[BACKEND] Update LLVM hash (triton-lang#5040)

Fixes triton-lang#4769

(cherry picked from commit f484cb8)

cmake/llvm-hash.txt

@@ -1 +1 @@
-b5cc222d7429fe6f18c787f633d5262fac2e676f
+b74e588e1f460eb48ceb1a30cf8ac870b7537dcc

lib/Conversion/TritonToTritonGPU/TritonGPUConversion.cpp

@@ -56,20 +56,19 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
   // This will create newArg, and map(origArg, newArg)
   addArgumentMaterialization([&](OpBuilder &builder,
                                  RankedTensorType tensorType, ValueRange inputs,
-                                 Location loc) -> std::optional<Value> {
+                                 Location loc) -> Value {
     llvm_unreachable("Argument rematerialization should not happen in Triton "
                      "-> TritonGPU conversion");
-    return std::nullopt;
+    return {};
   });
 
   // If the origValue still has live user(s), use this to
   // convert origValue to newValue
   addSourceMaterialization([&](OpBuilder &builder, RankedTensorType tensorType,
-                               ValueRange inputs,
-                               Location loc) -> std::optional<Value> {
+                               ValueRange inputs, Location loc) -> Value {
     llvm_unreachable("Source rematerialization should not happen in Triton -> "
                      "TritonGPU Conversion");
-    return std::nullopt;
+    return {};
   });
 
   // This will be called when (desiredType != newOperandType)
@@ -79,7 +78,7 @@ TritonGPUTypeConverter::TritonGPUTypeConverter(MLIRContext *context,
                                ValueRange inputs, Location loc) {
     auto cast =
         builder.create<triton::gpu::ConvertLayoutOp>(loc, tensorType, inputs);
-    return std::optional<Value>(cast.getResult());
+    return cast.getResult();
   });
 }
 

test/TritonGPU/amd/amd-convert-buffer-ops.mlir

@@ -42,7 +42,7 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 :
     %1 = arith.muli %0, %c1024_i32 : i32
     %sub = arith.subi %1, %c128_i32 : i32
     %cmp = arith.cmpi sgt, %sub, %c0_i32 : i32
-    "llvm.intr.assume"(%cmp) : (i1) -> ()
+    llvm.intr.assume %cmp : i1
     %2 = tt.splat %sub : i32 -> tensor<1024xi32, #blocked>
     %3 = tt.make_range {end = 1024 : i32, start = 0 : i32} : tensor<1024xi32, #blocked>
     // CHECK: %[[offset:.*]] = arith.addi