mirror of https://github.com/explosion/spaCy.git
Merge pull request #8112 from svlandeg/bugfix/replace-trf
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commit
5957ab74f7
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@ -1769,6 +1769,7 @@ class Language:
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raise ValueError(err)
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tok2vec = self.get_pipe(tok2vec_name)
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tok2vec_cfg = self.get_pipe_config(tok2vec_name)
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tok2vec_model = tok2vec.model
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if (
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not hasattr(tok2vec, "model")
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or not hasattr(tok2vec, "listener_map")
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@ -1777,6 +1778,7 @@ class Language:
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):
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raise ValueError(Errors.E888.format(name=tok2vec_name, pipe=type(tok2vec)))
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pipe_listeners = tok2vec.listener_map.get(pipe_name, [])
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pipe = self.get_pipe(pipe_name)
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pipe_cfg = self._pipe_configs[pipe_name]
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if listeners:
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util.logger.debug(f"Replacing listeners of component '{pipe_name}'")
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@ -1791,7 +1793,6 @@ class Language:
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n_listeners=len(pipe_listeners),
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)
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raise ValueError(err)
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pipe = self.get_pipe(pipe_name)
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# Update the config accordingly by copying the tok2vec model to all
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# sections defined in the listener paths
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for listener_path in listeners:
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@ -1803,10 +1804,17 @@ class Language:
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name=pipe_name, tok2vec=tok2vec_name, path=listener_path
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)
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raise ValueError(err)
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util.set_dot_to_object(pipe_cfg, listener_path, tok2vec_cfg["model"])
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new_config = tok2vec_cfg["model"]
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if "replace_listener_cfg" in tok2vec_model.attrs:
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replace_func = tok2vec_model.attrs["replace_listener_cfg"]
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new_config = replace_func(tok2vec_cfg["model"], pipe_cfg["model"]["tok2vec"])
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util.set_dot_to_object(pipe_cfg, listener_path, new_config)
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# Go over the listener layers and replace them
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for listener in pipe_listeners:
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util.replace_model_node(pipe.model, listener, tok2vec.model.copy())
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new_model = tok2vec_model.copy()
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if "replace_listener" in tok2vec_model.attrs:
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new_model = tok2vec_model.attrs["replace_listener"](new_model)
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util.replace_model_node(pipe.model, listener, new_model)
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tok2vec.remove_listener(listener, pipe_name)
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def to_disk(
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@ -218,6 +218,13 @@ def test_replace_listeners():
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nlp.replace_listeners("tok2vec", "tagger", ["model.yolo"])
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with pytest.raises(ValueError):
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nlp.replace_listeners("tok2vec", "tagger", ["model.tok2vec", "model.yolo"])
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# attempt training with the new pipeline
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optimizer = nlp.initialize(lambda: examples)
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for i in range(2):
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losses = {}
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nlp.update(examples, sgd=optimizer, losses=losses)
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assert losses["tok2vec"] == 0.0
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assert losses["tagger"] > 0.0
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cfg_string_multi = """
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