mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
ecc5e05854
158 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
Pearu Peterson
|
cffea773e3 |
Fix bsr_dense_mm with a non-contiguous out argument. (#113801)
Fixes https://github.com/pytorch/pytorch/issues/113754 Pull Request resolved: https://github.com/pytorch/pytorch/pull/113801 Approved by: https://github.com/cpuhrsch |
||
|
Pearu Peterson
|
e1c872e009 |
Add optimal triton kernel parameters to bsr_dense_mm and scatter_mm for bfloat16 and float32 dtypes (#113553)
As in the title. This PR is a follow-up to PR https://github.com/pytorch/pytorch/pull/112737 to address bfloat16 and float32 dtype cases. The performance increase is as follows (`NVIDIA A100-SXM4-80GB`): - bsr_scatter_mm and bfloat16 - for blocksize 16x16, the average/maximum speed up is about 29/75 %. - for blocksize 32x32, the average/maximum speed up is about 23/58 %. - for blocksize 64x64, the average/maximum speed up is about 27/66 %. - for blocksize 128x128, the average/maximum speed up is about 33/72 %. - bsr_dense_mm and bfloat16 - for blocksize 16x16, the average/maximum speed up is about 47/61 %. - for blocksize 32x32, the average/maximum speed up is about 29/43 %. - for blocksize 64x64, the average/maximum speed up is about 21/41 %. - for blocksize 128x128, the average/maximum speed up is about 12/29 %. - bsr_dense_mm and float32 - for blocksize 16x16, the average/maximum speed up is about 35/49 %. - for blocksize 32x32, the average/maximum speed up is about 2/5 %. - for blocksize 64x64, the average/maximum speed up is about 2/21 %. - for blocksize 128x128, the average/maximum speed up is about 79/84 %. Pull Request resolved: https://github.com/pytorch/pytorch/pull/113553 Approved by: https://github.com/cpuhrsch |
||
|
Siddharth Mishra
|
fe5d8850e2 |
Fixed docstring errors in _fuser.py, _state.py, __init__.py, _freeze.py, _async.py, _recursive.py, _tensorboard_vis.py, _trace.py, _await.py, _check.py, _serialization.py, _script.py, annotations.py, _monkeytype_config.py (#113371)
Fixes #113194 docstrings updated. Here are the outputs with the number before and after:- 1) torch/sparse/__init__.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:1 at module level: D104: Missing docstring in public package /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:183 in public function `sum`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:183 in public function `sum`: D400: First line should end with a period (not 'n') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:183 in public function `sum`: D401: First line should be in imperative mood (perhaps 'Return', not 'Returns') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:391 in public class `check_sparse_tensor_invariants`: D207: Docstring is under-indented /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:436 in public method `is_enabled`: D207: Docstring is under-indented /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:436 in public method `is_enabled`: D401: First line should be in imperative mood (perhaps 'Return', not 'Returns') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:448 in public method `enable`: D207: Docstring is under-indented /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:468 in public method `disable`: D207: Docstring is under-indented /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:475 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:479 in public method `__enter__`: D105: Missing docstring in magic method /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:486 in public method `__exit__`: D105: Missing docstring in magic method /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:492 in public method `__call__`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:502 in public function `as_sparse_gradcheck`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:502 in public function `as_sparse_gradcheck`: D400: First line should end with a period (not 'l') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:502 in public function `as_sparse_gradcheck`: D401: First line should be in imperative mood (perhaps 'Decorate', not 'Decorator') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:518 in private nested function `gradcheck_with_sparse_support`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:518 in private nested function `gradcheck_with_sparse_support`: D400: First line should end with a period (not 's') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:518 in private nested function `gradcheck_with_sparse_support`: D401: First line should be in imperative mood; try rephrasing (found 'Same') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:528 in private nested function `convert_to_strided_representation`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:528 in private nested function `convert_to_strided_representation`: D400: First line should end with a period (not 'n') /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:559 in private nested function `restore_from_strided_representation`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:559 in private nested function `restore_from_strided_representation`: D400: First line should end with a period (not 'd') 23 ``` After: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:1 at module level: D104: Missing docstring in public package /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:476 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:480 in public method `__enter__`: D105: Missing docstring in magic method /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:487 in public method `__exit__`: D105: Missing docstring in magic method /home/ubuntu/Desktop/Docathon/pytorch/torch/sparse/__init__.py:493 in public method `__call__`: D102: Missing docstring in public method 5 ``` 2) torch/contrib/_tensorboard_vis.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/contrib/_tensorboard_vis.py:21 in public function `dump_tensorboard_summary`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/contrib/_tensorboard_vis.py:54 in public function `visualize_graph_executor`: D401: First line should be in imperative mood (perhaps 'Append', not 'Appends') 2 ``` After: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/contrib/_tensorboard_vis.py:21 in public function `dump_tensorboard_summary`: D103: Missing docstring in public function 1 ``` 3) torch/jit/_state.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:1 at module level: D400: First line should end with a period (not 'e') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:20 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:25 in public method `parse_env`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:41 in public method `__bool__`: D105: Missing docstring in magic method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:48 in public function `disable`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:52 in public function `enable`: D103: Missing docstring in public function 6 ``` After: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:20 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:25 in public method `parse_env`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:41 in public method `__bool__`: D105: Missing docstring in magic method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:48 in public function `disable`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_state.py:52 in public function `enable`: D103: Missing docstring in public function 5 ``` 4) torch/jit/_monkeytype_config.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:27 in public function `is_torch_native_class`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:40 in public function `get_type`: D200: One-line docstring should fit on one line with quotes (found 3) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:40 in public function `get_type`: D401: First line should be in imperative mood; try rephrasing (found 'Helper') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:62 in public function `get_optional_of_element_type`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:62 in public function `get_optional_of_element_type`: D400: First line should end with a period (not 'l') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:62 in public function `get_optional_of_element_type`: D401: First line should be in imperative mood; try rephrasing (found 'Helper') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:75 in public function `get_qualified_name`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:84 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:87 in public method `log`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:90 in public class `JitTypeTraceStore`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:91 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:98 in public method `add`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:103 in public method `filter`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:111 in public method `analyze`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:122 in public method `consolidate_types`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:139 in public method `get_args_types`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:142 in public class `JitTypeTraceConfig`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:143 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:148 in public method `trace_logger`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:148 in public method `trace_logger`: D400: First line should end with a period (not 'd') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:148 in public method `trace_logger`: D401: First line should be in imperative mood (perhaps 'Return', not 'Returns') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:154 in public method `trace_store`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:157 in public method `code_filter`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:163 in public class `JitTypeTraceStoreLogger`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:164 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:167 in public class `JitTypeTraceStore`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:168 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:171 in public class `JitTypeTraceConfig`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:172 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:179 in public function `jit_code_filter`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:179 in public function `jit_code_filter`: D401: First line should be in imperative mood; try rephrasing (found 'Custom') 31 ``` After: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:27 in public function `is_torch_native_class`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:74 in public function `get_qualified_name`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:83 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:86 in public method `log`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:89 in public class `JitTypeTraceStore`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:90 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:97 in public method `add`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:102 in public method `filter`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:110 in public method `analyze`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:121 in public method `consolidate_types`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:138 in public method `get_args_types`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:141 in public class `JitTypeTraceConfig`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:142 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:150 in public method `trace_store`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:153 in public method `code_filter`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:159 in public class `JitTypeTraceStoreLogger`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:160 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:163 in public class `JitTypeTraceStore`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:164 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:167 in public class `JitTypeTraceConfig`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_monkeytype_config.py:168 in public method `__init__`: D107: Missing docstring in __init__ 21 ``` 5) torch/jit/_fuser.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_fuser.py:9 in public function `optimized_execution`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_fuser.py:9 in public function `optimized_execution`: D400: First line should end with a period (not 'n') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_fuser.py:9 in public function `optimized_execution`: D401: First line should be in imperative mood; try rephrasing (found 'A') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_fuser.py:23 in public function `fuser`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_fuser.py:23 in public function `fuser`: D400: First line should end with a period (not 'n') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_fuser.py:23 in public function `fuser`: D401: First line should be in imperative mood; try rephrasing (found 'A') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_fuser.py:136 in public function `set_fusion_strategy`: D401: First line should be in imperative mood (perhaps 'Set', not 'Sets') 7 ``` After: ``` 0 ``` 6) torch/jit/_async.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:1 at module level: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:1 at module level: D400: First line should end with a period (not 'I') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:20 in public function `fork`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:20 in public function `fork`: D400: First line should end with a period (not 'e') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:20 in public function `fork`: D401: First line should be in imperative mood (perhaps 'Create', not 'Creates') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:88 in public function `wait`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:88 in public function `wait`: D400: First line should end with a period (not 'e') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_async.py:88 in public function `wait`: D401: First line should be in imperative mood (perhaps 'Force', not 'Forces') 8 ``` After: ``` 0 ``` 7) torch/jit/_await.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:11 in private function `_awaitable`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:11 in private function `_awaitable`: D400: First line should end with a period (not ',') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:11 in private function `_awaitable`: D401: First line should be in imperative mood (perhaps 'Create', not 'Creates') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:19 in private function `_awaitable_wait`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:19 in private function `_awaitable_wait`: D400: First line should end with a period (not ',') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:19 in private function `_awaitable_wait`: D401: First line should be in imperative mood (perhaps 'Request', not 'Requests') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:27 in private function `_awaitable_nowait`: D200: One-line docstring should fit on one line with quotes (found 3) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_await.py:27 in private function `_awaitable_nowait`: D401: First line should be in imperative mood (perhaps 'Create', not 'Creates') 8 ``` After: ``` 0 ``` 8) torch/jit/_check.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:10 in public class `AttributeTypeIsSupportedChecker`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:10 in public class `AttributeTypeIsSupportedChecker`: D400: First line should end with a period (not 'e') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:10 in public class `AttributeTypeIsSupportedChecker`: D412: No blank lines allowed between a section header and its content ('Example') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:61 in public method `check`: D102: Missing docstring in public method /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:110 in public method `visit_Assign`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:110 in public method `visit_Assign`: D400: First line should end with a period (not 'n') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:132 in public method `visit_AnnAssign`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:132 in public method `visit_AnnAssign`: D400: First line should end with a period (not '`') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:187 in public method `visit_Call`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:187 in public method `visit_Call`: D400: First line should end with a period (not '`') 10 ``` After: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_check.py:58 in public method `check`: D102: Missing docstring in public method 1 ``` 9) torch/jit/_freeze.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:1 at module level: D400: First line should end with a period (not 'g') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:16 in public function `freeze`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:16 in public function `freeze`: D400: First line should end with a period (not 'd') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:127 in public function `run_frozen_optimizations`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:127 in public function `run_frozen_optimizations`: D401: First line should be in imperative mood (perhaps 'Run', not 'Runs') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:182 in public function `optimize_for_inference`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:182 in public function `optimize_for_inference`: D400: First line should end with a period (not 'e') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_freeze.py:182 in public function `optimize_for_inference`: D401: First line should be in imperative mood (perhaps 'Perform', not 'Performs') 8 ``` After: ``` 0 ``` 10) torch/jit/_recursive.py Before: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:69 in public function `make_stub`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:75 in public function `make_stub_from_method`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:90 in public function `make_stubs_from_exported_methods`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:103 in public function `jit_ignored_properties`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:155 in public class `SourceContext`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:156 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:160 in public function `get_annotations`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:186 in public function `infer_concrete_type_builder`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:186 in public function `infer_concrete_type_builder`: D400: First line should end with a period (not 's') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:423 in public class `ConcreteTypeStore`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:427 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:434 in public method `get_or_create_concrete_type`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:434 in public method `get_or_create_concrete_type`: D400: First line should end with a period (not 'T') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:459 in public function `create_methods_and_properties_from_stubs`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:474 in public function `create_hooks_from_stubs`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:485 in public function `get_module_concrete_type`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:485 in public function `get_module_concrete_type`: D400: First line should end with a period (not 'e') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:485 in public function `get_module_concrete_type`: D401: First line should be in imperative mood (perhaps 'Get', not 'Gets') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:539 in public function `create_script_module`: D400: First line should end with a period (not 'e') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:539 in public function `create_script_module`: D401: First line should be in imperative mood (perhaps 'Create', not 'Creates') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:725 in public function `script_model_defines_attr`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:735 in public function `add_python_attr_to_scripted_model`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:740 in public function `get_overload_annotations`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:772 in public function `get_overload_name_mapping`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:797 in public function `make_stubs_for_overloads`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:816 in public function `check_module_initialized`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:842 in public function `infer_methods_to_compile`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:842 in public function `infer_methods_to_compile`: D400: First line should end with a period (not 'g') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:842 in public function `infer_methods_to_compile`: D401: First line should be in imperative mood (perhaps 'Implement', not 'Implements') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:904 in public function `get_hook_stubs`: D200: One-line docstring should fit on one line with quotes (found 3) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:904 in public function `get_hook_stubs`: D400: First line should end with a period (not 's') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:904 in public function `get_hook_stubs`: D401: First line should be in imperative mood (perhaps 'Return', not 'Returns') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:940 in public function `get_property_stubs`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:940 in public function `get_property_stubs`: D400: First line should end with a period (not 'd') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:963 in public function `interface_script`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:963 in public function `interface_script`: D400: First line should end with a period (not 'r') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:963 in public function `interface_script`: D401: First line should be in imperative mood (perhaps 'Make', not 'Makes') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:977 in private nested function `infer_interface_methods_to_compile`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:977 in private nested function `infer_interface_methods_to_compile`: D400: First line should end with a period (not 'h') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:989 in public function `try_compile_fn`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1014 in public function `wrap_cpp_class`: D200: One-line docstring should fit on one line with quotes (found 3) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1021 in public function `wrap_cpp_module`: D200: One-line docstring should fit on one line with quotes (found 3) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1021 in public function `wrap_cpp_module`: D400: First line should end with a period (not 's') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1040 in public function `compile_unbound_method`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1052 in public function `lazy_bind`: D205: 1 blank line required between summary line and description (found 0) /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1052 in public function `lazy_bind`: D400: First line should end with a period (not 'd') /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1052 in public function `lazy_bind`: D401: First line should be in imperative mood (perhaps 'Return', not 'Returns') 47 ``` After: ``` /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:69 in public function `make_stub`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:75 in public function `make_stub_from_method`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:90 in public function `make_stubs_from_exported_methods`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:103 in public function `jit_ignored_properties`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:155 in public class `SourceContext`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:156 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:160 in public function `get_annotations`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:424 in public class `ConcreteTypeStore`: D101: Missing docstring in public class /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:428 in public method `__init__`: D107: Missing docstring in __init__ /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:457 in public function `create_methods_and_properties_from_stubs`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:472 in public function `create_hooks_from_stubs`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:724 in public function `script_model_defines_attr`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:734 in public function `add_python_attr_to_scripted_model`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:739 in public function `get_overload_annotations`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:771 in public function `get_overload_name_mapping`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:796 in public function `make_stubs_for_overloads`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:815 in public function `check_module_initialized`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:979 in public function `try_compile_fn`: D103: Missing docstring in public function /home/ubuntu/Desktop/Docathon/pytorch/torch/jit/_recursive.py:1026 in public function `compile_unbound_method`: D103: Missing docstring in public function 19 ``` @svekars Pull Request resolved: https://github.com/pytorch/pytorch/pull/113371 Approved by: https://github.com/davidberard98 |
||
|
Aaron Gokaslan
|
8219bf051b |
[BE]: Apply RUF015 to torch folder (#113025)
Removes unnecessary allocations of iterators. There is a small chance this may have side effects as the entire iterator is no longer consumed, but this is a way more efficient method for retrieving the first element. Pull Request resolved: https://github.com/pytorch/pytorch/pull/113025 Approved by: https://github.com/ezyang, https://github.com/malfet |
||
|
Pearu Peterson
|
e64d250210 |
Add a tool for a semi-automatic optimization of bsr_dense_mm meta parameters. (#112737)
Finding optimal meta parameters for bsr_dense_mm and bsr_scatter_mm triton kernels is a tedious job. This PR introduces a tool (a Python script `torch/sparse/_triton_ops_meta.py`) that finds the optimal set of meta parameters for a given set of matrix multiplication inputs and their block sizes. Currently, such a set is found for square bsr tensor inputs with sizes 256...16384 and square blocksizes 16...128, and dense tensor inputs with sizes 256...131072. As a result, bsr_dense_mm performance has increased as follows (`NVIDIA A100-SXM4-80GB`): - for blocksize 16x16, the average/maximum speed up is about 40/60 %. - for blocksize 32x32, the average/maximum speed up is about 28/45 %. - for blocksize 64x64, the average/maximum speed up is about 26/43 %. - for blocksize 128x128, the average/maximum speed up is about 12/28 %. To enable the performance improvements through meta parameter optimization for other CUDA devices, one must execute the `_triton_ops_meta.py` which will calculate the optimal meta parameters and store the results in a dictionary object defined in `_triton_ops_meta.py`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/112737 Approved by: https://github.com/cpuhrsch |
||
|
Pearu Peterson
|
33c41daf60 |
Fix scatter_mm kernel failure on non-contiguous tensor arguments (#112337)
This PR fixes ``` RuntimeError: Triton Error [CUDA]: an illegal memory access was encountered ``` that appears when using large non-contiguous tensor arguments in `scatter_mm` kernel launch. Pull Request resolved: https://github.com/pytorch/pytorch/pull/112337 Approved by: https://github.com/cpuhrsch ghstack dependencies: #112154, #112076 |
||
|
Pearu Peterson
|
cf6041e942 |
Use weakref in storing tensors as keys (follow-up to #111470) (#112076)
This PR addresses the discussion items in https://github.com/pytorch/pytorch/pull/111470#discussion_r1369008167, that is, - use weakref when storing tensors as keys, - add `storage_offset` to the key data, - and revise the description of the `TensorAsKey` utility. Pull Request resolved: https://github.com/pytorch/pytorch/pull/112076 Approved by: https://github.com/cpuhrsch ghstack dependencies: #112154 |
||
|
Jesse Cai
|
702aaf8aea |
[sparse] semi-structured sparse + torch.compile support (#111049)
Summary: This PR adds in torch.compile support for semi-structured sparsity, using the subclass tracing @bdhirsh added. Based on wether we are using cuSPARSELt or CUTLASS, we return a different representation of the inner tensors. Test Plan: ``` python test/test_sparse_semi_structured.py -k compile ``` Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/111049 Approved by: https://github.com/cpuhrsch |
||
|
Pearu Peterson
|
b969c675f5 |
Add batched dimensions support to the second operand of bsr_scatter_mm (#111796)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111796 Approved by: https://github.com/cpuhrsch ghstack dependencies: #110396, #111470, #111489, #111760 |
||
|
Pearu Peterson
|
6382011843 |
Add NVIDIA A100 optimized meta parameters to bsr_dense_mm (#111760)
As in the title. The figures below illustrate the performance differences of bsr_dense_mm with optimized parameters and bsr_dense_mm with default parameters (GPU: NVIDIA A100-SXM4-80GB). The first figure represents the performance equilibrium point in BSR tensor sparsity at which value bsr_dense_mm have the same performance characteristics as torch.matmul. The second figure represents speedups from using optimized meta parameters in bsr_dense_mm at its performance equilibrium points with respect to bsr_dense_mm with default meta parameters. In sum, this PR speeds up `bsr_dense_mm` about 50 % depending on the bsr tensor shape and blocksize and lowers the performance equilibrium points of BSR tensor sparsity and strided tensor for matmul operations. <img src="https://github.com/pytorch/pytorch/assets/402156/6fe9d35f-dd21-4aa0-bb01-6ee257254453" width="48%"> <img src="https://github.com/pytorch/pytorch/assets/402156/506921c6-3770-4209-ad3d-498d2ae4989d" width="48%"> Pull Request resolved: https://github.com/pytorch/pytorch/pull/111760 Approved by: https://github.com/cpuhrsch ghstack dependencies: #110396, #111470, #111489 |
||
|
Pearu Peterson
|
f3d08ab271 |
Use more performant bsr_scatter_mm within bsr_dense_mm when blocksize is 16. (#111489)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111489 Approved by: https://github.com/cpuhrsch ghstack dependencies: #110396, #111470 |
||
|
Pearu Peterson
|
6078ed95cc |
Use lru_cache to cache indices data for bsr_scatter_mm. (#111470)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/111470 Approved by: https://github.com/cpuhrsch ghstack dependencies: #110396 |
||
|
Pearu Peterson
|
d4708a6da7 |
Add scatter_mm and bsr_scatter_mm operations. (#110396)
This PR introduces `scatter_mm` operation (compute `mm` of arbitrary pairs of tensors given in batches of tensors) that is used to implement `bsr_scatter_mm` that is equivalent to `bsr_dense_mm` (the `mm` operation on bsr and strided tensors). The implementation is provided both in Triton (when tensor dimensions are multiples of 16) and in PyTorch (otherwise). The figures below illustrate the performance differences of `bsr_scatter_mm` and `bsr_dense_mm` (GPU: `NVIDIA GeForce RTX 2060 SUPER`). The first figure represents the performance equilibrium point in BSR tensor sparsity at which value `bsr_scatter_mm` or `bsr_dense_mm` have the same performance characteristics as `torch.matmul`. The second figure represents speedups from using `bsr_scatter_mm` at its performance equilibrium points with respect to `bsr_dense_mm`. <img src="https://github.com/pytorch/pytorch/assets/402156/526d182e-937f-4812-a6c4-904f52d6d5ab" width="48%"> <img src="https://github.com/pytorch/pytorch/assets/402156/ccb606ab-1f3f-4133-887c-b56285f4f168" width="48%"> The same figures for GPU card `NVIDIA A100-SXM4-80GB`: <img src="https://github.com/pytorch/pytorch/assets/402156/25466f1d-df34-4d1c-a975-afb478e4d9f0" width="48%"> <img src="https://github.com/pytorch/pytorch/assets/402156/6ada91f0-a20f-4f0d-8a48-1f4ccc60d08e" width="48%"> In sum: - `bsr_scatter_mm` is about 2x faster than `bsr_dense_mm` for small block sizes of 16 and 32 and large tensors [GPU: `NVIDIA GeForce RTX 2060 SUPER`]. - `bsr_scatter_mm` is up to 2x faster than `bsr_dense_mm` for small block sizes of 16 and large tensors [GPU: `NVIDIA A100-SXM4-80GB`]. - `bsr_dense_mm` is up to 20 % faster than `bsr_scatter_mm` for block sizes of 64 or larger [GPU: `NVIDIA GeForce RTX 2060 SUPER`]. - However, `bsr_dense_mm` fails with `OutOfResources` exception for block sizes of 256 or larger whereas `bsr_scatter_mm` succeeds. Pull Request resolved: https://github.com/pytorch/pytorch/pull/110396 Approved by: https://github.com/cpuhrsch |
||
|
PyTorch MergeBot
|
41490119f2 |
Revert "[sparse] semi-structured sparse + torch.compile support (#111049)"
This reverts commit |
||
|
Jesse Cai
|
408f210938 |
[sparse] semi-structured sparse + torch.compile support (#111049)
Summary: This PR adds in torch.compile support for semi-structured sparsity, using the subclass tracing @bdhirsh added. Based on wether we are using cuSPARSELt or CUTLASS, we return a different representation of the inner tensors. Test Plan: ``` python test/test_sparse_semi_structured.py -k compile ``` Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/111049 Approved by: https://github.com/cpuhrsch ghstack dependencies: #110583 |
||
|
PyTorch MergeBot
|
b4745d476c |
Revert "[sparse] semi-structured sparse + torch.compile support (#111049)"
This reverts commit
|
||
|
Jesse Cai
|
ac02531bab |
[sparse] semi-structured sparse + torch.compile support (#111049)
Summary: This PR adds in torch.compile support for semi-structured sparsity, using the subclass tracing @bdhirsh added. Based on wether we are using cuSPARSELt or CUTLASS, we return a different representation of the inner tensors. Test Plan: ``` python test/test_sparse_semi_structured.py -k compile ``` Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/111049 Approved by: https://github.com/cpuhrsch ghstack dependencies: #110583 |
||
|
Jesse Cai
|
8db72a430d |
[sparse] Add padding for dense matrices in semi-structured sparse (#110583)
Summary: Currently we have shape constraints in semi-structured sparsity for both CUTLASS and cuSPARSELt These shape constraints unfortunately apply to both the dense and sparse matrices in sparsedense matmul. This PR adds in support for calling `F.pad` in order to pad dense matrices to the right size with zeros and then pull out the corresponding rows from the resultant result matrix. We also throw a warning in this case. The tests have also been updated to take in a dense_input_shape parameter. Test Plan: ``` python test/test_sparse_semi_structured.py ``` Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/110583 Approved by: https://github.com/alexsamardzic, https://github.com/cpuhrsch |
||
|
Jesse Cai
|
f10aab03c4 |
[sparse] Fix semi-structured sparse shape mismatch bug (#110420)
Summary: Currently, PyTorch incorrectly calculates the size of the returned matrix when we pass a non-contiguous batched (>2d) input to the semi-structured sparse subclass. This is most common in MLP layers, where we have 2 linear layers back to back. This will lead to an error like the following: ``` RuntimeError: shape '[20, 64, 64, 3072]' is invalid for input of size 62914560 ``` Where the size of the sparse matmul result is off because we infer the output shape with the wrong tensor shape. This happens because of a bug where we did not update the subclass tensor shape when doing transpose. For semi-structured sparsity, transposing is a no-op where we just set the boolean flag, but we forgot to also update the tensor shape. Note that this error goes away in inference mode, since we avoid decomposing the aten.linear op and handle shape folding ourselves, which changes the execution path. An alternative way to fix this issue is to set TORCH_FLATTEN_LINEAR_3D=True, which will also fix this error. Test Plan: ``` python test/test_sparse_semi_structured.py -k test_mlp ``` Reviewers: Subscribers: Tasks: Tags: Pull Request resolved: https://github.com/pytorch/pytorch/pull/110420 Approved by: https://github.com/alexsamardzic, https://github.com/cpuhrsch |
||
|
Aleksandar Samardžić
|
6a202c36af |
Minor fixes in semi-structured sparse code (#105595)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105595 Approved by: https://github.com/jcaip |
||
|
Oguz Ulgen
|
1df14f1bf8 |
Move has_triton to top level triton utils so that dynamo can also access (#109832)
it without creating cyclic dependencies Pull Request resolved: https://github.com/pytorch/pytorch/pull/109832 Approved by: https://github.com/zou3519 |
||
|
Pearu Peterson
|
4e042cfed5 |
Improve triton bsr_dense_mm performance on column-major ordered inputs with float32 dtype (#108512)
As in the title. The bsr_dense_mm performance on inputs using column-major storage order is relevant for `linear(x, W)` operation that for BSR weights is defined as `bsr_dense_mm(W, x.transpose(-2, -1)).transpose(-2, 1)` so that the second argument to `bse_dense_mm` is a strided tensor using column-major storage order when `x` is C-contiguous. For large inputs (size > 1000) and moderate sparsity in the BSR input, the speed up can be more than 3 times, as illustrated in the following figure (raw data: [bench_bsr_dense_mm_1_results.txt](https://github.com/pytorch/pytorch/files/12512245/bench_bsr_dense_mm_1_results.txt)):  For small inputs (size=512), there exists a slight degradation of performance. For row-major ordered inputs, there is no change in performance (see raw data above). For inputs with float16 dtype, there is no considerable change in performance (see blue marks in the figure). Pull Request resolved: https://github.com/pytorch/pytorch/pull/108512 Approved by: https://github.com/cpuhrsch |
||
|
Pearu Peterson
|
c5ad44be1d |
Add torch.sparse.as_sparse_gradcheck decorator of gradcheck that allows gradcheck input function to receive and return sparse tensors (#107150)
Compared to #104848, this PR makes a step further: when the enable_sparse_support decorator is applied to `torch.autograd.gradcheck`, the resulting callable is equivalent to `torch.autograd.gradcheck` with an extra feature of supporting functions that can have input sparse tensors or/and can return sparse tensors. At the same time, the underlying call to `torch.autograd.gradcheck` will operate on strided tensors only. This basically means that torch/autograd/gradcheck.py can be cleaned up by removing the code that deals with sparse tensors. Pull Request resolved: https://github.com/pytorch/pytorch/pull/107150 Approved by: https://github.com/albanD, https://github.com/amjames, https://github.com/cpuhrsch ghstack dependencies: #107638, #107777 |
||
|
Christian Puhrsch
|
925d71e72e |
[core][sparse][pruning] cuSPARSELt Kernels and ops. (#107398)
Summary: This is a duplicate PR of 102133, which was reverted because it was failing internal tests. It seems like that internal builds did not like my guard to check if cuSPARSELt was available or not. Test Plan: python test/test_sparse_semi_structured.py Differential Revision: D48440330 Pull Request resolved: https://github.com/pytorch/pytorch/pull/107398 Approved by: https://github.com/cpuhrsch |
||
|
PyTorch MergeBot
|
fe594ab323 |
Revert "[core][pruning][feature] cuSPARSELt kernels and ops (#102133)"
This reverts commit
|
||
|
Jesse Cai
|
ad22f0ffb4 |
[core][pruning][feature] cuSPARSELt kernels and ops (#102133)
This PR contains two new private ops, added for cuSPARSELt support. These ops call into the cuSPASRELt kernels using the bindings they provide. For more information, see the documentation [here](https://docs.nvidia.com/cuda/cusparselt/index.html). The two new private ops added are: ``` _cslt_compress() _cslt_sparse_mm() ``` _cslt_compress is an op that reuturns the compressesed matrix given a sparse matrix that is passed in. _cslt_sparse_mm is an op that expects a compressed matrix (the result of _cslt_compress) and a dense matrix and performs sparse-dense matmul These ops will throw runtime errors if they cusparselt is not present. This PR also modifies the test and tensor sublass to reflect the new cuSPARSELt support. Pull Request resolved: https://github.com/pytorch/pytorch/pull/102133 Approved by: https://github.com/cpuhrsch |
||
|
Justin Chu
|
3721fa5612 |
[BE] Enable ruff's UP rules and autoformat optim/ (#105426)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105426 Approved by: https://github.com/malfet, https://github.com/albanD, https://github.com/aaronenyeshi, https://github.com/janeyx99 |
||
|
Aleksandar Samardžić
|
5d473a950f |
Make conversions from/to sparse semi-structured always @torch.compile-d (#105272)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/105272 Approved by: https://github.com/ezyang |
||
|
Nikita Shulga
|
5837e95d30 |
[Reland] Update mypy to 1.4.1 (#105227)
This PR re-lands - [Typing] Fix PEP 484 Violation (#105022) - Update mypy to 1.4.1 (#91983) That were reverted due to the conflict with internal source repo. Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - Add assert it `torch/optim/optimizer.py` that Optional list is not None TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Unrelated, to bypass CI failures due to the gcc9 dependency update in Ubuntu-18.04: - Add hack to squash older libstdc++ from conda environment in favor one from OS to `.ci/docker/install_conda.sh` - Update bazel cuda builds to focal, as with libstdc++-6.0.32 bazel builds loose the ability to catch exceptions (probably because they link with cupti statically, but I could not found where it is done) Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227 Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007 |
||
|
PyTorch MergeBot
|
15fd1ea118 |
Revert "[Reland] Update mypy to 1.4.1 (#105227)"
This reverts commit
|
||
|
Nikita Shulga
|
c9c4f8efc3 |
[Reland] Update mypy to 1.4.1 (#105227)
This PR re-lands - [Typing] Fix PEP 484 Violation (#105022) - Update mypy to 1.4.1 (#91983) That were reverted due to the conflict with internal source repo. Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - Add assert it `torch/optim/optimizer.py` that Optional list is not None TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/105227 Approved by: https://github.com/atalman, https://github.com/albanD, https://github.com/Skylion007 |
||
|
PyTorch MergeBot
|
3c5a494d7a |
Revert "Update mypy to 1.4.1 (#91983)"
This reverts commit
|
||
|
Aleksandar Samardžić
|
d7e6040efa |
Update sparse semi-structured linear operator (#104608)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/104608 Approved by: https://github.com/cpuhrsch |
||
|
Aleksandar Samardžić
|
fc2f87b281 |
Add semi-structured sparse conversions (#103830)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/103830 Approved by: https://github.com/amjames, https://github.com/jcaip, https://github.com/cpuhrsch |
||
|
nikitaved
|
44c8515d0d |
SDPA: frontend for BSR masks (#104042)
This PR implements a (yet private) frontend for scaled_dot_product_attention that works with BSR `attn_mask`. This function is directly comparable (with suitable masks) with `torch.nn.functional.scaled_dot_product_attention` once `attn_mask.dtype == torch.bool`, but it's behavior is different when `attn_mask.dtype != torch.bool`. This is because `torch.nn.functional.scaled_dot_product_attention` assumes that irrelevant values are supposed to be filled with `-inf`, while the selected ones should be `0`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/104042 Approved by: https://github.com/amjames, https://github.com/cpuhrsch |
||
|
Nikita Shulga
|
634659e262 |
Update mypy to 1.4.1 (#91983)
Mostly fixes for PEP-484 violation (i.e. when default arg is set to None, but type is not annotated as optional) Plus few real fixes: - Add missing `_get_upgraders_entry_map` to `torch/_C/__init__.pyi` - Add missing return statement to `torch._export. deserialize_graph` - Fix error message in `torch.ao.ns.fx.weight_utils.get_lstm_mod_weights` - TODO (in followup PR): - Fix erroneous `isinstance` check in `torch/ao/quantization/_pt2e/qat_utils.py` Pull Request resolved: https://github.com/pytorch/pytorch/pull/91983 Approved by: https://github.com/kit1980, https://github.com/ZainRizvi, https://github.com/huydhn, https://github.com/thiagocrepaldi, https://github.com/aaronenyeshi |
||
|
Jesse Cai
|
2da6cae43c |
[core][pruning][sparse][feature] SparseSemiStructured tensor subclass (#102135)
This PR adds in support for semi-structured sparsity via a tensor subclass. It currently uses the CUTLASS kernels merged in PR #100881. In the future we plan to add in cuSPARSELt support (see the other PRs in the stack), which will give us larger performance gains. This PR adds in 2 things: - a Tensor subclass, `SparseSemiStructuredTensor` to store the sparse tensor in copmressed form and override `__torch_dispatch__`. - a conversion function that takes in a dense tensor and a semi-structured sparse bool mask and creates an instance of the subclass. **SparseSemiStructuredTensor** The subclass stores the dense tensor in a contiguous flattened tensor for future compatability with cuSPARSELt, which expects this format. Note that the CUTLASS kernels do not have this limitation, as the specified values and the metadata are passed separately in `_structured_sparse_linear`. In the future we can use the cuSPARSELT bindings [here](https://github.com/pytorch/pytorch/pull/103700) for faster matmul, better dtype converage, and relaxed shape constraints. Since we currently don't have a way to go back from the sparse representation to the dense representation, and we store the weights in compressed form, we don't have a great way to handle .t(). Instead, we keep track of how often we've called transpose on our tensor, and if it's an unexpected number we throw an error. When the first argument is sparse, we expect an even number of calls to transpose, while when the second argument is sparse, we expect an odd number of calls. This is because we support second argument sparse matrix multiplications by using transpose properties. **to_sparse_semi_structured** This is a conversion function to convert a dense tensor and a semi-structured sparse bool mask into a subclass. Currently, we must pass in a bool mask, since we can't infer it becuase there may be additional zero elements in the dense tensor, so `tensor !=0` is not 2:4 sparse. Once we add either a method to derive the mask from the dense tensor or cuSPARSELt, we no longer need to pass in the mask. cuSPARSELt has it's own helper functions to create the metadata mask. **User Details** We have implemented support for the following ops for `torch.float16` and `torch.int8`: ``` torch.addmm(bias, dense, sparse.t()) torch.mm(dense, sparse) torch.mm(sparse, dense) aten.linear.default aten.t.default aten.t.detach ``` The end user interface to accelerate a nn.Linaer module with the subclass would look like this: ``` from torch.sparse import to_sparse_semi_structured mask = torch.Tensor([0, 0, 1, 1]).tile(128, 32).cuda().bool() linear = Model(128, 128).half().cuda() linear.weight = nn.Parameter(to_sparse_semi_structured(linear.weight, mask=linear.weight.bool()) ``` This also updates tests and the `torch.sparse` module docstring to reflect these changes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/102135 Approved by: https://github.com/albanD |
||
|
PyTorch MergeBot
|
b76a040b18 |
Revert "[core][pruning][sparse][feature] SparseSemiStructured tensor subclass (#102135)"
This reverts commit |
||
|
Jesse Cai
|
aea771de30 |
[core][pruning][sparse][feature] SparseSemiStructured tensor subclass (#102135)
This PR adds in support for semi-structured sparsity via a tensor subclass. It currently uses the CUTLASS kernels merged in PR #100881. In the future we plan to add in cuSPARSELt support (see the other PRs in the stack), which will give us larger performance gains. This PR adds in 2 things: - a Tensor subclass, `SparseSemiStructuredTensor` to store the sparse tensor in copmressed form and override `__torch_dispatch__`. - a conversion function that takes in a dense tensor and a semi-structured sparse bool mask and creates an instance of the subclass. **SparseSemiStructuredTensor** The subclass stores the dense tensor in a contiguous flattened tensor for future compatability with cuSPARSELt, which expects this format. Note that the CUTLASS kernels do not have this limitation, as the specified values and the metadata are passed separately in `_structured_sparse_linear`. In the future we can use the cuSPARSELT bindings [here](https://github.com/pytorch/pytorch/pull/103700) for faster matmul, better dtype converage, and relaxed shape constraints. Since we currently don't have a way to go back from the sparse representation to the dense representation, and we store the weights in compressed form, we don't have a great way to handle .t(). Instead, we keep track of how often we've called transpose on our tensor, and if it's an unexpected number we throw an error. When the first argument is sparse, we expect an even number of calls to transpose, while when the second argument is sparse, we expect an odd number of calls. This is because we support second argument sparse matrix multiplications by using transpose properties. **to_sparse_semi_structured** This is a conversion function to convert a dense tensor and a semi-structured sparse bool mask into a subclass. Currently, we must pass in a bool mask, since we can't infer it becuase there may be additional zero elements in the dense tensor, so `tensor !=0` is not 2:4 sparse. Once we add either a method to derive the mask from the dense tensor or cuSPARSELt, we no longer need to pass in the mask. cuSPARSELt has it's own helper functions to create the metadata mask. **User Details** We have implemented support for the following ops for `torch.float16` and `torch.int8`: ``` torch.addmm(bias, dense, sparse.t()) torch.mm(dense, sparse) torch.mm(sparse, dense) aten.linear.default aten.t.default aten.t.detach ``` The end user interface to accelerate a nn.Linaer module with the subclass would look like this: ``` from torch.sparse import to_sparse_semi_structured mask = torch.Tensor([0, 0, 1, 1]).tile(128, 32).cuda().bool() linear = Model(128, 128).half().cuda() linear.weight = nn.Parameter(to_sparse_semi_structured(linear.weight, mask=linear.weight.bool()) ``` This also updates tests and the `torch.sparse` module docstring to reflect these changes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/102135 Approved by: https://github.com/albanD |
||
|
PyTorch MergeBot
|
bfa08a1c67 |
Revert "[core][pruning][sparse][feature] SparseSemiStructured tensor subclass (#102135)"
This reverts commit |
||
|
Jesse Cai
|
cf5262a84f |
[core][pruning][sparse][feature] SparseSemiStructured tensor subclass (#102135)
This PR adds in support for semi-structured sparsity via a tensor subclass. It currently uses the CUTLASS kernels merged in PR #100881. In the future we plan to add in cuSPARSELt support (see the other PRs in the stack), which will give us larger performance gains. This PR adds in 2 things: - a Tensor subclass, `SparseSemiStructuredTensor` to store the sparse tensor in copmressed form and override `__torch_dispatch__`. - a conversion function that takes in a dense tensor and a semi-structured sparse bool mask and creates an instance of the subclass. **SparseSemiStructuredTensor** The subclass stores the dense tensor in a contiguous flattened tensor for future compatability with cuSPARSELt, which expects this format. Note that the CUTLASS kernels do not have this limitation, as the specified values and the metadata are passed separately in `_structured_sparse_linear`. In the future we can use the cuSPARSELT bindings [here](https://github.com/pytorch/pytorch/pull/103700) for faster matmul, better dtype converage, and relaxed shape constraints. Since we currently don't have a way to go back from the sparse representation to the dense representation, and we store the weights in compressed form, we don't have a great way to handle .t(). Instead, we keep track of how often we've called transpose on our tensor, and if it's an unexpected number we throw an error. When the first argument is sparse, we expect an even number of calls to transpose, while when the second argument is sparse, we expect an odd number of calls. This is because we support second argument sparse matrix multiplications by using transpose properties. **to_sparse_semi_structured** This is a conversion function to convert a dense tensor and a semi-structured sparse bool mask into a subclass. Currently, we must pass in a bool mask, since we can't infer it becuase there may be additional zero elements in the dense tensor, so `tensor !=0` is not 2:4 sparse. Once we add either a method to derive the mask from the dense tensor or cuSPARSELt, we no longer need to pass in the mask. cuSPARSELt has it's own helper functions to create the metadata mask. **User Details** We have implemented support for the following ops for `torch.float16` and `torch.int8`: ``` torch.addmm(bias, dense, sparse.t()) torch.mm(dense, sparse) torch.mm(sparse, dense) aten.linear.default aten.t.default aten.t.detach ``` The end user interface to accelerate a nn.Linaer module with the subclass would look like this: ``` from torch.sparse import to_sparse_semi_structured mask = torch.Tensor([0, 0, 1, 1]).tile(128, 32).cuda().bool() linear = Model(128, 128).half().cuda() linear.weight = nn.Parameter(to_sparse_semi_structured(linear.weight, mask=linear.weight.bool()) ``` This also updates tests and the `torch.sparse` module docstring to reflect these changes. Pull Request resolved: https://github.com/pytorch/pytorch/pull/102135 Approved by: https://github.com/albanD |
||
|
Nikita Vedeneev
|
39a22e2791 |
softmax: Triton kernel for BSR inputs (#102095)
Implements `softmax` Triton kernel for BSR inputs. So far, only over `dim=-1`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/102095 Approved by: https://github.com/cpuhrsch |
||
|
Nikita Vedeneev
|
6c7410ddc3 |
sampled_addmm: BSR support (#101163)
This PR implements a `sampled_addmm` kernel that works with a BSR mask. Pull Request resolved: https://github.com/pytorch/pytorch/pull/101163 Approved by: https://github.com/cpuhrsch |
||
|
Nikita Vedeneev
|
dd2c22f4bb |
bsr_dense_bmm(): enable more precise float32 support with float64 accumulators (#100882)
Float64 is there in Triton! This PR increases precision for float32 inputs with float64 accumulation dtype. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100882 Approved by: https://github.com/cpuhrsch |
||
|
Nikita Vedeneev
|
0141a242fd |
bsr_dense_bmm(): remove sparse_rowspace kernel and some dead code (#100876)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/100876 Approved by: https://github.com/cpuhrsch, https://github.com/Skylion007 |
||
|
Nikita Vedeneev
|
c4bc259f00 |
bsr_dense_mm(): better test coverage (#100543)
This PR improves test coverage for `bsr_dense_mm` by: - ~~enabling correctness tests for `float32`~~. - extending and testing input correctness checks. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100543 Approved by: https://github.com/cpuhrsch, https://github.com/malfet |
||
|
Nikita Vedeneev
|
cd8b82e5c6 |
bsr_dense_mm(): code refactoring (#100634)
Code unification/refactoring for better re-use. Intended for easier `sampled_addmm` implementation. Pull Request resolved: https://github.com/pytorch/pytorch/pull/100634 Approved by: https://github.com/cpuhrsch |
||
|
Nikita Vedeneev
|
05dda7ff65 |
bsr_dense_mm Triton kernel: fix out kwarg (#96648)
As per title. The kernel did not handle `out=` correctly and returned a different tensor which only shared storage with `out`. Pull Request resolved: https://github.com/pytorch/pytorch/pull/96648 Approved by: https://github.com/cpuhrsch |
||
|
Natalia Gimelshein
|
76cac70939 |
new triton main pin (#95896)
Fixes #ISSUE_NUMBER Pull Request resolved: https://github.com/pytorch/pytorch/pull/95896 Approved by: https://github.com/jansel, https://github.com/malfet |
||
|
PyTorch MergeBot
|
d0731271cd |
Revert "new triton main pin (#95896)"
This reverts commit |
||
|
Natalia Gimelshein
|
6e0359dd42 |
new triton main pin (#95896)
Fixes #ISSUE_NUMBER Pull Request resolved: https://github.com/pytorch/pytorch/pull/95896 Approved by: https://github.com/jansel |
||
|
Nikita Vedeneev
|
d809020fc8 |
Triton kernel for bsr @ dense (#94823)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/94823 Approved by: https://github.com/cpuhrsch, https://github.com/malfet |
||
|
Pearu Peterson
|
0c0694495b |
Fix a bug in nesting check_sparse_tensor_invariants context managers (#95372)
As in the title. The bug was reported in https://github.com/pytorch/pytorch/pull/94728#discussion_r1108892366 and has the following reproducer: ```python >>> import torch >>> check_ctx = torch.sparse.check_sparse_tensor_invariants(True) >>> no_check_ctx = torch.sparse.check_sparse_tensor_invariants(False) >>> with check_ctx: ... assert torch.sparse.check_sparse_tensor_invariants.is_enabled() ... with no_check_ctx: ... assert not torch.sparse.check_sparse_tensor_invariants.is_enabled() ... assert torch.sparse.check_sparse_tensor_invariants.is_enabled() ... Traceback (most recent call last): File "<stdin>", line 5, in <module> AssertionError ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/95372 Approved by: https://github.com/cpuhrsch |
||
|
mingfeima
|
c620ece726 |
port sparse_mm.reduce to pytorch and optimize it on CPU (#83727)
### Motivation of this PR This patch is to migrate `spmm_reduce` from `torch-sparse` (a 3rd party dependency for PyG) to `torch`, which is a response to the initial proposal for fusion of **Gather, Apply Scatter** in Message Passing of GNN inference/training. https://github.com/pytorch/pytorch/issues/71300 **GAS** is the major step for Message Passing, the behavior of **GAS** can be classified into 2 kinds depending on the storage type of `EdgeIndex` which records the connections of nodes: * COO: the hotspot is `scatter_reduce` * CSR: the hotspot is `spmm_reduce` The reduce type can be choose from: "max", "mean", "max", "min". extend `torch.sparse.mm` with an `reduce` argument, maps to `torch.sparse_mm.reduce` internally. `sparse_mm_reduce` is registered under the TensorTypeId of `SparseCsrCPU`, and this operator requires an internal interface `_sparse_mm_reduce_impl` which has dual outputs: * `out` - the actual output * `arg_out` - records output indices in the non zero elements if the reduce type is "max" or "min", this is only useful for training. So for inference, it will not be calculated. ### Performance Benchmark on GCN for obgn-products on Xeon single socket, the workload is improved by `4.3x` with this patch. Performance benefit for training will be bigger, the original backward impl for `sum|mean` is sequential; the original backward impl for `max|min` is not fused. #### before: ``` ----------------------------- ------------ ------------ ------------ ------------ ------------ ------------ Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls ----------------------------- ------------ ------------ ------------ ------------ ------------ ------------ torch_sparse::spmm_sum 97.09% 56.086s 97.09% 56.088s 6.232s 9 aten::linear 0.00% 85.000us 1.38% 795.485ms 88.387ms 9 aten::matmul 0.00% 57.000us 1.38% 795.260ms 88.362ms 9 aten::mm 1.38% 795.201ms 1.38% 795.203ms 88.356ms 9 aten::relu 0.00% 50.000us 0.76% 440.434ms 73.406ms 6 aten::clamp_min 0.76% 440.384ms 0.76% 440.384ms 73.397ms 6 aten::add_ 0.57% 327.801ms 0.57% 327.801ms 36.422ms 9 aten::log_softmax 0.00% 23.000us 0.10% 55.503ms 18.501ms 3 ``` #### after ``` ----------------------------- ------------ ------------ ------------ ------------ ------------ ------------ Name Self CPU % Self CPU CPU total % CPU total CPU time avg # of Calls ----------------------------- ------------ ------------ ------------ ------------ ------------ ------------ aten::spmm_sum 87.35% 11.826s 87.36% 11.827s 1.314s 9 aten::linear 0.00% 92.000us 5.87% 794.451ms 88.272ms 9 aten::matmul 0.00% 62.000us 5.87% 794.208ms 88.245ms 9 aten::mm 5.87% 794.143ms 5.87% 794.146ms 88.238ms 9 aten::relu 0.00% 53.000us 3.35% 452.977ms 75.496ms 6 aten::clamp_min 3.35% 452.924ms 3.35% 452.924ms 75.487ms 6 aten::add_ 2.58% 348.663ms 2.58% 348.663ms 38.740ms 9 aten::argmax 0.42% 57.473ms 0.42% 57.475ms 14.369ms 4 aten::log_softmax 0.00% 22.000us 0.39% 52.605ms 17.535ms 3 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/83727 Approved by: https://github.com/jgong5, https://github.com/cpuhrsch, https://github.com/rusty1s, https://github.com/pearu |
||
|
Aaron Gokaslan
|
8fce9a09cd |
[BE]: pyupgrade Python to 3.8 - imports and object inheritance only (#94308)
Apply parts of pyupgrade to torch (starting with the safest changes). This PR only does two things: removes the need to inherit from object and removes unused future imports. Pull Request resolved: https://github.com/pytorch/pytorch/pull/94308 Approved by: https://github.com/ezyang, https://github.com/albanD |
||
|
PyTorch MergeBot
|
7012d985fa |
Revert "Improve bsr @ strided performance in baddmm for bfloat16/half with Triton kernels. (#88078)"
This reverts commit
|
||
|
Nikita Vedeneev
|
46f16b9363 |
Improve bsr @ strided performance in baddmm for bfloat16/half with Triton kernels. (#88078)
As per title. Additionally we also introduce support for: - Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation). - Batch support with broadcasting for either of the arguments. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078 Approved by: https://github.com/cpuhrsch |
||
|
PyTorch MergeBot
|
60bf851931 |
Revert "Improve bsr @ strided performance in baddmm for bfloat16/half with Triton kernels. (#88078)"
This reverts commit
|
||
|
Nikita Vedeneev
|
8383b5c488 |
Improve bsr @ strided performance in baddmm for bfloat16/half with Triton kernels. (#88078)
As per title. Additionally we also introduce support for: - Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation). - Batch support with broadcasting for either of the arguments. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078 Approved by: https://github.com/cpuhrsch |
||
|
PyTorch MergeBot
|
89f1ad08b4 |
Revert "Improve bsr @ strided performance in baddmm for bfloat16/half with Triton kernels. (#88078)"
This reverts commit |
||
|
Nikita Vedeneev
|
7f256fff77 |
Improve bsr @ strided performance in baddmm for bfloat16/half with Triton kernels. (#88078)
As per title. Additionally we also introduce support for: - Rectangular block sizes which are powers of 2 and at least 16 (triton's `dot` limitation). - Batch support with broadcasting for either of the arguments. Pull Request resolved: https://github.com/pytorch/pytorch/pull/88078 Approved by: https://github.com/cpuhrsch |
||
|
Pearu Peterson
|
b3e4f5029b |
Add check-sparse-tensor-invariants flag to Context - 2nd try. (#92094)
This PR is a copy of https://github.com/pytorch/pytorch/pull/90849 that merge was reverted. The PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI: `torch.sparse.check_sparse_tensor_invariants` class provides different ways to enable/disable the invariant checking. `torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden. The PR fixes https://github.com/pytorch/pytorch/issues/90833 Pull Request resolved: https://github.com/pytorch/pytorch/pull/92094 Approved by: https://github.com/cpuhrsch |
||
|
mingfeima
|
3ab58fd5ed |
optimize sampled_addmm performance on CPU (SparseCSR) (#90978)
### Target and Background This PR is improving the performance of `sampled_addmm` on CPU device. This is part of effort for improving PyG performance on CPU for GNN training/inference. The current implementation is a reference design which converts `SparseCSR` tensor back to dense tensor and then do the addmm and convert back to `SparseCSR` again: this is going to be very slow and won't be able to run most of the datasets under https://github.com/snap-stanford/ogb (convert to dense would trigger `OOM`). ### Benchmarks Right now we don't have any hands-on benchmark or workload to test this since this operator is not used in PyG yet. I fetched the dataset from `ogb-products` where: * number of nodes: 2.4 * 10^6 * number of edges: 1.26 * 10^8 * number of features: 128 So if we store the **adjacency matrix** is dense, it is going to be 2.4 * 2.4 * 4 * 10^12 bytes, this will be OOB on current code. I abstract the first 1k rows to compare, **1100x** speedup: CPU: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz, dual socket, 20 cores per socket. ``` ### before: run 1000 rows from the whole dataset sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1212.000 ms! ### after: run 1000 rows from the whole dataset sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1.102 ms! ### after: run the whole dataset sampled_addmm: running dataset ogb-products (the whole dataset) 2449029 rows: each iter takes 873.306 ms! ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/90978 Approved by: https://github.com/pearu, https://github.com/cpuhrsch |
||
|
PyTorch MergeBot
|
c7a22bb7c7 |
Revert "Add check-sparse-tensor-invariants flag to Context. (#90849)"
This reverts commit
|
||
|
PyTorch MergeBot
|
c5836153f5 |
Revert "optimize sampled_addmm performance on CPU (SparseCSR) (#90978)"
This reverts commit
|
||
|
Pearu Peterson
|
b9a035c1c5 |
Add check-sparse-tensor-invariants flag to Context. (#90849)
This PR adds "check sparse tensor invariants" flag to Context that when enabled will trigger sparse tensor data invariants checks in unsafe methods of constructing sparse COO/CSR/CSC/BSR/BSC tensors. The feature includes the following changes to UI: - `torch.enable_check_sparse_tensor_invariants` and `torch.is_check_sparse_tensor_invariants_enabled` functions to globally enable/disable the invariant checks and to retrieve the state of the feature, respectively - `torch.sparse_coo/csr/csc/bsr/bsc/compressed_tensor` functions have a new optional argument `check_invariants` to enable/disable the invariant checks explicitly. When the `check_invariants` argument is specified, the global state of the feature is temporarily overridden. The PR also fixes https://github.com/pytorch/pytorch/issues/90833 # Main issue *The following content is outdated after merging the PRs in this ghstack but kept for the record.* The importance of this feature is that when enabling the invariants checks by default, say, via <details> ``` $ git diff diff --git a/torch/__init__.py b/torch/__init__.py index c8543057c7..19a91d0482 100644 --- a/torch/__init__.py +++ b/torch/__init__.py @@ -1239,3 +1239,8 @@ if 'TORCH_CUDA_SANITIZER' in os.environ: # Populate magic methods on SymInt and SymFloat import torch.fx.experimental.symbolic_shapes + +# temporarily enable sparse tensor arguments validation in unsafe +# constructors: + +torch._C._set_check_sparse_tensor_invariants(True) ``` </details> a massive number of test failures/errors occur in test_sparse_csr.py tests: ``` $ pytest -sv test/test_sparse_csr.py <snip> ==== 4293 failed, 1557 passed, 237 skipped, 2744 errors in 69.71s (0:01:09) ==== ``` that means that we are silently constructing sparse compressed tensors that do not satisfy the sparse tensor invariants. In particular, the following errors are raised: ``` AssertionError: "resize_as_sparse_compressed_tensor_: self and src must have the same layout" does not match "expected values to be a strided and contiguous tensor" RuntimeError: CUDA error: device-side assert triggered RuntimeError: `col_indices[..., crow_indices[..., i - 1]:crow_indices[..., i]] for all i = 1, ..., nrows are sorted and distinct along the last dimension values` is not satisfied. RuntimeError: expected col_indices to be a strided and contiguous tensor RuntimeError: expected row_indices to be a strided and contiguous tensor RuntimeError: expected values to be a strided and contiguous tensor RuntimeError: for_each: failed to synchronize: cudaErrorAssert: device-side assert triggered RuntimeError: tensor dimensionality must be sum of batch, base, and dense dimensionalities (=0 + 2 + 0) but got 3 ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/90849 Approved by: https://github.com/amjames, https://github.com/cpuhrsch |
||
|
mingfeima
|
645fb217c0 |
optimize sampled_addmm performance on CPU (SparseCSR) (#90978)
### Target and Background This PR is improving the performance of `sampled_addmm` on CPU device. This is part of effort for improving PyG performance on CPU for GNN training/inference. The current implementation is a reference design which converts `SparseCSR` tensor back to dense tensor and then do the addmm and convert back to `SparseCSR` again: this is going to be very slow and won't be able to run most of the datasets under https://github.com/snap-stanford/ogb (convert to dense would trigger `OOM`). ### Benchmarks Right now we don't have any hands-on benchmark or workload to test this since this operator is not used in PyG yet. I fetched the dataset from `ogb-products` where: * number of nodes: 2.4 * 10^6 * number of edges: 1.26 * 10^8 * number of features: 128 So if we store the **adjacency matrix** is dense, it is going to be 2.4 * 2.4 * 4 * 10^12 bytes, this will be OOB on current code. I abstract the first 1k rows to compare, **1100x** speedup: CPU: Intel(R) Xeon(R) Gold 6248 CPU @ 2.50GHz, dual socket, 20 cores per socket. ``` ### before: run 1000 rows from the whole dataset sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1212.000 ms! ### after: run 1000 rows from the whole dataset sampled_addmm: running dataset ogb-products first 1000 rows: each iter takes 1.102 ms! ### after: run the whole dataset sampled_addmm: running dataset ogb-products (the whole dataset) 2449029 rows: each iter takes 873.306 ms! ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/90978 Approved by: https://github.com/pearu, https://github.com/cpuhrsch |
||
|
joncrall
|
4618371da5 |
Integrate xdoctest - Rebased (#82797)
This is a new version of #15648 based on the latest master branch. Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR. In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.) Fixes https://github.com/pytorch/pytorch/issues/71105 @ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797 Approved by: https://github.com/ezyang |
||
|
Andrew M. James
|
5a4c9e8394 |
Add spdiags sparse matrix initialization (#78439)
Similar to [scipy.sparse.spdiags](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.spdiags.html#scipy-sparse-spdiags) Part of #70926 In other functions (ie (torch.diagonal)[https://pytorch.org/docs/stable/generated/torch.diagonal.html#torch.diagonal]) diagonals of a tensor are referenced using the offset and the two dimensions that the diagonal is taken with respect to. Here the reference implementation from scipy is only considering matrix output, so even if we only support 2-d output at first. It may be useful to consider how the dimensions corresponding to each diagonal would be specified for higher dimensional output. The proposed torch signature implies that all offsets refer to the diagonals with respect to the only two dimensions of the output: ``` torch.sparse.spdiags(Tensor diagonals, IntTensor offsets, int[] shape, Layout? layout=None) -> SparseTensor ``` Above it is required that: `diagonals.ndimension() == 2`, `offsets.ndimensions() == 1`, `offsets.shape[0] == diagonals.shape[0]` and `len(shape) == 2`. This would need to be altered for the case where `len(shape)` > 2. One options is: ``` torch.sparse.spdiags(Tensor[] diagonals, IntTensor[] offsets, IntTensor dims, int[] shape, Layout? layout=None) -> SparseTensor ``` Here `offsets` and `diagonals` becomes lists of tensors, and the `IntTensor dims` argument is introduced. This would require that `len(diagonals) == len(offsets) == dims.shape[0]`, `dims.ndimension() == 2` and `dims.shape[1] == 2` also the same restrictions as the 2d case above apply to the elements of `diagonals` and `offsets` pairwise (that is `diagonals[i].ndimension() == 2`, `offsets[i].ndimension() == 1` and `offsets[i].shape[0] == diagonals[i].shape[0]` for all i). This form of the signature would construct the sparse result by placing the values from `diagonals[i][j]` into the diagonal with offset `offset[i][j]` taken with respect to dimensions `dims[i]`. The specialization back to the original signature for the 2d case could be seen as allowing the single row of dims to default to `[0, 1]` when there is only one `diagonals`, `offsets` provided, and shape is `2-d`. This option allows the rows of an input element `diagonals[i]` to have a different length which may be appropriate as the max length of a diagonal along different dimension pairs will be different. Another option is to specify the dimensions the diagonal is taken with respect to for each offset. This signature would look like: ``` torch.sparse.spdiags(Tensor diagonals, IntTensor offsets, IntTensor dims, int[] shape, Layout? layout=None) -> SparseTensor ``` Here, `diagonals` is still 2-D with dimension 0 matching the length of 1-D `offsets` and the tensor input `dims` is also 2-D with dimension 0 matching the length of 1-D `offsets` and the second dimension being fixed at `2` in this case the sparse result is constructed by placing the elements from `diagonals[i]` into the output diagonal `output.diagonal(offset[i], dim0=dims[i][0], dim1=dims[i][1])` (with some additional consideration that makes it more complicated than simply asigning to that view). The specialization from this back to the 2-D form could be seen as assuming `dims = [[0, 1], [0, 1]... len(offsets) times ]` when `len shape==2`. In both proposed signatures for the N-D case the specialization back to the 2-D signature is a bit of a stretch for your typical default arguments logic, however I think the first is better choice as it offers more flexibility. I think some discussion is required about: - [x] Should the N-D output case be implemented from the outset - [x] If not, should the future addition of the N-D output case be considered when designing the interface. - [x] Other thoughts on the signature which includes the `dims` information for the N-D output case. **Resolution**: Since no one has offered a request for N-D output support, I think is fine to restrict this to sparse matrix generation. Should a request for N-D support come later, an overload accepting the additional `dims` could be added. Pull Request resolved: https://github.com/pytorch/pytorch/pull/78439 Approved by: https://github.com/nikitaved, https://github.com/cpuhrsch, https://github.com/pearu |
||
|
PyTorch MergeBot
|
56e3bc5215 |
Revert "Add spdiags sparse matrix initialization (#78439)"
This reverts commit |
||
|
Andrew M. James
|
cfb2034b65 |
Add spdiags sparse matrix initialization (#78439)
Similar to [scipy.sparse.spdiags](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.spdiags.html#scipy-sparse-spdiags) Part of #70926 In other functions (ie (torch.diagonal)[https://pytorch.org/docs/stable/generated/torch.diagonal.html#torch.diagonal]) diagonals of a tensor are referenced using the offset and the two dimensions that the diagonal is taken with respect to. Here the reference implementation from scipy is only considering matrix output, so even if we only support 2-d output at first. It may be useful to consider how the dimensions corresponding to each diagonal would be specified for higher dimensional output. The proposed torch signature implies that all offsets refer to the diagonals with respect to the only two dimensions of the output: ``` torch.sparse.spdiags(Tensor diagonals, IntTensor offsets, int[] shape, Layout? layout=None) -> SparseTensor ``` Above it is required that: `diagonals.ndimension() == 2`, `offsets.ndimensions() == 1`, `offsets.shape[0] == diagonals.shape[0]` and `len(shape) == 2`. This would need to be altered for the case where `len(shape)` > 2. One options is: ``` torch.sparse.spdiags(Tensor[] diagonals, IntTensor[] offsets, IntTensor dims, int[] shape, Layout? layout=None) -> SparseTensor ``` Here `offsets` and `diagonals` becomes lists of tensors, and the `IntTensor dims` argument is introduced. This would require that `len(diagonals) == len(offsets) == dims.shape[0]`, `dims.ndimension() == 2` and `dims.shape[1] == 2` also the same restrictions as the 2d case above apply to the elements of `diagonals` and `offsets` pairwise (that is `diagonals[i].ndimension() == 2`, `offsets[i].ndimension() == 1` and `offsets[i].shape[0] == diagonals[i].shape[0]` for all i). This form of the signature would construct the sparse result by placing the values from `diagonals[i][j]` into the diagonal with offset `offset[i][j]` taken with respect to dimensions `dims[i]`. The specialization back to the original signature for the 2d case could be seen as allowing the single row of dims to default to `[0, 1]` when there is only one `diagonals`, `offsets` provided, and shape is `2-d`. This option allows the rows of an input element `diagonals[i]` to have a different length which may be appropriate as the max length of a diagonal along different dimension pairs will be different. Another option is to specify the dimensions the diagonal is taken with respect to for each offset. This signature would look like: ``` torch.sparse.spdiags(Tensor diagonals, IntTensor offsets, IntTensor dims, int[] shape, Layout? layout=None) -> SparseTensor ``` Here, `diagonals` is still 2-D with dimension 0 matching the length of 1-D `offsets` and the tensor input `dims` is also 2-D with dimension 0 matching the length of 1-D `offsets` and the second dimension being fixed at `2` in this case the sparse result is constructed by placing the elements from `diagonals[i]` into the output diagonal `output.diagonal(offset[i], dim0=dims[i][0], dim1=dims[i][1])` (with some additional consideration that makes it more complicated than simply asigning to that view). The specialization from this back to the 2-D form could be seen as assuming `dims = [[0, 1], [0, 1]... len(offsets) times ]` when `len shape==2`. In both proposed signatures for the N-D case the specialization back to the 2-D signature is a bit of a stretch for your typical default arguments logic, however I think the first is better choice as it offers more flexibility. I think some discussion is required about: - [x] Should the N-D output case be implemented from the outset - [x] If not, should the future addition of the N-D output case be considered when designing the interface. - [x] Other thoughts on the signature which includes the `dims` information for the N-D output case. **Resolution**: Since no one has offered a request for N-D output support, I think is fine to restrict this to sparse matrix generation. Should a request for N-D support come later, an overload accepting the additional `dims` could be added. Pull Request resolved: https://github.com/pytorch/pytorch/pull/78439 Approved by: https://github.com/nikitaved, https://github.com/cpuhrsch, https://github.com/pearu |
||
|
Christian Puhrsch
|
8c608a79b4 |
Compressed sparse layout conversion stubs (#77489)
This PR unifies sparse layout conversions into a single location and adds stubs to raise a Runtime error for unsupported conversions. Pull Request resolved: https://github.com/pytorch/pytorch/pull/77489 Approved by: https://github.com/pearu, https://github.com/mruberry |
||
|
Christian Puhrsch
|
edf2deb81e |
Add private conversion function from CSR to block CSR
This PR adds a private function that converts a CSR Tensor into a [scipy-style block CSR Tensor](https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.bsr_matrix.html#scipy.sparse.bsr_matrix). It uses the scipy CSR to BSR conversion routines (and credits them accordingly). The main purpose of this function is to easily create a block CSR Tensor for matrix multiplication. Follow up work includes - Blocksize support for sparse_csr_tensor - Parallel CPU kernel - CUDA kernels - Faster arg sanitization - Benchmarking of cuSPARSE backend - Dense to/from block CSR - Autograd support - Column-major blocks - Block CSR to CSR conversion Pull Request resolved: https://github.com/pytorch/pytorch/pull/71582 Approved by: https://github.com/IvanYashchuk, https://github.com/albanD |
||
|
Ivan Yashchuk
|
ebd93f69db |
Enable CSR inputs for torch.sparse.mm (#73075)
Summary: Previously `torch.sparse.mm` supported only COO and dense inputs. Computing derivatives works wrt dense input for sparse_csr x dense -> dense Modified implementation of `torch.sparse.mm` to be directly bound to ATen function. Pull Request resolved: https://github.com/pytorch/pytorch/pull/73075 Reviewed By: mikaylagawarecki Differential Revision: D34342954 Pulled By: cpuhrsch fbshipit-source-id: a6ed914a0ce28b35276109479109095f7149d32b (cherry picked from commit 948de1816c46cd087bacbee36dc583cf409813f9) |
||
|
Ivan Yashchuk
|
8cdcc1181c |
Add missing entry for sampled_addmm in sparse.rst (#72312)
Summary:
Let's make the documentation for `torch.sparse.sampled_addmm` searchable in the PyTorch documentation.
This PR shall be cherry-picked for the next 1.11 release.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/72312
Reviewed By: davidberard98
Differential Revision: D34045230
Pulled By: cpuhrsch
fbshipit-source-id: c1b1dc907443284857f48c8ce1efab22c6701bbe
(cherry picked from commit
|
||
|
Ivan Yashchuk
|
89a145fd91 |
Sparse CSR CUDA: Add torch.sparse.sampled_addmm (#68007)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68007 This PR adds a new function to the sparse module. `sampled_addmm` computes α*(A @ B) * spy(C) + β*C, where C is a sparse CSR matrix and A, B are dense (strided) matrices. This function is currently restricted to single 2D matrices, it doesn't support batched input. cc nikitaved pearu cpuhrsch IvanYashchuk Test Plan: Imported from OSS Reviewed By: mrshenli Differential Revision: D32435799 Pulled By: cpuhrsch fbshipit-source-id: b1ffac795080aef3fa05eaeeded03402bc097392 |
||
|
Christian Puhrsch
|
75955e4ef8 |
[clone][sparse] Add torch._C._sparse namespace (#68672)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/68672 This PR adds `python_module: sparse` to `native_function.yaml`. These functions would appear in `torch._C._sparse` namespace instead of just `torch`. Test Plan: Imported from OSS Reviewed By: mruberry Differential Revision: D32517813 fbshipit-source-id: 7c3d6df57a24d7c7354d0fefe1b628dc89be9431 |
||
|
Shen Li
|
1022443168 |
Revert D30279364: [codemod][lint][fbcode/c*] Enable BLACK by default
Test Plan: revert-hammer
Differential Revision:
D30279364 (
|
||
|
Zsolt Dollenstein
|
b004307252 |
[codemod][lint][fbcode/c*] Enable BLACK by default
Test Plan: manual inspection & sandcastle Reviewed By: zertosh Differential Revision: D30279364 fbshipit-source-id: c1ed77dfe43a3bde358f92737cd5535ae5d13c9a |
||
|
Sam Estep
|
8c798e0622 |
Forbid trailing whitespace (#53406)
Summary: Context: https://github.com/pytorch/pytorch/pull/53299#discussion_r587882857 These are the only hand-written parts of this diff: - the addition to `.github/workflows/lint.yml` - the file endings changed in these four files (to appease FB-internal land-blocking lints): - `GLOSSARY.md` - `aten/src/ATen/core/op_registration/README.md` - `scripts/README.md` - `torch/csrc/jit/codegen/fuser/README.md` The rest was generated by running this command (on macOS): ``` git grep -I -l ' $' -- . ':(exclude)**/contrib/**' ':(exclude)third_party' | xargs gsed -i 's/ *$//' ``` I looked over the auto-generated changes and didn't see anything that looked problematic. Pull Request resolved: https://github.com/pytorch/pytorch/pull/53406 Test Plan: This run (after adding the lint but before removing existing trailing spaces) failed: - https://github.com/pytorch/pytorch/runs/2043032377 This run (on the tip of this PR) succeeded: - https://github.com/pytorch/pytorch/runs/2043296348 Reviewed By: walterddr, seemethere Differential Revision: D26856620 Pulled By: samestep fbshipit-source-id: 3f0de7f7c2e4b0f1c089eac9b5085a58dd7e0d97 |
||
|
Samuel Marks
|
e6779d4357 |
[*.py] Rename "Arguments:" to "Args:" (#49736)
Summary:
I've written custom parsers and emitters for everything from docstrings to classes and functions. However, I recently came across an issue when I was parsing/generating from the TensorFlow codebase: inconsistent use of `Args:` and `Arguments:` in its docstrings.
```sh
(pytorch#c348fae)$ for name in 'Args:' 'Arguments:'; do
printf '%-10s %04d\n' "$name" "$(rg -IFtpy --count-matches "$name" | paste -s -d+ -- | bc)"; done
Args: 1095
Arguments: 0336
```
It is easy enough to extend my parsers to support both variants, however it looks like `Arguments:` is wrong anyway, as per:
- https://google.github.io/styleguide/pyguide.html#doc-function-args @ [`ddccc0f`](https://github.com/google/styleguide/blob/ddccc0f/pyguide.md)
- https://chromium.googlesource.com/chromiumos/docs/+/master/styleguide/python.md#describing-arguments-in-docstrings @ [`9fc0fc0`](https://chromium.googlesource.com/chromiumos/docs/+/9fc0fc0/styleguide/python.md)
- https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html @ [`c0ae8e3`](https://github.com/sphinx-contrib/napoleon/blob/c0ae8e3/docs/source/example_google.rst)
Therefore, only `Args:` is valid. This PR replaces them throughout the codebase.
PS: For related PRs, see tensorflow/tensorflow/pull/45420
PPS: The trackbacks automatically appearing below are sending the same changes to other repositories in the [PyTorch](https://github.com/pytorch) organisation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/49736
Reviewed By: albanD
Differential Revision: D25710534
Pulled By: soumith
fbshipit-source-id: 61e8ff01abb433e9f78185c2d1d0cbd7c22c1619
|
||
|
Alexander
|
44ce0b8883 |
Sparse-sparse matrix multiplication (CPU/CUDA) (#39526)
Summary: This PR implements matrix multiplication support for 2-d sparse tensors using the COO sparse format. The current implementation of `torch.sparse.mm` support this configuration, `torch.sparse.mm(sparse_matrix1, sparse_matrix2.to_dense())`, but this could spend a lot of memory when sparse_matrix2's shape is large. This implementation extends `torch.sparse.mm` function to support `torch.sparse.mm(sparse_matrix1, sparse_matrix2)` Resolves #[20988](https://github.com/pytorch/pytorch/issues/20988) for CPU/CUDA. - [x] sparse matmul - [x] CPU/CUDA C++ implementation - [x] unittests - [x] update torch.sparse.mm documentation - [x] autograd support The CPU sparse-sparse matmul was implemented taking as a reference this work "Sparse Matrix Multiplication Package (SMMP)". The GPU sparse-sparse matmul is based on cuSparse, there is specific code for CUSPARSE when CUSPARSE_VERSION >= 11 and old version of CUSPARSE. Both CPU/CUDA rely on the sparse-sparse matmul algorithm using the CSR indices format as it is one of the fastest algorithm. Here it is the latest benchmark (script is here) results for torch.sparse.mm (CUDA) and torch.sparse.mm (CPU) and scipy, values are float32 scalars: size | density | sparse.mm(CUDA) | sparse.mm(CPU) | scipy_coo_matmul -- | -- | -- | -- | -- (32, 10000) | 0.01 | 822.7 | 79.4 | 704.1 (32, 10000) | 0.05 | 1741.1 | 402.6 | 1155.3 (32, 10000) | 0.1 | 2956.8 | 840.8 | 1885.4 (32, 10000) | 0.25 | 6417.7 | 2832.3 | 4665.2 (512, 10000) | 0.01 | 1010.2 | 3941.3 | 26937.7 (512, 10000) | 0.05 | 2216.2 | 26903.8 | 57343.7 (512, 10000) | 0.1 | 4868.4 | 87773.7 | 117477.0 (512, 10000) | 0.25 | 16639.3 | 608105.0 | 624290.4 (1024, 10000) | 0.01 | 1224.8 | 13088.1 | 110379.2 (1024, 10000) | 0.05 | 3897.5 | 94783.9 | 236541.8 (1024, 10000) | 0.1 | 10559.1 | 405312.5 | 525483.4 (1024, 10000) | 0.25 | 57456.3 | 2424337.5 | 2729318.7 A new backward algorithm was implemented using only `sparse @ sparse` and `sparse_mask` operations. Here is some benchmarking: ``` [------------------------- sparse.mm-backward -------------------------] | sparse.backward | dense.backward ----------------------------------------------------------------------- (32, 10000) | 0.01 | 13.5 | 2.4 (32, 10000) | 0.05 | 52.3 | 2.4 (512, 10000) | 0.01 | 1016.8 | 491.5 (512, 10000) | 0.05 | 1604.3 | 492.3 (1024, 10000) | 0.01 | 2384.1 | 1963.7 (1024, 10000) | 0.05 | 3965.8 | 1951.9 ``` I added new benchmark tests. Now I am using a real dataset used in recent studies [1, 2] with different sparsity levels. ``` [---------------------------------- matmul ---------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ------------------------------------------------------------------ (cpu) torch | 5.4 | 5.4 | 5.2 | 5.3 | 5.3 | 5.4 torch.sparse | 122.2 | 51.9 | 27.5 | 11.4 | 4.9 | 1.8 scipy | 150.1 | 87.4 | 69.2 | 56.8 | 38.4 | 17.1 (cuda) torch | 1.3 | 1.1 | 1.1 | 1.1 | 1.1 | 1.1 torch.sparse | 20.0 | 8.4 | 5.1 | 2.5 | 1.5 | 1.1 [----------------------------------- backward -----------------------------------] | 0.5 | 0.7 | 0.8 | 0.9 | 0.95 | 0.98 1 threads: ----------------------------------------------------------------------- (cpu) torch | 17.7 | 17.9 | 17.7 | 17.7 | 17.6 | 17.9 torch.sparse | 672.9 | 432.6 | 327.5 | 230.8 | 176.7 | 116.7 (cuda) torch | 3.8 | 3.6 | 3.5 | 3.5 | 3.6 | 3.5 torch.sparse | 68.8 | 46.2 | 35.6 | 24.2 | 17.8 | 11.9 Times are in milliseconds (ms). ``` In summary, I can say that the new `sparse @ sparse` backward algorithm is better as it is more about saving space than performance. Moreover, it is better than other options tested before. ## **References** 1. Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen. **Sparse GPU Kernels for Deep Learning.** Proceedings of the International Conference for High Performance Computing, 2020. [https://github.com/google-research/google-research/tree/master/sgk](https://github.com/google-research/google-research/tree/master/sgk) 2. Trevor Gale, Erich Elsen, Sara Hooker. **The State of Sparsity in Deep Neural Networks.** [https://github.com/google-research/google-research/tree/master/state_of_sparsity](https://github.com/google-research/google-research/tree/master/state_of_sparsity) Pull Request resolved: https://github.com/pytorch/pytorch/pull/39526 Reviewed By: mruberry Differential Revision: D25661239 Pulled By: ngimel fbshipit-source-id: b515ecd66d25f347d637e159d51aa45fb43b6938 |
||
|
Pearu Peterson
|
905ed3c840 |
Revised sparse tensor documentation. (#45400)
Summary: Fixes https://github.com/pytorch/pytorch/issues/44635. Pull Request resolved: https://github.com/pytorch/pytorch/pull/45400 Reviewed By: ezyang Differential Revision: D24359410 Pulled By: mruberry fbshipit-source-id: 37c691a49a7b0042c7a298e0ed1226702b097c8b |
||
|
Ralf Gommers
|
864f0cfb2d |
Fix type annotations for torch.sparse, enable in CI (#43108)
Summary: Closes gh-42982 Pull Request resolved: https://github.com/pytorch/pytorch/pull/43108 Reviewed By: malfet Differential Revision: D23167560 Pulled By: ezyang fbshipit-source-id: 0d660ca686ada2347bf440c6349551d1539f99ef |
||
|
wudenggang
|
9600ed9af3 |
typo fixes (#41632)
Summary: typo fixes Pull Request resolved: https://github.com/pytorch/pytorch/pull/41632 Reviewed By: ezyang Differential Revision: D22617827 Pulled By: mrshenli fbshipit-source-id: c2bfcb7cc36913a8dd32f13fc9adc3aa0a9b682f |
||
|
Pearu Peterson
|
48c0331e01 |
Sparse softmax support (CPU) (#36305)
Summary: This PR implements softmax support for sparse tensors. The sparse softmax is related to dense softmax when the values of unspecified sparse tensor entries are taken to be `-inf` that will have the effect of "zero entries ignored". This relation is used for testing the correctness of results here. Resolves https://github.com/pytorch/pytorch/issues/23651 for CPU. - [x] sparse softmax - [x] CPU C++ implementation - [x] unittests - [x] update softmax documentation - [x] autograd support - [x] sparse log_softmax - [x] CPU C++ implementation - [x] unittests - [x] update log_softmax documentation - [x] autograd support Pull Request resolved: https://github.com/pytorch/pytorch/pull/36305 Differential Revision: D21566540 Pulled By: ezyang fbshipit-source-id: a632ea69c38622f960721482e442efeb8d0a54fc |
||
|
Ralf Gommers
|
78d5707041 |
Fix type annotations and make MyPy run on torch/ (#36584)
Summary: This PR fixes a couple of syntax errors in `torch/` that prevent MyPy from running, fixes simple type annotation errors (e.g. missing `from typing import List, Tuple, Optional`), and adds granular ignores for errors in particular modules as well as for missing typing in third party packages. As a result, running `mypy` in the root dir of the repo now runs on: - `torch/` - `aten/src/ATen/function_wrapper.py` (the only file already covered in CI) In CI this runs on GitHub Actions, job Lint, sub-job "quick-checks", task "MyPy typecheck". It should give (right now): `Success: no issues found in 329 source files`. Here are the details of the original 855 errors when running `mypy torch` on current master (after fixing the couple of syntax errors that prevent `mypy` from running through): <details> ``` torch/utils/tensorboard/_proto_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_proto_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_proto_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/backcompat/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/for_onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch.for_onnx.onnx' torch/cuda/nvtx.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/utils/show_pickle.py:59: error: Name 'pickle._Unpickler' is not defined torch/utils/show_pickle.py:113: error: "Type[PrettyPrinter]" has no attribute "_dispatch" torch/utils/tensorboard/_onnx_graph.py:1: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.graph_pb2' torch/utils/tensorboard/_onnx_graph.py:2: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.node_def_pb2' torch/utils/tensorboard/_onnx_graph.py:3: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.versions_pb2' torch/utils/tensorboard/_onnx_graph.py:4: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.attr_value_pb2' torch/utils/tensorboard/_onnx_graph.py:5: error: Cannot find implementation or library stub for module named 'tensorboard.compat.proto.tensor_shape_pb2' torch/utils/tensorboard/_onnx_graph.py:9: error: Cannot find implementation or library stub for module named 'onnx' torch/contrib/_tensorboard_vis.py:10: error: Cannot find implementation or library stub for module named 'tensorflow.core.util' torch/contrib/_tensorboard_vis.py:11: error: Cannot find implementation or library stub for module named 'tensorflow.core.framework' torch/contrib/_tensorboard_vis.py:12: error: Cannot find implementation or library stub for module named 'tensorflow.python.summary.writer.writer' torch/utils/hipify/hipify_python.py:43: error: Need type annotation for 'CAFFE2_TEMPLATE_MAP' (hint: "CAFFE2_TEMPLATE_MAP: Dict[<type>, <type>] = ...") torch/utils/hipify/hipify_python.py:636: error: "object" has no attribute "items" torch/nn/_reduction.py:27: error: Name 'Optional' is not defined torch/nn/_reduction.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/_reduction.py:47: error: Name 'Optional' is not defined torch/nn/_reduction.py:47: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib.pyplot': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:17: error: Skipping analyzing 'matplotlib': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends.backend_agg': found module but no type hints or library stubs torch/utils/tensorboard/_utils.py:18: error: Skipping analyzing 'matplotlib.backends': found module but no type hints or library stubs torch/nn/modules/utils.py:27: error: Name 'List' is not defined torch/nn/modules/utils.py:27: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") caffe2/proto/caffe2_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/caffe2_pb2.py:25: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:31: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:35: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:39: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:47: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:51: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:55: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:59: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:63: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:67: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:71: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:75: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:108: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:112: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:124: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:134: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:138: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:142: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:146: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:150: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:154: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:158: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:162: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:166: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:170: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:174: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:194: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:200: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:204: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:208: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:212: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:224: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/caffe2_pb2.py:230: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:238: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:242: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:246: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:250: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/caffe2_pb2.py:267: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:288: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:295: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:302: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:327: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:334: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:341: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:364: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:371: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:378: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:385: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:392: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:399: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:406: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:413: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:420: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:448: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:455: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:462: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:488: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:495: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:502: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:509: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:516: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:523: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:530: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:537: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:544: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:551: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:558: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:565: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:572: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:596: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:603: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:627: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:634: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:641: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:648: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:655: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:662: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:686: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:693: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:717: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:724: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:731: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:738: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:763: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:770: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:777: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:784: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:808: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:815: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:822: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:829: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:836: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:843: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:850: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:857: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:864: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:871: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:878: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:885: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:892: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:916: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:923: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:930: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:937: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:944: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:951: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:958: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:982: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:989: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:996: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1003: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1010: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1017: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1024: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1031: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1038: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1045: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1052: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1059: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1066: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1090: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1097: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1104: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1128: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1135: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1142: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1166: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1173: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1180: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1187: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1194: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1218: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1225: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1232: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1239: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1246: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1253: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1260: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1267: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1274: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1281: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1305: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1312: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1319: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1326: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1333: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1340: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1347: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1354: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1361: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1368: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1375: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1382: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1389: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1396: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1420: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1427: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1434: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1441: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1465: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1472: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1479: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1486: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1493: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1500: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1507: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1514: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1538: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/caffe2_pb2.py:1545: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1552: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1559: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1566: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/caffe2_pb2.py:1667: error: "GeneratedProtocolMessageType" has no attribute "Segment" torch/multiprocessing/queue.py:4: error: No library stub file for standard library module 'multiprocessing.reduction' caffe2/proto/torch_pb2.py:18: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/torch_pb2.py:27: error: Unexpected keyword argument "serialized_options" for "EnumDescriptor" caffe2/proto/torch_pb2.py:33: error: Unexpected keyword argument "serialized_options" for "EnumValueDescriptor" caffe2/proto/torch_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:81: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:109: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:116: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:123: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:130: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:137: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:144: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:151: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:175: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:182: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:189: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:196: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:220: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:227: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:234: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:241: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:265: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:272: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:279: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:286: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:293: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:300: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:307: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:314: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:321: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:328: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:335: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:342: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:366: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:373: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:397: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/torch_pb2.py:404: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:411: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:418: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:425: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/torch_pb2.py:432: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:17: error: Unexpected keyword argument "serialized_options" for "FileDescriptor"; did you mean "serialized_pb"? caffe2/proto/metanet_pb2.py:29: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:36: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:43: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:50: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:57: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:64: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:88: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:95: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:102: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:126: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:133: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:140: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:164: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:171: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:178: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:202: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:209: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:216: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:240: error: Unexpected keyword argument "serialized_options" for "Descriptor" caffe2/proto/metanet_pb2.py:247: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:254: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:261: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:268: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:275: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:282: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:289: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/metanet_pb2.py:296: error: Unexpected keyword argument "serialized_options" for "FieldDescriptor" caffe2/proto/__init__.py:13: error: Skipping analyzing 'caffe2.caffe2.fb.session.proto': found module but no type hints or library stubs torch/multiprocessing/pool.py:3: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/pool.py:3: note: (Stub files are from https://github.com/python/typeshed) caffe2/python/scope.py:10: error: Skipping analyzing 'past.builtins': found module but no type hints or library stubs caffe2/python/__init__.py:7: error: Module has no attribute "CPU" caffe2/python/__init__.py:8: error: Module has no attribute "CUDA" caffe2/python/__init__.py:9: error: Module has no attribute "MKLDNN" caffe2/python/__init__.py:10: error: Module has no attribute "OPENGL" caffe2/python/__init__.py:11: error: Module has no attribute "OPENCL" caffe2/python/__init__.py:12: error: Module has no attribute "IDEEP" caffe2/python/__init__.py:13: error: Module has no attribute "HIP" caffe2/python/__init__.py:14: error: Module has no attribute "COMPILE_TIME_MAX_DEVICE_TYPES"; maybe "PROTO_COMPILE_TIME_MAX_DEVICE_TYPES"? caffe2/python/__init__.py:15: error: Module has no attribute "ONLY_FOR_TEST"; maybe "PROTO_ONLY_FOR_TEST"? caffe2/python/__init__.py:34: error: Item "_Loader" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:34: error: Item "None" of "Optional[_Loader]" has no attribute "exec_module" caffe2/python/__init__.py:35: error: Module has no attribute "cuda" caffe2/python/__init__.py:37: error: Module has no attribute "cuda" caffe2/python/__init__.py:49: error: Module has no attribute "add_dll_directory" torch/random.py:4: error: Cannot find implementation or library stub for module named 'torch._C' torch/_classes.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/onnx/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/hub.py:21: error: Skipping analyzing 'tqdm.auto': found module but no type hints or library stubs torch/hub.py:24: error: Skipping analyzing 'tqdm': found module but no type hints or library stubs torch/hub.py:27: error: Name 'tqdm' already defined (possibly by an import) torch/_tensor_str.py:164: error: Not all arguments converted during string formatting torch/_ops.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/_linalg_utils.py:26: error: Name 'Optional' is not defined torch/_linalg_utils.py:26: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:26: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Tensor' is not defined torch/_linalg_utils.py:63: error: Name 'Optional' is not defined torch/_linalg_utils.py:63: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Optional' is not defined torch/_linalg_utils.py:70: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:70: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Tensor' is not defined torch/_linalg_utils.py:88: error: Name 'Optional' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_linalg_utils.py:88: error: Name 'Tuple' is not defined torch/_linalg_utils.py:88: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_jit_internal.py:17: error: Need type annotation for 'boolean_dispatched' torch/_jit_internal.py:474: error: Need type annotation for '_overloaded_fns' (hint: "_overloaded_fns: Dict[<type>, <type>] = ...") torch/_jit_internal.py:512: error: Need type annotation for '_overloaded_methods' (hint: "_overloaded_methods: Dict[<type>, <type>] = ...") torch/_jit_internal.py:648: error: Incompatible types in assignment (expression has type "FinalCls", variable has type "_SpecialForm") torch/sparse/__init__.py:11: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Tensor' is not defined torch/sparse/__init__.py:71: error: Name 'Optional' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/sparse/__init__.py:71: error: Name 'Tuple' is not defined torch/sparse/__init__.py:71: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/nn/init.py:109: error: Name 'Tensor' is not defined torch/nn/init.py:126: error: Name 'Tensor' is not defined torch/nn/init.py:142: error: Name 'Tensor' is not defined torch/nn/init.py:165: error: Name 'Tensor' is not defined torch/nn/init.py:180: error: Name 'Tensor' is not defined torch/nn/init.py:194: error: Name 'Tensor' is not defined torch/nn/init.py:287: error: Name 'Tensor' is not defined torch/nn/init.py:315: error: Name 'Tensor' is not defined torch/multiprocessing/reductions.py:8: error: No library stub file for standard library module 'multiprocessing.util' torch/multiprocessing/reductions.py:9: error: No library stub file for standard library module 'multiprocessing.reduction' torch/multiprocessing/reductions.py:17: error: No library stub file for standard library module 'multiprocessing.resource_sharer' torch/jit/_builtins.py:72: error: Module has no attribute "_no_grad_embedding_renorm_" torch/jit/_builtins.py:80: error: Module has no attribute "stft" torch/jit/_builtins.py:81: error: Module has no attribute "cdist" torch/jit/_builtins.py:82: error: Module has no attribute "norm" torch/jit/_builtins.py:83: error: Module has no attribute "nuclear_norm" torch/jit/_builtins.py:84: error: Module has no attribute "frobenius_norm" torch/backends/cudnn/__init__.py:8: error: Cannot find implementation or library stub for module named 'torch._C' torch/backends/cudnn/__init__.py:86: error: Need type annotation for '_handles' (hint: "_handles: Dict[<type>, <type>] = ...") torch/autograd/profiler.py:13: error: Name 'ContextDecorator' already defined (possibly by an import) torch/autograd/function.py:2: error: Cannot find implementation or library stub for module named 'torch._C' torch/autograd/function.py:2: note: See https://mypy.readthedocs.io/en/latest/running_mypy.html#missing-imports torch/autograd/function.py:109: error: Unsupported dynamic base class "with_metaclass" torch/serialization.py:609: error: "Callable[[Any], Any]" has no attribute "cache" torch/_lowrank.py:11: error: Name 'Tensor' is not defined torch/_lowrank.py:13: error: Name 'Optional' is not defined torch/_lowrank.py:13: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Optional' is not defined torch/_lowrank.py:14: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:14: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Tensor' is not defined torch/_lowrank.py:82: error: Name 'Optional' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:82: error: Name 'Tuple' is not defined torch/_lowrank.py:82: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:130: error: Name 'Tensor' is not defined torch/_lowrank.py:130: error: Name 'Optional' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:130: error: Name 'Tuple' is not defined torch/_lowrank.py:130: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/_lowrank.py:167: error: Name 'Tensor' is not defined torch/_lowrank.py:167: error: Name 'Optional' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/_lowrank.py:167: error: Name 'Tuple' is not defined torch/_lowrank.py:167: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:45: error: Variable "torch.quantization.observer.ABC" is not valid as a type torch/quantization/observer.py:45: note: See https://mypy.readthedocs.io/en/latest/common_issues.html#variables-vs-type-aliases torch/quantization/observer.py:45: error: Invalid base class "ABC" torch/quantization/observer.py:127: error: Name 'Tensor' is not defined torch/quantization/observer.py:127: error: Name 'Tuple' is not defined torch/quantization/observer.py:127: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:172: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:172: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:192: error: Name 'Tensor' is not defined torch/quantization/observer.py:192: error: Name 'Tuple' is not defined torch/quantization/observer.py:192: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:233: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:233: error: Module has no attribute "per_channel_symmetric" torch/quantization/observer.py:534: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tensor' is not defined torch/quantization/observer.py:885: error: Name 'Tuple' is not defined torch/quantization/observer.py:885: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/quantization/observer.py:894: error: Cannot determine type of 'max_val' torch/quantization/observer.py:894: error: Cannot determine type of 'min_val' torch/quantization/observer.py:899: error: Cannot determine type of 'min_val' torch/quantization/observer.py:902: error: Name 'Tensor' is not defined torch/quantization/observer.py:925: error: Name 'Tensor' is not defined torch/quantization/observer.py:928: error: Cannot determine type of 'min_val' torch/quantization/observer.py:929: error: Cannot determine type of 'max_val' torch/quantization/observer.py:946: error: Argument "min" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:946: error: Argument "max" to "histc" has incompatible type "Tuple[Tensor, Tensor]"; expected "Union[int, float, bool]" torch/quantization/observer.py:1056: error: Module has no attribute "per_tensor_symmetric" torch/quantization/observer.py:1058: error: Module has no attribute "per_channel_symmetric" torch/nn/quantized/functional.py:76: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:76: error: Name 'BroadcastingList2' is not defined torch/nn/quantized/functional.py:259: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:259: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:259: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:289: error: Module has no attribute "ops" torch/nn/quantized/functional.py:290: error: Module has no attribute "ops" torch/nn/quantized/functional.py:308: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:326: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:356: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:371: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:400: error: Name 'Optional' is not defined torch/nn/quantized/functional.py:400: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/functional.py:430: error: Name 'Tensor' is not defined torch/nn/quantized/functional.py:448: error: Name 'Tensor' is not defined torch/nn/quantized/modules/linear.py:26: error: Module has no attribute "ops" torch/nn/quantized/modules/linear.py:28: error: Module has no attribute "ops" torch/nn/quantized/modules/functional_modules.py:40: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:47: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:54: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:61: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:68: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:68: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:68: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:75: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:140: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:146: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:151: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:157: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:162: error: Name 'List' is not defined torch/nn/quantized/modules/functional_modules.py:162: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/functional_modules.py:162: error: Name 'Tensor' is not defined torch/nn/quantized/modules/functional_modules.py:168: error: Name 'Tensor' is not defined torch/multiprocessing/spawn.py:9: error: Module 'torch.multiprocessing' has no attribute '_prctl_pr_set_pdeathsig' torch/multiprocessing/__init__.py:28: error: Module has no attribute "__all__" torch/jit/frontend.py:9: error: Cannot find implementation or library stub for module named 'torch._C._jit_tree_views' torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList2'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:6: error: Module 'torch._jit_internal' has no attribute 'BroadcastingList3'; maybe "BroadcastingList1" or "BroadcastingListCls"? torch/jit/annotations.py:9: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributions/distribution.py:16: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/distribution.py:74: error: Name 'arg_constraints' already defined on line 16 torch/distributions/distribution.py:84: error: Name 'support' already defined on line 15 torch/functional.py:114: error: Name 'Tuple' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:114: error: Name 'Optional' is not defined torch/functional.py:114: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:189: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:200: error: Argument 1 to "_indices_product" has incompatible type "Tuple[int, ...]"; expected "List[int]" torch/functional.py:204: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:204: note: Possible overload variants: torch/functional.py:204: note: def __setitem__(self, int, int) -> None torch/functional.py:204: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:204: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:204: note: def __getitem__(self, int) -> int torch/functional.py:204: note: def __getitem__(self, slice) -> List[int] torch/functional.py:207: error: "Tensor" has no attribute "copy_" torch/functional.py:212: error: No overload variant of "__setitem__" of "list" matches argument types "Tensor", "int" torch/functional.py:212: note: Possible overload variants: torch/functional.py:212: note: def __setitem__(self, int, int) -> None torch/functional.py:212: note: def __setitem__(self, slice, Iterable[int]) -> None torch/functional.py:212: error: No overload variant of "__getitem__" of "list" matches argument type "Tensor" torch/functional.py:212: note: def __getitem__(self, int) -> int torch/functional.py:212: note: def __getitem__(self, slice) -> List[int] torch/functional.py:215: error: Incompatible types in assignment (expression has type "None", variable has type "Tensor") torch/functional.py:334: error: Name 'Optional' is not defined torch/functional.py:334: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:429: error: Argument 2 to "pad" has incompatible type "Tuple[int, int]"; expected "List[int]" torch/functional.py:431: error: Module has no attribute "stft" torch/functional.py:766: error: Module has no attribute "cdist" torch/functional.py:768: error: Module has no attribute "cdist" torch/functional.py:770: error: Module has no attribute "cdist" torch/functional.py:775: error: Name 'Optional' is not defined torch/functional.py:775: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'Optional' is not defined torch/functional.py:780: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:780: error: Name 'number' is not defined torch/functional.py:780: error: Name 'norm' already defined on line 775 torch/functional.py:785: error: Name 'Optional' is not defined torch/functional.py:785: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:785: error: Name 'number' is not defined torch/functional.py:785: error: Name 'norm' already defined on line 775 torch/functional.py:790: error: Name 'Optional' is not defined torch/functional.py:790: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:790: error: Name 'norm' already defined on line 775 torch/functional.py:795: error: Name 'norm' already defined on line 775 torch/functional.py:960: error: Name 'Any' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Any") torch/functional.py:960: error: Name 'Tuple' is not defined torch/functional.py:960: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1036: error: Argument 1 to "len" has incompatible type "int"; expected "Sized" torch/functional.py:1041: error: Name 'Optional' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1041: error: Name 'Tuple' is not defined torch/functional.py:1041: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/functional.py:1056: error: Name 'Optional' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/functional.py:1056: error: Name 'Tuple' is not defined torch/functional.py:1056: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Tuple") torch/distributions/von_mises.py:87: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/negative_binomial.py:25: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/multivariate_normal.py:116: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/laplace.py:23: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/independent.py:34: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/cauchy.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/poisson.py:28: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/one_hot_categorical.py:32: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/normal.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/lowrank_multivariate_normal.py:79: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/gamma.py:30: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/exponential.py:23: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/fishersnedecor.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/dirichlet.py:44: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/nn/quantized/dynamic/modules/rnn.py:230: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:232: error: Incompatible types in assignment (expression has type "int", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:236: error: Incompatible return value type (got "Tuple[Any, Tensor, Any]", expected "Tuple[int, int, int]") torch/nn/quantized/dynamic/modules/rnn.py:351: error: Incompatible types in assignment (expression has type "Type[LSTM]", base class "RNNBase" defined the type as "Type[RNNBase]") torch/nn/quantized/dynamic/modules/rnn.py:381: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:385: error: Module has no attribute "quantized_lstm" torch/nn/quantized/dynamic/modules/rnn.py:414: error: Argument 1 to "forward_impl" of "LSTM" has incompatible type "PackedSequence"; expected "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:416: error: Incompatible types in assignment (expression has type "PackedSequence", variable has type "Tensor") torch/nn/quantized/dynamic/modules/rnn.py:418: error: Incompatible return value type (got "Tuple[Tensor, Tuple[Tensor, Tensor]]", expected "Tuple[PackedSequence, Tuple[Tensor, Tensor]]") torch/nn/quantized/dynamic/modules/rnn.py:420: error: Argument 1 of "permute_hidden" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/quantized/dynamic/modules/rnn.py:420: error: Return type "Tuple[Tensor, Tensor]" of "permute_hidden" incompatible with return type "Tensor" in supertype "RNNBase" torch/nn/quantized/dynamic/modules/rnn.py:426: error: Argument 2 of "check_forward_args" is incompatible with supertype "RNNBase"; supertype defines the argument type as "Tensor" torch/nn/intrinsic/qat/modules/conv_fused.py:232: error: Incompatible types in assignment (expression has type "Type[ConvBnReLU2d]", base class "ConvBn2d" defined the type as "Type[ConvBn2d]") torch/distributions/beta.py:27: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/geometric.py:31: error: Incompatible types in assignment (expression has type "_IntegerGreaterThan", base class "Distribution" defined the type as "None") torch/distributions/continuous_bernoulli.py:38: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/bernoulli.py:30: error: Incompatible types in assignment (expression has type "_Boolean", base class "Distribution" defined the type as "None") torch/quantization/fake_quantize.py:126: error: Module has no attribute "per_tensor_symmetric" torch/quantization/fake_quantize.py:132: error: Module has no attribute "per_channel_symmetric" torch/distributions/transformed_distribution.py:41: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/jit/__init__.py:1: error: Cannot find implementation or library stub for module named 'torch._C' torch/jit/__init__.py:15: error: Module 'torch.utils' has no attribute 'set_module' torch/jit/__init__.py:70: error: Name 'Attribute' already defined on line 68 torch/jit/__init__.py:213: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:215: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/jit/__init__.py:1524: error: Unsupported dynamic base class "with_metaclass" torch/jit/__init__.py:1869: error: Name 'ScriptModule' already defined on line 1524 torch/jit/__init__.py:1998: error: Need type annotation for '_jit_caching_layer' torch/jit/__init__.py:1999: error: Need type annotation for '_jit_function_overload_caching' torch/distributions/relaxed_categorical.py:34: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_categorical.py:108: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:31: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/relaxed_bernoulli.py:114: error: Incompatible types in assignment (expression has type "_Interval", base class "Distribution" defined the type as "None") torch/distributions/logistic_normal.py:31: error: Incompatible types in assignment (expression has type "_Simplex", base class "Distribution" defined the type as "None") torch/distributions/log_normal.py:26: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_normal.py:27: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/half_cauchy.py:28: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/gumbel.py:28: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/nn/quantized/modules/conv.py:18: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/quantized/modules/conv.py:209: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:209: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:214: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:321: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:321: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:323: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:447: error: Name 'Optional' is not defined torch/nn/quantized/modules/conv.py:447: note: Did you forget to import it from "typing"? (Suggestion: "from typing import Optional") torch/nn/quantized/modules/conv.py:449: error: Module has no attribute "ops" torch/nn/quantized/modules/conv.py:513: error: Name 'nn.modules.conv._ConvTransposeNd' is not defined torch/nn/quantized/modules/conv.py:525: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:525: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/quantized/modules/conv.py:527: error: Name 'List' is not defined torch/nn/quantized/modules/conv.py:527: note: Did you forget to import it from "typing"? (Suggestion: "from typing import List") torch/nn/intrinsic/quantized/modules/conv_relu.py:8: error: Module 'torch.nn.utils' has no attribute 'fuse_conv_bn_weights' torch/nn/intrinsic/quantized/modules/conv_relu.py:21: error: Incompatible types in assignment (expression has type "Type[ConvReLU2d]", base class "Conv2d" defined the type as "Type[Conv2d]") torch/nn/intrinsic/quantized/modules/conv_relu.py:62: error: Incompatible types in assignment (expression has type "Type[ConvReLU3d]", base class "Conv3d" defined the type as "Type[Conv3d]") torch/distributions/weibull.py:25: error: Incompatible types in assignment (expression has type "_GreaterThan", base class "Distribution" defined the type as "None") torch/distributions/kl.py:35: error: Need type annotation for '_KL_MEMOIZE' (hint: "_KL_MEMOIZE: Dict[<type>, <type>] = ...") torch/distributions/studentT.py:27: error: Incompatible types in assignment (expression has type "_Real", base class "Distribution" defined the type as "None") torch/distributions/mixture_same_family.py:48: error: Need type annotation for 'arg_constraints' (hint: "arg_constraints: Dict[<type>, <type>] = ...") torch/distributions/__init__.py:158: error: Name 'transforms' is not defined torch/onnx/utils.py:21: error: Cannot find implementation or library stub for module named 'torch._C' torch/distributed/rendezvous.py:4: error: Cannot find implementation or library stub for module named 'urlparse' torch/distributed/rendezvous.py:4: error: Name 'urlparse' already defined (possibly by an import) torch/distributed/rendezvous.py:4: error: Name 'urlunparse' already defined (possibly by an import) torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'FileStore' torch/distributed/rendezvous.py:9: error: Module 'torch.distributed' has no attribute 'TCPStore' torch/distributed/rendezvous.py:65: error: On Python 3 '{}'.format(b'abc') produces "b'abc'"; use !r if this is a desired behavior torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceOptions'; maybe "ReduceOptions" or "AllreduceCoalescedOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllreduceCoalescedOptions'; maybe "AllreduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'AllToAllOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'BroadcastOptions' torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'GatherOptions'; maybe "ScatterOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceOptions'; maybe "AllreduceOptions", "ReduceScatterOptions", or "ReduceOp"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ReduceScatterOptions'; maybe "ScatterOptions" or "ReduceOptions"? torch/distributed/distributed_c10d.py:11: error: Module 'torch.distributed' has no attribute 'ScatterOptions'; maybe "ReduceScatterOptions" or Pull Request resolved: https://github.com/pytorch/pytorch/pull/36584 Reviewed By: seemethere, ailzhang Differential Revision: D21155985 Pulled By: ezyang fbshipit-source-id: f628d4293992576207167e7c417998fad15898d1 |
||
|
Wanchao Liang
|
5fd3251c50 |
add some sparse tensor ops support in TorchScript (#24967)
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/24967 Fixes https://github.com/pytorch/pytorch/issues/24140 Test Plan: Imported from OSS Differential Revision: D16975865 fbshipit-source-id: 134ecfff6ecb7144079d4eae85b186293aa26dd3 |
||
|
ZhuBaohe
|
acf5ec07af |
Correct conv and pooling docstrings in nn module (#17052)
Summary: This PR fix conv and pooling docstrings in nn module Pull Request resolved: https://github.com/pytorch/pytorch/pull/17052 Differential Revision: D14068566 Pulled By: ezyang fbshipit-source-id: 3ec1de232ff6334b6a544dadefbb0ee6193d443a |
||
|
Gregory Chanan
|
ffed8bff6a |
Fix torch.sparse.sum parsing of dim. (#16517)
Summary: Fixes https://github.com/pytorch/pytorch/issues/16501. Pull Request resolved: https://github.com/pytorch/pytorch/pull/16517 Differential Revision: D13865322 Pulled By: gchanan fbshipit-source-id: fa0ac37a9e7b8f19a5bdf75e5771128e48c41612 |
||
|
Wei Yang
|
5ee8312b63 |
sparse.mm(), reland #14526 (#14661)
Summary: - reland reverted PR #14526 with doc fixes Pull Request resolved: https://github.com/pytorch/pytorch/pull/14661 Differential Revision: D13289047 Pulled By: weiyangfb fbshipit-source-id: 5b843a11a58b56aeada3af2680a27cf89ecef4d8 |
||
|
Alyssa Wang
|
1c21dc6e16 |
Revert D13252990: [pytorch][PR] [sparse] sparse.mm(S, D)
Differential Revision: D13252990 Original commit changeset: 8fdb14144405 fbshipit-source-id: 49b8b0759a6e647854689962ffa72a205b4a2088 |
||
|
Wei Yang
|
c3a2b1e155 |
sparse.mm(S, D) (#14526)
Summary: - add `sparse.mm(S, D)` with backward - for `sparse.addmm()`, relax input constraint so that sparse matrix input doesn't have to coalesced Pull Request resolved: https://github.com/pytorch/pytorch/pull/14526 Reviewed By: ezyang Differential Revision: D13252990 Pulled By: weiyangfb fbshipit-source-id: 8fdb14144405a2122d4b8447ad4055cd0330e6e8 |
||
|
Wei Yang
|
be7c618fd7 |
torch.sparse.sum() (#12430)
Summary: - to fix #12241 - add `_sparse_sum()` to ATen, and expose as `torch.sparse.sum()`, not support `SparseTensor.sum()` currently - this PR depends on #11253, and will need to be updated upon it lands - [x] implement forward - [x] implement backward - performance [benchmark script](https://gist.github.com/weiyangfb/f4c55c88b6092ef8f7e348f6b9ad8946#file-sparse_sum_benchmark-py): - sum all dims is fastest for sparse tensor - when input is sparse enough nnz = 0.1%, sum of sparse tensor is faster than dense in CPU, but not necessary in CUDA - CUDA backward is comparable (<2x) between `sum several dims` vs `sum all dims` in sparse - CPU backward uses binary search is still slow in sparse, takes `5x` time in `sum [0, 2, 3] dims` vs `sum all dims` - optimize CUDA backward for now - using thrust for sort and binary search, but runtime not improved - both of CPU and CUDA forward are slow in sparse (`sum several dims` vs `sum all dims`), at most `20x` slower in CPU, and `10x` in CUDA - improve CPU and CUDA forward kernels (nnz, sizes, sum_dims, keepdim, sum all or dims, bk=backward) | CPU (sparse vs dense) | CUDA(sparse vs dense) -- | -- | -- (1000, [1000, 1000, 2, 2], [0, 1], False, sumAll) | 8.77 µs vs 72.9 µs | 42.5 µs vs 108 µs (1000, [1000, 1000, 2, 2], [0, 1], False, sumD) | 112 µs vs 4.47 ms | 484 µs vs 407 µs (1000, [1000, 1000, 2, 2], [0, 1], False, sumAll, bk) | 141 µs vs 148 µs | 647 µs vs 231 µs (1000, [1000, 1000, 2, 2], [0, 1], False, sumD, bk) | 235 µs vs 1.23 ms | 781 µs vs 213 µs (1000, [1000, 1000, 2, 2], [2, 3], False, sumD) | 48.5 µs vs 360 µs | 160 µs vs 2.03 ms (1000, [1000, 1000, 2, 2], [2, 3], False, sumD, bk) | 258 µs vs 1.22 ms | 798 µs vs 224 µs (1000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD) | 204 µs vs 882 µs | 443 µs vs 133 µs (1000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD, bk) | 709 µs vs 1.15 ms | 893 µs vs 202 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumAll) | 39.8 µs vs 81 µs | 42.4 µs vs 113 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumD) | 747 µs vs 4.7 ms | 2.4 ms vs 414 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumAll, bk) | 1.04 ms vs 126 µs | 5.03 ms vs 231 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumD, bk) | 1.12 ms vs 1.24 ms | 5.99 ms vs 213 µs (10000, [1000, 1000, 2, 2], [2, 3], False, sumD) | 133 µs vs 366 µs | 463 µs vs 2.03 ms (10000, [1000, 1000, 2, 2], [2, 3], False, sumD, bk) | 1.56 ms vs 1.22 ms | 6.11 ms vs 229 µs (10000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD) | 1.53 ms vs 799 µs | 824 µs vs 134 µs (10000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD, bk) | 5.15 ms vs 1.09 ms | 7.02 ms vs 205 µs - after improving CPU and CUDA forward kernels - in `(1000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD)` forward, CPU takes ~~`171 µs`~~, in which `130 µs` is spent on `coalesce()`, for CUDA, total time is ~~`331 µs`~~, in which `141 µs` is spent on `coalesce()`, we need to reduce time at other places outside `coalesce()`. - after a few simple tweaks, now in the forward, it is at most `10x` slower in CPU, and `7x` in CUDA. And time takes in `sum dense dims only [2, 3]` is `~2x` of `sum all dims`. Speed of `sum all sparse dims [0, 1]` is on bar with `sum all dims` (nnz, sizes, sum_dims, keepdim, sum all or dims, bk=backward) | CPU (sparse vs dense) | CUDA(sparse vs dense) -- | -- | -- (1000, [1000, 1000, 2, 2], [0, 1], False, sumAll) | 7 µs vs 69.5 µs | 31.5 µs vs 61.6 µs (1000, [1000, 1000, 2, 2], [0, 1], False, sumD) | 11.3 µs vs 4.72 ms | 35.2 µs vs 285 µs (1000, [1000, 1000, 2, 2], [0, 1], False, sumAll, bk) | 197 µs vs 124 µs | 857 µs vs 134 µs (1000, [1000, 1000, 2, 2], [0, 1], False, sumD, bk) | 124 µs vs 833 µs | 796 µs vs 106 µs (1000, [1000, 1000, 2, 2], [2, 3], False, sumD) | 20.5 µs vs 213 µs | 39.4 µs vs 1.24 ms (1000, [1000, 1000, 2, 2], [2, 3], False, sumD, bk) | 131 µs vs 830 µs | 881 µs vs 132 µs (1000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD) | 95.8 µs vs 409 µs | 246 µs vs 87.2 µs (1000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD, bk) | 624 µs vs 820 µs | 953 µs vs 124 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumAll) | 45.3 µs vs 72.9 µs | 33.9 µs vs 57.2 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumD) | 81.4 µs vs 4.49 ms | 39.7 µs vs 280 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumAll, bk) | 984 µs vs 111 µs | 6.41 ms vs 121 µs (10000, [1000, 1000, 2, 2], [0, 1], False, sumD, bk) | 1.45 ms vs 828 µs | 6.77 ms vs 113 µs (10000, [1000, 1000, 2, 2], [2, 3], False, sumD) | 74.9 µs vs 209 µs | 37.7 µs vs 1.23 ms (10000, [1000, 1000, 2, 2], [2, 3], False, sumD, bk) | 1.48 ms vs 845 µs | 6.96 ms vs 132 µs (10000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD) | 1.14 ms vs 411 µs | 252 µs vs 87.8 µs (10000, [1000, 1000, 2, 2], [0, 2, 3], False, sumD, bk) | 4.53 ms vs 851 µs | 7.12 ms vs 128 µs - time takes in CUDA backward of sparse is super long with large variance (in case of nnz=10000, it normally takes 6-7ms). To improve backward of sparse ops, we will need to debug at places other than CUDA kernels. here is a benchmark of `torch.copy_()`: ``` >>> d = [1000, 1000, 2, 2] >>> nnz = 10000 >>> I = torch.cat([torch.randint(0, d[0], size=(nnz,)), torch.randint(0, d[1], size=(nnz,))], 0).reshape(2, nnz) >>> V = torch.randn(nnz, d[2], d[3]) >>> size = torch.Size(d) >>> S = torch.sparse_coo_tensor(I, V, size).coalesce().cuda() >>> S2 = torch.sparse_coo_tensor(I, V, size).coalesce().cuda().requires_grad_() >>> data = S2.clone() >>> S.copy_(S2) >>> y = S * 2 >>> torch.cuda.synchronize() >>> %timeit y.backward(data, retain_graph=True); torch.cuda.synchronize() 7.07 ms ± 3.06 ms per loop (mean ± std. dev. of 7 runs, 1000 loops each) ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/12430 Differential Revision: D12878313 Pulled By: weiyangfb fbshipit-source-id: e16dc7681ba41fdabf4838cf05e491ca9108c6fe |
||
|
Wei Yang
|
50bc9dc9c3 |
fix doc for sparse.addmm (#14403)
Summary: - fixing the doc issue in sparse.addmm ================ before change ==================   ================ post change ==================   Pull Request resolved: https://github.com/pytorch/pytorch/pull/14403 Differential Revision: D13216582 Pulled By: weiyangfb fbshipit-source-id: 52e0a20c6b341c37cfb31f281be3afe2a52ca532 |
||
|
Wei Yang
|
12558019a8 |
backward for sparse.addmm(D, S, D, alpha, beta) -> D (#13345)
Summary: - introduce `sparse.addmm()` with backward for sparse matrix input for https://github.com/pytorch/pytorch/issues/12308 Pull Request resolved: https://github.com/pytorch/pytorch/pull/13345 Differential Revision: D13094070 Pulled By: weiyangfb fbshipit-source-id: 136c08c3ca9bafb20577b60dd43d31c3e5cd5461 |
||
|
Sam Gross
|
48a3349c29
|
Delete dead Tensor code paths (#5417)
This deletes most of the dead Tensor code paths, including the TensorMethods cwrap and generic/Tensor.cpp. This also moves the THNN.cwrap/.cpp generation to generate_code which can use ninja if installed. |
||
|
Sam Gross
|
30ec06c140
|
Merge Variable and Tensor classes (#5225)
This replaces the torch.Tensor constructors with factories that produce Variables. Similarly, functions on the torch module (e.g. torch.randn) now return Variables. To keep the PR to a reasonable size, I've left most of the unused tensor code. Subsequent PRs will remove the dead code, clean-up calls to torch.autograd.Variable, and rename Variable to Tensor everywhere. There are some breaking changes because Variable and Tensors had slightly different semantics. There's a list of those changes here: https://github.com/pytorch/pytorch/wiki/Breaking-Changes-from-Variable-and-Tensor-merge |
||
|
Luca Antiga
|
af58bfbb1b |
Make integer parameters and buffers immune to float(), double() and half() (#3820)
* Avoid casting integer params and buffers to float(), double() and half() * Add test for immune integer buffers * Fix documentation for float(), double() and half() * Fix test |
||
|
Edward Z. Yang
|
4ec0435b39 |
Report overall size of sparse tensors. (#1461)
Signed-off-by: Edward Z. Yang <ezyang@fb.com> |
