diff --git a/cmake/llvm-hash.txt b/cmake/llvm-hash.txt
index 0952ab984c..50d0247946 100644
--- a/cmake/llvm-hash.txt
+++ b/cmake/llvm-hash.txt
@@ -1 +1 @@
-1f20eee6dc367bd202895e3eedb03974a628ef16
+86b69c31642e98f8357df62c09d118ad1da4e16a
diff --git a/lib/Dialect/Triton/Transforms/Combine.td b/lib/Dialect/Triton/Transforms/Combine.td
index 1f1de2c717..e3588f5877 100644
--- a/lib/Dialect/Triton/Transforms/Combine.td
+++ b/lib/Dialect/Triton/Transforms/Combine.td
@@ -17,7 +17,7 @@ def CombineDotAddIPattern : Pat<
         [(Constraint<CPred<"isZero($0)">> $c),
          (Constraint<CPred<"res->hasOneUse()">, "dot result has a single use">)]>;
 def CombineDotAddFPattern : Pat<
-        (Arith_AddFOp $d, (TT_DotOp:$res $a, $b, $c, $inputPrecision, $maxNumImpreciseAcc), $fastmath, $denorm),
+        (Arith_AddFOp $d, (TT_DotOp:$res $a, $b, $c, $inputPrecision, $maxNumImpreciseAcc), $fastmath),
         (TT_DotOp $a, $b, $d, $inputPrecision, $maxNumImpreciseAcc, (location $res)),
         [(Constraint<CPred<"isZero($0)">> $c),
          (Constraint<CPred<"::llvm::cast<::mlir::IntegerAttr>($0).getInt() == 0">> $maxNumImpreciseAcc),
@@ -29,7 +29,7 @@ def CombineDotAddIRevPattern : Pat<
         [(Constraint<CPred<"isZero($0)">> $c),
          (Constraint<CPred<"res->hasOneUse()">, "dot result has a single use">)]>;
 def CombineDotAddFRevPattern : Pat<
-        (Arith_AddFOp (TT_DotOp:$res $a, $b, $c, $inputPrecision, $maxNumImpreciseAcc), $d, $fastmath, $denorm),
+        (Arith_AddFOp (TT_DotOp:$res $a, $b, $c, $inputPrecision, $maxNumImpreciseAcc), $d, $fastmath),
         (TT_DotOp $a, $b, $d, $inputPrecision, $maxNumImpreciseAcc, (location $res)),
         [(Constraint<CPred<"isZero($0)">> $c),
          (Constraint<CPred<"::llvm::cast<::mlir::IntegerAttr>($0).getInt() == 0">> $maxNumImpreciseAcc),
diff --git a/python/test/regression/test_cast_matmul.py b/python/test/regression/test_cast_matmul.py
index 5a5d3b446f..2ea563b9d2 100644
--- a/python/test/regression/test_cast_matmul.py
+++ b/python/test/regression/test_cast_matmul.py
@@ -15,7 +15,7 @@
 import triton.language as tl
 from triton._internal_testing import is_hip_mi300, is_cuda, is_hip
 
-input_dtypes = ["float16", "float32", "float64"]
+input_dtypes = ["bfloat16", "float16", "float32", "float64"]
 if is_cuda():
     input_dtypes += ["int8", "float8_e5m2"]
     cc = torch.cuda.get_device_capability(0)
diff --git a/test/Conversion/tritongpu_to_llvm.mlir b/test/Conversion/tritongpu_to_llvm.mlir
index 32dff6e49f..8f214e25d5 100644
--- a/test/Conversion/tritongpu_to_llvm.mlir
+++ b/test/Conversion/tritongpu_to_llvm.mlir
@@ -1999,3 +1999,32 @@ tt.func @gather_in_shared_dot_input(%arg0: tensor<16x4xi32, #blocked>, %arg1: te
 }
 
 }
+
+// -----
+
+#mma = #ttg.nvidia_mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [2, 2], instrShape = [16, 8]}>
+
+module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 4 : i32, ttg.shared = 3072 : i32, ttg.target = "cuda:80", "ttg.threads-per-warp" = 32 : i32} {
+
+  tt.func public @ampere_s8_to_fp16_conversion_opIdx1(%1 : tensor<16x32xi8, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>>) attributes {noinline = false} {
+    // CHECK-LABEL: ampere_s8_to_fp16_conversion_opIdx1
+    // CHECK: llvm.sitofp %{{.*}} : i8 to f16
+    %2 = arith.sitofp %1 : tensor<16x32xi8, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>> to tensor<16x32xf16, #ttg.dot_op<{opIdx = 1, parent = #mma, kWidth = 4}>>
+    tt.return
+}
+
+}
+
+// -----
+
+#mma = #ttg.nvidia_mma<{versionMajor = 2, versionMinor = 0, warpsPerCTA = [2, 2], instrShape = [16, 8]}>
+
+module attributes {"ttg.num-ctas" = 1 : i32, "ttg.num-warps" = 4 : i32, ttg.shared = 3072 : i32, ttg.target = "cuda:80", "ttg.threads-per-warp" = 32 : i32} {
+  tt.func public @ampere_s8_to_fp16_conversion_opIdx0(%1 : tensor<32x16xi8, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>>) attributes {noinline = false} {
+    // CHECK-LABEL: @ampere_s8_to_fp16_conversion_opIdx0
+    // CHECK: llvm.sitofp %{{.*}} : i8 to f16
+    %2 = arith.sitofp %1 : tensor<32x16xi8, #ttg.dot_op<{opIdx = 0 , parent = #mma, kWidth = 4}>> to tensor<32x16xf16, #ttg.dot_op<{opIdx = 0, parent = #mma, kWidth = 4}>>
+    tt.return
+}
+
+}