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test_gpu.cpp
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#if defined(USE_CUDA)
#include <test/cpp/jit/test_base.h>
#include <torch/csrc/jit/codegen/cuda/arith.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/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(
const EvaluationContext* eval_context,
Val* val,
Int::ScalarType expected_value) {
TORCH_CHECK(val->isAnInt());
const auto actual_value = ExpressionEvaluator::evaluate(val, eval_context);
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)
void testGPU_IrGraphGenerator() {
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());
}
void testGPU_FusionDispatch() {
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
void testGPU_FusionExprEvalConstants() {
Fusion fusion;
FusionGuard fg(&fusion);
EvaluationContext eval_context(&fusion);
auto* a = new Int(7);
auto* b = new Int(3);
checkIntValue(&eval_context, neg(a), -7);
checkIntValue(&eval_context, add(a, b), 10);
checkIntValue(&eval_context, neg(mul(sub(a, b), div(a, b))), -8);
checkIntValue(&eval_context, mod(a, b), 1);
checkIntValue(&eval_context, ceilDiv(a, b), 3);
}
// Evaluate basic scalar operations with bound values
void testGPU_FusionExprEvalBindings() {
Fusion fusion;
FusionGuard fg(&fusion);
EvaluationContext eval_context(&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(!ExpressionEvaluator::evaluate(a, &eval_context).has_value());
TORCH_CHECK(!ExpressionEvaluator::evaluate(d, &eval_context).has_value());
eval_context.bind(a, 7);
eval_context.bind(b, 3);
// can't bind to the results of expressions
ASSERT_ANY_THROW(eval_context.bind(c, 100));
// can't bind to concrete values
ASSERT_ANY_THROW(eval_context.bind(e, 100));
checkIntValue(&eval_context, c, 10);
checkIntValue(&eval_context, sub(a, b), 4);
checkIntValue(&eval_context, mod(a, b), 1);
checkIntValue(&eval_context, ceilDiv(a, b), 3);
checkIntValue(&eval_context, d, -4);
// Reset evaluation context
eval_context = EvaluationContext(&fusion);
eval_context.bind(a, 2);
eval_context.bind(b, 5);
checkIntValue(&eval_context, c, 7);
checkIntValue(&eval_context, sub(a, b), -3);
checkIntValue(&eval_context, mod(a, b), 2);
checkIntValue(&eval_context, ceilDiv(a, b), 1);
checkIntValue(&eval_context, d, -2);
}
// Evaluate expressions in a simple IR
void testGPU_FusionExprEvalBasic() {
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 evaluation context
EvaluationContext eval_context(&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()`)
//
eval_context.bind(tv0->getRootDomain()[0]->extent(), 6);
eval_context.bind(tv0->getRootDomain()[1]->extent(), 128);
eval_context.bind(tv1->getRootDomain()[0]->extent(), 6);
eval_context.bind(tv1->getRootDomain()[1]->extent(), 128);
// 3. Evaluate and check result values
TORCH_CHECK(tv2->domain()->nDims() == 3);
checkIntValue(&eval_context, tv2->axis(0)->rawExtent(), 2);
checkIntValue(&eval_context, tv2->axis(1)->rawExtent(), 4);
checkIntValue(&eval_context, tv2->axis(2)->rawExtent(), 128);
TORCH_CHECK(tv3->domain()->nDims() == 3);
checkIntValue(&eval_context, tv3->axis(0)->rawExtent(), 2);
checkIntValue(&eval_context, tv3->axis(1)->rawExtent(), 4);
checkIntValue(&eval_context, tv3->axis(2)->rawExtent(), 128);
}
// Evaluate expressions in a more complex IR
void testGPU_FusionExprEvalComplex() {
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 evaluation context
EvaluationContext eval_context(&fusion);
// 2. Bind values
eval_context.bind(tv0->getRootDomain()[0]->extent(), 129);
eval_context.bind(tv0->getRootDomain()[1]->extent(), 127);
// Evaluate and check extent values
TORCH_CHECK(tv0->domain()->nDims() == 2);
checkIntValue(&eval_context, tv0->axis(0)->rawExtent(), 129);
checkIntValue(&eval_context, tv0->axis(1)->rawExtent(), 127);
TORCH_CHECK(tv3->domain()->nDims() == 2);
checkIntValue(&eval_context, tv3->axis(0)->rawExtent(), 129);
checkIntValue(&eval_context, tv3->axis(1)->rawExtent(), 127);
TORCH_CHECK(tv4->domain()->nDims() == 2);
checkIntValue(&eval_context, tv4->axis(0)->rawExtent(), 129);
checkIntValue(&eval_context, tv4->axis(1)->rawExtent(), 127);
TORCH_CHECK(tv5->domain()->nDims() == 1);
checkIntValue(&eval_context, tv5->axis(0)->rawExtent(), 16383);
TORCH_CHECK(tv6->domain()->nDims() == 3);
checkIntValue(&eval_context, tv6->axis(0)->rawExtent(), 26);
checkIntValue(&eval_context, tv6->axis(1)->rawExtent(), 5);
checkIntValue(&eval_context, tv6->axis(2)->rawExtent(), 127);
}
// Evaluate expressions post lowering
void testGPU_FusionExprEvalPostLower() {
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);
std::stringstream kernel;
gpulw.printKernel(kernel);
// 1. Create an evaluation context
EvaluationContext eval_context(&fusion);
// 2. Bind values
eval_context.bind(tv0->getRootDomain()[0]->extent(), 6);
eval_context.bind(tv0->getRootDomain()[1]->extent(), 128);
eval_context.bind(tv1->getRootDomain()[0]->extent(), 6);
eval_context.bind(tv1->getRootDomain()[1]->extent(), 128);
// 3. Evaluate and check result values
TORCH_CHECK(tv2->domain()->nDims() == 3);
checkIntValue(&eval_context, tv2->axis(0)->rawExtent(), 2);
checkIntValue(&eval_context, tv2->axis(1)->rawExtent(), 4);
checkIntValue(&eval_context, tv2->axis(2)->rawExtent(), 128);
TORCH_CHECK(tv3->domain()->nDims() == 3);
checkIntValue(&eval_context, tv3->axis(0)->rawExtent(), 2);
checkIntValue(&eval_context, tv3->axis(1)->rawExtent(), 4);
checkIntValue(&eval_context, tv3->axis(2)->rawExtent(), 128);
checkIntValue(&eval_context, bid_x, 2);
checkIntValue(&eval_context, tid_x, 128);
}
void testGPU_FusionClear() {
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]));
}
void testGPU_FusionCopy() {
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::stringstream original_kernel;
{
GpuLower lower(&original_fusion);
lower.printKernel(original_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::stringstream clone_kernel;
{
GpuLower lower(&before_lowering);
lower.printKernel(clone_kernel);
}
ASSERT_EQ(original_kernel.str(), clone_kernel.str());
}
void testGPU_FusionMove() {
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
std::stringstream kernel;
GpuLower lower(&another_fusion);
lower.printKernel(kernel);
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());
}
void testGPU_FusionSimpleArith() {
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.");
}
void testGPU_FusionSimpleTypePromote() {
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);
}
};
void testGPU_FusionMutator() {
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);
}
void testGPU_FusionRegister() {
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;
};
void testGPU_FusionTopoSort() {
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);
}
void testGPU_FusionTensor() {
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]);
}
}
{
auto tensor = at::randn({2, 1, 4}, 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));
}
TORCH_CHECK(fuser_tensor->domain()->contiguity()[2]);
// temporary WAR to disable contig & bcast; issue # 230
// TODO: insert the check where broadcast & contiguous cannot be marked
// together
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[0]);
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[1]);
}
{
auto tensor = at::randn({2, 3, 1}, 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));
}
TORCH_CHECK(fuser_tensor->domain()->contiguity()[0]);
// temporary WAR to disable contig & bcast; issue # 230
// TODO: insert the check where broadcast & contiguous cannot be marked
// together
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[1]);
TORCH_CHECK(!fuser_tensor->domain()->contiguity()[2]);
}
// 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]);
}
}
void testGPU_FusionFilterVals() {
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");
}
void testGPU_FusionTVSplit() {
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);
}
void testGPU_FusionTVMerge() {
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());
}
void testGPU_FusionTVReorder() {
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());