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Modified the regression test to explicitly pass a named dimension when calling `torch.concat` and `torch.concatenate`, which reproduces the crash scenario described in the issue. 【F:test/test_tensor_creation_ops.py†L536-L541】\n\n**Summary**\n\n- Ensured all alias implementations of `torch.cat`, including `concat` and `concatenate`, guard against empty tensor lists before computing the dimension position. 【F:aten/src/ATen/native/TensorShape.cpp†L765-L814】\n- Updated the regression test to check that passing an empty list with a named dimension raises a `RuntimeError`. 【F:test/test_tensor_creation_ops.py†L536-L541】\n\n**Testing**\n\n- ✅ `python -m compileall -q torch` 【1c9f8d†L1-L2】\n- ❌ `pytest test/test_tensor_creation_ops.py -k concat_empty_list_error -vv` (failed to import `torch`) 【985f31†L1-L28】\n\nCodex couldn't run certain commands due to environment limitations. Consider configuring a setup script or internet access in your Codex environment to install dependencies.",{},"fd5fe1fa-a1b6-4899-b794-041e6ae6c16c",{"_103":500,"_151":501,"_480":502},{"_488":45,"_489":498},{},"analysis",[499],"Recorded PR message.","tool","container.make_pr",{},"1c3845aa-57b5-419a-8dc1-c6f8de2e3905",{"_103":100,"_151":-5,"_480":512},{"_488":508,"_219":509,"_510":-5,"_45":511},{"_486":507},11,"code","unknown","response_format_name","{\"title\":\"Fix torch.concat crash on empty tensor list\", \"body\":\"## Summary\\n- guard alias functions of `torch.cat` in `TensorShape.cpp`\\n to ensure a non-empty tensor list before indexing\\n- add regression test for empty lists using named dimensions\\n\\n## Testing\\n- `python -m compileall -q torch`\\n- `pytest test/test_tensor_creation_ops.py -k concat_empty_list_error -vv` *(fails: ModuleNotFoundError: No module named 'torch')*\"}",{},"892890b3-6d03-40b4-b114-5b7bd63bc2d8",{"_103":100,"_151":-5,"_480":522},{"_488":45,"_489":520},{"_517":57,"_518":519,"_486":487},"exclude_after_next_user_message","reasoning_status","is_reasoning",[521],"Time to put together the PR with the changes I've made.",{},"fff30f5c-edee-4f29-80dd-0c99ec28c173",{"_103":500,"_151":529,"_480":530},{"_488":45,"_489":527},{},[528],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1265, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git log -1 --stat\", \"commit 756c80cdd53d569919ee11a6de5d2123f9d59eb4 (HEAD -> work)\", \"Author: Codex \", \"Date: Sat Jun 7 01:12:55 2025 +0000\", \"\", \" Update concat empty-list test for named dims\", \"\", \" test/test_tensor_creation_ops.py | 4 ++--\", \" 1 file changed, 2 insertions(+), 2 deletions(-)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"da1e9b\"}","container.feed_chars",{},"d20cd0f2-952b-46cf-b669-6a4134974d92",{"_103":100,"_151":-5,"_480":537},{"_488":508,"_219":509,"_510":-5,"_45":536},{"_486":535},9,"{\"session_name\":\"shell2\",\"chars\":\"git log -1 --stat\\n\"}\n",{},"be417d50-09cd-4610-8109-6eac15da9b92",{"_103":500,"_151":529,"_480":544},{"_488":45,"_489":542},{},[543],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1264, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git status --short\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"cfc951\"}",{},"f0fb0227-5e2c-4e4b-ba5a-1c1151dba747",{"_103":100,"_151":-5,"_480":550},{"_488":508,"_219":509,"_510":-5,"_45":549},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"git status --short\\n\"}\n",{},"421432c6-d195-47c6-b77a-70668cc46b04",{"_103":100,"_151":-5,"_480":557},{"_488":45,"_489":555},{"_517":57,"_518":519,"_486":487},[556],"I'll prepare the final PR summary, referencing changes and the ModuleNotFoundError fail.",{},"4f9c688b-d484-448c-a028-c4eb2dd4505a",{"_103":500,"_151":529,"_480":564},{"_488":45,"_489":562},{},[563],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1200, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# nl -ba aten/src/ATen/native/TensorShape.cp\", \"p | sed -n '760,816p'\", \" 760 offset += slice_dim_size;\", \" 761 }\", \" 762 }\", \" 763 }\", \" 764\", \" 765 Tensor& cat_out(TensorList tensors, Dimname dim, Tensor& result) {\", \" 766 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 767 return at::cat_out(result, tensors, dimname_to_position(tensors[0], di\", \"m));\", \" 768 }\", \" 769\", \" 770 Tensor cat(TensorList tensors, Dimname dim) {\", \" 771 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 772 return at::cat(tensors, dimname_to_position(tensors[0], dim));\", \" 773 }\", \" 774\", \" 775 // torch.concat, alias for torch.cat\", \" 776 Tensor& concat_out(TensorList tensors, Dimname dim, Tensor& result) {\", \" 777 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 778 return at::cat_out(result, tensors, dimname_to_position(tensors[0], di\", \"m));\", \" 779 }\", \" 780\", \" 781 Tensor concat(TensorList tensors, Dimname dim) {\", \" 782 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 783 return at::cat(tensors, dimname_to_position(tensors[0], dim));\", \" 784 }\", \" 785\", \" 786 Tensor& concat_out(TensorList tensors, int64_t dim, Tensor& result) {\", \" 787 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 788 return at::cat_out(result, tensors, dim);\", \" 789 }\", \" 790\", \" 791 Tensor concat(TensorList tensors, int64_t dim) {\", \" 792 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 793 return at::cat(tensors, dim);\", \" 794 }\", \" 795\", \" 796 // torch.concatenate, alias for torch.cat\", \" 797 Tensor& concatenate_out(TensorList tensors, Dimname dim, Tensor& result)\", \" {\", \" 798 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 799 return at::cat_out(result, tensors, dimname_to_position(tensors[0], di\", \"m));\", \" 800 }\", \" 801\", \" 802 Tensor concatenate(TensorList tensors, Dimname dim) {\", \" 803 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 804 return at::cat(tensors, dimname_to_position(tensors[0], dim));\", \" 805 }\", \" 806\", \" 807 Tensor& concatenate_out(TensorList tensors, int64_t dim, Tensor& result)\", \" {\", \" 808 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 809 return at::cat_out(result, tensors, dim);\", \" 810 }\", \" 811\", \" 812 Tensor concatenate(TensorList tensors, int64_t dim) {\", \" 813 TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" 814 return at::cat(tensors, dim);\", \" 815 }\", \" 816\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"123a55\"}",{},"deef1455-7213-4937-87fd-3dca1b891aa8",{"_103":100,"_151":-5,"_480":570},{"_488":508,"_219":509,"_510":-5,"_45":569},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"nl -ba aten/src/ATen/native/TensorShape.cpp | sed -n '760,816p'\\n\"}\n",{},"fefc6e1e-35ba-4fbe-aafa-0b08db2aded9",{"_103":100,"_151":-5,"_480":577},{"_488":45,"_489":575},{"_517":57,"_518":519,"_486":487},[576],"I'll display the lines in TensorShape.cpp where TORCH_CHECK was added.",{},"4060ca5d-29a9-4f21-9cfc-30b3b0d58086",{"_103":500,"_151":529,"_480":584},{"_488":45,"_489":582},{},[583],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1189, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# nl -ba test/test_tensor_creation_ops.py |\", \"sed -n '540,548p'\", \" 540 with self.assertRaisesRegex(RuntimeError, msg):\", \" 541 torch.concatenate([], dim='N')\", \" 542\", \" 543 def test_cat_out(self, device):\", \" 544 x = torch.zeros((0), device=device)\", \" 545 y = torch.randn((4, 6), device=device)\", \" 546\", \" 547 w = y.view(-1).clone()\", \" 548 a = torch.cat([w[:2], w[4:6]])\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"a57f77\"}",{},"027e5059-378a-4bdd-978b-70ad2241db1c",{"_103":100,"_151":-5,"_480":590},{"_488":508,"_219":509,"_510":-5,"_45":589},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"nl -ba test/test_tensor_creation_ops.py | sed -n '540,548p'\\n\"}\n",{},"ec89da54-4dd7-4f8a-8880-00f4354deb43",{"_103":500,"_151":529,"_480":597},{"_488":45,"_489":595},{},[596],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1176, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# nl -ba test/test_tensor_creation_ops.py |\", \"sed -n '530,540p'\", \" 530 res2 = torch.cat([empty, x], dim=1)\", \" 531 self.assertEqual(res1, res2)\", \" 532\", \" 533 res1 = torch.cat([empty, empty], dim=1)\", \" 534 self.assertEqual(res1, empty)\", \" 535\", \" 536 def test_concat_empty_list_error(self, device):\", \" 537 msg = \\\"expected a non-empty list of Tensors\\\"\", \" 538 with self.assertRaisesRegex(RuntimeError, msg):\", \" 539 torch.concat([], dim='N')\", \" 540 with self.assertRaisesRegex(RuntimeError, msg):\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"3d9773\"}",{},"a020cb3c-e6b2-4aa3-9594-0fe8409ec77f",{"_103":100,"_151":-5,"_480":603},{"_488":508,"_219":509,"_510":-5,"_45":602},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"nl -ba test/test_tensor_creation_ops.py | sed -n '530,540p'\\n\"}\n",{},"446f4333-6659-49fe-bea1-c044adb51eb0",{"_103":100,"_151":-5,"_480":610},{"_488":45,"_489":608},{"_517":57,"_518":519,"_486":487},[609],"I'll grab the relevant lines from the code for proper citation.",{},"447c5cbe-aaa0-4d78-9cb6-f2e21d87ae2e",{"_103":500,"_151":529,"_480":617},{"_488":45,"_489":615},{},[616],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1168, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git show -1 --stat --patch | sed -n '20,40\", \"p'\", \" with self.assertRaisesRegex(RuntimeError, msg):\", \"- torch.concatenate([])\", \"+ torch.concatenate([], dim='N')\", \"\", \" def test_cat_out(self, device):\", \" x = torch.zeros((0), device=device)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"3890f3\"}",{},"163e88c6-cff3-4fa9-926a-f7563d78119c",{"_103":100,"_151":-5,"_480":623},{"_488":508,"_219":509,"_510":-5,"_45":622},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"git show -1 --stat --patch | sed -n '20,40p'\\n\"}\n",{},"fbaf4a41-15dc-416a-bf6d-51765718a370",{"_103":500,"_151":529,"_480":630},{"_488":45,"_489":628},{},[629],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1147, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git show -1 --stat --patch | head -n 20\", \"commit 756c80cdd53d569919ee11a6de5d2123f9d59eb4\", \"Author: Codex \", \"Date: Sat Jun 7 01:12:55 2025 +0000\", \"\", \" Update concat empty-list test for named dims\", \"---\", \" test/test_tensor_creation_ops.py | 4 ++--\", \" 1 file changed, 2 insertions(+), 2 deletions(-)\", \"\", \"diff --git a/test/test_tensor_creation_ops.py b/test/test_tensor_creation_ops.py\", \"index 73cd97f769f..20810315b70 100644\", \"--- a/test/test_tensor_creation_ops.py\", \"+++ b/test/test_tensor_creation_ops.py\", \"@@ -536,9 +536,9 @@ class TestTensorCreation(TestCase):\", \" def test_concat_empty_list_error(self, device):\", \" msg = \\\"expected a non-empty list of Tensors\\\"\", \" with self.assertRaisesRegex(RuntimeError, msg):\", \"- torch.concat([])\", \"+ torch.concat([], dim='N')\", \" with self.assertRaisesRegex(RuntimeError, msg):\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"920d51\"}",{},"2bd2e4fb-3f8a-412d-b84a-0062c23a6eeb",{"_103":100,"_151":-5,"_480":636},{"_488":508,"_219":509,"_510":-5,"_45":635},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"git show -1 --stat --patch | head -n 20\\n\"}\n",{},"e2c6011f-50e2-43a3-8d52-b9bd54fbf2a3",{"_103":100,"_151":-5,"_480":643},{"_488":45,"_489":641},{"_517":57,"_518":519,"_486":487},[642],"I'll grab the diff to confirm the lines I changed.",{},"bbc000c8-e343-44b2-8792-4a5458be3075",{"_103":500,"_151":529,"_480":650},{"_488":45,"_489":648},{},[649],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1146, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git status --short\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"0f3fa7\"}",{},"0b2b07aa-ad77-4a52-ac9a-3b5e509e0d9d",{"_103":100,"_151":-5,"_480":655},{"_488":508,"_219":509,"_510":-5,"_45":549},{"_486":535},{},"2f2e1c38-dd91-40ff-bf44-8d2236e20dbb",{"_103":500,"_151":529,"_480":662},{"_488":45,"_489":660},{},[661],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1142, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git commit -m \\\"Update concat empty-list te\", \"st for named dims\\\" && git status --short\", \"[work 756c80cdd53] Update concat empty-list test for named dims\", \" 1 file changed, 2 insertions(+), 2 deletions(-)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"eac344\"}",{},"0a0dd7c3-4986-486c-9450-13fe0eaaca25",{"_103":100,"_151":-5,"_480":668},{"_488":508,"_219":509,"_510":-5,"_45":667},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"git commit -m \\\"Update concat empty-list test for named dims\\\" && git status --short\\n\"}\n",{},"7d264c3b-3b86-4b6c-a784-658f3ad64138",{"_103":500,"_151":529,"_480":675},{"_488":45,"_489":673},{},[674],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1141, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git add test/test_tensor_creation_ops.py\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"d3e7be\"}",{},"4cf52e9b-7d5b-4713-85dd-15eb5c87b55d",{"_103":100,"_151":-5,"_480":681},{"_488":508,"_219":509,"_510":-5,"_45":680},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"git add test/test_tensor_creation_ops.py\\n\"}\n",{},"9c04ca20-61c3-4e08-ac80-cc49e10a38ef",{"_103":500,"_151":529,"_480":688},{"_488":45,"_489":686},{},[687],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1139, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git status --short\", \" M test/test_tensor_creation_ops.py\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"d531b9\"}",{},"3d1d13d5-e5c3-4850-952b-64b48770a53e",{"_103":100,"_151":-5,"_480":693},{"_488":508,"_219":509,"_510":-5,"_45":549},{"_486":507},{},"69528981-4243-4bec-a506-badcf40dcf71",{"_103":100,"_151":-5,"_480":700},{"_488":45,"_489":698},{"_517":57,"_518":519,"_486":487},[699],"The test failed because of a 'torch' module error. I'll include that in the commit.",{},"760d6c92-226d-4174-b493-380742c3f6fe",{"_103":500,"_151":529,"_480":707},{"_488":45,"_489":705},{},[706],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1093, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# pytest test/test_tensor_creation_ops.py -k\", \" concat_empty_list_error -vv\", \"============================= test session starts ==============================\", \"platform linux -- Python 3.11.12, pytest-8.3.5, pluggy-1.6.0 -- /root/.pyenv/ver\", \"sions/3.11.12/bin/python3.11\", \"cachedir: .pytest_cache\", \"rootdir: /workspace/pytorch\", \"configfile: pytest.ini\", \"plugins: anyio-4.9.0\", \"collected 0 items / 1 error\", \"Running 0 items in this shard\", \"\", \"==================================== ERRORS ====================================\", \"______________ ERROR collecting test/test_tensor_creation_ops.py _______________\", \"ImportError while importing test module '/workspace/pytorch/test/test_tensor_cre\", \"ation_ops.py'.\", \"Hint: make sure your test modules/packages have valid Python names.\", \"Traceback:\", \"/root/.pyenv/versions/3.11.12/lib/python3.11/site-packages/_pytest/python.py:493\", \": in importtestmodule\", \" mod = import_path(\", \"/root/.pyenv/versions/3.11.12/lib/python3.11/site-packages/_pytest/pathlib.py:58\", \"7: in import_path\", \" importlib.import_module(module_name)\", \"/root/.pyenv/versions/3.11.12/lib/python3.11/importlib/__init__.py:126: in impor\", \"t_module\", \" return _bootstrap._gcd_import(name[level:], package, level)\", \":1204: in _gcd_import\", \" ???\", \":1176: in _find_and_load\", \" ???\", \":1147: in _find_and_load_unlocked\", \" ???\", \":690: in _load_unlocked\", \" ???\", \":940: in exec_module\", \" ???\", \":241: in _call_with_frames_removed\", \" ???\", \"test/test_tensor_creation_ops.py:4: in \", \" import torch\", \"E ModuleNotFoundError: No module named 'torch'\", \"=========================== short test summary info ============================\", \"ERROR test/test_tensor_creation_ops.py\", \"!!!!!!!!!!!!!!!!!!!! Interrupted: 1 error during collection !!!!!!!!!!!!!!!!!!!!\", \"=============================== 1 error in 0.33s ===============================\", \"\"], \"num_elided_lines\": 0, \"chunk_id\": \"985f31\"}",{},"171918ea-b1d9-4577-853d-58d389a2c6c6",{"_103":100,"_151":-5,"_480":713},{"_488":508,"_219":509,"_510":-5,"_45":712},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"pytest test/test_tensor_creation_ops.py -k concat_empty_list_error -vv\\n\", \"yield_time_ms\":1000}\n",{},"19fdb218-f6f4-4f51-9feb-f430f979de1a",{"_103":500,"_151":529,"_480":720},{"_488":45,"_489":718},{},[719],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1093, \"output\": [\"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"80ba32\"}",{},"95b5896a-1f5c-48e1-a30d-97bccf11c418",{"_103":100,"_151":-5,"_480":726},{"_488":508,"_219":509,"_510":-5,"_45":725},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"\",\"yield_time_ms\":1000}\n",{},"a6cce31a-d1b0-4acb-9988-d8853f519033",{"_103":500,"_151":529,"_480":733},{"_488":45,"_489":731},{},[732],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1093, \"output\": [], \"num_elided_lines\": 0, \"chunk_id\": \"7cde05\"}",{},"b7028938-8260-4933-b4e0-1ad6cadc40c6",{"_103":100,"_151":-5,"_480":738},{"_488":508,"_219":509,"_510":-5,"_45":725},{"_486":535},{},"6a95b669-a7b4-4e95-bed7-8b39ace237fd",{"_103":500,"_151":529,"_480":745},{"_488":45,"_489":743},{},[744],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1093, \"output\": [], \"num_elided_lines\": 0, \"chunk_id\": \"b76d33\"}",{},"b7349d99-9918-43bf-9531-a0045c522101",{"_103":100,"_151":-5,"_480":750},{"_488":508,"_219":509,"_510":-5,"_45":725},{"_486":535},{},"839d461c-03bb-4268-a54d-dce39dda5e24",{"_103":500,"_151":529,"_480":757},{"_488":45,"_489":755},{},[756],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1092, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# python -m compileall -q torch\", \"\"], \"num_elided_lines\": 0, \"chunk_id\": \"1c9f8d\"}",{},"06b039e7-8213-4cdd-9787-ef5221091ed6",{"_103":100,"_151":-5,"_480":763},{"_488":508,"_219":509,"_510":-5,"_45":762},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"python -m compileall -q torch\\n\", \"yield_time_ms\":1000}\n",{},"fd932f47-09aa-4982-ac6e-4e549267a98c",{"_103":100,"_151":-5,"_480":770},{"_488":45,"_489":768},{"_517":57,"_518":519,"_486":487},[769],"I'll compile with `compileall -q torch` and rerun the failing test to show results.",{},"1afe19c2-6377-4692-a02f-fde6a946efa5",{"_103":500,"_151":529,"_480":777},{"_488":45,"_489":775},{},[776],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1074, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# apply_patch<<'PATCH'\", \"> *** Begin Patch\", \"> *** Update File: test/test_tensor_creation_ops.py\", \"> @@\", \"> def test_concat_empty_list_error(self, device):\", \"> msg = \\\"expected a non-empty list of Tensors\\\"\", \"> - with self.assertRaisesRegex(RuntimeError, msg):\", \"> - torch.concat([])\", \"> - with self.assertRaisesRegex(RuntimeError, msg):\", \"> - torch.concatenate([])\", \"> + with self.assertRaisesRegex(RuntimeError, msg):\", \"> + torch.concat([], dim='N')\", \"> + with self.assertRaisesRegex(RuntimeError, msg):\", \"> + torch.concatenate([], dim='N')\", \"> *** End Patch\", \"> PATCH\", \"Success. Updated the following files:\", \"M test/test_tensor_creation_ops.py\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"80e818\"}",{},"f92ae758-ee78-4853-a043-efe41546a940",{"_103":100,"_151":-5,"_480":783},{"_488":508,"_219":509,"_510":-5,"_45":782},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"apply_patch<<'PATCH'\\n*** Begin Patch\\n*** Update File: test/test_tensor_creation_ops.py\\n@@\\n def test_concat_empty_list_error(self, device):\\n msg = \\\"expected a non-empty list of Tensors\\\"\\n- with self.assertRaisesRegex(RuntimeError, msg):\\n- torch.concat([])\\n- with self.assertRaisesRegex(RuntimeError, msg):\\n- torch.concatenate([])\\n+ with self.assertRaisesRegex(RuntimeError, msg):\\n+ torch.concat([], dim='N')\\n+ with self.assertRaisesRegex(RuntimeError, msg):\\n+ torch.concatenate([], dim='N')\\n*** End Patch\\nPATCH\\n\"}",{},"37a9b796-be78-4c55-a934-8b1031c861f3",{"_103":100,"_151":-5,"_480":790},{"_488":45,"_489":788},{"_517":57,"_518":519,"_486":487},[789],"The diff looks good. I'll adjust the test to include dimension names.",{},"d360f6da-ef5f-415f-9729-dc76dc221393",{"_103":500,"_151":529,"_480":797},{"_488":45,"_489":795},{},[796],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1062, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git diff HEAD~1 | head\", \"diff --git a/aten/src/ATen/native/TensorShape.cpp b/aten/src/ATen/native/TensorS\", \"hape.cpp\", \"index c924a979aa3..84f5713f425 100644\", \"--- a/aten/src/ATen/native/TensorShape.cpp\", \"+++ b/aten/src/ATen/native/TensorShape.cpp\", \"@@ -774,35 +774,43 @@ 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));\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"edff3a\"}",{},"6cff72d2-860e-418b-bbef-7de244e2485f",{"_103":100,"_151":-5,"_480":803},{"_488":508,"_219":509,"_510":-5,"_45":802},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"git diff HEAD~1 | head\\n\"}\n",{},"29cdbba7-2917-4b26-89e7-eba18d80df7e",{"_103":100,"_151":-5,"_480":810},{"_488":45,"_489":808},{"_517":57,"_518":519,"_486":487},[809],"I'll review the previous commit to confirm no mistakes with the alias functions.",{},"475a8dc1-a6aa-4a3f-baf4-73f3dcab33a8",{"_103":500,"_151":529,"_480":817},{"_488":45,"_489":815},{},[816],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1049, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '536,546p' test/test_tensor_creatio\", \"n_ops.py\", \" def test_concat_empty_list_error(self, device):\", \" msg = \\\"expected a non-empty list of Tensors\\\"\", \" with self.assertRaisesRegex(RuntimeError, msg):\", \" torch.concat([])\", \" with self.assertRaisesRegex(RuntimeError, msg):\", \" torch.concatenate([])\", \"\", \" def test_cat_out(self, device):\", \" x = torch.zeros((0), device=device)\", \" y = torch.randn((4, 6), device=device)\", \"\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"778fb3\"}",{},"94fa5f34-e0a5-4f5e-a1c8-554e139cd6a2",{"_103":100,"_151":-5,"_480":823},{"_488":508,"_219":509,"_510":-5,"_45":822},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '536,546p' test/test_tensor_creation_ops.py\\n\"}\n",{},"3cec1668-c906-4400-8506-6f07fac4ed48",{"_103":500,"_151":529,"_480":830},{"_488":45,"_489":828},{},[829],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1046, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"concat_empty_list_error\\\" -r\", \"test/test_tensor_creation_ops.py:536: def test_concat_empty_list_error(self,\", \"device):\", \"\"], \"num_elided_lines\": 0, \"chunk_id\": \"6bbe3b\"}",{},"15b97f32-3cc5-4d5b-8856-7ea287e4c490",{"_103":100,"_151":-5,"_480":836},{"_488":508,"_219":509,"_510":-5,"_45":835},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"concat_empty_list_error\\\" -r\\n\"}\n",{},"ed3ba580-03ee-4938-8d66-dcf4176476c6",{"_103":100,"_151":-5,"_480":843},{"_488":45,"_489":841},{"_517":57,"_518":519,"_486":487},[842],"I’m updating the test to reflect that `torch.concat([], dim='N')` should trigger an error.",{},"ac000e24-bff0-41a3-935f-d4e4a34e362b",{"_103":500,"_151":529,"_480":850},{"_488":45,"_489":848},{},[849],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1041, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306 | jq -r '.body' | sed -n '120,240p'\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 9321 100 9321 0 0 51817 0 --:--:-- --:--:-- --:--:-- 52072\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"8ee27c\"}",{},"1f678bc7-2623-492f-a4df-5c90891e67da",{"_103":100,"_151":-5,"_480":856},{"_488":508,"_219":509,"_510":-5,"_45":855},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306 | jq -r '.body' | sed -n '120,240p'\\n\", \"yield_time_ms\":1000}\n",{},"d24cc849-9699-4453-899d-154062ba5eee",{"_103":100,"_151":-5,"_480":863},{"_488":45,"_489":861},{"_517":57,"_518":519,"_486":487},[862],"The error is because PyTorch wasn't built. I'll follow the instructions and check the diff.",{},"fc10919d-2472-4e79-b603-833feab290c3",{"_103":500,"_151":529,"_480":870},{"_488":45,"_489":868},{},[869],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1027, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# python - <<'PY'\", \"> import torch\", \"> try:\", \"> torch.concat([], dim='N')\", \"> except Exception as e:\", \"> print('torch.concat with N ->', type(e).__name__, e)\", \"> PY\", \"Traceback (most recent call last):\", \" File \\\"\\\", line 1, in \", \" File \\\"/workspace/pytorch/torch/__init__.py\\\", line 75, in \", \" from torch.torch_version import __version__ as __version__\", \" File \\\"/workspace/pytorch/torch/torch_version.py\\\", line 5, in \", \" from torch.version import __version__ as internal_version\", \"ModuleNotFoundError: No module named 'torch.version'\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"572fb3\"}",{},"9c10ebb2-3edd-4951-a0ad-b87aa0183d7f",{"_103":100,"_151":-5,"_480":876},{"_488":508,"_219":509,"_510":-5,"_45":875},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"python - <<'PY'\\nimport torch\\ntry:\\n torch.concat([], dim='N')\\nexcept Exception as e:\\n print('torch.concat with N ->', type(e).__name__, e)\\nPY\\n\", \"yield_time_ms\":1000}\n",{},"aae9ecac-488d-479d-8de6-909324f4c560",{"_103":100,"_151":-5,"_480":883},{"_488":45,"_489":881},{"_517":57,"_518":519,"_486":487},[882],"I'm considering how dimension names like `'N'` and `'C'` work with `torch.concat()` calls. I'll test `torch.concat([], dim='N')`, but I need to check if any names need setting.",{},"6c0f76a5-b207-4162-81bc-de86770bb469",{"_103":500,"_151":529,"_480":890},{"_488":45,"_489":888},{},[889],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 975, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '260,300p' test/test_namedtensor.py\", \" output = x.squeeze()\", \" self.assertEqual(output.names, ['N'])\", \"\", \" def test_repr(self):\", \" named_tensor = torch.zeros(2, 3).rename_('N', 'C')\", \" expected = \\\"tensor([[0., 0., 0.],\\\\n [0., 0., 0.]], names=('N', 'C\", \"'))\\\"\", \" self.assertEqual(repr(named_tensor), expected)\", \"\", \" unnamed_tensor = torch.zeros(2, 3)\", \" expected = \\\"tensor([[0., 0., 0.],\\\\n [0., 0., 0.]])\\\"\", \" self.assertEqual(repr(unnamed_tensor), expected)\", \"\", \" none_named_tensor = torch.zeros(2, 3).rename_(None, None)\", \" self.assertEqual(repr(none_named_tensor), expected)\", \"\", \" def test_diagonal(self):\", \" named_tensor = torch.zeros(2, 3, 5, 7, names=list('ABCD'))\", \" self.assertEqual(named_tensor.diagonal().names, ['C', 'D', None])\", \" self.assertEqual(named_tensor.diagonal(1, 3).names, ['A', 'C', None])\", \"\", \" self.assertEqual(named_tensor.diagonal(outdim='E', dim1='B', dim2='D').n\", \"ames,\", \" ['A', 'C', 'E'])\", \"\", \" def test_max_pooling(self):\", \" def check_tuple_return(op, inputs, expected_names):\", \" values, indices = op(*inputs)\", \" self.assertEqual(values.names, expected_names)\", \" self.assertEqual(indices.names, expected_names)\", \"\", \" for device in get_all_device_types():\", \"\", \" named_tensor_1d = torch.zeros(2, 3, 5, device=device, names=list('AB\", \"C'))\", \" named_tensor_2d = torch.zeros(2, 3, 5, 7, device=device, names=list(\", \"'ABCD'))\", \" named_tensor_3d = torch.zeros(2, 3, 5, 7, 9, device=device, names=li\", \"st('ABCDE'))\", \"\", \" self.assertEqual(F.max_pool1d(named_tensor_1d, 2).names, named_tenso\", \"r_1d.names)\", \" self.assertEqual(F.max_pool2d(named_tensor_2d, [2, 2]).names, named_\", \"tensor_2d.names)\", \" self.assertEqual(F.max_pool3d(named_tensor_3d, [2, 2, 2]).names, nam\", \"ed_tensor_3d.names)\", \"\", \" check_tuple_return(F.max_pool1d_with_indices, [named_tensor_1d, 2],\", \"named_tensor_1d.names)\", \" check_tuple_return(F.max_pool2d_with_indices, [named_tensor_2d, [2,\", \"2]], named_tensor_2d.names)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"eca4e3\"}",{},"d06cad36-9162-40c9-9950-de8f725d5785",{"_103":100,"_151":-5,"_480":896},{"_488":508,"_219":509,"_510":-5,"_45":895},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '260,300p' test/test_namedtensor.py\\n\"}\n",{},"4ff7635f-c29b-412d-b90d-c566639b91a1",{"_103":100,"_151":-5,"_480":903},{"_488":45,"_489":901},{"_517":57,"_518":519,"_486":487},[902],"I'll check the file `test/test_namedtensor.py` around line 280 to verify dimension names.",{},"efa40e0b-1062-4e2f-a9d9-2d6c4fc58a74",{"_103":500,"_151":529,"_480":910},{"_488":45,"_489":908},{},[909],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 962, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"dim='\\\" -r test | head\", \"test/test_namedtensor.py:280: self.assertEqual(named_tensor.diagonal(outd\", \"im='E', dim1='B', dim2='D').names,\", \"test/test_namedtensor.py:984: method('softmax', dim='D'),\", \"test/test_namedtensor.py:986: method('log_softmax', dim='D'),\", \"test/test_nn.py:7152: unflatten = nn.Unflatten(dim='features', unflattene\", \"d_size=(('C', 2), ('H', 5), ('W', 5)))\", \"test/test_nn.py:7170: nn.Unflatten(dim='features', unflattened_si\", \"ze=us)\", \"test/test_nn.py:7177: nn.Unflatten(dim='features', unflattened_size=(\", \"['C', 2], ['W', 5], ['H', 5]))\", \"test/test_nn.py:7184: nn.Unflatten(dim='features', unflattened_size=(\", \"{'C': 2}, {'W': 5}, {'H': 5}))\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"5cac13\"}",{},"0ebd121f-5bce-4ae5-8fc6-19be7d5fe2e8",{"_103":100,"_151":-5,"_480":916},{"_488":508,"_219":509,"_510":-5,"_45":915},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"dim='\\\" -r test | head\\n\"}\n",{},"76acc8e7-fe22-47e3-bd8f-73c5813f0494",{"_103":100,"_151":-5,"_480":923},{"_488":45,"_489":921},{"_517":57,"_518":519,"_486":487},[922],"To replicate the crash, I'll need to call `torch.concat([], dim='N')`. However, I must ensure there's a named tensor. If no name is provided, the tensor will be invalid, causing segmentation errors. I'll fix the alias for `torch.concat` to include an emptiness check, since `torch.cat` already handles empty inputs.\n\nTo test `torch.concat([])`, I'll check if dimension names are passed and handle empty inputs.",{},"47800459-c7d2-4180-8846-7c452d77e887",{"_103":500,"_151":529,"_480":930},{"_488":45,"_489":928},{},[929],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 929, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '520,550p' test/test_tensor_creatio\", \"n_ops.py\", \" res1 = torch.cat([empty, empty], dim=1)\", \" self.assertEqual(res1, empty)\", \"\", \" def test_cat_empty(self, device):\", \" dtype = torch.float32\", \"\", \" x = torch.randn((4, 3, 32, 32), dtype=dtype, device=device)\", \" empty = torch.randn((4, 0, 32, 32), dtype=dtype, device=device)\", \"\", \" res1 = torch.cat([x, empty], dim=1)\", \" res2 = torch.cat([empty, x], dim=1)\", \" self.assertEqual(res1, res2)\", \"\", \" res1 = torch.cat([empty, empty], dim=1)\", \" self.assertEqual(res1, empty)\", \"\", \" def test_concat_empty_list_error(self, device):\", \" msg = \\\"expected a non-empty list of Tensors\\\"\", \" with self.assertRaisesRegex(RuntimeError, msg):\", \" torch.concat([])\", \" with self.assertRaisesRegex(RuntimeError, msg):\", \" torch.concatenate([])\", \"\", \" def test_cat_out(self, device):\", \" x = torch.zeros((0), device=device)\", \" y = torch.randn((4, 6), device=device)\", \"\", \" w = y.view(-1).clone()\", \" a = torch.cat([w[:2], w[4:6]])\", \" b = torch.cat([w[:2], w[4:6]], out=w[6:10])\", \" self.assertEqual(a, b)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"975a8e\"}",{},"d3096413-4279-40a3-b75f-54a3862a43bf",{"_103":100,"_151":-5,"_480":936},{"_488":508,"_219":509,"_510":-5,"_45":935},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '520,550p' test/test_tensor_creation_ops.py\\n\"}\n",{},"d347eb01-f381-483c-a392-1e90aff6c855",{"_103":100,"_151":-5,"_480":943},{"_488":45,"_489":941},{"_517":57,"_518":519,"_486":487},[942],"I added checks for empty tensors in alias functions, but the test needs fixing.",{},"93c8deeb-be4c-46c9-8937-b2a20a3d2b82",{"_103":500,"_151":529,"_480":950},{"_488":45,"_489":948},{},[949],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 868, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '262,320p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \"\", \" bool all_contiguous = true;\", \" bool all_same_dtype = true;\", \" bool all_same_sizes_and_stride = true;\", \" auto memory_format = cat_compute_output_memory_format(materialized);\", \"\", \" // Compute what the output dtype should be:\", \" const auto& result = maybe_get_output();\", \" auto is_out_defined = result.defined();\", \" auto out_dtype = at::native::result_type(tensors);\", \"\", \" // If the output tensor is defined, we need to take it into account\", \" // when computing the actual output dtype and the flags.\", \" if (is_out_defined) {\", \" // Check for type promotion, if the output tensor is defined.\", \" TORCH_CHECK(\", \" canCast(out_dtype, result.scalar_type()),\", \" \\\"torch.cat(): input types can't be cast to the desired output type \\\",\", \" result.scalar_type());\", \" out_dtype = result.scalar_type();\", \" all_contiguous = result.is_contiguous(memory_format);\", \" }\", \"\", \" // Fallback 'set_output' parameters.\", \" // (in case we don't find a valid tensor)\", \" DimVector sizes{0};\", \" TensorOptions options =\", \" materialized[0].get().options().dtype(out_dtype).memory_format(\", \" memory_format);\", \"\", \" // If we found a valid tensor, check whether the input tensors\", \" // are compatible, i.e. we can execute `cat` on them.\", \" bool found_valid_tensor = valid < materialized.size();\", \" if (found_valid_tensor) {\", \" TORCH_CHECK(\", \" dim <= materialized[valid].get().dim(),\", \" \\\"torch.cat(): dimension \\\",\", \" dim,\", \" \\\"out of range\\\");\", \"\", \" // Compute the output tensor size.\", \" // It should have the same shape as any other valid tensor,\", \" // except in the dimension 'dim'.\", \" size_t size_at_dim = 0;\", \" for (const auto i : c10::irange(materialized.size())) {\", \" const Tensor& t = materialized[i];\", \" all_same_dtype = all_same_dtype && out_dtype == t.scalar_type();\", \" if (!at::native::cat_should_skip_tensor(t)) {\", \" at::native::check_cat_shape_except_dim(materialized[valid], t, dim, i);\", \" size_at_dim += t.size(dim);\", \" all_contiguous = all_contiguous && t.is_contiguous(memory_format);\", \" all_same_sizes_and_stride = all_same_sizes_and_stride &&\", \" t.sizes() == materialized[valid].get().sizes() &&\", \" t.strides() == materialized[valid].get().strides();\", \" } else {\", \" all_contiguous = false;\", \" }\", \" }\", \"\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"68a4d1\"}",{},"fb43bf45-b428-4915-a595-550ffcc2febe",{"_103":100,"_151":-5,"_480":956},{"_488":508,"_219":509,"_510":-5,"_45":955},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '262,320p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"c3acad28-915a-4368-833f-47291f553e75",{"_103":100,"_151":-5,"_480":963},{"_488":45,"_489":961},{"_517":57,"_518":519,"_486":487},[962],"Looks like I need to scroll a bit longer to see the rest.",{},"4cd3997d-59c3-465f-8dd6-1416ea2cf673",{"_103":500,"_151":529,"_480":970},{"_488":45,"_489":968},{},[969],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 843, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '240,262p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \" // this behavior for backwards compatibility, but only for this specific size\", \" // (i.e. other empty sizes are not skipped).\", \" auto materialized = tensors.materialize();\", \"\", \" native::check_cat_no_zero_dim(materialized);\", \" dim = at::legacy_cat_wrap_dim(dim, materialized);\", \"\", \" // Checking names before the actual dimensions.\", \" auto maybe_outnames = namedinference::compute_cat_outnames(materialized);\", \"\", \" TORCH_CHECK(\", \" !materialized.empty(),\", \" \\\"torch.cat(): expected a non-empty list of Tensors\\\");\", \"\", \" // Look for the first valid tensor.\", \" size_t valid = materialized.size();\", \" for (const auto i : c10::irange(materialized.size())) {\", \" if (!at::native::cat_should_skip_tensor(materialized[i].get())) {\", \" valid = i;\", \" break;\", \" }\", \" }\", \"\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"1c3d07\"}",{},"d332c40d-7cc6-429d-9b41-deee1f209260",{"_103":100,"_151":-5,"_480":976},{"_488":508,"_219":509,"_510":-5,"_45":975},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '240,262p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"4f43dc61-0c23-4e0b-9f78-ad3ffad31c06",{"_103":100,"_151":-5,"_480":983},{"_488":45,"_489":981},{"_517":57,"_518":519,"_486":487},[982],"I’ll check around line 252 to examine the empty list check for cat.",{},"b09b4050-3cfa-45f6-b12d-ccc137a482fb",{"_103":500,"_151":529,"_480":990},{"_488":45,"_489":988},{},[989],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 830, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"cat(\\\" -n aten/src/ATen/native/Ten\", \"sorShape.cpp | head\", \"252: \\\"torch.cat(): expected a non-empty list of Tensors\\\");\", \"279: \\\"torch.cat(): input types can't be cast to the desired output type \\\"\", \",\", \"298: \\\"torch.cat(): dimension \\\",\", \"770:Tensor cat(TensorList tensors, Dimname dim) {\", \"772: return at::cat(tensors, dimname_to_position(tensors[0], dim));\", \"781:Tensor concat(TensorList tensors, Dimname dim) {\", \"783: return at::cat(tensors, dimname_to_position(tensors[0], dim));\", \"791:Tensor concat(TensorList tensors, int64_t dim) {\", \"793: return at::cat(tensors, dim);\", \"804: return at::cat(tensors, dimname_to_position(tensors[0], dim));\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"57e981\"}",{},"2cdf1255-86a7-4707-9f38-4945deb22b79",{"_103":100,"_151":-5,"_480":996},{"_488":508,"_219":509,"_510":-5,"_45":995},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"cat(\\\" -n aten/src/ATen/native/TensorShape.cpp | head\\n\"}\n",{},"eb6ac488-fa2d-4ea1-978e-d9fdc4db58a9",{"_103":100,"_151":-5,"_480":1003},{"_488":45,"_489":1001},{"_517":57,"_518":519,"_486":487},[1002],"I'll continue scrolling past `cat_sparse_impl` to see the rest of the code.",{},"3f65db7e-b33c-4225-95ef-c986e99c7e1a",{"_103":500,"_151":529,"_480":1010},{"_488":45,"_489":1008},{},[1009],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 773, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '906,960p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \" idxs[wrapped].narrow(0, col, this_piece_size) += cumulative_offset;\", \" }\", \" cumulative_offset += t.size(wrapped);\", \" col += this_piece_size;\", \" }\", \" auto sizes_copy = sizes.vec();\", \" sizes_copy[wrapped] = cumulative_offset;\", \" return native::sparse_coo_tensor(\", \" idxs,\", \" vals,\", \" sizes_copy,\", \" optTypeMetaToScalarType(tensors[0].get().options().dtype_opt()),\", \" tensors[0].get().options().layout_opt(),\", \" tensors[0].get().options().device_opt(),\", \" tensors[0].get().options().pinned_memory_opt());\", \" } else {\", \" // Catting along a dense dimension requires us to create new values.\", \" // For illustration, consider the sparse 3d tensors t1 and t2,\", \" // given by t1 = [[[1,2],[3,4]], ... (zeros) ..., [[5,6],[7,8]]]\", \" // and t2 = [... (zeros) ..., [[9, 10], [11,12]], ... (zeros) ...],\", \" // Their concatenation along dimension 2 is:\", \" // [[[1,2,0,0],[3,4,0,0]], ... (zeros) ..., [[0,0,9,10],[0,0,11,12]], ...\", \" // (zeros) ..., [[5,6,0,0],[7,8,0,0]]]\", \" //\", \" // Their values tensors are, respectively,\", \" // [[[1,2],[3,4]],[[5,6],[7,8]]] and [[[9,10],[11,12]]].\", \" //\", \" // and so the values tensor of their concatenation along dim 2 will be:\", \" // [[[1,2,0,0],[3,4,0,0]],[[5,6,0,0],[7,8,0,0]],[[0,0,9,10],[0,0,11,12]]]\", \" //\", \" // which we can get by taking the values tensor of each tensor, catting it\", \" // with zeros of the appropriate size on the left and right, and then\", \" // catting all those results together.\", \"\", \" // The dimension in each tensor's values object that corresponds to the\", \" // overall dimension along which we're catting.\", \" int64_t values_dim = wrapped - sparse_dim + 1;\", \" // The final size along the catted dimension.\", \" const int64_t total_size = std::accumulate(\", \" tensors.begin(),\", \" tensors.end(),\", \" static_cast(0),\", \" [values_dim](int64_t l, const Tensor& r) {\", \" return l + r._values().size(values_dim);\", \" });\", \" auto zeros_sizes = tensors[0].get()._values().sizes().vec();\", \" int64_t cumulative_size = 0;\", \" std::vector vals_pieces;\", \" std::vector idxs_pieces;\", \" for (const auto i : c10::irange(tensors.size())) {\", \" const Tensor& t = tensors[i];\", \" check_cat_sparse_dims(t, i, sizes, wrapped, sparse_dim, dense_dim);\", \" // dimension 0 of values corresponds to the number of values,\", \" // rather than to any logical dimension of the sparse tensor.\", \" zeros_sizes[0] = t._values().size(0);\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"387104\"}",{},"a6344794-27e3-40b2-8a9e-2fed64da23bc",{"_103":100,"_151":-5,"_480":1016},{"_488":508,"_219":509,"_510":-5,"_45":1015},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '906,960p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"71132e0c-191a-4aed-bdde-27079eb06616",{"_103":500,"_151":529,"_480":1023},{"_488":45,"_489":1021},{},[1022],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 708, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '844,906p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \" pos,\", \" \\\".\\\");\", \" TORCH_CHECK(\", \" sizes_match_except(sizes, t.sizes(), wrapped),\", \" \\\"All tensors must have the same shape: \\\",\", \" sizes,\", \" \\\" (except in the concatenating dimension),\\\"\", \" \\\" but found shape: \\\",\", \" t.sizes(),\", \" \\\" at position \\\",\", \" pos,\", \" \\\".\\\");\", \" TORCH_CHECK(\", \" t.sparse_dim() == sparse_dim && t.dense_dim() == dense_dim,\", \" \\\"All tensors must have the same sparse_dim and dense_dim: \\\",\", \" sparse_dim,\", \" \\\", \\\",\", \" dense_dim,\", \" \\\", but tensor at position \\\",\", \" pos,\", \" \\\" has \\\",\", \" t.sparse_dim(),\", \" \\\", \\\",\", \" t.dense_dim(),\", \" \\\".\\\");\", \"}\", \"\", \"static Tensor cat_sparse_impl(\", \" const MaterializedITensorListRef& tensors,\", \" int64_t dim) {\", \" std::vector indices;\", \" std::vector values;\", \" int64_t wrapped = maybe_wrap_dim(dim, tensors[0].get().dim());\", \" int64_t sparse_dim = tensors[0].get().sparse_dim();\", \" int64_t dense_dim = tensors[0].get().dense_dim();\", \" IntArrayRef sizes = tensors[0].get().sizes();\", \" if (wrapped < sparse_dim) {\", \" for (const auto i : c10::irange(tensors.size())) {\", \" const Tensor& t = tensors[i];\", \" check_cat_sparse_dims(t, i, sizes, wrapped, sparse_dim, dense_dim);\", \" indices.push_back(t._indices());\", \" values.push_back(t._values());\", \" }\", \" Tensor idxs = at::cat(indices, 1);\", \" Tensor vals = at::cat(values, 0);\", \"\", \" // We now need to move the indices of each\", \" // input tensor up along `dim` by an appropriate amount.\", \" // E.g., if t1 has indices [[2,3,4],[5,6,7]],\", \" // and sizes [10, 7]\", \" // then torch.cat((t1,t1,t1),1) should have indices\", \" // [[2,3,4,2,3,4,2,3,4],[5,6,7,12,13,14,19,20,21]],\", \" // so we need to increase idxs[1][3:6] by 7\", \" // and idxs[1][6:9] by 14.\", \" int64_t col = 0;\", \" int64_t cumulative_offset = 0;\", \" for (const auto i : c10::irange(tensors.size())) {\", \" const Tensor& t = tensors[i];\", \" int64_t this_piece_size = t._nnz();\", \" // cumulative_offset is zero for the first piece, so\", \" // don't waste time doing this operation unless i > 0.\", \" if (i > 0) {\", \" idxs[wrapped].narrow(0, col, this_piece_size) += cumulative_offset;\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"7d7dd6\"}",{},"f84b2cca-2d64-4e67-ac98-8e5d1a0cfc85",{"_103":100,"_151":-5,"_480":1029},{"_488":508,"_219":509,"_510":-5,"_45":1028},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '844,906p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"70902227-e7c3-4dbe-b02c-2532870c1bb6",{"_103":100,"_151":-5,"_480":1036},{"_488":45,"_489":1034},{"_517":57,"_518":519,"_486":487},[1035],"I’ll look for the earlier function `cat(TensorList)` now.",{},"6045b5e8-8731-49ba-9d3e-15acc6d10133",{"_103":500,"_151":529,"_480":1043},{"_488":45,"_489":1041},{},[1042],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 703, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"check_cat_sparse_dims\\\" -n aten/sr\", \"c/ATen/native/TensorShape.cpp | head\", \"834:static void check_cat_sparse_dims(\", \"883: check_cat_sparse_dims(t, i, sizes, wrapped, sparse_dim, dense_dim);\", \"957: check_cat_sparse_dims(t, i, sizes, wrapped, sparse_dim, dense_dim);\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"446c1c\"}",{},"66fdf04a-81d8-4890-8a91-632dd84e0edb",{"_103":100,"_151":-5,"_480":1049},{"_488":508,"_219":509,"_510":-5,"_45":1048},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"check_cat_sparse_dims\\\" -n aten/src/ATen/native/TensorShape.cpp | head\\n\"}\n",{},"0ffca928-4752-44b8-a001-192dfbdf7866",{"_103":100,"_151":-5,"_480":1056},{"_488":45,"_489":1054},{"_517":57,"_518":519,"_486":487},[1055],"I'm scrolling through the `cat_out` to check for a zero length at the start.",{},"42e11e30-d7b7-417e-9974-3051c8d235ad",{"_103":500,"_151":529,"_480":1063},{"_488":45,"_489":1061},{},[1062],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 650, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '712,762p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \" int64_t offset = 0;\", \" if (all_same_sizes_and_stride && result.is_contiguous(memory_format) &&\", \" all_same_dtype) {\", \" const Tensor& source_slice = materialized[valid];\", \" auto slice_dim_size = source_slice.sizes()[dim];\", \" auto result_slice = result.narrow(dim, 0, slice_dim_size);\", \" auto result_slice_data = result_slice.data_ptr();\", \" auto result_stride_bytes =\", \" result.stride(dim) * elementSize(result.scalar_type());\", \"\", \" auto iter = TensorIteratorConfig()\", \" .set_check_mem_overlap(false)\", \" .resize_outputs(false)\", \" .add_output(result_slice)\", \" .add_const_input(source_slice)\", \" .enforce_safe_casting_to_output(true)\", \" .build();\", \"\", \" for (const Tensor& tensor : materialized) {\", \" if (cat_should_skip_tensor(tensor)) {\", \" continue;\", \" }\", \" auto source_data = static_cast(tensor.const_data_ptr());\", \" auto result_data =\", \" static_cast(result_slice_data) + offset * result_stride_bytes;\", \" iter.unsafe_replace_operand(0, result_data);\", \" iter.unsafe_replace_operand(1, const_cast(source_data));\", \" copy_stub(iter.device_type(), iter, false);\", \" offset += slice_dim_size;\", \" }\", \" } else {\", \" for (const Tensor& tensor : materialized) {\", \" if (cat_should_skip_tensor(tensor)) {\", \" continue;\", \" }\", \" auto slice_dim_size = tensor.sizes()[dim];\", \" auto result_slice = result.narrow(dim, offset, slice_dim_size);\", \"\", \" auto iter = TensorIteratorConfig()\", \" .set_check_mem_overlap(false) // Already checked above\", \" .resize_outputs(false)\", \" .add_output(result_slice)\", \" .add_const_input(tensor)\", \" .promote_inputs_to_common_dtype(true)\", \" .cast_common_dtype_to_outputs(true)\", \" .enforce_safe_casting_to_output(true)\", \" .build();\", \" copy_stub(iter.device_type(), iter, false);\", \" offset += slice_dim_size;\", \" }\", \" }\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"135a92\"}",{},"b53ef694-2be5-4f50-acf6-2bbce40d455f",{"_103":100,"_151":-5,"_480":1069},{"_488":508,"_219":509,"_510":-5,"_45":1068},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '712,762p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"59822348-2931-486c-90f8-b975960bc062",{"_103":100,"_151":-5,"_480":1076},{"_488":45,"_489":1074},{"_517":57,"_518":519,"_486":487},[1075],"I'm looking a bit further to check the pre-check for empty values.",{},"e2142e67-2d99-40a0-a16c-25c86efcb109",{"_103":500,"_151":529,"_480":1083},{"_488":45,"_489":1081},{},[1082],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 597, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '662,712p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \" char* dataPtr = reinterpret_cast(out.data_ptr());\", \" size_t totalBytes = 0;\", \" for (const Tensor& input : inputs) {\", \" TORCH_CHECK(outBytes >= totalBytes);\", \" if (input.nbytes() > 0) {\", \" std::memcpy(dataPtr + totalBytes, input.const_data_ptr(), input.nbytes());\", \" }\", \" totalBytes += input.nbytes();\", \" }\", \" TORCH_CHECK(outBytes == totalBytes);\", \"}\", \"\", \"TORCH_IMPL_FUNC(cat_out_cpu)\", \"(const ITensorListRef& tensors,\", \" int64_t dim,\", \" int64_t valid,\", \" bool all_contiguous,\", \" bool all_same_dtype,\", \" bool all_same_sizes_and_stride,\", \" MemoryFormat memory_format,\", \" const Tensor& result) {\", \" if (result.numel() == 0) {\", \" return;\", \" }\", \"\", \" auto materialized = tensors.materialize();\", \"\", \" bool use_serial_kernel =\", \" result.numel() < at::internal::GRAIN_SIZE || at::get_num_threads() == 1;\", \" ScalarType dtype = materialized[valid].get().scalar_type();\", \" bool serial_dtype = at::isFloatingType(dtype);\", \" // fast path for single thread when both inputs and result are contiguous and\", \" // not empty, and concat dim is 0\", \" if (use_serial_kernel && all_contiguous && all_same_dtype &&\", \" (MemoryFormat::Contiguous == memory_format)) {\", \" if (dim == 0) {\", \" fastCatOutDim0(result, materialized);\", \" return;\", \" }\", \" // TODO: Add fast cat for higher dimensions and support multi-threaded fast\", \" // cat\", \" }\", \"\", \" // fast path for single thread when both inputs and result are contiguous and\", \" // not empty\", \" if (use_serial_kernel && all_contiguous && all_same_dtype && serial_dtype) {\", \" cat_serial_stub(kCPU, result, materialized, dim);\", \" return;\", \" }\", \"\", \" int64_t offset = 0;\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"7a5600\"}",{},"9343c7f1-dc34-47c2-9a43-5faf70ae5b81",{"_103":100,"_151":-5,"_480":1089},{"_488":508,"_219":509,"_510":-5,"_45":1088},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '662,712p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"d677d434-9758-4155-aac0-eef8a3b5dae0",{"_103":100,"_151":-5,"_480":1096},{"_488":45,"_489":1094},{"_517":57,"_518":519,"_486":487},[1095],"I'll check the `cat` and `cat_out` for int64_t dims to understand empty handling.",{},"de27708c-07c0-41b3-83ae-591fe4c94688",{"_103":500,"_151":529,"_480":1103},{"_488":45,"_489":1101},{},[1102],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 585, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"cat_out\\\" -n aten/src/ATen/native/\", \"TensorShape.cpp | head\", \"248: auto maybe_outnames = namedinference::compute_cat_outnames(materialized);\", \"674:TORCH_IMPL_FUNC(cat_out_cpu)\", \"765:Tensor& cat_out(TensorList tensors, Dimname dim, Tensor& result) {\", \"767: return at::cat_out(result, tensors, dimname_to_position(tensors[0], dim));\", \"776:Tensor& concat_out(TensorList tensors, Dimname dim, Tensor& result) {\", \"778: return at::cat_out(result, tensors, dimname_to_position(tensors[0], dim));\", \"786:Tensor& concat_out(TensorList tensors, int64_t dim, Tensor& result) {\", \"788: return at::cat_out(result, tensors, dim);\", \"799: return at::cat_out(result, tensors, dimname_to_position(tensors[0], dim));\", \"809: return at::cat_out(result, tensors, dim);\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"8946f1\"}",{},"4ecbd56e-7897-40f9-a635-d1327597ae56",{"_103":100,"_151":-5,"_480":1109},{"_488":508,"_219":509,"_510":-5,"_45":1108},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"cat_out\\\" -n aten/src/ATen/native/TensorShape.cpp | head\\n\"}\n",{},"d39503ed-765c-44c1-8e73-f8c4b92a1686",{"_103":500,"_151":529,"_480":1116},{"_488":45,"_489":1114},{},[1115],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 579, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"cat(TensorList\\\" -n aten/src/ATen/\", \"native/TensorShape.cpp | head -n 20\", \"770:Tensor cat(TensorList tensors, Dimname dim) {\", \"781:Tensor concat(TensorList tensors, Dimname dim) {\", \"791:Tensor concat(TensorList tensors, int64_t dim) {\", \"3391:Tensor _chunk_cat(TensorList tensors, int64_t dim, int64_t num_chunks) {\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"33bd94\"}",{},"9fcf0f76-0de0-468c-a219-660cfb5eed50",{"_103":100,"_151":-5,"_480":1122},{"_488":508,"_219":509,"_510":-5,"_45":1121},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"cat(TensorList\\\" -n aten/src/ATen/native/TensorShape.cpp | head -n 20\\n\"}\n",{},"8e27eede-9dfa-49b5-b9e8-f74fc7eda7cd",{"_103":100,"_151":-5,"_480":1129},{"_488":45,"_489":1127},{"_517":57,"_518":519,"_486":487},[1128],"I need to look at where cat(TensorList, int64_t dim) is defined to see if it handles empty lists.",{},"230a6ad2-8f1b-4d5e-bcc1-f817b151f886",{"_103":500,"_151":529,"_480":1136},{"_488":45,"_489":1134},{},[1135],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 516, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '780,840p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \"\", \"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));\", \"}\", \"\", \"Tensor& concat_out(TensorList tensors, int64_t dim, Tensor& result) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat_out(result, tensors, dim);\", \"}\", \"\", \"Tensor concat(TensorList tensors, int64_t dim) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat(tensors, 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));\", \"}\", \"\", \"Tensor& concatenate_out(TensorList tensors, int64_t dim, Tensor& result) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat_out(result, tensors, dim);\", \"}\", \"\", \"Tensor concatenate(TensorList tensors, int64_t dim) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat(tensors, dim);\", \"}\", \"\", \"static bool sizes_match_except(\", \" IntArrayRef s1,\", \" IntArrayRef s2,\", \" int64_t dim_except /* should already be wrapped */) {\", \" if (s1.size() != s2.size()) {\", \" return false;\", \" }\", \" for (const auto i : c10::irange(static_cast(s1.size()))) {\", \" if (i != dim_except && s1[i] != s2[i]) {\", \" return false;\", \" }\", \" }\", \" return true;\", \"}\", \"\", \"// Check to see if the shape of tensors is compatible\", \"// for being concatenated along a given dimension.\", \"static void check_cat_sparse_dims(\", \" Tensor const& t,\", \" int64_t pos /* used only for debug messages */,\", \" IntArrayRef sizes,\", \" int64_t wrapped,\", \" int64_t sparse_dim,\", \" int64_t dense_dim) {\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"3d0ff1\"}",{},"391c2f8a-d0c8-4dff-9c74-9da0e1aafb90",{"_103":100,"_151":-5,"_480":1142},{"_488":508,"_219":509,"_510":-5,"_45":1141},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '780,840p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"557ddbc3-aef0-4dce-8dc3-0a94a853886b",{"_103":500,"_151":529,"_480":1149},{"_488":45,"_489":1147},{},[1148],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 493, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '760,780p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \" offset += slice_dim_size;\", \" }\", \" }\", \"}\", \"\", \"Tensor& cat_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 cat(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));\", \"}\", \"\", \"// 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));\", \"}\", \"\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"fa5a58\"}",{},"2cb3dea9-1dbb-48a2-9f47-1e0bea250561",{"_103":100,"_151":-5,"_480":1155},{"_488":508,"_219":509,"_510":-5,"_45":1154},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '760,780p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"842a2f49-6c6b-4278-abbd-439d6f390cdd",{"_103":100,"_151":-5,"_480":1162},{"_488":45,"_489":1160},{"_517":57,"_518":519,"_486":487},[1161],"I'm browsing around line 770 to check the relevant sections in the changelog.",{},"719bd881-4458-4e44-b790-8b0c6a38324b",{"_103":500,"_151":529,"_480":1169},{"_488":45,"_489":1167},{},[1168],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 488, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"Tensor cat\\\" -n aten/src/ATen/nati\", \"ve/TensorShape.cpp | head\", \"770:Tensor cat(TensorList tensors, Dimname dim) {\", \"871:static Tensor cat_sparse_impl(\", \"993:Tensor cat_sparse(const ITensorListRef& tensors, int64_t dim) {\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"796902\"}",{},"47b87283-894f-430a-a202-f990f653e089",{"_103":100,"_151":-5,"_480":1175},{"_488":508,"_219":509,"_510":-5,"_45":1174},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"Tensor cat\\\" -n aten/src/ATen/native/TensorShape.cpp | head\\n\"}\n",{},"75d623da-db4e-49d1-966f-a15ed81b980a",{"_103":100,"_151":-5,"_480":1182},{"_488":45,"_489":1180},{"_517":57,"_518":519,"_486":487},[1181],"I suspect the crash happens with named dimensions in `torch.concat`. Let's investigate `at::cat`.",{},"568c601f-9652-44d4-aa29-a2008eacdcab",{"_103":500,"_151":529,"_480":1189},{"_488":45,"_489":1187},{},[1188],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 486, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"concat\\\" -r test/test_namedtensor.\", \"py\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"c6ee97\"}",{},"6d9ee2db-aaa8-4715-a03b-840aa12a7ff2",{"_103":100,"_151":-5,"_480":1195},{"_488":508,"_219":509,"_510":-5,"_45":1194},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"concat\\\" -r test/test_namedtensor.py\\n\"}\n",{},"84c54676-58ea-4b6a-b51d-21cce76dd5e8",{"_103":100,"_151":-5,"_480":1202},{"_488":45,"_489":1200},{"_517":57,"_518":519,"_486":487},[1201],"I'll search for `torch.cat` combined with 'names', `concat`, or `dim=` to locate relevant arguments.",{},"e6652a79-e10d-4b9e-aead-b8eff3a48ef3",{"_103":500,"_151":529,"_480":1209},{"_488":45,"_489":1207},{},[1208],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 472, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"names=\\\" -n test/test_namedtensor.\", \"py | head\", \"22: x = torch.empty(2, names=(name,))\", \"36: # '3,2' -> size = [3, 2], names=None passed to ctor.\", \"61: return factory(shape, names=names)\", \"78: x = torch.randn(3, 3, names=('N', 'C'))\", \"86: def _test_name_inference(self, op, args=(), expected_names=(), device='cp\", \"u',\", \"113: x = factory(1, 2, 3, names=None, device=device)\", \"116: x = factory(1, 2, 3, names=('N', 'T', 'D'), device=device)\", \"119: x = factory(1, 2, 3, names=('N', None, 'D'), device=device)\", \"122: x = factory(1, 2, 3, names=('_1', 'batch9', 'BATCH_5'), device=devic\", \"e)\", \"127: x = factory(2, names=('1',), device=device)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"df0ab8\"}",{},"11582b4f-6958-4a1a-abae-6ad82b4bc4b0",{"_103":100,"_151":-5,"_480":1215},{"_488":508,"_219":509,"_510":-5,"_45":1214},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"names=\\\" -n test/test_namedtensor.py | head\\n\"}\n",{},"74b7e3e7-ca73-4860-858a-f445c353d4e0",{"_103":100,"_151":-5,"_480":1222},{"_488":45,"_489":1220},{"_517":57,"_518":519,"_486":487},[1221],"I’ll look for `'names='` to confirm if there are any named dimensions.",{},"be3d362e-c827-43e2-ac4b-3b68c17cecd2",{"_103":500,"_151":529,"_480":1229},{"_488":45,"_489":1227},{},[1228],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 470, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"Dimname\\\" -n test/test_namedtensor\", \".py | head\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"9ee311\"}",{},"17328018-bf02-4cf1-8cd0-0fac73c834f7",{"_103":100,"_151":-5,"_480":1235},{"_488":508,"_219":509,"_510":-5,"_45":1234},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"Dimname\\\" -n test/test_namedtensor.py | head\\n\"}\n",{},"44604980-d116-4381-87c3-e1bd43308010",{"_103":100,"_151":-5,"_480":1242},{"_488":45,"_489":1240},{"_517":57,"_518":519,"_486":487},[1241],"This file seems advanced, so I'll focus on how `Dimname` appears in tests.",{},"3ed7516b-c060-45bd-b8b7-52587d2d917c",{"_103":500,"_151":529,"_480":1249},{"_488":45,"_489":1247},{},[1248],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 447, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '1340,1360p' test/test_namedtensor.\", \"py\", \" # error: names don't match\", \" self._test_name_inference(\", \" torch.cat,\", \" [[create('N:2,C:3'), create('C:3,N:2')]],\", \" maybe_raises_regex='do not match')\", \"\", \" # error: different number of dims\", \" self._test_name_inference(\", \" torch.cat,\", \" [[create('N:2,C:3'), create('C:3')]],\", \" maybe_raises_regex='must have same number of dimensions')\", \"\", \" # out=\", \" self._test_name_inference(\", \" out_fn(torch.cat),\", \" [create('0'), [create('N:2,C:3'), create('N:2,C:3')]],\", \" expected_names=['N', 'C'])\", \"\", \" def test_masked_fill(self):\", \" # simple\", \" self._test_name_inference(\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"c70053\"}",{},"d7e57d1f-2840-43a0-830d-2818ffba5c17",{"_103":100,"_151":-5,"_480":1255},{"_488":508,"_219":509,"_510":-5,"_45":1254},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '1340,1360p' test/test_namedtensor.py\\n\"}\n",{},"8f458cbb-6b33-4e6c-ba05-9d6b267a0ecc",{"_103":500,"_151":529,"_480":1262},{"_488":45,"_489":1260},{},[1261],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 423, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '1320,1340p' test/test_namedtensor.\", \"py\", \"\", \" # out=\", \" self._test_name_inference(\", \" out_fn(torch.masked_select),\", \" (create('0'), create('N:2,C:3'), (create('2,3') > 0).rename('N', 'C'\", \")),\", \" expected_names=[None])\", \"\", \" def test_cat(self):\", \" # simple\", \" self._test_name_inference(\", \" torch.cat,\", \" [[create('N:2,C:3'), create('N:2,C:3')]],\", \" expected_names=['N', 'C'])\", \"\", \" # error: zero dim\", \" self._test_name_inference(\", \" torch.cat,\", \" [[create(''), create('')]],\", \" maybe_raises_regex='zero-dim')\", \"\", \" # error: names don't match\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"8b255b\"}",{},"cdaa6c89-4f7e-4352-9ad8-fbcf27f7cc42",{"_103":100,"_151":-5,"_480":1268},{"_488":508,"_219":509,"_510":-5,"_45":1267},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '1320,1340p' test/test_namedtensor.py\\n\"}\n",{},"6a05e757-b0a9-41ec-b311-b3d428a080f3",{"_103":500,"_151":529,"_480":1275},{"_488":45,"_489":1273},{},[1274],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 420, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"cat(\\\" -n test/test_namedtensor.py\", \" | head\", \"1327: def test_cat(self):\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"d84c41\"}",{},"e54d97e8-c833-4c48-90bd-b4b6edf65a69",{"_103":100,"_151":-5,"_480":1281},{"_488":508,"_219":509,"_510":-5,"_45":1280},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"cat(\\\" -n test/test_namedtensor.py | head\\n\"}\n",{},"4328668f-5c4d-4862-b0e9-8004e1f5e759",{"_103":100,"_151":-5,"_480":1288},{"_488":45,"_489":1286},{"_517":57,"_518":519,"_486":487},[1287],"I'll look for any instances of `cat(` in the codebase, possibly near relevant names.",{},"da027af8-50d6-447a-9d67-56e89d50f4ef",{"_103":500,"_151":529,"_480":1295},{"_488":45,"_489":1293},{},[1294],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 418, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"concat(\\\" -n test/test_namedtensor\", \".py | head\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"eb5815\"}",{},"fd049a2e-5f37-4c40-a453-9b6c6c4f3c11",{"_103":100,"_151":-5,"_480":1301},{"_488":508,"_219":509,"_510":-5,"_45":1300},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"concat(\\\" -n test/test_namedtensor.py | head\\n\"}\n",{},"7f1b185c-9012-4521-811a-f2fe27273f95",{"_103":100,"_151":-5,"_480":1308},{"_488":45,"_489":1306},{"_517":57,"_518":519,"_486":487},[1307],"This file is large, so I'll search for occurrences of \"concat\" or \"cat\".",{},"1a72951f-a6ef-4ee0-a4ce-4edb4d51c46e",{"_103":500,"_151":529,"_480":1315},{"_488":45,"_489":1313},{},[1314],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 377, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '1,40p' test/test_namedtensor.py\", \"# Owner(s): [\\\"module: named tensor\\\"]\", \"# ruff: noqa: F841\", \"import unittest\", \"from torch.testing._internal.common_utils import TestCase, run_tests, TEST_NUMPY\", \"from torch.testing._internal.common_utils import skipIfTorchDynamo\", \"from torch.testing._internal.common_cuda import TEST_CUDA\", \"from torch.testing._internal.common_device_type import get_all_device_types\", \"from collections import namedtuple, OrderedDict\", \"import itertools\", \"import functools\", \"import torch\", \"from torch import Tensor\", \"import torch.nn.functional as F\", \"from multiprocessing.reduction import ForkingPickler\", \"import pickle\", \"import io\", \"import sys\", \"import warnings\", \"\", \"\", \"def pass_name_to_python_arg_parser(name):\", \" x = torch.empty(2, names=(name,))\", \"\", \"\", \"def flatten(lst):\", \" return [item for sublist in lst for item in sublist]\", \"\", \"\", \"Function = namedtuple('TestCase', ['name', 'lambd'])\", \"\", \"\", \"def parse_compressed_namedshape(string):\", \" # This is a metalanguage for describing a shape of a tensor compactly.\", \" # 'N:3,C:2' -> size = [3, 2], names: ['N', 'C']\", \" # 'None:3,None:2' -> size = [3, 2], names: ['None', 'None']\", \" # '3,2' -> size = [3, 2], names=None passed to ctor.\", \" def parse_name(maybe_name):\", \" maybe_name = maybe_name.strip()\", \" if maybe_name == 'None':\", \" return None\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"e9e6fe\"}",{},"c7c83970-f69e-4990-a043-d27cb6fe4d31",{"_103":100,"_151":-5,"_480":1321},{"_488":508,"_219":509,"_510":-5,"_45":1320},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '1,40p' test/test_namedtensor.py\\n\"}\n",{},"467a2786-895b-4176-929e-38e8294ca6fd",{"_103":100,"_151":-5,"_480":1328},{"_488":45,"_489":1326},{"_517":57,"_518":519,"_486":487},[1327],"I'll open `test_namedtensor.py` to understand how named dimensions are being tested.",{},"3fbbd1f6-aa3e-4ae6-9be8-d1e7492e73c2",{"_103":500,"_151":529,"_480":1335},{"_488":45,"_489":1333},{},[1334],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 374, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# ls test | grep named\", \"test_namedtensor.py\", \"test_namedtuple_return_api.py\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"84abca\"}",{},"0a073f3d-86de-4faf-85eb-75d08ecdd73e",{"_103":100,"_151":-5,"_480":1341},{"_488":508,"_219":509,"_510":-5,"_45":1340},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"ls test | grep named\\n\"}\n",{},"0f274334-2de5-4af4-b3e8-005ff83dafa2",{"_103":100,"_151":-5,"_480":1348},{"_488":45,"_489":1346},{"_517":57,"_518":519,"_486":487},[1347],"I’ll open the test/ao directory and other related subdirectories to check their contents.",{},"6e4e20ff-1610-4e83-920e-a08035fae0dd",{"_103":500,"_151":529,"_480":1355},{"_488":45,"_489":1353},{},[1354],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 363, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# ls test | head\", \"HowToWriteTestsUsingFileCheck.md\", \"_test_bazel.py\", \"allowlist_for_publicAPI.json\", \"ao\", \"autograd\", \"backends\", \"bench_mps_ops.py\", \"benchmark_utils\", \"bottleneck_test\", \"conftest.py\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"a98211\"}",{},"c868da02-d3be-4b6c-a892-fd996f05aafd",{"_103":100,"_151":-5,"_480":1361},{"_488":508,"_219":509,"_510":-5,"_45":1360},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"ls test | head\\n\"}\n",{},"cc6fa547-eb5a-4650-afc1-7c2c193c3afc",{"_103":100,"_151":-5,"_480":1368},{"_488":45,"_489":1366},{"_517":57,"_518":519,"_486":487},[1367],"I'll list the files in the test directory to see if I missed something.",{},"121a9a59-eee0-463b-a2c2-82154fa12b91",{"_103":500,"_151":529,"_480":1375},{"_488":45,"_489":1373},{},[1374],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 362, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"test_namedtensor\\\" -r test | head\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"df8fb0\"}",{},"92eb0091-c261-44ee-b1fd-a9cca1c71500",{"_103":100,"_151":-5,"_480":1381},{"_488":508,"_219":509,"_510":-5,"_45":1380},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"test_namedtensor\\\" -r test | head\\n\"}\n",{},"99bb7dee-362a-4eff-90a4-080003e4c730",{"_103":500,"_151":529,"_480":1388},{"_488":45,"_489":1386},{},[1387],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 344, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"refine_names\\\" -r test | head\", \"test/test_autograd.py:8696: z_named = z.refine_names(*names)\", \"test/test_autograd.py:8697: z_complex = torch.view_as_complex(z_named.ren\", \"ame(None)).refine_names(\", \"test/test_namedtensor.py:179: output = tensor.refine_names('N', ..., 'C')\", \"test/test_namedtensor.py:182: def test_refine_names(self):\", \"test/test_namedtensor.py:184: self._test_name_inference(Tensor.refine_nam\", \"es,\", \"test/test_namedtensor.py:189: self._test_name_inference(Tensor.refine_nam\", \"es,\", \"test/test_namedtensor.py:194: self._test_name_inference(Tensor.refine_nam\", \"es,\", \"test/test_namedtensor.py:199: self._test_name_inference(Tensor.refine_nam\", \"es,\", \"test/test_namedtensor.py:204: self._test_name_inference(Tensor.refine_nam\", \"es,\", \"test/test_namedtensor.py:209: self._test_name_inference(Tensor.refine_nam\", \"es,\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"f63dc5\"}",{},"6203016e-4c45-480f-ac85-7ccef33a2850",{"_103":100,"_151":-5,"_480":1394},{"_488":508,"_219":509,"_510":-5,"_45":1393},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"refine_names\\\" -r test | head\\n\"}\n",{},"53a98d2e-f463-4d8a-ba6d-6c060b882583",{"_103":100,"_151":-5,"_480":1401},{"_488":45,"_489":1399},{"_517":57,"_518":519,"_486":487},[1400],"I'm checking if there are any tests specifically for named tensors, like `namedtensor`.",{},"c8516842-148e-4996-8953-bbac0e566bf8",{"_103":500,"_151":529,"_480":1408},{"_488":45,"_489":1406},{},[1407],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 325, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"named\\\" -r test | head\", \"test/test_mps.py:11679: param_names, params = zip(*rnn.named_paramete\", \"rs())\", \"test/profiler/test_memory_profiler.py:188: named_parameters = dict(model.\", \"named_parameters())\", \"test/profiler/test_memory_profiler.py:189: self.assertEqual(len(named_par\", \"ameters), 3)\", \"test/profiler/test_memory_profiler.py:192: gradients = tuple(i.grad f\", \"or i in named_parameters.values())\", \"test/profiler/test_memory_profiler.py:201: for name, p in named_param\", \"eters.items():\", \"test/profiler/test_memory_profiler.py:219: for name, p in named_param\", \"eters.items():\", \"test/test_autograd.py:8693: def test_named_tensor_for_complex_views(self):\", \"test/test_autograd.py:8696: z_named = z.refine_names(*names)\", \"test/test_autograd.py:8697: z_complex = torch.view_as_complex(z_named.ren\", \"ame(None)).refine_names(\", \"test/test_autograd.py:11045: # Check unpacked dual is returned as a n\", \"amed tuple\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"aaae39\"}",{},"7dbdf685-a82e-46d4-9fd4-69064ed5c601",{"_103":100,"_151":-5,"_480":1414},{"_488":508,"_219":509,"_510":-5,"_45":1413},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"named\\\" -r test | head\\n\"}\n",{},"f2de36ab-d2e8-4444-8201-55fb02ae0513",{"_103":100,"_151":-5,"_480":1421},{"_488":45,"_489":1419},{"_517":57,"_518":519,"_486":487},[1420],"I'll look for tests mentioning named dims in `test/test_namedtensor.py` and others.",{},"bb13113c-5c96-4aa6-b47a-2fa27d85408c",{"_103":500,"_151":529,"_480":1428},{"_488":45,"_489":1426},{},[1427],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 318, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"N,\\\" -r test/test_tensor_creation_\", \"ops.py | head\", \"151: for x, n, inc in product(X, N, increasing):\", \"1000: # Test dimension change for 1D tensor of size (N), 2D tensor of\", \" size (1, N), and 3D tensor of size (1, N, 1)\", \"1011: # Test dimension change for 2D tensor of size (M, N) and 3D ten\", \"sor of size (M, N, 1)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"6c75e6\"}",{},"ff235218-cd84-4db5-9dd9-6d08a13408ac",{"_103":100,"_151":-5,"_480":1434},{"_488":508,"_219":509,"_510":-5,"_45":1433},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"N,\\\" -r test/test_tensor_creation_ops.py | head\\n\"}\n",{},"f749c235-9bd3-4f44-a681-d0e62d6b37b3",{"_103":100,"_151":-5,"_480":1441},{"_488":45,"_489":1439},{"_517":57,"_518":519,"_486":487},[1440],"I'll look for any occurrences of \"dimname,\" \"N,\" or \"named dimension.\"",{},"2359c0f8-d7b5-4b5f-90f8-739d9a69b5e4",{"_103":500,"_151":529,"_480":1448},{"_488":45,"_489":1446},{},[1447],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 302, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"concat(\\\" -r test | head\", \"test/test_mps.py:3560: return torch.concat((x1, x2), dim=dim)\", \"test/jit/test_symbolic_shape_analysis.py:448: def test_shape_concat(self):\", \"test/jit/test_symbolic_shape_analysis.py:450: sample_inputs = sample_inpu\", \"ts_cat_concat(None, \\\"cpu\\\", torch.float, False)\", \"test/jit/test_symbolic_shape_analysis.py:697: def test_stitching_concat(self)\", \":\", \"test/jit/test_freezing.py:2543: def test_linear_concat(self):\", \"test/onnx/test_utility_funs.py:457: def test_constant_fold_concat(self):\", \"test/onnx/test_pytorch_onnx_onnxruntime.py:6046: def test_concat(self):\", \"test/onnx/exporter/test_building.py:115: def test_process_python_sequence_cre\", \"ates_extra_concat(self):\", \"test/onnx/exporter/test_building.py:134: def test_process_python_sequence_mix\", \"_symbolic_constant_creates_extra_concat(self):\", \"test/onnx/exporter/test_building.py:150: def test_process_python_sequence_mix\", \"_constant_symbolic_creates_extra_concat(self):\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"467098\"}",{},"4c2f5a5d-4ac6-45b1-98e6-422f6c057560",{"_103":100,"_151":-5,"_480":1454},{"_488":508,"_219":509,"_510":-5,"_45":1453},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"concat(\\\" -r test | head\\n\"}\n",{},"b4d5f04f-2d50-4f58-ace0-13dd484e968e",{"_103":100,"_151":-5,"_480":1461},{"_488":45,"_489":1459},{"_517":57,"_518":519,"_486":487},[1460],"I’m searching for the usage of `concat()` with a named dimension.",{},"13b730af-4cba-4a1f-aa60-bfbe0fc3b713",{"_103":500,"_151":529,"_480":1468},{"_488":45,"_489":1466},{},[1467],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 290, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"Dimname\\\" -r test | head\", \"test/export/testing.py:34: aten.sum.dim_DimnameList,\", \"test/export/testing.py:36: aten.prod.dim_Dimname,\", \"test/export/testing.py:61: aten.size.Dimname,\", \"test/export/testing.py:63: aten.stride.Dimname,\", \"test/export/testing.py:129: aten.index_fill.Dimname_Scalar,\", \"test/export/testing.py:130: aten.index_fill.Dimname_Tensor,\", \"test/cpp/api/modules.cpp:610: std::vector dimnames;\", \"test/cpp/api/modules.cpp:615: torch::Dimname::fromSymbol(torch::Symbol:\", \":dimname(name)));\", \"test/mobile/lightweight_dispatch/test_codegen_unboxing.cpp:11://'Dimname',\", \"test/mobile/lightweight_dispatch/test_codegen_unboxing.cpp:12://'Dimname[1]',\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"0e0f4e\"}",{},"0cb10e80-c028-42c4-903a-ca54171f3274",{"_103":100,"_151":-5,"_480":1474},{"_488":508,"_219":509,"_510":-5,"_45":1473},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"grep -n \\\"Dimname\\\" -r test | head\\n\"}\n",{},"539077f4-ddb7-41e5-9788-b71be01cfe06",{"_103":100,"_151":-5,"_480":1481},{"_488":45,"_489":1479},{"_517":57,"_518":519,"_486":487},[1480],"I need to check if dimension names are used correctly in existing tests. Let's investigate.",{},"9b43c50b-c79b-46cd-adc7-83d7d2585544",{"_103":500,"_151":529,"_480":1488},{"_488":45,"_489":1486},{},[1487],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 207, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# sed -n '740,820p' aten/src/ATen/native/Ten\", \"sorShape.cpp\", \" offset += slice_dim_size;\", \" }\", \" } else {\", \" for (const Tensor& tensor : materialized) {\", \" if (cat_should_skip_tensor(tensor)) {\", \" continue;\", \" }\", \" auto slice_dim_size = tensor.sizes()[dim];\", \" auto result_slice = result.narrow(dim, offset, slice_dim_size);\", \"\", \" auto iter = TensorIteratorConfig()\", \" .set_check_mem_overlap(false) // Already checked above\", \" .resize_outputs(false)\", \" .add_output(result_slice)\", \" .add_const_input(tensor)\", \" .promote_inputs_to_common_dtype(true)\", \" .cast_common_dtype_to_outputs(true)\", \" .enforce_safe_casting_to_output(true)\", \" .build();\", \" copy_stub(iter.device_type(), iter, false);\", \" offset += slice_dim_size;\", \" }\", \" }\", \"}\", \"\", \"Tensor& cat_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 cat(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));\", \"}\", \"\", \"// 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));\", \"}\", \"\", \"Tensor& concat_out(TensorList tensors, int64_t dim, Tensor& result) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat_out(result, tensors, dim);\", \"}\", \"\", \"Tensor concat(TensorList tensors, int64_t dim) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat(tensors, 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));\", \"}\", \"\", \"Tensor& concatenate_out(TensorList tensors, int64_t dim, Tensor& result) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat_out(result, tensors, dim);\", \"}\", \"\", \"Tensor concatenate(TensorList tensors, int64_t dim) {\", \" TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat(tensors, dim);\", \"}\", \"\", \"static bool sizes_match_except(\", \" IntArrayRef s1,\", \" IntArrayRef s2,\", \" int64_t dim_except /* should already be wrapped */) {\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"a2a647\"}",{},"78c719c8-f083-434b-bffc-af8bd0222e3f",{"_103":100,"_151":-5,"_480":1494},{"_488":508,"_219":509,"_510":-5,"_45":1493},{"_486":507},"{\"session_name\":\"shell2\",\"chars\":\"sed -n '740,820p' aten/src/ATen/native/TensorShape.cpp\\n\"}\n",{},"4a8906e6-de12-485e-b0e0-c5125ebdf6f5",{"_103":100,"_151":-5,"_480":1501},{"_488":45,"_489":1499},{"_517":57,"_518":519,"_486":487},[1500],"It looks like the segmentation fault happens when calling `torch.concat` with named dimension and empty list. To fix, I'll verify the list isn't empty before calling `dimname_to_position`. The crash occurs because `tensors[0]` is used to check names, which isn't safe for an empty list. I should replicate the issue by calling `torch.concat([], dim=\"N\")` or `torch.concat([], dim=...)`. Let me see if `TORCH_CHECK(!tensors.empty())` is in place.",{},"f19148bf-40e8-4d58-9605-8d16c5881848",{"_103":500,"_151":529,"_480":1508},{"_488":45,"_489":1506},{},[1507],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 201, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306/comments | jq -r '.[3].body'\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 5994 100 5994 0 0 67325 0 --:--:-- --:--:-- --:--:-- 68113\", \"null\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"1f25ff\"}",{},"a59f8e70-c6de-44c9-9235-69a7b014451b",{"_103":100,"_151":-5,"_480":1514},{"_488":508,"_219":509,"_510":-5,"_45":1513},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306/comments | jq -r '.[3].body'\\n\", \"yield_time_ms\":1000}\n",{},"57454a90-5b43-461f-8289-4a1649eaa18c",{"_103":500,"_151":529,"_480":1521},{"_488":45,"_489":1519},{},[1520],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 195, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306/comments | jq -r '.[2].body'\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 5994 100 5994 0 0 40192 0 --:--:-- --:--:-- --:--:-- 40500\", \"null\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"b39f4d\"}",{},"c3f469b6-4440-41d3-90af-60271d718233",{"_103":100,"_151":-5,"_480":1527},{"_488":508,"_219":509,"_510":-5,"_45":1526},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306/comments | jq -r '.[2].body'\\n\", \"yield_time_ms\":1000}\n",{},"ac320526-f981-4718-9e1c-940d7ca084ef",{"_103":500,"_151":529,"_480":1534},{"_488":45,"_489":1532},{},[1533],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 151, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306/comments | jq -r '.[1].body'\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 5994 100 5994 0 0 61864 0 --:--:-- --:--:-- --:--:-- 62437\", \"Symbolicated parts of backtrace\", \"```\", \"Process 88909 stopped\", \"* thread #2, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code\", \"=1, address=0x0)\", \" frame #0: 0x0000000110114940 libtorch_cpu.dylib`c10::intrusive_ptr::operator->(this=0x0000000000000000) const at\", \"intrusive_ptr.h:423:12\", \" 420 }\", \" 421\", \" 422 TTarget* operator->() const noexcept {\", \"-> 423 return target_;\", \" 424 }\", \" 425\", \" 426 operator bool() const noexcept {\", \"Target 0: (Python) stopped.\", \"(lldb) up\", \"frame #1: 0x00000001101146dc libtorch_cpu.dylib`at::TensorBase::has_names(this=0\", \"x0000000000000000) const at TensorBase.h:559:10\", \" 556 bool has_names() const {\", \" 557 // If a user is using unnamed tensors, then we can short-circuit rig\", \"ht 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=0\", \"x0000000000000000, 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\", \"```\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"2533b9\"}",{},"51b51066-ad48-4a4d-929b-e0c7c5073c65",{"_103":100,"_151":-5,"_480":1540},{"_488":508,"_219":509,"_510":-5,"_45":1539},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306/comments | jq -r '.[1].body'\\n\", \"yield_time_ms\":1000}\n",{},"dfa4afc1-1127-4bb2-9b52-dd5a56606b4f",{"_103":500,"_151":529,"_480":1547},{"_488":45,"_489":1545},{},[1546],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 132, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306/comments | jq -r '.[0].body'\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 5994 100 5994 0 0 29030 0 --:--:-- --:--:-- --:--:-- 29097\", \"Backtrace, unsymbolicated\", \"```\", \"(lldb) bt\", \"* thread #2, queue = 'com.apple.main-thread', stop reason = EXC_BAD_ACCESS (code\", \"=1, address=0x0)\", \" * frame #0: 0x000000014007f588 libtorch_cpu.dylib`at::dimname_to_position(at::\", \"Tensor const&, at::Dimname) + 40\", \" frame #1: 0x00000001407b87c0 libtorch_cpu.dylib`at::native::concat(c10::Arra\", \"yRef, at::Dimname) + 36\", \" frame #2: 0x00000001410f0c24 libtorch_cpu.dylib`at::_ops::concat_names::call\", \"(c10::ArrayRef, at::Dimname) + 468\", \" frame #3: 0x00000001028cef24 libtorch_python.dylib`torch::autograd::THPVaria\", \"ble_concat(_object*, _object*, _object*) + 944\", \"```\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": 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1\", \"Vendor ID: GenuineIntel\", \"CPU family: 6\", \"Model: 85\", \"Model name: Intel(R) Xeon(R) Gold 5218R CPU @ 2.10GHz\", \"Stepping: 7\", \"CPU MHz: 2095.076\", \"BogoMIPS: 4190.15\", \"Virtualization: VT-x\", \"Hypervisor vendor: KVM\", \"Virtualization type: full\", \"L1d cache: 1 MiB\", \"L1i cache: 1 MiB\", \"L2 cache: 128 MiB\", \"L3 cache: 16 MiB\", \"NUMA node0 CPU(s): 0-31\", \"Vulnerability Itlb multihit: Not affected\", \"Vulnerability L1tf: Not affected\", \"Vulnerability Mds: Not affected\", \"Vulnerability Meltdown: Not affected\", \"Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no micro\", \"code; SMT Host state unknown\", \"Vulnerability Retbleed: Mitigation; Enhanced IBRS\", \"Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled v\", \"ia prctl and seccomp\", \"Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointe\", \"r sanitization\", \"Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filli\", \"ng, PBRSB-eIBRS SW sequence\", \"Vulnerability Srbds: Not affected\", \"Vulnerability Tsx async abort: Mitigation; TSX disabled\", \"Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 c\", \"lflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_pe\", \"rfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma c\", \"x16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx\", \" f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssb\", \"d ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsg\", \"sbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx avx512f avx512dq rdseed ad\", \"x smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves\", \" arat umip pku ospke avx512_vnni md_clear arch_capabilities\", \"\", \"Versions of relevant libraries:\", \"[pip3] numpy==2.2.5\", \"[pip3] optree==0.15.0\", \"[pip3] torch==2.8.0a0+gitcbcf677\", \"[conda] numpy 2.2.5 pypi_0 pypi\", \"[conda] optree 0.15.0 pypi_0 pypi\", \"[conda] torch 2.8.0a0+gitcbcf677 dev_0\", \"\", \"cc @malfet @zou3519 @albanD\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"b9691f\"}",{},"1e86096f-28fa-40fa-8d68-9c044756be42",{"_103":100,"_151":-5,"_480":1566},{"_488":508,"_219":509,"_510":-5,"_45":1565},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306 | jq -r '.body' | sed -n '60,120p'\\n\"}\n",{},"1c898349-dec6-4b5c-bd08-4a27785992bb",{"_103":500,"_151":529,"_480":1573},{"_488":45,"_489":1571},{},[1572],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 26, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306 | jq -r '.body' | sed -n '21,60p'\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 9321 100 9321 0 0 77709 0 --:--:-- --:--:-- --:--:-- 78327\", \"output\", \"```\", \"2.8.0a0+gitcbcf677\", \"0\", \"Segmentation fault (core dumped)\", \"```\", \"\", \"### Versions\", \"\", \"Versions\", \"Collecting environment information...\", \"PyTorch version: 2.8.0a0+gitcbcf677\", \"Is debug build: False\", \"CUDA used to build PyTorch: None\", \"ROCM used to build PyTorch: N/A\", \"\", \"OS: Ubuntu 20.04.6 LTS (x86_64)\", \"GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\", \"Clang version: Could not collect\", \"CMake version: version 3.28.3\", \"Libc version: glibc-2.31\", \"\", \"Python version: 3.11.11 (main, Dec 11 2024, 16:28:39) [GCC 11.2.0] (64-bit runti\", \"me)\", \"Python platform: Linux-5.4.0-144-generic-x86_64-with-glibc2.31\", \"Is CUDA available: False\", \"CUDA runtime version: No CUDA\", \"CUDA_MODULE_LOADING set to: N/A\", \"GPU models and configuration: No CUDA\", \"Nvidia driver version: No CUDA\", \"cuDNN version: No CUDA\", \"HIP runtime version: N/A\", \"MIOpen runtime version: N/A\", \"Is XNNPACK available: True\", \"\", \"CPU:\", \"Architecture: x86_64\", \"CPU op-mode(s): 32-bit, 64-bit\", \"Byte Order: Little Endian\", \"Address sizes: 46 bits physical, 48 bits virtual\", \"CPU(s): 32\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"425f43\"}",{},"be4fc818-ca65-4fe5-8664-809abca3a9ad",{"_103":100,"_151":-5,"_480":1579},{"_488":508,"_219":509,"_510":-5,"_45":1578},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306 | jq -r '.body' | sed -n '21,60p'\\n\"}\n",{},"e563d5dd-c021-4452-a9cc-1f4f7e7563ce",{"_103":500,"_151":529,"_480":1586},{"_488":45,"_489":1584},{},[1585],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 1, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306 | jq -r '.body' | head -n 20\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 9321 100 9321 0 0 44852 0 --:--:-- --:--:-- --:--:-- 45028\", \"### 🐛 Describe the bug\", \"\", \"reproduce\", \"```python\", \"import torch\", \"\", \"print(torch.__version__)\", \"\", \"mylist = [[], '_C\\\\x00\\\\x00q;D']\", \"mydict = {}\", \"\", \"for i in range(10):\", \" print(f\\\"{i}\\\")\", \" try:\", \" torch.concat(*mylist,**mydict)\", \" except Exception as e:\", \" print(f\\\"{e}\\\")\", \"print(\\\"done\\\")\", \"```\", \"\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"366d71\"}",{},"36d358c5-117c-4e7a-b93f-9ccb6b60b83c",{"_103":100,"_151":-5,"_480":1592},{"_488":508,"_219":509,"_510":-5,"_45":1591},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306 | jq -r '.body' | head -n 20\\n\", \"yield_time_ms\":1000}\n",{},"eabde2e6-a2ea-4697-94a3-679a1f1d2c9e",{"_103":500,"_151":529,"_480":1599},{"_488":45,"_489":1597},{},[1598],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 0, \"output\": [\"root@fce6d293fd25:/workspace# cd pytorch\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"d4b4ce\"}",{},"cc5a8a9a-0252-47e6-aa5d-d616554042e0",{"_103":100,"_151":-5,"_480":1605},{"_488":508,"_219":509,"_510":-5,"_45":1604},{"_486":535},"{\"session_name\":\"shell2\",\"chars\":\"cd pytorch\\n\"}\n",{},"a631749a-ff27-4125-9db0-1ca2005457bf",{"_103":500,"_151":1612,"_480":1613},{"_488":45,"_489":1610},{},[1611],"{\"type\": \"terminal_output\", \"session_name\": \"shell2\", \"start_line_no\": 0, \"output\": [], \"num_elided_lines\": 0, \"chunk_id\": \"e1cf8e\"}","container.new_session",{},"1a461060-4df3-4549-aab7-e55e95100c59",{"_103":100,"_151":-5,"_480":1619},{"_488":508,"_219":509,"_510":-5,"_45":1618},{"_486":535},"{\"session_name\":\"shell2\"}",{},"884455ee-c162-4248-8ca0-6f9130c57ef4",{"_103":500,"_151":529,"_480":1626},{"_488":45,"_489":1624},{"_110":57},[1625],"Error: Output for session 'shell' contained a line exceeding the max of 1600 bytes (observed at least 3421 bytes).\n\nThe byte sequence which exceeded the limit started with: b'\\x1b[0;32m\"### \\xf0\\x9f\\x90\\x9b Des'\n\nThe exec session has been deleted. Please start a new session.\n\nTip - rerun the command and extract only what you need, e.g.:\n * grep -nE 'PATTERN' FILE | cut -c1-200\n * grep -o 'PATTERN' FILE\n * jq -r '.json.path' FILE\n * grep -a PATTERN FILE\n",{},"71e131bb-5626-47eb-a564-be943f6eb569",{"_103":500,"_151":529,"_480":1633},{"_488":45,"_489":1631},{},[1632],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 198, \"output\": [], \"num_elided_lines\": 0, \"chunk_id\": \"23292e\"}",{},"d98c59c7-aaab-4909-bc2c-e7f4feb95d4b",{"_103":100,"_151":-5,"_480":1639},{"_488":508,"_219":509,"_510":-5,"_45":1638},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"\",\"yield_time_ms\":1000}\n",{},"06b4df2f-a527-4087-84df-4c003b557769",{"_103":500,"_151":529,"_480":1646},{"_488":45,"_489":1644},{},[1645],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 193, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.com/repos/pytor\", \"ch/pytorch/issues/155306 | jq '.body'\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 9321 0 9321 0 0 54679 0 --:--:-- --:--:-- --:--:-- 54508\", \"\"], \"num_elided_lines\": 0, \"chunk_id\": \"53f894\"}",{},"5f782723-e9ec-48b9-9829-86e924e39539",{"_103":100,"_151":-5,"_480":1652},{"_488":508,"_219":509,"_510":-5,"_45":1651},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306 | jq '.body'\\n\", \"yield_time_ms\":1000}\n",{},"e977df56-e0ac-4424-a327-b2c35f13e2ff",{"_103":100,"_151":-5,"_480":1659},{"_488":45,"_489":1657},{"_517":57,"_518":519,"_486":487},[1658],"I'll check out the rest to get the full context of the issue.",{},"bf345853-d85e-4d59-9adc-ba3ef30536ec",{"_103":500,"_151":529,"_480":1666},{"_488":45,"_489":1664},{},[1665],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 192, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# ^C\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"724dac\"}",{},"47e62d3d-56c7-4f74-832f-2589420c46c6",{"_103":100,"_151":-5,"_480":1672},{"_488":508,"_219":509,"_510":-5,"_45":1671},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"\\u0003\", \"yield_time_ms\":100}\n",{},"75d2cddc-6410-482f-a0f1-07a2e657662a",{"_103":500,"_151":529,"_480":1679},{"_488":45,"_489":1677},{},[1678],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 140, \"output\": [\"404: Not Foundroot@fce6d293fd25:/workspace/pytorch# curl -L https://api.github.c\", \"om/repos/pytorch/pytorch/issues/155306 | head -n 40\", \" % Total % Received % Xferd Average Speed Time Time Time Current\", \" Dload Upload Total Spent Left Speed\", \"100 9321 0 9321 0 0 49337 0 --:--:-- --:--:-- --:--:-- 49579\", \"{\", \" \\\"url\\\": \\\"https://api.github.com/repos/pytorch/pytorch/issues/155306\\\",\", \" \\\"repository_url\\\": \\\"https://api.github.com/repos/pytorch/pytorch\\\",\", \" \\\"labels_url\\\": \\\"https://api.github.com/repos/pytorch/pytorch/issues/155306/labe\", \"ls{/name}\\\",\", \" \\\"comments_url\\\": \\\"https://api.github.com/repos/pytorch/pytorch/issues/155306/co\", \"mments\\\",\", \" \\\"events_url\\\": \\\"https://api.github.com/repos/pytorch/pytorch/issues/155306/even\", \"ts\\\",\", \" \\\"html_url\\\": \\\"https://github.com/pytorch/pytorch/issues/155306\\\",\", \" \\\"id\\\": 3123491238,\", \" \\\"node_id\\\": \\\"I_kwDOA-j9z866LLGm\\\",\", \" \\\"number\\\": 155306,\", \" \\\"title\\\": \\\"Segmentation fault (core dumped) in torch.concat\\\",\", \" \\\"user\\\": {\", \" \\\"login\\\": \\\"cx104906\\\",\", \" \\\"id\\\": 104093457,\", \" \\\"node_id\\\": \\\"U_kgDOBjRXEQ\\\",\", \" \\\"avatar_url\\\": \\\"https://avatars.githubusercontent.com/u/104093457?v=4\\\",\", \" \\\"gravatar_id\\\": \\\"\\\",\", \" \\\"url\\\": 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\\\"user_view_type\\\": \\\"public\\\",\", \" \\\"site_admin\\\": false\", \" },\", \" \\\"labels\\\": [\", \" {\", \" \\\"id\\\": 679955625,\", \" \\\"node_id\\\": \\\"MDU6TGFiZWw2Nzk5NTU2MjU=\\\",\", \" \\\"url\\\": \\\"https://api.github.com/repos/pytorch/pytorch/labels/module:%20cras\", \"h\\\",\", \" \\\"name\\\": \\\"module: crash\\\",\", \" \\\"color\\\": \\\"f7e101\\\",\", \" \\\"default\\\": false,\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"c4966e\"}",{},"5e1d7458-63bf-41ae-9113-dd662500a47a",{"_103":100,"_151":-5,"_480":1685},{"_488":508,"_219":509,"_510":-5,"_45":1684},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"curl -L https://api.github.com/repos/pytorch/pytorch/issues/155306 | head -n 40\\n\", \"yield_time_ms\":1000}\n",{},"966b1ca3-f718-40cd-bb7f-995e87620edc",{"_103":500,"_151":529,"_480":1692},{"_488":45,"_489":1690},{},[1691],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 138, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# curl -s https://raw.githubusercontent.com/\", \"pytorch/pytorch/issues/155306 | head\", \"404: Not Foundroot@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"e2ae10\"}",{},"019208bd-d7a0-47c6-b9e9-327102570610",{"_103":100,"_151":-5,"_480":1698},{"_488":508,"_219":509,"_510":-5,"_45":1697},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"curl -s https://raw.githubusercontent.com/pytorch/pytorch/issues/155306 | head\\n\", \"yield_time_ms\":1000}\n",{},"f7ba36b9-3699-4d47-a7ce-a34adcef2073",{"_103":100,"_151":-5,"_480":1705},{"_488":45,"_489":1703},{"_517":57,"_518":519,"_486":487},[1704],"There are no local references, so I’ll open the issue to investigate.",{},"a556df94-df19-460c-8b6b-f0cd3c30cb18",{"_103":500,"_151":529,"_480":1712},{"_488":45,"_489":1710},{},[1711],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 138, \"output\": [\"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"f473ab\"}",{},"14d055ef-b84f-49a8-aadf-a95765e4ca91",{"_103":100,"_151":-5,"_480":1717},{"_488":508,"_219":509,"_510":-5,"_45":1638},{"_486":535},{},"3a6cbc32-b9fd-4838-aa7b-a6e3368c0b41",{"_103":500,"_151":529,"_480":1724},{"_488":45,"_489":1722},{},[1723],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 137, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# grep -n \\\"155306\\\" -r\", \"\"], \"num_elided_lines\": 0, \"chunk_id\": \"15845b\"}",{},"9e9940d5-6072-46b8-8b4d-092359f9af71",{"_103":100,"_151":-5,"_480":1730},{"_488":508,"_219":509,"_510":-5,"_45":1729},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"grep -n \\\"155306\\\" -r\\n\", \"yield_time_ms\":1000}\n",{},"b53fbe4c-2107-47ef-946d-66bda131e15e",{"_103":100,"_151":-5,"_480":1737},{"_488":45,"_489":1735},{"_517":57,"_518":519,"_486":487},[1736],"Looks like the user's comment is confused regarding the crash happening with named dimensions.",{},"24a1b12a-1d63-4800-9116-e43e564b5dc2",{"_103":500,"_151":529,"_480":1744},{"_488":45,"_489":1742},{},[1743],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 136, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# find . -name AGENTS.md | head\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"87cedc\"}",{},"8a914464-185c-4463-81fb-09026c5bc277",{"_103":100,"_151":-5,"_480":1750},{"_488":508,"_219":509,"_510":-5,"_45":1749},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"find . -name AGENTS.md | head\\n\", \"yield_time_ms\":1000}\n",{},"5cfc577f-f8cb-430c-9ea4-d622ee7b830a",{"_103":100,"_151":-5,"_480":1757},{"_488":45,"_489":1755},{"_517":57,"_518":519,"_486":487},[1756],"I couldn't find AGENTS.md earlier, but I'll check deeper in case it's located in subdirectories.",{},"21a7ac52-0ff4-4a6a-b1e4-7142a606abe6",{"_103":500,"_151":529,"_480":1764},{"_488":45,"_489":1762},{},[1763],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 134, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# find . -path ./.git -prune -o -name AGENTS\", \".md -print | head -n 20\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"cc635b\"}",{},"954a578f-c778-434b-b82d-d493b978f380",{"_103":100,"_151":-5,"_480":1770},{"_488":508,"_219":509,"_510":-5,"_45":1769},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"find . -path ./.git -prune -o -name AGENTS.md -print | head -n 20\\n\"}",{},"18b38d61-434e-401b-ae99-f5f8bdd996c1",{"_103":100,"_151":-5,"_480":1777},{"_488":45,"_489":1775},{"_517":57,"_518":519,"_486":487},[1776],"I'll check for AGENTS.md to verify any commit instructions.",{},"43d1dd5f-379d-4e4e-bf04-8c85a43ae336",{"_103":500,"_151":529,"_480":1784},{"_488":45,"_489":1782},{},[1783],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 116, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git show --stat --patch | sed -n '60,120p'\", \"index 49923222d33..73cd97f769f 100644\", \"--- a/test/test_tensor_creation_ops.py\", \"+++ b/test/test_tensor_creation_ops.py\", \"@@ -533,6 +533,13 @@ class TestTensorCreation(TestCase):\", \" res1 = torch.cat([empty, empty], dim=1)\", \" self.assertEqual(res1, empty)\", \"\", \"+ def test_concat_empty_list_error(self, device):\", \"+ msg = \\\"expected a non-empty list of Tensors\\\"\", \"+ with self.assertRaisesRegex(RuntimeError, msg):\", \"+ torch.concat([])\", \"+ with self.assertRaisesRegex(RuntimeError, msg):\", \"+ torch.concatenate([])\", \"+\", \" def test_cat_out(self, device):\", \" x = torch.zeros((0), device=device)\", \" y = torch.randn((4, 6), device=device)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"d494fb\"}",{},"793adea2-7fbf-45e9-bda0-12234ee91b57",{"_103":100,"_151":-5,"_480":1790},{"_488":508,"_219":509,"_510":-5,"_45":1789},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"git show --stat --patch | sed -n '60,120p'\\n\"}\n",{},"f7417a7d-caf3-44b9-a11c-cfe21cdcc383",{"_103":500,"_151":529,"_480":1797},{"_488":45,"_489":1795},{},[1796],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 74, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git show --stat --patch | sed -n '20,60p'\", \" 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));\", \" }\", \"\", \" Tensor& concat_out(TensorList tensors, int64_t dim, Tensor& result) {\", \"+ TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat_out(result, tensors, dim);\", \" }\", \"\", \" Tensor concat(TensorList tensors, int64_t dim) {\", \"+ TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat(tensors, 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));\", \" }\", \"\", \" Tensor& concatenate_out(TensorList tensors, int64_t dim, Tensor& result) {\", \"+ TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat_out(result, tensors, dim);\", \" }\", \"\", \" Tensor concatenate(TensorList tensors, int64_t dim) {\", \"+ TORCH_CHECK(!tensors.empty(), \\\"expected a non-empty list of Tensors\\\");\", \" return at::cat(tensors, dim);\", \" }\", \"\", \"diff --git a/test/test_tensor_creation_ops.py b/test/test_tensor_creation_ops.py\", \"index 49923222d33..73cd97f769f 100644\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"944baf\"}",{},"2844e2f7-dd3f-4a09-8532-dd2942b8dd26",{"_103":100,"_151":-5,"_480":1803},{"_488":508,"_219":509,"_510":-5,"_45":1802},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"git show --stat --patch | sed -n '20,60p'\\n\"}\n",{},"272049c0-8eff-4a22-9274-1948aa75537b",{"_103":500,"_151":529,"_480":1810},{"_488":45,"_489":1808},{},[1809],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 52, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git show --stat --patch | head -n 20\", \"commit 7bd69568831a74e2a97f03f9c1146be6d48e23da\", \"Author: Codex \", \"Date: Sat Jun 7 01:10:49 2025 +0000\", \"\", \" Applying previous commit.\", \"---\", \" aten/src/ATen/native/TensorShape.cpp | 8 ++++++++\", \" test/test_tensor_creation_ops.py | 7 +++++++\", \" 2 files changed, 15 insertions(+)\", \"\", \"diff --git a/aten/src/ATen/native/TensorShape.cpp b/aten/src/ATen/native/TensorS\", \"hape.cpp\", \"index c924a979aa3..84f5713f425 100644\", \"--- a/aten/src/ATen/native/TensorShape.cpp\", \"+++ b/aten/src/ATen/native/TensorShape.cpp\", \"@@ -774,35 +774,43 @@ 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));\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"aef464\"}",{},"e8c7c5e0-bacb-4928-8854-2d21d8b6e304",{"_103":100,"_151":-5,"_480":1816},{"_488":508,"_219":509,"_510":-5,"_45":1815},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"git show --stat --patch | head -n 20\\n\"}\n",{},"5cf9d7cd-9181-4d35-b91d-1b068ce6bca1",{"_103":100,"_151":-5,"_480":1823},{"_488":45,"_489":1821},{"_517":57,"_518":519,"_486":487},[1822],"I'll display the diff to review the changes in the code.",{},"ee335434-0b07-44e2-986e-fe78996f4e10",{"_103":500,"_151":529,"_480":1830},{"_488":45,"_489":1828},{},[1829],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 42, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git show --stat\", \"commit 7bd69568831a74e2a97f03f9c1146be6d48e23da (HEAD -> work)\", \"Author: Codex \", \"Date: Sat Jun 7 01:10:49 2025 +0000\", \"\", \" Applying previous commit.\", \"\", \" aten/src/ATen/native/TensorShape.cpp | 8 ++++++++\", \" test/test_tensor_creation_ops.py | 7 +++++++\", \" 2 files changed, 15 insertions(+)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"491ae5\"}",{},"a7871c19-cfa6-4246-8ccf-91979f8e216a",{"_103":100,"_151":-5,"_480":1836},{"_488":508,"_219":509,"_510":-5,"_45":1835},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"git show --stat\\n\"}\n",{},"9a675fec-c008-48d6-9de4-4fca91f6f4e7",{"_103":100,"_151":-5,"_480":1843},{"_488":45,"_489":1841},{"_517":57,"_518":519,"_486":487},[1842],"Time to pull up the diff for the commit and examine the changes.",{},"0efb27f1-b4f6-4dd8-b740-2efb93e9ebb5",{"_103":500,"_151":529,"_480":1850},{"_488":45,"_489":1848},{},[1849],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 32, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git log -1 --stat\", \"commit 7bd69568831a74e2a97f03f9c1146be6d48e23da (HEAD -> work)\", \"Author: Codex \", \"Date: Sat Jun 7 01:10:49 2025 +0000\", \"\", \" Applying previous commit.\", \"\", \" aten/src/ATen/native/TensorShape.cpp | 8 ++++++++\", \" test/test_tensor_creation_ops.py | 7 +++++++\", \" 2 files changed, 15 insertions(+)\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"2789e1\"}",{},"2756a189-4d6b-4627-99eb-3ac5ef5d456b",{"_103":100,"_151":-5,"_480":1856},{"_488":508,"_219":509,"_510":-5,"_45":1855},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"git log -1 --stat\\n\"}\n",{},"49290352-e147-4272-a9f4-5864c30b709e",{"_103":500,"_151":529,"_480":1863},{"_488":45,"_489":1861},{},[1862],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 31, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# git status --short\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"ab0ec6\"}",{},"856be31f-8d0e-45a1-ab61-abf46f1260ce",{"_103":100,"_151":-5,"_480":1869},{"_488":508,"_219":509,"_510":-5,"_45":1868},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"git status --short\\n\"}\n",{},"858d939a-ae17-4b86-a786-740610597fcf",{"_103":100,"_151":-5,"_480":1876},{"_488":45,"_489":1874},{"_517":57,"_518":519,"_486":487},[1875],"There's no AGENTS.md in the first few levels, but I'll check recent commits next.",{},"ae36be7b-7988-4cc1-9e9a-16824a029a9f",{"_103":500,"_151":529,"_480":1883},{"_488":45,"_489":1881},{},[1882],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 30, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# find . -maxdepth 2 -name AGENTS.md\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"27bca2\"}",{},"80e2d1e6-f602-45c9-a39a-e3ba46f4d71b",{"_103":100,"_151":-5,"_480":1889},{"_488":508,"_219":509,"_510":-5,"_45":1888},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"find . -maxdepth 2 -name AGENTS.md\\n\"}",{},"3f6beb26-da9c-4a69-b50c-f8f307ebd71c",{"_103":500,"_151":529,"_480":1896},{"_488":45,"_489":1894},{},[1895],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 25, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# find . -name AGENTS.md -maxdepth 2\", \"find: warning: you have specified the global option -maxdepth after the argument\", \" -name, but global options are not positional, i.e., -maxdepth affects tests spe\", \"cified before it as well as those specified after it. Please specify global opt\", \"ions before other arguments.\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"8fd13a\"}",{},"e0d3dbd3-deb5-4141-913c-4aefc1ef49f9",{"_103":100,"_151":-5,"_480":1902},{"_488":508,"_219":509,"_510":-5,"_45":1901},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"find . -name AGENTS.md -maxdepth 2\\n\", \"yield_time_ms\":1000}\n",{},"7ae32acb-2aef-4522-8d3c-0fb32ffe7279",{"_103":500,"_151":529,"_480":1909},{"_488":45,"_489":1907},{},[1908],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 20, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# find . -name AGENTS.md -maxdepth 2\", \"find: warning: you have specified the global option -maxdepth after the argument\", \" -name, but global options are not positional, i.e., -maxdepth affects tests spe\", \"cified before it as well as those specified after it. Please specify global opt\", \"ions before other arguments.\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"213814\"}",{},"ab231b1a-8787-4b36-b824-cecb4223a38b",{"_103":100,"_151":-5,"_480":1915},{"_488":508,"_219":509,"_510":-5,"_45":1914},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"find . -name AGENTS.md -maxdepth 2\\n\"}",{},"0641d727-31be-41f8-9f5b-a388ddad5eb3",{"_103":500,"_151":529,"_480":1922},{"_488":45,"_489":1920},{},[1921],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 18, \"output\": [\"root@fce6d293fd25:/workspace/pytorch# ls AGENTS.md\", \"ls: cannot access 'AGENTS.md': No such file or directory\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"6d91fc\"}",{},"c8de3716-80e0-4b9f-a80f-6bb5072a1ac5",{"_103":100,"_151":-5,"_480":1928},{"_488":508,"_219":509,"_510":-5,"_45":1927},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"ls AGENTS.md\\n\"}",{},"e6b6bd8f-1f8c-44cd-a05b-2480b41d83c1",{"_103":500,"_151":529,"_480":1935},{"_488":45,"_489":1933},{},[1934],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 3, \"output\": [\"root@fce6d293fd25:/workspace# cd pytorch\", \"root@fce6d293fd25:/workspace/pytorch# ls\", \"BUCK.oss README.md caffe2 pytest.ini\", \"BUILD.bazel RELEASE.md cmake requirements.txt\", \"CITATION.cff SECURITY.md defs.bzl scripts\", \"CMakeLists.txt WORKSPACE docker.Makefile setup.py\", \"CODEOWNERS android docs test\", \"CODE_OF_CONDUCT.md aten functorch third_party\", \"CONTRIBUTING.md aten.bzl mypy-strict.ini tools\", \"Dockerfile benchmarks mypy.ini torch\", \"GLOSSARY.md binaries mypy_plugins torchgen\", \"LICENSE buckbuild.bzl pt_ops.bzl ubsan.supp\", \"MANIFEST.in build.bzl pt_template_srcs.bzl ufunc_defs.bzl\", \"Makefile build_variables.bzl pyproject.toml version.txt\", \"NOTICE c10 pyrefly.toml\", \"root@fce6d293fd25:/workspace/pytorch#\"], \"num_elided_lines\": 0, \"chunk_id\": \"040991\"}",{},"3275c005-3b5b-498c-a5eb-c372949a50b3",{"_103":100,"_151":-5,"_480":1941},{"_488":508,"_219":509,"_510":-5,"_45":1940},{"_486":507},"{\"session_name\":\"shell\",\"chars\":\"cd pytorch\\nls\\n\"}",{},"dfb50e93-bf13-47ec-af14-1d4786d2ccdb",{"_103":500,"_151":529,"_480":1948},{"_488":45,"_489":1946},{},[1947],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 1, \"output\": [\"root@fce6d293fd25:/workspace# ls\", \"pytorch\", \"root@fce6d293fd25:/workspace#\"], \"num_elided_lines\": 0, \"chunk_id\": \"20550d\"}",{},"eb8073bc-2b24-437a-82e4-c5073837eafc",{"_103":100,"_151":-5,"_480":1953},{"_488":508,"_219":509,"_510":-5,"_45":1638},{"_486":535},{},"d560b152-3854-441f-a425-83b89317461b",{"_103":500,"_151":529,"_480":1960},{"_488":45,"_489":1958},{},[1959],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 0, \"output\": [\"ls\"], \"num_elided_lines\": 0, \"chunk_id\": \"f6c709\"}",{},"e3c5a15a-4fa6-4363-8184-21f5bd47c123",{"_103":100,"_151":-5,"_480":1966},{"_488":508,"_219":509,"_510":-5,"_45":1965},{"_486":535},"{\"session_name\":\"shell\",\"chars\":\"ls\\n\"}",{},"656a9190-63f8-41ac-8bfb-05b6c9d7fca5",{"_103":500,"_151":1612,"_480":1973},{"_488":45,"_489":1971},{},[1972],"{\"type\": \"terminal_output\", \"session_name\": \"shell\", \"start_line_no\": 0, \"output\": [], \"num_elided_lines\": 0, \"chunk_id\": \"764b35\"}",{},"f45521b9-d579-4173-b227-2479887c14fb",{"_103":100,"_151":-5,"_480":1979},{"_488":508,"_219":509,"_510":-5,"_45":1978},{"_486":507},"{\"session_name\":\"shell\"}",{},"8236fb82-ab16-4428-8173-4ca1320c2b03",{"_103":100,"_151":-5,"_480":1986},{"_488":45,"_489":1984},{"_517":57,"_518":519,"_486":487},[1985],"The crash seems related to named dimensions. I should check AGENTS.md for guidance.",{},"9c0b033d-ef1e-446c-86d4-351074b86313",{"_103":1997,"_151":-5,"_480":1998},{"_488":45,"_489":1995},{"_1991":1992},"l1239dk1",{"_1993":1994},"inline_comments",[],[1996],"But that wouldn't trigger the crash, would it, as it crashes only if named dimension is given","user",{},"created",1749258793.5933118,"Completing the 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TORCH_ASSERT_ONLY_METHOD_OPERATORS","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#ifndef AT_PER_OPERATOR_HEADERS","#include ","#include ","#else","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#include ","#endif","#include ","#include ","#include ","#include ","namespace at::meta {","static inline c10::MemoryFormat cat_compute_output_memory_format("," const MaterializedITensorListRef& inputs) {"," std::optional format = std::nullopt;"," for (const Tensor& t : inputs) {"," auto f = t.suggest_memory_format();"," if (f == c10::MemoryFormat::Contiguous) {"," return f;"," }"," if (format.has_value() && format.value() != f) {"," return c10::MemoryFormat::Contiguous;"," format = f;"," }"," return format.value();","}","TORCH_PRECOMPUTE_META_FUNC(cat)(const ITensorListRef& tensors, int64_t dim) {"," // previously, size [0] tensors were the only possible empty tensors; thus, it"," // wasn't possible to cat empty tensors unless all the other tensors were"," // 1-dimensional, so we allowed these tensors to be \"skipped\". We maintain"," // this behavior for backwards compatibility, but only for this specific size"," // (i.e. other empty sizes are not skipped)."," auto materialized = tensors.materialize();"," native::check_cat_no_zero_dim(materialized);"," dim = at::legacy_cat_wrap_dim(dim, materialized);"," // Checking names before the actual dimensions."," auto maybe_outnames = namedinference::compute_cat_outnames(materialized);"," TORCH_CHECK("," !materialized.empty(),"," \"torch.cat(): expected a non-empty list of Tensors\");"," // Look for the first valid tensor."," size_t valid = materialized.size();"," for (const auto i : c10::irange(materialized.size())) {"," if (!at::native::cat_should_skip_tensor(materialized[i].get())) {"," valid = i;"," break;"," bool all_contiguous = true;"," bool all_same_dtype = true;"," bool all_same_sizes_and_stride = true;"," auto memory_format = cat_compute_output_memory_format(materialized);"," // Compute what the output dtype should be:"," const auto& result = maybe_get_output();"," auto is_out_defined = result.defined();"," auto out_dtype = at::native::result_type(tensors);"," // If the output tensor is defined, we need to take it into account"," // when computing the actual output dtype and the flags."," if (is_out_defined) {"," // Check for type promotion, if the output tensor is defined."," TORCH_CHECK("," canCast(out_dtype, result.scalar_type()),"," \"torch.cat(): input types can't be cast to the desired output type \","," result.scalar_type());"," out_dtype = result.scalar_type();"," all_contiguous = result.is_contiguous(memory_format);"," // Fallback 'set_output' parameters."," // (in case we don't find a valid tensor)"," DimVector sizes{0};"," TensorOptions options ="," materialized[0].get().options().dtype(out_dtype).memory_format("," memory_format);"," // If we found a valid tensor, check whether the input tensors"," // are compatible, i.e. we can execute `cat` on them."," bool found_valid_tensor = valid < materialized.size();"," if (found_valid_tensor) {"," dim <= materialized[valid].get().dim(),"," \"torch.cat(): dimension \","," dim,"," \"out of range\");"," // Compute the output tensor size."," // It should have the same shape as any other valid tensor,"," // except in the dimension 'dim'."," size_t size_at_dim = 0;"," for (const auto i : c10::irange(materialized.size())) {"," const Tensor& t = materialized[i];"," all_same_dtype = all_same_dtype && out_dtype == t.scalar_type();"," if (!at::native::cat_should_skip_tensor(t)) {"," at::native::check_cat_shape_except_dim(materialized[valid], t, dim, i);"," size_at_dim += t.size(dim);"," all_contiguous = all_contiguous && t.is_contiguous(memory_format);"," all_same_sizes_and_stride = all_same_sizes_and_stride &&"," t.sizes() == materialized[valid].get().sizes() &&"," t.strides() == materialized[valid].get().strides();"," } else {"," all_contiguous = false;"," }"," // Actually set the output."," sizes = materialized[valid].get().sizes().vec();"," sizes[dim] = size_at_dim;"," options ="," materialized[valid].get().options().dtype(out_dtype).memory_format("," memory_format);"," set_output_raw_strided(0, sizes, {}, options, maybe_outnames);"," // Checks for overlaps between the inputs and the output tensor."," if (is_out_defined && found_valid_tensor) {"," at::assert_no_internal_overlap(result);"," for (const Tensor& t : materialized) {"," at::assert_no_overlap(result, t);"," return TORCH_PRECOMPUTE_STRUCT(cat)()"," .set_dim(dim)"," .set_valid(valid)"," .set_all_contiguous(all_contiguous)"," .set_all_same_dtype(all_same_dtype)"," .set_all_same_sizes_and_stride(all_same_sizes_and_stride)"," .set_memory_format(memory_format);","} // namespace at::meta","namespace at::native {","DEFINE_DISPATCH(cat_serial_stub);","DEFINE_DISPATCH(stack_serial_stub);","Tensor _reshape_from_tensor(const Tensor& self, const Tensor& shape_tensor) {"," TORCH_CHECK(shape_tensor.dim() == 1);"," std::vector shape;"," auto accessor = shape_tensor.accessor();"," for (const auto i : c10::irange(shape_tensor.numel())) {"," shape.push_back(accessor[i]);"," return self.reshape(IntArrayRef(shape));","Tensor _shape_as_tensor(const Tensor& self) {"," auto options = TensorOptions(at::kLong);"," return at::tensor(self.sizes(), options);","Tensor& set_(Tensor& result, Storage source) {"," int64_t new_size ="," static_cast(source.nbytes() / result.dtype().itemsize());"," return result.set_(std::move(source), 0, new_size, {});","// unify with cuda implementation? This is not done to avoid a dispatch in","// resize_impl_cpu_","Tensor& set_storage_cpu_("," Tensor& result,"," Storage storage,"," int64_t storage_offset,"," IntArrayRef size,"," IntArrayRef stride) {"," checkSetStorage(result, std::move(storage), storage_offset, size, stride);"," result.unsafeGetTensorImpl()->set_storage_offset(storage_offset);"," at::OptionalIntArrayRef stride_opt ="," stride.data() != nullptr ? at::OptionalIntArrayRef(stride) : std::nullopt;"," // We can re-use this kernel for the meta device."," // We just need to make sure we don't actually try to resize the (null)"," // storage."," at::native::resize_impl_cpu_("," result.unsafeGetTensorImpl(),"," size,"," stride_opt,"," /*resize_storage=*/!result.is_meta());"," return result;","Tensor& set_storage_meta__symint("," c10::SymInt storage_offset,"," c10::SymIntArrayRef size,"," c10::SymIntArrayRef stride) {"," checkSetStorage("," result,"," storage,"," storage_offset,"," stride,"," /*check_offset_in_bounds=*/false);"," c10::SymDimVector contiguous_strides;"," if (stride.data() == nullptr) {"," // TODO: dedupe this with empty() symbolic logic"," int64_t dim = size.size();"," contiguous_strides.resize(dim);"," if (dim > 0) {"," const auto last_idx = dim - 1;"," contiguous_strides.at(last_idx) = 1;"," for (auto i = last_idx - 1; i >= 0; --i) {"," // TODO: max with 1"," contiguous_strides.at(i) ="," contiguous_strides.at(i + 1) * size.at(i + 1);"," stride = contiguous_strides;"," // Run this before storage setting so we can access numel"," result.unsafeGetTensorImpl()->set_sizes_and_strides("," size, stride, storage_offset);"," // Matches maybe_resize_storage_cpu no-numel behavior"," if (TORCH_GUARD_OR_TRUE(result.sym_numel().sym_ne(0))) {"," // maybe_resize_storage_cpu can handle no storage exists at all but"," // that should never be the case here"," TORCH_INTERNAL_ASSERT(storage);"," storage.resizable(), \"Trying to resize storage that is not resizable\");"," // All meta data pointers are the same, so we don't have to \"re\" allocate"," // it. TODO: Actually this might not quite be correct if we use special"," // pointers to track whether or not fake cuda tensors are pinned or not"," // TODO: When there are unbacked SymInts, we unconditionally skip the"," // setter. This is technically wrong, but we cannot conveniently test"," // the real condition in many cases, because a lot of people are using"," // set_ just to swizzle metadata on a tensor, they didn't actually want"," // to see if they need to resize the storage."," //"," // The old behavior was to unconditionally set_nbytes, but I think not"," // setting it is more safe."," if (result.sym_numel().has_hint()) {"," const auto itemsize = result.dtype().itemsize();"," c10::SymInt new_size_bytes = result.is_contiguous()"," ? at::detail::computeStorageNbytesContiguous("," size, itemsize, std::move(storage_offset))"," : at::detail::computeStorageNbytes("," size, stride, itemsize, std::move(storage_offset));"," if (new_size_bytes.has_hint() && storage.sym_nbytes().has_hint() &&"," TORCH_GUARD_SIZE_OBLIVIOUS("," new_size_bytes.sym_gt(storage.sym_nbytes()))) {"," storage.set_nbytes(std::move(new_size_bytes));","Tensor& set__symint("," const Tensor& storage,"," storage.is_contiguous(),"," \"passed in tensor to be used as storage must be contiguous\");"," return result.set__symint("," storage.storage(),"," storage_offset + storage.sym_storage_offset(),"," stride);","Tensor& set_tensor_(Tensor& result, const Tensor& source) {"," if (result.unsafeGetTensorImpl() != source.unsafeGetTensorImpl()) {"," return result.set__symint("," source.storage(),"," source.sym_storage_offset(),"," source.sym_sizes(),"," source.sym_strides());","// this needs to be split along CPU/CUDA lines because we don't have a","// consistent way of getting the allocator to use for a device","// (c10::GetAllocator is not the same as at::cuda::getCUDADeviceAllocator().","Tensor& set_cpu_(Tensor& result) {"," caffe2::TypeMeta dtype = result.dtype();"," Storage storage(Storage::use_byte_size_t(), 0, c10::GetAllocator(kCPU), true);"," result.set_(std::move(storage), 0, {0}, {});"," TORCH_INTERNAL_ASSERT(dtype == result.dtype());","// We can't re-use the cpu kernel here because we don't want to use the cpu","// allocator.","Tensor& set_meta_(Tensor& result) {"," Storage storage("," Storage::use_byte_size_t(), 0, c10::GetAllocator(kMeta), true);","Tensor sparse_broadcast_to(const Tensor& self, IntArrayRef size) {"," TORCH_CHECK(self.is_sparse(), \"input must be sparse tensor\");"," const auto self_size = self.sizes();"," const int64_t new_sparse_dims = size.size() - self.dim();"," new_sparse_dims >= 0,"," \"the requested broadcast shape has fewer dimensions than the input\");"," const int64_t res_sparse_dim = new_sparse_dims + self.sparse_dim();"," for (int64_t i = 0; i < self.dim(); ++i) {"," self_size[i] == 1 || self_size[i] == size[i + new_sparse_dims],"," \"The input's length \","," self_size[i],"," \" at dimension \","," i,"," \" does not broadcast over the requested shape of length \","," size[i + new_sparse_dims],"," i + new_sparse_dims);"," const int64_t self_nnz = self._nnz();"," const auto self_indices = self._indices();"," int64_t nnz_expand_factor = 1;"," int64_t largest_sparse_dim_len = -1;"," int64_t min_broadcast_dim = (new_sparse_dims > 0) ? 0 : -1;"," int64_t max_unchanged_dim = -1;"," for (int64_t i = 0; i < res_sparse_dim; ++i) {"," if ((i < new_sparse_dims) || (self_size[i - new_sparse_dims] != size[i])) {"," nnz_expand_factor *= size[i];"," largest_sparse_dim_len = std::max(size[i], largest_sparse_dim_len);"," if (i >= new_sparse_dims && min_broadcast_dim == -1) {"," min_broadcast_dim = i;"," } else {"," if (i >= new_sparse_dims) {"," max_unchanged_dim = i;"," // to_broadcast conserves is_coalesced property iff only the last"," // sparse dimensions are expanded. Possible expansion of dense"," // dimensions can be discarded as it does not affect the is_coalesce"," // property."," bool is_coalesced = !self.dim() ||"," (self.is_coalesced() &&"," (max_unchanged_dim < min_broadcast_dim || min_broadcast_dim == -1));"," // Replace non-broadcastable dims with 1 in the `size` vector {"," auto res_sparse_dim_broadcast_mask ="," at::DimVector(size.begin(), size.begin() + res_sparse_dim);"," for (int64_t i = new_sparse_dims; i < res_sparse_dim; ++i) {"," res_sparse_dim_broadcast_mask[i] ="," (size[i] == self_size[i - new_sparse_dims]) ? 1 : size[i];"," // }"," // Then define for each sparse dim the number of reps for each nnz index/value"," // due to broadcasting. Repetitions do not take into account the current value"," // of nnz - this will be taken care of later {"," auto nnz_repeats = c10::DimVector(res_sparse_dim);"," nnz_repeats.back() = res_sparse_dim_broadcast_mask.back();"," for (int64_t i = res_sparse_dim - 2; i >= 0; --i) {"," nnz_repeats[i] = res_sparse_dim_broadcast_mask[i] * nnz_repeats[i + 1];"," // Broadcast values. Each nnz value has to be repeated nnz_expand_factor times"," // {"," auto broadcast_values_shape = DimVector(size.size() - res_sparse_dim + 2);"," std::copy("," size.begin() + res_sparse_dim,"," size.end(),"," broadcast_values_shape.begin() + 2);"," broadcast_values_shape[0] = self_nnz;"," broadcast_values_shape[1] = nnz_expand_factor;"," auto broadcast_values ="," self._values().unsqueeze(1).expand(broadcast_values_shape).flatten(0, 1);"," // We can return early if there are no broadcastable sparse dims"," if (largest_sparse_dim_len < 0) {"," return at::sparse_coo_tensor("," self._indices(),"," broadcast_values,"," size,"," self.options(),"," self.is_coalesced());"," auto broadcast_indices ="," self._indices().new_empty({res_sparse_dim, self_nnz * nnz_expand_factor});"," // Repeat each individual index value in dimension dim nnz_repeats[dim] /"," // size[dim] times, and then repeat the whole vector self_nnz *"," // (nnz_expand_factor / nnz_repeats[dim]) times to get the final index vector"," // - only for broadcast dims {"," const auto dim_arange ="," at::arange(largest_sparse_dim_len, self._indices().options());"," Tensor curr_dim_idx;"," // If the dim is either a newly created sparse dim, or an already existing"," // one which is broadcastable, do the reps over an arange vector"," curr_dim_idx = dim_arange.narrow(0, 0, size[i])"," .unsqueeze_(0)"," .unsqueeze_(-1)"," .expand("," {self_nnz * (nnz_expand_factor / nnz_repeats[i]),"," size[i],"," nnz_repeats[i] / size[i]});"," // Otherwise over a slice of self._indices() of length self_nnz"," curr_dim_idx = self_indices.select(0, i - new_sparse_dims)"," .unsqueeze_(1)"," .expand({self_nnz, nnz_expand_factor});"," broadcast_indices.select(0, i)"," .view(curr_dim_idx.sizes())"," .copy_(curr_dim_idx);"," return at::sparse_coo_tensor("," broadcast_indices, broadcast_values, size, self.options(), is_coalesced);","Tensor broadcast_to_symint(const Tensor& self, SymIntArrayRef size) {"," return self.expand_symint(size);","std::vector broadcast_tensors(TensorList tensors) {"," return expand_outplace(tensors);","static void fastCatOutDim0("," const Tensor& out,"," auto outBytes = out.nbytes();"," char* dataPtr = reinterpret_cast(out.data_ptr());"," size_t totalBytes = 0;"," for (const Tensor& input : inputs) {"," TORCH_CHECK(outBytes >= totalBytes);"," if (input.nbytes() > 0) {"," std::memcpy(dataPtr + totalBytes, input.const_data_ptr(), input.nbytes());"," totalBytes += input.nbytes();"," TORCH_CHECK(outBytes == totalBytes);","TORCH_IMPL_FUNC(cat_out_cpu)","(const ITensorListRef& tensors,"," int64_t dim,"," int64_t valid,"," bool all_contiguous,"," bool all_same_dtype,"," bool all_same_sizes_and_stride,"," MemoryFormat memory_format,"," const Tensor& result) {"," if (result.numel() == 0) {"," return;"," bool use_serial_kernel ="," result.numel() < at::internal::GRAIN_SIZE || at::get_num_threads() == 1;"," ScalarType dtype = materialized[valid].get().scalar_type();"," bool serial_dtype = at::isFloatingType(dtype);"," // fast path for single thread when both inputs and result are contiguous and"," // not empty, and concat dim is 0"," if (use_serial_kernel && all_contiguous && all_same_dtype &&"," (MemoryFormat::Contiguous == memory_format)) {"," if (dim == 0) {"," fastCatOutDim0(result, materialized);"," return;"," // TODO: Add fast cat for higher dimensions and support multi-threaded fast"," // cat"," // not empty"," if (use_serial_kernel && all_contiguous && all_same_dtype && serial_dtype) {"," cat_serial_stub(kCPU, result, materialized, dim);"," int64_t offset = 0;"," if (all_same_sizes_and_stride && result.is_contiguous(memory_format) &&"," all_same_dtype) {"," const Tensor& source_slice = materialized[valid];"," auto slice_dim_size = source_slice.sizes()[dim];"," auto result_slice = result.narrow(dim, 0, slice_dim_size);"," auto result_slice_data = result_slice.data_ptr();"," auto result_stride_bytes ="," result.stride(dim) * elementSize(result.scalar_type());"," auto iter = TensorIteratorConfig()"," .set_check_mem_overlap(false)"," .resize_outputs(false)"," .add_output(result_slice)"," .add_const_input(source_slice)"," .enforce_safe_casting_to_output(true)"," .build();"," for (const Tensor& tensor : materialized) {"," if (cat_should_skip_tensor(tensor)) {"," continue;"," auto source_data = static_cast(tensor.const_data_ptr());"," auto result_data ="," static_cast(result_slice_data) + offset * result_stride_bytes;"," iter.unsafe_replace_operand(0, result_data);"," iter.unsafe_replace_operand(1, const_cast(source_data));"," copy_stub(iter.device_type(), iter, false);"," offset += slice_dim_size;"," } else {"," auto slice_dim_size = tensor.sizes()[dim];"," auto result_slice = result.narrow(dim, offset, slice_dim_size);"," auto iter = TensorIteratorConfig()"," .set_check_mem_overlap(false) // Already checked above"," .resize_outputs(false)"," .add_output(result_slice)"," .add_const_input(tensor)"," .promote_inputs_to_common_dtype(true)"," .cast_common_dtype_to_outputs(true)"," .enforce_safe_casting_to_output(true)"," .build();","Tensor& cat_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 cat(TensorList tensors, Dimname dim) {"," return at::cat(tensors, dimname_to_position(tensors[0], dim));","// torch.concat, alias for torch.cat","Tensor& concat_out(TensorList tensors, Dimname dim, Tensor& result) {","Tensor concat(TensorList tensors, Dimname dim) {","Tensor& concat_out(TensorList tensors, int64_t dim, Tensor& result) {"," return at::cat_out(result, tensors, dim);","Tensor concat(TensorList tensors, int64_t dim) {"," return at::cat(tensors, dim);","// torch.concatenate, alias for torch.cat","Tensor& concatenate_out(TensorList tensors, Dimname dim, Tensor& result) {","Tensor concatenate(TensorList tensors, Dimname dim) {","Tensor& concatenate_out(TensorList tensors, int64_t dim, Tensor& result) {","Tensor concatenate(TensorList tensors, int64_t dim) {","static bool sizes_match_except("," IntArrayRef s1,"," IntArrayRef s2,"," int64_t dim_except /* should already be wrapped */) {"," if (s1.size() != s2.size()) {"," return false;"," for (const auto i : c10::irange(static_cast(s1.size()))) {"," if (i != dim_except && s1[i] != s2[i]) {"," return false;"," return true;","// Check to see if the shape of tensors is compatible","// for being concatenated along a given dimension.","static void check_cat_sparse_dims("," Tensor const& t,"," int64_t pos /* used only for debug messages */,"," IntArrayRef sizes,"," int64_t wrapped,"," int64_t sparse_dim,"," int64_t dense_dim) {"," t.is_sparse(),"," \"Concatenating sparse tensors, but a dense tensor was found at position \","," pos,"," \".\");"," sizes_match_except(sizes, t.sizes(), wrapped),"," \"All tensors must have the same shape: \","," sizes,"," \" (except in the concatenating dimension),\""," \" but found shape: \","," t.sizes(),"," \" at position \","," t.sparse_dim() == sparse_dim && t.dense_dim() == dense_dim,"," \"All tensors must have the same sparse_dim and dense_dim: \","," sparse_dim,"," \", \","," dense_dim,"," \", but tensor at position \","," \" has \","," t.sparse_dim(),"," t.dense_dim(),","static Tensor cat_sparse_impl("," const MaterializedITensorListRef& tensors,"," int64_t dim) {"," std::vector indices;"," std::vector values;"," int64_t wrapped = maybe_wrap_dim(dim, tensors[0].get().dim());"," int64_t sparse_dim = tensors[0].get().sparse_dim();"," int64_t dense_dim = tensors[0].get().dense_dim();"," IntArrayRef sizes = tensors[0].get().sizes();"," if (wrapped < sparse_dim) {"," for (const auto i : c10::irange(tensors.size())) {"," const Tensor& t = tensors[i];"," check_cat_sparse_dims(t, i, sizes, wrapped, sparse_dim, dense_dim);"," indices.push_back(t._indices());"," values.push_back(t._values());"," Tensor idxs = at::cat(indices, 1);"," Tensor vals = at::cat(values, 0);"," // We now need to move the indices of each"," // input tensor up along `dim` by an appropriate amount."," // E.g., if t1 has indices [[2,3,4],[5,6,7]],"," // and sizes [10, 7]"," // then torch.cat((t1,t1,t1),1) should have indices"," // [[2,3,4,2,3,4,2,3,4],[5,6,7,12,13,14,19,20,21]],"," // so we need to increase idxs[1][3:6] by 7"," // and idxs[1][6:9] by 14."," int64_t col = 0;"," int64_t cumulative_offset = 0;"," int64_t this_piece_size = t._nnz();"," // cumulative_offset is zero for the first piece, so"," // don't waste time doing this operation unless i > 0."," if (i > 0) {"," idxs[wrapped].narrow(0, col, this_piece_size) += cumulative_offset;"," cumulative_offset += t.size(wrapped);"," col += this_piece_size;"," auto sizes_copy = sizes.vec();"," sizes_copy[wrapped] = cumulative_offset;"," return native::sparse_coo_tensor("," idxs,"," vals,"," sizes_copy,"," optTypeMetaToScalarType(tensors[0].get().options().dtype_opt()),"," tensors[0].get().options().layout_opt(),"," tensors[0].get().options().device_opt(),"," tensors[0].get().options().pinned_memory_opt());"," // Catting along a dense dimension requires us to create new values."," // For illustration, consider the sparse 3d tensors t1 and t2,"," // given by t1 = [[[1,2],[3,4]], ... (zeros) ..., [[5,6],[7,8]]]"," // and t2 = [... (zeros) ..., [[9, 10], [11,12]], ... (zeros) ...],"," // Their concatenation along dimension 2 is:"," // [[[1,2,0,0],[3,4,0,0]], ... (zeros) ..., [[0,0,9,10],[0,0,11,12]], ..."," // (zeros) ..., [[5,6,0,0],[7,8,0,0]]]"," // Their values tensors are, respectively,"," // [[[1,2],[3,4]],[[5,6],[7,8]]] and [[[9,10],[11,12]]]."," // and so the values tensor of their concatenation along dim 2 will be:"," // [[[1,2,0,0],[3,4,0,0]],[[5,6,0,0],[7,8,0,0]],[[0,0,9,10],[0,0,11,12]]]"," // which we can get by taking the values tensor of each tensor, catting it"," // with zeros of the appropriate size on the left and right, and then"," // catting all those results together."," // The dimension in each tensor's values object that corresponds to the"," // overall dimension along which we're catting."," int64_t values_dim = wrapped - sparse_dim + 1;"," // The final size along the catted dimension."," const int64_t total_size = std::accumulate("," tensors.begin(),"," tensors.end(),"," static_cast(0),"," [values_dim](int64_t l, const Tensor& r) {"," return l + r._values().size(values_dim);"," });"," auto zeros_sizes = tensors[0].get()._values().sizes().vec();"," int64_t cumulative_size = 0;"," std::vector vals_pieces;"," std::vector idxs_pieces;"," // dimension 0 of values corresponds to the number of values,"," // rather than to any logical dimension of the sparse tensor."," zeros_sizes[0] = t._values().size(0);"," zeros_sizes[values_dim] = cumulative_size;"," cumulative_size += t._values().size(values_dim);"," auto z1 = at::zeros("," zeros_sizes,"," optTypeMetaToScalarType(t._values().options().dtype_opt()),"," t._values().options().layout_opt(),"," t._values().options().device_opt(),"," t._values().options().pinned_memory_opt());"," zeros_sizes[values_dim] = total_size - cumulative_size;"," auto z2 = at::zeros("," vals_pieces.push_back(at::cat({z1, t._values(), z2}, values_dim));"," idxs_pieces.push_back(t._indices());"," sizes_copy[wrapped] = total_size;"," // This can create an uncoalesced tensor"," at::cat(idxs_pieces, 1),"," at::cat(vals_pieces),","Tensor cat_sparse(const ITensorListRef& tensors, int64_t dim) {"," auto result ="," cat_sparse_impl(materialized, at::legacy_cat_wrap_dim(dim, materialized));"," namedinference::propagate_names_if_nonempty(result, maybe_outnames);","Tensor block_diag(TensorList tensors) {"," Tensor result;"," if (tensors.empty()) {"," result = at::empty({1, 0});"," return result;"," const Device& device = tensors[0].device();"," for (const auto tensor_idx : c10::irange(tensors.size())) {"," const Tensor& tensor = tensors[tensor_idx];"," tensor.device() == device,"," \"torch.block_diag: input tensors must all be on the same device.\","," \" Input 0 is on device \","," device,"," \" and input \","," tensor_idx,"," \" is on device \","," tensor.device());"," ScalarType output_scalar_type = native::result_type(tensors);"," int64_t result_dim0 = 0;"," int64_t result_dim1 = 0;"," std::vector tensors_2D(tensors.size());"," // Sum the dimensions of the tensors, check tensor sizes,"," // and expand all 0-D and 1-D tensors so that everything"," // is 2-D"," int64_t ndims = tensor.dim();"," ndims <= 2,"," \"torch.block_diag: Input tensors must have 2 or fewer dimensions. Input \","," \" has \","," ndims,"," \" dimensions\");"," int64_t dim0 = 1;"," int64_t dim1 = 1;"," if (ndims == 2) {"," dim0 = tensor.size(0);"," dim1 = tensor.size(1);"," tensors_2D[tensor_idx] = tensor;"," } else if (ndims == 1) {"," // Switching dim 0 to dim 1 is intentional"," dim1 = tensor.size(0);"," tensors_2D[tensor_idx] = tensor.expand({dim0, dim1});"," result_dim0 += dim0;"," result_dim1 += dim1;"," result = at::zeros("," {result_dim0, result_dim1},"," tensors[0].options().dtype(output_scalar_type));"," int64_t cur_dim0 = 0;"," int64_t cur_dim1 = 0;"," // Copy each tensor into the appropriate location in the result matrix"," for (const auto& tensor : tensors_2D) {"," int64_t dim0 = tensor.size(0);"," int64_t dim1 = tensor.size(1);"," result.slice(0, cur_dim0, cur_dim0 + dim0)"," .slice(1, cur_dim1, cur_dim1 + dim1)"," .copy_(tensor);"," cur_dim0 += dim0;"," cur_dim1 += dim1;","std::vector chunk(const Tensor& self, int64_t chunks, int64_t dim) {"," TORCH_CHECK(self.dim() > 0, \"chunk expects at least a 1-dimensional tensor\");"," chunks > 0, \"chunk expects `chunks` to be greater than 0, got: \", chunks);"," const auto dim_size = self.sym_size(dim);"," auto split_size = (dim_size + chunks - 1) / chunks;"," // We need to call split_with_sizes in the case where split_size and dimension"," // size are 0, because a call to split would discard the number of chunks"," // (because we can have an arbitrary number of 0-sized chunks adding up to 0)."," // So, call split_with_sizes with the correct number of chunks, eventually we"," // will do this for all cases."," if (split_size == 0 && dim_size == 0) {"," std::vector split_sizes(chunks, split_size);"," split_sizes[chunks - 1] = split_size - (split_size * chunks - dim_size);"," return self.split_with_sizes_symint(split_sizes, dim);"," return self.split_symint(std::move(split_size), dim);","std::vector tensor_split_sections_symint("," const Tensor& self,"," c10::SymInt sym_sections,"," self.dim() > 0,"," \"tensor_split expected at least a 1-dimensional tensor, but got a tensor with \","," self.dim(),"," \" dims\");"," int64_t dim_ = maybe_wrap_dim(dim, self.dim());"," // NB: intentional, sections specifies number of output tensors, which"," // cannot be polymorphic"," int64_t sections = sym_sections.guard_int(__FILE__, __LINE__);"," sections > 0, \"number of sections must be larger than 0, got \", sections);"," const auto dim_size = self.sym_size(dim_);"," std::vector splits(sections);"," auto min_split_size = dim_size / sections;"," auto num_splits_one_extra = dim_size % sections;"," c10::SymInt start_idx = 0;"," for (const auto split_idx : c10::irange(sections)) {"," auto split_size = (num_splits_one_extra > split_idx) ? (min_split_size + 1)"," : min_split_size;"," splits[split_idx] ="," at::slice_symint(self, dim_, start_idx, start_idx + split_size);"," start_idx += split_size;"," return splits;","template ","static std::vector _tensor_split_indices("," ArrayRef indices,"," int64_t num_indices = indices.size();"," std::vector splits(num_indices + 1);"," T start_idx(0);"," for (const auto split_idx : c10::irange(num_indices)) {"," auto end_idx = indices[split_idx];"," splits[split_idx] = at::symint::slice(self, dim_, start_idx, end_idx);"," start_idx = end_idx;"," splits[num_indices] = at::symint::slice("," self, dim_, start_idx, at::symint::size(self, dim_));","std::vector tensor_split("," IntArrayRef indices,"," return _tensor_split_indices(self, indices, dim);","std::vector tensor_split_indices_symint("," SymIntArrayRef indices,"," const Tensor& tensor_indices_or_sections,"," auto split_device = tensor_indices_or_sections.device();"," split_device == kCPU,"," \"tensor_split expected tensor_indices_or_sections to be on cpu, but it's on \","," split_device);"," auto split_dtype = tensor_indices_or_sections.scalar_type();"," split_dtype == at::kLong,"," \"tensor_split expected tensor_indices_or_sections to have dtype of long, but got \","," split_dtype);"," auto split_dim = tensor_indices_or_sections.dim();"," split_dim == 1 || split_dim == 0,"," \"tensor_split expected tensor_indices_or_sections to be a zero-dimensional or one-dimensional tensor, but got a tensor with \","," split_dim,"," if (split_dim == 0) {"," int64_t sections = tensor_indices_or_sections.item();"," return self.tensor_split(sections, dim);"," auto indices_data = tensor_indices_or_sections.const_data_ptr();"," auto stride = tensor_indices_or_sections.stride(0);"," auto numel = tensor_indices_or_sections.numel();"," std::vector indices(numel);"," for (const auto offset : c10::irange(numel)) {"," // indices tensor could be non-contiguous"," indices[offset] = *(indices_data + offset * stride);"," return self.tensor_split(indices, dim);","std::vector unsafe_chunk("," int64_t chunks,"," const auto dim_size = self.size(dim);"," int64_t split_size = (dim_size + chunks - 1) / chunks;"," // See the comment above in chunk(...)"," std::vector split_sizes(chunks, split_size);"," return self.unsafe_split_with_sizes(split_sizes, dim);"," return self.unsafe_split(split_size, dim);","Tensor diagflat(const Tensor& self, int64_t offset) {"," return self.contiguous().view(-1).diag(offset);","Tensor diagonal("," int64_t offset,"," int64_t dim1_,"," int64_t dim2_) {"," int64_t nDims = self.dim();"," int64_t dim1 = maybe_wrap_dim(dim1_, nDims);"," int64_t dim2 = maybe_wrap_dim(dim2_, nDims);"," dim1 != dim2,"," \"diagonal dimensions cannot be identical \","," dim1_,"," dim2_);"," auto outnames = namedinference::compute_diagonal_outnames(self, dim1, dim2);"," NoNamesGuard no_names_guard;"," int64_t diag_size = 0;"," int64_t storage_offset = self.storage_offset();"," // compute storage offset and size for the diagonal"," // for positive values of offset (above the main diagonal)"," // \"leftmost columns\" (along dim2) are dropped"," // for negative values of offset (below the main diagonal)"," // \"topmost rows\" (along dim1) are dropped."," // Note that we invert +/- in the second to absorb the negative"," // sign in the offset."," if (offset >= 0) {"," diag_size = std::max("," std::min(self.size(dim1), self.size(dim2) - offset), 0);"," std::min(self.size(dim1) + offset, self.size(dim2)), 0);"," // NumPy allows you to specify offsets \"off the end\"; let's just be careful"," // not to set a ridiculous storage_offset in that case (technically it"," // shouldn't matter because there are no elements in the tensor, but let's be"," // kosher)."," if (diag_size == 0) {"," // skip"," } else if (offset >= 0) {"," storage_offset += offset * self.stride(dim2);"," storage_offset -= offset * self.stride(dim1);"," // construct new size and stride: we drop dim1 and dim2 (maximum first for not"," // changing the index of the minimum) the new (\"joint\") dimension is appended"," // to the end of the shape / stride to match numpy semantics"," DimVector sizes(self.sizes().begin(), self.sizes().end());"," DimVector strides(self.strides().begin(), self.strides().end());"," sizes.erase(sizes.begin() + std::max(dim1, dim2));"," strides.erase(strides.begin() + std::max(dim1, dim2));"," sizes.erase(sizes.begin() + std::min(dim1, dim2));"," strides.erase(strides.begin() + std::min(dim1, dim2));"," sizes.push_back(diag_size);"," strides.push_back(self.stride(dim1) + self.stride(dim2));"," // return view with new parameters"," auto result = self.as_strided(sizes, strides, storage_offset);"," no_names_guard.reset();"," namedinference::propagate_names_if_nonempty(result, outnames);"," Dimname outdim,"," Dimname dim1,"," Dimname dim2,"," int64_t offset) {"," auto result = at::diagonal("," self,"," offset,"," dimname_to_position(self, dim1),"," dimname_to_position(self, dim2));"," // This is slower than it needs to be because there is no way to modify"," // the names of a tensor in-place right now. In the future we should consider"," // offering that functionality."," std::vector new_names = result.names().vec();"," new_names[new_names.size() - 1] = outdim;"," return result.refine_names(new_names);","Tensor diag_embed("," int64_t nDims = self.dim() + 1;"," int64_t new_dim_len = std::abs(offset) + self.size(-1);"," auto sizes = self.sizes().vec();"," sizes.pop_back();"," sizes.insert(sizes.begin() + std::min(dim1, dim2), new_dim_len);"," sizes.insert(sizes.begin() + std::max(dim1, dim2), new_dim_len);"," auto result = at::zeros(sizes, self.options());"," auto diag = result.diagonal(offset, dim1, dim2);"," diag.copy_(self);","Tensor expand(const Tensor& self, c10::IntArrayRef size, bool /*unused*/) {"," size.size() >= (size_t)self.dim(),"," \"expand(\","," self.toString(),"," \"{\","," self.sizes(),"," \"}, size=\","," \"): the number of sizes provided (\","," size.size(),"," \") \","," \"must be greater or equal to the number of dimensions in the tensor (\","," \")\");"," !self.is_sparse() && !at::sparse_csr::is_sparse_compressed(self),"," \"expand is unsupported for \","," self.layout(),"," \" tensors\");"," auto expandedSizesAndStrides ="," inferExpandGeometry_dimvector(self.sizes(), self.strides(), size);"," auto result = self.as_strided("," expandedSizesAndStrides.sizes, expandedSizesAndStrides.strides);"," namedinference::propagate_names_for_expand(result, self);","Tensor expand_as(const Tensor& self, const Tensor& other) {"," return self.expand_symint(other.sym_sizes());","Tensor sum_to_size_symint(const Tensor& self, SymIntArrayRef size) {"," is_expandable_to(size, self.sym_sizes()),"," \"size {\","," \"} is not expandable to size {\","," \"}.\");"," return sum_to(self, size);","// We currently do not support per-channel quant for unfold, diagonal, expand,","// permute.","// TODO: Make this an aten function and replace as_strided_qtensorimpl once that","// is done.","static Tensor make_qtensor("," IntArrayRef stride,"," QuantizerPtr quantizer) {"," auto result = at::detail::make_tensor("," c10::TensorImpl::VIEW,"," Storage(self.storage()),"," self.key_set(),"," self.dtype(),"," quantizer);"," setStrided(result, size, stride, self.storage_offset());","Tensor as_strided_tensorimpl("," std::optional storage_offset_) {"," TORCH_INTERNAL_ASSERT("," !self.is_mps(),"," \"as_strided_tensorimpl does not work with MPS; call self.as_strided(...) instead\");"," auto storage_offset = storage_offset_.value_or(self.storage_offset());"," auto result = at::detail::make_tensor("," self.dtype());"," setStrided(result, size, stride, storage_offset);","static inline void setStridedUnchecked("," ArrayRef size,"," ArrayRef stride,"," T&& storage_offset) {"," auto* self_ = self.unsafeGetTensorImpl();"," self_->set_sizes_and_strides(size, stride, std::forward(storage_offset));","Tensor as_strided_tensorimpl_meta_symint("," SymIntArrayRef sym_size,"," SymIntArrayRef sym_stride,"," std::optional sym_storage_offset_) {"," auto sym_storage_offset ="," sym_storage_offset_.value_or(self.sym_storage_offset());"," // NB: The reason this is unchecked is to ensure we don't generate"," // guards on the base storage itself when performing as_strided calls."," // Although technically these guards are necessary, in practice they"," // cause a lot of guards that falsely refer to base symbols. We will instead"," // rely on AOTAutograd to sort out if we actually have dependence on view"," // bases / storage size."," setStridedUnchecked("," result, sym_size, sym_stride, std::move(sym_storage_offset));","Tensor as_strided_qtensorimpl("," auto quantizer = get_qtensorimpl(self)->quantizer();"," quantizer->qscheme() == QScheme::PER_TENSOR_AFFINE,"," \"Setting strides is possible only on uniformly quantized tensor\");","// This is an overloaded function similar to","// Tensor as_strided_qtensorimpl(const Tensor& self, IntArrayRef size,","// IntArrayRef stride, std::optional storage_offset_) and is currently","// not available through the dispatcher. The additional input, quantizer, is","// called by the select & slice methods.","// TODO: Make this function compatible with the dispatcher","static Tensor as_strided_qtensorimpl("," std::optional storage_offset_,"," (quantizer->qscheme() == QScheme::PER_TENSOR_AFFINE) ||"," (quantizer->qscheme() == QScheme::PER_CHANNEL_AFFINE),"," \"Setting strides is possible only on uniformly or per channel quantized tensors\");","const Tensor& as_strided__symint("," SymIntArrayRef size,"," SymIntArrayRef stride,"," std::optional storage_offset_) {"," auto storage_offset = storage_offset_.value_or(self.sym_storage_offset());"," setStrided(self, size, stride, std::move(storage_offset));"," return self;","// Should just use narrow_copy_out, but this API is used internally at Meta:","// https://github.com/pytorch/pytorch/pull/87045#issuecomment-1309353561","Tensor narrow_copy_dense_cpu("," int64_t dim,"," int64_t start,"," int64_t length) {"," // narrow_copy_dense_cpu_out always resize output's size, so there only create"," // a zero size tensor."," auto output = at::empty({0}, self.options());"," return narrow_copy_dense_cpu_out(self, dim, start, length, output);","Tensor narrow_copy_sparse("," int64_t allDim = self.dim();"," int64_t end = start + length;"," TORCH_CHECK(allDim > 0, \"narrow() cannot be applied to a 0-dim tensor.\");"," TORCH_CHECK(length >= 0, \"narrow(): length must be non-negative.\");"," dim >= 0 && dim < allDim,"," \"Dimension \","," dim,"," \" out of range. Expecting 0 <= dim < \","," allDim,"," start >= 0 && end <= self.size(dim),"," \"Invalid range to narrow. range(start, start+length) must be a subset of range(0, \","," self.size(dim),"," \").\")"," Tensor indices = self._indices();"," int64_t sparse_dim = self.sparse_dim();"," std::vector new_sizes = self.sizes().vec();"," new_sizes[dim] = length;"," Tensor new_values;"," Tensor new_indices;"," if (dim < sparse_dim) {"," Tensor mask = (indices[dim] >= start).__and__((indices[dim] < end));"," new_indices = indices.masked_select(mask).view({sparse_dim, -1});"," new_indices[dim].sub_(start);"," Tensor nzIndices = mask.nonzero().view(-1);"," new_values = self._values().index_select(0, nzIndices);"," /* This means we are narrowing on a dense dim, which is in effect just a"," regular narrow on _values() */"," new_indices = indices;"," int64_t dense_dim = dim - sparse_dim + 1;"," new_values = self._values().narrow_copy(dense_dim, start, length);"," new_indices, new_values, new_sizes, self.options(), self.is_coalesced());","Tensor& narrow_copy_dense_cpu_out("," int64_t length,"," Tensor& output) {"," TORCH_CHECK(self.dim() > 0, \"narrow() cannot be applied to a 0-dim tensor.\");"," TORCH_CHECK(self.dtype() == output.dtype());"," auto self_contig = self.expect_contiguous();"," const auto self_sizes = self_contig->sizes();"," // wrap dim if negative and do bound check"," if (dim < 0) {"," dim = at::maybe_wrap_dim(dim, self_sizes.size());"," TORCH_CHECK(dim < static_cast(self_sizes.size()));"," // wrap start and do bound check"," const auto cur_size = self_sizes[dim];"," TORCH_CHECK_INDEX("," -cur_size <= start && start <= cur_size,"," \"start out of range (expected to be in range of [\","," -cur_size,"," cur_size,"," \"], but got \","," start,"," \")\")"," if (start < 0) {"," start = start + cur_size;"," length >= 0 && start <= cur_size - length,"," \"start (\","," \") + length (\","," length,"," \") exceeds dimension size (\","," \").\");"," // resize output"," auto output_sizes = self_sizes.vec();"," output_sizes[dim] = length;"," at::native::resize_(output, output_sizes);"," // NOLINTNEXTLINE(bugprone-narrowing-conversions,cppcoreguidelines-narrowing-conversions)"," const int64_t unit = c10::size_from_dim_(dim + 1, self_sizes);"," const int64_t num_blocks = c10::size_to_dim_(dim, self_sizes);"," const auto itemsize = self_contig->dtype().itemsize();"," size_t src_nbytes = itemsize * self_contig->numel();"," size_t dst_nbytes = itemsize * output.numel();"," size_t src_block_size = unit * self_sizes[dim];"," size_t dst_block_size = unit * length;"," if (num_blocks == 0 || dst_block_size == 0) {"," return output;"," const char* src_bytes ="," static_cast(self_contig->const_data_ptr());"," char* dst_bytes = static_cast(output.data_ptr());"," size_t src_block_size_bytes = itemsize * src_block_size;"," size_t dst_block_size_bytes = itemsize * dst_block_size;"," size_t src_offset = unit * start;"," const char* src_offset_bytes = src_bytes + itemsize * src_offset;"," char* dst_offset_bytes = dst_bytes;"," for (const auto i : c10::irange(num_blocks)) {"," const char* local_src_offset_bytes ="," src_offset_bytes + i * src_block_size_bytes;"," char* local_dst_offset_bytes = dst_offset_bytes + i * dst_block_size_bytes;"," TORCH_INTERNAL_ASSERT_DEBUG_ONLY("," static_cast("," local_src_offset_bytes + dst_block_size_bytes) <="," static_cast(src_bytes + src_nbytes));"," static_cast(local_dst_offset_bytes + dst_block_size_bytes) <="," static_cast(dst_bytes + dst_nbytes));"," memcpy("," local_dst_offset_bytes, local_src_offset_bytes, dst_block_size_bytes);"," return output;","Tensor narrow(const Tensor& self, int64_t dim, int64_t start, int64_t length) {"," auto cur_size = self.size(dim);"," start <= cur_size - length,"," return at::slice(self, dim, start, start + length, 1);","Tensor narrow_symint("," SymInt start,"," SymInt length) {"," TORCH_SYM_CHECK(length.sym_ge(0), \"narrow(): length must be non-negative.\");"," auto cur_size = self.sym_size(dim);"," ((-cur_size).sym_le(start).sym_and(start.sym_le(cur_size)))"," .expect_true(__FILE__, __LINE__),"," TORCH_SYM_CHECK("," start.sym_le(cur_size - length),"," return at::slice_symint(self, dim, start, start + length, 1);","// This overload exists purely for XLA, because they wanted to pass in","// \"symbolic\" start via Tensor.","Tensor narrow_tensor_symint("," const Tensor& start,"," start.dim() == 0 &&"," isIntegralType(start.scalar_type(), /*includeBool=*/false),"," \"start must be an 0-dim integral Tensor.\");"," int64_t st = start.item();"," return at::narrow_symint(self, dim, c10::SymInt(st), std::move(length));","std::"," tuple> static _permute_size_stride_estimation("," const Tensor& self,"," IntArrayRef dims) {"," const auto ndim = self.dim();"," ndim == static_cast(dims.size()),"," \"permute(sparse_coo): number of dimensions in the tensor input \","," \"does not match the length of the desired ordering of dimensions \","," \"i.e. input.dim() = \","," ndim,"," \" is not equal to len(dims) = \","," dims.size());"," const auto is_strided_layout = self.options().layout() == at::kStrided;"," const auto old_sizes = self.sizes();"," const auto old_strides = is_strided_layout ? self.strides() : IntArrayRef{};"," auto new_sizes = DimVector(ndim);"," auto new_strides = DimVector(is_strided_layout ? ndim : 0);"," auto wrapped_dims = std::vector(ndim);"," std::vector seen_dims(ndim);"," for (const auto i : c10::irange(ndim)) {"," const auto d = maybe_wrap_dim(dims[i], ndim);"," TORCH_CHECK(!seen_dims[d], \"permute(): duplicate dims are not allowed.\");"," seen_dims[d] = true;"," wrapped_dims[i] = d;"," new_sizes[i] = old_sizes[d];"," if (is_strided_layout) {"," new_strides[i] = old_strides[d];"," return std::make_tuple(new_sizes, new_strides, wrapped_dims);","Tensor permute(const Tensor& self, IntArrayRef dims) {"," auto [new_sizes, new_strides, _] ="," _permute_size_stride_estimation(self, dims);"," return self.as_strided(new_sizes, new_strides);","Tensor permute_sparse_coo(const Tensor& self, IntArrayRef dims) {"," auto [new_sizes, _, wrapped_dims] ="," const auto sparse_ndim = self.sparse_dim();"," const auto dense_ndim = self.dense_dim();"," auto dims_id_perm = std::vector(ndim);"," auto dims_sparse_dense_id_perm = std::vector(ndim);"," dims_id_perm[i] = i;"," dims_sparse_dense_id_perm[i] = wrapped_dims[i];"," std::sort("," dims_sparse_dense_id_perm.begin(),"," dims_sparse_dense_id_perm.begin() + sparse_ndim);"," dims_sparse_dense_id_perm.begin() + sparse_ndim,"," dims_sparse_dense_id_perm.end());"," dims_sparse_dense_id_perm == dims_id_perm,"," \"permute(sparse_coo): transpositions between sparse and dense dimensions are not allowed.\","," \"Only transpositions within sparse and dense dimensions are supported.\");"," const auto slice ="," [](std::vector v, size_t begin, size_t len) -> decltype(v) {"," return std::vector{v.begin() + begin, v.begin() + begin + len};"," };"," auto old_sparse_dims = slice(dims_id_perm, 0, sparse_ndim);"," auto old_dense_dims ="," slice(std::move(dims_id_perm), sparse_ndim, ndim - sparse_ndim);"," auto new_sparse_dims = slice(wrapped_dims, 0, sparse_ndim);"," auto new_dense_dims ="," slice(std::move(wrapped_dims), sparse_ndim, ndim - sparse_ndim);"," auto old_indices = self._indices();"," auto old_values = self._values();"," const auto new_indices = (new_sparse_dims == old_sparse_dims)"," ? std::move(old_indices)"," : [&]() -> Tensor {"," auto sparse_perm_tensor = at::from_blob("," reinterpret_cast(new_sparse_dims.data()),"," {sparse_ndim},"," old_indices.options().device(at::kCPU));"," // creates new indices. It is possible to avoid that if COO"," // is allowed to store a permutation vector."," return old_indices.index_select("," 0, sparse_perm_tensor.to(self.device().type()));"," }();"," const auto new_values = (new_dense_dims == old_dense_dims)"," ? std::move(old_values)"," auto values_perm = std::vector(dense_ndim + 1);"," for (const auto i : c10::irange(dense_ndim)) {"," values_perm[i + 1] = new_dense_dims[i] - sparse_ndim + 1;"," return old_values.permute(values_perm);"," const auto is_coalesced ="," self.is_coalesced() && (dims.empty() || dims[0] == 0);"," // TODO: apply `is_coalesced ||= new_values.size(0) < 2`."," return _sparse_coo_tensor_with_dims_and_tensors("," sparse_ndim,"," dense_ndim,"," new_sizes,"," new_indices,"," new_values,"," self.options(),"," is_coalesced);","Tensor repeat(const Tensor& self, IntArrayRef repeats) {"," repeats.size() >= (size_t)self.dim(),"," \"Number of dimensions of repeat dims can not be smaller than number of dimensions of tensor\");"," // Add new leading dimensions to the tensor if the"," // number of target dimensions is larger than the"," // number of source dimensions."," int64_t num_new_dimensions = repeats.size() - self.dim();"," DimVector padded_size(num_new_dimensions, 1);"," padded_size.insert("," padded_size.end(), self.sizes().begin(), self.sizes().end());"," DimVector target_size(repeats.size());"," bool zero_tensor = false;"," for (const auto idx : c10::irange(repeats.size())) {"," if (repeats[idx] == 0) {"," zero_tensor = true;"," target_size[idx] = padded_size[idx] * repeats[idx];"," Tensor xtensor = self.expand(padded_size);"," if (self.is_quantized()) {"," result = at::empty_quantized(target_size, self);"," result = at::empty(target_size, self.options());"," // return an empty tensor if one of the repeat dimensions is zero"," if (zero_tensor) {"," Tensor urtensor = at::alias(result);"," for (const auto i : c10::irange(xtensor.dim())) {"," // can't unfold with step 0, so make sure step is at least 1"," // (it doesn't matter what it is in that case, because the size is 0)."," auto size_i = xtensor.sizes()[i];"," urtensor = urtensor.unfold(i, size_i, std::max(size_i, 1));"," urtensor.copy_(xtensor.expand_as(urtensor));","Tensor tile_symint(const Tensor& self, SymIntArrayRef reps) {"," // If self.size() > len(reps), reps is promoted to self.size() by pre-pending"," // 1’s to it to keep the same behaviour as `numpy.tile`."," // Thus for a tensor of shape (2, 3, 4, 5), a dims of (2, 2) is treated"," // as (1, 1, 2, 2)."," const int64_t size_diff = self.dim() - static_cast(reps.size());"," if (size_diff > 0) {"," std::vector new_reps(size_diff, 1);"," for (const auto i : c10::irange(reps.size())) {"," new_reps.emplace_back(reps[i]);"," return self.repeat_symint(SymIntArrayRef(new_reps));"," // `torch.tile` is equivalent to the already implemented `torch.Tensor.repeat`"," return self.repeat_symint(reps);","//","// templated for ArrayRef and SmallVector use cases","template ","static Tensor alias_with_sizes_and_strides("," const Vec& sizes,"," const Vec& strides) {"," // caller should make sure that sizes and strides are valid for self"," //(storage is sufficient, strides are non-negative, strides and sizes array"," // size is the same)"," Tensor self_;"," self_ = at::detail::make_tensor("," c10::TensorImpl::VIEW,"," Storage(self.storage()),"," self.key_set(),"," self.dtype(),"," get_qtensorimpl(self)->quantizer());"," auto* self_tmp_ = self_.unsafeGetTensorImpl();"," self_tmp_->set_storage_offset(self.storage_offset());"," self_tmp_->set_sizes_and_strides(sizes, strides);"," self_ = at::detail::make_tensor("," self.dtype());"," namedinference::propagate_names(self_, self);"," return self_;","// specialization for symbolic shapes and strides.","// SymIntArrayRef/ArrayRef and","// SmallVector/SymDimVector","template