[AutoAccept][Codemod][FBSourceClangFormatLinter] Daily arc lint --take CLANGFORMAT

Reviewed By: zertosh

Differential Revision: D33577744

fbshipit-source-id: 7ecc8367998ee1dffde54c2f4dd3cfafe19a53c9

Author: CodemodService FBSourceClangFormatLinterBot

test/cpp/jit/test_code_template.cpp

@@ -1,7 +1,7 @@
 #include <gtest/gtest.h>
 
-#include <test/cpp/jit/test_utils.h>
 #include <ATen/code_template.h>
+#include <test/cpp/jit/test_utils.h>
 
 namespace torch {
 namespace jit {

test/cpp/tensorexpr/test_kernel.cpp

@@ -1,8 +1,8 @@
 #include <gtest/gtest.h>
 
+#include <ATen/code_template.h>
 #include <c10/util/irange.h>
 #include <test/cpp/tensorexpr/test_base.h>
-#include <ATen/code_template.h>
 #include <torch/csrc/jit/ir/ir.h>
 #include <torch/csrc/jit/ir/irparser.h>
 #include <torch/csrc/jit/passes/constant_propagation.h>

torch/csrc/jit/backends/backend_detail.cpp

@@ -1,11 +1,11 @@
 #include <torch/csrc/jit/backends/backend_detail.h>
 
+#include <ATen/code_template.h>
 #include <ATen/core/jit_type.h>
 #include <torch/csrc/jit/backends/backend.h>
 #include <torch/csrc/jit/backends/backend_debug_handler.h>
 #include <torch/csrc/jit/backends/backend_debug_info.h>
 #include <torch/csrc/jit/backends/backend_resolver.h>
-#include <ATen/code_template.h>
 
 #include <memory>
 #include <stack>

torch/csrc/jit/codegen/fuser/codegen.cpp

@@ -1,11 +1,11 @@
 #include <torch/csrc/jit/codegen/fuser/codegen.h>
 
 #include <ATen/ATen.h>
+#include <ATen/code_template.h>
 #include <c10/util/Exception.h>
 #include <torch/csrc/jit/codegen/fuser/compiler.h>
 #include <torch/csrc/jit/codegen/fuser/interface.h>
 #include <torch/csrc/jit/codegen/fuser/tensor_info.h>
-#include <ATen/code_template.h>
 #include <torch/csrc/jit/ir/ir.h>
 
 #include <torch/csrc/jit/codegen/fuser/cpu/resource_strings.h>

torch/csrc/jit/codegen/fuser/cpu/fused_kernel.cpp

@@ -1,11 +1,11 @@
 #include <torch/csrc/jit/codegen/fuser/cpu/fused_kernel.h>
 
 #include <ATen/DynamicLibrary.h>
+#include <ATen/code_template.h>
 #include <c10/util/Exception.h>
 #include <c10/util/Optional.h>
 #include <torch/csrc/jit/codegen/fuser/compiler.h>
 #include <torch/csrc/jit/codegen/fuser/cpu/temp_file.h>
-#include <ATen/code_template.h>
 #include <torch/csrc/utils/memory.h>
 
 #include <cstdlib>

torch/csrc/jit/codegen/fuser/cuda/resource_strings.h

@@ -1,7 +1,7 @@
 #pragma once
 
-#include <torch/csrc/Export.h>
 #include <ATen/code_template.h>
+#include <torch/csrc/Export.h>
 
 namespace torch {
 namespace jit {

torch/csrc/jit/frontend/builtin_functions.cpp

@@ -1,8 +1,8 @@
 #include <torch/csrc/jit/frontend/builtin_functions.h>
 
+#include <ATen/code_template.h>
 #include <caffe2/serialize/versions.h>
 #include <torch/csrc/api/include/torch/jit.h>
-#include <ATen/code_template.h>
 #include <torch/csrc/jit/frontend/resolver.h>
 
 namespace torch {

torch/csrc/jit/passes/frozen_conv_add_relu_fusion_cuda.cpp

@@ -1,7 +1,7 @@
 #include <ATen/Utils.h>
 
-#include <ATen/cuda/CUDAConfig.h>
 #include <ATen/code_template.h>
+#include <ATen/cuda/CUDAConfig.h>
 #include <torch/csrc/jit/ir/constants.h>
 #include <torch/csrc/jit/ir/ir.h>
 #include <torch/csrc/jit/ir/subgraph_matcher.h>

torch/csrc/jit/passes/shape_analysis.cpp

@@ -50,7 +50,7 @@ bool mergeTypes(
   return changed;
 }
 
-void applyTypes(ArrayRef<Value*> src, ArrayRef<Value*> dst){
+void applyTypes(ArrayRef<Value*> src, ArrayRef<Value*> dst) {
   AT_ASSERT(src.size() == dst.size());
   for (const auto i : c10::irange(src.size())) {
     dst[i]->setType(src[i]->type());
@@ -90,7 +90,9 @@ void PropertyPropBase::processLoop(Node* node) {
     // note: inserting expands is unsafe at this point, we don't know
     // if the types are stable yet, so the arguments to expand may change
   } while (mergeTypes(
-      loop.bodyCarriedInputs(), loop.bodyCarriedOutputs(), loop.bodyCarriedInputs()));
+      loop.bodyCarriedInputs(),
+      loop.bodyCarriedOutputs(),
+      loop.bodyCarriedInputs()));
 
   // now that the types are stable, we can insert the expands
   propagateBlock(loop.bodyBlock(), /*insert_expands=*/true);
@@ -111,7 +113,6 @@ namespace prim {
 using namespace ::c10::prim;
 }
 
-
 #define SHAPE_ASSERT(cond) \
   if (!(cond))             \
   throw propagation_error()
@@ -189,7 +190,7 @@ c10::optional<std::vector<TensorTypePtr>> gatherTensorTypes(
 
 int64_t wrapDim(int64_t dim, at::IntArrayRef sizes) {
   if (dim < 0) {
-    dim += (int64_t) sizes.size();
+    dim += (int64_t)sizes.size();
   }
   return dim;
 }
@@ -204,7 +205,6 @@ c10::ScalarType unionScalarTypes(
   }
 }
 
-
 // Promotes result types for arithmetic operations on Tensor operands using
 // new type promotion logic. See tensor_attributes.rst for details.
 // This doesn't handle the case of arithmetic ops with Scalar arguments (when
@@ -251,7 +251,7 @@ c10::optional<c10::ScalarType> getPromotedTypeForArithmeticOp(Node* node) {
   return zerodim;
 }
 
-class ShapePropagator: public PropertyPropBase {
+class ShapePropagator : public PropertyPropBase {
  public:
   explicit ShapePropagator(const std::shared_ptr<Graph>& graph)
       : PropertyPropBase(graph), aliasDb_(graph) {
@@ -585,7 +585,7 @@ class ShapePropagator: public PropertyPropBase {
     return in_resize;
   }
 
-   void propagateNode(Node* node, bool insert_expands = true) override {
+  void propagateNode(Node* node, bool insert_expands = true) override {
     // Certain ops like resize_ change the input tensors size. Because our
     // analysis is flow invariant, we set any Tensor that can alias a resized
     // Tensor to the base Tensor Type without size information.
@@ -1469,7 +1469,7 @@ class ShapePropagator: public PropertyPropBase {
           if (auto type =
                   node->namedInput(attr::self)->type()->cast<TensorType>()) {
             if (type->dim()) {
-              return factory_like_with_ndim(node, (int) *type->dim());
+              return factory_like_with_ndim(node, (int)*type->dim());
             }
           }
           return {};

torch/csrc/jit/passes/shape_analysis.h

@@ -11,12 +11,11 @@ struct Graph;
 
 struct propagation_error : std::exception {};
 
-class PropertyPropBase{
+class PropertyPropBase {
   // Used for both Shape Propagation and Dtype/Device Propagation
-  public:
+ public:
   explicit PropertyPropBase(std::shared_ptr<Graph> graph)
-      : graph_(std::move(graph)) {
-  }
+      : graph_(std::move(graph)) {}
   virtual ~PropertyPropBase() = default;
 
   void propagateBlock(Block* block, bool insert_expands = true);
@@ -25,7 +24,7 @@ class PropertyPropBase{
   void processIf(Node* node);
   void processLoop(Node* node);
 
-  protected:
+ protected:
   virtual void propagateNode(Node* node, bool insert_expands = true) = 0;
   void setUnshapedType(Value* o);
   void setUnshapedType(Node* node);

torch/csrc/jit/python/init.cpp

@@ -23,8 +23,8 @@
 #include <torch/csrc/jit/passes/cuda_graph_fuser.h>
 #include <torch/csrc/jit/passes/dead_code_elimination.h>
 #include <torch/csrc/jit/passes/decompose_ops.h>
-#include <torch/csrc/jit/passes/dtype_analysis.h>
 #include <torch/csrc/jit/passes/device_type_analysis.h>
+#include <torch/csrc/jit/passes/dtype_analysis.h>
 #include <torch/csrc/jit/passes/erase_number_types.h>
 #include <torch/csrc/jit/passes/fold_conv_bn.h>
 #include <torch/csrc/jit/passes/freeze_module.h>

torch/csrc/jit/python/python_ir.cpp

@@ -821,8 +821,7 @@ void initPythonIRBindings(PyObject* module_) {
       .def(
           "device",
           [](Type& t) -> py::object {
-            auto device =
-                t.expectRef<TensorType>().device();
+            auto device = t.expectRef<TensorType>().device();
             if (!device) {
               return py::none();
             }

torch/csrc/jit/tensorexpr/ir_simplifier.cpp

@@ -1866,7 +1866,7 @@ class ModRound {
   ExprPtr mod_divisor;
 };
 
-c10::optional< class ModRound > isModRound(TermPtr e) {
+c10::optional<class ModRound> isModRound(TermPtr e) {
   DivPtr div{nullptr};
   ModPtr mod{nullptr};
   ExprPtr denom{nullptr};