format

Author: zasdfgbnm

torch/csrc/jit/codegen/cuda/arith.cpp

@@ -630,10 +630,12 @@ TensorView* eye(Val* size, DataType dtype) {
 
 // UNARY OPERATIONS
 
-#define NVFUSER_DEFINE_UNARY_OP(op_name, op_type)                   \
-  Val* op_name(Val* v) { return unaryOp(UnaryOpType::op_type, v); } \
-  TensorView* op_name(TensorView* tv) {                             \
-    return unaryOp(UnaryOpType::op_type, tv);                       \
+#define NVFUSER_DEFINE_UNARY_OP(op_name, op_type) \
+  Val* op_name(Val* v) {                          \
+    return unaryOp(UnaryOpType::op_type, v);      \
+  }                                               \
+  TensorView* op_name(TensorView* tv) {           \
+    return unaryOp(UnaryOpType::op_type, tv);     \
   }
 
 NVFUSER_DEFINE_UNARY_OP(set, Set)
@@ -763,10 +765,12 @@ NVFUSER_DEFINE_UNARY_FLOAT_OP(tan, Tan)
 NVFUSER_DEFINE_UNARY_FLOAT_OP(tanh, Tanh)
 #undef NVFUSER_DEFINE_UNARY_FLOAT_OP
 
-#define NVFUSER_DEFINE_UNARY_IS_OP(op_name, op_type)                  \
-  Val* op_name(Val* v) { return unaryIsOp(UnaryOpType::op_type, v); } \
-  TensorView* op_name(TensorView* tv) {                               \
-    return unaryIsOp(UnaryOpType::op_type, tv);                       \
+#define NVFUSER_DEFINE_UNARY_IS_OP(op_name, op_type) \
+  Val* op_name(Val* v) {                             \
+    return unaryIsOp(UnaryOpType::op_type, v);       \
+  }                                                  \
+  TensorView* op_name(TensorView* tv) {              \
+    return unaryIsOp(UnaryOpType::op_type, tv);      \
   }
 
 NVFUSER_DEFINE_UNARY_IS_OP(isfinite, IsFinite)

torch/csrc/jit/codegen/cuda/ir_iostream.cpp

@@ -506,10 +506,8 @@ void IrPrinter::handle(const RNGOp* rop) {
 
 void IrPrinter::handle(const SelectOp* sop) {
   indent() << sop->output(0) << "\n";
-  indent() << "   = select( " << sop->input(0)
-           << ", dim = " << sop->getDim()
-           << ", index = " <<  sop->input(1)
-           << " )\n";
+  indent() << "   = select( " << sop->input(0) << ", dim = " << sop->getDim()
+           << ", index = " << sop->input(1) << " )\n";
 }
 
 void IrPrinter::handle(const ReductionOp* rop) {

torch/csrc/jit/codegen/cuda/ir_utils.cpp

@@ -266,18 +266,17 @@ struct SubstituteInExpr : public OptInDispatch {
   }
 
   void handle(SelectOp* select_expr) final {
-    auto input = reference_->sameAs(select_expr->input(0)) ? substitute_
-                                                       : select_expr->input(0);
-    auto index = reference_->sameAs(select_expr->input(1)) ? substitute_
-                                                       : select_expr->input(1);
-    auto out = reference_->sameAs(select_expr->output(0)) ? substitute_
-                                                       : select_expr->output(0);
+    auto input = reference_->sameAs(select_expr->input(0))
+        ? substitute_
+        : select_expr->input(0);
+    auto index = reference_->sameAs(select_expr->input(1))
+        ? substitute_
+        : select_expr->input(1);
+    auto out = reference_->sameAs(select_expr->output(0))
+        ? substitute_
+        : select_expr->output(0);
     expr_ = IrBuilder::create<SelectOp>(
-        select_expr->container(),
-        out,
-        input,
-        select_expr->getDim(),
-        index);
+        select_expr->container(), out, input, select_expr->getDim(), index);
   }
 
   void handle(RNGOp* rng_expr) final {

torch/csrc/jit/codegen/cuda/scheduler/pointwise_utils.cpp

@@ -27,7 +27,8 @@ bool DomainMap::areAllInputIdsMappedTo(TensorView* input_tv, TensorView* tv)
     // like T0[I0, b] + T1[b, I1]
     auto concrete =
         ca_map_.getConcreteMappedID(in_id, IdMappingMode::PERMISSIVE);
-    if (!concrete->isBroadcast() && !in_id->isReduction() && !isSelectId(in_id)) {
+    if (!concrete->isBroadcast() && !in_id->isReduction() &&
+        !isSelectId(in_id)) {
       in_concrete_ids.insert(concrete);
     }
   }

torch/csrc/jit/codegen/cuda/scheduler/utils.cpp

@@ -975,7 +975,8 @@ std::vector<TensorView*> cacheInputs(Fusion* fusion, bool unroll) {
   // If we're going to unroll, make a cache of the inputs
   auto in_tvs = ir_utils::filterByType<TensorView>(fusion->inputs());
   for (auto tv : in_tvs) {
-    if (tv->uses().empty() || tv->isFusionOutput() || scheduler_utils::isSelectInput(tv)) {
+    if (tv->uses().empty() || tv->isFusionOutput() ||
+        scheduler_utils::isSelectInput(tv)) {
       continue;
     }
     auto cached_tv = tv->cacheAfter();

torch/csrc/jit/codegen/cuda/test/test_gpu_indexing_ops.cpp

@@ -133,4 +133,4 @@ TEST_F(NVFuserTest, FusionSelectOpPersistent_CUDA) {
 
 } // namespace jit
 } // namespace torch
-#endif // #if defined(USE_CUDA)
+#endif // #if defined(USE_CUDA)