spaCy/spacy/_ml.py

import ujson
from thinc.api import add, layerize, chain, clone, concatenate, with_flatten
from thinc.neural import Model, Maxout, Softmax, Affine
from thinc.neural._classes.hash_embed import HashEmbed
from thinc.neural.ops import NumpyOps, CupyOps
from thinc.neural.util import get_array_module
import random

from thinc.neural._classes.convolution import ExtractWindow
from thinc.neural._classes.static_vectors import StaticVectors
from thinc.neural._classes.batchnorm import BatchNorm
from thinc.neural._classes.resnet import Residual
from thinc.neural import ReLu
from thinc.neural._classes.selu import SELU
from thinc import describe
from thinc.describe import Dimension, Synapses, Biases, Gradient
from thinc.neural._classes.affine import _set_dimensions_if_needed
from thinc.api import FeatureExtracter, with_getitem
from thinc.neural.pooling import Pooling, max_pool, mean_pool, sum_pool
from thinc.neural._classes.attention import ParametricAttention
from thinc.linear.linear import LinearModel
from thinc.api import uniqued, wrap, flatten_add_lengths

from .attrs import ID, ORTH, LOWER, NORM, PREFIX, SUFFIX, SHAPE, TAG, DEP
from .tokens.doc import Doc

import numpy
import io


@layerize
def _flatten_add_lengths(seqs, pad=0, drop=0.):
    ops = Model.ops
    lengths = ops.asarray([len(seq) for seq in seqs], dtype='i')
    def finish_update(d_X, sgd=None):
        return ops.unflatten(d_X, lengths, pad=pad)
    X = ops.flatten(seqs, pad=pad)
    return (X, lengths), finish_update


@layerize
def _logistic(X, drop=0.):
    xp = get_array_module(X)
    if not isinstance(X, xp.ndarray):
        X = xp.asarray(X)
    # Clip to range (-10, 10)
    X = xp.minimum(X, 10., X)
    X = xp.maximum(X, -10., X)
    Y = 1. / (1. + xp.exp(-X))
    def logistic_bwd(dY, sgd=None):
        dX = dY * (Y * (1-Y))
        return dX
    return Y, logistic_bwd


@layerize
def add_tuples(X, drop=0.):
    """Give inputs of sequence pairs, where each sequence is (vals, length),
    sum the values, returning a single sequence.

    If input is:
    ((vals1, length), (vals2, length)
    Output is:
    (vals1+vals2, length)

    vals are a single tensor for the whole batch.
    """
    (vals1, length1), (vals2, length2) = X
    assert length1 == length2

    def add_tuples_bwd(dY, sgd=None):
        return (dY, dY)

    return (vals1+vals2, length), add_tuples_bwd


def _zero_init(model):
    def _zero_init_impl(self, X, y):
        self.W.fill(0)
    model.on_data_hooks.append(_zero_init_impl)
    if model.W is not None:
        model.W.fill(0.)
    return model


@layerize
def _preprocess_doc(docs, drop=0.):
    keys = [doc.to_array([LOWER]) for doc in docs]
    keys = [a[:, 0] for a in keys]
    ops = Model.ops
    lengths = ops.asarray([arr.shape[0] for arr in keys])
    keys = ops.xp.concatenate(keys)
    vals = ops.allocate(keys.shape[0]) + 1
    return (keys, vals, lengths), None


def _init_for_precomputed(W, ops):
    if (W**2).sum() != 0.:
        return
    reshaped = W.reshape((W.shape[1], W.shape[0] * W.shape[2]))
    ops.xavier_uniform_init(reshaped)
    W[:] = reshaped.reshape(W.shape)


@describe.on_data(_set_dimensions_if_needed)
@describe.attributes(
    nI=Dimension("Input size"),
    nF=Dimension("Number of features"),
    nO=Dimension("Output size"),
    W=Synapses("Weights matrix",
        lambda obj: (obj.nF, obj.nO, obj.nI),
        lambda W, ops: _init_for_precomputed(W, ops)),
    b=Biases("Bias vector",
        lambda obj: (obj.nO,)),
    d_W=Gradient("W"),
    d_b=Gradient("b")
)
class PrecomputableAffine(Model):
    def __init__(self, nO=None, nI=None, nF=None, **kwargs):
        Model.__init__(self, **kwargs)
        self.nO = nO
        self.nI = nI
        self.nF = nF

    def begin_update(self, X, drop=0.):
        # X: (b, i)
        # Yf: (b, f, i)
        # dY: (b, o)
        # dYf: (b, f, o)
        #Yf = numpy.einsum('bi,foi->bfo', X, self.W)
        Yf = self.ops.xp.tensordot(
                X, self.W, axes=[[1], [2]])
        Yf += self.b
        def backward(dY_ids, sgd=None):
            tensordot = self.ops.xp.tensordot
            dY, ids = dY_ids
            Xf = X[ids]

            #dXf = numpy.einsum('bo,foi->bfi', dY, self.W)
            dXf = tensordot(dY, self.W, axes=[[1], [1]])
            #dW = numpy.einsum('bo,bfi->ofi', dY, Xf)
            dW = tensordot(dY, Xf, axes=[[0], [0]])
            # ofi -> foi
            self.d_W += dW.transpose((1, 0, 2))
            self.d_b += dY.sum(axis=0)

            if sgd is not None:
                sgd(self._mem.weights, self._mem.gradient, key=self.id)
            return dXf
        return Yf, backward


@describe.on_data(_set_dimensions_if_needed)
@describe.attributes(
    nI=Dimension("Input size"),
    nF=Dimension("Number of features"),
    nP=Dimension("Number of pieces"),
    nO=Dimension("Output size"),
    W=Synapses("Weights matrix",
        lambda obj: (obj.nF, obj.nO, obj.nP, obj.nI),
        lambda W, ops: ops.xavier_uniform_init(W)),
    b=Biases("Bias vector",
        lambda obj: (obj.nO, obj.nP)),
    d_W=Gradient("W"),
    d_b=Gradient("b")
)
class PrecomputableMaxouts(Model):
    def __init__(self, nO=None, nI=None, nF=None, nP=3, **kwargs):
        Model.__init__(self, **kwargs)
        self.nO = nO
        self.nP = nP
        self.nI = nI
        self.nF = nF

    def begin_update(self, X, drop=0.):
        # X: (b, i)
        # Yfp: (b, f, o, p)
        # Xf: (f, b, i)
        # dYp: (b, o, p)
        # W: (f, o, p, i)
        # b: (o, p)

        # bi,opfi->bfop
        # bop,fopi->bfi
        # bop,fbi->opfi : fopi

        tensordot = self.ops.xp.tensordot
        ascontiguous = self.ops.xp.ascontiguousarray

        Yfp = tensordot(X, self.W, axes=[[1], [3]])
        Yfp += self.b

        def backward(dYp_ids, sgd=None):
            dYp, ids = dYp_ids
            Xf = X[ids]

            dXf = tensordot(dYp, self.W, axes=[[1, 2], [1,2]])
            dW = tensordot(dYp, Xf, axes=[[0], [0]])

            self.d_W += dW.transpose((2, 0, 1, 3))
            self.d_b += dYp.sum(axis=0)

            if sgd is not None:
                sgd(self._mem.weights, self._mem.gradient, key=self.id)
            return dXf
        return Yfp, backward


def Tok2Vec(width, embed_size, preprocess=None):
    cols = [ID, NORM, PREFIX, SUFFIX, SHAPE]
    with Model.define_operators({'>>': chain, '|': concatenate, '**': clone, '+': add}):
        norm = get_col(cols.index(NORM))   >> HashEmbed(width, embed_size, name='embed_lower')
        prefix = get_col(cols.index(PREFIX)) >> HashEmbed(width, embed_size//2, name='embed_prefix')
        suffix = get_col(cols.index(SUFFIX)) >> HashEmbed(width, embed_size//2, name='embed_suffix')
        shape = get_col(cols.index(SHAPE))   >> HashEmbed(width, embed_size//2, name='embed_shape')

        embed = (norm | prefix | suffix | shape )
        tok2vec = (
            with_flatten(
                asarray(Model.ops, dtype='uint64')
                >> embed
                >> Maxout(width, width*4, pieces=3)
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> Maxout(width, width*3)),
            pad=4)
        )
        if preprocess not in (False, None):
            tok2vec = preprocess >> tok2vec
        # Work around thinc API limitations :(. TODO: Revise in Thinc 7
        tok2vec.nO = width
        tok2vec.embed = embed
    return tok2vec


def asarray(ops, dtype):
    def forward(X, drop=0.):
        return ops.asarray(X, dtype=dtype), None
    return layerize(forward)


def foreach(layer):
    def forward(Xs, drop=0.):
        results = []
        backprops = []
        for X in Xs:
            result, bp = layer.begin_update(X, drop=drop)
            results.append(result)
            backprops.append(bp)
        def backward(d_results, sgd=None):
            dXs = []
            for d_result, backprop in zip(d_results, backprops):
                dXs.append(backprop(d_result, sgd))
            return dXs
        return results, backward
    model = layerize(forward)
    model._layers.append(layer)
    return model


def rebatch(size, layer):
    ops = layer.ops
    def forward(X, drop=0.):
        if X.shape[0] < size:
            return layer.begin_update(X)
        parts = _divide_array(X, size)
        results, bp_results = zip(*[layer.begin_update(p, drop=drop)
                                    for p in parts])
        y = ops.flatten(results)
        def backward(dy, sgd=None):
            d_parts = [bp(y, sgd=sgd) for bp, y in
                       zip(bp_results, _divide_array(dy, size))]
            try:
                dX = ops.flatten(d_parts)
            except TypeError:
                dX = None
            except ValueError:
                dX = None
            return dX
        return y, backward
    model = layerize(forward)
    model._layers.append(layer)
    return model


def _divide_array(X, size):
    parts = []
    index = 0
    while index < len(X):
        parts.append(X[index : index + size])
        index += size
    return parts


def get_col(idx):
    assert idx >= 0, idx
    def forward(X, drop=0.):
        assert idx >= 0, idx
        if isinstance(X, numpy.ndarray):
            ops = NumpyOps()
        else:
            ops = CupyOps()
        output = ops.xp.ascontiguousarray(X[:, idx], dtype=X.dtype)
        def backward(y, sgd=None):
            assert idx >= 0, idx
            dX = ops.allocate(X.shape)
            dX[:, idx] += y
            return dX
        return output, backward
    return layerize(forward)


def zero_init(model):
    def _hook(self, X, y=None):
        self.W.fill(0)
    model.on_data_hooks.append(_hook)
    return model


def doc2feats(cols=None):
    cols = [ID, NORM, PREFIX, SUFFIX, SHAPE]
    def forward(docs, drop=0.):
        feats = []
        for doc in docs:
            feats.append(doc.to_array(cols))
        return feats, None
    model = layerize(forward)
    model.cols = cols
    return model


def print_shape(prefix):
    def forward(X, drop=0.):
        return X, lambda dX, **kwargs: dX
    return layerize(forward)


@layerize
def get_token_vectors(tokens_attrs_vectors, drop=0.):
    ops = Model.ops
    tokens, attrs, vectors = tokens_attrs_vectors
    def backward(d_output, sgd=None):
        return (tokens, d_output)
    return vectors, backward


def fine_tune(model1, combine=None):
    def fine_tune_fwd(docs_tokvecs, drop=0.):
        docs, tokvecs = docs_tokvecs
        lengths = model.ops.asarray([len(doc) for doc in docs], dtype='i')
        X1, bp_X1 = model1.begin_update(docs)

        def fine_tune_bwd(d_output, sgd=None):
            bp_X1(model1.ops.flatten(d_output), sgd=sgd)
            return d_output

        return model1.ops.unflatten(X1+X2, lengths), fine_tune_bwd
    model = wrap(fine_tune_fwd)
    return model


@layerize
def flatten(seqs, drop=0.):
    if isinstance(seqs[0], numpy.ndarray):
        ops = NumpyOps()
    elif hasattr(CupyOps.xp, 'ndarray') and isinstance(seqs[0], CupyOps.xp.ndarray):
        ops = CupyOps()
    else:
        raise ValueError("Unable to flatten sequence of type %s" % type(seqs[0]))
    lengths = [len(seq) for seq in seqs]
    def finish_update(d_X, sgd=None):
        return ops.unflatten(d_X, lengths)
    X = ops.xp.vstack(seqs)
    return X, finish_update


@layerize
def logistic(X, drop=0.):
    xp = get_array_module(X)
    if not isinstance(X, xp.ndarray):
        X = xp.asarray(X)
    # Clip to range (-10, 10)
    X = xp.minimum(X, 10., X)
    X = xp.maximum(X, -10., X)
    Y = 1. / (1. + xp.exp(-X))
    def logistic_bwd(dY, sgd=None):
        dX = dY * (Y * (1-Y))
        return dX
    return Y, logistic_bwd


def zero_init(model):
    def _zero_init_impl(self, X, y):
        self.W.fill(0)
    model.on_data_hooks.append(_zero_init_impl)
    return model

@layerize
def preprocess_doc(docs, drop=0.):
    keys = [doc.to_array([LOWER]) for doc in docs]
    keys = [a[:, 0] for a in keys]
    ops = Model.ops
    lengths = ops.asarray([arr.shape[0] for arr in keys])
    keys = ops.xp.concatenate(keys)
    vals = ops.allocate(keys.shape[0]) + 1
    return (keys, vals, lengths), None


def build_tagger_model(nr_class, token_vector_width, **cfg):
    with Model.define_operators({'>>': chain, '+': add}):
        # Input: (doc, tensor) tuples
        embed_docs = ( 
            FeatureExtracter([NORM])
            >> flatten
            >> HashEmbed(token_vector_width, 1000)
        )
 
        model = ( 
            fine_tune(embed_docs)
            >> with_flatten(
                Maxout(token_vector_width, token_vector_width)
                >> Softmax(nr_class, token_vector_width)
            )
        )
    model.nI = None
    return model


def build_text_classifier(nr_class, width=64, **cfg):
    nr_vector = cfg.get('nr_vector', 200)
    with Model.define_operators({'>>': chain, '+': add, '|': concatenate, '**': clone}):
        embed_lower = HashEmbed(width, nr_vector, column=1)
        embed_prefix = HashEmbed(width//2, nr_vector, column=2)
        embed_suffix = HashEmbed(width//2, nr_vector, column=3)
        embed_shape = HashEmbed(width//2, nr_vector, column=4)

        cnn_model = (
            FeatureExtracter([ORTH, LOWER, PREFIX, SUFFIX, SHAPE])
            >> _flatten_add_lengths
            >> with_getitem(0,
                uniqued(
                  (embed_lower | embed_prefix | embed_suffix | embed_shape) 
                  >> Maxout(width, width+(width//2)*3))
                >> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
                >> Residual(ExtractWindow(nW=1) >> ReLu(width, width*3))
            )
            >> ParametricAttention(width,)
            >> Pooling(sum_pool)
            >> ReLu(width, width)
            >> zero_init(Affine(nr_class, width, drop_factor=0.0))
        )
        linear_model = (
            _preprocess_doc
            >> LinearModel(nr_class, drop_factor=0.)
        )

        model = (
            (linear_model | cnn_model)
            >> zero_init(Affine(nr_class, nr_class*2, drop_factor=0.0))
            >> logistic
        )
 
    model.lsuv = False
    return model