PSRoiPool: SymInt support + meta-implem

test/test_ops.py

@@ -228,6 +228,7 @@ def func(z):
     @needs_cuda
     @pytest.mark.parametrize("x_dtype", (torch.float, torch.half))
     @pytest.mark.parametrize("rois_dtype", (torch.float, torch.half))
+    @pytest.mark.opcheck_only_one()
     def test_autocast(self, x_dtype, rois_dtype):
         with torch.cuda.amp.autocast():
             self.test_forward(torch.device("cuda"), contiguous=False, x_dtype=x_dtype, rois_dtype=rois_dtype)

torchvision/_meta_registrations.py

@@ -51,6 +51,40 @@ def meta_roi_align_backward(
     return grad.new_empty((batch_size, channels, height, width))
 
 
+@register_meta("ps_roi_pool")
+def meta_ps_roi_pool(input, rois, spatial_scale, pooled_height, pooled_width):
+    torch._check(rois.size(1) == 5, lambda: "rois must have shape as Tensor[K, 5]")
+    torch._check(
+        input.dtype == rois.dtype,
+        lambda: (
+            "Expected tensor for input to have the same type as tensor for rois; "
+            f"but type {input.dtype} does not equal {rois.dtype}"
+        ),
+    )
+    channels = input.size(1)
+    torch._check(
+        channels % (pooled_height * pooled_width) == 0,
+        "input channels must be a multiple of pooling height * pooling width",
+    )
+    num_rois = rois.size(0)
+    out_size = (num_rois, channels // (pooled_height * pooled_width), pooled_height, pooled_width)
+    return input.new_empty(out_size), torch.empty(out_size, device="meta", dtype=torch.int32)
+
+
+@register_meta("_ps_roi_pool_backward")
+def meta_ps_roi_pool_backward(
+    grad, rois, channel_mapping, spatial_scale, pooled_height, pooled_width, batch_size, channels, height, width
+):
+    torch._check(
+        grad.dtype == rois.dtype,
+        lambda: (
+            "Expected tensor for grad to have the same type as tensor for rois; "
+            f"but type {grad.dtype} does not equal {rois.dtype}"
+        ),
+    )
+    return grad.new_empty((batch_size, channels, height, width))
+
+
 @torch._custom_ops.impl_abstract("torchvision::nms")
 def meta_nms(dets, scores, iou_threshold):
     torch._check(dets.dim() == 2, lambda: f"boxes should be a 2d tensor, got {dets.dim()}D")

torchvision/csrc/ops/autograd/ps_roi_pool_kernel.cpp

@@ -15,15 +15,15 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
       const torch::autograd::Variable& input,
       const torch::autograd::Variable& rois,
       double spatial_scale,
-      int64_t pooled_height,
-      int64_t pooled_width) {
+      c10::SymInt pooled_height,
+      c10::SymInt 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();
+    ctx->saved_data["input_shape"] = input.sym_sizes();
     at::AutoDispatchBelowADInplaceOrView g;
-    auto result =
-        ps_roi_pool(input, rois, spatial_scale, pooled_height, pooled_width);
+    auto result = ps_roi_pool_symint(
+        input, rois, spatial_scale, pooled_height, pooled_width);
 
     auto output = std::get<0>(result);
     auto channel_mapping = std::get<1>(result);
@@ -40,18 +40,18 @@ class PSROIPoolFunction : public torch::autograd::Function<PSROIPoolFunction> {
     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 = detail::_ps_roi_pool_backward(
+    auto input_shape = ctx->saved_data["input_shape"].toList();
+    auto grad_in = detail::_ps_roi_pool_backward_symint(
         grad_output[0],
         rois,
         channel_mapping,
         ctx->saved_data["spatial_scale"].toDouble(),
-        ctx->saved_data["pooled_height"].toInt(),
-        ctx->saved_data["pooled_width"].toInt(),
-        input_shape[0],
-        input_shape[1],
-        input_shape[2],
-        input_shape[3]);
+        ctx->saved_data["pooled_height"].toSymInt(),
+        ctx->saved_data["pooled_width"].toSymInt(),
+        input_shape[0].get().toSymInt(),
+        input_shape[1].get().toSymInt(),
+        input_shape[2].get().toSymInt(),
+        input_shape[3].get().toSymInt());
 
     return {
         grad_in,
@@ -72,14 +72,14 @@ class PSROIPoolBackwardFunction
       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) {
+      c10::SymInt pooled_height,
+      c10::SymInt pooled_width,
+      c10::SymInt batch_size,
+      c10::SymInt channels,
+      c10::SymInt height,
+      c10::SymInt width) {
     at::AutoDispatchBelowADInplaceOrView g;
-    auto grad_in = detail::_ps_roi_pool_backward(
+    auto grad_in = detail::_ps_roi_pool_backward_symint(
         grad,
         rois,
         channel_mapping,
@@ -105,8 +105,8 @@ std::tuple<at::Tensor, at::Tensor> ps_roi_pool_autograd(
     const at::Tensor& input,
     const at::Tensor& rois,
     double spatial_scale,
-    int64_t pooled_height,
-    int64_t pooled_width) {
+    c10::SymInt pooled_height,
+    c10::SymInt pooled_width) {
   auto result = PSROIPoolFunction::apply(
       input, rois, spatial_scale, pooled_height, pooled_width);
 
@@ -118,12 +118,12 @@ at::Tensor ps_roi_pool_backward_autograd(
     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) {
+    c10::SymInt pooled_height,
+    c10::SymInt pooled_width,
+    c10::SymInt batch_size,
+    c10::SymInt channels,
+    c10::SymInt height,
+    c10::SymInt width) {
   return PSROIPoolBackwardFunction::apply(
       grad,
       rois,

torchvision/csrc/ops/ps_roi_pool.cpp

@@ -20,6 +20,19 @@ std::tuple<at::Tensor, at::Tensor> ps_roi_pool(
   return op.call(input, rois, spatial_scale, pooled_height, pooled_width);
 }
 
+std::tuple<at::Tensor, at::Tensor> ps_roi_pool_symint(
+    const at::Tensor& input,
+    const at::Tensor& rois,
+    double spatial_scale,
+    c10::SymInt pooled_height,
+    c10::SymInt pooled_width) {
+  C10_LOG_API_USAGE_ONCE("torchvision.csrc.ops.ps_roi_pool.ps_roi_pool");
+  static auto op = c10::Dispatcher::singleton()
+                       .findSchemaOrThrow("torchvision::ps_roi_pool", "")
+                       .typed<decltype(ps_roi_pool_symint)>();
+  return op.call(input, rois, spatial_scale, pooled_height, pooled_width);
+}
+
 namespace detail {
 
 at::Tensor _ps_roi_pool_backward(
@@ -50,13 +63,41 @@ at::Tensor _ps_roi_pool_backward(
       width);
 }
 
+at::Tensor _ps_roi_pool_backward_symint(
+    const at::Tensor& grad,
+    const at::Tensor& rois,
+    const at::Tensor& channel_mapping,
+    double spatial_scale,
+    c10::SymInt pooled_height,
+    c10::SymInt pooled_width,
+    c10::SymInt batch_size,
+    c10::SymInt channels,
+    c10::SymInt height,
+    c10::SymInt width) {
+  static auto op =
+      c10::Dispatcher::singleton()
+          .findSchemaOrThrow("torchvision::_ps_roi_pool_backward", "")
+          .typed<decltype(_ps_roi_pool_backward_symint)>();
+  return op.call(
+      grad,
+      rois,
+      channel_mapping,
+      spatial_scale,
+      pooled_height,
+      pooled_width,
+      batch_size,
+      channels,
+      height,
+      width);
+}
+
 } // namespace detail
 
 TORCH_LIBRARY_FRAGMENT(torchvision, m) {
   m.def(TORCH_SELECTIVE_SCHEMA(
-      "torchvision::ps_roi_pool(Tensor input, Tensor rois, float spatial_scale, int pooled_height, int pooled_width) -> (Tensor, Tensor)"));
+      "torchvision::ps_roi_pool(Tensor input, Tensor rois, float spatial_scale, SymInt pooled_height, SymInt pooled_width) -> (Tensor, Tensor)"));
   m.def(TORCH_SELECTIVE_SCHEMA(
-      "torchvision::_ps_roi_pool_backward(Tensor grad, Tensor rois, Tensor channel_mapping, float spatial_scale, int pooled_height, int pooled_width, int batch_size, int channels, int height, int width) -> Tensor"));
+      "torchvision::_ps_roi_pool_backward(Tensor grad, Tensor rois, Tensor channel_mapping, float spatial_scale, SymInt pooled_height, SymInt pooled_width, SymInt batch_size, SymInt channels, SymInt height, SymInt width) -> Tensor"));
 }
 
 } // namespace ops

torchvision/csrc/ops/ps_roi_pool.h

@@ -13,6 +13,13 @@ VISION_API std::tuple<at::Tensor, at::Tensor> ps_roi_pool(
     int64_t pooled_height,
     int64_t pooled_width);
 
+VISION_API std::tuple<at::Tensor, at::Tensor> ps_roi_pool_symint(
+    const at::Tensor& input,
+    const at::Tensor& rois,
+    double spatial_scale,
+    c10::SymInt pooled_height,
+    c10::SymInt pooled_width);
+
 namespace detail {
 
 at::Tensor _ps_roi_pool_backward(
@@ -27,6 +34,18 @@ at::Tensor _ps_roi_pool_backward(
     int64_t height,
     int64_t width);
 
+at::Tensor _ps_roi_pool_backward_symint(
+    const at::Tensor& grad,
+    const at::Tensor& rois,
+    const at::Tensor& channel_mapping,
+    double spatial_scale,
+    c10::SymInt pooled_height,
+    c10::SymInt pooled_width,
+    c10::SymInt batch_size,
+    c10::SymInt channels,
+    c10::SymInt height,
+    c10::SymInt width);
+
 } // namespace detail
 
 } // namespace ops