diff --git a/torchvision/csrc/PSROIAlign.h b/torchvision/csrc/PSROIAlign.h
index ce8e49363c0..92f4390a0f9 100644
--- a/torchvision/csrc/PSROIAlign.h
+++ b/torchvision/csrc/PSROIAlign.h
@@ -3,72 +3,75 @@
 #include "cpu/vision_cpu.h"
 
 #ifdef WITH_CUDA
+#include "autocast.h"
 #include "cuda/vision_cuda.h"
 #endif
 #ifdef WITH_HIP
+#include "autocast.h"
 #include "hip/vision_cuda.h"
 #endif
 
 #include <iostream>
 
-std::tuple<at::Tensor, at::Tensor> PSROIAlign_forward(
+// TODO: put this stuff in torchvision namespace
+
+std::tuple<at::Tensor, at::Tensor> ps_roi_align(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio) {
-  if (input.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    return PSROIAlign_forward_cuda(
-        input,
-        rois,
-        spatial_scale,
-        pooled_height,
-        pooled_width,
-        sampling_ratio);
-#else
-    TORCH_CHECK(false, "Not compiled with GPU support");
-#endif
-  }
-  return PSROIAlign_forward_cpu(
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio) {
+  static auto op = c10::Dispatcher::singleton()
+                       .findSchemaOrThrow("torchvision::ps_roi_align", "")
+                       .typed<decltype(ps_roi_align)>();
+  return op.call(
       input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
 }
 
-at::Tensor PSROIAlign_backward(
-    const at::Tensor& grad,
-    const at::Tensor& rois,
-    const at::Tensor& mapping_channel,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width) {
-  if (grad.is_cuda()) {
 #if defined(WITH_CUDA) || defined(WITH_HIP)
-    return PSROIAlign_backward_cuda(
-        grad,
-        rois,
-        mapping_channel,
-        spatial_scale,
-        pooled_height,
-        pooled_width,
-        sampling_ratio,
-        batch_size,
-        channels,
-        height,
-        width);
-#else
-    TORCH_CHECK(false, "Not compiled with GPU support");
+std::tuple<at::Tensor, at::Tensor> PSROIAlign_autocast(
+    const at::Tensor& input,
+    const at::Tensor& rois,
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio) {
+  c10::impl::ExcludeDispatchKeyGuard no_autocast(c10::DispatchKey::Autocast);
+  auto result = ps_roi_align(
+      at::autocast::cached_cast(at::kFloat, input),
+      at::autocast::cached_cast(at::kFloat, rois),
+      spatial_scale,
+      pooled_height,
+      pooled_width,
+      sampling_ratio);
+
+  return std::make_tuple(
+      std::get<0>(result).to(input.scalar_type()),
+      std::get<1>(result).to(input.scalar_type()));
+}
 #endif
-  }
-  return PSROIAlign_backward_cpu(
+
+at::Tensor _ps_roi_align_backward(
+    const at::Tensor& grad,
+    const at::Tensor& rois,
+    const at::Tensor& channel_mapping,
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio,
+    int64_t batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width) {
+  static auto op =
+      c10::Dispatcher::singleton()
+          .findSchemaOrThrow("torchvision::_ps_roi_align_backward", "")
+          .typed<decltype(_ps_roi_align_backward)>();
+  return op.call(
       grad,
       rois,
-      mapping_channel,
+      channel_mapping,
       spatial_scale,
       pooled_height,
       pooled_width,
@@ -84,40 +87,43 @@ class PSROIAlignFunction
  public:
   static torch::autograd::variable_list forward(
       torch::autograd::AutogradContext* ctx,
-      torch::autograd::Variable input,
-      torch::autograd::Variable rois,
-      const double spatial_scale,
-      const int64_t pooled_height,
-      const int64_t pooled_width,
-      const int64_t sampling_ratio) {
+      const torch::autograd::Variable& input,
+      const torch::autograd::Variable& rois,
+      double spatial_scale,
+      int64_t pooled_height,
+      int64_t pooled_width,
+      int64_t sampling_ratio) {
     ctx->saved_data["spatial_scale"] = spatial_scale;
     ctx->saved_data["pooled_height"] = pooled_height;
     ctx->saved_data["pooled_width"] = pooled_width;
     ctx->saved_data["sampling_ratio"] = sampling_ratio;
     ctx->saved_data["input_shape"] = input.sizes();
-    auto result = PSROIAlign_forward(
+    at::AutoNonVariableTypeMode g;
+    auto result = ps_roi_align(
         input,
         rois,
         spatial_scale,
         pooled_height,
         pooled_width,
         sampling_ratio);
+
     auto output = std::get<0>(result);
     auto channel_mapping = std::get<1>(result);
     ctx->save_for_backward({rois, channel_mapping});
     ctx->mark_non_differentiable({channel_mapping});
+
     return {output, channel_mapping};
   }
 
   static torch::autograd::variable_list backward(
       torch::autograd::AutogradContext* ctx,
-      torch::autograd::variable_list grad_output) {
+      const torch::autograd::variable_list& grad_output) {
     // Use data saved in forward
     auto saved = ctx->get_saved_variables();
     auto rois = saved[0];
     auto channel_mapping = saved[1];
     auto input_shape = ctx->saved_data["input_shape"].toIntList();
-    auto grad_in = PSROIAlign_backward(
+    auto grad_in = _ps_roi_align_backward(
         grad_output[0],
         rois,
         channel_mapping,
@@ -129,6 +135,7 @@ class PSROIAlignFunction
         input_shape[1],
         input_shape[2],
         input_shape[3]);
+
     return {grad_in,
             torch::autograd::Variable(),
             torch::autograd::Variable(),
@@ -138,14 +145,82 @@ class PSROIAlignFunction
   }
 };
 
-std::tuple<at::Tensor, at::Tensor> ps_roi_align(
+// TODO: There should be an easier way to do this
+class PSROIAlignBackwardFunction
+    : public torch::autograd::Function<PSROIAlignBackwardFunction> {
+ public:
+  static torch::autograd::variable_list forward(
+      torch::autograd::AutogradContext* ctx,
+      const torch::autograd::Variable& grad,
+      const torch::autograd::Variable& rois,
+      const torch::autograd::Variable& channel_mapping,
+      double spatial_scale,
+      int64_t pooled_height,
+      int64_t pooled_width,
+      int64_t sampling_ratio,
+      int64_t batch_size,
+      int64_t channels,
+      int64_t height,
+      int64_t width) {
+    at::AutoNonVariableTypeMode g;
+    auto grad_in = _ps_roi_align_backward(
+        grad,
+        rois,
+        channel_mapping,
+        spatial_scale,
+        pooled_height,
+        pooled_width,
+        sampling_ratio,
+        batch_size,
+        channels,
+        height,
+        width);
+
+    return {grad_in};
+  }
+
+  static torch::autograd::variable_list backward(
+      torch::autograd::AutogradContext* ctx,
+      const torch::autograd::variable_list& grad_output) {
+    TORCH_CHECK(0, "double backwards on ps_roi_align not supported");
+  }
+};
+
+std::tuple<at::Tensor, at::Tensor> PSROIAlign_autograd(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const double spatial_scale,
-    const int64_t pooled_height,
-    const int64_t pooled_width,
-    const int64_t sampling_ratio) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio) {
   auto result = PSROIAlignFunction::apply(
       input, rois, spatial_scale, pooled_height, pooled_width, sampling_ratio);
-  return std::tuple<at::Tensor, at::Tensor>(result[0], result[1]);
+
+  return std::make_tuple(result[0], result[1]);
 }
+
+at::Tensor PSROIAlign_backward_autograd(
+    const at::Tensor& grad,
+    const at::Tensor& rois,
+    const at::Tensor& channel_mapping,
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio,
+    int64_t batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width) {
+  return PSROIAlignBackwardFunction::apply(
+      grad,
+      rois,
+      channel_mapping,
+      spatial_scale,
+      pooled_height,
+      pooled_width,
+      sampling_ratio,
+      batch_size,
+      channels,
+      height,
+      width)[0];
+}
\ No newline at end of file
diff --git a/torchvision/csrc/cpu/PSROIAlign_cpu.cpp b/torchvision/csrc/cpu/PSROIAlign_cpu.cpp
index e5eb051cb91..899dbb208b6 100644
--- a/torchvision/csrc/cpu/PSROIAlign_cpu.cpp
+++ b/torchvision/csrc/cpu/PSROIAlign_cpu.cpp
@@ -5,11 +5,11 @@
 template <typename T>
 T bilinear_interpolate(
     const T* input,
-    const int height,
-    const int width,
+    int height,
+    int width,
     T y,
     T x,
-    const int index /* index for debug only*/) {
+    int index /* index for debug only*/) {
   // deal with cases that inverse elements are out of feature map boundary
   if (y < -1.0 || y > height || x < -1.0 || x > width) {
     // empty
@@ -58,17 +58,17 @@ T bilinear_interpolate(
 
 template <typename T>
 void PSROIAlignForwardCPU(
-    const int nthreads,
+    int nthreads,
     const T* input,
     const T spatial_scale,
-    const int channels,
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
+    int channels,
+    int height,
+    int width,
+    int pooled_height,
+    int pooled_width,
+    int sampling_ratio,
     const T* rois,
-    const int channels_out,
+    int channels_out,
     T* output,
     int* channel_mapping) {
   int num_rois = nthreads / channels_out / pooled_width / pooled_height;
@@ -139,8 +139,8 @@ void PSROIAlignForwardCPU(
 
 template <typename T>
 void bilinear_interpolate_gradient(
-    const int height,
-    const int width,
+    int height,
+    int width,
     T y,
     T x,
     T& w1,
@@ -151,7 +151,7 @@ void bilinear_interpolate_gradient(
     int& x_high,
     int& y_low,
     int& y_high,
-    const int index /* index for debug only*/) {
+    int index /* index for debug only*/) {
   // deal with cases that inverse elements are out of feature map boundary
   if (y < -1.0 || y > height || x < -1.0 || x > width) {
     // empty
@@ -203,18 +203,18 @@ inline void add(T* address, const T& val) {
 
 template <typename T>
 void PSROIAlignBackwardCPU(
-    const int nthreads,
+    int nthreads,
     const T* grad_output,
     const int* channel_mapping,
-    const int num_rois,
+    int num_rois,
     const T spatial_scale,
-    const int channels,
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const int channels_out,
+    int channels,
+    int height,
+    int width,
+    int pooled_height,
+    int pooled_width,
+    int sampling_ratio,
+    int channels_out,
     T* grad_input,
     const T* rois) {
   for (int index = 0; index < nthreads; index++) {
@@ -301,10 +301,10 @@ void PSROIAlignBackwardCPU(
 std::tuple<at::Tensor, at::Tensor> PSROIAlign_forward_cpu(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio) {
   // Check if input tensors are CPU tensors
   TORCH_CHECK(input.device().is_cpu(), "input must be a CPU tensor");
   TORCH_CHECK(rois.device().is_cpu(), "rois must be a CPU tensor");
@@ -361,14 +361,14 @@ at::Tensor PSROIAlign_backward_cpu(
     const at::Tensor& grad,
     const at::Tensor& rois,
     const at::Tensor& channel_mapping,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio,
+    int64_t batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width) {
   // Check if input tensors are CPU tensors
   TORCH_CHECK(grad.device().is_cpu(), "grad must be a CPU tensor");
   TORCH_CHECK(rois.device().is_cpu(), "rois must be a CPU tensor");
diff --git a/torchvision/csrc/cpu/vision_cpu.h b/torchvision/csrc/cpu/vision_cpu.h
index 69b1bbf555d..248225479d3 100644
--- a/torchvision/csrc/cpu/vision_cpu.h
+++ b/torchvision/csrc/cpu/vision_cpu.h
@@ -65,23 +65,23 @@ VISION_API at::Tensor PSROIPool_backward_cpu(
 VISION_API std::tuple<at::Tensor, at::Tensor> PSROIAlign_forward_cpu(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio);
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio);
 
 VISION_API at::Tensor PSROIAlign_backward_cpu(
     const at::Tensor& grad,
     const at::Tensor& rois,
-    const at::Tensor& mapping_channel,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width);
+    const at::Tensor& channel_mapping,
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio,
+    int64_t batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width);
 
 VISION_API at::Tensor nms_cpu(
     const at::Tensor& dets,
diff --git a/torchvision/csrc/cuda/PSROIAlign_cuda.cu b/torchvision/csrc/cuda/PSROIAlign_cuda.cu
index 709b0bda208..e6912d8c7ee 100644
--- a/torchvision/csrc/cuda/PSROIAlign_cuda.cu
+++ b/torchvision/csrc/cuda/PSROIAlign_cuda.cu
@@ -10,11 +10,11 @@
 template <typename T>
 __device__ T bilinear_interpolate(
     const T* input,
-    const int height,
-    const int width,
+    int height,
+    int width,
     T y,
     T x,
-    const int index /* index for debug only*/) {
+    int index /* index for debug only*/) {
   // deal with cases that inverse elements are out of feature map boundary
   if (y < -1.0 || y > height || x < -1.0 || x > width) {
     // empty
@@ -63,17 +63,17 @@ __device__ T bilinear_interpolate(
 
 template <typename T>
 __global__ void PSROIAlignForwardCUDA(
-    const int nthreads,
+    int nthreads,
     const T* input,
     const T spatial_scale,
-    const int channels,
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
+    int channels,
+    int height,
+    int width,
+    int pooled_height,
+    int pooled_width,
+    int sampling_ratio,
     const T* rois,
-    const int channels_out,
+    int channels_out,
     T* output,
     int* channel_mapping) {
   CUDA_1D_KERNEL_LOOP(index, nthreads) {
@@ -137,8 +137,8 @@ __global__ void PSROIAlignForwardCUDA(
 
 template <typename T>
 __device__ void bilinear_interpolate_gradient(
-    const int height,
-    const int width,
+    int height,
+    int width,
     T y,
     T x,
     T& w1,
@@ -149,7 +149,7 @@ __device__ void bilinear_interpolate_gradient(
     int& x_high,
     int& y_low,
     int& y_high,
-    const int index /* index for debug only*/) {
+    int index /* index for debug only*/) {
   // deal with cases that inverse elements are out of feature map boundary
   if (y < -1.0 || y > height || x < -1.0 || x > width) {
     // empty
@@ -196,18 +196,18 @@ __device__ void bilinear_interpolate_gradient(
 
 template <typename T>
 __global__ void PSROIAlignBackwardCUDA(
-    const int nthreads,
+    int nthreads,
     const T* grad_output,
     const int* channel_mapping,
-    const int num_rois,
+    int num_rois,
     const T spatial_scale,
-    const int channels,
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const int channels_out,
+    int channels,
+    int height,
+    int width,
+    int pooled_height,
+    int pooled_width,
+    int sampling_ratio,
+    int channels_out,
     T* grad_input,
     const T* rois) {
   CUDA_1D_KERNEL_LOOP(index, nthreads) {
@@ -295,10 +295,10 @@ __global__ void PSROIAlignBackwardCUDA(
 std::tuple<at::Tensor, at::Tensor> PSROIAlign_forward_cuda(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio) {
   // Check if input tensors are CUDA tensors
   TORCH_CHECK(input.is_cuda(), "input must be a CUDA tensor");
   TORCH_CHECK(rois.is_cuda(), "rois must be a CUDA tensor");
@@ -369,14 +369,14 @@ at::Tensor PSROIAlign_backward_cuda(
     const at::Tensor& grad,
     const at::Tensor& rois,
     const at::Tensor& channel_mapping,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio,
+    int64_t batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width) {
   // Check if input tensors are CUDA tensors
   TORCH_CHECK(grad.is_cuda(), "grad must be a CUDA tensor");
   TORCH_CHECK(rois.is_cuda(), "rois must be a CUDA tensor");
diff --git a/torchvision/csrc/cuda/vision_cuda.h b/torchvision/csrc/cuda/vision_cuda.h
index 2481cfc63c2..00db2cacf90 100644
--- a/torchvision/csrc/cuda/vision_cuda.h
+++ b/torchvision/csrc/cuda/vision_cuda.h
@@ -65,23 +65,23 @@ VISION_API at::Tensor PSROIPool_backward_cuda(
 VISION_API std::tuple<at::Tensor, at::Tensor> PSROIAlign_forward_cuda(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio);
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio);
 
 VISION_API at::Tensor PSROIAlign_backward_cuda(
     const at::Tensor& grad,
     const at::Tensor& rois,
-    const at::Tensor& mapping_channel,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width,
-    const int sampling_ratio,
-    const int batch_size,
-    const int channels,
-    const int height,
-    const int width);
+    const at::Tensor& channel_mapping,
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t sampling_ratio,
+    int64_t batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width);
 
 VISION_API at::Tensor nms_cuda(
     const at::Tensor& dets,
diff --git a/torchvision/csrc/vision.cpp b/torchvision/csrc/vision.cpp
index f56a671d6e5..d4f5ca2c917 100644
--- a/torchvision/csrc/vision.cpp
+++ b/torchvision/csrc/vision.cpp
@@ -52,7 +52,10 @@ TORCH_LIBRARY(torchvision, m) {
       "_roi_align_backward(Tensor grad, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int batch_size, int channels, int height, int width, int sampling_ratio, bool aligned) -> Tensor");
   m.def("roi_pool", &roi_pool);
   m.def("_new_empty_tensor_op", &new_empty_tensor);
-  m.def("ps_roi_align", &ps_roi_align);
+  m.def(
+      "ps_roi_align(Tensor input, Tensor rois, float spatial_scale, int pooled_height, int pooled_width, int sampling_ratio) -> (Tensor, Tensor)");
+  m.def(
+      "_ps_roi_align_backward(Tensor grad, Tensor rois, Tensor channel_mapping, float spatial_scale, int pooled_height, int pooled_width, int sampling_ratio, int batch_size, int channels, int height, int width) -> Tensor");
   m.def("ps_roi_pool", &ps_roi_pool);
   m.def(
       "deform_conv2d(Tensor input, Tensor weight, Tensor offset, Tensor bias, int stride_h, int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, int groups, int offset_groups) -> Tensor");
@@ -67,6 +70,8 @@ TORCH_LIBRARY_IMPL(torchvision, CPU, m) {
   m.impl("deform_conv2d", DeformConv2d_forward_cpu);
   m.impl("_deform_conv2d_backward", DeformConv2d_backward_cpu);
   m.impl("nms", nms_cpu);
+  m.impl("ps_roi_align", PSROIAlign_forward_cpu);
+  m.impl("_ps_roi_align_backward", PSROIAlign_backward_cpu);
 }
 
 // TODO: Place this in a hypothetical separate torchvision_cuda library
@@ -77,6 +82,8 @@ TORCH_LIBRARY_IMPL(torchvision, CUDA, m) {
   m.impl("deform_conv2d", DeformConv2d_forward_cuda);
   m.impl("_deform_conv2d_backward", DeformConv2d_backward_cuda);
   m.impl("nms", nms_cuda);
+  m.impl("ps_roi_align", PSROIAlign_forward_cuda);
+  m.impl("_ps_roi_align_backward", PSROIAlign_backward_cuda);
 }
 #endif
 
@@ -86,6 +93,7 @@ TORCH_LIBRARY_IMPL(torchvision, Autocast, m) {
   m.impl("roi_align", ROIAlign_autocast);
   m.impl("deform_conv2d", DeformConv2d_autocast);
   m.impl("nms", nms_autocast);
+  m.impl("ps_roi_align", PSROIAlign_autocast);
 }
 #endif
 
@@ -94,4 +102,6 @@ TORCH_LIBRARY_IMPL(torchvision, Autograd, m) {
   m.impl("_roi_align_backward", ROIAlign_backward_autograd);
   m.impl("deform_conv2d", DeformConv2d_autograd);
   m.impl("_deform_conv2d_backward", DeformConv2d_backward_autograd);
+  m.impl("ps_roi_align", PSROIAlign_autograd);
+  m.impl("_ps_roi_align_backward", PSROIAlign_backward_autograd);
 }