[NFC reformat] Run pre-commit on all files and format misc.

This is part 1 of ~3, formatting all miscellaneous text files and CPP files matched by a first run of pre-commit. These tend to be low change-traffic and are likely not disruptive.

Subsequent patches will format Python files and remaining CPP files.

CMakeLists.txt

@@ -247,4 +247,4 @@ add_subdirectory(projects)
 # Finish with top-level Python bindings so it can handle additional deps.
 if(MLIR_ENABLE_BINDINGS_PYTHON)
   add_subdirectory(python)
-endif()
+endif()

build_tools/ci/test_posix.sh

@@ -30,7 +30,7 @@ echo "::endgroup::"
 
 case $torch_version in
   nightly)
-    # Failing with: NotImplementedError: 
+    # Failing with: NotImplementedError:
     #   Could not run 'aten::empty.memory_format' with arguments from the 'Lazy' backend.
     # As of 2024-01-07
     # echo "::group::Run Lazy Tensor Core e2e integration tests"

build_tools/python_deploy/build_linux_packages.sh

@@ -282,7 +282,7 @@ function _check_file_not_changed_by() {
 
 function test_in_tree() {
   local torch_version="$1"
-  
+
   echo ":::: Test in-tree"
   cmake --build /main_checkout/torch-mlir/build --target check-torch-mlir-all
 

docs/importers/onnx_importer.md

@@ -140,4 +140,3 @@ torch-mlir's representation:
 
 * `ConstantOfShape`: Mapped to `torch.vtensor.literal` with
   a corresponding `value` attribute.
-

docs/roadmap.md

@@ -277,4 +277,3 @@ directly provided a way to plug into this.
 
 Additionally, we can leverage the [`pytorch-jit-paritybench`](https://github.com/jansel/pytorch-jit-paritybench)
 to verify our end-to-end correctness on real models.
-

include/CMakeLists.txt

@@ -1,2 +1,2 @@
 add_subdirectory(torch-mlir)
-add_subdirectory(torch-mlir-dialects)
+add_subdirectory(torch-mlir-dialects)

include/torch-mlir/Dialect/Torch/IR/TorchOps.td

@@ -756,12 +756,12 @@ def Torch_ConstantNumberOp : Torch_Op<"constant.number",
     [ConstantLike, Pure]> {
   let summary = "Materialize a constant `number` value.";
   let description = [{
-    This op is used as a workaround to the fact that the constant 
-    materialization in MLIR must materialize a constant with a single op. 
-    To materialize ops with a static `!torch.number` type, we must use this op, 
+    This op is used as a workaround to the fact that the constant
+    materialization in MLIR must materialize a constant with a single op.
+    To materialize ops with a static `!torch.number` type, we must use this op,
     even though we statically know if it is an integer or a float.
 
-    Note: This op unconditionally canonicalizes to 
+    Note: This op unconditionally canonicalizes to
     `torch.constant.{float,int}` + `torch.derefine`
   }];
   let arguments = (ins
@@ -846,7 +846,7 @@ def Torch_OperatorOp : Torch_Op<"operator", [
   let regions = (region VariadicRegion<AnyRegion>:$regions);
 
   let assemblyFormat = [{
-    $name `(` $operands `)` attr-dict `:` functional-type($operands, $results) $regions 
+    $name `(` $operands `)` attr-dict `:` functional-type($operands, $results) $regions
   }];
 }
 
@@ -1146,10 +1146,10 @@ def Torch_PromoteDtypesOp: Torch_Op<"promote_dtypes", [
   let assemblyFormat = "$ranks `,` $dtypes attr-dict `:` functional-type(operands, results)";
 }
 
-// To handle runtime assertions, torchscript provides us `torch._assert` operation. 
-// But TS compiler introduces control flow for `torch._assert` operation. The 
-// `torch._assert` would introduce control flow like: 
-// 
+// To handle runtime assertions, torchscript provides us `torch._assert` operation.
+// But TS compiler introduces control flow for `torch._assert` operation. The
+// `torch._assert` would introduce control flow like:
+//
 //            %cond = "torch.aten.Bool.Tensor"(%0) : (!torch.tensor) -> !torch.bool
 //            "torch.prim.If"(%cond) ({
 //              "torch.prim.If.yield"() : () -> ()

include/torch-mlir/Dialect/Torch/Transforms/Passes.td

@@ -369,7 +369,7 @@ def LowerToBackendContract
     to the backend contract. This pass does not do any global program
     restructuring -- it works entirely within a single semantic model
     of a `builtin.module` with `torch.global_slot` ops and `func.func` ops.
-    
+
     This pass runs a set of simplifications within that semantic model until
     the backend contract is satisfied, and fails if it cannot be satisfied.
     In particular, the backend contract consists of:

lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

@@ -628,42 +628,39 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
                       binder.op, resultType, operand);
                   return success();
                 });
-  patterns.onOp("Not", 1,
-                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
-                  Torch::ValueTensorType resultType;
-                  Value operand;
-                  if (binder.tensorOperand(operand) ||
-                      binder.tensorResultType(resultType)) {
-                    return failure();
-                  }
+  patterns.onOp(
+      "Not", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Torch::ValueTensorType resultType;
+        Value operand;
+        if (binder.tensorOperand(operand) ||
+            binder.tensorResultType(resultType)) {
+          return failure();
+        }
 
-                  auto loc = binder.getLoc();
-                  auto operandTy =
-                      cast<Torch::ValueTensorType>(operand.getType());
-                  auto eTy = operandTy.getDtype();
-
-                  if (!eTy.isInteger(1)) {
-                    auto i1ty = rewriter.getI1Type();
-                    auto ty = rewriter.getType<Torch::ValueTensorType>(
-                        operandTy.getSizes(), i1ty);
-                    auto torchqTy = Torch::getScalarTypeForType(i1ty);
-                    Value tyConst = rewriter.create<Torch::ConstantIntOp>(
-                        binder.getLoc(), rewriter.getType<Torch::IntType>(),
-                        rewriter.getIntegerAttr(
-                            rewriter.getIntegerType(64),
-                            static_cast<int64_t>(torchqTy)));
-                    Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
-                    Value cstFalse =
-                        rewriter.create<Torch::ConstantBoolOp>(loc, false);
-                    operand = rewriter.create<Torch::AtenToDtypeOp>(
-                        loc, ty, operand, tyConst,
-                        /*non_blocking=*/cstFalse, /*copy=*/cstFalse,
-                        /*memory_format=*/none);
-                  }
-                  rewriter.replaceOpWithNewOp<Torch::AtenBitwiseNotOp>(
-                      binder.op, resultType, operand);
-                  return success();
-                });
+        auto loc = binder.getLoc();
+        auto operandTy = cast<Torch::ValueTensorType>(operand.getType());
+        auto eTy = operandTy.getDtype();
+
+        if (!eTy.isInteger(1)) {
+          auto i1ty = rewriter.getI1Type();
+          auto ty = rewriter.getType<Torch::ValueTensorType>(
+              operandTy.getSizes(), i1ty);
+          auto torchqTy = Torch::getScalarTypeForType(i1ty);
+          Value tyConst = rewriter.create<Torch::ConstantIntOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(),
+              rewriter.getIntegerAttr(rewriter.getIntegerType(64),
+                                      static_cast<int64_t>(torchqTy)));
+          Value none = rewriter.create<Torch::ConstantNoneOp>(loc);
+          Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
+          operand = rewriter.create<Torch::AtenToDtypeOp>(
+              loc, ty, operand, tyConst,
+              /*non_blocking=*/cstFalse, /*copy=*/cstFalse,
+              /*memory_format=*/none);
+        }
+        rewriter.replaceOpWithNewOp<Torch::AtenBitwiseNotOp>(
+            binder.op, resultType, operand);
+        return success();
+      });
   patterns.onOp("Or", 1,
                 [](OpBinder binder, ConversionPatternRewriter &rewriter) {
                   Torch::ValueTensorType resultType;

lib/Conversion/TorchToStablehlo/StablehloLegalizeUtils.cpp

@@ -189,9 +189,8 @@ Value promoteAndBroadcast(ConversionPatternRewriter &rewriter, Value input,
       do_bcast = true;
     } else {
       op->emitError("The size of tensor a (")
-          << inDim << ")"
-          << "must match the size of tensor b (" << outDim << ")"
-          << "at non-singleton dimension " << inPos;
+          << inDim << ")" << "must match the size of tensor b (" << outDim
+          << ")" << "at non-singleton dimension " << inPos;
     }
   }
   std::reverse(bcastDims.begin(), bcastDims.end());

lib/Conversion/TorchToTosa/TosaLegalizeUtils.cpp

@@ -287,19 +287,19 @@ static LogicalResult checkValidityOfCast(Type src, Type dest) {
       (src.isInteger(1) && dest.isInteger(64)) ||
       (src.isInteger(1) && dest.isF32()) ||
       // f64 -> *
-      (src.isF64() && dest.isF32()) || 
+      (src.isF64() && dest.isF32()) ||
       (src.isF64() && dest.isBF16()) ||
       // f32 -> *
-      (src.isF32() && dest.isF64()) || 
+      (src.isF32() && dest.isF64()) ||
       (src.isF32() && dest.isBF16()) ||
-      (src.isF32() && dest.isF16()) || 
+      (src.isF32() && dest.isF16()) ||
       (src.isF32() && dest.isInteger(8)) ||
       (src.isF32() && dest.isInteger(64)) ||
       (src.isF32() && dest.isInteger(1)) ||
       // bf16 -> *
       (src.isBF16() && dest.isInteger(8)) ||
       (src.isBF16() && dest.isInteger(16)) ||
-      (src.isBF16() && dest.isInteger(32)) || 
+      (src.isBF16() && dest.isInteger(32)) ||
       (src.isBF16() && dest.isF32())) {
     return success();
   }

lib/Dialect/TMTensor/Transforms/CMakeLists.txt

@@ -22,4 +22,4 @@ add_mlir_library(TorchMLIRTMTensorPasses
   MLIRTransforms
 )
 
-torch_mlir_target_includes(TorchMLIRTMTensorPasses)
+torch_mlir_target_includes(TorchMLIRTMTensorPasses)

lib/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -305,8 +305,7 @@ class LowerToBackendContractPass
         return signalPassFailure();
     } while (!satisfiesBackendContract(module, target));
     LLVM_DEBUG({
-      llvm::dbgs() << "LowerToBackendContractPass: "
-                   << "succeeded after " << i
+      llvm::dbgs() << "LowerToBackendContractPass: " << "succeeded after " << i
                    << " iterations of the simplification pipeline\n";
     });
   }

lib/Dialect/TorchConversion/Transforms/CMakeLists.txt

@@ -21,7 +21,7 @@ endif()
 
 add_mlir_library(TorchMLIRTorchConversionPasses
   BackendTypeConversion.cpp
-  BackendTypeConversionPasses.cpp  
+  BackendTypeConversionPasses.cpp
   Passes.cpp
   ConvertCustomQuantOp.cpp
   UnpackQuantTensor.cpp

projects/CMakeLists.txt

@@ -44,16 +44,16 @@ if(TORCH_MLIR_ENABLE_JIT_IR_IMPORTER OR TORCH_MLIR_ENABLE_LTC)
       message(FATAL_ERROR "Without TORCH_MLIR_USE_INSTALLED_PYTORCH, expected to find Torch configuration at ${Torch_DIR}, which does not exist")
     endif()
   endif()
-  
+
   find_package(Torch 1.11 REQUIRED)
-  
+
   set(TORCHGEN_DIR ${Torch_ROOT}/../../../torchgen)
-  
+
   include_directories(BEFORE
     ${TORCH_INCLUDE_DIRS}
     ${Python3_INCLUDE_DIRS}
   )
-  link_directories("${TORCH_INSTALL_PREFIX}/lib")  
+  link_directories("${TORCH_INSTALL_PREFIX}/lib")
   message(STATUS "TORCH_CXXFLAGS is = ${TORCH_CXXFLAGS}")
   if(${CMAKE_SYSTEM_NAME} STREQUAL "Linux" AND NOT TORCH_CXXFLAGS)
     message(WARNING

projects/ltc/csrc/base_lazy_backend/mlir_native_functions.cpp

@@ -713,4 +713,4 @@ at::Tensor &LazyNativeFunctions::logsumexp_out(const at::Tensor &self,
 void InitializeAtenBindings() {}
 
 } // namespace lazy
-} // namespace torch
+} // namespace torch

projects/ltc/csrc/base_lazy_backend/ops/unbind_int.h

@@ -34,4 +34,4 @@ class UnbindCopyInt : public torch::lazy::TorchMlirNode {
 };
 
 } // namespace lazy
-} // namespace torch
+} // namespace torch

projects/pt1/python/CMakeLists.txt

@@ -56,7 +56,7 @@ endif()
 # Can we build the JIT IR importer with `declare_mlir_python_extension`?
 # Then it would "just work".
 if(TORCH_MLIR_ENABLE_JIT_IR_IMPORTER)
-  add_dependencies(TorchMLIRPythonTorchExtensionsSources 
+  add_dependencies(TorchMLIRPythonTorchExtensionsSources
     TorchMLIRJITIRImporter
     TorchMLIRJITIRImporterPybind
     TorchMLIRE2ETestPythonModules
@@ -65,7 +65,7 @@ endif()
 
 if(TORCH_MLIR_ENABLE_LTC)
   # Add Torch-MLIR LTC backend as dependency
-  add_dependencies(TorchMLIRPythonTorchExtensionsSources 
+  add_dependencies(TorchMLIRPythonTorchExtensionsSources
     torch_mlir_ltc_backend
     reference_lazy_backend
   )

projects/pt1/python/torch_mlir/_torch_mlir_custom_op_example/CMakeLists.txt

@@ -28,4 +28,3 @@ set_target_properties(torch_mlir_custom_op_example PROPERTIES
 )
 torch_mlir_python_target_compile_options(torch_mlir_custom_op_example)
 mlir_check_all_link_libraries(torch_mlir_custom_op_example)
-

projects/pt1/test/CMakeLists.txt

@@ -13,7 +13,7 @@ configure_lit_site_cfg(
 set(TORCH_MLIR_TEST_DEPENDS
         FileCheck count not
         TorchMLIRPythonModules
-        torch-mlir-opt 
+        torch-mlir-opt
         torch-mlir-capi-torch-test
         )
 

test/CAPI/lit.local.cfg

@@ -1 +1 @@
-config.suffixes.add('.c')
+config.suffixes.add('.c')

test/CAPI/torch.c

@@ -36,7 +36,7 @@ static void testTensor(MlirContext ctx, intptr_t numSizes, int64_t *sizes,
     fprintf(stderr, #TTT "Type %s rank: %zu\n", testName,                      \
             torchMlirTorch##TTT##TypeGetRank(TTT##Type));                      \
     int64_t *TTT##Sizes = malloc(sizeof(int64_t) * numSizes);                  \
-    torchMlirTorch##TTT##TypeGetSizes(TTT##Type, TTT##Sizes);    \
+    torchMlirTorch##TTT##TypeGetSizes(TTT##Type, TTT##Sizes);                  \
     for (int i = 0; i < numSizes; ++i) {                                       \
       fprintf(stderr, #TTT "Type %s pos %d size: %ld\n", testName, i,          \
               TTT##Sizes[i]);                                                  \
@@ -157,22 +157,26 @@ static void testTypeMetaDataAccessors(MlirContext ctx) {
   MlirType dictType1 = torchMlirTorchDictTypeGet(strType, floatType);
 
   fprintf(stderr, "dict keyType: ");
-  mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType1), printToStderr, NULL);
+  mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType1), printToStderr,
+                NULL);
   fprintf(stderr, "\n");
   // CHECK: dict keyType: !torch.str
   fprintf(stderr, "dict valueType: ");
-  mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType1), printToStderr, NULL);
+  mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType1), printToStderr,
+                NULL);
   fprintf(stderr, "\n");
   // CHECK: dict valueType: !torch.float
 
   MlirType dictType2 = torchMlirTorchDictTypeGet(floatType, strType);
 
   fprintf(stderr, "dict keyType: ");
-  mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType2), printToStderr, NULL);
+  mlirTypePrint(torchMlirTorchDictTypeGetKeyType(dictType2), printToStderr,
+                NULL);
   fprintf(stderr, "\n");
   // CHECK: dict keyType: !torch.float
   fprintf(stderr, "dict valueType: ");
-  mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType2), printToStderr, NULL);
+  mlirTypePrint(torchMlirTorchDictTypeGetValueType(dictType2), printToStderr,
+                NULL);
   fprintf(stderr, "\n");
   // CHECK: dict valueType: !torch.str
 }

test/CMakeLists.txt

@@ -14,7 +14,7 @@ configure_lit_site_cfg(
 set(TORCH_MLIR_TEST_DEPENDS
         FileCheck count not
         TorchMLIRPythonModules
-        torch-mlir-opt 
+        torch-mlir-opt
         torch-mlir-capi-torch-test
         )