[inductor] Don't require_dense for grid_sampler_2d_backward (#163415)

Fixes #163372

Pull Request resolved: https://github.com/pytorch/pytorch/pull/163415
Approved by: https://github.com/Skylion007
ghstack dependencies: #163386, #163398, #163387, #163414

test/inductor/test_torchinductor.py

@@ -7409,6 +7409,56 @@ def forward(self, arg0_1: "Sym(s77)", arg1_1: "Sym(s27)", arg2_1: "Sym(s53)", ar
             rtol=1.3e-06,
         )
 
+    @requires_gpu()
+    def test_grid_sampler_expand_preserves_view(self):
+        if not self.device.startswith("cuda"):
+            self.skipTest("requires CUDA")
+
+        torch.manual_seed(0)
+        torch._dynamo.reset()
+
+        repeats = 9000
+        batch = 48
+        channels = 3
+        img = 224
+        grid_size = 13
+        device = self.device
+
+        class ExpandGridSampler(torch.nn.Module):
+            def __init__(self) -> None:
+                super().__init__()
+                self.grid = torch.nn.Parameter(
+                    torch.randn(repeats, grid_size, grid_size, 2, device=device)
+                )
+                self.fc = torch.nn.Linear(grid_size * grid_size * channels, 16).to(
+                    device
+                )
+
+            def forward(self, x: torch.Tensor) -> torch.Tensor:
+                per_channel = []
+                for i in range(channels):
+                    channel = x[:, i, ...].expand(repeats, -1, -1, -1)
+                    patch = torch.nn.functional.grid_sample(
+                        channel,
+                        self.grid,
+                        mode="bilinear",
+                        align_corners=False,
+                        padding_mode="border",
+                    )
+                    patch = patch.transpose(0, 1).flatten(start_dim=2)
+                    per_channel.append(patch)
+                x = torch.cat(per_channel, dim=2)
+                return self.fc(x)
+
+        model = ExpandGridSampler().to(device)
+        compiled = torch.compile(model, backend="inductor")
+        inp = torch.randn(batch, channels, img, img, device=device)
+
+        out = compiled(inp)
+        out.sum().backward()
+
+        self.assertIsNotNone(model.grid.grad)
+
     def test_upsample_bicubic2d(self):
         def fn(a):
             return (

torch/_inductor/lowering.py

@@ -2795,7 +2795,7 @@ make_fallback(aten.replication_pad2d_backward)
 make_fallback(aten.upsample_linear1d_backward)
 make_fallback(aten.upsample_bicubic2d_backward, require_contiguous)
 make_fallback(aten.upsample_trilinear3d_backward)
-make_fallback(aten.grid_sampler_2d_backward, require_dense)
+make_fallback(aten.grid_sampler_2d_backward)
 make_fallback(aten._pdist_backward)