diff --git a/lib/Dialect/Torch/Utils/Utils.cpp b/lib/Dialect/Torch/Utils/Utils.cpp
index 7e9f9a947..906243e14 100644
--- a/lib/Dialect/Torch/Utils/Utils.cpp
+++ b/lib/Dialect/Torch/Utils/Utils.cpp
@@ -55,6 +55,8 @@ torch_upstream::ScalarType Torch::getScalarTypeForType(Type type) {
     return torch_upstream::ScalarType::Bool;
   if (type.isBF16())
     return torch_upstream::ScalarType::BFloat16;
+  if (type.isF16())
+    return torch_upstream::ScalarType::Half;
   llvm::report_fatal_error("unhandled type for getScalarTypeForType");
 }
 
@@ -74,6 +76,8 @@ Type Torch::getTypeForScalarType(
     return IntegerType::get(context, 1);
   case torch_upstream::ScalarType::BFloat16:
     return mlir::FloatType::getBF16(context);
+  case torch_upstream::ScalarType::Half:
+    return mlir::FloatType::getF16(context);
   default:
     return Type();
   }
diff --git a/python/torch_mlir/dialects/torch/importer/jit_ir/csrc/torch_to_mlir_utils.cpp b/python/torch_mlir/dialects/torch/importer/jit_ir/csrc/torch_to_mlir_utils.cpp
index 32c2e157c..471e2d570 100644
--- a/python/torch_mlir/dialects/torch/importer/jit_ir/csrc/torch_to_mlir_utils.cpp
+++ b/python/torch_mlir/dialects/torch/importer/jit_ir/csrc/torch_to_mlir_utils.cpp
@@ -353,6 +353,10 @@ MlirAttribute torch_mlir::convertTensorToMlirElementsAttr(at::Tensor tensor,
   case ScalarType::BFloat16:
     return mlirDenseElementsAttrBFloat16Get(
         shapedType, numElements, static_cast<const uint16_t *>(tensorData));
+  case ScalarType::Half:
+    return mlirDenseElementsAttrFloat16Get(
+        shapedType, numElements, static_cast<const uint16_t *>(tensorData));
+
   default:
     throwUnsupportedTensorError();
   }
diff --git a/test/Conversion/TorchToLinalg/basic.mlir b/test/Conversion/TorchToLinalg/basic.mlir
index 1e0f66582..dd07e3848 100644
--- a/test/Conversion/TorchToLinalg/basic.mlir
+++ b/test/Conversion/TorchToLinalg/basic.mlir
@@ -220,3 +220,16 @@ func.func @torch.aten.neg.bf16(%arg0: !torch.vtensor<[?,?],bf16>) -> !torch.vten
   %0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],bf16> -> !torch.vtensor<[?,?],bf16>
   return %0 : !torch.vtensor<[?,?],bf16>
 }
+
+// -----
+
+// CHECK-LABEL:     func.func @torch.aten.neg.f16
+// CHECK: linalg.generic {{.*}} {
+// CHECK-NEXT:    ^bb0(%[[LHS:.*]]: f16, %{{.*}}: f16):
+// CHECK-NEXT:      %[[NEG:.*]] = arith.negf %[[LHS]] : f16
+// CHECK-NEXT:      linalg.yield %[[NEG]] : f16
+// CHECK-NEXT:    } -> tensor<?x?xf16>
+func.func @torch.aten.neg.f16(%arg0: !torch.vtensor<[?,?],f16>) -> !torch.vtensor<[?,?],f16> {
+  %0 = torch.aten.neg %arg0 : !torch.vtensor<[?,?],f16> -> !torch.vtensor<[?,?],f16>
+  return %0 : !torch.vtensor<[?,?],f16>
+}
diff --git a/test/python/importer/jit_ir/ivalue_import/tensors.py b/test/python/importer/jit_ir/ivalue_import/tensors.py
index 02a82f06e..314c245f5 100644
--- a/test/python/importer/jit_ir/ivalue_import/tensors.py
+++ b/test/python/importer/jit_ir/ivalue_import/tensors.py
@@ -22,6 +22,7 @@ class TestModule(torch.nn.Module):
         self.ones_f64 = torch.ones(1, dtype=torch.float64)
         self.ones_bool = torch.ones(1, dtype=torch.bool)
         self.ones_bf16 = torch.ones(1, dtype=torch.bfloat16)
+        self.ones_f16 = torch.ones(1, dtype=torch.half)
         self.ones_qint8 =  torch.quantize_per_tensor(torch.ones(1), 1.0, 0, torch.qint8)
         self.ones_quint8 =  torch.quantize_per_tensor(torch.ones(1), 1.0, 0, torch.quint8)
         self.arange = torch.nn.Parameter(torch.arange(3.0))
@@ -34,6 +35,7 @@ class TestModule(torch.nn.Module):
 # CHECK: %[[ONES_F64:.*]] = torch.tensor.literal(dense<1.000000e+00> : tensor<1xf64>) : !torch.tensor<[1],f64>
 # CHECK: %[[ONES_BOOL:.*]] = torch.tensor.literal(dense<true> : tensor<1xi1>) : !torch.tensor<[1],i1>
 # CHECK: %[[ONES_BF16:.*]] = torch.tensor.literal(dense<1.000000e+00> : tensor<1xbf16>) : !torch.tensor<[1],bf16>
+# CHECK: %[[ONES_F16:.*]] = torch.tensor.literal(dense<1.000000e+00> : tensor<1xf16>) : !torch.tensor<[1],f16>
 # CHECK: %[[ONES_QINT8_DATA:.*]] = torch.tensor.literal(dense<1> : tensor<1xsi8>) : !torch.tensor<[1],si8>
 # CHECK: %[[SCALE:.*]] = torch.constant.float 1.000000e+00
 # CHECK: %[[ZERO_POINT:.*]] = torch.constant.int 0
@@ -49,6 +51,7 @@ class TestModule(torch.nn.Module):
 # CHECK:   torch.slot "ones_f64", %[[ONES_F64]] : !torch.tensor<[1],f64>
 # CHECK:   torch.slot "ones_bool", %[[ONES_BOOL]] : !torch.tensor<[1],i1>
 # CHECK:   torch.slot "ones_bf16", %[[ONES_BF16]] : !torch.tensor<[1],bf16>
+# CHECK:   torch.slot "ones_f16", %[[ONES_F16]] : !torch.tensor<[1],f16>
 # CHECK:   torch.slot "ones_qint8", %[[ONES_QINT8]] : !torch.tensor<[1],!torch.qint8>
 # CHECK:   torch.slot "ones_quint8", %[[ONES_QUINT8]] : !torch.tensor<[1],!torch.quint8>
 # CHECK: }