new unit test

NVIDIA · Dec 21, 2024 · e92c575 · e92c575
1 parent 49b0862
commit e92c575
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diff --git a/csrc/ops/arith.cpp b/csrc/ops/arith.cpp
@@ -494,6 +494,35 @@ TensorView* eye(Val* size, DataType dtype) {
   return eye(size, size, dtype);
 }
 
+TensorView* triu(TensorView* tv, Val* offset) {
+  NVF_CHECK(
+      offset->getDataType() == DataType::Int, "offset must have type Int");
+
+  NVF_CHECK(
+      tv->nDims() >= 2,
+      "triu is only supported for 2+D tensors, but got ",
+      tv->nDims(),
+      "D tensor");
+
+  auto tv_rows = iota(
+      tv->domain()->logical()[1]->extent(),
+      IrBuilder::create<Val>(0, DataType::Index),
+      IrBuilder::create<Val>(1, DataType::Index),
+      DataType::Int);
+
+  auto tv_columns = iota(
+      tv->domain()->logical()[2]->extent(),
+      SimplifyingIrBuilder::mulExpr(
+          offset, IrBuilder::create<Val>(-1, DataType::Index)),
+      IrBuilder::create<Val>(1, DataType::Index),
+      DataType::Int);
+
+  auto tv_rows_b = broadcast(tv_rows, {false, true});
+  auto tv_cols_b = broadcast(tv_columns, {true, false});
+  auto mask = le(tv_rows_b, tv_cols_b);
+  return where(mask, tv, IrBuilder::create<Val>(0, tv->dtype()));
+}
+
 // UNARY OPERATIONS
 
 #define NVFUSER_DEFINE_UNARY_OP(operator_name, operator_type) \

diff --git a/csrc/ops/arith.h b/csrc/ops/arith.h
@@ -251,6 +251,7 @@ NVF_API TensorView* arange(
     DataType dtype = DataType::Int);
 NVF_API TensorView* eye(Val* size, DataType dtype);
 NVF_API TensorView* eye(Val* rows, Val* cols, DataType dtype);
+NVF_API TensorView* triu(TensorView* tv, Val* offset);
 
 // UNARY OPERATIONS
 // abs

diff --git a/tests/cpp/test_tensor_factories.cpp b/tests/cpp/test_tensor_factories.cpp
@@ -230,6 +230,28 @@ TEST_F(TensorFactoryTest, StandaloneIota) {
   }
 }
 
+TEST_F(TensorFactoryTest, SimpleTriu) {
+  auto fusion = std::make_unique<Fusion>();
+
+  FusionGuard gf(fusion.get());
+
+  auto tv_to_triu_on = makeSymbolicTensor(3, DataType::Half);
+  fusion->addInput(tv_to_triu_on);
+
+  int64_t k_factor = -2;
+  auto out = triu(tv_to_triu_on, IrBuilder::create<Val>(k_factor, DataType::Int));
+  fusion->addOutput(out);
+
+
+  auto options = at::TensorOptions().dtype(at::kHalf).device(at::kCUDA);
+  auto in_tensor = at::randn({4, 4, 8}, options);
+
+  FusionExecutorCache executor_cache(std::move(fusion));
+  auto cg_outputs = executor_cache.runFusionWithInputs({in_tensor});
+
+  EXPECT_TRUE(cg_outputs[0].allclose(at::triu(in_tensor, k_factor), .001, .001));
+}
+
 TEST_F(TensorFactoryTest, StandaloneARange) {
   auto starts_ends = {-1., 0., 10.3, 1024. * 256};
   auto steps = {-1.5, 1., 2.};