[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.

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.
-

lib/Conversion/TorchOnnxToTorch/DefaultDomainGtoP.cpp

@@ -628,8 +628,8 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
                       binder.op, resultType, operand);
                   return success();
                 });
-  patterns.onOp("Not", 1,
-                [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+  patterns.onOp(
+      "Not", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
         Torch::ValueTensorType resultType;
         Value operand;
         if (binder.tensorOperand(operand) ||
@@ -638,8 +638,7 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
         }
 
         auto loc = binder.getLoc();
-                  auto operandTy =
-                      cast<Torch::ValueTensorType>(operand.getType());
+        auto operandTy = cast<Torch::ValueTensorType>(operand.getType());
         auto eTy = operandTy.getDtype();
 
         if (!eTy.isInteger(1)) {
@@ -649,12 +648,10 @@ void mlir::torch::onnx_c::populateDefaultDomainGtoP(
           auto torchqTy = Torch::getScalarTypeForType(i1ty);
           Value tyConst = rewriter.create<Torch::ConstantIntOp>(
               binder.getLoc(), rewriter.getType<Torch::IntType>(),
-                        rewriter.getIntegerAttr(
-                            rewriter.getIntegerType(64),
+              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);
+          Value cstFalse = rewriter.create<Torch::ConstantBoolOp>(loc, false);
           operand = rewriter.create<Torch::AtenToDtypeOp>(
               loc, ty, operand, tyConst,
               /*non_blocking=*/cstFalse, /*copy=*/cstFalse,

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/Dialect/Torch/Transforms/LowerToBackendContract.cpp

@@ -305,8 +305,7 @@ public:
         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";
     });
   }

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)
-

test/CAPI/torch.c

@@ -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/Conversion/TorchToLinalg/flatten.mlir

@@ -82,5 +82,3 @@ func.func @torch.aten.flatten.using_ints$rank0(%arg0: !torch.vtensor<[],f32>) ->
   %0 = torch.aten.flatten.using_ints %arg0, %int0, %int0 : !torch.vtensor<[],f32>, !torch.int, !torch.int -> !torch.vtensor<[1],f32>
   return %0 : !torch.vtensor<[1],f32>
 }
-
-

test/Conversion/TorchToLinalg/gridsampler.mlir

@@ -86,4 +86,3 @@ func.func @grid_sampler3(%arg0: !torch.vtensor<[?,?,?,?],f32>, %arg1: !torch.vte
   %4 = torch.aten.grid_sampler %arg0, %arg1, %int0, %int1, %false : !torch.vtensor<[?,?,?,?],f32>, !torch.vtensor<[?,?,?,?],f32>, !torch.int, !torch.int, !torch.bool -> !torch.vtensor<[?,?,?,?],f32>
   return %4 : !torch.vtensor<[?,?,?,?],f32>
 }
-

test/Conversion/TorchToLinalg/view.mlir

@@ -254,4 +254,3 @@ func.func @torch.aten.view$dynamicInferredSame(%arg0: !torch.vtensor<[10,?,2,3],
   %1 = torch.aten.view %arg0, %0 : !torch.vtensor<[10,?,2,3],f32>, !torch.list<int> -> !torch.vtensor<[2,5,?,6],f32>
   return %1 : !torch.vtensor<[2,5,?,6],f32>
 }
-

test/Conversion/TorchToStablehlo/gather.mlir

@@ -63,4 +63,3 @@ func.func @torch.aten.embedding$rank_two_indices(%weight: !torch.vtensor<[?,?],f
   %ret = torch.aten.embedding %weight, %indices, %int-1, %false, %false : !torch.vtensor<[?,?],f32>, !torch.vtensor<[?,1], si64>, !torch.int, !torch.bool, !torch.bool -> !torch.vtensor<[?,1,?],f32>
   return %ret: !torch.vtensor<[?,1,?],f32>
 }
-

test/Conversion/TorchToStablehlo/view_like.mlir

@@ -565,4 +565,3 @@ func.func @torch.aten.unsqueeze$from_end(%arg0: !torch.vtensor<[?,?,?,?],f32>) -
   %0 = torch.aten.unsqueeze %arg0, %int-2 : !torch.vtensor<[?,?,?,?],f32>, !torch.int -> !torch.vtensor<[?,?,?,1,?],f32>
   return %0 : !torch.vtensor<[?,?,?,1,?],f32>
 }
-

test/Conversion/TorchToTosa/cast_fp32_to_fp16.mlir

@@ -8,5 +8,3 @@ func.func @forward(%arg0: !torch.vtensor<[1,32,220,220],f32>) -> !torch.vtensor<
   %out = torch.aten.to.dtype %arg0, %int5, %false, %false, %none : !torch.vtensor<[1,32,220,220],f32>, !torch.int, !torch.bool, !torch.bool, !torch.none -> !torch.vtensor<[1,32,220,220],f16>
   return %out : !torch.vtensor<[1,32,220,220],f16>
 }
-
-

test/Conversion/TorchToTosa/conv2d_transpose.mlir

@@ -15,4 +15,3 @@ func.func @forward(%input: !torch.vtensor<[1,64,1,100],f32>) -> !torch.vtensor<[
   %output = torch.aten.convolution %input, %weight, %bias, %stride, %int1x1, %int1x1, %true, %int1x1, %int1 : !torch.vtensor<[1,64,1,100],f32>, !torch.vtensor<[64,64,3,3],f32>, !torch.vtensor<[64],f32>, !torch.list<int>, !torch.list<int>, !torch.list<int>, !torch.bool, !torch.list<int>, !torch.int -> !torch.vtensor<[1,64,2,200],f32>
   return %output : !torch.vtensor<[1,64,2,200],f32>
 }
-

test/Dialect/Torch/ops.mlir

@@ -186,4 +186,3 @@ func.func @torch.permute$negative_index_valid (%arg0: !torch.vtensor<[1,2,3],f32
   %3 = torch.aten.permute %arg0, %perm : !torch.vtensor<[1,2,3],f32>, !torch.list<int> -> !torch.vtensor<[1,2,3],f32>
    return %3 : !torch.vtensor<[1,2,3],f32>
 }
-