)->parallelize(ParallelType::TIDx);
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1, t2});
auto t3 = t0.add(1.0);
auto t4 = t3.add(t1);
auto t5 = t3.add(t2);
auto t6 = t4.add(t5);
TORCH_CHECK(t6.allclose(outputs[0]));
}
TEST(NVFuserTest, FusionSimpleBCast_CUDA) {
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1.5));
TensorView* tv2 = makeDummyTensor(2);
fusion.addInput(tv2);
TensorView* tv3 = makeDummyTensor(2);
fusion.addInput(tv3);
TensorView* tv4 = sub(tv2, tv3);
TensorView* tv5 = broadcast(tv1, {false, false, true});
TensorView* tv6 = broadcast(tv4, {true, false, false});
TensorView* tv7 = add(tv5, tv6);
fusion.addOutput(tv7);
tv7->split(-1, 4);
tv7->split(0, 8);
tv0->computeAt(tv7, -1);
tv2->computeAt(tv7, -1);
tv7->axis(0)->parallelize(ParallelType::BIDx);
tv7->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int x = 63, y = 33, z = 15;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({x, y}, options);
at::Tensor t1 = t0.add(1.5);
at::Tensor t2 = at::randn({y, z}, options);
at::Tensor t3 = at::randn({y, z}, options);
at::Tensor t4 = t2.sub(t3);
at::Tensor t5 = t1.unsqueeze(-1).expand({x, y, z});
at::Tensor t6 = t4.expand({x, y, z});
at::Tensor t7 = t5.add(t6);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t2, t3});
TORCH_CHECK(t7.allclose(outputs[0]));
}
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = makeDummyTensor(2);
fusion.addInput(tv1);
TensorView* tv2 = add(tv0, tv1);
TensorView* tv3 = broadcast(tv2, {false, false, true});
TensorView* tv4 = makeDummyTensor(2);
fusion.addInput(tv4);
TensorView* tv5 = sub(tv4, new Float(0.1));
TensorView* tv6 = broadcast(tv5, {true, false, false});
TensorView* tv7 = add(tv3, tv6);
fusion.addOutput(tv7);
tv7->merge(0, 1);
tv0->computeAt(tv7, -1);
tv4->computeAt(tv7, -1);
tv7->axis(0)->parallelize(ParallelType::BIDx);
tv7->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int x = 63, y = 33, z = 15;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({x, y}, options);
at::Tensor t1 = at::randn({x, y}, options);
at::Tensor t2 = t0.add(t1);
at::Tensor t3 = t2.unsqueeze(-1).expand({x, y, z});
at::Tensor t4 = at::randn({y, z}, options);
at::Tensor t5 = t4.sub(0.1);
at::Tensor t6 = t5.expand({x, y, z});
at::Tensor t7 = t3.add(t6);
at::Tensor cg_output = at::empty({x, y, z}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0, t1, t4}, {cg_output});
TORCH_CHECK(t7.allclose(cg_output));
}
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
std::vector<IterDomain*> dom;
dom.push_back(new IterDomain(new Int(0), new Int()));
dom.push_back(new IterDomain(
new Int(0),
new Int(1),
ParallelType::Serial,
IterType::BroadcastWithStride));
// tv0[I1, B{1}]
TensorView* tv0 = new TensorView(new TensorDomain(dom), DataType::Float);
fusion.addInput(tv0);
// tv1[I0, I1, I2]
TensorView* tv2 = makeDummyTensor(3);
fusion.addInput(tv2);
TensorView* tv3 = add(tv0, tv2);
fusion.addOutput(tv3);
tv3->merge(0);
tv3->merge(0);
tv0->computeAt(tv3, -1);
tv2->computeAt(tv3, -1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
constexpr int x = 2, y = 3, z = 4;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({y, 1}, options);
at::Tensor t2 = at::randn({x, y, z}, options);
auto t3 = t0.add(t2);
at::Tensor cg_output = at::empty({x, y, z}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0, t2}, {cg_output});
TORCH_CHECK(t3.allclose(cg_output));
}
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
std::vector<IterDomain*> dom;
dom.push_back(new IterDomain(
new Int(0),
new Int(1),
ParallelType::Serial,
IterType::BroadcastWithStride));
dom.push_back(new IterDomain(new Int(0), new Int()));
TensorView* tv0 = new TensorView(new TensorDomain(dom), DataType::Float);
TensorView* tv1 = makeDummyTensor(3);
fusion.addInput(tv0);
fusion.addInput(tv1);
TensorView* tv3 = add(tv0, tv1);
tv3->merge(0);
tv3->merge(0);
tv3->split(0, 128);
tv3->split(0, 4);
fusion.addOutput(tv3);
tv0->computeAt(tv3, -1);
tv1->computeAt(tv3, -1);
tv3->axis(0)->parallelize(ParallelType::BIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-2)->parallelize(ParallelType::Unroll);
constexpr int x = 63, y = 33, z = 15;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({1, z}, options);
at::Tensor t1 = at::randn({x, y, z}, options);
at::Tensor cg_output = at::empty({x, y, z}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0, t1}, {cg_output});
auto t3 = t0.add(t1);
TORCH_CHECK(t3.allclose(cg_output));
}
{
Fusion fusion;
FusionGuard fg(&fusion);
constexpr int m = 2, k = 3, n = 4;
auto zero = new Int(0);
auto M = new IterDomain(zero, new Int(m));
auto K = new IterDomain(zero, new Int(k));
auto N = new IterDomain(zero, new Int(n));
// Set up your input tensor views
TensorView* tv0 =
new TensorView(new TensorDomain({M, K}, {true, true}), DataType::Float);
TensorView* tv1 =
new TensorView(new TensorDomain({K, N}, {true, true}), DataType::Float);
fusion.addInput(tv0);
fusion.addInput(tv1);
TensorView* tv2 = broadcast(tv0, {false, false, true});
TensorView* tv3 = broadcast(tv1, {true, false, false});
TensorView* tv4 = add(tv2, tv3);
fusion.addOutput(tv4);
tv4->merge(0);
tv4->merge(0);
tv0->computeAt(tv4, -1);
tv1->computeAt(tv4, -1);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({m, k}, options);
at::Tensor t1 = at::randn({k, n}, options);
at::Tensor cg_output = at::empty({m, k, n}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0, t1}, {cg_output});
auto t2 = t0.unsqueeze(-1).expand({m, k, n});
auto t3 = t1.expand({m, k, n});
auto t4 = t2.add(t3);
TORCH_CHECK(t4.allclose(cg_output));
}
}
TEST(NVFuserTest, FusionComplexBCast_CUDA) {
{
Fusion fusion;
FusionGuard fg(&fusion);
int x = 2, y = 3, z = 4;
auto tv0 = makeConcreteTensor({y});
auto tv1 = div(tv0, new Float(2.0));
auto tv2 = broadcast(tv1, {false, true});
auto tv3 = makeConcreteTensor({y, z});
auto tv4 = mul(tv2, tv3);
auto tv5 = broadcast(tv4, {true, false, false});
auto tv6 = makeConcreteTensor({x, y, z});
auto tv7 = add(tv5, tv6);
// tv0[ i1 ] = input
// tv1[ i1 ] = tv0/2.0
// tv2[ i1, b2] = bcast(tv1)
// tv3[ i1, i2] = input
// tv4[ i1, i2] = tv2 * tv3
// tv5[b0, i1, i2] = bcast(tv4)
// tv6[i0, i1, i2] = input
// tv7[i0, i1, i2] = tv5 + tv6
// tv4 = bcast(tv1) * tv3
// tv7 = bcast(tv4) + tv6
fusion.addInput(tv0);
fusion.addInput(tv3);
fusion.addInput(tv6);
fusion.addOutput(tv7);
tv7->merge(0);
tv7->merge(0);
tv0->computeAt(tv7, -1);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({y}, options);
at::Tensor t3 = at::randn({y, z}, options);
at::Tensor t6 = at::randn({x, y, z}, options);
auto t4 = t0.div(2.0).unsqueeze(-1).expand({y, z}) * t3;
auto t7 = t4.unsqueeze(0).expand({x, y, z}) + t6;
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t3, t6});
TORCH_CHECK(t7.allclose(outputs[0]));
}
{
Fusion fusion;
FusionGuard fg(&fusion);
int x = 2, y = 3, z = 4;
auto tv0 = makeConcreteTensor({y, z});
auto tv1 = div(tv0, new Float(2.0));
auto tv2 = sum(tv1, {1});
auto tv3 = broadcast(tv2, {true, false});
auto tv4 = makeConcreteTensor({x, y});
auto tv5 = add(tv3, tv4);
// tv0[ i1, i2] = input
// tv1[ i1, i2] = tv0/2.0
// tv2[ i1 ] = sum(tv1, 1)
// tv3[b0, i1 ] = bcast(tv2)
// tv4[i0, i1 ] = input
// tv5[i0, i1 ] = tv3 + tv4
// tv2 = sum(tv0/2.0, 1)
// tv5 = bcast(tv2) + tv4
fusion.addInput(tv0);
fusion.addInput(tv4);
fusion.addOutput(tv5);
tv5->merge(0);
tv0->computeAt(tv5, -1);
tv1->computeAt(tv2, -1);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({y, z}, options);
auto t1 = t0.div(2.0);
auto t2 = t1.sum(1);
auto t3 = t2.unsqueeze(0).expand({x, y});
at::Tensor t4 = at::randn({x, y}, options);
auto t5 = t3.add(t4);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t4});
TORCH_CHECK(t5.allclose(outputs[0]));
}
}
TEST(NVFuserTest, FusionAdvancedIndexing_CUDA) {
// Merging left to right is still broken in some instances. Indexing can't
// complete because we assume we can simply traverse consumer->producer in the
// index/extent map, but this case breaks this assumption.
{
Fusion fusion;
FusionGuard fg(&fusion);
int w = 3, x = 4, y = 7, z = 8;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
auto tv0 = makeDummyTensor(3);
auto tv1 = makeDummyTensor(4);
fusion.addInput(tv0);
fusion.addInput(tv1);
auto tv2 = add(tv0, new Float(1.0));
auto tv3 = broadcast(tv2, {true, false, false, false});
auto tv4 = add(tv3, tv1);
fusion.addOutput(tv4);
tv4->merge(0);
tv4->merge(0);
tv4->merge(0);
tv4->split(0, 128);
tv4->split(0, 4);
tv2->computeAt(tv4, 1);
tv4->axis(0)->parallelize(ParallelType::BIDx);
tv4->axis(1)->parallelize(ParallelType::Unroll);
tv4->axis(2)->parallelize(ParallelType::TIDx);
tv3->axis(1)->parallelize(ParallelType::Unroll);
tv3->axis(2)->parallelize(ParallelType::TIDx);
tv2->axis(1)->parallelize(ParallelType::Unroll);
tv2->axis(2)->parallelize(ParallelType::TIDx);
torch::jit::fuser::cuda::FusionExecutor fe;
at::Tensor t0 = at::randn({x, y, z}, options);
at::Tensor t1 = at::randn({w, x, y, z}, options);
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
auto t3 = t0.add(1.0);
auto t4 = t3.add(t1);
TORCH_CHECK(t4.allclose(outputs[0]));
}
// Merging right to left actually does work.
{
Fusion fusion;
FusionGuard fg(&fusion);
int w = 3, x = 4, y = 7, z = 8;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
auto tv0 = makeDummyTensor(3);
auto tv1 = makeDummyTensor(4);
fusion.addInput(tv0);
fusion.addInput(tv1);
auto tv2 = add(tv0, new Float(1.0));
auto tv3 = broadcast(tv2, {true, false, false, false});
auto tv4 = add(tv3, tv1);
fusion.addOutput(tv4);
tv4->merge(-2);
tv4->merge(-2);
tv4->merge(-2);
tv4->split(0, 128);
tv4->split(0, 4);
tv2->computeAt(tv4, 1);
tv4->axis(0)->parallelize(ParallelType::BIDx);
tv4->axis(1)->parallelize(ParallelType::Unroll);
tv4->axis(2)->parallelize(ParallelType::TIDx);
tv3->axis(1)->parallelize(ParallelType::Unroll);
tv3->axis(2)->parallelize(ParallelType::TIDx);
tv2->axis(1)->parallelize(ParallelType::Unroll);
tv2->axis(2)->parallelize(ParallelType::TIDx);
torch::jit::fuser::cuda::FusionExecutor fe;
at::Tensor t0 = at::randn({x, y, z}, options);
at::Tensor t1 = at::randn({w, x, y, z}, options);
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
auto t3 = t0.add(1.0);
auto t4 = t3.add(t1);
TORCH_CHECK(t4.allclose(outputs[0]));
}
// Same issue as the first one in this section
{
Fusion fusion;
FusionGuard fg(&fusion);
int w = 3, x = 4, y = 7, z = 8;
auto tv0 = makeDummyTensor(3);
auto tv1 = makeDummyTensor(4);
fusion.addInput(tv0);
fusion.addInput(tv1);
auto tv2 = add(tv0, new Float(1.0));
auto tv3 = add(tv2, tv1);
fusion.addOutput(tv3);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({x, y, z}, options);
at::Tensor t1 = at::randn({w, x, y, z}, options);
fuser::cuda::scheduleFusion(&fusion, {t0, t1});
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
auto t2 = t0.add(1.0);
auto t3 = t2.add(t1);
TORCH_CHECK(t3.allclose(outputs[0]));
}
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeConcreteTensor({10, 20});
fusion.addInput(tv0);
TensorView* tv1 = makeConcreteTensor({10, 10, 20});
fusion.addInput(tv1);
TensorView* tv2 = add(tv0, new Float(1));
TensorView* tv3 = broadcast(tv2, {true, false, false});
TensorView* tv4 = add(tv3, tv1);
fusion.addOutput(tv4);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({10, 20}, options);
at::Tensor t1 = at::randn({10, 10, 20}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
auto t2 = t0.add(1.0);
auto t3 = t2.add(t1);
TORCH_CHECK(t3.allclose(outputs[0]));
}
}
// Test a simple Gemm but also play around with fusion executor features
TEST(NVFuserTest, FusionSimpleGemm_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2); // M, K
TensorView* tv1 = makeDummyTensor(2); // K, N
fusion.addInput(tv0);
fusion.addInput(tv1);
TensorView* tv2 = broadcast(tv0, {false, false, true});
// tv2[I0, I1, B] = tv0[I0, I1]
TensorView* tv3 = broadcast(tv1, {true, false, false});
// tv3[B, I1, I2] = tv1[I1, I2]
// tv4[I0, I1, I2] = tv2[I0, I1, B] * tv3[B, I1, I2]
TensorView* tv4 = mul(tv2, tv3);
// tv5[I0, R1, I2] = tv4[I0, I1, I2]
TensorView* tv5 = sum(tv4, {1});
fusion.addOutput(tv5);
tv5->split(1, 32);
// tv5[I0, R1o, R1i{32}, I2]
auto tv6 = tv5->rFactor({1});
// tv6[I0, R1o, I1i{32}, I2] = tv4[I0, I1, I2]
// tv5[I0, , R1i{32}, I2] = tv6[I0, R1o, I1i{32}, I2]
tv5->split(0, 4);
tv5->split(-1, 4);
// tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}]
// tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}]
tv0->computeAt(tv5, -1);
tv1->computeAt(tv5, -1);
// tv6[I0o, I0i{4}, R1o, I1i{32}, I2o, I2i{4}]
// tv5[I0o, I0i{4}, , R1i{32}, I2o, I2i{4}]
//--> (line symbolizes compute at location)
// tv4[I0o, I0i{4}, I1i{32}, I2o, I2i{4}|, I1o]
// tv6[I0o, I0i{4}, I1i{32}, I2o, I2i{4}|, R1o]
// tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}|]
tv0->computeAt(tv6, -1);
tv1->computeAt(tv6, -1);
// tv4[I0o, I0i{4}, I1i{32}, I2o, I2i{4}, I1o |]
// tv6[I0o, I0i{4}, I1i{32}, I2o, I2i{4}, R1o |]
// tv5[I0o, I0i{4}, R1i{32}, I2o, I2i{4}|]
tv5->axis(0)->parallelize(ParallelType::BIDz);
tv5->axis(1)->parallelize(ParallelType::TIDz);
tv5->axis(-2)->parallelize(ParallelType::BIDy);
tv5->axis(-1)->parallelize(ParallelType::TIDy);
tv5->axis(2)->parallelize(ParallelType::TIDx);
tv6->axis(2)->parallelize(ParallelType::TIDx);
constexpr int M = 65, K = 33, N = 17;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M, K}, options);
at::Tensor t1 = at::randn({K, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
// Lets specify a few bounds in launch params to make sure it works
fe.runFusion(
{t0, t1}, torch::jit::fuser::cuda::LaunchParams(1, -1, -1, 32, 4, 4));
// Make sure bad launch params throws
ASSERT_ANY_THROW(fe.runFusion(
{t0, t1}, torch::jit::fuser::cuda::LaunchParams(1, 2, 3, 4, 5, 6)));
// Don't specify any launch params
auto outputs = fe.runFusion({t0, t1});
auto t2 = t0.matmul(t1);
TORCH_CHECK(
t2.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
t2.sub(outputs[0]).abs().max());
}
// Softmax with a 1D tensor. Parallelized only with a single thread block.
TEST(NVFuserTest, FusionSoftmax1D_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int tidx = 128;
const int dimx = 1000;
// Set up your input tensor views
TensorView* input_tv0 = makeDummyTensor(1);
fusion.addInput(input_tv0);
TensorView* exp_tv1 = unaryOp(UnaryOpType::Exp, input_tv0);
TensorView* sum_exp_tv2 = sum(exp_tv1, {-1});
TensorView* bcast_sum_tv3 = broadcast(sum_exp_tv2, {true});
// Replicate exp_tv4 as exp_tv4_copy because exp_tv4 is going to be
// computed at sum_exp_rf_tv8.
TensorView* exp_tv1_copy = unaryOp(UnaryOpType::Exp, input_tv0);
TensorView* output_tv4 = div(exp_tv1_copy, bcast_sum_tv3);
fusion.addOutput(output_tv4);
bcast_sum_tv3->split(0, tidx);
sum_exp_tv2->split(-1, tidx);
TensorView* sum_exp_rf_tv5 = sum_exp_tv2->rFactor({-2});
output_tv4->split(-1, tidx);
exp_tv1->computeAt(sum_exp_rf_tv5, -1);
exp_tv1_copy->computeAt(output_tv4, -1);
TensorView* tensors_to_parallelize[] = {
sum_exp_tv2, bcast_sum_tv3, output_tv4, sum_exp_rf_tv5};
for (auto tv : tensors_to_parallelize) {
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({dimx}, options);
at::Tensor cg_output = at::empty({dimx}, options);
at::Tensor t3_output = at::empty_like(cg_output, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {cg_output});
auto t2 = at::_softmax(t0, -1, false);
TORCH_CHECK(
t2.allclose(cg_output, 1e-5, 1e-5),
"Error of: ",
t2.sub(cg_output).abs().max());
}
// Softmax with a 1D tensor with input normalization.
TEST(NVFuserTest, FusionSoftmax1DNormalized_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int tidx = 128;
const int dimx = 1000;
// Set up your input tensor views
TensorView* input_tv0 = makeDummyTensor(1);
fusion.addInput(input_tv0);
// Normalize with the max value before computing exp.
TensorView* max_val_tv1 =
reductionOp(BinaryOpType::Max, {-1}, new Float(0), input_tv0);
TensorView* bcast_max_tv2 = broadcast(max_val_tv1, {true});
TensorView* sub_tv3 = sub(input_tv0, bcast_max_tv2);
TensorView* exp_tv4 = unaryOp(UnaryOpType::Exp, sub_tv3);
TensorView* sum_exp_tv5 = sum(exp_tv4, {-1});
TensorView* bcast_sum_tv6 = broadcast(sum_exp_tv5, {true});
// Replicate exp_tv4 as exp_tv4_copy because exp_tv4 is going to be
// computed at sum_exp_rf_tv8.
TensorView* sub_tv3_copy = sub(input_tv0, bcast_max_tv2);
TensorView* exp_tv4_copy = unaryOp(UnaryOpType::Exp, sub_tv3_copy);
TensorView* output_tv7 = div(exp_tv4_copy, bcast_sum_tv6);
fusion.addOutput(output_tv7);
bcast_max_tv2->split(0, tidx);
bcast_sum_tv6->split(0, tidx);
max_val_tv1->split(-1, tidx);
TensorView* max_val_rf_tv8 = max_val_tv1->rFactor({-2});
sum_exp_tv5->split(-1, tidx);
TensorView* sum_exp_rf_tv9 = sum_exp_tv5->rFactor({-2});
output_tv7->split(-1, tidx);
sub_tv3->computeAt(sum_exp_rf_tv9, -1);
sub_tv3_copy->computeAt(output_tv7, -1);
TensorView* tensors_to_parallelize[] = {max_val_tv1,
bcast_max_tv2,
sum_exp_tv5,
bcast_sum_tv6,
output_tv7,
max_val_rf_tv8,
sum_exp_rf_tv9};
for (auto tv : tensors_to_parallelize) {
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({dimx}, options);
at::Tensor t3_output = at::empty({dimx}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0});
auto t2 = at::_softmax(t0, -1, false);
TORCH_CHECK(
t2.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
t2.sub(outputs[0]).abs().max());
}
// Softmax with a 3D tensor, where the inner-most 3rd dimension is
// normalized. Pallelized with multiple thread blocks.
TEST(NVFuserTest, FusionSoftmax3D_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int tidx = 32;
const int dimx = 32;
const int dimy = 16;
const int dimz = 130;
// Set up your input tensor views
TensorView* input_tv0 = makeDummyTensor(3);
fusion.addInput(input_tv0);
TensorView* exp_tv1 = unaryOp(UnaryOpType::Exp, input_tv0);
TensorView* sum_exp_tv2 = sum(exp_tv1, {-1});
TensorView* bcast_sum_tv3 = broadcast(sum_exp_tv2, {false, false, true});
// Replicate exp_tv4 as exp_tv4_copy because exp_tv4 is going to be
// computed at sum_exp_rf_tv8.
TensorView* exp_tv1_copy = unaryOp(UnaryOpType::Exp, input_tv0);
TensorView* output_tv4 = div(exp_tv1_copy, bcast_sum_tv3);
fusion.addOutput(output_tv4);
bcast_sum_tv3->split(-1, tidx);
sum_exp_tv2->split(-1, tidx);
TensorView* sum_exp_rf_tv5 = sum_exp_tv2->rFactor({-2});
output_tv4->split(-1, tidx);
exp_tv1->computeAt(sum_exp_rf_tv5, -1);
exp_tv1_copy->computeAt(output_tv4, -1);
TensorView* tensors_to_parallelize[] = {
sum_exp_tv2, bcast_sum_tv3, output_tv4, sum_exp_rf_tv5};
for (auto tv : tensors_to_parallelize) {
tv->axis(0)->parallelize(ParallelType::BIDx);
tv->axis(1)->parallelize(ParallelType::BIDy);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({dimx, dimy, dimz}, options);
at::Tensor cg_output = at::empty({dimx, dimy, dimz}, options);
at::Tensor t3_output = at::empty_like(cg_output, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0}, {cg_output});
auto t2 = at::_softmax(t0, -1, false);
TORCH_CHECK(
t2.allclose(cg_output, 1e-5, 1e-5),
"Error of: ",
t2.sub(cg_output).abs().max());
}
// Softmax with a 3D tensor with input normalization.
TEST(NVFuserTest, FusionSoftmax3DNormalized_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int tidx = 32;
const int dimx = 32;
const int dimy = 16;
const int dimz = 130;
// Set up your input tensor views
TensorView* input_tv0 = makeDummyTensor(3);
fusion.addInput(input_tv0);
// Normalize with the max value before computing exp.
TensorView* max_val_tv1 =
reductionOp(BinaryOpType::Max, {-1}, new Float(0), input_tv0);
TensorView* bcast_max_tv2 = broadcast(max_val_tv1, {false, false, true});
TensorView* sub_tv3 = sub(input_tv0, bcast_max_tv2);
TensorView* exp_tv4 = unaryOp(UnaryOpType::Exp, sub_tv3);
TensorView* sum_exp_tv5 = sum(exp_tv4, {-1});
TensorView* bcast_sum_tv6 = broadcast(sum_exp_tv5, {false, false, true});
// Replicate exp_tv4 as exp_tv4_copy because exp_tv4 is going to be
// computed at sum_exp_rf_tv8.
TensorView* sub_tv3_copy = sub(input_tv0, bcast_max_tv2);
TensorView* exp_tv4_copy = unaryOp(UnaryOpType::Exp, sub_tv3_copy);
TensorView* output_tv7 = div(exp_tv4_copy, bcast_sum_tv6);
fusion.addOutput(output_tv7);
bcast_max_tv2->split(-1, tidx);
bcast_sum_tv6->split(-1, tidx);
max_val_tv1->split(-1, tidx);
TensorView* max_val_rf_tv8 = max_val_tv1->rFactor({-2});
sum_exp_tv5->split(-1, tidx);
TensorView* sum_exp_rf_tv9 = sum_exp_tv5->rFactor({-2});
output_tv7->split(-1, tidx);
sub_tv3->computeAt(sum_exp_rf_tv9, -1);
sub_tv3_copy->computeAt(output_tv7, -1);
TensorView* tensors_to_parallelize[] = {max_val_tv1,
bcast_max_tv2,
sum_exp_tv5,
bcast_sum_tv6,
output_tv7,
max_val_rf_tv8,
sum_exp_rf_tv9};
for (auto tv : tensors_to_parallelize) {
tv->axis(0)->parallelize(ParallelType::BIDx);
tv->axis(1)->parallelize(ParallelType::BIDy);
tv->axis(-1)->parallelize(ParallelType::TIDx);
}
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({dimx, dimy, dimz}, options);
at::Tensor t3_output = at::empty({dimx, dimy, dimz}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0});
auto t2 = at::_softmax(t0, -1, false);
TORCH_CHECK(
t2.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
t2.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionSoftmaxComputeAt_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
auto tv1 = sum(tv0, {1});
auto tv2 = broadcast(tv1, {false, true});
auto tv3 = add(tv0, new Float(1.0));
auto tv4 = mul(tv2, tv3);
auto tv5 = sum(tv4, {1});
auto tv6 = broadcast(tv5, {false, true});
auto tv7 = sub(tv6, tv4);
fusion.addOutput(tv7);
tv1->computeAt(tv7, 1);
ASSERT_ANY_THROW(tv1->computeAt(tv7, -1));
}
// Similar to FusionReduction but uses grid reduction
TEST(NVFuserTest, FusionGridReduction1_CUDA) {
const int gdimx = 32;
const int bdimx = 128;
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(1, bdimx);
// tv1[I0, R1o, R1i{128}] = tv0[I0, I1]
tv1->split(1, gdimx);
// tv1[I0, R1oo, R1oi{32}, R1i{128}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{32}, Ir1i{128}] = tv0[I0, I1]
// tv1[I0, R1oi{32}, R1i{128}] = tv2[I0, R1oo, Ir1oi{32}, Ir1i{128}]
// Incrementally, can print in between for debugging
tv0->computeAt(tv2, 1);
tv2->computeAt(tv1, 1);
// Re do it all at once, because why not.
tv0->computeAt(tv1, 1);
tv1->axis(0)->parallelize(ParallelType::BIDy);
tv1->axis(1)->parallelize(ParallelType::BIDx);
tv2->axis(2)->parallelize(ParallelType::BIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
int numel_x = 10000;
int numel_y = 65000;
// fusion.printKernel();
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
at::Tensor cg_output = at::empty({numel_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(cg_output));
}
// Same test as the above but uses BIDy and TIDx for reduction
TEST(NVFuserTest, FusionGridReduction2_CUDA) {
const int gdimy = 32;
const int bdimx = 128;
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(1, bdimx);
// tv1[I0, R1o, R1i{128}] = tv0[I0, I1]
tv1->split(1, gdimy);
// tv1[I0, R1oo, R1oi{32}, R1i{128}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{32}, Ir1i{128}] = tv0[I0, I1]
// tv1[I0, R1oi{32}, R1i{128}] = tv2[I0, R1oo, Ir1oi{32}, Ir1i{128}]
// Incrementally, can print in between for debugging
tv0->computeAt(tv2, 1);
tv2->computeAt(tv1, 1);
// Re do it all at once, because why not.
tv0->computeAt(tv1, 1);
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(1)->parallelize(ParallelType::BIDy);
tv2->axis(2)->parallelize(ParallelType::BIDy);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
int numel_x = 10000;
int numel_y = 65000;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(outputs[0]));
}
// Same test but uses BIDy and BIDz for reduction. No TID used.
TEST(NVFuserTest, FusionGridReduction3dim1_CUDA) {
const int gdimz = 32;
const int gdimy = 128;
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(1, gdimy);
// tv1[I0, R1o, R1i{128}] = tv0[I0, I1]
tv1->split(1, gdimz);
// tv1[I0, R1oo, R1oi{32}, R1i{128}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{32}, Ir1i{128}] = tv0[I0, I1]
// tv1[I0, R1oi{32}, R1i{128}] = tv2[I0, R1oo, Ir1oi{32}, Ir1i{128}]
// Incrementally, can print in between for debugging
tv0->computeAt(tv2, 1);
tv2->computeAt(tv1, 1);
// Re do it all at once, because why not.
tv0->computeAt(tv1, 1);
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(1)->parallelize(ParallelType::BIDz);
tv2->axis(2)->parallelize(ParallelType::BIDz);
tv1->axis(-1)->parallelize(ParallelType::BIDy);
tv2->axis(-1)->parallelize(ParallelType::BIDy);
int numel_x = 100;
int numel_y = 6500;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
at::Tensor cg_output = at::empty({numel_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(cg_output));
}
// Same as testGPU_FusionGridReduction3dim1 but reduces dimension 0
TEST(NVFuserTest, FusionGridReduction3dim0_CUDA) {
const int rdim = 0;
const int gdimy = 128;
const int gdimz = 32;
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[R0, I1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {rdim}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(rdim, gdimy);
// tv1[R0o, R0i{128}, I1] = tv0[I0, I1]
tv1->split(rdim, gdimz);
// tv1[R0oo, R0oi{32}, R0i{128}, I1] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({rdim});
// tv2[R0oo, I0oi{32}, I0i{128}, I1] = tv0[I0, I1]
// tv1[ R0oi{32}, R0i{128}, I1] = tv2[R0oo, I0oi{32}, I0i{128}, I1]
// Note that computeAt isn't going to make anything better as there
// is no dynamically sized dimension.
// Map parallelism as [Serial, BIDz, BIDy, BIDx]
tv1->axis(-1)->parallelize(ParallelType::BIDx);
tv2->axis(-1)->parallelize(ParallelType::BIDx);
tv1->axis(-2)->parallelize(ParallelType::BIDy);
tv2->axis(-2)->parallelize(ParallelType::BIDy);
tv1->axis(-3)->parallelize(ParallelType::BIDz);
tv2->axis(-3)->parallelize(ParallelType::BIDz);
int numel_x = 6500;
int numel_y = 100;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = input.sum({0});
TORCH_CHECK(aten_output.allclose(outputs[0]));
}
// This is similar to the FusionReduction, but swaps BIDx and TIDx
TEST(NVFuserTest, FusionGridReduction4_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int bdimx = 128;
const int gdimx = 1024;
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(1, gdimx);
// tv1[I0, R1o, R1i{1024}] = tv0[I0, I1]
tv1->split(1, 4);
// tv1[I0, R1oo, R1oi{4}, R1i{128}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{4}, Ir1i{1024}] = tv0[I0, I1]
// tv1[I0, R1oi{4}, R1i{1024}] = tv2[I0, R1oo, Ir1oi{4}, Ir1i{1024}]
TensorView* tv3 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{4}, Ir1i{1024}] = tv0[I0, I1]
// tv3[I0, R1oi{4}, Ir1i{1024}] = tv2[I0, R1oo, Ir1oi{4}, Ir1i{1024}]
// tv1[I0, R1i{1024}] = tv3[I0, R1oi{4}, Ir1i{1024}]
// Incrementally, can print in between for debugging
tv0->computeAt(tv2, 1);
tv2->computeAt(tv3, 1);
tv3->computeAt(tv1, 1);
// Re do it all at once, because why not.
tv0->computeAt(tv1, 1);
tv2->axis(2)->parallelize(ParallelType::Unroll);
tv1->axis(0)->parallelize(ParallelType::TIDx);
tv1->axis(-1)->parallelize(ParallelType::BIDx);
tv2->axis(-1)->parallelize(ParallelType::BIDx);
tv3->axis(-1)->parallelize(ParallelType::BIDx);
int numel_x = bdimx;
int numel_y = 65000;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
at::Tensor cg_output = at::empty({numel_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(cg_output));
}
// Grid reduction with 2D thread blocks but only TIDx and BIDx are
// mapped to a reduction dim
TEST(NVFuserTest, FusionGridReduction5_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int bdimx = 64;
const int bdimy = 16;
const int gdimx = 4;
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
tv1->split(1, bdimx);
// tv1[I0, R1o, R1i{64}] = tv0[I0, I1]
tv1->split(1, gdimx);
// tv1[I0, R1oo, R1oi{4}, R1i{64}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{4}, Ir1i{64}] = tv0[I0, I1]
// tv1[I0, R1oi{4}, R1i{64}] = tv2[I0, R1oo, Ir1oi{4}, Ir1i{64}]
tv0->computeAt(tv1, 1);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(-2)->parallelize(ParallelType::BIDx);
tv2->axis(-2)->parallelize(ParallelType::BIDx);
tv1->axis(0)->parallelize(ParallelType::TIDy);
int numel_x = bdimy;
int numel_y = 6500;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(outputs[0]));
}
// Similar to FusionGridReduction1 but with 3D tensors
TEST(NVFuserTest, FusionGridReduction6_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(3);
fusion.addInput(tv0);
// tv1[I0, R1, R2] = tv0[I0, I1, I2]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1, 2}, new Float(0), tv0);
fusion.addOutput(tv1);
TORCH_CHECK(fusion.hasReduction(), "Could not detect reduction in fusion.");
// Splitting for TID
tv1->split(2, 128);
// tv1[I0, R1, R2o, R2i{128}] = tv0[I0, I1, I2]
// Splitting for BID
tv1->split(1, 128);
// tv1[I0, R1o, R1i{128}, R2o, R2i{128}] = tv0[I0, I1, I2]
TensorView* tv2 = tv1->rFactor({3});
// tv2[I0, I1o, I1i{128}, R2o, I2i{128}]
// tv1[I0, R1o, R1i{128}, R2i{128}]
TensorView* tv3 = tv1->rFactor({1});
// tv2[I0, I1o, I1i{128}, R2o, I2i{128}]
// tv3[I0, R1o, I1i{128}, I2i{128}]
// tv1[I0, R1i{128}, R2i{128}]
tv3->computeAt(tv1, 1);
tv2->computeAt(tv3, 3);
tv1->axis(0)->parallelize(ParallelType::BIDy);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(-2)->parallelize(ParallelType::BIDx);
tv2->axis(-3)->parallelize(ParallelType::BIDx);
tv3->axis(-2)->parallelize(ParallelType::BIDx);
int numel_x = 6500;
int numel_y = 200;
int numel_z = numel_y;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y, numel_z}, options);
at::Tensor cg_output = at::empty({numel_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output});
auto aten_output = input.sum({1, 2});
TORCH_CHECK(aten_output.allclose(cg_output));
}
TEST(NVFuserTest, FusionNonRedAxisBind_CUDA) {
int bid_x = 3;
int tid_x = 2;
int red_dim = 0;
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 =
reductionOp(BinaryOpType::Add, {red_dim}, new Float(0), tv0);
fusion.addOutput(tv1);
tv1->split(-1, tid_x);
tv1->axis(-2)->parallelize(ParallelType::BIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({16, bid_x * tid_x}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = input.sum({red_dim});
TORCH_CHECK(
aten_output.allclose(outputs[0]),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionSplitBCast_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* input_tv0 = makeDummyTensor(3);
TensorView* input_tv1 = makeDummyTensor(3);
fusion.addInput(input_tv0);
fusion.addInput(input_tv1);
TensorView* sum_tv2 =
reductionOp(BinaryOpType::Add, {2}, new Float(0), input_tv0);
TensorView* bcast_tv3 = broadcast(sum_tv2, {false, false, true});
TensorView* output_tv4 = div(input_tv1, bcast_tv3);
sum_tv2->split(-1, 32);
TensorView* sum_rf_tv5 = sum_tv2->rFactor({-2});
bcast_tv3->split(-1, 32);
output_tv4->split(-1, 32);
sum_rf_tv5->axis(0)->parallelize(ParallelType::BIDx);
sum_tv2->axis(0)->parallelize(ParallelType::BIDx);
bcast_tv3->axis(0)->parallelize(ParallelType::BIDx);
output_tv4->axis(0)->parallelize(ParallelType::BIDx);
sum_rf_tv5->axis(1)->parallelize(ParallelType::BIDy);
sum_tv2->axis(1)->parallelize(ParallelType::BIDy);
bcast_tv3->axis(1)->parallelize(ParallelType::BIDy);
output_tv4->axis(1)->parallelize(ParallelType::BIDy);
sum_rf_tv5->axis(-1)->parallelize(ParallelType::TIDx);
sum_tv2->axis(-1)->parallelize(ParallelType::TIDx);
bcast_tv3->axis(-1)->parallelize(ParallelType::TIDx);
output_tv4->axis(-1)->parallelize(ParallelType::TIDx);
fusion.addOutput(output_tv4);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({32, 32, 128}, options);
at::Tensor t1 = at::randn({32, 32, 128}, options);
at::Tensor cg_output = at::empty({32, 32, 128}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({t0, t1}, {cg_output});
}
TEST(NVFuserTest, FusionBCastInnerDim_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// reduce then broadcast
auto tv1 = sum(tv0, {0});
auto tv2 = broadcast(tv1, {false, true});
TORCH_CHECK(!tv2->axis(0)->isReduction() && tv2->axis(1)->isBroadcast());
}
TEST(NVFuserTest, FusionBCastReduce_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
auto tv1 = broadcast(tv0, {true, false, false});
auto tv2 = sum(tv1, {1});
TORCH_CHECK(
tv2->axis(0)->isBroadcast() && tv2->axis(1)->isReduction() &&
!tv2->axis(2)->isBroadcast() && !tv2->axis(2)->isReduction());
}
// Multiple consumer reduction with computeAt
// https://github.com/csarofeen/pytorch/issues/110
TEST(NVFuserTest, FusionReductionMultiConsumer_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
auto tv1 = unaryOp(UnaryOpType::Exp, tv0);
auto tv2 = reductionOp(BinaryOpType::Max, {-1}, new Float(0), tv1);
auto tv3 = reductionOp(BinaryOpType::Min, {-1}, new Float(0), tv1);
auto tv4 = add(tv2, tv3);
fusion.addOutput(tv4);
tv1->computeAt(tv2, -1);
TORCH_CHECK(
(tv1->getComputeAtView() == tv2 || tv1->getComputeAtView() == tv3) &&
tv1->getThisComputeAtAxis() == 2 && tv1->getRelativeComputeAtAxis() == 2);
}
TEST(NVFuserTest, FusionComputeAtExprOrder_CUDA) {
{
for (int i = 0; i < 2; ++i) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
auto tv1 = add(tv0, new Float(1));
auto tv2 = add(tv0, new Float(1));
TensorView* tv3 = add(tv1, tv2);
if (i == 0) {
tv1->computeAt(tv3, -1);
fusion.addOutput(tv2);
} else {
tv2->computeAt(tv3, -1);
fusion.addOutput(tv1);
}
fusion.addOutput(tv3);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({100}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = (input + 1) * 2;
TORCH_CHECK(
aten_output.allclose(outputs[1]),
"Error of: ",
aten_output.sub(outputs[1]).abs().max());
}
}
{
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
auto tv1 = add(tv0, new Float(1));
auto tv2 = add(tv0, new Float(1));
TensorView* tv3 = add(tv1, tv2);
fusion.addOutput(tv3);
tv3->split(-1, 32);
tv1->computeAt(tv3, -1);
tv2->computeAt(tv3, -2);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({100, 100}, options);
at::Tensor output = at::empty_like(input, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {output});
auto aten_output = (input + 1) * 2;
TORCH_CHECK(
aten_output.allclose(output),
"Error of: ",
aten_output.sub(output).abs().max());
}
}
TEST(NVFuserTest, FusionZeroDimComputeAt_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
auto tv1 = sum(tv0, {0});
auto tv2 = add(tv1, new Float(1));
fusion.addOutput(tv2);
TORCH_CHECK(tv2->nDims() == 0);
tv1->computeAt(tv2, 0);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({100}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = input.sum() + 1;
TORCH_CHECK(
aten_output.allclose(outputs[0]),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionZeroDimBroadcast_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(0);
fusion.addInput(tv0);
auto tv1 = broadcast(tv0, {true, true});
TORCH_CHECK(tv1->nDims() == 2);
TensorView* tv2 = makeDummyTensor(2);
fusion.addInput(tv2);
auto tv3 = add(tv1, tv2);
auto tv4 = sum(tv3, {0, 1});
fusion.addOutput(tv4);
tv3->computeAt(tv4, -1);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input1 = at::rand({}, options);
at::Tensor input2 = at::rand({10, 10}, options);
at::Tensor output = at::empty({}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input1, input2}, {output});
auto aten_output =
(input1.unsqueeze(-1).unsqueeze(-1).expand({10, 10}) + input2).sum();
TORCH_CHECK(
aten_output.allclose(output),
"Error of: ",
aten_output.sub(output).abs().max());
}
TEST(NVFuserTest, FusionZeroDimReduction_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int bdimx = 32;
const int gdimx = 32;
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
auto tv1 = sum(tv0, {0});
fusion.addOutput(tv1);
tv1->split(0, bdimx);
tv1->split(0, gdimx);
auto tv2 = tv1->rFactor({0});
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(-2)->parallelize(ParallelType::BIDx);
tv2->axis(-2)->parallelize(ParallelType::BIDx);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({1000}, options);
at::Tensor output = at::empty({}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {output});
auto aten_output = input.sum();
TORCH_CHECK(
aten_output.allclose(output),
"Error of: ",
aten_output.sub(output).abs().max());
}
TEST(NVFuserTest, FusionBCastAfterReduce_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
const int tidx = 128;
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
auto tv1 = sum(tv0, {1});
auto tv2 = broadcast(tv1, {false, true});
tv1->split(1, tidx);
auto tv3 = tv1->rFactor({-2});
TensorView* tv4 = makeDummyTensor(2);
fusion.addInput(tv4);
auto tv5 = add(tv2, tv4);
fusion.addOutput(tv5);
tv5->split(1, tidx);
tv3->computeAt(tv5, 1);
tv2->split(1, tidx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv5->axis(-1)->parallelize(ParallelType::TIDx);
tv5->axis(0)->parallelize(ParallelType::BIDx);
int x = 63, y = 200;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({x, y}, options);
at::Tensor t4 = at::randn({x, y}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t4});
auto t3 = t0.sum({1}).unsqueeze(-1).expand({x, y});
auto t5 = t3.add(t4);
// Error is larger than the default threshold
TORCH_CHECK(t5.allclose(outputs[0], 1e-5, 1e-5));
}
TEST(NVFuserTest, FusionReductionScheduler_CUDA) {
constexpr int bid_x = 80;
constexpr int tid_x = 4096;
constexpr int red_dim = 1;
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 =
reductionOp(BinaryOpType::Add, {red_dim}, new Float(0), tv0);
fusion.addOutput(tv1);
const auto options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::randn({bid_x, tid_x}, options);
// Apply reduction heuristic
auto reduction_params = cuda::getReductionHeuristics(&fusion, {input}, tv1);
TORCH_CHECK(reduction_params, "Reduction schedule was not generated!");
cuda::scheduleReduction(&fusion, reduction_params.value(), tv1, {});
cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
// no broadcasting needed, omitting the last optional argument;
auto outputs = fe.runFusion({input}, reduction_params.value().lparams);
auto aten_output = input.sum({red_dim});
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-04, 1e-04),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
// Simple reduction parallelized on a symbolic size.
TEST(NVFuserTest, FusionSymbolicReduction_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
// tv1[I0, R1] = tv0[I0, I1]
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addOutput(tv1);
// Interface should just be a direct split with a Parallel type. We can
// include the parallelize call if we do this.
tv1->split(1, NamedScalar::getParallelDim(ParallelType::TIDx));
// tv1[I0, R1o, R1i{BIDx}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({1});
// tv2[I0, R1oo, Ir1oi{4}, Ir1i{BIDx}] = tv0[I0, I1]
// tv1[I0, R1oi{4}, R1i{BIDx}] = tv2[I0, R1oo, Ir1oi{4}, Ir1i{BIDx}]
// Incrementally, can print in between for debugging
tv0->computeAt(tv2, 1);
tv2->computeAt(tv1, 1);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
int numel_x = 65000;
int numel_y = 1025;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
// How many threads to use for the block reduction
int runtime_threadIdx_dim = 128;
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion(
{input},
torch::jit::fuser::cuda::LaunchParams(
-1, -1, -1, runtime_threadIdx_dim, -1, -1));
auto aten_output = input.sum({1});
TORCH_CHECK(aten_output.allclose(outputs[0]));
}
TEST(NVFuserTest, FusionReductionSchedulerMultiDimNonFastest_CUDA) {
const std::vector<int> red_dims = {0, 2};
// Copy is because CodeGen requires int and Pytorch requires int64_t
// for a vector of reduction dimensions
const std::vector<int64_t> red_dims64 = {0, 2};
const std::vector<int64_t> tensor_dims_in = {5, 10, 15, 20};
const std::vector<int64_t> tensor_dims_out = {10, 20};
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(tensor_dims_in.size());
fusion.addInput(tv0);
TensorView* tv1 = reductionOp(BinaryOpType::Add, red_dims, new Float(0), tv0);
fusion.addOutput(tv1);
const auto options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::randn(tensor_dims_in, options);
at::Tensor cg_output = at::empty(tensor_dims_out, options);
// Apply reduction heuristic
auto reduction_params = cuda::getReductionHeuristics(&fusion, {input}, tv1);
TORCH_CHECK(reduction_params, "Reduction schedule was not generated!");
cuda::scheduleReduction(&fusion, reduction_params.value(), tv1, {});
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input}, reduction_params.value().lparams);
auto aten_output = input.sum(red_dims64);
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-04, 1e-04),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionReductionSchedulerMultiDimFastest_CUDA) {
const std::vector<int> red_dims = {1, 3};
// Copy is because CodeGen requires int and Pytorch requires int64_t
// for a vector of reduction dimensions
const std::vector<int64_t> red_dims64 = {1, 3};
const std::vector<int64_t> tensor_dims_in = {5, 10, 15, 20};
const std::vector<int64_t> tensor_dims_out = {5, 15};
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(tensor_dims_in.size());
fusion.addInput(tv0);
TensorView* tv1 = reductionOp(BinaryOpType::Add, red_dims, new Float(0), tv0);
fusion.addOutput(tv1);
const auto options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::randn(tensor_dims_in, options);
auto reduction_params = cuda::getReductionHeuristics(&fusion, {input}, tv1);
TORCH_CHECK(reduction_params, "Reduction schedule was not generated!");
cuda::scheduleReduction(&fusion, reduction_params.value(), tv1, {});
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input}, reduction_params.value().lparams);
auto aten_output = input.sum(red_dims64);
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-05, 1e-05),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionReductionSchedulerDimShmoo_CUDA) {
std::vector<bool> fp16_usage = {true, false};
std::vector<int> red_axis = {1, 0};
std::vector<int> output_dims = {320, 640};
std::vector<int> red_dims;
// Tried to cut down the number iterations with just
// doing every other power of 2.
for (int i = 1; i <= 1024 * 1024; i <<= 2) {
red_dims.push_back(i);
}
for (auto fp16 : fp16_usage) {
for (auto& axis : red_axis) {
for (auto& odim : output_dims) {
for (auto& rdim : red_dims) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 =
makeDummyTensor(2, (fp16 ? DataType::Half : DataType::Float));
fusion.addInput(tv0);
torch::jit::fuser::Val* tv0_cast = nullptr;
if (fp16) {
tv0_cast = castOp(DataType::Float, tv0);
}
TensorView* tv1 = reductionOp(
BinaryOpType::Add,
{axis},
new Float(0),
(fp16 ? tv0_cast->as<TensorView>() : tv0));
TensorView* tv1_cast = nullptr;
if (fp16) {
tv1_cast = castOp(DataType::Half, tv1);
}
fusion.addOutput((fp16 ? tv1_cast : tv1));
auto options = at::TensorOptions()
.dtype((fp16 ? at::kHalf : at::kFloat))
.device(at::kCUDA, 0);
at::Tensor input =
(axis ? at::randn({odim, rdim}, options)
: at::randn({rdim, odim}, options));
std::vector<TensorView*> outputs_of_red;
if (fp16) {
outputs_of_red.push_back(tv1_cast);
}
auto reduction_params =
cuda::getReductionHeuristics(&fusion, {input}, tv1);
TORCH_CHECK(reduction_params.has_value(), "Reduction is not found!");
cuda::scheduleReduction(
&fusion, reduction_params.value(), tv1, outputs_of_red);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs =
fe.runFusion({input}, reduction_params.value().lparams);
auto aten_output = input.sum({axis});
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-03, 1e-03),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
}
}
}
}
TEST(NVFuserTest, FusionCacheBefore_CUDA) {
// TVM Cache Write
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = add(tv0, new Float(1.0));
TensorView* tv2 = mul(tv1, new Float(3.0));
fusion.addInput(tv0);
fusion.addOutput(tv2);
// Before: TV2 = TV1 * 3
// After: TV3 = TV1 * 3;
// TV2 = TV3;
constexpr int BSX = 32;
tv2->split(-1, BSX);
tv0->computeAt(tv2, -1);
// cache_before automatically applies ComputeAt to the cache TensorView
tv2->cache_before();
// Thread and Block binding
tv2->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 32, N = 750;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({M, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
at::Tensor aten_output = (input + 1.0) * 3.0;
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().sum());
}
TEST(NVFuserTest, FusionCacheAfter_CUDA) {
// TVM Cache Read
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = add(tv0, new Float(1.0));
TensorView* tv2 = mul(tv1, new Float(3.0));
fusion.addInput(tv0);
fusion.addOutput(tv2);
// Before: TV1 = TV0 + 1
// After: TV3 = TV0;
// TV1 = TV3 + 1
constexpr int BSX = 32;
tv2->split(-1, BSX);
tv0->computeAt(tv2, -1);
// cache_after automatically applies ComputeAt to the cache TensorView
tv0->cache_after();
// Thread and Block binding
tv2->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 32, N = 457;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({M, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
at::Tensor aten_output = (input + 1.0) * 3.0;
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().sum());
}
TEST(NVFuserTest, FusionCacheIndirect_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
TensorView* tv2 = makeDummyTensor(2);
TensorView* tv3 = makeDummyTensor(2);
TensorView* tv4 = sub(tv2, tv3);
TensorView* tv5 = add(tv1, tv4);
TensorView* tv6 = sub(tv5, tv0);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addInput(tv2);
fusion.addInput(tv3);
fusion.addOutput(tv6);
// t6 = ((t1 + (t2 - t3)) - t0)
// cache_after on inputs placed before schedule
constexpr int BSX = 32;
tv6->split(-1, BSX);
tv2->computeAt(tv6, -1);
tv5->cache_after();
tv5->cache_before();
// Thread and Block binding
tv6->axis(0)->parallelize(ParallelType::BIDx);
tv6->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 32, N = 810;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor in0 = at::rand({M, N}, options);
at::Tensor in1 = at::rand({M, N}, options);
at::Tensor in2 = at::rand({M, N}, options);
at::Tensor in3 = at::rand({M, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({in0, in1, in2, in3});
at::Tensor aten_output = (in1 + (in2 - in3)) - in0;
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().sum());
}
TEST(NVFuserTest, FusionCacheBcast_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Algorithm
TensorView* tv0 = makeDummyTensor(1); // (M, 1)
TensorView* tv1 = broadcast(tv0, {false, true});
TensorView* tv2 = makeDummyTensor(1); // (1, N)
TensorView* tv3 = broadcast(tv2, {true, false});
TensorView* tv4 = mul(tv1, tv3);
fusion.addInput(tv0);
fusion.addInput(tv2);
fusion.addOutput(tv4);
constexpr int BSX = 128;
tv4->split(0, BSX);
tv4->split(-1, BSX);
tv4->reorder({{0, 0}, {1, 2}, {2, 1}, {3, 3}});
// M/BSX, N/BSY, BSX, BSY
tv0->computeAt(tv4, 2);
tv2->computeAt(tv4, 2);
// 0, 1 | 2, 3, 4
// Case 1
tv0->cache_after();
// Case 2
tv1->cache_before();
// Case 3
tv1->cache_after();
// Case 4
TensorView* tv8 = tv4->cache_before();
tv4->axis(0)->parallelize(ParallelType::BIDx);
tv4->axis(1)->parallelize(ParallelType::BIDy);
tv4->axis(-1)->parallelize(ParallelType::TIDx);
// Manual Replay on TV3
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv8->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 92, N = 500;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M}, options);
at::Tensor t1 = at::randn({N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
at::Tensor aten_output = t0.unsqueeze(1).matmul(t1.unsqueeze(0));
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionCacheComplex_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2); // (N, N)
TensorView* tv1 = makeDummyTensor(1); // (N)
TensorView* tv2 = sum(tv0, {1}); // (N)
TensorView* tv3 = broadcast(tv2, {false, true}); // (N, 1)
TensorView* tv4 = broadcast(tv1, {true, false}); // (1, N)
TensorView* tv5 = mul(tv3, tv4); // (N, N)
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv5);
// Exception: Cache-Before on reduction Op
// TensorView* tv9 = tv2->cache_before();
constexpr int BSX = 128;
tv5->split(0, BSX);
tv5->split(-1, BSX);
// M/BSX, BSX, N/BSX, BSX
tv5->reorder({{0, 0}, {1, 2}, {2, 1}, {3, 3}});
// M/BSX, N/BSY, BSX, BSY
tv0->computeAt(tv5, 2);
tv1->computeAt(tv5, 2);
// 0, 1 | 2, 3, 4
tv2->cache_after();
TensorView* tv7 = tv5->cache_before();
tv5->axis(0)->parallelize(ParallelType::BIDx);
tv5->axis(1)->parallelize(ParallelType::BIDy);
tv5->axis(-1)->parallelize(ParallelType::TIDx);
tv4->axis(-1)->parallelize(ParallelType::TIDx);
tv7->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int N = 800;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input1 = at::rand({N, N}, options);
at::Tensor input2 = at::rand({N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input1, input2});
at::Tensor aten_output =
matmul(sum(input1, 1).unsqueeze(1), input2.unsqueeze(0));
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().sum());
}
TEST(NVFuserTest, FusionCacheMultiConsumer_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv1, new Float(2));
TensorView* tv3 = add(tv0, new Float(1));
TensorView* tv4 = add(tv3, new Float(2));
fusion.addInput(tv0);
fusion.addOutput(tv2);
fusion.addOutput(tv4);
tv1->computeAt(tv2, -1);
tv3->computeAt(tv4, -1);
auto tv5 = tv1->cache_before();
auto tv6 = tv3->cache_before();
tv5->setMemoryType(MemoryType::Shared);
tv6->setMemoryType(MemoryType::Shared);
// Fails because tensor must be recomputed twice
// auto tv7 = tv0->cache_after();
constexpr int N = 800;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = (input + 1) + 2;
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().sum());
TORCH_CHECK(
aten_output.allclose(outputs[1], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[1]).abs().sum());
}
TEST(NVFuserTest, FusionSmem_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Algorithm
TensorView* tv0 = makeDummyTensor(2); // (M, N)
TensorView* tv1 = makeDummyTensor(2); // (M, N)
TensorView* tv2 = mul(tv0, tv1);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv2);
// Schedule
TensorView* tv3 = tv0->cache_after();
TensorView* tv4 = tv1->cache_after();
tv3->setMemoryType(MemoryType::Shared);
tv4->setMemoryType(MemoryType::Shared);
constexpr int BSY = 32;
constexpr int BSX = 128;
tv2->split(0, BSY);
tv2->split(2, BSX);
// M/BSX, BSX, N/BSX, BSX
tv2->reorder({{0, 0}, {1, 2}, {2, 1}, {3, 3}});
// M/BSX, N/BSX, BSX, BSX
tv0->computeAt(tv2, 2);
tv1->computeAt(tv2, 2);
// Thread and Block binding
tv2->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(1)->parallelize(ParallelType::BIDy);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
// Manual Binding
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv4->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 128, N = 10240;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M, N}, options);
at::Tensor t1 = at::randn({M, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
at::Tensor aten_output = mul(t0, t1);
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 0);
}
TEST(NVFuserTest, FusionSmemReduce_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Algorithm
TensorView* tv0 = makeDummyTensor(3); // M, K, N
TensorView* tv1 = sum(tv0, {1}); // M, R, N
fusion.addInput(tv0);
fusion.addOutput(tv1);
TensorView* tv2 = tv0->cache_after();
tv2->setMemoryType(MemoryType::Shared);
// Schedule
constexpr int BSX = 32;
tv1->split(2, BSX);
tv1->split(1, 128);
tv1->split(0, BSX);
// M/BSX, BSX, K/BSX, BSX, N/BSX, BSX
tv1->reorder({{0, 0}, {1, 2}, {2, 4}, {3, 5}, {4, 1}, {5, 3}});
TensorView* tv3 = tv1->rFactor({-2});
tv0->computeAt(tv1, -2);
tv0->computeAt(tv3, -2);
// Thread and Block binding
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(1)->parallelize(ParallelType::BIDy);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
// Manual Binding
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 154, K = 45, N = 1524;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M, K, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0});
at::Tensor aten_output = sum(t0, {1});
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 1);
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.count(24) == 1);
}
TEST(NVFuserTest, FusionSmemBlockGemm_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Algorithm
TensorView* tv0 = makeDummyTensor(2); // (M, K)
TensorView* tv1 = makeDummyTensor(2); // (K, N)
TensorView* tv2 = broadcast(tv0, {false, false, true}); // (M, K, B)
TensorView* tv3 = broadcast(tv1, {true, false, false}); // (B, K, N)
TensorView* tv4 = mul(tv2, tv3); // M, K, N
TensorView* tv5 = sum(tv4, {1}); // M, R, N
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv5);
// Schedule
constexpr int BSX = 16;
tv5->split(2, BSX);
tv5->split(1, BSX);
tv5->split(0, BSX);
// M/BSX, BSX, K/BSX, BSX, N/BSX, BSX
tv5->reorder({{0, 0}, {1, 3}, {2, 2}, {3, 5}, {4, 1}, {5, 4}});
// M/BSX, N/BSX, K/BSX, MSX, NSX, KSX
TensorView* tv6 = tv5->rFactor({-1});
tv2->setMemoryType(MemoryType::Shared);
tv3->setMemoryType(MemoryType::Shared);
tv4->setMemoryType(MemoryType::Shared);
tv6->setMemoryType(MemoryType::Shared);
tv0->computeAt(tv5, 3);
tv1->computeAt(tv5, 3);
// Thread and Block binding
tv5->axis(0)->parallelize(ParallelType::BIDx);
tv5->axis(1)->parallelize(ParallelType::BIDy);
tv5->axis(-2)->parallelize(ParallelType::TIDy);
tv5->axis(-1)->parallelize(ParallelType::TIDx);
// Manual Binding
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv4->axis(-1)->parallelize(ParallelType::TIDx);
tv6->axis(-3)->parallelize(ParallelType::TIDy);
tv6->axis(-2)->parallelize(ParallelType::TIDx);
constexpr int M = 154, K = 45, N = 1524;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M, K}, options);
at::Tensor t1 = at::randn({K, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
at::Tensor aten_output = matmul(t0, t1);
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 0);
}
TEST(NVFuserTest, FusionSmemBlockGemmCache_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Algorithm
TensorView* tv0 = makeDummyTensor(2); // (M, K)
TensorView* tv1 = makeDummyTensor(2); // (K, N)
TensorView* tv2 = broadcast(tv0, {false, false, true}); // (M, K, B)
TensorView* tv3 = broadcast(tv1, {true, false, false}); // (B, K, N)
TensorView* tv4 = mul(tv2, tv3); // M, K, N
TensorView* tv5 = sum(tv4, {1}); // M, R, N
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv5);
// Schedule
// Remove reduction axis from tv5
// tv6 = (M, R, N)
// tv5 = (M, N)
TensorView* tv6 = tv5->cache_before();
constexpr int BSX = 16;
tv5->split(1, BSX);
tv5->split(0, BSX);
// M/BSX, BSX, N/BSX, BSX
tv5->reorder({{0, 0}, {1, 2}, {2, 1}, {3, 3}});
// tv5 = M/BSX, N/BSX, MSX, NSX
tv6->computeAt(tv5, 2);
tv6->computeAt(tv5, 2);
tv6->split(-1, BSX);
// M/BSX, BSX, K/BSX, BSX, N/BSX, BSX
tv6->reorder({{0, 0}, {1, 1}, {2, 3}, {3, 4}, {4, 2}, {5, 5}});
// M/BSX, N/BSX, K/BSX, MSX, NSX, KSX
TensorView* tv7 = tv6->rFactor({-1});
// tv7 = M/BSX, N/BSX, K/BSXrf, MSX, NSX, KSXr
// tv6 = M/BSX, N/BSX, K/BSXr, MSX, NSX
tv0->computeAt(tv6, 3);
tv1->computeAt(tv6, 3);
tv0->computeAt(tv7, 3);
tv1->computeAt(tv7, 3);
tv2->setMemoryType(MemoryType::Shared);
tv3->setMemoryType(MemoryType::Shared);
tv4->setMemoryType(MemoryType::Shared);
tv6->setMemoryType(MemoryType::Shared);
tv7->setMemoryType(MemoryType::Shared);
// Memory Type
// Thread and Block binding
tv5->axis(0)->parallelize(ParallelType::BIDx);
tv5->axis(1)->parallelize(ParallelType::BIDy);
tv5->axis(-2)->parallelize(ParallelType::TIDy);
tv5->axis(-1)->parallelize(ParallelType::TIDx);
// Manual Binding
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
tv4->axis(-1)->parallelize(ParallelType::TIDx);
tv7->axis(-3)->parallelize(ParallelType::TIDy);
tv7->axis(-2)->parallelize(ParallelType::TIDx);
tv6->axis(-2)->parallelize(ParallelType::TIDy);
tv6->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 154, K = 45, N = 1524;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M, K}, options);
at::Tensor t1 = at::randn({K, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({t0, t1});
at::Tensor aten_output = matmul(t0, t1);
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 0);
}
TEST(NVFuserTest, FusionSmemDynamicReductionSymbolic_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addInput(tv0);
fusion.addOutput(tv1);
// tv1[I0, R1] = tv0[I0, I1]
// Interface should just be a direct split with a Parallel type. We can
// include the parallelize call if we do this.
tv1->split(1, NamedScalar::getParallelDim(ParallelType::TIDx));
// tv1[I0, R1o, R1i{BIDx}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({2});
tv2->setMemoryType(MemoryType::Shared);
// tv2[I0, R1oo, Ir1i{BIDx}] = tv0[I0, I1]
// tv1[I0, R1i{BIDx}] = tv2[I0, R1oo, Ir1i{BIDx}]
tv0->computeAt(tv1, 1);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(0)->parallelize(ParallelType::BIDx);
constexpr int numel_x = 65000, numel_y = 1024;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
// How many threads to use for the block reduction
constexpr int runtime_threadIdx_dim = 128;
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion(
{input},
torch::jit::fuser::cuda::LaunchParams(
-1, -1, -1, runtime_threadIdx_dim, -1, -1));
auto aten_output = input.sum({1});
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 0);
}
TEST(NVFuserTest, FusionSmemDynamicReductionSymbolicArg_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Algorithm
Int* sym_bsx = new Int();
TensorView* tv0 = makeDummyTensor(3); // M, K, N
fusion.addInput(tv0);
fusion.addInput(sym_bsx);
TensorView* tv1 = sum(tv0, {1}); // M, R, N
fusion.addOutput(tv1);
TensorView* tv2 = tv0->cache_after();
tv2->setMemoryType(MemoryType::Shared);
// Schedule
constexpr int BSX = 32;
tv1->split(2, BSX);
tv1->split(1, sym_bsx);
tv1->split(0, BSX);
// M/BSX, BSX, K/BSX, BSX, N/BSX, BSX
tv1->reorder({{0, 0}, {1, 2}, {2, 4}, {3, 5}, {4, 1}, {5, 3}});
TensorView* tv3 = tv1->rFactor({-2});
tv0->computeAt(tv1, -2);
tv0->computeAt(tv3, -2);
// Thread and Block binding
tv1->axis(0)->parallelize(ParallelType::BIDx);
tv1->axis(1)->parallelize(ParallelType::BIDy);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
// Manual Binding
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
constexpr int M = 154, K = 45, N = 1524;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M, K, N}, options);
// How many threads to use for the block reduction
constexpr int runtime_threadIdx_dim = 128;
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion(
{t0, runtime_threadIdx_dim},
torch::jit::fuser::cuda::LaunchParams(
-1, -1, -1, runtime_threadIdx_dim, -1, -1));
at::Tensor aten_output = sum(t0, {1});
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 1);
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.count(24) == 1);
}
TEST(NVFuserTest, FusionSmemDynamicPwiseMulSymbolicArgWAR_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Int* sym_bsx = new Int();
TensorView* tv0 = makeDummyTensor(2); // (M, K)
TensorView* tv1 = makeDummyTensor(2); // (K, N)
TensorView* tv2 = broadcast(tv0, {false, false, true}); // (M, K, B)
TensorView* tv3 = broadcast(tv1, {true, false, false}); // (B, K, N)
TensorView* tv4 = mul(tv2, tv3); // M, K, N
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addInput(sym_bsx);
fusion.addOutput(tv4);
// Algorithm
tv2->setMemoryType(MemoryType::Shared);
tv3->setMemoryType(MemoryType::Shared);
constexpr int BSX = 32;
tv4->split(2, BSX);
tv4->split(1, sym_bsx);
tv4->split(0, BSX);
// M/BSX, BSX, K/BSX, BSX, N/BSX, BSX
tv4->reorder({{0, 0}, {1, 3}, {2, 1}, {3, 4}, {4, 2}, {5, 5}});
// M/BSX, K/BSX, N/BSX, MSX, KSX, NSX
tv0->computeAt(tv4, 3);
tv1->computeAt(tv4, 3);
// Schedule
tv4->axis(0)->parallelize(ParallelType::BIDx);
tv4->axis(2)->parallelize(ParallelType::BIDy);
// Manual Binding
tv2->axis(-2)->parallelize(ParallelType::TIDx);
tv3->axis(-1)->parallelize(ParallelType::TIDx);
// Thread and Block binding
constexpr int M = 128, K = 457, N = 1024;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({M, K}, options);
at::Tensor t1 = at::randn({K, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion(
{t0, t1, BSX},
torch::jit::fuser::cuda::LaunchParams(-1, -1, -1, BSX, -1, -1));
at::Tensor aten_output = mul(t0.unsqueeze(2), t1.unsqueeze(0));
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 1);
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.count(22) == 1);
}
TEST(NVFuserTest, FusionSmemDynamicTiledGemm_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Symbolic integers we will use for runtime tiling
Int* symbolic_m_tile_dim = new Int(); // bound to threadIdx.z
Int* symbolic_split_k_tile_dim = new Int(); // bound to blockIdx.x
Int* symbolic_block_k_tile_dim = new Int(); // bound to threadIdx.x
// Compile-time integer for tiling
int n_smem_tile = 8; // bound to threadIdx.y
// Symbolic 2D tensors TV0[M, K], TV1[K, N]
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
// Broadcast tv0 to [M, K, *]
TensorView* tv2 = broadcast(tv0, {false, false, true});
// Broadcast tv1 to [*, K, N]
TensorView* tv3 = broadcast(tv1, {true, false, false});
// Pointwise multiplication resulting in tv3[M, K, N]
TensorView* tv4 = mul(tv2, tv3);
// Turn the K-dimension of tv4 into a reduction dimension
TensorView* tv5 = sum(tv4, {1});
// Register inputs and outputs
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addOutput(tv5);
// Register runtime tile dims as inputs
fusion.addInput(symbolic_m_tile_dim);
fusion.addInput(symbolic_split_k_tile_dim);
fusion.addInput(symbolic_block_k_tile_dim);
// Make a 3D tile, mix of symbolic and constant, do in reverse order because
// dims are inserted
tv5->split(2, n_smem_tile);
tv5->split(1, symbolic_block_k_tile_dim);
tv5->split(1, symbolic_split_k_tile_dim);
tv5->split(0, symbolic_m_tile_dim);
// Reorder so all outer tiles are in the leftmost 3 positions
tv5->reorder({{1, 5}, {5, 1}});
// Factor out the outer reduction IterDomain, then run the inter-cta
// reduction, and intra-cta reduction
auto tv6 = tv5->rFactor({2});
// Scope computations
tv6->computeAt(tv5, 2);
// RFactor moves reduction axes around, reorder to match ordering of tv5
tv6->reorder({
{2, -2},
{3, -1},
{4, 2},
{5, 3},
{6, 4},
});
// Setup compute at schedule
tv0->computeAt(tv6, 3);
tv1->computeAt(tv6, 3);
tv4->computeAt(tv6, -1);
//
// T2[Mo, bNo, Koo, Koi, Kii, Mi, bNi] CA(4, 3)
// T3[bMo, No, Koo, Koi, Kii, bMi, Ni] CA(4, 3)
// T4[ Mo, No, Koo, Koi, Kii, Mi, Ni]
// T6[ Mo, No, rKoo, Koi, Kii, Mi, Ni]
// T5[ Mo, No, rKoi, rKii, Mi, Ni]
// Cache smem tiles
tv2->setMemoryType(MemoryType::Shared);
tv3->setMemoryType(MemoryType::Shared);
tv4->setMemoryType(MemoryType::Local);
tv6->setMemoryType(MemoryType::Local);
tv5->axis(0)->parallelize(ParallelType::BIDz);
tv5->axis(1)->parallelize(ParallelType::BIDy);
std::vector<TensorView*> tv_list = {tv2, tv3, tv4, tv5, tv6};
for (auto tv : tv_list) {
tv->axis(-2)->parallelize(ParallelType::TIDz);
tv->axis(-1)->parallelize(ParallelType::TIDy);
}
tv2->axis(3)->parallelize(ParallelType::TIDx);
tv3->axis(3)->parallelize(ParallelType::TIDx);
tv4->axis(3)->parallelize(ParallelType::TIDx);
tv6->axis(3)->parallelize(ParallelType::TIDx);
tv5->axis(2)->parallelize(ParallelType::TIDx);
tv2->axis(4)->parallelize(ParallelType::BIDx);
tv3->axis(4)->parallelize(ParallelType::BIDx);
tv4->axis(4)->parallelize(ParallelType::BIDx);
tv6->axis(4)->parallelize(ParallelType::BIDx);
tv5->axis(3)->parallelize(ParallelType::BIDx);
constexpr int M = 31, K = 65, N = 33;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor A = at::randn({M, K}, options);
at::Tensor B = at::randn({K, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
// Generate CUDA and compile with nvRTC
fe.compileFusion(&fusion);
// Runtime tiling
int m_tile = 4; // bound to threadIdx.z
int split_k = 7; // bound to blockIdx.x
int intra_cta = 8; // bound to threadIdx.x
auto fuser_outputs = fe.runFusion({A, B, m_tile, split_k, intra_cta});
auto C_fuser = fuser_outputs[0];
at::Tensor aten_C = mul(A.unsqueeze(2), B.unsqueeze(0)).sum(1);
TORCH_CHECK(
aten_C.allclose(C_fuser, 1e-5, 1e-5),
"Error of: ",
aten_C.sub(C_fuser).abs().max());
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.size() == 1);
TORCH_CHECK(fe.kernel()->summary().war_hazard_syncs.count(41) == 1);
}
TEST(NVFuserTest, FusionGlobalIntermediate_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
fusion.addInput(tv0);
fusion.addOutput(tv1);
// tv1[I0, R1] = tv0[I0, I1]
// Interface should just be a direct split with a Parallel type. We can
// include the parallelize call if we do this.
tv1->split(1, NamedScalar::getParallelDim(ParallelType::TIDx));
// tv1[I0, R1o, R1i{BIDx}] = tv0[I0, I1]
TensorView* tv2 = tv1->rFactor({2});
tv2->setMemoryType(MemoryType::Global);
// tv2[I0, R1oo, Ir1i{BIDx}] = tv0[I0, I1]
// tv1[I0, R1i{BIDx}] = tv2[I0, R1oo, Ir1i{BIDx}]
tv0->computeAt(tv1, 1);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv1->axis(0)->parallelize(ParallelType::BIDx);
constexpr int numel_x = 65000, numel_y = 1024;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
// How many threads to use for the block reduction
constexpr int runtime_threadIdx_dim = 128;
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion(
{input},
torch::jit::fuser::cuda::LaunchParams(
-1, -1, -1, runtime_threadIdx_dim, -1, -1));
auto aten_output = input.sum({1});
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionGlobalIntermediateDefaultSchedule_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
TensorView* tv1 = makeDummyTensor(2);
TensorView* tv2 = makeDummyTensor(2);
TensorView* tv3 = makeDummyTensor(2);
TensorView* tv4 = sub(tv2, tv3);
TensorView* tv5 = add(tv1, tv4);
TensorView* tv6 = sub(tv5, tv0);
fusion.addInput(tv0);
fusion.addInput(tv1);
fusion.addInput(tv2);
fusion.addInput(tv3);
fusion.addOutput(tv6);
// t6 = ((t1 + (t2 - t3)) - t0)
tv4->setMemoryType(MemoryType::Global);
tv5->setMemoryType(MemoryType::Global);
tv6->setMemoryType(MemoryType::Global);
constexpr int M = 32, N = 810;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor in0 = at::rand({M, N}, options);
at::Tensor in1 = at::rand({M, N}, options);
at::Tensor in2 = at::rand({M, N}, options);
at::Tensor in3 = at::rand({M, N}, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({in0, in1, in2, in3});
at::Tensor aten_output = (in1 + (in2 - in3)) - in0;
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-5, 1e-5),
"Error of: ",
aten_output.sub(outputs[0]).abs().sum());
}
TEST(NVFuserTest, FusionConstCheck_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
auto one = new Int(1);
TORCH_CHECK(one->isConstScalar());
auto one_x2 = mul(one, one);
TORCH_CHECK(one_x2->isConstScalar());
auto one_x3 = mul(one_x2, one);
TORCH_CHECK(one_x3->isConstScalar());
auto one_x4 = mul(one_x3, one);
TORCH_CHECK(one_x4->isConstScalar());
}
TEST(NVFuserTest, FusionUnrollWithAlloc_CUDA) {
const std::vector<int64_t> tensor_dims_in = {128, 128};
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(tensor_dims_in.size());
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(0));
TensorView* tv2 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv1);
fusion.addOutput(tv2);
const auto options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand(tensor_dims_in, options);
at::Tensor cg_output = at::empty({tensor_dims_in[0]}, options);
// const at::ArrayRef<c10::IValue> inputs({input});
// Schedule
tv2->split(1, 32);
tv2->split(1, 4); // unroll
auto tv2_rf = tv2->rFactor({-3, -2});
tv2->axis(0)->parallelize(ParallelType::BIDx);
tv2->axis(-1)->parallelize(ParallelType::TIDx);
tv2_rf->axis(0)->parallelize(ParallelType::BIDx);
tv2_rf->axis(-1)->parallelize(ParallelType::TIDx);
tv2_rf->axis(-2)->parallelize(ParallelType::Unroll);
tv1->computeAt(tv2_rf, -1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto aten_output = (input + 0).sum(1);
TORCH_CHECK(
aten_output.allclose(outputs[0]),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
// Test isZeroInt
TEST(NVFuserTest, FusionIsZeroInt_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Int* x = new Int(0);
Int* y = new Int(1);
Val* z = mul(x, y);
TORCH_CHECK(x->isZeroInt());
TORCH_CHECK(!y->isZeroInt());
TORCH_CHECK(!z->isZeroInt());
}
// Test isOneInt
TEST(NVFuserTest, FusionIsOneInt_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
Int* x = new Int(1);
Int* y = new Int(1);
Val* z = mul(x, y);
TORCH_CHECK(x->isOneInt());
TORCH_CHECK(y->isOneInt());
TORCH_CHECK(!z->isOneInt());
}
// This is to verify no cycle of computeAt is created. A more complex
// variation of this pattern appears in one of the Python tests
// (test_random_topo).
TEST(NVFuserTest, FusionComputeAtNonterminatingOutput_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
// Common intermediate tensor
auto tv1 = add(tv0, new Float(1));
// tv1 -> tv2
auto tv2 = add(tv1, new Float(2));
// tv1 -> tv3 -> tv4
auto tv3 = add(tv1, new Float(3));
auto tv4 = add(tv3, new Float(4));
// NOTE: This should no longer occur as of PR #201.
// The order of adding outputs matters. If tv3 is added before tv4,
// it should be fine. However, if tv4 is added before tv3, there
// will be a cycle of tv3->tv4 and tv4->tv3. tv3->tv4 is created
// first, and then tv4->tv3 is created at the final phase of
// computeAt (ComputeAt::setupOutputs).
fusion.addOutput(tv2);
fusion.addOutput(tv4);
fusion.addOutput(tv3);
tv0->computeAt(tv2, -1);
TORCH_CHECK(
!(tv3->getComputeAtView() == tv4 && tv4->getComputeAtView() == tv3),
"ComputeAt cycle detected between tv3 and tv4");
const auto options =
at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand(100, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion({input});
auto& output_tv2 = outputs[0];
auto& output_tv4 = outputs[1];
auto& output_tv3 = outputs[2];
auto aten_t1 = input + 1;
auto aten_t2 = aten_t1 + 2;
auto aten_t3 = aten_t1 + 3;
auto aten_t4 = aten_t3 + 4;
TORCH_CHECK(
aten_t2.allclose(output_tv2),
"Error of: ",
aten_t2.sub(output_tv2).abs().max());
TORCH_CHECK(
aten_t3.allclose(output_tv3),
"Error of: ",
aten_t3.sub(output_tv3).abs().max());
TORCH_CHECK(
aten_t4.allclose(output_tv4),
"Error of: ",
aten_t4.sub(output_tv4).abs().max());
return;
}
TEST(NVFuserTest, FusionTraversalOrder1_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv0, new Float(2));
TensorView* tv3 = add(tv1, new Float(3));
TensorView* tv4 = add(tv1, new Float(4));
fusion.addOutput(tv2);
fusion.addOutput(tv3);
fusion.addOutput(tv4);
tv1->computeAt(tv3, -1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({10, 10}, options);
at::Tensor cg_output_tv2 = at::empty_like(input, options);
at::Tensor cg_output_tv3 = at::empty_like(input, options);
at::Tensor cg_output_tv4 = at::empty_like(input, options);
fe.runFusion({input}, {cg_output_tv2, cg_output_tv3, cg_output_tv4});
auto t1 = input + 1;
auto t2 = input + 2;
auto t3 = t1 + 3;
auto t4 = t1 + 4;
TORCH_CHECK(
t2.allclose(cg_output_tv2),
"tv2 error of: ",
t2.sub(cg_output_tv2).abs().max());
TORCH_CHECK(
t3.allclose(cg_output_tv3),
"tv5 error of: ",
t3.sub(cg_output_tv3).abs().max());
TORCH_CHECK(
t4.allclose(cg_output_tv4),
"tv4 error of: ",
t4.sub(cg_output_tv4).abs().max());
}
TEST(NVFuserTest, FusionTraversalOrder2_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv1, new Float(2));
TensorView* tv3 = add(tv0, new Float(3));
TensorView* tv4 = add(tv3, new Float(4));
TensorView* tv5 = add(tv1, tv3);
fusion.addOutput(tv2);
fusion.addOutput(tv4);
fusion.addOutput(tv5);
tv1->computeAt(tv5, -1);
tv3->computeAt(tv5, -1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({10, 10}, options);
at::Tensor cg_output_tv2 = at::empty_like(input, options);
at::Tensor cg_output_tv4 = at::empty_like(input, options);
at::Tensor cg_output_tv5 = at::empty_like(input, options);
fe.runFusion({input}, {cg_output_tv2, cg_output_tv4, cg_output_tv5});
auto t1 = input + 1;
auto t2 = t1 + 2;
auto t3 = input + 3;
auto t4 = t3 + 4;
auto t5 = t1 + t3;
TORCH_CHECK(
t2.allclose(cg_output_tv2),
"tv2 error of: ",
t2.sub(cg_output_tv2).abs().max());
TORCH_CHECK(
t4.allclose(cg_output_tv4),
"tv4 error of: ",
t4.sub(cg_output_tv4).abs().max());
TORCH_CHECK(
t5.allclose(cg_output_tv5),
"tv5 error of: ",
t5.sub(cg_output_tv5).abs().max());
}
TEST(NVFuserTest, FusionTraversalOrder3_CUDA) {
for (int i = 0; i < 2; ++i) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv1, new Float(2));
TensorView* tv3 = add(tv0, new Float(3));
TensorView* tv4 = add(tv3, new Float(4));
TensorView* tv5 = add(tv1, tv3);
fusion.addOutput(tv2);
fusion.addOutput(tv4);
fusion.addOutput(tv5);
const int tile = 32;
tv1->split(-1, tile);
tv2->split(-1, tile);
tv3->split(-1, tile);
tv4->split(-1, tile);
tv5->split(-1, tile);
auto compute_at_outer = tv1;
auto compute_at_inner = tv3;
if (i == 1) {
std::swap(compute_at_inner, compute_at_outer);
}
compute_at_outer->computeAt(tv5, -2);
compute_at_inner->computeAt(tv5, -1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({100}, options);
at::Tensor cg_output_tv2 = at::empty_like(input, options);
at::Tensor cg_output_tv4 = at::empty_like(input, options);
at::Tensor cg_output_tv5 = at::empty_like(input, options);
fe.runFusion({input}, {cg_output_tv2, cg_output_tv4, cg_output_tv5});
auto t1 = input + 1;
auto t2 = t1 + 2;
auto t3 = input + 3;
auto t4 = t3 + 4;
auto t5 = t1 + t3;
TORCH_CHECK(
t2.allclose(cg_output_tv2),
"tv2 error of: ",
t2.sub(cg_output_tv2).abs().max());
TORCH_CHECK(
t4.allclose(cg_output_tv4),
"tv4 error of: ",
t4.sub(cg_output_tv4).abs().max());
TORCH_CHECK(
t5.allclose(cg_output_tv5),
"tv5 error of: ",
t5.sub(cg_output_tv5).abs().max());
}
}
TEST(NVFuserTest, FusionTraversalOrder4_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// First tree
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv1, new Float(2));
TensorView* tv3 = add(tv1, new Float(3));
fusion.addOutput(tv2);
fusion.addOutput(tv3);
// Second tree
TensorView* tv4 = makeDummyTensor(1);
fusion.addInput(tv4);
TensorView* tv5 = add(tv4, new Float(5));
TensorView* tv6 = add(tv5, new Float(6));
TensorView* tv7 = add(tv5, new Float(7));
fusion.addOutput(tv6);
fusion.addOutput(tv7);
tv1->computeAt(tv2, -1);
tv5->computeAt(tv6, -1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::rand({100}, options);
at::Tensor t4 = at::rand_like(t0, options);
at::Tensor cg_output_tv2 = at::empty_like(t0, options);
at::Tensor cg_output_tv3 = at::empty_like(t0, options);
at::Tensor cg_output_tv6 = at::empty_like(t0, options);
at::Tensor cg_output_tv7 = at::empty_like(t0, options);
fe.runFusion(
{t0, t4}, {cg_output_tv2, cg_output_tv3, cg_output_tv6, cg_output_tv7});
auto t1 = t0 + 1;
auto t2 = t1 + 2;
auto t3 = t1 + 3;
auto t5 = t4 + 5;
auto t6 = t5 + 6;
auto t7 = t5 + 7;
TORCH_CHECK(
t2.allclose(cg_output_tv2),
"tv2 error of: ",
t2.sub(cg_output_tv2).abs().max());
TORCH_CHECK(
t3.allclose(cg_output_tv3),
"tv3 error of: ",
t3.sub(cg_output_tv3).abs().max());
TORCH_CHECK(
t6.allclose(cg_output_tv6),
"tv6 error of: ",
t6.sub(cg_output_tv6).abs().max());
TORCH_CHECK(
t7.allclose(cg_output_tv7),
"tv7 error of: ",
t7.sub(cg_output_tv7).abs().max());
}
TEST(NVFuserTest, FusionTraversalOrder5_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv1, new Float(2));
TensorView* tv3 = add(tv0, new Float(3));
TensorView* tv4 = add(tv3, new Float(4));
TensorView* tv5 = add(tv2, tv4);
fusion.addOutput(tv1);
fusion.addOutput(tv3);
fusion.addOutput(tv5);
tv2->computeAt(tv5, -1);
tv4->computeAt(tv5, -1);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::rand({100}, options);
at::Tensor cg_output_tv1 = at::empty_like(t0, options);
at::Tensor cg_output_tv3 = at::empty_like(t0, options);
at::Tensor cg_output_tv5 = at::empty_like(t0, options);
fe.runFusion({t0}, {cg_output_tv1, cg_output_tv3, cg_output_tv5});
auto t1 = t0 + 1;
auto t2 = t1 + 2;
auto t3 = t0 + 3;
auto t4 = t3 + 4;
auto t5 = t2 + t4;
TORCH_CHECK(
t1.allclose(cg_output_tv1),
"tv1 error of: ",
t1.sub(cg_output_tv1).abs().max());
TORCH_CHECK(
t3.allclose(cg_output_tv3),
"tv3 error of: ",
t3.sub(cg_output_tv3).abs().max());
TORCH_CHECK(
t5.allclose(cg_output_tv5),
"tv5 error of: ",
t5.sub(cg_output_tv5).abs().max());
}
TEST(NVFuserTest, FusionTraversalOrder6_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv0, new Float(2));
TensorView* tv3 = add(tv1, tv2);
TensorView* tv4 = add(tv3, new Float(4));
fusion.addOutput(tv4);
tv1->split(0, 32);
tv2->split(0, 32);
tv3->split(0, 32);
tv4->split(0, 32);
tv3->computeAt(tv4, -2);
tv1->computeAt(tv3, -1);
tv2->computeAt(tv3, -2);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::rand({100}, options);
at::Tensor cg_output_tv4 = at::empty_like(t0, options);
fe.runFusion({t0}, {cg_output_tv4});
auto t1 = t0 + 1;
auto t2 = t0 + 2;
auto t3 = t1 + t2;
auto t4 = t3 + 4;
TORCH_CHECK(
t4.allclose(cg_output_tv4),
"tv4 error of: ",
t4.sub(cg_output_tv4).abs().max());
}
TEST(NVFuserTest, FusionTraversalOrder7_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = add(tv0, new Float(1));
TensorView* tv2 = add(tv1, new Float(2));
TensorView* tv3 = add(tv0, new Float(3));
TensorView* tv4 = add(tv3, new Float(4));
TensorView* tv5 = add(tv2, tv4);
fusion.addOutput(tv5);
TensorView* tvs[] = {tv1, tv2, tv3, tv4, tv5};
for (auto tv : tvs) {
tv->split(0, 2);
tv->split(0, 4);
tv->split(0, 8);
}
// computeAt into inner loop nests
tv1->computeAt(tv2, -1);
tv3->computeAt(tv4, -2);
tv2->computeAt(tv5, -4);
tv4->computeAt(tv5, -3);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::rand({100}, options);
at::Tensor cg_output_tv5 = at::empty_like(t0, options);
fe.runFusion({t0}, {cg_output_tv5});
auto t1 = t0 + 1;
auto t2 = t1 + 2;
auto t3 = t0 + 3;
auto t4 = t3 + 4;
auto t5 = t2 + t4;
TORCH_CHECK(
t5.allclose(cg_output_tv5),
"tv5 error of: ",
t5.sub(cg_output_tv5).abs().max());
}
// Test predication of grid reduction
TEST(NVFuserTest, FusionThreadPredicate_CUDA) {
const int gdimx = 4;
const int bdimx = 128;
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tv0 = makeDummyTensor(2);
fusion.addInput(tv0);
TensorView* tv1 = reductionOp(BinaryOpType::Add, {1}, new Float(0), tv0);
TensorView* tv2 = unaryOp(UnaryOpType::Neg, tv1);
TensorView* tv3 = add(tv0, new Float(2));
fusion.addOutput(tv3);
fusion.addOutput(tv2);
tv1->split(1, bdimx);
tv1->split(1, gdimx);
tv3->split(1, bdimx);
tv3->split(1, gdimx);
TensorView* tv1_rf = tv1->rFactor({1});
tv1->computeAt(tv2, -1);
tv1->axis(0)->parallelize(ParallelType::BIDy);
tv1_rf->axis(0)->parallelize(ParallelType::BIDy);
tv2->axis(0)->parallelize(ParallelType::BIDy);
tv1->axis(-2)->parallelize(ParallelType::BIDx);
tv1_rf->axis(-2)->parallelize(ParallelType::BIDx);
tv1->axis(-1)->parallelize(ParallelType::TIDx);
tv1_rf->axis(-1)->parallelize(ParallelType::TIDx);
tv3->axis(3)->parallelize(ParallelType::TIDx);
tv3->axis(2)->parallelize(ParallelType::BIDx);
tv3->axis(0)->parallelize(ParallelType::BIDy);
int numel_x = 100;
int numel_y = 1000;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor input = at::rand({numel_x, numel_y}, options);
at::Tensor cg_output_tv2 = at::empty({numel_x}, options);
at::Tensor cg_output_tv3 = at::empty_like(input, options);
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
fe.runFusion({input}, {cg_output_tv3, cg_output_tv2});
auto aten_output_tv2 = -input.sum({1});
TORCH_CHECK(aten_output_tv2.allclose(cg_output_tv2));
auto aten_output_tv3 = input + 2.0;
TORCH_CHECK(aten_output_tv3.allclose(cg_output_tv3));
}
TEST(NVFuserTest, FusionLSTMCell_CUDA) {
const int hidden_features = 512;
const int batch_size = 64;
Fusion fusion;
FusionGuard fg(&fusion);
TensorView* tvs[16];
for (size_t i = 0; i < 16; i++) {
tvs[i] = makeDummyTensor(2);
fusion.addInput(tvs[i]);
}
auto ingate = unaryOp(
UnaryOpType::Sigmoid, add(add(add(tvs[0], tvs[1]), tvs[2]), tvs[3]));
auto forgetgate = unaryOp(
UnaryOpType::Sigmoid, add(add(add(tvs[4], tvs[5]), tvs[6]), tvs[7]));
auto cellgate = unaryOp(
UnaryOpType::Tanh, add(add(add(tvs[8], tvs[9]), tvs[10]), tvs[11]));
auto outgate = unaryOp(
UnaryOpType::Sigmoid, add(add(add(tvs[12], tvs[13]), tvs[14]), tvs[15]));
auto cx = makeContigTensor(2);
fusion.addInput(cx);
auto cy = add(mul(forgetgate, cx), mul(ingate, cellgate));
auto hy = mul(outgate, unaryOp(UnaryOpType::Tanh, cy));
fusion.addOutput(cy);
fusion.addOutput(hy);
std::vector<c10::IValue> inputs;
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor large_tensor0 =
at::randn({batch_size, hidden_features * 4}, options);
at::Tensor large_tensor1 =
at::randn({batch_size, hidden_features * 4}, options);
at::Tensor large_tensor2 =
at::randn({batch_size, hidden_features * 4}, options);
at::Tensor large_tensor3 =
at::randn({batch_size, hidden_features * 4}, options);
auto chunked0 = large_tensor0.chunk(4, 1);
auto chunked1 = large_tensor1.chunk(4, 1);
auto chunked2 = large_tensor2.chunk(4, 1);
auto chunked3 = large_tensor3.chunk(4, 1);
inputs.insert(inputs.end(), chunked0.begin(), chunked0.end());
inputs.insert(inputs.end(), chunked1.begin(), chunked1.end());
inputs.insert(inputs.end(), chunked2.begin(), chunked2.end());
inputs.insert(inputs.end(), chunked3.begin(), chunked3.end());
auto at_ingate =
chunked0[0].add(chunked0[1]).add(chunked0[2]).add(chunked0[3]).sigmoid();
auto at_forgetgate =
chunked1[0].add(chunked1[1]).add(chunked1[2]).add(chunked1[3]).sigmoid();
auto at_cellgate =
chunked2[0].add(chunked2[1]).add(chunked2[2]).add(chunked2[3]).tanh();
auto at_outgate =
chunked3[0].add(chunked3[1]).add(chunked3[2]).add(chunked3[3]).sigmoid();
auto at_cx = at::randn({batch_size, hidden_features}, options);
inputs.push_back(at_cx);
auto at_cy = at_forgetgate.mul(at_cx).add(at_ingate.mul(at_cellgate));
auto at_hy = at_outgate.mul(at_cy.tanh());
fuser::cuda::scheduleFusion(&fusion, c10::ArrayRef<c10::IValue>(inputs));
torch::jit::fuser::cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
auto outputs = fe.runFusion(c10::ArrayRef<c10::IValue>(inputs));
TORCH_CHECK(at_cy.allclose(outputs[0], 1e-4, 1e-7));
TORCH_CHECK(at_hy.allclose(outputs[1], 1e-4, 1e-7));
}
TEST(NVFuserTest, FusionComputeAtMultiBCast_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(1);
fusion.addInput(tv0);
TensorView* tv1 = mul(tv0, new Float(0.5));
TensorView* tv2 = broadcast(tv1, {true, false});
TensorView* tv3 = broadcast(tv1, {false, true});
TensorView* tv4 = add(tv2, tv3);
fusion.addOutput(tv4);
// This is not supported and should throw an exception.
ASSERT_ANY_THROW(tv1->computeAt(tv3, -1));
}
TEST(NVFuserTest, FusionReductionHalf_CUDA) {
Fusion fusion;
FusionGuard fg(&fusion);
// Set up your input tensor views
TensorView* tv0 = makeDummyTensor(3, DataType::Half);
fusion.addInput(tv0);
auto tv1 = castOp(DataType::Float, tv0);
auto tv2 = add(tv1, new Float(1.0));
auto tv3 = sum(tv2, {2});
auto tv4 = castOp(DataType::Half, tv3);
fusion.addOutput(tv4);
const auto options =
at::TensorOptions().dtype(at::kHalf).device(at::kCUDA, 0);
at::Tensor input = at::randn({8, 8, 16}, options);
auto reduction_tv = tv3;
auto outputsOfReduction = DependencyCheck::getAllOutputsOf({reduction_tv});
// Grab only tensor views, though there shouldn't be any other type
auto tv_entries = ir_utils::filterByType<TensorView>(outputsOfReduction);
std::vector<TensorView*> tvOutputsOfReduction(
tv_entries.begin(), tv_entries.end());
auto reduction_params =
cuda::getReductionHeuristics(&fusion, {input}, reduction_tv);
TORCH_CHECK(reduction_params, "Reduction schedule was not generated!");
cuda::scheduleReduction(
&fusion, reduction_params.value(), reduction_tv, tvOutputsOfReduction);
TORCH_CHECK(reduction_params, "Reduction schedule was not generated!");
cuda::FusionExecutor fe;
fe.compileFusion(&fusion);
// no broadcasting needed, omitting the last optional argument;
auto outputs = fe.runFusion({input}, reduction_params.value().lparams);
auto aten_output = input.to(c10::ScalarType::Float)
.add(1.0)
.sum({2})
.to(c10::ScalarType::Half);
TORCH_CHECK(
aten_output.allclose(outputs[0], 1e-04, 1e-04),
"Error of: ",
aten_output.sub(outputs[0]).abs().max());
}
TEST(NVFuserTest, FusionInputsIdLookup_CUDA) {
auto options = at::TensorOptions().dtype(at::kFloat).device(at::kCUDA, 0);
at::Tensor t0 = at::randn({16, 8, 8}, options);
at::Tensor t1 = at::randn({8, 8}, options);
at::Tensor t2 = at::randn({6, 4}, options);
// create a cache with max size 2;
auto inputs_id_lookup = torch::jit::fuser::cuda::InputsIdLookup(2);
// testing basic function, same encoding for identical inputs
auto id_0 = inputs_id_lookup.lookupId({t0, t1, 5.0});
auto id_0_lookup = inputs_id_lookup.lookupId({t0, t1, 2.5});
TORCH_CHECK(id_0.id == id_0_lookup.id);
TORCH_CHECK(inputs_id_lookup.size() == 1);
TORCH_CHECK(id_0.eviction == false);
// new input (even tho same shape, but we have different signature because of
// missing scalar input
auto id_1 = inputs_id_lookup.lookupId({t0, t1});
auto id_1_lookup = inputs_id_lookup.lookupId({t0, t1});
TORCH_CHECK(id_1.id == id_1_lookup.id);
TORCH_CHECK(inputs_id_lookup.size() == 2);
TORCH_CHECK(id_1.eviction == false);
// eviction should happen at this point
auto id_2 = inputs_id_lookup.lookupId({t2, t1});
TORCH_CHECK(id_2.id != id_0.id);
TORCH_CHECK(id_2.id != id_1.id);
TORCH_CHECK(inputs_id_lookup.size() == 2);
TORCH_CHECK(id_2.eviction == true);
TORCH_CHECK(id_2.evict_id == id_0.id);
// look at input 1 again
auto id_1_relook = inputs_id_lookup.lookupId({t0, t1});
TORCH_CHECK(id_1_relook.id == id_1.id);
TORCH_CHECK(id_1_relook.eviction == false);
}
} // namespace jit
} // namespace torch
#endif // #if defined(USE_CUDA)
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