mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 18:06:29 +03:00
986 lines
39 KiB
Cython
986 lines
39 KiB
Cython
# cython: infer_types=True
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# cython: cdivision=True
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# cython: boundscheck=False
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# cython: profile=True
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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 OrderedDict
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import ujson
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import json
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import numpy
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cimport cython.parallel
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import cytoolz
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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_XDECREF
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from cpython.exc cimport PyErr_CheckSignals, PyErr_SetFromErrno
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from libc.math cimport exp
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from libcpp.vector cimport vector
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from libc.string cimport memset
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from libc.stdlib cimport calloc, free
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from cymem.cymem cimport Pool
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from thinc.typedefs cimport weight_t, class_t, hash_t
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from thinc.extra.search cimport Beam
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from thinc.api import chain, clone
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from thinc.v2v import Model, Maxout, Affine
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from thinc.misc import LayerNorm
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from thinc.neural.ops import CupyOps
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from thinc.neural.util import get_array_module
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from thinc.linalg cimport Vec, VecVec
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from .._ml import zero_init, PrecomputableAffine, Tok2Vec, flatten
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from .._ml import link_vectors_to_models, create_default_optimizer
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from ..compat import json_dumps, copy_array
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from ..tokens.doc cimport Doc
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from ..gold cimport GoldParse
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from .. import util
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from .stateclass cimport StateClass
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from ._state cimport StateC
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from .transition_system cimport Transition
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from . import _beam_utils, nonproj
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def get_templates(*args, **kwargs):
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return []
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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 np.ndarray bias
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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,
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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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if not isinstance(lower_model.b, numpy.ndarray):
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self.bias = lower_model.b.get()
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else:
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self.bias = lower_model.b
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self.nF = cached.shape[1]
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self.nP = getattr(lower_model, 'nP', 1)
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self.nO = cached.shape[2]
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self.ops = lower_model.ops
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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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cdef const float* get_feat_weights(self) except NULL:
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if not self._is_synchronized 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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return <float*>self._cached.data
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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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cdef np.ndarray state_vector = numpy.zeros(
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(token_ids.shape[0], self.nO, self.nP), dtype='f')
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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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bp_hiddens = self._bp_hiddens
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feat_weights = self.get_feat_weights()
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cdef int[:, ::1] ids = token_ids
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sum_state_features(<float*>state_vector.data,
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feat_weights, &ids[0,0],
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token_ids.shape[0], self.nF, self.nO*self.nP)
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state_vector += self.bias
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state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
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def backward(d_state_vector_ids, sgd=None):
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d_state_vector, token_ids = d_state_vector_ids
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d_state_vector = bp_nonlinearity(d_state_vector, sgd)
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# This will usually be on GPU
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if not isinstance(d_state_vector, self.ops.xp.ndarray):
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d_state_vector = self.ops.xp.array(d_state_vector)
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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 state_vector, backward
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def _nonlinearity(self, state_vector):
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if self.nP == 1:
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state_vector = state_vector.reshape(state_vector.shape[:-1])
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mask = state_vector >= 0.
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state_vector *= mask
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else:
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state_vector, mask = self.ops.maxout(state_vector)
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def backprop_nonlinearity(d_best, sgd=None):
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if self.nP == 1:
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d_best *= mask
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d_best = d_best.reshape((d_best.shape + (1,)))
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return d_best
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else:
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return self.ops.backprop_maxout(d_best, mask, self.nP)
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return state_vector, backprop_nonlinearity
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cdef void sum_state_features(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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padding = cached
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cached += F * O
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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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feature = &padding[f*O]
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else:
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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, **cfg):
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depth = util.env_opt('parser_hidden_depth', cfg.get('hidden_depth', 1))
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if depth != 1:
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raise ValueError("Currently parser depth is hard-coded to 1.")
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parser_maxout_pieces = util.env_opt('parser_maxout_pieces',
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cfg.get('maxout_pieces', 2))
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token_vector_width = util.env_opt('token_vector_width',
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cfg.get('token_vector_width', 128))
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hidden_width = util.env_opt('hidden_width', cfg.get('hidden_width', 200))
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embed_size = util.env_opt('embed_size', cfg.get('embed_size', 7000))
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hist_size = util.env_opt('history_feats', cfg.get('hist_size', 0))
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hist_width = util.env_opt('history_width', cfg.get('hist_width', 0))
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if hist_size != 0:
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raise ValueError("Currently history size is hard-coded to 0")
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if hist_width != 0:
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raise ValueError("Currently history width is hard-coded to 0")
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tok2vec = Tok2Vec(token_vector_width, embed_size,
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pretrained_dims=cfg.get('pretrained_dims', 0))
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tok2vec = chain(tok2vec, flatten)
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lower = PrecomputableAffine(hidden_width,
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nF=cls.nr_feature, nI=token_vector_width,
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nP=parser_maxout_pieces)
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lower.nP = parser_maxout_pieces
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with Model.use_device('cpu'):
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upper = chain(
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clone(LayerNorm(Maxout(hidden_width, hidden_width)), depth-1),
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zero_init(Affine(nr_class, hidden_width, drop_factor=0.0))
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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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cfg = {
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'nr_class': nr_class,
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'hidden_depth': depth,
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'token_vector_width': token_vector_width,
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'hidden_width': hidden_width,
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'maxout_pieces': parser_maxout_pieces,
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'hist_size': hist_size,
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'hist_width': hist_width
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}
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return (tok2vec, lower, upper), cfg
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def create_optimizer(self):
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return create_default_optimizer(self.model[0].ops,
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**self.cfg.get('optimizer', {}))
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def __init__(self, Vocab vocab, moves=True, model=True, **cfg):
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"""Create a Parser.
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vocab (Vocab): The vocabulary object. Must be shared with documents
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to be processed. The value is set to the `.vocab` attribute.
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moves (TransitionSystem): Defines how the parse-state is created,
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updated and evaluated. The value is set to the .moves attribute
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unless True (default), in which case a new instance is created with
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`Parser.Moves()`.
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model (object): Defines how the parse-state is created, updated and
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evaluated. The value is set to the .model attribute unless True
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(default), in which case a new instance is created with
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`Parser.Model()`.
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**cfg: 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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if 'beam_width' not in cfg:
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cfg['beam_width'] = util.env_opt('beam_width', 1)
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if 'beam_density' not in cfg:
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cfg['beam_density'] = util.env_opt('beam_density', 0.0)
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if 'pretrained_dims' not in cfg:
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cfg['pretrained_dims'] = self.vocab.vectors.data.shape[1]
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cfg.setdefault('cnn_maxout_pieces', 3)
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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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self._multitasks = []
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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 doc, beam_width=None, beam_density=None):
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"""Apply the parser or entity recognizer, setting the annotations onto
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the `Doc` object.
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doc (Doc): The document to be processed.
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"""
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if beam_width is None:
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beam_width = self.cfg.get('beam_width', 1)
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if beam_density is None:
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beam_density = self.cfg.get('beam_density', 0.0)
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cdef Beam beam
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if beam_width == 1:
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states, tokvecs = self.parse_batch([doc])
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self.set_annotations([doc], states, tensors=tokvecs)
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return doc
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else:
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beams, tokvecs = self.beam_parse([doc],
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beam_width=beam_width,
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beam_density=beam_density)
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beam = beams[0]
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output = self.moves.get_beam_annot(beam)
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state = <StateClass>beam.at(0)
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self.set_annotations([doc], [state], tensors=tokvecs)
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_cleanup(beam)
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return output
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def pipe(self, docs, int batch_size=256, int n_threads=2,
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beam_width=None, beam_density=None):
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"""Process a stream of documents.
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stream: The sequence of documents to process.
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batch_size (int): Number of documents to accumulate into a working set.
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n_threads (int): The number of threads with which to work on the buffer
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in parallel.
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YIELDS (Doc): Documents, in order.
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"""
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if beam_width is None:
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beam_width = self.cfg.get('beam_width', 1)
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if beam_density is None:
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beam_density = self.cfg.get('beam_density', 0.0)
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cdef Doc doc
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cdef Beam beam
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for batch in cytoolz.partition_all(batch_size, docs):
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batch = list(batch)
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by_length = sorted(list(batch), key=lambda doc: len(doc))
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for subbatch in cytoolz.partition_all(8, by_length):
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subbatch = list(subbatch)
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if beam_width == 1:
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parse_states, tokvecs = self.parse_batch(subbatch)
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beams = []
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else:
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beams, tokvecs = self.beam_parse(subbatch,
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beam_width=beam_width,
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beam_density=beam_density)
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parse_states = []
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for beam in beams:
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parse_states.append(<StateClass>beam.at(0))
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self.set_annotations(subbatch, parse_states, tensors=tokvecs)
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yield from batch
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def parse_batch(self, docs):
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cdef:
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precompute_hiddens state2vec
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StateClass stcls
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Pool mem
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const float* feat_weights
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StateC* st
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vector[StateC*] states
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int guess, nr_class, nr_feat, nr_piece, nr_dim, nr_state, nr_step
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int j
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if isinstance(docs, Doc):
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docs = [docs]
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cuda_stream = util.get_cuda_stream()
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(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(
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docs, cuda_stream, 0.0)
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nr_state = len(docs)
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nr_class = self.moves.n_moves
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nr_dim = tokvecs.shape[1]
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nr_feat = self.nr_feature
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nr_piece = state2vec.nP
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state_objs = self.moves.init_batch(docs)
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for stcls in state_objs:
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if not stcls.c.is_final():
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states.push_back(stcls.c)
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feat_weights = state2vec.get_feat_weights()
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cdef int i
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cdef np.ndarray hidden_weights = numpy.ascontiguousarray(
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vec2scores._layers[-1].W.T)
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cdef np.ndarray hidden_bias = vec2scores._layers[-1].b
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hW = <float*>hidden_weights.data
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hb = <float*>hidden_bias.data
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bias = <float*>state2vec.bias.data
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cdef int nr_hidden = hidden_weights.shape[0]
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cdef int nr_task = states.size()
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with nogil:
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for i in range(nr_task):
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self._parseC(states[i],
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feat_weights, bias, hW, hb,
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nr_class, nr_hidden, nr_feat, nr_piece)
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PyErr_CheckSignals()
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tokvecs = self.model[0].ops.unflatten(tokvecs,
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[len(doc) for doc in docs])
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return state_objs, tokvecs
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cdef void _parseC(self, StateC* state,
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const float* feat_weights, const float* bias,
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const float* hW, const float* hb,
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int nr_class, int nr_hidden, int nr_feat, int nr_piece) nogil:
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token_ids = <int*>calloc(nr_feat, sizeof(int))
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is_valid = <int*>calloc(nr_class, sizeof(int))
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vectors = <float*>calloc(nr_hidden * nr_piece, sizeof(float))
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scores = <float*>calloc(nr_class, sizeof(float))
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if not (token_ids and is_valid and vectors and scores):
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with gil:
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PyErr_SetFromErrno(MemoryError)
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PyErr_CheckSignals()
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cdef float feature
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while not state.is_final():
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state.set_context_tokens(token_ids, nr_feat)
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memset(vectors, 0, nr_hidden * nr_piece * sizeof(float))
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memset(scores, 0, nr_class * sizeof(float))
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sum_state_features(vectors,
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feat_weights, token_ids, 1, nr_feat, nr_hidden * nr_piece)
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for i in range(nr_hidden * nr_piece):
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vectors[i] += bias[i]
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V = vectors
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W = hW
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for i in range(nr_hidden):
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if nr_piece == 1:
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feature = V[0] if V[0] >= 0. else 0.
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elif nr_piece == 2:
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feature = V[0] if V[0] >= V[1] else V[1]
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else:
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feature = Vec.max(V, nr_piece)
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for j in range(nr_class):
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scores[j] += feature * W[j]
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W += nr_class
|
|
V += nr_piece
|
|
for i in range(nr_class):
|
|
scores[i] += hb[i]
|
|
self.moves.set_valid(is_valid, state)
|
|
guess = arg_max_if_valid(scores, is_valid, nr_class)
|
|
action = self.moves.c[guess]
|
|
action.do(state, action.label)
|
|
state.push_hist(guess)
|
|
free(token_ids)
|
|
free(is_valid)
|
|
free(vectors)
|
|
free(scores)
|
|
|
|
def beam_parse(self, docs, int beam_width=3, float beam_density=0.001):
|
|
cdef Beam beam
|
|
cdef np.ndarray scores
|
|
cdef Doc doc
|
|
cdef int nr_class = self.moves.n_moves
|
|
cdef StateClass stcls, output
|
|
cuda_stream = util.get_cuda_stream()
|
|
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(
|
|
docs, cuda_stream, 0.0)
|
|
beams = []
|
|
cdef int offset = 0
|
|
cdef int j = 0
|
|
cdef int k
|
|
for doc in docs:
|
|
beam = Beam(nr_class, beam_width, min_density=beam_density)
|
|
beam.initialize(self.moves.init_beam_state, doc.length, doc.c)
|
|
for i in range(beam.width):
|
|
stcls = <StateClass>beam.at(i)
|
|
stcls.c.offset = offset
|
|
offset += len(doc)
|
|
beam.check_done(_check_final_state, NULL)
|
|
while not beam.is_done:
|
|
states = []
|
|
for i in range(beam.size):
|
|
stcls = <StateClass>beam.at(i)
|
|
# This way we avoid having to score finalized states
|
|
# We do have to take care to keep indexes aligned, though
|
|
if not stcls.is_final():
|
|
states.append(stcls)
|
|
token_ids = self.get_token_ids(states)
|
|
vectors = state2vec(token_ids)
|
|
if self.cfg.get('hist_size', 0):
|
|
hists = numpy.asarray([st.history[:self.cfg['hist_size']]
|
|
for st in states], dtype='i')
|
|
scores = vec2scores((vectors, hists))
|
|
else:
|
|
scores = vec2scores(vectors)
|
|
j = 0
|
|
c_scores = <float*>scores.data
|
|
for i in range(beam.size):
|
|
stcls = <StateClass>beam.at(i)
|
|
if not stcls.is_final():
|
|
self.moves.set_valid(beam.is_valid[i], stcls.c)
|
|
for k in range(nr_class):
|
|
beam.scores[i][k] = c_scores[j * scores.shape[1] + k]
|
|
j += 1
|
|
beam.advance(_transition_state, _hash_state, <void*>self.moves.c)
|
|
beam.check_done(_check_final_state, NULL)
|
|
beams.append(beam)
|
|
tokvecs = self.model[0].ops.unflatten(tokvecs,
|
|
[len(doc) for doc in docs])
|
|
return beams, tokvecs
|
|
|
|
def update(self, docs, golds, drop=0., sgd=None, losses=None):
|
|
if not any(self.moves.has_gold(gold) for gold in golds):
|
|
return None
|
|
if self.cfg.get('beam_width', 1) >= 2 and numpy.random.random() >= 0.5:
|
|
return self.update_beam(docs, golds,
|
|
self.cfg['beam_width'], self.cfg['beam_density'],
|
|
drop=drop, sgd=sgd, losses=losses)
|
|
if losses is not None and self.name not in losses:
|
|
losses[self.name] = 0.
|
|
if isinstance(docs, Doc) and isinstance(golds, GoldParse):
|
|
docs = [docs]
|
|
golds = [golds]
|
|
cuda_stream = util.get_cuda_stream()
|
|
states, golds, max_steps = self._init_gold_batch(docs, golds)
|
|
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(docs, cuda_stream,
|
|
drop)
|
|
todo = [(s, g) for (s, g) in zip(states, golds)
|
|
if not s.is_final() and g is not None]
|
|
if not todo:
|
|
return None
|
|
|
|
backprops = []
|
|
# Add a padding vector to the d_tokvecs gradient, so that missing
|
|
# values don't affect the real gradient.
|
|
d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1]))
|
|
cdef float loss = 0.
|
|
n_steps = 0
|
|
while todo:
|
|
states, golds = zip(*todo)
|
|
token_ids = self.get_token_ids(states)
|
|
vector, bp_vector = state2vec.begin_update(token_ids, drop=0.0)
|
|
if drop != 0:
|
|
mask = vec2scores.ops.get_dropout_mask(vector.shape, drop)
|
|
vector *= mask
|
|
hists = numpy.asarray([st.history for st in states], dtype='i')
|
|
if self.cfg.get('hist_size', 0):
|
|
scores, bp_scores = vec2scores.begin_update((vector, hists), drop=drop)
|
|
else:
|
|
scores, bp_scores = vec2scores.begin_update(vector, drop=drop)
|
|
|
|
d_scores = self.get_batch_loss(states, golds, scores)
|
|
d_scores /= len(docs)
|
|
d_vector = bp_scores(d_scores, sgd=sgd)
|
|
if drop != 0:
|
|
d_vector *= mask
|
|
|
|
if isinstance(self.model[0].ops, CupyOps) \
|
|
and not isinstance(token_ids, state2vec.ops.xp.ndarray):
|
|
# Move token_ids and d_vector to GPU, asynchronously
|
|
backprops.append((
|
|
util.get_async(cuda_stream, token_ids),
|
|
util.get_async(cuda_stream, d_vector),
|
|
bp_vector
|
|
))
|
|
else:
|
|
backprops.append((token_ids, d_vector, bp_vector))
|
|
self.transition_batch(states, scores)
|
|
todo = [(st, gold) for (st, gold) in todo
|
|
if not st.is_final()]
|
|
if losses is not None:
|
|
losses[self.name] += (d_scores**2).sum()
|
|
n_steps += 1
|
|
if n_steps >= max_steps:
|
|
break
|
|
self._make_updates(d_tokvecs,
|
|
bp_tokvecs, backprops, sgd, cuda_stream)
|
|
|
|
def update_beam(self, docs, golds, width=None, density=None,
|
|
drop=0., sgd=None, losses=None):
|
|
if not any(self.moves.has_gold(gold) for gold in golds):
|
|
return None
|
|
if not golds:
|
|
return None
|
|
if width is None:
|
|
width = self.cfg.get('beam_width', 2)
|
|
if density is None:
|
|
density = self.cfg.get('beam_density', 0.0)
|
|
if losses is not None and self.name not in losses:
|
|
losses[self.name] = 0.
|
|
lengths = [len(d) for d in docs]
|
|
assert min(lengths) >= 1
|
|
states = self.moves.init_batch(docs)
|
|
for gold in golds:
|
|
self.moves.preprocess_gold(gold)
|
|
cuda_stream = util.get_cuda_stream()
|
|
(tokvecs, bp_tokvecs), state2vec, vec2scores = self.get_batch_model(
|
|
docs, cuda_stream, drop)
|
|
states_d_scores, backprops = _beam_utils.update_beam(
|
|
self.moves, self.nr_feature, 500, states, golds, state2vec,
|
|
vec2scores, width, density, self.cfg.get('hist_size', 0),
|
|
drop=drop, losses=losses)
|
|
backprop_lower = []
|
|
cdef float batch_size = len(docs)
|
|
for i, d_scores in enumerate(states_d_scores):
|
|
d_scores /= batch_size
|
|
if losses is not None:
|
|
losses[self.name] += (d_scores**2).sum()
|
|
ids, bp_vectors, bp_scores = backprops[i]
|
|
d_vector = bp_scores(d_scores, sgd=sgd)
|
|
if isinstance(self.model[0].ops, CupyOps) \
|
|
and not isinstance(ids, state2vec.ops.xp.ndarray):
|
|
backprop_lower.append((
|
|
util.get_async(cuda_stream, ids),
|
|
util.get_async(cuda_stream, d_vector),
|
|
bp_vectors))
|
|
else:
|
|
backprop_lower.append((ids, d_vector, bp_vectors))
|
|
# Add a padding vector to the d_tokvecs gradient, so that missing
|
|
# values don't affect the real gradient.
|
|
d_tokvecs = state2vec.ops.allocate((tokvecs.shape[0]+1, tokvecs.shape[1]))
|
|
self._make_updates(d_tokvecs, bp_tokvecs, backprop_lower, sgd,
|
|
cuda_stream)
|
|
|
|
def _init_gold_batch(self, whole_docs, whole_golds):
|
|
"""Make a square batch, of length equal to the shortest doc. A long
|
|
doc will get multiple states. Let's say we have a doc of length 2*N,
|
|
where N is the shortest doc. We'll make two states, one representing
|
|
long_doc[:N], and another representing long_doc[N:]."""
|
|
cdef:
|
|
StateClass state
|
|
Transition action
|
|
whole_states = self.moves.init_batch(whole_docs)
|
|
max_length = max(5, min(50, min([len(doc) for doc in whole_docs])))
|
|
max_moves = 0
|
|
states = []
|
|
golds = []
|
|
for doc, state, gold in zip(whole_docs, whole_states, whole_golds):
|
|
gold = self.moves.preprocess_gold(gold)
|
|
if gold is None:
|
|
continue
|
|
oracle_actions = self.moves.get_oracle_sequence(doc, gold)
|
|
start = 0
|
|
while start < len(doc):
|
|
state = state.copy()
|
|
n_moves = 0
|
|
while state.B(0) < start and not state.is_final():
|
|
action = self.moves.c[oracle_actions.pop(0)]
|
|
action.do(state.c, action.label)
|
|
state.c.push_hist(action.clas)
|
|
n_moves += 1
|
|
has_gold = self.moves.has_gold(gold, start=start,
|
|
end=start+max_length)
|
|
if not state.is_final() and has_gold:
|
|
states.append(state)
|
|
golds.append(gold)
|
|
max_moves = max(max_moves, n_moves)
|
|
start += min(max_length, len(doc)-start)
|
|
max_moves = max(max_moves, len(oracle_actions))
|
|
return states, golds, max_moves
|
|
|
|
def _make_updates(self, d_tokvecs, bp_tokvecs, backprops, sgd, cuda_stream=None):
|
|
# Tells CUDA to block, so our async copies complete.
|
|
if cuda_stream is not None:
|
|
cuda_stream.synchronize()
|
|
xp = get_array_module(d_tokvecs)
|
|
for ids, d_vector, bp_vector in backprops:
|
|
d_state_features = bp_vector((d_vector, ids), sgd=sgd)
|
|
ids = ids.flatten()
|
|
d_state_features = d_state_features.reshape(
|
|
(ids.size, d_state_features.shape[2]))
|
|
self.model[0].ops.scatter_add(d_tokvecs, ids,
|
|
d_state_features)
|
|
# Padded -- see update()
|
|
bp_tokvecs(d_tokvecs[:-1], sgd=sgd)
|
|
|
|
@property
|
|
def move_names(self):
|
|
names = []
|
|
for i in range(self.moves.n_moves):
|
|
name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label)
|
|
names.append(name)
|
|
return names
|
|
|
|
def get_batch_model(self, docs, stream, dropout):
|
|
tok2vec, lower, upper = self.model
|
|
tokvecs, bp_tokvecs = tok2vec.begin_update(docs, drop=dropout)
|
|
state2vec = precompute_hiddens(len(docs), tokvecs,
|
|
lower, stream, drop=0.0)
|
|
return (tokvecs, bp_tokvecs), state2vec, upper
|
|
|
|
nr_feature = 13
|
|
|
|
def get_token_ids(self, states):
|
|
cdef StateClass state
|
|
cdef int n_tokens = self.nr_feature
|
|
cdef np.ndarray ids = numpy.zeros((len(states), n_tokens),
|
|
dtype='i', order='C')
|
|
c_ids = <int*>ids.data
|
|
for i, state in enumerate(states):
|
|
if not state.is_final():
|
|
state.c.set_context_tokens(c_ids, n_tokens)
|
|
c_ids += ids.shape[1]
|
|
return ids
|
|
|
|
def transition_batch(self, states, float[:, ::1] scores):
|
|
cdef StateClass state
|
|
cdef int[500] is_valid # TODO: Unhack
|
|
cdef float* c_scores = &scores[0, 0]
|
|
for state in states:
|
|
self.moves.set_valid(is_valid, state.c)
|
|
guess = arg_max_if_valid(c_scores, is_valid, scores.shape[1])
|
|
action = self.moves.c[guess]
|
|
action.do(state.c, action.label)
|
|
c_scores += scores.shape[1]
|
|
state.c.push_hist(guess)
|
|
|
|
def get_batch_loss(self, states, golds, float[:, ::1] scores):
|
|
cdef StateClass state
|
|
cdef GoldParse gold
|
|
cdef Pool mem = Pool()
|
|
cdef int i
|
|
is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
|
|
costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
|
|
cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
|
|
dtype='f', order='C')
|
|
c_d_scores = <float*>d_scores.data
|
|
for i, (state, gold) in enumerate(zip(states, golds)):
|
|
memset(is_valid, 0, self.moves.n_moves * sizeof(int))
|
|
memset(costs, 0, self.moves.n_moves * sizeof(float))
|
|
self.moves.set_costs(is_valid, costs, state, gold)
|
|
cpu_log_loss(c_d_scores,
|
|
costs, is_valid, &scores[i, 0], d_scores.shape[1])
|
|
c_d_scores += d_scores.shape[1]
|
|
return d_scores
|
|
|
|
def set_annotations(self, docs, states, tensors=None):
|
|
cdef StateClass state
|
|
cdef Doc doc
|
|
for i, (state, doc) in enumerate(zip(states, docs)):
|
|
self.moves.finalize_state(state.c)
|
|
for j in range(doc.length):
|
|
doc.c[j] = state.c._sent[j]
|
|
if tensors is not None:
|
|
if isinstance(doc.tensor, numpy.ndarray) \
|
|
and not isinstance(tensors[i], numpy.ndarray):
|
|
doc.extend_tensor(tensors[i].get())
|
|
else:
|
|
doc.extend_tensor(tensors[i])
|
|
self.moves.finalize_doc(doc)
|
|
|
|
for hook in self.postprocesses:
|
|
for doc in docs:
|
|
hook(doc)
|
|
|
|
@property
|
|
def tok2vec(self):
|
|
'''Return the embedding and convolutional layer of the model.'''
|
|
if self.model in (None, True, False):
|
|
return None
|
|
else:
|
|
return self.model[0]
|
|
|
|
@property
|
|
def postprocesses(self):
|
|
# Available for subclasses, e.g. to deprojectivize
|
|
return []
|
|
|
|
def add_label(self, label):
|
|
resized = False
|
|
for action in self.moves.action_types:
|
|
added = self.moves.add_action(action, label)
|
|
if added:
|
|
# Important that the labels be stored as a list! We need the
|
|
# order, or the model goes out of synch
|
|
self.cfg.setdefault('extra_labels', []).append(label)
|
|
resized = True
|
|
if self.model not in (True, False, None) and resized:
|
|
# Weights are stored in (nr_out, nr_in) format, so we're basically
|
|
# just adding rows here.
|
|
smaller = self.model[-1]._layers[-1]
|
|
larger = Affine(self.moves.n_moves, smaller.nI)
|
|
copy_array(larger.W[:smaller.nO], smaller.W)
|
|
copy_array(larger.b[:smaller.nO], smaller.b)
|
|
self.model[-1]._layers[-1] = larger
|
|
|
|
def begin_training(self, gold_tuples, pipeline=None, sgd=None, **cfg):
|
|
if 'model' in cfg:
|
|
self.model = cfg['model']
|
|
gold_tuples = nonproj.preprocess_training_data(gold_tuples,
|
|
label_freq_cutoff=100)
|
|
actions = self.moves.get_actions(gold_parses=gold_tuples)
|
|
for action, labels in actions.items():
|
|
for label in labels:
|
|
self.moves.add_action(action, label)
|
|
if self.model is True:
|
|
cfg['pretrained_dims'] = self.vocab.vectors_length
|
|
self.model, cfg = self.Model(self.moves.n_moves, **cfg)
|
|
if sgd is None:
|
|
sgd = self.create_optimizer()
|
|
self.init_multitask_objectives(gold_tuples, pipeline, sgd=sgd, **cfg)
|
|
link_vectors_to_models(self.vocab)
|
|
self.cfg.update(cfg)
|
|
elif sgd is None:
|
|
sgd = self.create_optimizer()
|
|
return sgd
|
|
|
|
def init_multitask_objectives(self, gold_tuples, pipeline, **cfg):
|
|
'''Setup models for secondary objectives, to benefit from multi-task
|
|
learning. This method is intended to be overridden by subclasses.
|
|
|
|
For instance, the dependency parser can benefit from sharing
|
|
an input representation with a label prediction model. These auxiliary
|
|
models are discarded after training.
|
|
'''
|
|
pass
|
|
|
|
def preprocess_gold(self, docs_golds):
|
|
for doc, gold in docs_golds:
|
|
yield doc, gold
|
|
|
|
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, **exclude):
|
|
serializers = {
|
|
'tok2vec_model': lambda p: p.open('wb').write(
|
|
self.model[0].to_bytes()),
|
|
'lower_model': lambda p: p.open('wb').write(
|
|
self.model[1].to_bytes()),
|
|
'upper_model': lambda p: p.open('wb').write(
|
|
self.model[2].to_bytes()),
|
|
'vocab': lambda p: self.vocab.to_disk(p),
|
|
'moves': lambda p: self.moves.to_disk(p, strings=False),
|
|
'cfg': lambda p: p.open('w').write(json_dumps(self.cfg))
|
|
}
|
|
util.to_disk(path, serializers, exclude)
|
|
|
|
def from_disk(self, path, **exclude):
|
|
deserializers = {
|
|
'vocab': lambda p: self.vocab.from_disk(p),
|
|
'moves': lambda p: self.moves.from_disk(p, strings=False),
|
|
'cfg': lambda p: self.cfg.update(ujson.load(p.open())),
|
|
'model': lambda p: None
|
|
}
|
|
util.from_disk(path, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
path = util.ensure_path(path)
|
|
if self.model is True:
|
|
self.cfg['pretrained_dims'] = self.vocab.vectors_length
|
|
self.model, cfg = self.Model(**self.cfg)
|
|
else:
|
|
cfg = {}
|
|
with (path / 'tok2vec_model').open('rb') as file_:
|
|
bytes_data = file_.read()
|
|
self.model[0].from_bytes(bytes_data)
|
|
with (path / 'lower_model').open('rb') as file_:
|
|
bytes_data = file_.read()
|
|
self.model[1].from_bytes(bytes_data)
|
|
with (path / 'upper_model').open('rb') as file_:
|
|
bytes_data = file_.read()
|
|
self.model[2].from_bytes(bytes_data)
|
|
self.cfg.update(cfg)
|
|
return self
|
|
|
|
def to_bytes(self, **exclude):
|
|
serializers = OrderedDict((
|
|
('tok2vec_model', lambda: self.model[0].to_bytes()),
|
|
('lower_model', lambda: self.model[1].to_bytes()),
|
|
('upper_model', lambda: self.model[2].to_bytes()),
|
|
('vocab', lambda: self.vocab.to_bytes()),
|
|
('moves', lambda: self.moves.to_bytes(strings=False)),
|
|
('cfg', lambda: json.dumps(self.cfg, indent=2, sort_keys=True))
|
|
))
|
|
if 'model' in exclude:
|
|
exclude['tok2vec_model'] = True
|
|
exclude['lower_model'] = True
|
|
exclude['upper_model'] = True
|
|
exclude.pop('model')
|
|
return util.to_bytes(serializers, exclude)
|
|
|
|
def from_bytes(self, bytes_data, **exclude):
|
|
deserializers = OrderedDict((
|
|
('vocab', lambda b: self.vocab.from_bytes(b)),
|
|
('moves', lambda b: self.moves.from_bytes(b, strings=False)),
|
|
('cfg', lambda b: self.cfg.update(json.loads(b))),
|
|
('tok2vec_model', lambda b: None),
|
|
('lower_model', lambda b: None),
|
|
('upper_model', lambda b: None)
|
|
))
|
|
msg = util.from_bytes(bytes_data, deserializers, exclude)
|
|
if 'model' not in exclude:
|
|
if self.model is True:
|
|
self.model, cfg = self.Model(**self.cfg)
|
|
cfg['pretrained_dims'] = self.vocab.vectors_length
|
|
else:
|
|
cfg = {}
|
|
cfg['pretrained_dims'] = self.vocab.vectors_length
|
|
if 'tok2vec_model' in msg:
|
|
self.model[0].from_bytes(msg['tok2vec_model'])
|
|
if 'lower_model' in msg:
|
|
self.model[1].from_bytes(msg['lower_model'])
|
|
if 'upper_model' in msg:
|
|
self.model[2].from_bytes(msg['upper_model'])
|
|
self.cfg.update(cfg)
|
|
return self
|
|
|
|
|
|
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
|
|
|
|
|
|
# These are passed as callbacks to thinc.search.Beam
|
|
cdef int _transition_state(void* _dest, void* _src, class_t clas, void* _moves) except -1:
|
|
dest = <StateClass>_dest
|
|
src = <StateClass>_src
|
|
moves = <const Transition*>_moves
|
|
dest.clone(src)
|
|
moves[clas].do(dest.c, moves[clas].label)
|
|
dest.c.push_hist(clas)
|
|
|
|
|
|
cdef int _check_final_state(void* _state, void* extra_args) except -1:
|
|
return (<StateClass>_state).is_final()
|
|
|
|
|
|
def _cleanup(Beam beam):
|
|
for i in range(beam.width):
|
|
Py_XDECREF(<PyObject*>beam._states[i].content)
|
|
Py_XDECREF(<PyObject*>beam._parents[i].content)
|
|
|
|
|
|
cdef hash_t _hash_state(void* _state, void* _) except 0:
|
|
state = <StateClass>_state
|
|
if state.c.is_final():
|
|
return 1
|
|
else:
|
|
return state.c.hash()
|