diff --git a/test/test_autocast.py b/test/test_autocast.py
index 9d7b9f7b7b1e..acbd0e03be39 100644
--- a/test/test_autocast.py
+++ b/test/test_autocast.py
@@ -484,20 +484,6 @@ def test_autocast_tpu_check_dtype(self):
       assert not torch.is_autocast_xla_enabled()
 
 
-class TestOtherOps(unittest.TestCase):
-
-  @unittest.skipIf(not (xm.get_xla_supported_devices("TPU") or
-                        xm.get_xla_supported_devices("GPU")),
-                   f"bfloat16 is only enabled for TPU and GPU")
-  def test_batch_norm(self):
-    device = xm.xla_device()
-    data = torch.randn(4, 16, 32, 32, device=device, dtype=torch.bfloat16)
-    with autocast(device, dtype=torch.bfloat16):
-      output = torch.nn.BatchNorm2d(16)(data)
-    xm.mark_step()
-    self.assertEqual(output.dtype, torch.bfloat16)
-
-
 if __name__ == "__main__":
   test = unittest.main(verbosity=FLAGS.verbosity, exit=False)
   sys.exit(0 if test.result.wasSuccessful() else 1)
diff --git a/torch_xla/csrc/batch_norm.cpp b/torch_xla/csrc/batch_norm.cpp
index fa9a365e7743..c33fc0c523c6 100644
--- a/torch_xla/csrc/batch_norm.cpp
+++ b/torch_xla/csrc/batch_norm.cpp
@@ -8,17 +8,10 @@
 namespace torch_xla {
 namespace {
 
-bool IsF32BatchNormWithLowerFPInputs(const xla::XlaOp& input,
-                                     const xla::XlaOp& weight) {
-  static constexpr std::array<xla::PrimitiveType, 9> lowerPrecistionTypes = {
-      xla::PrimitiveType::F8E5M2,     xla::PrimitiveType::F8E4M3,
-      xla::PrimitiveType::F8E4M3FN,   xla::PrimitiveType::F8E4M3B11FNUZ,
-      xla::PrimitiveType::F8E3M4,     xla::PrimitiveType::F8E5M2FNUZ,
-      xla::PrimitiveType::F8E4M3FNUZ, xla::PrimitiveType::F16,
-      xla::PrimitiveType::BF16};
-  if (std::find(lowerPrecistionTypes.begin(), lowerPrecistionTypes.end(),
-                ShapeHelper::ShapeOfXlaOp(input).element_type()) !=
-          lowerPrecistionTypes.end() &&
+bool IsF32BatchNormWithFP16Inputs(const xla::XlaOp& input,
+                                  const xla::XlaOp& weight) {
+  if (ShapeHelper::ShapeOfXlaOp(input).element_type() ==
+          xla::PrimitiveType::F16 &&
       ShapeHelper::ShapeOfXlaOp(weight).element_type() ==
           xla::PrimitiveType::F32) {
     return true;
@@ -46,10 +39,10 @@ xla::XlaOp BatchNormVarianceInvert(xla::XlaOp variance, float eps_value) {
 
 BatchNormOutput BuildBatchNormTraining(xla::XlaOp input, xla::XlaOp weight,
                                        xla::XlaOp bias, float eps_value) {
-  bool is_batchnorm_with_lower_fp_inputs =
-      IsF32BatchNormWithLowerFPInputs(input, weight);
+  bool is_batchnorm_with_fp16_inputs =
+      IsF32BatchNormWithFP16Inputs(input, weight);
   // Handle the mixed precision use case.
-  if (is_batchnorm_with_lower_fp_inputs) {
+  if (is_batchnorm_with_fp16_inputs) {
     input = xla::ConvertElementType(input, xla::PrimitiveType::F32);
   }
   xla::XlaOp outputs = xla::BatchNormTraining(input, weight, bias, eps_value,
@@ -57,9 +50,8 @@ BatchNormOutput BuildBatchNormTraining(xla::XlaOp input, xla::XlaOp weight,
   xla::XlaOp output = xla::GetTupleElement(outputs, 0);
   xla::XlaOp batch_mean = xla::GetTupleElement(outputs, 1);
   xla::XlaOp batch_variance = xla::GetTupleElement(outputs, 2);
-  if (is_batchnorm_with_lower_fp_inputs) {
-    output = xla::ConvertElementType(
-        output, ShapeHelper::ShapeOfXlaOp(input).element_type());
+  if (is_batchnorm_with_fp16_inputs) {
+    output = xla::ConvertElementType(output, xla::PrimitiveType::F16);
   }
   return {output, batch_mean, batch_variance};
 }
@@ -67,18 +59,17 @@ BatchNormOutput BuildBatchNormTraining(xla::XlaOp input, xla::XlaOp weight,
 xla::XlaOp BuildBatchNormInference(xla::XlaOp input, xla::XlaOp weight,
                                    xla::XlaOp bias, xla::XlaOp mean,
                                    xla::XlaOp variance, float eps_value) {
-  bool is_batchnorm_with_lower_fp_inputs =
-      IsF32BatchNormWithLowerFPInputs(input, weight);
+  bool is_batchnorm_with_fp16_inputs =
+      IsF32BatchNormWithFP16Inputs(input, weight);
   // Handle the mixed precision use case.
-  if (is_batchnorm_with_lower_fp_inputs) {
+  if (is_batchnorm_with_fp16_inputs) {
     input = xla::ConvertElementType(input, xla::PrimitiveType::F32);
   }
   xla::XlaOp output =
       xla::BatchNormInference(input, weight, bias, mean, variance, eps_value,
                               /*feature_index=*/1);
-  if (is_batchnorm_with_lower_fp_inputs) {
-    output = xla::ConvertElementType(
-        output, ShapeHelper::ShapeOfXlaOp(input).element_type());
+  if (is_batchnorm_with_fp16_inputs) {
+    output = xla::ConvertElementType(output, xla::PrimitiveType::F16);
   }
   return output;
 }
@@ -87,10 +78,10 @@ BatchNormGrads BuildBatchNormBackward(xla::XlaOp grad, xla::XlaOp input,
                                       xla::XlaOp weight, xla::XlaOp save_mean,
                                       xla::XlaOp save_invstd, bool training,
                                       float eps_value) {
-  bool is_batchnorm_with_lower_fp_inputs =
-      IsF32BatchNormWithLowerFPInputs(input, weight);
+  bool is_batchnorm_with_fp16_inputs =
+      IsF32BatchNormWithFP16Inputs(input, weight);
   // Handle the mixed precision use case.
-  if (is_batchnorm_with_lower_fp_inputs) {
+  if (is_batchnorm_with_fp16_inputs) {
     input = xla::ConvertElementType(input, xla::PrimitiveType::F32);
     grad = xla::ConvertElementType(grad, xla::PrimitiveType::F32);
   }
@@ -100,9 +91,8 @@ BatchNormGrads BuildBatchNormBackward(xla::XlaOp grad, xla::XlaOp input,
   xla::XlaOp grad_input = xla::GetTupleElement(grads, 0);
   xla::XlaOp grad_weight = xla::GetTupleElement(grads, 1);
   xla::XlaOp grad_bias = xla::GetTupleElement(grads, 2);
-  if (is_batchnorm_with_lower_fp_inputs) {
-    grad_input = xla::ConvertElementType(
-        grad_input, ShapeHelper::ShapeOfXlaOp(input).element_type());
+  if (is_batchnorm_with_fp16_inputs) {
+    grad_input = xla::ConvertElementType(grad_input, xla::PrimitiveType::F16);
   }
   return {grad_input, grad_weight, grad_bias};
 }