Use standard PyTorch decomposition for upsample_nearest2d.

Replaced custom implementations of `upsample_nearest2d` decompositions with the standard decomposition provided by PyTorch. This aligns with upstream practices and reduces maintenance overhead.

PiperOrigin-RevId: 854101690

Author: ai-edge-bot

ai_edge_torch/odml_torch/lowerings/_decomp_registry.py

@@ -95,6 +95,7 @@ def _upsample_nearest2d_common(input, h_indices, w_indices):
         torch.ops.aten.replication_pad3d,
         torch.ops.aten.upsample_bilinear2d.vec,
         torch.ops.aten.addmm,
+        torch.ops.aten.upsample_nearest2d.vec,
     ])
 )
 
@@ -117,19 +118,7 @@ def get_scale_value(scales, idx):
   return scales[idx]
 
 
-@functools.partial(
-    fx_infra.decomp.add_pre_lower_decomp,
-    torch.ops.aten.upsample_nearest2d.vec,
-)
-@fx_infra.annotate_force_decomp
-def upsample_nearest2d_vec(input, output_size, scale_factors):
-  osize = upsample_compute_output_size(input.size(), output_size, scale_factors)
-  scale_h = get_scale_value(scale_factors, 0)
-  scale_w = get_scale_value(scale_factors, 1)
 
-  return torch.ops.aten.upsample_nearest2d.default(
-      input, osize, scale_h, scale_w
-  )
 
 
 fx_infra.decomp.remove_pre_lower_decomp(torch.ops.aten.roll)