mirror of
https://github.com/explosion/spaCy.git
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534 lines
20 KiB
Cython
534 lines
20 KiB
Cython
# cython: infer_types=True
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# cython: profile=True
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# cython: cdivision=True
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# cython: boundscheck=False
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# coding: utf-8
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from __future__ import unicode_literals, print_function
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from collections import Counter
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import ujson
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import contextlib
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from libc.math cimport exp
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cimport cython
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cimport cython.parallel
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import cytoolz
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import dill
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import numpy.random
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cimport numpy as np
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from cpython.ref cimport PyObject, Py_INCREF, Py_XDECREF
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from cpython.exc cimport PyErr_CheckSignals
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from libc.stdint cimport uint32_t, uint64_t
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from libc.string cimport memset, memcpy
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from libc.stdlib cimport malloc, calloc, free
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from thinc.typedefs cimport weight_t, class_t, feat_t, atom_t, hash_t
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from thinc.linear.avgtron cimport AveragedPerceptron
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from thinc.linalg cimport VecVec
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from thinc.structs cimport SparseArrayC, FeatureC, ExampleC
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from thinc.extra.eg cimport Example
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from cymem.cymem cimport Pool, Address
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from murmurhash.mrmr cimport hash64
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from preshed.maps cimport MapStruct
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from preshed.maps cimport map_get
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from thinc.api import layerize, chain
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from thinc.neural import Model, Affine, ELU, ReLu, Maxout
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from thinc.neural.ops import NumpyOps
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from .. import util
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from ..util import get_async, get_cuda_stream
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from .._ml import zero_init, PrecomputableAffine, PrecomputableMaxouts
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from .._ml import Tok2Vec, doc2feats
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from . import _parse_features
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from ._parse_features cimport CONTEXT_SIZE
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from ._parse_features cimport fill_context
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from .stateclass cimport StateClass
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from ._state cimport StateC
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from .nonproj import PseudoProjectivity
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from .transition_system import OracleError
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from .transition_system cimport TransitionSystem, Transition
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from ..structs cimport TokenC
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from ..tokens.doc cimport Doc
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from ..strings cimport StringStore
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from ..gold cimport GoldParse
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from ..attrs cimport TAG, DEP
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def get_templates(*args, **kwargs):
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return []
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USE_FTRL = True
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DEBUG = False
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def set_debug(val):
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global DEBUG
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DEBUG = val
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cdef class precompute_hiddens:
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'''Allow a model to be "primed" by pre-computing input features in bulk.
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This is used for the parser, where we want to take a batch of documents,
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and compute vectors for each (token, position) pair. These vectors can then
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be reused, especially for beam-search.
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Let's say we're using 12 features for each state, e.g. word at start of
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buffer, three words on stack, their children, etc. In the normal arc-eager
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system, a document of length N is processed in 2*N states. This means we'll
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create 2*N*12 feature vectors --- but if we pre-compute, we only need
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N*12 vector computations. The saving for beam-search is much better:
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if we have a beam of k, we'll normally make 2*N*12*K computations --
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so we can save the factor k. This also gives a nice CPU/GPU division:
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we can do all our hard maths up front, packed into large multiplications,
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and do the hard-to-program parsing on the CPU.
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'''
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cdef int nF, nO, nP
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cdef bint _is_synchronized
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cdef public object ops
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cdef np.ndarray _features
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cdef np.ndarray _cached
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cdef object _cuda_stream
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cdef object _bp_hiddens
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def __init__(self, batch_size, tokvecs, lower_model, cuda_stream=None, drop=0.):
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gpu_cached, bp_features = lower_model.begin_update(tokvecs, drop=drop)
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cdef np.ndarray cached
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if not isinstance(gpu_cached, numpy.ndarray):
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# Note the passing of cuda_stream here: it lets
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# cupy make the copy asynchronously.
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# We then have to block before first use.
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cached = gpu_cached.get(stream=cuda_stream)
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else:
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cached = gpu_cached
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self.nF = cached.shape[1]
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self.nO = cached.shape[2]
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self.nP = cached.shape[3]
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self.ops = lower_model.ops
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self._features = numpy.zeros((batch_size, self.nO, self.nP), dtype='f')
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self._is_synchronized = False
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self._cuda_stream = cuda_stream
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self._cached = cached
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self._bp_hiddens = bp_features
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def __call__(self, X):
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return self.begin_update(X)[0]
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def begin_update(self, token_ids, drop=0.):
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self._features.fill(0)
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if not self._is_synchronized \
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and self._cuda_stream is not None:
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self._cuda_stream.synchronize()
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self._is_synchronized = True
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# This is tricky, but (assuming GPU available);
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# - Input to forward on CPU
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# - Output from forward on CPU
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# - Input to backward on GPU!
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# - Output from backward on GPU
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cdef np.ndarray state_vector = self._features[:len(token_ids)]
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cdef np.ndarray hiddens = self._cached
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bp_hiddens = self._bp_hiddens
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cdef int[:, ::1] ids = token_ids
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self._sum_features(<float*>state_vector.data,
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<float*>hiddens.data, &ids[0,0],
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token_ids.shape[0], self.nF, self.nO*self.nP)
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output, bp_output = self._apply_nonlinearity(state_vector)
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def backward(d_output, sgd=None):
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# This will usually be on GPU
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if isinstance(d_output, numpy.ndarray):
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d_output = self.ops.xp.array(d_output)
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d_state_vector = bp_output(d_output, sgd)
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d_tokens = bp_hiddens((d_state_vector, token_ids), sgd)
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return d_tokens
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return output, backward
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def _apply_nonlinearity(self, X):
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if self.nP < 2:
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return X.reshape(X.shape[:2]), lambda dX, sgd=None: dX.reshape(X.shape)
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best, which = self.ops.maxout(X)
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return best, lambda dX, sgd=None: self.ops.backprop_maxout(dX, which, self.nP)
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cdef void _sum_features(self, float* output,
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const float* cached, const int* token_ids, int B, int F, int O) nogil:
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cdef int idx, b, f, i
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cdef const float* feature
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for b in range(B):
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for f in range(F):
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if token_ids[f] < 0:
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continue
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idx = token_ids[f] * F * O + f*O
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feature = &cached[idx]
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for i in range(O):
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output[i] += feature[i]
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output += O
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token_ids += F
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cdef void cpu_log_loss(float* d_scores,
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const float* costs, const int* is_valid, const float* scores,
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int O) nogil:
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"""Do multi-label log loss"""
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cdef double max_, gmax, Z, gZ
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best = arg_max_if_gold(scores, costs, is_valid, O)
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guess = arg_max_if_valid(scores, is_valid, O)
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Z = 1e-10
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gZ = 1e-10
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max_ = scores[guess]
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gmax = scores[best]
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for i in range(O):
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if is_valid[i]:
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Z += exp(scores[i] - max_)
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if costs[i] <= costs[best]:
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gZ += exp(scores[i] - gmax)
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for i in range(O):
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if not is_valid[i]:
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d_scores[i] = 0.
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elif costs[i] <= costs[best]:
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d_scores[i] = (exp(scores[i]-max_) / Z) - (exp(scores[i]-gmax)/gZ)
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else:
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d_scores[i] = exp(scores[i]-max_) / Z
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cdef void cpu_regression_loss(float* d_scores,
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const float* costs, const int* is_valid, const float* scores,
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int O) nogil:
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cdef float eps = 2.
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best = arg_max_if_gold(scores, costs, is_valid, O)
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for i in range(O):
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if not is_valid[i]:
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d_scores[i] = 0.
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elif scores[i] < scores[best]:
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d_scores[i] = 0.
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else:
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# I doubt this is correct?
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# Looking for something like Huber loss
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diff = scores[i] - -costs[i]
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if diff > eps:
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d_scores[i] = eps
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elif diff < -eps:
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d_scores[i] = -eps
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else:
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d_scores[i] = diff
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cdef class Parser:
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"""
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Base class of the DependencyParser and EntityRecognizer.
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"""
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@classmethod
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def Model(cls, nr_class, token_vector_width=128, hidden_width=128, **cfg):
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token_vector_width = util.env_opt('token_vector_width', token_vector_width)
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hidden_width = util.env_opt('hidden_width', hidden_width)
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maxout_pieces = util.env_opt('parser_maxout_pieces', 1)
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lower = PrecomputableMaxouts(hidden_width,
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nF=cls.nr_feature,
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nI=token_vector_width,
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pieces=maxout_pieces)
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with Model.use_device('cpu'):
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upper = chain(
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Maxout(hidden_width),
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zero_init(Affine(nr_class))
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)
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# TODO: This is an unfortunate hack atm!
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# Used to set input dimensions in network.
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lower.begin_training(lower.ops.allocate((500, token_vector_width)))
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upper.begin_training(upper.ops.allocate((500, hidden_width)))
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return lower, upper
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def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
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"""
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Create a Parser.
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Arguments:
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vocab (Vocab):
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The vocabulary object. Must be shared with documents to be processed.
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The value is set to the .vocab attribute.
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moves (TransitionSystem):
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Defines how the parse-state is created, updated and evaluated.
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The value is set to the .moves attribute unless True (default),
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in which case a new instance is created with Parser.Moves().
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model (object):
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Defines how the parse-state is created, updated and evaluated.
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The value is set to the .model attribute unless True (default),
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in which case a new instance is created with Parser.Model().
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**cfg:
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Arbitrary configuration parameters. Set to the .cfg attribute
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"""
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self.vocab = vocab
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if moves is True:
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self.moves = self.TransitionSystem(self.vocab.strings, {})
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else:
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self.moves = moves
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self.cfg = cfg
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if 'actions' in self.cfg:
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for action, labels in self.cfg.get('actions', {}).items():
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for label in labels:
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self.moves.add_action(action, label)
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self.model = model
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def __reduce__(self):
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return (Parser, (self.vocab, self.moves, self.model), None, None)
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def __call__(self, Doc tokens, state=None):
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"""
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Apply the parser or entity recognizer, setting the annotations onto the Doc object.
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Arguments:
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doc (Doc): The document to be processed.
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Returns:
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None
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"""
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self.parse_batch([tokens], state['tokvecs'])
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return state
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def pipe(self, stream, int batch_size=1000, int n_threads=2):
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"""
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Process a stream of documents.
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Arguments:
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stream: The sequence of documents to process.
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batch_size (int):
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The number of documents to accumulate into a working set.
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n_threads (int):
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The number of threads with which to work on the buffer in parallel.
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Yields (Doc): Documents, in order.
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"""
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cdef StateClass parse_state
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cdef Doc doc
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queue = []
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for batch in cytoolz.partition_all(batch_size, stream):
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batch = list(batch)
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docs, states = zip(*batch)
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parse_states = self.parse_batch(docs, states[0]['tokvecs'])
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self.set_annotations(docs, parse_states)
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yield from zip(docs, states)
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def parse_batch(self, docs, tokvecs):
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cuda_stream = get_cuda_stream()
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states = self.moves.init_batch(docs)
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state2vec, vec2scores = self.get_batch_model(len(states), tokvecs,
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cuda_stream, 0.0)
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todo = [st for st in states if not st.is_final()]
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while todo:
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token_ids = self.get_token_ids(states)
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vectors = state2vec(token_ids)
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scores = vec2scores(vectors)
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self.transition_batch(states, scores)
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todo = [st for st in states if not st.is_final()]
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return states
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def update(self, docs, golds, state=None, drop=0., sgd=None):
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assert state is not None
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assert 'tokvecs' in state
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assert 'bp_tokvecs' in state
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if isinstance(docs, Doc) and isinstance(golds, GoldParse):
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docs = [docs]
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golds = [golds]
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cuda_stream = get_cuda_stream()
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for gold in golds:
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self.moves.preprocess_gold(gold)
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tokvecs = state['tokvecs']
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bp_tokvecs = state['bp_tokvecs']
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states = self.moves.init_batch(docs)
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state2vec, vec2scores = self.get_batch_model(len(states), tokvecs, cuda_stream,
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drop)
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todo = [(s, g) for s, g in zip(states, golds) if not s.is_final()]
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backprops = []
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cdef float loss = 0.
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cutoff = max(1, len(todo) // 10)
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while len(todo) >= cutoff:
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states, golds = zip(*todo)
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token_ids = self.get_token_ids(states)
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vector, bp_vector = state2vec.begin_update(token_ids, drop=drop)
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scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
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d_scores = self.get_batch_loss(states, golds, scores)
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d_vector = bp_scores(d_scores, sgd=sgd)
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loss += (d_scores**2).sum()
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if not isinstance(tokvecs, state2vec.ops.xp.ndarray):
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backprops.append((token_ids, d_vector, bp_vector))
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else:
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# Move token_ids and d_vector to CPU, asynchronously
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backprops.append((
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get_async(cuda_stream, token_ids),
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get_async(cuda_stream, d_vector),
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bp_vector
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))
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self.transition_batch(states, scores)
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todo = [st for st in todo if not st[0].is_final()]
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# Tells CUDA to block, so our async copies complete.
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if cuda_stream is not None:
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cuda_stream.synchronize()
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d_tokvecs = state2vec.ops.allocate(tokvecs.shape)
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xp = state2vec.ops.xp # Handle for numpy/cupy
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for token_ids, d_vector, bp_vector in backprops:
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d_state_features = bp_vector(d_vector, sgd=sgd)
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active_feats = token_ids * (token_ids >= 0)
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active_feats = active_feats.reshape((token_ids.shape[0], token_ids.shape[1], 1))
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if hasattr(xp, 'scatter_add'):
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xp.scatter_add(d_tokvecs,
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token_ids, d_state_features * active_feats)
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else:
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xp.add.at(d_tokvecs,
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token_ids, d_state_features * active_feats)
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bp_tokvecs(d_tokvecs, sgd)
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state['parser_loss'] = loss
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return state
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def get_batch_model(self, batch_size, tokvecs, stream, dropout):
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lower, upper = self.model
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state2vec = precompute_hiddens(batch_size, tokvecs,
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lower, stream, drop=dropout)
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return state2vec, upper
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nr_feature = 13
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def get_token_ids(self, states):
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cdef StateClass state
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cdef int n_tokens = self.nr_feature
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ids = numpy.zeros((len(states), n_tokens), dtype='i', order='C')
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for i, state in enumerate(states):
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state.set_context_tokens(ids[i])
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return ids
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def transition_batch(self, states, float[:, ::1] scores):
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cdef StateClass state
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cdef int[500] is_valid # TODO: Unhack
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cdef float* c_scores = &scores[0, 0]
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for state in states:
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self.moves.set_valid(is_valid, state.c)
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guess = arg_max_if_valid(c_scores, is_valid, scores.shape[1])
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action = self.moves.c[guess]
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action.do(state.c, action.label)
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c_scores += scores.shape[1]
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def get_batch_loss(self, states, golds, float[:, ::1] scores):
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cdef StateClass state
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cdef GoldParse gold
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cdef Pool mem = Pool()
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cdef int i
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is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
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costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
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cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
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dtype='f', order='C')
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c_d_scores = <float*>d_scores.data
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for i, (state, gold) in enumerate(zip(states, golds)):
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memset(is_valid, 0, self.moves.n_moves * sizeof(int))
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memset(costs, 0, self.moves.n_moves * sizeof(float))
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self.moves.set_costs(is_valid, costs, state, gold)
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cpu_log_loss(c_d_scores,
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costs, is_valid, &scores[i, 0], d_scores.shape[1])
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c_d_scores += d_scores.shape[1]
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return d_scores
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def set_annotations(self, docs, states):
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cdef StateClass state
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cdef Doc doc
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for state, doc in zip(states, docs):
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self.moves.finalize_state(state.c)
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for i in range(doc.length):
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doc.c[i] = state.c._sent[i]
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self.moves.finalize_doc(doc)
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def add_label(self, label):
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# Doesn't set label into serializer -- subclasses override it to do that.
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for action in self.moves.action_types:
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added = self.moves.add_action(action, label)
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if added:
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# Important that the labels be stored as a list! We need the
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# order, or the model goes out of synch
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self.cfg.setdefault('extra_labels', []).append(label)
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def begin_training(self, gold_tuples, **cfg):
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if 'model' in cfg:
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self.model = cfg['model']
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actions = self.moves.get_actions(gold_parses=gold_tuples)
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for action, labels in actions.items():
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for label in labels:
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self.moves.add_action(action, label)
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if self.model is True:
|
|
self.model = self.Model(self.moves.n_moves, **cfg)
|
|
|
|
def use_params(self, params):
|
|
# Can't decorate cdef class :(. Workaround.
|
|
with self.model[0].use_params(params):
|
|
with self.model[1].use_params(params):
|
|
yield
|
|
|
|
def to_disk(self, path):
|
|
path = util.ensure_path(path)
|
|
with (path / 'model.bin').open('wb') as file_:
|
|
dill.dump(self.model, file_)
|
|
|
|
def from_disk(self, path):
|
|
path = util.ensure_path(path)
|
|
with (path / 'model.bin').open('wb') as file_:
|
|
self.model = dill.load(file_)
|
|
|
|
def to_bytes(self):
|
|
pass
|
|
|
|
def from_bytes(self, data):
|
|
pass
|
|
|
|
|
|
class ParserStateError(ValueError):
|
|
def __init__(self, doc):
|
|
ValueError.__init__(self,
|
|
"Error analysing doc -- no valid actions available. This should "
|
|
"never happen, so please report the error on the issue tracker. "
|
|
"Here's the thread to do so --- reopen it if it's closed:\n"
|
|
"https://github.com/spacy-io/spaCy/issues/429\n"
|
|
"Please include the text that the parser failed on, which is:\n"
|
|
"%s" % repr(doc.text))
|
|
|
|
|
|
cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs, const int* is_valid, int n) nogil:
|
|
# Find minimum cost
|
|
cdef float cost = 1
|
|
for i in range(n):
|
|
if is_valid[i] and costs[i] < cost:
|
|
cost = costs[i]
|
|
# Now find best-scoring with that cost
|
|
cdef int best = -1
|
|
for i in range(n):
|
|
if costs[i] <= cost and is_valid[i]:
|
|
if best == -1 or scores[i] > scores[best]:
|
|
best = i
|
|
return best
|
|
|
|
|
|
cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil:
|
|
cdef int best = -1
|
|
for i in range(n):
|
|
if is_valid[i] >= 1:
|
|
if best == -1 or scores[i] > scores[best]:
|
|
best = i
|
|
return best
|
|
|
|
|
|
cdef int _arg_max_clas(const weight_t* scores, int move, const Transition* actions,
|
|
int nr_class) except -1:
|
|
cdef weight_t score = 0
|
|
cdef int mode = -1
|
|
cdef int i
|
|
for i in range(nr_class):
|
|
if actions[i].move == move and (mode == -1 or scores[i] >= score):
|
|
mode = i
|
|
score = scores[i]
|
|
return mode
|