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eaf66e7431
* `TrainablePipe`: Add NVTX range decorator * Annotate `TrainablePipe` subclasses with NVTX ranges * Export function signature to allow introspection of args in tests * Revert "Annotate `TrainablePipe` subclasses with NVTX ranges" This reverts commitd8684f7372. * Revert "Export function signature to allow introspection of args in tests" This reverts commitf4405ca3ad. * Revert "`TrainablePipe`: Add NVTX range decorator" This reverts commit26536eb6b8. * Add `spacy.pipes_with_nvtx_range` pipeline callback * Show warnings for all missing user-defined pipe functions that need to be annotated Fix imports, typos * Rename `DEFAULT_ANNOTATABLE_PIPE_METHODS` to `DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS` Reorder import * Walk model nodes directly whilst applying NVTX ranges Ignore pipe method wrapper when applying range
122 lines
3.4 KiB
Python
122 lines
3.4 KiB
Python
from typing import Type, Callable, Dict, TYPE_CHECKING, List, Optional, Set
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import functools
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import inspect
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import types
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import warnings
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from thinc.layers import with_nvtx_range
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from thinc.model import Model, wrap_model_recursive
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from thinc.util import use_nvtx_range
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from ..errors import Warnings
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from ..util import registry
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if TYPE_CHECKING:
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# This lets us add type hints for mypy etc. without causing circular imports
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from ..language import Language # noqa: F401
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DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS = [
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"pipe",
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"predict",
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"set_annotations",
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"update",
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"rehearse",
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"get_loss",
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"initialize",
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"begin_update",
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"finish_update",
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"update",
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]
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def models_with_nvtx_range(nlp, forward_color: int, backprop_color: int):
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pipes = [
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pipe
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for _, pipe in nlp.components
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if hasattr(pipe, "is_trainable") and pipe.is_trainable
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]
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seen_models: Set[int] = set()
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for pipe in pipes:
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for node in pipe.model.walk():
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if id(node) in seen_models:
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continue
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seen_models.add(id(node))
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with_nvtx_range(
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node, forward_color=forward_color, backprop_color=backprop_color
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)
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return nlp
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@registry.callbacks("spacy.models_with_nvtx_range.v1")
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def create_models_with_nvtx_range(
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forward_color: int = -1, backprop_color: int = -1
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) -> Callable[["Language"], "Language"]:
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return functools.partial(
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models_with_nvtx_range,
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forward_color=forward_color,
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backprop_color=backprop_color,
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)
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def nvtx_range_wrapper_for_pipe_method(self, func, *args, **kwargs):
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if isinstance(func, functools.partial):
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return func(*args, **kwargs)
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else:
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with use_nvtx_range(f"{self.name} {func.__name__}"):
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return func(*args, **kwargs)
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def pipes_with_nvtx_range(
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nlp, additional_pipe_functions: Optional[Dict[str, List[str]]]
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):
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for _, pipe in nlp.components:
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if additional_pipe_functions:
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extra_funcs = additional_pipe_functions.get(pipe.name, [])
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else:
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extra_funcs = []
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for name in DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS + extra_funcs:
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func = getattr(pipe, name, None)
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if func is None:
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if name in extra_funcs:
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warnings.warn(Warnings.W121.format(method=name, pipe=pipe.name))
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continue
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wrapped_func = functools.partial(
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types.MethodType(nvtx_range_wrapper_for_pipe_method, pipe), func
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)
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# Try to preserve the original function signature.
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try:
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wrapped_func.__signature__ = inspect.signature(func) # type: ignore
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except:
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pass
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try:
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setattr(
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pipe,
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name,
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wrapped_func,
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)
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except AttributeError:
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warnings.warn(Warnings.W122.format(method=name, pipe=pipe.name))
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return nlp
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@registry.callbacks("spacy.models_and_pipes_with_nvtx_range.v1")
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def create_models_and_pipes_with_nvtx_range(
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forward_color: int = -1,
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backprop_color: int = -1,
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additional_pipe_functions: Optional[Dict[str, List[str]]] = None,
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) -> Callable[["Language"], "Language"]:
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def inner(nlp):
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nlp = models_with_nvtx_range(nlp, forward_color, backprop_color)
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nlp = pipes_with_nvtx_range(nlp, additional_pipe_functions)
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return nlp
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return inner
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