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Reduction rand like patch #2031

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Merged
merged 3 commits into from
Oct 5, 2022
Merged

Reduction rand like patch #2031

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7e1658c
fixing "*_like" in arith to filter out reduction domains
jjsjann123 Oct 4, 2022
0571c63
cpp tests added for reduction + *_like
jjsjann123 Oct 4, 2022
cd06128
quick patch on tests
jjsjann123 Oct 5, 2022
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18 changes: 10 additions & 8 deletions torch/csrc/jit/codegen/cuda/arith.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -471,17 +471,18 @@ TensorView* uniform(
return out;
}

TensorView* rand_like(TensorView* v) {
TensorView* rand_like(TensorView* tv) {
TORCH_CHECK(
isFloatingPointType(v->dtype()),
isFloatingPointType(tv->dtype()),
"input must have floating point type, but got ",
v->dtype());
tv->dtype());
std::vector<Val*> shape;
shape.reserve(v->getMaybeRFactorDomain().size());
for (auto id : v->getMaybeRFactorDomain()) {
auto dom = TensorDomain::noReductions(tv->getMaybeRFactorDomain());
shape.reserve(dom.size());
for (auto id : dom) {
shape.emplace_back(id->getMaybeExpandedExtent());
}
return rand(shape, v->dtype());
return rand(shape, tv->dtype());
}

Val* rand_like(Val* v) {
Expand All @@ -505,8 +506,9 @@ TensorView* full(

TensorView* full_like(TensorView* tv, Val* fill_value) {
std::vector<Val*> shape;
shape.reserve(tv->getMaybeRFactorDomain().size());
for (auto id : tv->getMaybeRFactorDomain()) {
auto dom = TensorDomain::noReductions(tv->getMaybeRFactorDomain());
shape.reserve(dom.size());
for (auto id : dom) {
shape.emplace_back(id->getMaybeExpandedExtent());
}
return full(shape, fill_value, tv->dtype());
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30 changes: 30 additions & 0 deletions torch/csrc/jit/codegen/cuda/test/test_gpu_rng.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -365,5 +365,35 @@ TEST_F(NVFuserTest, FusionUniform_CUDA) {
}
}

TEST_F(NVFuserTest, FusionRandLikeReduction_CUDA) {
auto dtype = kFloat;
std::unique_ptr<Fusion> fusion_ptr = std::make_unique<Fusion>();
auto fusion = fusion_ptr.get();
FusionGuard fg(fusion);

TensorView* tv0 = makeSymbolicTensor(2, aten_to_data_type(dtype));
fusion->addInput(tv0);
auto tv1 = sum(tv0, {0});
auto tv2 = rand_like(tv1);
auto tv3 = add(tv1, tv2);
fusion->addOutput(tv3);

FusionExecutorCache fec(std::move(fusion_ptr));

auto options = at::TensorOptions().dtype(dtype).device(at::kCUDA, 0);
at::Tensor t0 = at::zeros({2, 3}, options);

at::manual_seed(0);
auto cg_outputs = fec.runFusionWithInputs({t0});
auto out = cg_outputs[0];

at::manual_seed(0);
auto t1 = t0.sum(0);
auto t2 = generate_uniform(3, dtype).expand_as(t1);
auto t3 = t1.add(t2);

testValidate(fec.fusion(), {out}, {t0}, {t3}, __LINE__, __FILE__);
}

} // namespace jit
} // namespace torch