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We want users to be able to define custom ops in C++ but put the abstract impl in Python (since it is easier to write them in Python and the abstract impl better models device semantics and data-dependent operators). `m.impl_abstract_pystub(opname, python_module, context)` declares the abstract_impl of the operator to exist in the given python module. When the abstract_impl needs to be accessed (either via FakeTensor or Meta), and it does not exist, the PyTorch Dispatcher will yell with a descriptive error message. Some details: - We construct a new global AbstractImplPyStub mapping in Dispatcher.cpp. Read/write to this map is protected by the Dispatcher lock. - We add a new Meta Tensor fallback kernel. The fallback errors out if there is no meta kernel, but also offers a nicer error message if we see that there is a pystub. - We create a `torch._utils_internal.throw_abstract_impl_not_imported_error` helper function to throw errors. This way, we can throw different error messages in OSS PyTorch vs internal PyTorch. To invoke this from C++, we added a PyInterpreter::throw_abstract_impl_not_imported_error. Differential Revision: [D49464753](https://our.internmc.facebook.com/intern/diff/D49464753/) Differential Revision: [D49464753](https://our.internmc.facebook.com/intern/diff/D49464753) Pull Request resolved: https://github.com/pytorch/pytorch/pull/109529 Approved by: https://github.com/ezyang, https://github.com/bdhirsh
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
import logging
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import os
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import tempfile
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from typing import Any, Dict
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import torch
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log = logging.getLogger(__name__)
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# this arbitrary-looking assortment of functionality is provided here
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# to have a central place for overrideable behavior. The motivating
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# use is the FB build environment, where this source file is replaced
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# by an equivalent.
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if torch._running_with_deploy():
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# __file__ is meaningless in the context of frozen torch used in torch deploy.
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# setting empty torch_parent should allow below functions to operate without crashing,
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# but it's unclear if there is a valid use case for them in the context of deploy.
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torch_parent = ""
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else:
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if os.path.basename(os.path.dirname(__file__)) == "shared":
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torch_parent = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
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else:
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torch_parent = os.path.dirname(os.path.dirname(__file__))
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def get_file_path(*path_components: str) -> str:
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return os.path.join(torch_parent, *path_components)
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def get_file_path_2(*path_components: str) -> str:
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return os.path.join(*path_components)
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def get_writable_path(path: str) -> str:
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if os.access(path, os.W_OK):
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return path
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return tempfile.mkdtemp(suffix=os.path.basename(path))
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def prepare_multiprocessing_environment(path: str) -> None:
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pass
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def resolve_library_path(path: str) -> str:
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return os.path.realpath(path)
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def throw_abstract_impl_not_imported_error(opname, module, context):
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raise NotImplementedError(
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f"{opname}: We could not find the abstract impl for this operator. "
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f"The operator specified that you need to import the '{module}' Python "
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f"module to load the abstract impl. {context}"
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)
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# Meta only, see
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# https://www.internalfb.com/intern/wiki/ML_Workflow_Observability/User_Guides/Adding_instrumentation_to_your_code/
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#
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# This will cause an event to get logged to Scuba via the signposts API. You
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# can view samples on the API at https://fburl.com/scuba/workflow_signpost/zh9wmpqs
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# we log to subsystem "torch", and the category and name you provide here.
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# Each of the arguments translate into a Scuba column. We're still figuring
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# out local conventions in PyTorch, but category should be something like
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# "dynamo" or "inductor", and name should be a specific string describing what
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# kind of event happened.
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#
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# Killswitch is at
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# https://www.internalfb.com/intern/justknobs/?name=pytorch%2Fsignpost#event
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def signpost_event(category: str, name: str, parameters: Dict[str, Any]):
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log.info("%s %s: %r", category, name, parameters)
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def log_compilation_event(metrics):
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log.info("%s", metrics)
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TEST_MASTER_ADDR = "127.0.0.1"
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TEST_MASTER_PORT = 29500
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# USE_GLOBAL_DEPS controls whether __init__.py tries to load
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# libtorch_global_deps, see Note [Global dependencies]
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USE_GLOBAL_DEPS = True
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# USE_RTLD_GLOBAL_WITH_LIBTORCH controls whether __init__.py tries to load
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# _C.so with RTLD_GLOBAL during the call to dlopen.
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USE_RTLD_GLOBAL_WITH_LIBTORCH = False
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