Manual merge.

Author: NiklasGustafsson

src/TorchSharp/NN/ParamLessModule.cs

@@ -24,11 +24,9 @@ protected ParamLessModule(IntPtr handle, IntPtr boxedHandle) : base(handle, boxe
 
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this;
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this;
+            protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this;
 
             public override void register_buffer(string name, Tensor tensor, bool persistent = true)
             {
@@ -60,11 +58,10 @@ protected ParamLessModule(IntPtr handle, IntPtr boxedHandle) : base(handle, boxe
 
             // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
+            protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this;
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this;
+            protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this;
 
-            protected internal override nn.Module _to(ScalarType dtype) => this;
 
             public override void register_buffer(string name, Tensor tensor, bool persistent = true)
             {
@@ -94,13 +91,11 @@ protected ParamLessModule(string name) : base(name) { }
 
             protected ParamLessModule(IntPtr handle, IntPtr boxedHandle) : base(handle, boxedHandle) {}
 
-            // Rather than spending cycles only to discover that this module has neither
+              // Rather than spending cycles only to discover that this module has neither
             // parameters nor buffers, just shortcut the move completely.
-            protected internal override nn.Module _to(Device device, ScalarType dtype) => this;
-
-            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex = -1) => this;
-
-            protected internal override nn.Module _to(ScalarType dtype) => this;
+            protected internal override nn.Module _to(Device device, ScalarType dtype, bool non_blocking) => this;
+            protected internal override nn.Module _to(DeviceType deviceType, int deviceIndex, bool non_blocking) => this;
+            protected internal override nn.Module _to(ScalarType dtype, bool non_blocking) => this;
 
             public override void register_buffer(string name, Tensor tensor, bool persistent = true)
             {

src/TorchSharp/NN/Parameter.cs

@@ -29,7 +29,7 @@ public Parameter(Tensor data, bool requires_grad = true) :
                 var scope = data.OwningDisposeScope;
                 if (scope is not null) {
                     this.OwningDisposeScope = scope;
-                    scope.Include(this);
+                    scope.Attach(this);
                     scope.Detach(data);
                 }
             }

src/TorchSharp/NN/Upsample.cs

@@ -17,8 +17,8 @@ public sealed class Upsample : ParamLessModule<Tensor, Tensor>
         {
             internal Upsample(long[]? size, double[]? scale_factor, UpsampleMode mode, bool? align_corners, bool? recompute_scale_factor) : base(nameof(Upsample))
             {
-                this.size = size;
-                this.scale_factor = scale_factor;
+                this._size = size;
+                this._scale_factor = scale_factor;
                 this.mode = mode;
                 this.align_corners = align_corners;
                 this.recompute_scale_factor = recompute_scale_factor;
@@ -31,7 +31,7 @@ internal Upsample(long[]? size, double[]? scale_factor, UpsampleMode mode, bool?
             /// <returns></returns>
             public override Tensor forward(Tensor input)
             {
-                return torch.nn.functional.interpolate(input, size, scale_factor, (InterpolationMode)mode, align_corners, recompute_scale_factor ?? false);
+                return torch.nn.functional.interpolate(input, _size, _scale_factor, (InterpolationMode)mode, align_corners, recompute_scale_factor ?? false);
             }
 
             public bool? recompute_scale_factor { get; set; }