build: update llvm tag to bebc9695 (#1415)

Summary of changes:
 - Renamed OptionalArrayRefParameter since the name conflicts with an
   upstream symbol that has a different meaning
   (https://reviews.llvm.org/D133819)
 - Removed extraneous dependency between TorchMLIRTorchToMhlo and
   ChloOps, since the existing dependency on MhloDialect is sufficient
 - Fixed code to prevent warnings related to comparisons between signed
   and unsigned values

externals/llvm-project

@@ -1 +1 @@
-Subproject commit 458598ccc50c5118107f05d60f3d043772a91f26
+Subproject commit bebc96956b76bdbc36f1d82a788c810e5b12e2c5

externals/mlir-hlo

@@ -1 +1 @@
-Subproject commit cd9da150e729fd046109e7962e5f63f5fe067a3b
+Subproject commit 7b0ecf7827e3fc07d2af90e147bcedc165bc78ac

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

@@ -45,7 +45,7 @@ def Torch_NnModuleType : Torch_Type<"NnModule", "nn.Module"> {
 }
 
 // For standard ArrayRefs, which require allocation.
-class OptionalArrayRefParameter<string arrayOf, string desc = ""> :
+class OptionalArrayRefTorchParameter<string arrayOf, string desc = ""> :
     AttrOrTypeParameter<
       "::llvm::Optional<::llvm::ArrayRef<" # arrayOf # ">>", desc> {
   let allocator = [{
@@ -146,7 +146,7 @@ class AnyTorchTensorType<string name, string typeMnemonic>
     - `getElementType()` -> `getDtype()` (but be sure that `hasDtype()` though).
   }];
   let parameters = (ins
-    OptionalArrayRefParameter<"int64_t", "sizes of dimensions">:$optionalSizes,
+    OptionalArrayRefTorchParameter<"int64_t", "sizes of dimensions">:$optionalSizes,
     "::mlir::Type":$optionalDtype
   );
   let genVerifyDecl = 1;

lib/Conversion/TorchToMhlo/CMakeLists.txt

@@ -22,7 +22,6 @@ add_mlir_conversion_library(TorchMLIRTorchToMhlo
   Core
 
   LINK_LIBS PUBLIC
-  ChloOps
   MLIRIR
   MLIRPass
   MhloDialect

lib/Conversion/TorchToMhlo/Linear.cpp

@@ -102,13 +102,13 @@ RankedTensorType castContractingDim(PatternRewriter &rewriter, Operation *op,
   }
   SmallVector<int64_t> outShape;
   // set batch dims, will skip invalid dimensions
-  for (size_t k = 0; k < lhsShape.size(); ++k) {
+  for (int64_t k = 0; k < static_cast<int64_t>(lhsShape.size()); ++k) {
     if (k == lhsResultDim || k == lhsContractingDim)
       continue;
     outShape.push_back(lhsShape[k]);
   }
-  for (size_t k = 0, b = 0; k < rhsShape.size(); ++k) {
-    if (b >= outShape.size())
+  for (int64_t k = 0, b = 0; k < static_cast<int64_t>(rhsShape.size()); ++k) {
+    if (b >= static_cast<int64_t>(outShape.size()))
       break;
     if (k == rhsResultDim || k == rhsContractingDim)
       continue;
@@ -119,10 +119,10 @@ RankedTensorType castContractingDim(PatternRewriter &rewriter, Operation *op,
   }
 
   // set result dimensions
-  if (lhsResultDim < lhsShape.size() && lhsResultDim >= 0) {
+  if (lhsResultDim < static_cast<int64_t>(lhsShape.size()) && lhsResultDim >= 0) {
     outShape.push_back(lhsShape[lhsResultDim]);
   }
-  if (rhsResultDim < rhsShape.size() && rhsResultDim >= 0) {
+  if (rhsResultDim < static_cast<int64_t>(rhsShape.size()) && rhsResultDim >= 0) {
     outShape.push_back(rhsShape[rhsResultDim]);
   }
   return RankedTensorType::get(outShape, lhsTy.getElementType());