diff --git a/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp b/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp
index cd9fde2ae..d35aedf27 100644
--- a/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp
+++ b/maskrcnn_benchmark/csrc/cpu/ROIAlign_cpu.cpp
@@ -239,7 +239,7 @@ at::Tensor ROIAlign_forward_cpu(const at::Tensor& input,
     return output;
   }
 
-  AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "ROIAlign_forward", [&] {
+  AT_DISPATCH_FLOATING_TYPES(input.type(), "ROIAlign_forward", [&] {
     ROIAlignForward_cpu_kernel<scalar_t>(
          output_size,
          input.data<scalar_t>(),
diff --git a/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp b/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp
index 639ca472e..1153dea04 100644
--- a/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp
+++ b/maskrcnn_benchmark/csrc/cpu/nms_cpu.cpp
@@ -68,7 +68,7 @@ at::Tensor nms_cpu(const at::Tensor& dets,
                const at::Tensor& scores,
                const float threshold) {
   at::Tensor result;
-  AT_DISPATCH_FLOATING_TYPES(dets.scalar_type(), "nms", [&] {
+  AT_DISPATCH_FLOATING_TYPES(dets.type(), "nms", [&] {
     result = nms_cpu_kernel<scalar_t>(dets, scores, threshold);
   });
   return result;
diff --git a/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu b/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu
index 170771aa8..1142fb375 100644
--- a/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu
+++ b/maskrcnn_benchmark/csrc/cuda/ROIAlign_cuda.cu
@@ -280,7 +280,7 @@ at::Tensor ROIAlign_forward_cuda(const at::Tensor& input,
     return output;
   }
 
-  AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "ROIAlign_forward", [&] {
+  AT_DISPATCH_FLOATING_TYPES(input.type(), "ROIAlign_forward", [&] {
     RoIAlignForward<scalar_t><<<grid, block, 0, stream>>>(
          output_size,
          input.contiguous().data<scalar_t>(),
@@ -326,7 +326,7 @@ at::Tensor ROIAlign_backward_cuda(const at::Tensor& grad,
     return grad_input;
   }
 
-  AT_DISPATCH_FLOATING_TYPES(grad.scalar_type(), "ROIAlign_backward", [&] {
+  AT_DISPATCH_FLOATING_TYPES(grad.type(), "ROIAlign_backward", [&] {
     RoIAlignBackwardFeature<scalar_t><<<grid, block, 0, stream>>>(
          grad.numel(),
          grad.contiguous().data<scalar_t>(),
diff --git a/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu b/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu
index cef3beaa4..8f072ffc2 100644
--- a/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu
+++ b/maskrcnn_benchmark/csrc/cuda/ROIPool_cuda.cu
@@ -134,7 +134,7 @@ std::tuple<at::Tensor, at::Tensor> ROIPool_forward_cuda(const at::Tensor& input,
     return std::make_tuple(output, argmax);
   }
 
-  AT_DISPATCH_FLOATING_TYPES(input.scalar_type(), "ROIPool_forward", [&] {
+  AT_DISPATCH_FLOATING_TYPES(input.type(), "ROIPool_forward", [&] {
     RoIPoolFForward<scalar_t><<<grid, block, 0, stream>>>(
          output_size,
          input.contiguous().data<scalar_t>(),
@@ -182,7 +182,7 @@ at::Tensor ROIPool_backward_cuda(const at::Tensor& grad,
     return grad_input;
   }
 
-  AT_DISPATCH_FLOATING_TYPES(grad.scalar_type(), "ROIPool_backward", [&] {
+  AT_DISPATCH_FLOATING_TYPES(grad.type(), "ROIPool_backward", [&] {
     RoIPoolFBackward<scalar_t><<<grid, block, 0, stream>>>(
          grad.numel(),
          grad.contiguous().data<scalar_t>(),
diff --git a/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu b/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu
index cd9b4c96b..7d40767bb 100644
--- a/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu
+++ b/maskrcnn_benchmark/csrc/cuda/SigmoidFocalLoss_cuda.cu
@@ -125,7 +125,7 @@ at::Tensor SigmoidFocalLoss_forward_cuda(
     return losses;
   }
 
-  AT_DISPATCH_FLOATING_TYPES(logits.scalar_type(), "SigmoidFocalLoss_forward", [&] {
+  AT_DISPATCH_FLOATING_TYPES(logits.type(), "SigmoidFocalLoss_forward", [&] {
     SigmoidFocalLossForward<scalar_t><<<grid, block, 0, stream>>>(
          losses_size,
          logits.contiguous().data<scalar_t>(),
@@ -169,7 +169,7 @@ at::Tensor SigmoidFocalLoss_backward_cuda(
     return d_logits;
   }
 
-  AT_DISPATCH_FLOATING_TYPES(logits.scalar_type(), "SigmoidFocalLoss_backward", [&] {
+  AT_DISPATCH_FLOATING_TYPES(logits.type(), "SigmoidFocalLoss_backward", [&] {
     SigmoidFocalLossBackward<scalar_t><<<grid, block, 0, stream>>>(
          d_logits_size,
          logits.contiguous().data<scalar_t>(),