PSROIPool + Dispatcher + Autocast + Code Cleanup

torchvision/csrc/PSROIAlign.h

@@ -223,4 +223,4 @@ at::Tensor PSROIAlign_backward_autograd(
       channels,
       height,
       width)[0];
-}
+}

torchvision/csrc/PSROIPool.h

@@ -3,62 +3,68 @@
 #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
 
-std::tuple<at::Tensor, at::Tensor> PSROIPool_forward(
+// TODO: put this stuff in torchvision namespace
+
+std::tuple<at::Tensor, at::Tensor> ps_roi_pool(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width) {
-  if (input.is_cuda()) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width) {
+  static auto op = c10::Dispatcher::singleton()
+                       .findSchemaOrThrow("torchvision::ps_roi_pool", "")
+                       .typed<decltype(ps_roi_pool)>();
+  return op.call(input, rois, spatial_scale, pooled_height, pooled_width);
+}
+
 #if defined(WITH_CUDA) || defined(WITH_HIP)
-    return PSROIPool_forward_cuda(
-        input, rois, spatial_scale, pooled_height, pooled_width);
-#else
-    TORCH_CHECK(false, "Not compiled with GPU support");
-#endif
-  }
-  return PSROIPool_forward_cpu(
-      input, rois, spatial_scale, pooled_height, pooled_width);
+std::tuple<at::Tensor, at::Tensor> PSROIPool_autocast(
+    const at::Tensor& input,
+    const at::Tensor& rois,
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width) {
+  c10::impl::ExcludeDispatchKeyGuard no_autocast(c10::DispatchKey::Autocast);
+  auto result = ps_roi_pool(
+      at::autocast::cached_cast(at::kFloat, input),
+      at::autocast::cached_cast(at::kFloat, rois),
+      spatial_scale,
+      pooled_height,
+      pooled_width);
+
+  return std::make_tuple(
+      std::get<0>(result).to(input.scalar_type()),
+      std::get<1>(result).to(input.scalar_type()));
 }
+#endif
 
-at::Tensor PSROIPool_backward(
+at::Tensor _ps_roi_pool_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 batch_size,
-    const int channels,
-    const int height,
-    const int width) {
-  if (grad.is_cuda()) {
-#if defined(WITH_CUDA) || defined(WITH_HIP)
-    return PSROIPool_backward_cuda(
-        grad,
-        rois,
-        mapping_channel,
-        spatial_scale,
-        pooled_height,
-        pooled_width,
-        batch_size,
-        channels,
-        height,
-        width);
-#else
-    TORCH_CHECK(false, "Not compiled with GPU support");
-#endif
-  }
-  return PSROIPool_backward_cpu(
+    const at::Tensor& channel_mapping,
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    int64_t batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width) {
+  static auto op =
+      c10::Dispatcher::singleton()
+          .findSchemaOrThrow("torchvision::_ps_roi_pool_backward", "")
+          .typed<decltype(_ps_roi_pool_backward)>();
+  return op.call(
       grad,
       rois,
-      mapping_channel,
+      channel_mapping,
       spatial_scale,
       pooled_height,
       pooled_width,
@@ -72,33 +78,36 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
  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 torch::autograd::Variable& input,
+      const torch::autograd::Variable& rois,
+      double spatial_scale,
+      int64_t pooled_height,
+      int64_t pooled_width) {
     ctx->saved_data["spatial_scale"] = spatial_scale;
     ctx->saved_data["pooled_height"] = pooled_height;
     ctx->saved_data["pooled_width"] = pooled_width;
     ctx->saved_data["input_shape"] = input.sizes();
-    auto result = PSROIPool_forward(
-        input, rois, spatial_scale, pooled_height, pooled_width);
+    at::AutoNonVariableTypeMode g;
+    auto result =
+        ps_roi_pool(input, rois, spatial_scale, pooled_height, pooled_width);
+
     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 = PSROIPool_backward(
+    auto grad_in = _ps_roi_pool_backward(
         grad_output[0],
         rois,
         channel_mapping,
@@ -109,6 +118,7 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
         input_shape[1],
         input_shape[2],
         input_shape[3]);
+
     return {grad_in,
             torch::autograd::Variable(),
             torch::autograd::Variable(),
@@ -117,13 +127,77 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
   }
 };
 
-std::tuple<at::Tensor, at::Tensor> ps_roi_pool(
+// TODO: There should be an easier way to do this
+class PSROIPoolBackwardFunction
+    : public torch::autograd::Function<PSROIPoolBackwardFunction> {
+ 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 batch_size,
+      int64_t channels,
+      int64_t height,
+      int64_t width) {
+    at::AutoNonVariableTypeMode g;
+    auto grad_in = _ps_roi_pool_backward(
+        grad,
+        rois,
+        channel_mapping,
+        spatial_scale,
+        pooled_height,
+        pooled_width,
+        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_pool not supported");
+  }
+};
+
+std::tuple<at::Tensor, at::Tensor> PSROIPool_autograd(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const double spatial_scale,
-    const int64_t pooled_height,
-    const int64_t pooled_width) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width) {
   auto result = PSROIPoolFunction::apply(
       input, rois, spatial_scale, pooled_height, pooled_width);
-  return std::tuple<at::Tensor, at::Tensor>(result[0], result[1]);
+
+  return std::make_tuple(result[0], result[1]);
+}
+
+at::Tensor PSROIPool_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 batch_size,
+    int64_t channels,
+    int64_t height,
+    int64_t width) {
+  return PSROIPoolBackwardFunction::apply(
+      grad,
+      rois,
+      channel_mapping,
+      spatial_scale,
+      pooled_height,
+      pooled_width,
+      batch_size,
+      channels,
+      height,
+      width)[0];
 }

torchvision/csrc/cpu/PSROIPool_cpu.cpp

@@ -12,14 +12,14 @@ template <typename T>
 void PSROIPoolForward(
     const T* input,
     const T spatial_scale,
-    const int channels,
-    const int height,
-    const int width,
-    const int pooled_height,
-    const int pooled_width,
+    int channels,
+    int height,
+    int width,
+    int pooled_height,
+    int pooled_width,
     const T* rois,
-    const int channels_out,
-    const int num_rois,
+    int channels_out,
+    int num_rois,
     T* output,
     int* channel_mapping) {
   for (int n = 0; n < num_rois; ++n) {
@@ -82,14 +82,14 @@ template <typename T>
 void PSROIPoolBackward(
     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 channels_out,
+    int channels,
+    int height,
+    int width,
+    int pooled_height,
+    int pooled_width,
+    int channels_out,
     T* grad_input,
     const T* rois) {
   for (int n = 0; n < num_rois; ++n) {
@@ -146,9 +146,9 @@ void PSROIPoolBackward(
 std::tuple<at::Tensor, at::Tensor> PSROIPool_forward_cpu(
     const at::Tensor& input,
     const at::Tensor& rois,
-    const float spatial_scale,
-    const int pooled_height,
-    const int pooled_width) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width) {
   // 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");
@@ -204,13 +204,13 @@ at::Tensor PSROIPool_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 batch_size,
-    const int channels,
-    const int height,
-    const int width) {
+    double spatial_scale,
+    int64_t pooled_height,
+    int64_t pooled_width,
+    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");