Fix triangular_solve meta function out parameter names. (#140186)

This PR replaces the parameter names specified in the `triangular_solve_meta`
function (specifically in its `@out_wrapper(...)` decorator) by those written in the
_native_functions.yaml_ file.

This name mismatch caused the operation to fail when using the meta device (see error
below):

```python
Traceback (most recent call last):
  File "examples/test.py", line 23, in <module>
    torch.triangular_solve(b.to("meta"), A.to("meta"), out=meta_out)
  File "torch/_decomp/__init__.py", line 100, in _fn
    return f(*args, **kwargs, out=None if is_none else out_kwargs)
  File "torch/_prims_common/wrappers.py", line 289, in _fn
    result = fn(*args, **kwargs)
TypeError: triangular_solve_meta() got an unexpected keyword argument 'X'
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/140186
Approved by: https://github.com/ezyang

test/test_meta.py

@@ -6,7 +6,7 @@ import os
 import numpy as np
 from enum import Enum
 from torch.overrides import resolve_name
-from torch.utils._pytree import tree_map, tree_flatten, tree_unflatten
+from torch.utils._pytree import tree_map, tree_map_only, tree_flatten, tree_unflatten
 from torch.utils import _pytree as pytree
 from torch._subclasses.meta_utils import MetaConverter, assert_metadata_eq, is_sparse_any
 import torch.utils._python_dispatch
@@ -1755,6 +1755,24 @@ class TestMeta(TestCase):
             meta_tensor = torch.randn(1, device='meta')
             meta_tensor.item()
 
+    def test_triangular_solve_out(self):
+        # Get what's the expected output for the given example.
+        A = torch.randn(2, 2).triu()
+        b = torch.randn(2, 3)
+        out = torch.triangular_solve(b, A)
+
+        # Call the function again, transforming every tensor input (including the out tensor)
+        # into a meta tensor.
+        meta_out = tree_map_only(torch.Tensor, lambda t: t.to("meta"), out)
+        torch.triangular_solve(b.to("meta"), A.to("meta"), out=meta_out)
+
+        self.assertEqual(out[0].shape, meta_out[0].shape)
+        self.assertEqual(out[0].dtype, meta_out[0].dtype)
+
+        self.assertEqual(out[1].shape, meta_out[1].shape)
+        self.assertEqual(out[1].dtype, meta_out[1].dtype)
+
+
 instantiate_device_type_tests(TestMeta, globals())
 
 def print_op_str_if_not_supported(op_str):

test/test_ops.py

@@ -222,6 +222,7 @@ meta_consistency_out_dtype_mismatch_xfails = {
     xfail("take"),
     xfail("transpose_copy"),
     xfail("tril"),
+    xfail("triangular_solve"),
     xfail("triu"),
     xfail("trunc"),
     xfail("unfold_copy"),

test/test_proxy_tensor.py

@@ -2054,7 +2054,6 @@ out_symbolic_tensor_failures = {
     xfail('scatter_add', ''),
     xfail('scatter', ''),
     xfail('take_along_dim', ''),
-    xfail('triangular_solve', ''),
 
     # SymIntArrayRef expected to contain only concrete
     xfail('ones', ''),

torch/_meta_registrations.py

@@ -1495,7 +1495,7 @@ def linalg_solve_triangular_meta(
 
 
 @register_meta(aten.triangular_solve)
-@out_wrapper("solution", "cloned_coefficient")
+@out_wrapper("X", "M")
 def triangular_solve_meta(
     self: Tensor,
     A: Tensor,