mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-13 05:07:03 +03:00
8661218fe8
* Work on refactoring greedy parser * Compile updated parser * Fix refactored parser * Update test * Fix refactored parser * Fix refactored parser * Readd beam search after refactor * Fix beam search after refactor * Fix parser * Fix beam parsing * Support oracle segmentation in ud-train CLI command * Avoid relying on final gold check in beam search * Add a keyword argument sink to GoldParse * Bug fixes to beam search after refactor * Avoid importing fused token symbol in ud-run-test, untl that's added * Avoid importing fused token symbol in ud-run-test, untl that's added * Don't modify Token in global scope * Fix error in beam gradient calculation * Default to beam_update_prob 1 * Set a more aggressive threshold on the max violn update * Disable some tests to figure out why CI fails * Disable some tests to figure out why CI fails * Add some diagnostics to travis.yml to try to figure out why build fails * Tell Thinc to link against system blas on Travis * Point thinc to libblas on Travis * Try running sudo=true for travis * Unhack travis.sh * Restore beam_density argument for parser beam * Require thinc 6.11.1.dev16 * Revert hacks to tests * Revert hacks to travis.yml * Update thinc requirement * Fix parser model loading * Fix size limits in training data * Add missing name attribute for parser * Fix appveyor for Windows
403 lines
15 KiB
Cython
403 lines
15 KiB
Cython
# cython: infer_types=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 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 libc.math cimport exp
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from libcpp.vector cimport vector
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from libc.string cimport memset, memcpy
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from libc.stdlib cimport calloc, free, realloc
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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 thinc cimport openblas
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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 ..errors import Errors, TempErrors
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from .. import util
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from .stateclass cimport StateClass
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from .transition_system cimport Transition
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from . import _beam_utils
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from . import nonproj
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cdef WeightsC get_c_weights(model) except *:
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cdef WeightsC output
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cdef precompute_hiddens state2vec = model.state2vec
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output.feat_weights = state2vec.get_feat_weights()
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output.feat_bias = <const float*>state2vec.bias.data
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cdef np.ndarray vec2scores_W = model.vec2scores.W
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cdef np.ndarray vec2scores_b = model.vec2scores.b
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output.hidden_weights = <const float*>vec2scores_W.data
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output.hidden_bias = <const float*>vec2scores_b.data
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cdef np.ndarray tokvecs = model.tokvecs
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output.vectors = <float*>tokvecs.data
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return output
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cdef SizesC get_c_sizes(model, int batch_size) except *:
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cdef SizesC output
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output.states = batch_size
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output.classes = model.vec2scores.nO
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output.hiddens = model.state2vec.nO
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output.pieces = model.state2vec.nP
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output.feats = model.state2vec.nF
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output.embed_width = model.tokvecs.shape[1]
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return output
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cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
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if n.states <= A._max_size:
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A._curr_size = n.states
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return
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if A._max_size == 0:
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A.token_ids = <int*>calloc(n.states * n.feats, sizeof(A.token_ids[0]))
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A.vectors = <float*>calloc(n.states * n.embed_width, sizeof(A.vectors[0]))
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A.scores = <float*>calloc(n.states * n.classes, sizeof(A.scores[0]))
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A.unmaxed = <float*>calloc(n.states * n.hiddens * n.pieces, sizeof(A.unmaxed[0]))
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A.hiddens = <float*>calloc(n.states * n.hiddens, sizeof(A.hiddens[0]))
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A.is_valid = <int*>calloc(n.states * n.classes, sizeof(A.is_valid[0]))
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A._max_size = n.states
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else:
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A.token_ids = <int*>realloc(A.token_ids,
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n.states * n.feats * sizeof(A.token_ids[0]))
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A.vectors = <float*>realloc(A.vectors,
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n.states * n.embed_width * sizeof(A.vectors[0]))
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A.scores = <float*>realloc(A.scores,
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n.states * n.classes * sizeof(A.scores[0]))
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A.unmaxed = <float*>realloc(A.unmaxed,
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n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]))
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A.hiddens = <float*>realloc(A.hiddens,
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n.states * n.hiddens * sizeof(A.hiddens[0]))
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A.is_valid = <int*>realloc(A.is_valid,
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n.states * n.classes * sizeof(A.is_valid[0]))
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A._max_size = n.states
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A._curr_size = n.states
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cdef void predict_states(ActivationsC* A, StateC** states,
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const WeightsC* W, SizesC n) nogil:
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resize_activations(A, n)
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memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float))
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memset(A.hiddens, 0, n.states * n.hiddens * sizeof(float))
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for i in range(n.states):
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states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
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sum_state_features(A.unmaxed,
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W.feat_weights, A.token_ids, n.states, n.feats, n.hiddens * n.pieces)
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for i in range(n.states):
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VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces],
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W.feat_bias, 1., n.hiddens * n.pieces)
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for j in range(n.hiddens):
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index = i * n.hiddens * n.pieces + j * n.pieces
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which = Vec.arg_max(&A.unmaxed[index], n.pieces)
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A.hiddens[i*n.hiddens + j] = A.unmaxed[index + which]
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memset(A.scores, 0, n.states * n.classes * sizeof(float))
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# Compute hidden-to-output
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openblas.simple_gemm(A.scores, n.states, n.classes,
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A.hiddens, n.states, n.hiddens,
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W.hidden_weights, n.classes, n.hiddens, 0, 1)
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# Add bias
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for i in range(n.states):
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VecVec.add_i(&A.scores[i*n.classes],
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W.hidden_bias, 1., n.classes)
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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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cdef int id_stride = F*O
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cdef float one = 1.
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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] * id_stride + f*O
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feature = &cached[idx]
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openblas.simple_axpy(&output[b*O], O,
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feature, one)
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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 int arg_max_if_gold(const weight_t* scores, const weight_t* costs,
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const int* is_valid, int n) nogil:
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# Find minimum cost
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cdef float cost = 1
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for i in range(n):
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if is_valid[i] and costs[i] < cost:
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cost = costs[i]
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# Now find best-scoring with that cost
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cdef int best = -1
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for i in range(n):
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if costs[i] <= cost and is_valid[i]:
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if best == -1 or scores[i] > scores[best]:
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best = i
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return best
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cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil:
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cdef int best = -1
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for i in range(n):
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if is_valid[i] >= 1:
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if best == -1 or scores[i] > scores[best]:
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best = i
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return best
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class ParserModel(Model):
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def __init__(self, tok2vec, lower_model, upper_model):
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Model.__init__(self)
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self._layers = [tok2vec, lower_model, upper_model]
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def begin_update(self, docs, drop=0.):
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step_model = ParserStepModel(docs, self._layers, drop=drop)
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def finish_parser_update(golds, sgd=None):
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step_model.make_updates(sgd)
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return None
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return step_model, finish_parser_update
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def resize_output(self, new_output):
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# Weights are stored in (nr_out, nr_in) format, so we're basically
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# just adding rows here.
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smaller = self._layers[-1]._layers[-1]
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larger = Affine(self.moves.n_moves, smaller.nI)
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copy_array(larger.W[:smaller.nO], smaller.W)
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copy_array(larger.b[:smaller.nO], smaller.b)
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self._layers[-1]._layers[-1] = larger
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@property
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def tok2vec(self):
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return self._layers[0]
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@property
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def lower(self):
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return self._layers[1]
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@property
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def upper(self):
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return self._layers[2]
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class ParserStepModel(Model):
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def __init__(self, docs, layers, drop=0.):
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self.tokvecs, self.bp_tokvecs = layers[0].begin_update(docs, drop=drop)
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self.state2vec = precompute_hiddens(len(docs), self.tokvecs, layers[1],
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drop=drop)
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self.vec2scores = layers[-1]
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self.cuda_stream = util.get_cuda_stream()
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self.backprops = []
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@property
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def nO(self):
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return self.state2vec.nO
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def begin_update(self, states, drop=0.):
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token_ids = self.get_token_ids(states)
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vector, get_d_tokvecs = self.state2vec.begin_update(token_ids, drop=0.0)
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mask = self.ops.get_dropout_mask(vector.shape, drop)
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if mask is not None:
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vector *= mask
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scores, get_d_vector = self.vec2scores.begin_update(vector, drop=drop)
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def backprop_parser_step(d_scores, sgd=None):
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d_vector = get_d_vector(d_scores, sgd=sgd)
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if mask is not None:
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d_vector *= mask
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if isinstance(self.ops, CupyOps) \
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and not isinstance(token_ids, self.state2vec.ops.xp.ndarray):
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# Move token_ids and d_vector to GPU, asynchronously
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self.backprops.append((
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util.get_async(self.cuda_stream, token_ids),
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util.get_async(self.cuda_stream, d_vector),
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get_d_tokvecs
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))
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else:
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self.backprops.append((token_ids, d_vector, get_d_tokvecs))
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return None
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return scores, backprop_parser_step
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def get_token_ids(self, batch):
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states = _beam_utils.collect_states(batch)
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cdef StateClass state
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states = [state for state in states if not state.is_final()]
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cdef np.ndarray ids = numpy.zeros((len(states), self.state2vec.nF),
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dtype='i', order='C')
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ids.fill(-1)
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c_ids = <int*>ids.data
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for state in states:
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state.c.set_context_tokens(c_ids, ids.shape[1])
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c_ids += ids.shape[1]
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return ids
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def make_updates(self, sgd):
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# Tells CUDA to block, so our async copies complete.
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if self.cuda_stream is not None:
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self.cuda_stream.synchronize()
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# Add a padding vector to the d_tokvecs gradient, so that missing
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# values don't affect the real gradient.
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d_tokvecs = self.ops.allocate((self.tokvecs.shape[0]+1, self.tokvecs.shape[1]))
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for ids, d_vector, bp_vector in self.backprops:
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d_state_features = bp_vector((d_vector, ids), sgd=sgd)
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ids = ids.flatten()
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d_state_features = d_state_features.reshape(
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(ids.size, d_state_features.shape[2]))
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self.ops.scatter_add(d_tokvecs, ids,
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d_state_features)
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# Padded -- see update()
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self.bp_tokvecs(d_tokvecs[:-1], sgd=sgd)
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return d_tokvecs
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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 readonly 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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