mirror of https://github.com/explosion/spaCy.git
125 lines
3.7 KiB
Python
125 lines
3.7 KiB
Python
from typing import Type, Callable, Dict, TYPE_CHECKING, List, Optional, Set
|
|
import functools
|
|
import inspect
|
|
import types
|
|
import warnings
|
|
|
|
from thinc.layers import with_nvtx_range
|
|
from thinc.model import Model, wrap_model_recursive
|
|
from thinc.util import use_nvtx_range
|
|
|
|
from ..errors import Warnings
|
|
from ..util import registry
|
|
|
|
if TYPE_CHECKING:
|
|
# This lets us add type hints for mypy etc. without causing circular imports
|
|
from ..language import Language # noqa: F401
|
|
|
|
|
|
DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS = [
|
|
"pipe",
|
|
"predict",
|
|
"set_annotations",
|
|
"update",
|
|
"rehearse",
|
|
"get_loss",
|
|
"initialize",
|
|
"begin_update",
|
|
"finish_update",
|
|
"update",
|
|
]
|
|
|
|
|
|
def models_with_nvtx_range(nlp, forward_color: int, backprop_color: int):
|
|
pipes = [
|
|
pipe
|
|
for _, pipe in nlp.components
|
|
if hasattr(pipe, "is_trainable") and pipe.is_trainable
|
|
]
|
|
|
|
seen_models: Set[int] = set()
|
|
for pipe in pipes:
|
|
for node in pipe.model.walk():
|
|
if id(node) in seen_models:
|
|
continue
|
|
seen_models.add(id(node))
|
|
with_nvtx_range(
|
|
node, forward_color=forward_color, backprop_color=backprop_color
|
|
)
|
|
|
|
return nlp
|
|
|
|
|
|
@registry.callbacks("spacy.models_with_nvtx_range.v1")
|
|
def create_models_with_nvtx_range(
|
|
forward_color: int = -1, backprop_color: int = -1
|
|
) -> Callable[["Language"], "Language"]:
|
|
return functools.partial(
|
|
models_with_nvtx_range,
|
|
forward_color=forward_color,
|
|
backprop_color=backprop_color,
|
|
)
|
|
|
|
|
|
def nvtx_range_wrapper_for_pipe_method(self, func, *args, **kwargs):
|
|
if isinstance(func, functools.partial):
|
|
return func(*args, **kwargs)
|
|
else:
|
|
with use_nvtx_range(f"{self.name} {func.__name__}"):
|
|
return func(*args, **kwargs)
|
|
|
|
|
|
def pipes_with_nvtx_range(
|
|
nlp, additional_pipe_functions: Optional[Dict[str, List[str]]]
|
|
):
|
|
for _, pipe in nlp.components:
|
|
if additional_pipe_functions:
|
|
extra_funcs = additional_pipe_functions.get(pipe.name, [])
|
|
else:
|
|
extra_funcs = []
|
|
|
|
for name in DEFAULT_NVTX_ANNOTATABLE_PIPE_METHODS + extra_funcs:
|
|
func = getattr(pipe, name, None)
|
|
if func is None:
|
|
if name in extra_funcs:
|
|
warnings.warn(Warnings.W121.format(method=name, pipe=pipe.name))
|
|
continue
|
|
|
|
wrapped_func = functools.partial(
|
|
types.MethodType(nvtx_range_wrapper_for_pipe_method, pipe), func
|
|
)
|
|
|
|
# We need to preserve the original function signature so that
|
|
# the original parameters are passed to pydantic for validation downstream.
|
|
try:
|
|
wrapped_func.__signature__ = inspect.signature(func) # type: ignore
|
|
except:
|
|
# Can fail for Cython methods that do not have bindings.
|
|
warnings.warn(Warnings.W122.format(method=name, pipe=pipe.name))
|
|
continue
|
|
|
|
try:
|
|
setattr(
|
|
pipe,
|
|
name,
|
|
wrapped_func,
|
|
)
|
|
except AttributeError:
|
|
warnings.warn(Warnings.W122.format(method=name, pipe=pipe.name))
|
|
|
|
return nlp
|
|
|
|
|
|
@registry.callbacks("spacy.models_and_pipes_with_nvtx_range.v1")
|
|
def create_models_and_pipes_with_nvtx_range(
|
|
forward_color: int = -1,
|
|
backprop_color: int = -1,
|
|
additional_pipe_functions: Optional[Dict[str, List[str]]] = None,
|
|
) -> Callable[["Language"], "Language"]:
|
|
def inner(nlp):
|
|
nlp = models_with_nvtx_range(nlp, forward_color, backprop_color)
|
|
nlp = pipes_with_nvtx_range(nlp, additional_pipe_functions)
|
|
return nlp
|
|
|
|
return inner
|