Add checks for empty tensor list (pytorch#155383)

Vibe-coded with Codex, after collecting a backtrace, see https://chatgpt.com/s/cd_68438be8a1248191adbfa0a5f000e60b

Even though, check for empty tensor list exists in `at::cat` crash might happens while resolving named dimension to position, by calling `dimname_to_position(tensors[0], dim)`, see backtrace below
```
(lldb) up
frame #1: 0x00000001101146dc libtorch_cpu.dylib`at::TensorBase::has_names(this=0x0000000000000000) const at TensorBase.h:559:10
   556 	  bool has_names() const {
   557 	    // If a user is using unnamed tensors, then we can short-circuit right here.
   558 	    // Otherwise, impl::has_names attempts to retrieve names.
-> 559 	    if (!impl_->has_named_tensor_meta()) {
   560 	      return false;
   561 	    }
   562 	    return impl::has_names(unsafeGetTensorImpl());
(lldb) up
frame #2: 0x00000001101144c4 libtorch_cpu.dylib`at::dimname_to_position(tensor=0x0000000000000000, dim=Dimname @ 0x000000016fdfe348) at NamedTensorUtils.cpp:23:3
   20  	int64_t dimname_to_position(const Tensor& tensor, Dimname dim) {
   21  	  TORCH_CHECK(dim.type() != NameType::WILDCARD,
   22  	      "Please look up dimensions by name, got: name = None.");
-> 23  	  TORCH_CHECK(tensor.has_names(),
   24  	      "Name ", dim, " not found in ", toDimnameRepr(tensor), ".");
   25  	  const auto names = tensor.names();
   26
```

TODOs:
 - May be move test from `test_tensor_creation.py` to OpInfo (not sure which one is more readable)
 - Replace  `TORCH_CHECK` with `TORCH_CHECK_VALUE` and adjust unit tests

Fixes pytorch#155306
Pull Request resolved: pytorch#155383
Approved by: https://github.com/cyyever, https://github.com/ezyang
ghstack dependencies: pytorch#155382

Author: malfet

aten/src/ATen/native/TensorShape.cpp

@@ -774,10 +774,12 @@ Tensor cat(TensorList tensors, Dimname dim) {
 
 // torch.concat, alias for torch.cat
 Tensor& concat_out(TensorList tensors, Dimname dim, Tensor& result) {
+  TORCH_CHECK(!tensors.empty(), "expected a non-empty list of Tensors");
   return at::cat_out(result, tensors, dimname_to_position(tensors[0], dim));
 }
 
 Tensor concat(TensorList tensors, Dimname dim) {
+  TORCH_CHECK(!tensors.empty(), "expected a non-empty list of Tensors");
   return at::cat(tensors, dimname_to_position(tensors[0], dim));
 }
 
@@ -791,10 +793,12 @@ Tensor concat(TensorList tensors, int64_t dim) {
 
 // torch.concatenate, alias for torch.cat
 Tensor& concatenate_out(TensorList tensors, Dimname dim, Tensor& result) {
+  TORCH_CHECK(!tensors.empty(), "expected a non-empty list of Tensors");
   return at::cat_out(result, tensors, dimname_to_position(tensors[0], dim));
 }
 
 Tensor concatenate(TensorList tensors, Dimname dim) {
+  TORCH_CHECK(!tensors.empty(), "expected a non-empty list of Tensors");
   return at::cat(tensors, dimname_to_position(tensors[0], dim));
 }
 

test/test_tensor_creation_ops.py

@@ -533,6 +533,14 @@ def test_cat_empty(self, device):
         res1 = torch.cat([empty, empty], dim=1)
         self.assertEqual(res1, empty)
 
+    def test_concat_empty_list_error(self, device):
+        # Regression test for https://github.com/pytorch/pytorch/issues/155306
+        msg = "expected a non-empty list of Tensors"
+        with self.assertRaisesRegex(RuntimeError, msg):
+            torch.concat([], dim='N')
+        with self.assertRaisesRegex(RuntimeError, msg):
+            torch.concatenate([], dim='N')
+
     def test_cat_out(self, device):
         x = torch.zeros((0), device=device)
         y = torch.randn((4, 6), device=device)