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bede11b67c
This patch does a few smallish things that tighten up the training workflow a little, and allow memory use during training to be reduced by letting the GoldCorpus stream data properly. Previously, the parser and entity recognizer read and saved labels as lists, with extra labels noted separately. Lists were used becaue ordering is very important, to ensure that the label-to-class mapping is stable. We now manage labels as nested dictionaries, first keyed by the action, and then keyed by the label. Values are frequencies. The trick is, how do we save new labels? We need to make sure we iterate over these in the same order they're added. Otherwise, we'll get different class IDs, and the model's predictions won't make sense. To allow stable sorting, we map the new labels to negative values. If we have two new labels, they'll be noted as having "frequency" -1 and -2. The next new label will then have "frequency" -3. When we sort by (frequency, label), we then get a stable sort. Storing frequencies then allows us to make the next nice improvement. Previously we had to iterate over the whole training set, to pre-process it for the deprojectivisation. This led to storing the whole training set in memory. This was most of the required memory during training. To prevent this, we now store the frequencies as we stream in the data, and deprojectivize as we go. Once we've built the frequencies, we can then apply a frequency cut-off when we decide how many classes to make. Finally, to allow proper data streaming, we also have to have some way of shuffling the iterator. This is awkward if the training files have multiple documents in them. To solve this, the GoldCorpus class now writes the training data to disk in msgpack files, one per document. We can then shuffle the data by shuffling the paths. This is a squash merge, as I made a lot of very small commits. Individual commit messages below. * Simplify label management for TransitionSystem and its subclasses * Fix serialization for new label handling format in parser * Simplify and improve GoldCorpus class. Reduce memory use, write to temp dir * Set actions in transition system * Require thinc 6.11.1.dev4 * Fix error in parser init * Add unicode declaration * Fix unicode declaration * Update textcat test * Try to get model training on less memory * Print json loc for now * Try rapidjson to reduce memory use * Remove rapidjson requirement * Try rapidjson for reduced mem usage * Handle None heads when projectivising * Stream json docs * Fix train script * Handle projectivity in GoldParse * Fix projectivity handling * Add minibatch_by_words util from ud_train * Minibatch by number of words in spacy.cli.train * Move minibatch_by_words util to spacy.util * Fix label handling * More hacking at label management in parser * Fix encoding in msgpack serialization in GoldParse * Adjust batch sizes in parser training * Fix minibatch_by_words * Add merge_subtokens function to pipeline.pyx * Register merge_subtokens factory * Restore use of msgpack tmp directory * Use minibatch-by-words in train * Handle retokenization in scorer * Change back-off approach for missing labels. Use 'dep' label * Update NER for new label management * Set NER tags for over-segmented words * Fix label alignment in gold * Fix label back-off for infrequent labels * Fix int type in labels dict key * Fix int type in labels dict key * Update feature definition for 8 feature set * Update ud-train script for new label stuff * Fix json streamer * Print the line number if conll eval fails * Update children and sentence boundaries after deprojectivisation * Export set_children_from_heads from doc.pxd * Render parses during UD training * Remove print statement * Require thinc 6.11.1.dev6. Try adding wheel as install_requires * Set different dev version, to flush pip cache * Update thinc version * Update GoldCorpus docs * Remove print statements * Fix formatting and links [ci skip]
64 lines
1.4 KiB
Cython
64 lines
1.4 KiB
Cython
from cymem.cymem cimport Pool
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cimport numpy as np
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from preshed.counter cimport PreshCounter
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from ..vocab cimport Vocab
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from ..structs cimport TokenC, LexemeC
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from ..typedefs cimport attr_t
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from ..attrs cimport attr_id_t
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cdef attr_t get_token_attr(const TokenC* token, attr_id_t feat_name) nogil
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ctypedef const LexemeC* const_Lexeme_ptr
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ctypedef const TokenC* const_TokenC_ptr
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ctypedef fused LexemeOrToken:
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const_Lexeme_ptr
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const_TokenC_ptr
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cdef int token_by_start(const TokenC* tokens, int length, int start_char) except -2
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cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2
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cdef class Doc:
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cdef readonly Pool mem
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cdef readonly Vocab vocab
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cdef public object _vector
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cdef public object _vector_norm
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cdef public object tensor
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cdef public object cats
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cdef public object user_data
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cdef TokenC* c
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cdef public bint is_tagged
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cdef public bint is_parsed
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cdef public float sentiment
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cdef public dict user_hooks
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cdef public dict user_token_hooks
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cdef public dict user_span_hooks
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cdef public list _py_tokens
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cdef int length
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cdef int max_length
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cdef public object noun_chunks_iterator
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cdef object __weakref__
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cdef int push_back(self, LexemeOrToken lex_or_tok, bint has_space) except -1
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cpdef np.ndarray to_array(self, object features)
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cdef void set_parse(self, const TokenC* parsed) nogil
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