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💫 Allow passing of config parameters to specific pipeline components (#3386)
* Add component_cfg kwarg to begin_training * Document component_cfg arg to begin_training * Update docs and auto-format * Support component_cfg across Language * Format * Update docs and docstrings [ci skip] * Fix begin_training
This commit is contained in:
Matthew Honnibal
Ines Montani
parent
8dbf1e9037
commit
98acf5ffe4
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@ -106,6 +106,7 @@ class Language(object):
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DOCS: https://spacy.io/api/language
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"""
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Defaults = BaseDefaults
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lang = None
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@ -344,13 +345,15 @@ class Language(object):
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raise ValueError(Errors.E001.format(name=name, opts=self.pipe_names))
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return self.pipeline.pop(self.pipe_names.index(name))
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def __call__(self, text, disable=[]):
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def __call__(self, text, disable=[], component_cfg=None):
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"""Apply the pipeline to some text. The text can span multiple sentences,
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and can contain arbtrary whitespace. Alignment into the original string
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is preserved.
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text (unicode): The text to be processed.
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disable (list): Names of the pipeline components to disable.
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component_cfg (dict): An optional dictionary with extra keyword arguments
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for specific components.
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RETURNS (Doc): A container for accessing the annotations.
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EXAMPLE:
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@ -363,12 +366,14 @@ class Language(object):
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Errors.E088.format(length=len(text), max_length=self.max_length)
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)
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doc = self.make_doc(text)
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if component_cfg is None:
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component_cfg = {}
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for name, proc in self.pipeline:
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if name in disable:
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continue
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if not hasattr(proc, "__call__"):
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raise ValueError(Errors.E003.format(component=type(proc), name=name))
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doc = proc(doc)
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doc = proc(doc, **component_cfg.get(name, {}))
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if doc is None:
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raise ValueError(Errors.E005.format(name=name))
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return doc
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@ -396,7 +401,7 @@ class Language(object):
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def make_doc(self, text):
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return self.tokenizer(text)
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def update(self, docs, golds, drop=0.0, sgd=None, losses=None):
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def update(self, docs, golds, drop=0.0, sgd=None, losses=None, component_cfg=None):
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"""Update the models in the pipeline.
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docs (iterable): A batch of `Doc` objects.
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@ -443,11 +448,15 @@ class Language(object):
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pipes = list(self.pipeline)
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random.shuffle(pipes)
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if component_cfg is None:
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component_cfg = {}
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for name, proc in pipes:
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if not hasattr(proc, "update"):
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continue
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grads = {}
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proc.update(docs, golds, drop=drop, sgd=get_grads, losses=losses)
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kwargs = component_cfg.get(name, {})
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kwargs.setdefault("drop", drop)
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proc.update(docs, golds, sgd=get_grads, losses=losses, **kwargs)
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for key, (W, dW) in grads.items():
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sgd(W, dW, key=key)
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@ -517,11 +526,12 @@ class Language(object):
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for doc, gold in docs_golds:
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yield doc, gold
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def begin_training(self, get_gold_tuples=None, sgd=None, **cfg):
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def begin_training(self, get_gold_tuples=None, sgd=None, component_cfg=None, **cfg):
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"""Allocate models, pre-process training data and acquire a trainer and
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optimizer. Used as a contextmanager.
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get_gold_tuples (function): Function returning gold data
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component_cfg (dict): Config parameters for specific components.
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**cfg: Config parameters.
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RETURNS: An optimizer
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"""
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@ -543,10 +553,17 @@ class Language(object):
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if sgd is None:
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sgd = create_default_optimizer(Model.ops)
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self._optimizer = sgd
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if component_cfg is None:
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component_cfg = {}
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for name, proc in self.pipeline:
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if hasattr(proc, "begin_training"):
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kwargs = component_cfg.get(name, {})
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kwargs.update(cfg)
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proc.begin_training(
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get_gold_tuples, pipeline=self.pipeline, sgd=self._optimizer, **cfg
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get_gold_tuples,
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pipeline=self.pipeline,
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sgd=self._optimizer,
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**kwargs
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)
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return self._optimizer
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@ -574,20 +591,27 @@ class Language(object):
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proc._rehearsal_model = deepcopy(proc.model)
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return self._optimizer
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def evaluate(self, docs_golds, verbose=False, batch_size=256):
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scorer = Scorer()
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def evaluate(
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self, docs_golds, verbose=False, batch_size=256, scorer=None, component_cfg=None
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):
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if scorer is None:
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scorer = Scorer()
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docs, golds = zip(*docs_golds)
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docs = list(docs)
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golds = list(golds)
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for name, pipe in self.pipeline:
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kwargs = component_cfg.get(name, {})
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kwargs.setdefault("batch_size", batch_size)
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if not hasattr(pipe, "pipe"):
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docs = (pipe(doc) for doc in docs)
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docs = (pipe(doc, **kwargs) for doc in docs)
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else:
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docs = pipe.pipe(docs, batch_size=batch_size)
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docs = pipe.pipe(docs, **kwargs)
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for doc, gold in zip(docs, golds):
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if verbose:
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print(doc)
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scorer.score(doc, gold, verbose=verbose)
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kwargs = component_cfg.get("scorer", {})
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kwargs.setdefault("verbose", verbose)
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scorer.score(doc, gold, **kwargs)
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return scorer
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@contextmanager
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@ -630,6 +654,7 @@ class Language(object):
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batch_size=1000,
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disable=[],
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cleanup=False,
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component_cfg=None,
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):
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"""Process texts as a stream, and yield `Doc` objects in order.
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@ -643,6 +668,8 @@ class Language(object):
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disable (list): Names of the pipeline components to disable.
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cleanup (bool): If True, unneeded strings are freed,
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to control memory use. Experimental.
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component_cfg (dict): An optional dictionary with extra keyword arguments
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for specific components.
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YIELDS (Doc): Documents in the order of the original text.
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EXAMPLE:
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@ -655,20 +682,30 @@ class Language(object):
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texts = (tc[0] for tc in text_context1)
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contexts = (tc[1] for tc in text_context2)
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docs = self.pipe(
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texts, n_threads=n_threads, batch_size=batch_size, disable=disable
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texts,
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n_threads=n_threads,
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batch_size=batch_size,
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disable=disable,
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component_cfg=component_cfg,
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)
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for doc, context in izip(docs, contexts):
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yield (doc, context)
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return
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docs = (self.make_doc(text) for text in texts)
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if component_cfg is None:
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component_cfg = {}
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for name, proc in self.pipeline:
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if name in disable:
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continue
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kwargs = component_cfg.get(name, {})
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# Allow component_cfg to overwrite the top-level kwargs.
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kwargs.setdefault("batch_size", batch_size)
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kwargs.setdefault("n_threads", n_threads)
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if hasattr(proc, "pipe"):
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docs = proc.pipe(docs, n_threads=n_threads, batch_size=batch_size)
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docs = proc.pipe(docs, **kwargs)
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else:
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# Apply the function, but yield the doc
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docs = _pipe(proc, docs)
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docs = _pipe(proc, docs, kwargs)
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# Track weakrefs of "recent" documents, so that we can see when they
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# expire from memory. When they do, we know we don't need old strings.
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# This way, we avoid maintaining an unbounded growth in string entries
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@ -861,7 +898,7 @@ class DisabledPipes(list):
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self[:] = []
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def _pipe(func, docs):
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def _pipe(func, docs, kwargs):
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for doc in docs:
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doc = func(doc)
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doc = func(doc, **kwargs)
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yield doc
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@ -91,13 +91,14 @@ multiprocessing.
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> assert doc.is_parsed
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> ```
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| Name | Type | Description |
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| ------------ | ----- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `texts` | - | A sequence of unicode objects. |
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| `as_tuples` | bool | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. |
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| `batch_size` | int | The number of texts to buffer. |
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| `disable` | list | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
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| **YIELDS** | `Doc` | Documents in the order of the original text. |
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| Name | Type | Description |
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| -------------------------------------------- | ----- | ---------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `texts` | - | A sequence of unicode objects. |
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| `as_tuples` | bool | If set to `True`, inputs should be a sequence of `(text, context)` tuples. Output will then be a sequence of `(doc, context)` tuples. Defaults to `False`. |
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| `batch_size` | int | The number of texts to buffer. |
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| `disable` | list | Names of pipeline components to [disable](/usage/processing-pipelines#disabling). |
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| `component_cfg` <Tag variant="new">2.1</Tag> | dict | Config parameters for specific pipeline components, keyed by component name. |
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| **YIELDS** | `Doc` | Documents in the order of the original text. |
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## Language.update {#update tag="method"}
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@ -112,13 +113,14 @@ Update the models in the pipeline.
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> nlp.update([doc], [gold], drop=0.5, sgd=optimizer)
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> ```
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| Name | Type | Description |
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| ----------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `docs` | iterable | A batch of `Doc` objects or unicode. If unicode, a `Doc` object will be created from the text. |
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| `golds` | iterable | A batch of `GoldParse` objects or dictionaries. Dictionaries will be used to create [`GoldParse`](/api/goldparse) objects. For the available keys and their usage, see [`GoldParse.__init__`](/api/goldparse#init). |
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| `drop` | float | The dropout rate. |
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| `sgd` | callable | An optimizer. |
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| **RETURNS** | dict | Results from the update. |
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| Name | Type | Description |
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| -------------------------------------------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `docs` | iterable | A batch of `Doc` objects or unicode. If unicode, a `Doc` object will be created from the text. |
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| `golds` | iterable | A batch of `GoldParse` objects or dictionaries. Dictionaries will be used to create [`GoldParse`](/api/goldparse) objects. For the available keys and their usage, see [`GoldParse.__init__`](/api/goldparse#init). |
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| `drop` | float | The dropout rate. |
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| `sgd` | callable | An optimizer. |
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| `component_cfg` <Tag variant="new">2.1</Tag> | dict | Config parameters for specific pipeline components, keyed by component name. |
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| **RETURNS** | dict | Results from the update. |
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## Language.begin_training {#begin_training tag="method"}
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@ -130,11 +132,12 @@ Allocate models, pre-process training data and acquire an optimizer.
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> optimizer = nlp.begin_training(gold_tuples)
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> ```
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| Name | Type | Description |
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| ------------- | -------- | ---------------------------- |
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| `gold_tuples` | iterable | Gold-standard training data. |
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| `**cfg` | - | Config parameters. |
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| **RETURNS** | callable | An optimizer. |
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| Name | Type | Description |
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| -------------------------------------------- | -------- | ---------------------------------------------------------------------------- |
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| `gold_tuples` | iterable | Gold-standard training data. |
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| `component_cfg` <Tag variant="new">2.1</Tag> | dict | Config parameters for specific pipeline components, keyed by component name. |
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| `**cfg` | - | Config parameters (sent to all components). |
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| **RETURNS** | callable | An optimizer. |
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## Language.use_params {#use_params tag="contextmanager, method"}
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