-
Notifications
You must be signed in to change notification settings - Fork 551
disable disables gradient calculation when getting the prop constant tensor #3948
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Conversation
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/3948
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 566484b with merge base 1345bc2 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This pull request was exported from Phabricator. Differential Revision: D58436236 |
…tensor (pytorch#3948) Summary: Pull Request resolved: pytorch#3948 As title, to fix the error when exporting/lowering lstm, the error message is: ``` Cell In[13], line 3 1 from executorch.backends.xnnpack.partition.xnnpack_partitioner import XnnpackPartitioner ----> 3 edge_manager = edge_manager.to_backend(XnnpackPartitioner()) 5 print(edge_manager.exported_program()) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/executorch/exir/program/_program.py:1166, in EdgeProgramManager.to_backend(self, partitioner) 1164 else: # apply partitioner to every method 1165 for name, program in self._edge_programs.items(): -> 1166 new_edge_programs[name] = to_backend(program, partitioner) 1168 config = EdgeCompileConfig(_check_ir_validity=False) 1169 return EdgeProgramManager( 1170 new_edge_programs, copy.deepcopy(self._config_methods), config 1171 ) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/functools.py:889, in singledispatch.<locals>.wrapper(*args, **kw) 885 if not args: 886 raise TypeError(f'{funcname} requires at least ' 887 '1 positional argument') --> 889 return dispatch(args[0].__class__)(*args, **kw) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/executorch/exir/backend/backend_api.py:384, in _(edge_program, partitioner_instance) 381 for tag, _ in partitioner_result.partition_tags.items(): 382 _maybe_duplicate_constant_nodes(tagged_exported_program, tag, edge_program) --> 384 tagged_graph_module = _partition_and_lower( 385 tagged_exported_program.graph_module, partitioner_result, edge_program 386 ) 388 # TODO(angelayi): Update this signature in a less manual way (maybe through 389 # retracing) 390 new_signature, new_state_dict, new_constants = _get_new_signature( 391 edge_program, 392 tagged_graph_module, 393 ) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/executorch/exir/backend/backend_api.py:299, in _partition_and_lower(tagged_graph_module, partition_result, owning_program) 290 def _partition_and_lower( 291 tagged_graph_module: torch.fx.GraphModule, 292 partition_result: PartitionResult, 293 owning_program: ExportedProgram, 294 ) -> torch.fx.GraphModule: 295 """ 296 Partitions the graph module into submodules based on tags, and then lowered the nodes with the same tag as one lowered module, including the submodule from control flow 297 """ --> 299 partitioned_module = _partition_and_lower_one_graph_module( 300 tagged_graph_module, partition_result, owning_program 301 ) 303 # Recursively partition and lower for submodules 304 for name, submod, _node in get_control_flow_submodules(partitioned_module): File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/executorch/exir/backend/backend_api.py:230, in _partition_and_lower_one_graph_module(tagged_graph_module, partition_result, owning_program) 224 logging.debug(f"Partitioned graph module: {tagged_graph_module}") 226 submodule_program = create_exported_program_from_submodule( 227 submodule, owning_program, tag 228 ) --> 230 lowered_submodule = to_backend( 231 delegation_spec.backend_id, 232 submodule_program, 233 delegation_spec.compile_specs, 234 ) 236 # call delegate args should only use user_inputs 237 call_delegate_args = [] File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/functools.py:889, in singledispatch.<locals>.wrapper(*args, **kw) 885 if not args: 886 raise TypeError(f'{funcname} requires at least ' 887 '1 positional argument') --> 889 return dispatch(args[0].__class__)(*args, **kw) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/executorch/exir/backend/backend_api.py:113, in _(backend_id, edge_program, compile_specs) 111 for cls in BackendDetails.__subclasses__(): 112 if backend_id == cls.__name__: --> 113 copied_edge_program = copy.deepcopy(edge_program) 114 preprocess_result: PreprocessResult = cls.preprocess( 115 copied_edge_program, 116 compile_specs, 117 ) 118 lowered_module = LoweredBackendModule( 119 edge_program=edge_program, 120 backend_id=backend_id, 121 processed_bytes=preprocess_result.processed_bytes, 122 compile_specs=compile_specs, 123 ) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/copy.py:172, in deepcopy(x, memo, _nil) 170 y = x 171 else: --> 172 y = _reconstruct(x, memo, *rv) 174 # If is its own copy, don't memoize. 175 if y is not x: File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/copy.py:271, in _reconstruct(x, memo, func, args, state, listiter, dictiter, deepcopy) 269 if state is not None: 270 if deep: --> 271 state = deepcopy(state, memo) 272 if hasattr(y, '__setstate__'): 273 y.__setstate__(state) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/copy.py:146, in deepcopy(x, memo, _nil) 144 copier = _deepcopy_dispatch.get(cls) 145 if copier is not None: --> 146 y = copier(x, memo) 147 else: 148 if issubclass(cls, type): File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/copy.py:231, in _deepcopy_dict(x, memo, deepcopy) 229 memo[id(x)] = y 230 for key, value in x.items(): --> 231 y[deepcopy(key, memo)] = deepcopy(value, memo) 232 return y File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/runtime/lib/python3.10/copy.py:153, in deepcopy(x, memo, _nil) 151 copier = getattr(x, "__deepcopy__", None) 152 if copier is not None: --> 153 y = copier(memo) 154 else: 155 reductor = dispatch_table.get(cls) File /mnt/xarfuse/uid-27416/e8d86d8d-seed-nspid4026533405_cgpid10356714-ns-4026533402/torch/_tensor.py:86, in Tensor.__deepcopy__(self, memo) 84 return handle_torch_function(Tensor.__deepcopy__, (self,), self, memo) 85 if not self.is_leaf: ---> 86 raise RuntimeError( 87 "Only Tensors created explicitly by the user " 88 "(graph leaves) support the deepcopy protocol at the moment. " 89 "If you were attempting to deepcopy a module, this may be because " 90 "of a torch.nn.utils.weight_norm usage, " 91 "see pytorch/pytorch#103001" 92 ) 93 if id(self) in memo: 94 return memo[id(self)] RuntimeError: Only Tensors created explicitly by the user (graph leaves) support the deepcopy protocol at the moment. If you were attempting to deepcopy a module, this may be because of a torch.nn.utils.weight_norm usage, see pytorch/pytorch#103001 ``` The reason is the constant tensor has the grad_fn when run it without the contextmgr Reviewed By: angelayi Differential Revision: D58436236
This pull request was exported from Phabricator. Differential Revision: D58436236 |
02dc6e2
to
566484b
Compare
This pull request has been merged in 4ed5bc7. |
Summary:
As title, to fix the error when exporting/lowering lstm, the error message is:
The reason is the constant tensor has the grad_fn when run it without the contextmgr
Reviewed By: angelayi
Differential Revision: D58436236