Merge pull request #10103 from svlandeg/refactor/parser-gpu

Consolidate parser refactor branches

setup.py

@@ -30,12 +30,9 @@ MOD_NAMES = [
     "spacy.vocab",
     "spacy.attrs",
     "spacy.kb",
-    "spacy.ml.parser_model",
     "spacy.morphology",
-    "spacy.pipeline.dep_parser",
     "spacy.pipeline.morphologizer",
     "spacy.pipeline.multitask",
-    "spacy.pipeline.ner",
     "spacy.pipeline.pipe",
     "spacy.pipeline.trainable_pipe",
     "spacy.pipeline.sentencizer",
@@ -205,7 +202,11 @@ def setup_package():
     for name in MOD_NAMES:
         mod_path = name.replace(".", "/") + ".pyx"
         ext = Extension(
-            name, [mod_path], language="c++", include_dirs=include_dirs, extra_compile_args=["-std=c++11"]
+            name,
+            [mod_path],
+            language="c++",
+            include_dirs=include_dirs,
+            extra_compile_args=["-std=c++11"],
         )
         ext_modules.append(ext)
     print("Cythonizing sources")

spacy/cli/templates/quickstart_training.jinja

@@ -81,12 +81,11 @@ grad_factor = 1.0
 factory = "parser"
 
 [components.parser.model]
-@architectures = "spacy.TransitionBasedParser.v2"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "parser"
 extra_state_tokens = false
 hidden_width = 128
 maxout_pieces = 3
-use_upper = false
 nO = null
 
 [components.parser.model.tok2vec]
@@ -102,12 +101,11 @@ grad_factor = 1.0
 factory = "ner"
 
 [components.ner.model]
-@architectures = "spacy.TransitionBasedParser.v2"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "ner"
 extra_state_tokens = false
 hidden_width = 64
 maxout_pieces = 2
-use_upper = false
 nO = null
 
 [components.ner.model.tok2vec]
@@ -259,12 +257,11 @@ width = ${components.tok2vec.model.encode.width}
 factory = "parser"
 
 [components.parser.model]
-@architectures = "spacy.TransitionBasedParser.v2"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "parser"
 extra_state_tokens = false
 hidden_width = 128
 maxout_pieces = 3
-use_upper = true
 nO = null
 
 [components.parser.model.tok2vec]
@@ -277,12 +274,11 @@ width = ${components.tok2vec.model.encode.width}
 factory = "ner"
 
 [components.ner.model]
-@architectures = "spacy.TransitionBasedParser.v2"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "ner"
 extra_state_tokens = false
 hidden_width = 64
 maxout_pieces = 2
-use_upper = true
 nO = null
 
 [components.ner.model.tok2vec]

spacy/ml/_precomputable_affine.py

@@ -1,158 +1,2 @@
-from thinc.api import Model, normal_init
+class PrecomputableAffine:
-
+    pass
-from ..util import registry
-
-
-@registry.layers("spacy.PrecomputableAffine.v1")
-def PrecomputableAffine(nO, nI, nF, nP, dropout=0.1):
-    model = Model(
-        "precomputable_affine",
-        forward,
-        init=init,
-        dims={"nO": nO, "nI": nI, "nF": nF, "nP": nP},
-        params={"W": None, "b": None, "pad": None},
-        attrs={"dropout_rate": dropout},
-    )
-    return model
-
-
-def forward(model, X, is_train):
-    nF = model.get_dim("nF")
-    nO = model.get_dim("nO")
-    nP = model.get_dim("nP")
-    nI = model.get_dim("nI")
-    W = model.get_param("W")
-    Yf = model.ops.gemm(X, W.reshape((nF * nO * nP, nI)), trans2=True)
-    Yf = Yf.reshape((Yf.shape[0], nF, nO, nP))
-    Yf = model.ops.xp.vstack((model.get_param("pad"), Yf))
-
-    def backward(dY_ids):
-        # This backprop is particularly tricky, because we get back a different
-        # thing from what we put out. We put out an array of shape:
-        # (nB, nF, nO, nP), and get back:
-        # (nB, nO, nP) and ids (nB, nF)
-        # The ids tell us the values of nF, so we would have:
-        #
-        # dYf = zeros((nB, nF, nO, nP))
-        # for b in range(nB):
-        #     for f in range(nF):
-        #         dYf[b, ids[b, f]] += dY[b]
-        #
-        # However, we avoid building that array for efficiency -- and just pass
-        # in the indices.
-        dY, ids = dY_ids
-        assert dY.ndim == 3
-        assert dY.shape[1] == nO, dY.shape
-        assert dY.shape[2] == nP, dY.shape
-        # nB = dY.shape[0]
-        model.inc_grad("pad", _backprop_precomputable_affine_padding(model, dY, ids))
-        Xf = X[ids]
-        Xf = Xf.reshape((Xf.shape[0], nF * nI))
-
-        model.inc_grad("b", dY.sum(axis=0))
-        dY = dY.reshape((dY.shape[0], nO * nP))
-
-        Wopfi = W.transpose((1, 2, 0, 3))
-        Wopfi = Wopfi.reshape((nO * nP, nF * nI))
-        dXf = model.ops.gemm(dY.reshape((dY.shape[0], nO * nP)), Wopfi)
-
-        dWopfi = model.ops.gemm(dY, Xf, trans1=True)
-        dWopfi = dWopfi.reshape((nO, nP, nF, nI))
-        # (o, p, f, i) --> (f, o, p, i)
-        dWopfi = dWopfi.transpose((2, 0, 1, 3))
-        model.inc_grad("W", dWopfi)
-        return dXf.reshape((dXf.shape[0], nF, nI))
-
-    return Yf, backward
-
-
-def _backprop_precomputable_affine_padding(model, dY, ids):
-    nB = dY.shape[0]
-    nF = model.get_dim("nF")
-    nP = model.get_dim("nP")
-    nO = model.get_dim("nO")
-    # Backprop the "padding", used as a filler for missing values.
-    # Values that are missing are set to -1, and each state vector could
-    # have multiple missing values. The padding has different values for
-    # different missing features. The gradient of the padding vector is:
-    #
-    # for b in range(nB):
-    #     for f in range(nF):
-    #         if ids[b, f] < 0:
-    #             d_pad[f] += dY[b]
-    #
-    # Which can be rewritten as:
-    #
-    # (ids < 0).T @ dY
-    mask = model.ops.asarray(ids < 0, dtype="f")
-    d_pad = model.ops.gemm(mask, dY.reshape(nB, nO * nP), trans1=True)
-    return d_pad.reshape((1, nF, nO, nP))
-
-
-def init(model, X=None, Y=None):
-    """This is like the 'layer sequential unit variance', but instead
-    of taking the actual inputs, we randomly generate whitened data.
-
-    Why's this all so complicated? We have a huge number of inputs,
-    and the maxout unit makes guessing the dynamics tricky. Instead
-    we set the maxout weights to values that empirically result in
-    whitened outputs given whitened inputs.
-    """
-    if model.has_param("W") and model.get_param("W").any():
-        return
-
-    nF = model.get_dim("nF")
-    nO = model.get_dim("nO")
-    nP = model.get_dim("nP")
-    nI = model.get_dim("nI")
-    W = model.ops.alloc4f(nF, nO, nP, nI)
-    b = model.ops.alloc2f(nO, nP)
-    pad = model.ops.alloc4f(1, nF, nO, nP)
-
-    ops = model.ops
-    W = normal_init(ops, W.shape, mean=float(ops.xp.sqrt(1.0 / nF * nI)))
-    pad = normal_init(ops, pad.shape, mean=1.0)
-    model.set_param("W", W)
-    model.set_param("b", b)
-    model.set_param("pad", pad)
-
-    ids = ops.alloc((5000, nF), dtype="f")
-    ids += ops.xp.random.uniform(0, 1000, ids.shape)
-    ids = ops.asarray(ids, dtype="i")
-    tokvecs = ops.alloc((5000, nI), dtype="f")
-    tokvecs += ops.xp.random.normal(loc=0.0, scale=1.0, size=tokvecs.size).reshape(
-        tokvecs.shape
-    )
-
-    def predict(ids, tokvecs):
-        # nS ids. nW tokvecs. Exclude the padding array.
-        hiddens = model.predict(tokvecs[:-1])  # (nW, f, o, p)
-        vectors = model.ops.alloc((ids.shape[0], nO * nP), dtype="f")
-        # need nS vectors
-        hiddens = hiddens.reshape((hiddens.shape[0] * nF, nO * nP))
-        model.ops.scatter_add(vectors, ids.flatten(), hiddens)
-        vectors = vectors.reshape((vectors.shape[0], nO, nP))
-        vectors += b
-        vectors = model.ops.asarray(vectors)
-        if nP >= 2:
-            return model.ops.maxout(vectors)[0]
-        else:
-            return vectors * (vectors >= 0)
-
-    tol_var = 0.01
-    tol_mean = 0.01
-    t_max = 10
-    W = model.get_param("W").copy()
-    b = model.get_param("b").copy()
-    for t_i in range(t_max):
-        acts1 = predict(ids, tokvecs)
-        var = model.ops.xp.var(acts1)
-        mean = model.ops.xp.mean(acts1)
-        if abs(var - 1.0) >= tol_var:
-            W /= model.ops.xp.sqrt(var)
-            model.set_param("W", W)
-        elif abs(mean) >= tol_mean:
-            b -= mean
-            model.set_param("b", b)
-        else:
-            break

spacy/ml/models/parser.py

@@ -1,23 +1,42 @@
-from typing import Optional, List, cast
+from typing import Optional, List, Tuple, Any
-from thinc.api import Model, chain, list2array, Linear, zero_init, use_ops
 from thinc.types import Floats2d
+from thinc.api import Model
 
 from ...errors import Errors
 from ...compat import Literal
 from ...util import registry
-from .._precomputable_affine import PrecomputableAffine
 from ..tb_framework import TransitionModel
-from ...tokens import Doc
+from ...tokens.doc import Doc
+
+TransitionSystem = Any  # TODO
+State = Any  # TODO
+
+
+@registry.architectures.register("spacy.TransitionBasedParser.v3")
+def transition_parser_v3(
+    tok2vec: Model[List[Doc], List[Floats2d]],
+    state_type: Literal["parser", "ner"],
+    extra_state_tokens: bool,
+    hidden_width: int,
+    maxout_pieces: int,
+    nO: Optional[int] = None,
+) -> Model:
+    return build_tb_parser_model(
+        tok2vec,
+        state_type,
+        extra_state_tokens,
+        hidden_width,
+        maxout_pieces,
+        nO=nO,
+    )
 
 
-@registry.architectures("spacy.TransitionBasedParser.v2")
 def build_tb_parser_model(
     tok2vec: Model[List[Doc], List[Floats2d]],
     state_type: Literal["parser", "ner"],
     extra_state_tokens: bool,
     hidden_width: int,
     maxout_pieces: int,
-    use_upper: bool,
     nO: Optional[int] = None,
 ) -> Model:
     """
@@ -51,14 +70,7 @@ def build_tb_parser_model(
         feature sets (for the NER) or 13 (for the parser).
     hidden_width (int): The width of the hidden layer.
     maxout_pieces (int): How many pieces to use in the state prediction layer.
-        Recommended values are 1, 2 or 3. If 1, the maxout non-linearity
+        Recommended values are 1, 2 or 3.
-        is replaced with a ReLu non-linearity if use_upper=True, and no
-        non-linearity if use_upper=False.
-    use_upper (bool): Whether to use an additional hidden layer after the state
-        vector in order to predict the action scores. It is recommended to set
-        this to False for large pretrained models such as transformers, and True
-        for smaller networks. The upper layer is computed on CPU, which becomes
-        a bottleneck on larger GPU-based models, where it's also less necessary.
     nO (int or None): The number of actions the model will predict between.
         Usually inferred from data at the beginning of training, or loaded from
         disk.
@@ -69,106 +81,11 @@ def build_tb_parser_model(
         nr_feature_tokens = 6 if extra_state_tokens else 3
     else:
         raise ValueError(Errors.E917.format(value=state_type))
-    t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
+    return TransitionModel(
-    tok2vec = chain(
+        tok2vec=tok2vec,
-        tok2vec,
+        state_tokens=nr_feature_tokens,
-        cast(Model[List["Floats2d"], Floats2d], list2array()),
+        hidden_width=hidden_width,
-        Linear(hidden_width, t2v_width),
+        maxout_pieces=maxout_pieces,
+        nO=nO,
+        unseen_classes=set(),
     )
-    tok2vec.set_dim("nO", hidden_width)
-    lower = _define_lower(
-        nO=hidden_width if use_upper else nO,
-        nF=nr_feature_tokens,
-        nI=tok2vec.get_dim("nO"),
-        nP=maxout_pieces,
-    )
-    upper = None
-    if use_upper:
-        with use_ops("cpu"):
-            # Initialize weights at zero, as it's a classification layer.
-            upper = _define_upper(nO=nO, nI=None)
-    return TransitionModel(tok2vec, lower, upper, resize_output)
-
-
-def _define_upper(nO, nI):
-    return Linear(nO=nO, nI=nI, init_W=zero_init)
-
-
-def _define_lower(nO, nF, nI, nP):
-    return PrecomputableAffine(nO=nO, nF=nF, nI=nI, nP=nP)
-
-
-def resize_output(model, new_nO):
-    if model.attrs["has_upper"]:
-        return _resize_upper(model, new_nO)
-    return _resize_lower(model, new_nO)
-
-
-def _resize_upper(model, new_nO):
-    upper = model.get_ref("upper")
-    if upper.has_dim("nO") is None:
-        upper.set_dim("nO", new_nO)
-        return model
-    elif new_nO == upper.get_dim("nO"):
-        return model
-
-    smaller = upper
-    nI = smaller.maybe_get_dim("nI")
-    with use_ops("cpu"):
-        larger = _define_upper(nO=new_nO, nI=nI)
-    # it could be that the model is not initialized yet, then skip this bit
-    if smaller.has_param("W"):
-        larger_W = larger.ops.alloc2f(new_nO, nI)
-        larger_b = larger.ops.alloc1f(new_nO)
-        smaller_W = smaller.get_param("W")
-        smaller_b = smaller.get_param("b")
-        # Weights are stored in (nr_out, nr_in) format, so we're basically
-        # just adding rows here.
-        if smaller.has_dim("nO"):
-            old_nO = smaller.get_dim("nO")
-            larger_W[:old_nO] = smaller_W
-            larger_b[:old_nO] = smaller_b
-            for i in range(old_nO, new_nO):
-                model.attrs["unseen_classes"].add(i)
-
-        larger.set_param("W", larger_W)
-        larger.set_param("b", larger_b)
-    model._layers[-1] = larger
-    model.set_ref("upper", larger)
-    return model
-
-
-def _resize_lower(model, new_nO):
-    lower = model.get_ref("lower")
-    if lower.has_dim("nO") is None:
-        lower.set_dim("nO", new_nO)
-        return model
-
-    smaller = lower
-    nI = smaller.maybe_get_dim("nI")
-    nF = smaller.maybe_get_dim("nF")
-    nP = smaller.maybe_get_dim("nP")
-    larger = _define_lower(nO=new_nO, nI=nI, nF=nF, nP=nP)
-    # it could be that the model is not initialized yet, then skip this bit
-    if smaller.has_param("W"):
-        larger_W = larger.ops.alloc4f(nF, new_nO, nP, nI)
-        larger_b = larger.ops.alloc2f(new_nO, nP)
-        larger_pad = larger.ops.alloc4f(1, nF, new_nO, nP)
-        smaller_W = smaller.get_param("W")
-        smaller_b = smaller.get_param("b")
-        smaller_pad = smaller.get_param("pad")
-        # Copy the old weights and padding into the new layer
-        if smaller.has_dim("nO"):
-            old_nO = smaller.get_dim("nO")
-            larger_W[:, 0:old_nO, :, :] = smaller_W
-            larger_pad[:, :, 0:old_nO, :] = smaller_pad
-            larger_b[0:old_nO, :] = smaller_b
-            for i in range(old_nO, new_nO):
-                model.attrs["unseen_classes"].add(i)
-
-        larger.set_param("W", larger_W)
-        larger.set_param("b", larger_b)
-        larger.set_param("pad", larger_pad)
-    model._layers[1] = larger
-    model.set_ref("lower", larger)
-    return model

spacy/ml/parser_model.pxd

@@ -1,48 +0,0 @@
-from libc.string cimport memset, memcpy
-from ..typedefs cimport weight_t, hash_t
-from ..pipeline._parser_internals._state cimport StateC
-
-
-cdef struct SizesC:
-    int states
-    int classes
-    int hiddens
-    int pieces
-    int feats
-    int embed_width
-
-
-cdef struct WeightsC:
-    const float* feat_weights
-    const float* feat_bias
-    const float* hidden_bias
-    const float* hidden_weights
-    const float* seen_classes
-
-
-cdef struct ActivationsC:
-    int* token_ids
-    float* unmaxed
-    float* scores
-    float* hiddens
-    int* is_valid
-    int _curr_size
-    int _max_size
-
-
-cdef WeightsC get_c_weights(model) except *
-
-cdef SizesC get_c_sizes(model, int batch_size) except *
-
-cdef ActivationsC alloc_activations(SizesC n) nogil
-
-cdef void free_activations(const ActivationsC* A) nogil
-
-cdef void predict_states(ActivationsC* A, StateC** states,
-        const WeightsC* W, SizesC n) nogil
- 
-cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil
-
-cdef void cpu_log_loss(float* d_scores,
-        const float* costs, const int* is_valid, const float* scores, int O) nogil
- 

spacy/ml/parser_model.pyx

@@ -1,489 +0,0 @@
-# cython: infer_types=True, cdivision=True, boundscheck=False
-cimport numpy as np
-from libc.math cimport exp
-from libc.string cimport memset, memcpy
-from libc.stdlib cimport calloc, free, realloc
-from thinc.backends.linalg cimport Vec, VecVec
-cimport blis.cy
-
-import numpy
-import numpy.random
-from thinc.api import Model, CupyOps, NumpyOps
-
-from .. import util
-from ..typedefs cimport weight_t, class_t, hash_t
-from ..pipeline._parser_internals.stateclass cimport StateClass
-
-
-cdef WeightsC get_c_weights(model) except *:
-    cdef WeightsC output
-    cdef precompute_hiddens state2vec = model.state2vec
-    output.feat_weights = state2vec.get_feat_weights()
-    output.feat_bias = <const float*>state2vec.bias.data
-    cdef np.ndarray vec2scores_W
-    cdef np.ndarray vec2scores_b
-    if model.vec2scores is None:
-        output.hidden_weights = NULL
-        output.hidden_bias = NULL
-    else:
-        vec2scores_W = model.vec2scores.get_param("W")
-        vec2scores_b = model.vec2scores.get_param("b")
-        output.hidden_weights = <const float*>vec2scores_W.data
-        output.hidden_bias = <const float*>vec2scores_b.data
-    cdef np.ndarray class_mask = model._class_mask
-    output.seen_classes = <const float*>class_mask.data
-    return output
-
-
-cdef SizesC get_c_sizes(model, int batch_size) except *:
-    cdef SizesC output
-    output.states = batch_size
-    if model.vec2scores is None:
-        output.classes = model.state2vec.get_dim("nO")
-    else:
-        output.classes = model.vec2scores.get_dim("nO")
-    output.hiddens = model.state2vec.get_dim("nO")
-    output.pieces = model.state2vec.get_dim("nP")
-    output.feats = model.state2vec.get_dim("nF")
-    output.embed_width = model.tokvecs.shape[1]
-    return output
-
-
-cdef ActivationsC alloc_activations(SizesC n) nogil:
-    cdef ActivationsC A
-    memset(&A, 0, sizeof(A))
-    resize_activations(&A, n)
-    return A
-
-
-cdef void free_activations(const ActivationsC* A) nogil:
-    free(A.token_ids)
-    free(A.scores)
-    free(A.unmaxed)
-    free(A.hiddens)
-    free(A.is_valid)
-
-
-cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
-    if n.states <= A._max_size:
-        A._curr_size = n.states
-        return
-    if A._max_size == 0:
-        A.token_ids = <int*>calloc(n.states * n.feats, sizeof(A.token_ids[0]))
-        A.scores = <float*>calloc(n.states * n.classes, sizeof(A.scores[0]))
-        A.unmaxed = <float*>calloc(n.states * n.hiddens * n.pieces, sizeof(A.unmaxed[0]))
-        A.hiddens = <float*>calloc(n.states * n.hiddens, sizeof(A.hiddens[0]))
-        A.is_valid = <int*>calloc(n.states * n.classes, sizeof(A.is_valid[0]))
-        A._max_size = n.states
-    else:
-        A.token_ids = <int*>realloc(A.token_ids,
-            n.states * n.feats * sizeof(A.token_ids[0]))
-        A.scores = <float*>realloc(A.scores,
-            n.states * n.classes * sizeof(A.scores[0]))
-        A.unmaxed = <float*>realloc(A.unmaxed,
-            n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]))
-        A.hiddens = <float*>realloc(A.hiddens,
-            n.states * n.hiddens * sizeof(A.hiddens[0]))
-        A.is_valid = <int*>realloc(A.is_valid,
-            n.states * n.classes * sizeof(A.is_valid[0]))
-        A._max_size = n.states
-    A._curr_size = n.states
-
-
-cdef void predict_states(ActivationsC* A, StateC** states,
-        const WeightsC* W, SizesC n) nogil:
-    cdef double one = 1.0
-    resize_activations(A, n)
-    for i in range(n.states):
-        states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
-    memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float))
-    memset(A.hiddens, 0, n.states * n.hiddens * sizeof(float))
-    sum_state_features(A.unmaxed,
-        W.feat_weights, A.token_ids, n.states, n.feats, n.hiddens * n.pieces)
-    for i in range(n.states):
-        VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces],
-            W.feat_bias, 1., n.hiddens * n.pieces)
-        for j in range(n.hiddens):
-            index = i * n.hiddens * n.pieces + j * n.pieces
-            which = Vec.arg_max(&A.unmaxed[index], n.pieces)
-            A.hiddens[i*n.hiddens + j] = A.unmaxed[index + which]
-    memset(A.scores, 0, n.states * n.classes * sizeof(float))
-    if W.hidden_weights == NULL:
-        memcpy(A.scores, A.hiddens, n.states * n.classes * sizeof(float))
-    else:
-        # Compute hidden-to-output
-        blis.cy.gemm(blis.cy.NO_TRANSPOSE, blis.cy.TRANSPOSE,
-            n.states, n.classes, n.hiddens, one,
-            <float*>A.hiddens, n.hiddens, 1,
-            <float*>W.hidden_weights, n.hiddens, 1,
-            one,
-            <float*>A.scores, n.classes, 1)
-        # Add bias
-        for i in range(n.states):
-            VecVec.add_i(&A.scores[i*n.classes],
-                W.hidden_bias, 1., n.classes)
-    # Set unseen classes to minimum value
-    i = 0
-    min_ = A.scores[0]
-    for i in range(1, n.states * n.classes):
-        if A.scores[i] < min_:
-            min_ = A.scores[i]
-    for i in range(n.states):
-        for j in range(n.classes):
-            if not W.seen_classes[j]:
-                A.scores[i*n.classes+j] = min_
-
-
-cdef void sum_state_features(float* output,
-        const float* cached, const int* token_ids, int B, int F, int O) nogil:
-    cdef int idx, b, f, i
-    cdef const float* feature
-    padding = cached
-    cached += F * O
-    cdef int id_stride = F*O
-    cdef float one = 1.
-    for b in range(B):
-        for f in range(F):
-            if token_ids[f] < 0:
-                feature = &padding[f*O]
-            else:
-                idx = token_ids[f] * id_stride + f*O
-                feature = &cached[idx]
-            blis.cy.axpyv(blis.cy.NO_CONJUGATE, O, one,
-                <float*>feature, 1,
-                &output[b*O], 1)
-        token_ids += F
-
-
-cdef void cpu_log_loss(float* d_scores,
-        const float* costs, const int* is_valid, const float* scores,
-        int O) nogil:
-    """Do multi-label log loss"""
-    cdef double max_, gmax, Z, gZ
-    best = arg_max_if_gold(scores, costs, is_valid, O)
-    guess = Vec.arg_max(scores, O)
-    if best == -1 or guess == -1:
-        # These shouldn't happen, but if they do, we want to make sure we don't
-        # cause an OOB access.
-        return
-    Z = 1e-10
-    gZ = 1e-10
-    max_ = scores[guess]
-    gmax = scores[best]
-    for i in range(O):
-        Z += exp(scores[i] - max_)
-        if costs[i] <= costs[best]:
-            gZ += exp(scores[i] - gmax)
-    for i in range(O):
-        if costs[i] <= costs[best]:
-            d_scores[i] = (exp(scores[i]-max_) / Z) - (exp(scores[i]-gmax)/gZ)
-        else:
-            d_scores[i] = exp(scores[i]-max_) / Z
-
-
-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
-
-
-
-class ParserStepModel(Model):
-    def __init__(self, docs, layers, *, has_upper, unseen_classes=None, train=True,
-            dropout=0.1):
-        Model.__init__(self, name="parser_step_model", forward=step_forward)
-        self.attrs["has_upper"] = has_upper
-        self.attrs["dropout_rate"] = dropout
-        self.tokvecs, self.bp_tokvecs = layers[0](docs, is_train=train)
-        if layers[1].get_dim("nP") >= 2:
-            activation = "maxout"
-        elif has_upper:
-            activation = None
-        else:
-            activation = "relu"
-        self.state2vec = precompute_hiddens(len(docs), self.tokvecs, layers[1],
-                                            activation=activation, train=train)
-        if has_upper:
-            self.vec2scores = layers[-1]
-        else:
-            self.vec2scores = None
-        self.cuda_stream = util.get_cuda_stream(non_blocking=True)
-        self.backprops = []
-        self._class_mask = numpy.zeros((self.nO,), dtype='f')
-        self._class_mask.fill(1)
-        if unseen_classes is not None:
-            for class_ in unseen_classes:
-                self._class_mask[class_] = 0.
-
-    def clear_memory(self):
-        del self.tokvecs
-        del self.bp_tokvecs
-        del self.state2vec
-        del self.backprops
-        del self._class_mask
-
-    @property
-    def nO(self):
-        if self.attrs["has_upper"]:
-            return self.vec2scores.get_dim("nO")
-        else:
-            return self.state2vec.get_dim("nO")
-
-    def class_is_unseen(self, class_):
-        return self._class_mask[class_]
-
-    def mark_class_unseen(self, class_):
-        self._class_mask[class_] = 0
-
-    def mark_class_seen(self, class_):
-        self._class_mask[class_] = 1
-
-    def get_token_ids(self, states):
-        cdef StateClass state
-        states = [state for state in states if not state.is_final()]
-        cdef np.ndarray ids = numpy.zeros((len(states), self.state2vec.nF),
-                                          dtype='i', order='C')
-        ids.fill(-1)
-        c_ids = <int*>ids.data
-        for state in states:
-            state.c.set_context_tokens(c_ids, ids.shape[1])
-            c_ids += ids.shape[1]
-        return ids
-
-    def backprop_step(self, token_ids, d_vector, get_d_tokvecs):
-        if isinstance(self.state2vec.ops, CupyOps) \
-        and not isinstance(token_ids, self.state2vec.ops.xp.ndarray):
-            # Move token_ids and d_vector to GPU, asynchronously
-            self.backprops.append((
-                util.get_async(self.cuda_stream, token_ids),
-                util.get_async(self.cuda_stream, d_vector),
-                get_d_tokvecs
-            ))
-        else:
-            self.backprops.append((token_ids, d_vector, get_d_tokvecs))
-
-
-    def finish_steps(self, golds):
-        # Add a padding vector to the d_tokvecs gradient, so that missing
-        # values don't affect the real gradient.
-        d_tokvecs = self.ops.alloc((self.tokvecs.shape[0]+1, self.tokvecs.shape[1]))
-        # Tells CUDA to block, so our async copies complete.
-        if self.cuda_stream is not None:
-            self.cuda_stream.synchronize()
-        for ids, d_vector, bp_vector in self.backprops:
-            d_state_features = bp_vector((d_vector, ids))
-            ids = ids.flatten()
-            d_state_features = d_state_features.reshape(
-                (ids.size, d_state_features.shape[2]))
-            self.ops.scatter_add(d_tokvecs, ids,
-                d_state_features)
-        # Padded -- see update()
-        self.bp_tokvecs(d_tokvecs[:-1])
-        return d_tokvecs
-
-NUMPY_OPS = NumpyOps()
-
-def step_forward(model: ParserStepModel, states, is_train):
-    token_ids = model.get_token_ids(states)
-    vector, get_d_tokvecs = model.state2vec(token_ids, is_train)
-    mask = None
-    if model.attrs["has_upper"]:
-        dropout_rate = model.attrs["dropout_rate"]
-        if is_train and dropout_rate > 0:
-            mask = NUMPY_OPS.get_dropout_mask(vector.shape, 0.1)
-            vector *= mask
-        scores, get_d_vector = model.vec2scores(vector, is_train)
-    else:
-        scores = NumpyOps().asarray(vector)
-        get_d_vector = lambda d_scores: d_scores
-    # If the class is unseen, make sure its score is minimum
-    scores[:, model._class_mask == 0] = numpy.nanmin(scores)
-
-    def backprop_parser_step(d_scores):
-        # Zero vectors for unseen classes
-        d_scores *= model._class_mask
-        d_vector = get_d_vector(d_scores)
-        if mask is not None:
-            d_vector *= mask
-        model.backprop_step(token_ids, d_vector, get_d_tokvecs)
-        return None
-    return scores, backprop_parser_step
-
-
-cdef class precompute_hiddens:
-    """Allow a model to be "primed" by pre-computing input features in bulk.
-
-    This is used for the parser, where we want to take a batch of documents,
-    and compute vectors for each (token, position) pair. These vectors can then
-    be reused, especially for beam-search.
-
-    Let's say we're using 12 features for each state, e.g. word at start of
-    buffer, three words on stack, their children, etc. In the normal arc-eager
-    system, a document of length N is processed in 2*N states. This means we'll
-    create 2*N*12 feature vectors --- but if we pre-compute, we only need
-    N*12 vector computations. The saving for beam-search is much better:
-    if we have a beam of k, we'll normally make 2*N*12*K computations --
-    so we can save the factor k. This also gives a nice CPU/GPU division:
-    we can do all our hard maths up front, packed into large multiplications,
-    and do the hard-to-program parsing on the CPU.
-    """
-    cdef readonly int nF, nO, nP
-    cdef bint _is_synchronized
-    cdef public object ops
-    cdef public object numpy_ops
-    cdef np.ndarray _features
-    cdef np.ndarray _cached
-    cdef np.ndarray bias
-    cdef object _cuda_stream
-    cdef object _bp_hiddens
-    cdef object activation
-
-    def __init__(self, batch_size, tokvecs, lower_model, cuda_stream=None,
-                 activation="maxout", train=False):
-        gpu_cached, bp_features = lower_model(tokvecs, train)
-        cdef np.ndarray cached
-        if not isinstance(gpu_cached, numpy.ndarray):
-            # Note the passing of cuda_stream here: it lets
-            # cupy make the copy asynchronously.
-            # We then have to block before first use.
-            cached = gpu_cached.get(stream=cuda_stream)
-        else:
-            cached = gpu_cached
-        if not isinstance(lower_model.get_param("b"), numpy.ndarray):
-            self.bias = lower_model.get_param("b").get(stream=cuda_stream)
-        else:
-            self.bias = lower_model.get_param("b")
-        self.nF = cached.shape[1]
-        if lower_model.has_dim("nP"):
-            self.nP = lower_model.get_dim("nP")
-        else:
-            self.nP = 1
-        self.nO = cached.shape[2]
-        self.ops = lower_model.ops
-        self.numpy_ops = NumpyOps()
-        assert activation in (None, "relu", "maxout")
-        self.activation = activation
-        self._is_synchronized = False
-        self._cuda_stream = cuda_stream
-        self._cached = cached
-        self._bp_hiddens = bp_features
-
-    cdef const float* get_feat_weights(self) except NULL:
-        if not self._is_synchronized and self._cuda_stream is not None:
-            self._cuda_stream.synchronize()
-            self._is_synchronized = True
-        return <float*>self._cached.data
-
-    def has_dim(self, name):
-        if name == "nF":
-            return self.nF if self.nF is not None else True
-        elif name == "nP":
-            return self.nP if self.nP is not None else True
-        elif name == "nO":
-            return self.nO if self.nO is not None else True
-        else:
-            return False
-
-    def get_dim(self, name):
-        if name == "nF":
-            return self.nF
-        elif name == "nP":
-            return self.nP
-        elif name == "nO":
-            return self.nO
-        else:
-            raise ValueError(f"Dimension {name} invalid -- only nO, nF, nP")
-
-    def set_dim(self, name, value):
-        if name == "nF":
-            self.nF = value
-        elif name == "nP":
-            self.nP = value
-        elif name == "nO":
-            self.nO = value
-        else:
-            raise ValueError(f"Dimension {name} invalid -- only nO, nF, nP")
-
-    def __call__(self, X, bint is_train):
-        if is_train:
-            return self.begin_update(X)
-        else:
-            return self.predict(X), lambda X: X
-
-    def predict(self, X):
-        return self.begin_update(X)[0]
-
-    def begin_update(self, token_ids):
-        cdef np.ndarray state_vector = numpy.zeros(
-            (token_ids.shape[0], self.nO, self.nP), dtype='f')
-        # This is tricky, but (assuming GPU available);
-        # - Input to forward on CPU
-        # - Output from forward on CPU
-        # - Input to backward on GPU!
-        # - Output from backward on GPU
-        bp_hiddens = self._bp_hiddens
-
-        feat_weights = self.get_feat_weights()
-        cdef int[:, ::1] ids = token_ids
-        sum_state_features(<float*>state_vector.data,
-            feat_weights, &ids[0,0],
-            token_ids.shape[0], self.nF, self.nO*self.nP)
-        state_vector += self.bias
-        state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
-
-        def backward(d_state_vector_ids):
-            d_state_vector, token_ids = d_state_vector_ids
-            d_state_vector = bp_nonlinearity(d_state_vector)
-            d_tokens = bp_hiddens((d_state_vector, token_ids))
-            return d_tokens
-        return state_vector, backward
-
-    def _nonlinearity(self, state_vector):
-        if self.activation == "maxout":
-            return self._maxout_nonlinearity(state_vector)
-        else:
-            return self._relu_nonlinearity(state_vector)
-
-    def _maxout_nonlinearity(self, state_vector):
-        state_vector, mask = self.numpy_ops.maxout(state_vector)
-        # We're outputting to CPU, but we need this variable on GPU for the
-        # backward pass.
-        mask = self.ops.asarray(mask)
-
-        def backprop_maxout(d_best):
-            return self.ops.backprop_maxout(d_best, mask, self.nP)
-        
-        return state_vector, backprop_maxout
-
-    def _relu_nonlinearity(self, state_vector):
-        state_vector = state_vector.reshape((state_vector.shape[0], -1))
-        mask = state_vector >= 0.
-        state_vector *= mask
-        # We're outputting to CPU, but we need this variable on GPU for the
-        # backward pass.
-        mask = self.ops.asarray(mask)
-
-        def backprop_relu(d_best):
-            d_best *= mask
-            return d_best.reshape((d_best.shape + (1,)))
- 
-        return state_vector, backprop_relu

spacy/ml/tb_framework.py

@@ -1,50 +1,438 @@
-from thinc.api import Model, noop
+from typing import List, Tuple, Any, Optional, cast
-from .parser_model import ParserStepModel
+from thinc.api import Ops, Model, normal_init, chain, list2array, Linear
+from thinc.api import uniform_init, glorot_uniform_init, zero_init
+from thinc.types import Floats1d, Floats2d, Floats3d, Ints2d, Floats4d
+import numpy
+from ..tokens.doc import Doc
 from ..util import registry
 
 
-@registry.layers("spacy.TransitionModel.v1")
+TransitionSystem = Any  # TODO
+State = Any  # TODO
+
+
+@registry.layers("spacy.TransitionModel.v2")
 def TransitionModel(
-    tok2vec, lower, upper, resize_output, dropout=0.2, unseen_classes=set()
+    *,
-):
+    tok2vec: Model[List[Doc], List[Floats2d]],
-    """Set up a stepwise transition-based model"""
+    state_tokens: int,
-    if upper is None:
+    hidden_width: int,
-        has_upper = False
+    maxout_pieces: int,
-        upper = noop()
+    nO: Optional[int] = None,
-    else:
+    unseen_classes=set(),
-        has_upper = True
+) -> Model[Tuple[List[Doc], TransitionSystem], List[Tuple[State, List[Floats2d]]]]:
-    # don't define nO for this object, because we can't dynamically change it
+    """Set up a transition-based parsing model, using a maxout hidden
+    layer and a linear output layer.
+    """
+    t2v_width = tok2vec.get_dim("nO") if tok2vec.has_dim("nO") else None
+    tok2vec_projected = chain(tok2vec, list2array(), Linear(hidden_width, t2v_width))  # type: ignore
+    tok2vec_projected.set_dim("nO", hidden_width)
+
     return Model(
         name="parser_model",
         forward=forward,
-        dims={"nI": tok2vec.maybe_get_dim("nI")},
-        layers=[tok2vec, lower, upper],
-        refs={"tok2vec": tok2vec, "lower": lower, "upper": upper},
         init=init,
+        layers=[tok2vec_projected],
+        refs={"tok2vec": tok2vec_projected},
+        params={
+            "lower_W": None,  # Floats2d W for the hidden layer
+            "lower_b": None,  # Floats1d bias for the hidden layer
+            "lower_pad": None,  # Floats1d padding for the hidden layer
+            "upper_W": None,  # Floats2d W for the output layer
+            "upper_b": None,  # Floats1d bias for the output layer
+        },
+        dims={
+            "nO": None,  # Output size
+            "nP": maxout_pieces,
+            "nH": hidden_width,
+            "nI": tok2vec_projected.maybe_get_dim("nO"),
+            "nF": state_tokens,
+        },
         attrs={
-            "has_upper": has_upper,
             "unseen_classes": set(unseen_classes),
             "resize_output": resize_output,
         },
     )
 
 
-def forward(model, X, is_train):
+def resize_output(model: Model, new_nO: int) -> Model:
-    step_model = ParserStepModel(
+    old_nO = model.maybe_get_dim("nO")
-        X,
+    if old_nO is None:
-        model.layers,
+        model.set_dim("nO", new_nO)
-        unseen_classes=model.attrs["unseen_classes"],
+        return model
-        train=is_train,
+    elif new_nO <= old_nO:
-        has_upper=model.attrs["has_upper"],
+        return model
+    elif model.has_param("upper_W"):
+        nH = model.get_dim("nH")
+        new_W = model.ops.alloc2f(new_nO, nH)
+        new_b = model.ops.alloc1f(new_nO)
+        old_W = model.get_param("upper_W")
+        old_b = model.get_param("upper_b")
+        new_W[:old_nO] = old_W  # type: ignore
+        new_b[:old_nO] = old_b  # type: ignore
+        for i in range(old_nO, new_nO):
+            model.attrs["unseen_classes"].add(i)
+        model.set_param("upper_W", new_W)
+        model.set_param("upper_b", new_b)
+    # TODO: Avoid this private intrusion
+    model._dims["nO"] = new_nO
+    if model.has_grad("upper_W"):
+        model.set_grad("upper_W", model.get_param("upper_W") * 0)
+    if model.has_grad("upper_b"):
+        model.set_grad("upper_b", model.get_param("upper_b") * 0)
+    return model
+
+
+def init(
+    model,
+    X: Optional[Tuple[List[Doc], TransitionSystem]] = None,
+    Y: Optional[Tuple[List[State], List[Floats2d]]] = None,
+):
+    if X is not None:
+        docs, moves = X
+        model.get_ref("tok2vec").initialize(X=docs)
+    else:
+        model.get_ref("tok2vec").initialize()
+    inferred_nO = _infer_nO(Y)
+    if inferred_nO is not None:
+        current_nO = model.maybe_get_dim("nO")
+        if current_nO is None:
+            model.set_dim("nO", inferred_nO)
+        elif current_nO != inferred_nO:
+            model.attrs["resize_output"](model, inferred_nO)
+    nO = model.get_dim("nO")
+    nP = model.get_dim("nP")
+    nH = model.get_dim("nH")
+    nI = model.get_dim("nI")
+    nF = model.get_dim("nF")
+    ops = model.ops
+
+    Wl = ops.alloc2f(nH * nP, nF * nI)
+    bl = ops.alloc1f(nH * nP)
+    padl = ops.alloc1f(nI)
+    Wu = ops.alloc2f(nO, nH)
+    bu = ops.alloc1f(nO)
+    Wu = zero_init(ops, Wu.shape)
+    # Wl = zero_init(ops, Wl.shape)
+    Wl = glorot_uniform_init(ops, Wl.shape)
+    padl = uniform_init(ops, padl.shape)  # type: ignore
+    # TODO: Experiment with whether better to initialize upper_W
+    model.set_param("lower_W", Wl)
+    model.set_param("lower_b", bl)
+    model.set_param("lower_pad", padl)
+    model.set_param("upper_W", Wu)
+    model.set_param("upper_b", bu)
+    # model = _lsuv_init(model)
+    return model
+
+
+def forward(model, docs_moves: Tuple[List[Doc], TransitionSystem], is_train: bool):
+    nF = model.get_dim("nF")
+    tok2vec = model.get_ref("tok2vec")
+    lower_pad = model.get_param("lower_pad")
+    lower_W = model.get_param("lower_W")
+    lower_b = model.get_param("lower_b")
+    upper_W = model.get_param("upper_W")
+    upper_b = model.get_param("upper_b")
+    nH = model.get_dim("nH")
+    nP = model.get_dim("nP")
+    nO = model.get_dim("nO")
+    nI = model.get_dim("nI")
+
+    ops = model.ops
+    docs, moves = docs_moves
+    states = moves.init_batch(docs)
+    tokvecs, backprop_tok2vec = tok2vec(docs, is_train)
+    tokvecs = model.ops.xp.vstack((tokvecs, lower_pad))
+    feats, backprop_feats = _forward_precomputable_affine(model, tokvecs, is_train)
+    all_ids = []
+    all_which = []
+    all_statevecs = []
+    all_scores = []
+    next_states = [s for s in states if not s.is_final()]
+    unseen_mask = _get_unseen_mask(model)
+    ids = numpy.zeros((len(states), nF), dtype="i")
+    arange = model.ops.xp.arange(nF)
+    while next_states:
+        ids = ids[: len(next_states)]
+        for i, state in enumerate(next_states):
+            state.set_context_tokens(ids, i, nF)
+        # Sum the state features, add the bias and apply the activation (maxout)
+        # to create the state vectors.
+        preacts2f = feats[ids, arange].sum(axis=1)  # type: ignore
+        preacts2f += lower_b
+        preacts = model.ops.reshape3f(preacts2f, preacts2f.shape[0], nH, nP)
+        assert preacts.shape[0] == len(next_states), preacts.shape
+        statevecs, which = ops.maxout(preacts)
+        # Multiply the state-vector by the scores weights and add the bias,
+        # to get the logits.
+        scores = model.ops.gemm(statevecs, upper_W, trans2=True)
+        scores += upper_b
+        scores[:, unseen_mask == 0] = model.ops.xp.nanmin(scores)
+        # Transition the states, filtering out any that are finished.
+        next_states = moves.transition_states(next_states, scores)
+        all_scores.append(scores)
+        if is_train:
+            # Remember intermediate results for the backprop.
+            all_ids.append(ids.copy())
+            all_statevecs.append(statevecs)
+            all_which.append(which)
+
+    def backprop_parser(d_states_d_scores):
+        d_tokvecs = model.ops.alloc2f(tokvecs.shape[0], tokvecs.shape[1])
+        ids = model.ops.xp.vstack(all_ids)
+        which = ops.xp.vstack(all_which)
+        statevecs = model.ops.xp.vstack(all_statevecs)
+        _, d_scores = d_states_d_scores
+        if model.attrs.get("unseen_classes"):
+            # If we have a negative gradient (i.e. the probability should
+            # increase) on any classes we filtered out as unseen, mark
+            # them as seen.
+            for clas in set(model.attrs["unseen_classes"]):
+                if (d_scores[:, clas] < 0).any():
+                    model.attrs["unseen_classes"].remove(clas)
+        d_scores *= unseen_mask
+        # Calculate the gradients for the parameters of the upper layer.
+        # The weight gemm is (nS, nO) @ (nS, nH).T
+        model.inc_grad("upper_b", d_scores.sum(axis=0))
+        model.inc_grad("upper_W", model.ops.gemm(d_scores, statevecs, trans1=True))
+        # Now calculate d_statevecs, by backproping through the upper linear layer.
+        # This gemm is (nS, nO) @ (nO, nH)
+        d_statevecs = model.ops.gemm(d_scores, upper_W)
+        # Backprop through the maxout activation
+        d_preacts = model.ops.backprop_maxout(d_statevecs, which, nP)
+        d_preacts2f = model.ops.reshape2f(d_preacts, d_preacts.shape[0], nH * nP)
+        model.inc_grad("lower_b", d_preacts2f.sum(axis=0))
+        # We don't need to backprop the summation, because we pass back the IDs instead
+        d_state_features = backprop_feats((d_preacts2f, ids))
+        d_tokvecs = model.ops.alloc2f(tokvecs.shape[0], tokvecs.shape[1])
+        model.ops.scatter_add(d_tokvecs, ids, d_state_features)
+        model.inc_grad("lower_pad", d_tokvecs[-1])
+        return (backprop_tok2vec(d_tokvecs[:-1]), None)
+
+    return (states, all_scores), backprop_parser
+
+
+def _forward_reference(
+    model, docs_moves: Tuple[List[Doc], TransitionSystem], is_train: bool
+):
+    """Slow reference implementation, without the precomputation"""
+    nF = model.get_dim("nF")
+    tok2vec = model.get_ref("tok2vec")
+    lower_pad = model.get_param("lower_pad")
+    lower_W = model.get_param("lower_W")
+    lower_b = model.get_param("lower_b")
+    upper_W = model.get_param("upper_W")
+    upper_b = model.get_param("upper_b")
+    nH = model.get_dim("nH")
+    nP = model.get_dim("nP")
+    nO = model.get_dim("nO")
+    nI = model.get_dim("nI")
+
+    ops = model.ops
+    docs, moves = docs_moves
+    states = moves.init_batch(docs)
+    tokvecs, backprop_tok2vec = tok2vec(docs, is_train)
+    tokvecs = model.ops.xp.vstack((tokvecs, lower_pad))
+    all_ids = []
+    all_which = []
+    all_statevecs = []
+    all_scores = []
+    all_tokfeats = []
+    next_states = [s for s in states if not s.is_final()]
+    unseen_mask = _get_unseen_mask(model)
+    ids = numpy.zeros((len(states), nF), dtype="i")
+    while next_states:
+        ids = ids[: len(next_states)]
+        for i, state in enumerate(next_states):
+            state.set_context_tokens(ids, i, nF)
+        # Sum the state features, add the bias and apply the activation (maxout)
+        # to create the state vectors.
+        tokfeats3f = tokvecs[ids]
+        tokfeats = model.ops.reshape2f(tokfeats3f, tokfeats3f.shape[0], -1)
+        preacts2f = model.ops.gemm(tokfeats, lower_W, trans2=True)
+        preacts2f += lower_b
+        preacts = model.ops.reshape3f(preacts2f, preacts2f.shape[0], nH, nP)
+        statevecs, which = ops.maxout(preacts)
+        # Multiply the state-vector by the scores weights and add the bias,
+        # to get the logits.
+        scores = model.ops.gemm(statevecs, upper_W, trans2=True)
+        scores += upper_b
+        scores[:, unseen_mask == 0] = model.ops.xp.nanmin(scores)
+        # Transition the states, filtering out any that are finished.
+        next_states = moves.transition_states(next_states, scores)
+        all_scores.append(scores)
+        if is_train:
+            # Remember intermediate results for the backprop.
+            all_tokfeats.append(tokfeats)
+            all_ids.append(ids.copy())
+            all_statevecs.append(statevecs)
+            all_which.append(which)
+
+    nS = sum(len(s.history) for s in states)
+
+    def backprop_parser(d_states_d_scores):
+        d_tokvecs = model.ops.alloc2f(tokvecs.shape[0], tokvecs.shape[1])
+        ids = model.ops.xp.vstack(all_ids)
+        which = ops.xp.vstack(all_which)
+        statevecs = model.ops.xp.vstack(all_statevecs)
+        tokfeats = model.ops.xp.vstack(all_tokfeats)
+        _, d_scores = d_states_d_scores
+        if model.attrs.get("unseen_classes"):
+            # If we have a negative gradient (i.e. the probability should
+            # increase) on any classes we filtered out as unseen, mark
+            # them as seen.
+            for clas in set(model.attrs["unseen_classes"]):
+                if (d_scores[:, clas] < 0).any():
+                    model.attrs["unseen_classes"].remove(clas)
+        d_scores *= unseen_mask
+        assert statevecs.shape == (nS, nH), statevecs.shape
+        assert d_scores.shape == (nS, nO), d_scores.shape
+        # Calculate the gradients for the parameters of the upper layer.
+        # The weight gemm is (nS, nO) @ (nS, nH).T
+        model.inc_grad("upper_b", d_scores.sum(axis=0))
+        model.inc_grad("upper_W", model.ops.gemm(d_scores, statevecs, trans1=True))
+        # Now calculate d_statevecs, by backproping through the upper linear layer.
+        # This gemm is (nS, nO) @ (nO, nH)
+        d_statevecs = model.ops.gemm(d_scores, upper_W)
+        # Backprop through the maxout activation
+        d_preacts = model.ops.backprop_maxout(d_statevecs, which, nP)
+        d_preacts2f = model.ops.reshape2f(d_preacts, d_preacts.shape[0], nH * nP)
+        # Now increment the gradients for the lower layer.
+        # The gemm here is (nS, nH*nP) @ (nS, nF*nI)
+        model.inc_grad("lower_b", d_preacts2f.sum(axis=0))
+        model.inc_grad("lower_W", model.ops.gemm(d_preacts2f, tokfeats, trans1=True))
+        # Caclulate d_tokfeats
+        # The gemm here is (nS, nH*nP) @ (nH*nP, nF*nI)
+        d_tokfeats = model.ops.gemm(d_preacts2f, lower_W)
+        # Get the gradients of the tokvecs and the padding
+        d_tokfeats3f = model.ops.reshape3f(d_tokfeats, nS, nF, nI)
+        model.ops.scatter_add(d_tokvecs, ids, d_tokfeats3f)
+        model.inc_grad("lower_pad", d_tokvecs[-1])
+        return (backprop_tok2vec(d_tokvecs[:-1]), None)
+
+    return (states, all_scores), backprop_parser
+
+
+def _get_unseen_mask(model: Model) -> Floats1d:
+    mask = model.ops.alloc1f(model.get_dim("nO"))
+    mask.fill(1)
+    for class_ in model.attrs.get("unseen_classes", set()):
+        mask[class_] = 0
+    return mask
+
+
+def _forward_precomputable_affine(model, X: Floats2d, is_train: bool):
+    W: Floats2d = model.get_param("lower_W")
+    nF = model.get_dim("nF")
+    nH = model.get_dim("nH")
+    nP = model.get_dim("nP")
+    nI = model.get_dim("nI")
+    # The weights start out (nH * nP, nF * nI). Transpose and reshape to (nF * nH *nP, nI)
+    W3f = model.ops.reshape3f(W, nH * nP, nF, nI)
+    W3f = W3f.transpose((1, 0, 2))
+    W2f = model.ops.reshape2f(W3f, nF * nH * nP, nI)
+    assert X.shape == (X.shape[0], nI), X.shape
+    Yf_ = model.ops.gemm(X, W2f, trans2=True)
+    Yf = model.ops.reshape3f(Yf_, Yf_.shape[0], nF, nH * nP)
+
+    def backward(dY_ids: Tuple[Floats3d, Ints2d]):
+        # This backprop is particularly tricky, because we get back a different
+        # thing from what we put out. We put out an array of shape:
+        # (nB, nF, nH, nP), and get back:
+        # (nB, nH, nP) and ids (nB, nF)
+        # The ids tell us the values of nF, so we would have:
+        #
+        # dYf = zeros((nB, nF, nH, nP))
+        # for b in range(nB):
+        #     for f in range(nF):
+        #         dYf[b, ids[b, f]] += dY[b]
+        #
+        # However, we avoid building that array for efficiency -- and just pass
+        # in the indices.
+        dY, ids = dY_ids
+        dXf = model.ops.gemm(dY, W)
+        Xf = X[ids].reshape((ids.shape[0], -1))
+        dW = model.ops.gemm(dY, Xf, trans1=True)
+        model.inc_grad("lower_W", dW)
+        return model.ops.reshape3f(dXf, dXf.shape[0], nF, nI)
+
+    return Yf, backward
+
+
+def _infer_nO(Y: Optional[Tuple[List[State], List[Floats2d]]]) -> Optional[int]:
+    if Y is None:
+        return None
+    _, scores = Y
+    if len(scores) == 0:
+        return None
+    assert scores[0].shape[0] >= 1
+    assert len(scores[0].shape) == 2
+    return scores[0].shape[1]
+
+
+def _lsuv_init(model: Model):
+    """This is like the 'layer sequential unit variance', but instead
+    of taking the actual inputs, we randomly generate whitened data.
+
+    Why's this all so complicated? We have a huge number of inputs,
+    and the maxout unit makes guessing the dynamics tricky. Instead
+    we set the maxout weights to values that empirically result in
+    whitened outputs given whitened inputs.
+    """
+    W = model.maybe_get_param("lower_W")
+    if W is not None and W.any():
+        return
+
+    nF = model.get_dim("nF")
+    nH = model.get_dim("nH")
+    nP = model.get_dim("nP")
+    nI = model.get_dim("nI")
+    W = model.ops.alloc4f(nF, nH, nP, nI)
+    b = model.ops.alloc2f(nH, nP)
+    pad = model.ops.alloc4f(1, nF, nH, nP)
+
+    ops = model.ops
+    W = normal_init(ops, W.shape, mean=float(ops.xp.sqrt(1.0 / nF * nI)))
+    pad = normal_init(ops, pad.shape, mean=1.0)
+    model.set_param("W", W)
+    model.set_param("b", b)
+    model.set_param("pad", pad)
+
+    ids = ops.alloc_f((5000, nF), dtype="f")
+    ids += ops.xp.random.uniform(0, 1000, ids.shape)
+    ids = ops.asarray(ids, dtype="i")
+    tokvecs = ops.alloc_f((5000, nI), dtype="f")
+    tokvecs += ops.xp.random.normal(loc=0.0, scale=1.0, size=tokvecs.size).reshape(
+        tokvecs.shape
     )
 
-    return step_model, step_model.finish_steps
+    def predict(ids, tokvecs):
+        # nS ids. nW tokvecs. Exclude the padding array.
+        hiddens, _ = _forward_precomputable_affine(model, tokvecs[:-1], False)
+        vectors = model.ops.alloc2f(ids.shape[0], nH * nP)
+        # need nS vectors
+        hiddens = hiddens.reshape((hiddens.shape[0] * nF, nH * nP))
+        model.ops.scatter_add(vectors, ids.flatten(), hiddens)
+        vectors3f = model.ops.reshape3f(vectors, vectors.shape[0], nH, nP)
+        vectors3f += b
+        return model.ops.maxout(vectors3f)[0]
 
-
+    tol_var = 0.01
-def init(model, X=None, Y=None):
+    tol_mean = 0.01
-    model.get_ref("tok2vec").initialize(X=X)
+    t_max = 10
-    lower = model.get_ref("lower")
+    W = cast(Floats4d, model.get_param("lower_W").copy())
-    lower.initialize()
+    b = cast(Floats2d, model.get_param("lower_b").copy())
-    if model.attrs["has_upper"]:
+    for t_i in range(t_max):
-        statevecs = model.ops.alloc2f(2, lower.get_dim("nO"))
+        acts1 = predict(ids, tokvecs)
-        model.get_ref("upper").initialize(X=statevecs)
+        var = model.ops.xp.var(acts1)
+        mean = model.ops.xp.mean(acts1)
+        if abs(var - 1.0) >= tol_var:
+            W /= model.ops.xp.sqrt(var)
+            model.set_param("lower_W", W)
+        elif abs(mean) >= tol_mean:
+            b -= mean
+            model.set_param("lower_b", b)
+        else:
+            break
+    return model

spacy/pipeline/_parser_internals/_state.pxd

@@ -33,6 +33,7 @@ cdef cppclass StateC:
     vector[ArcC] _left_arcs
     vector[ArcC] _right_arcs
     vector[libcpp.bool] _unshiftable
+    vector[int] history
     set[int] _sent_starts
     TokenC _empty_token
     int length
@@ -387,3 +388,4 @@ cdef cppclass StateC:
         this._b_i = src._b_i
         this.offset = src.offset
         this._empty_token = src._empty_token
+        this.history = src.history

spacy/pipeline/_parser_internals/arc_eager.pyx

@@ -772,6 +772,8 @@ cdef class ArcEager(TransitionSystem):
         return list(arcs)
 
     def has_gold(self, Example eg, start=0, end=None):
+        if end is not None and end < 0:
+            end = None
         for word in eg.y[start:end]:
             if word.dep != 0:
                 return True
@@ -857,6 +859,7 @@ cdef class ArcEager(TransitionSystem):
                             state.print_state()
                         )))
                     action.do(state.c, action.label)
+                    state.c.history.push_back(i)
                     break
             else:
                 failed = False

spacy/pipeline/_parser_internals/ner.pyx

@@ -307,6 +307,8 @@ cdef class BiluoPushDown(TransitionSystem):
             for span in eg.y.spans.get(neg_key, []):
                 if span.start >= start and span.end <= end:
                     return True
+        if end is not None and end < 0:
+            end = None
         for word in eg.y[start:end]:
             if word.ent_iob != 0:
                 return True
@@ -387,9 +389,9 @@ cdef class Begin:
         elif st.B_(1).ent_iob == 3:
             # If the next word is B, we can't B now
             return False
-        elif st.B_(1).sent_start == 1:
+        #elif st.B_(1).sent_start == 1:
-            # Don't allow entities to extend across sentence boundaries
+        #    # Don't allow entities to extend across sentence boundaries
-            return False
+        #    return False
         # Don't allow entities to start on whitespace
         elif Lexeme.get_struct_attr(st.B_(0).lex, IS_SPACE):
             return False
@@ -465,9 +467,9 @@ cdef class In:
                 # Otherwise, force acceptance, even if we're across a sentence
                 # boundary or the token is whitespace.
                 return True
-        elif st.B(1) != -1 and st.B_(1).sent_start == 1:
+        #elif st.B(1) != -1 and st.B_(1).sent_start == 1:
-            # Don't allow entities to extend across sentence boundaries
+        #    # Don't allow entities to extend across sentence boundaries
-            return False
+        #    return False
         else:
             return True
 

spacy/pipeline/_parser_internals/stateclass.pyx

@@ -20,6 +20,10 @@ cdef class StateClass:
         if self._borrowed != 1:
             del self.c
 
+    @property
+    def history(self):
+        return list(self.c.history)
+
     @property
     def stack(self):
         return [self.S(i) for i in range(self.c.stack_depth())]
@@ -176,3 +180,6 @@ cdef class StateClass:
 
     def clone(self, StateClass src):
         self.c.clone(src.c)
+
+    def set_context_tokens(self, int[:, :] output, int row, int n_feats):
+        self.c.set_context_tokens(&output[row, 0], n_feats)

spacy/pipeline/_parser_internals/transition_system.pyx

@@ -1,6 +1,8 @@
 # cython: infer_types=True
 from __future__ import print_function
 from cymem.cymem cimport Pool
+from libc.stdlib cimport calloc, free
+from libcpp.vector cimport vector
 
 from collections import Counter
 import srsly
@@ -73,7 +75,18 @@ cdef class TransitionSystem:
             offset += len(doc)
         return states
 
+    def follow_history(self, doc, history):
+        cdef int clas
+        cdef StateClass state = StateClass(doc)
+        for clas in history:
+            action = self.c[clas]
+            action.do(state.c, action.label)
+            state.c.history.push_back(clas)
+        return state
+
     def get_oracle_sequence(self, Example example, _debug=False):
+        if not self.has_gold(example):
+            return []
         states, golds, _ = self.init_gold_batch([example])
         if not states:
             return []
@@ -85,6 +98,8 @@ cdef class TransitionSystem:
             return self.get_oracle_sequence_from_state(state, gold)
 
     def get_oracle_sequence_from_state(self, StateClass state, gold, _debug=None):
+        if state.is_final():
+            return []
         cdef Pool mem = Pool()
         # n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
         assert self.n_moves > 0
@@ -110,6 +125,7 @@ cdef class TransitionSystem:
                             "S0 head?", str(state.has_head(state.S(0))),
                         )))
                     action.do(state.c, action.label)
+                    state.c.history.push_back(i)
                     break
             else:
                 if _debug:
@@ -137,6 +153,17 @@ cdef class TransitionSystem:
             raise ValueError(Errors.E170.format(name=name))
         action = self.lookup_transition(name)
         action.do(state.c, action.label)
+        state.c.history.push_back(action.clas)
+
+    def transition_states(self, states, float[:, ::1] scores):
+        assert len(states) == scores.shape[0]
+        cdef StateClass state
+        cdef float* c_scores = &scores[0, 0]
+        cdef vector[StateC*] c_states
+        for state in states:
+            c_states.push_back(state.c)
+        c_transition_batch(self, &c_states[0], c_scores, scores.shape[1], scores.shape[0])
+        return [state for state in states if not state.c.is_final()]
 
     cdef Transition lookup_transition(self, object name) except *:
         raise NotImplementedError
@@ -250,3 +277,31 @@ cdef class TransitionSystem:
             self.cfg.update(msg['cfg'])
         self.initialize_actions(labels)
         return self
+
+
+cdef void c_transition_batch(TransitionSystem moves, StateC** states, const float* scores,
+    int nr_class, int batch_size) nogil:
+    is_valid = <int*>calloc(moves.n_moves, sizeof(int))
+    cdef int i, guess
+    cdef Transition action
+    for i in range(batch_size):
+        moves.set_valid(is_valid, states[i])
+        guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class)
+        if guess == -1:
+            # This shouldn't happen, but it's hard to raise an error here,
+            # and we don't want to infinite loop. So, force to end state.
+            states[i].force_final()
+        else:
+            action = moves.c[guess]
+            action.do(states[i], action.label)
+            states[i].history.push_back(guess)
+    free(is_valid)
+
+
+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

spacy/pipeline/dep_parser.py

@@ -4,8 +4,8 @@ from typing import Optional, Iterable, Callable
 from thinc.api import Model, Config
 
 from ._parser_internals.transition_system import TransitionSystem
-from .transition_parser cimport Parser
+from .transition_parser import Parser
-from ._parser_internals.arc_eager cimport ArcEager
+from ._parser_internals.arc_eager import ArcEager
 
 from .functions import merge_subtokens
 from ..language import Language
@@ -17,12 +17,11 @@ from ..util import registry
 
 default_model_config = """
 [model]
-@architectures = "spacy.TransitionBasedParser.v2"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "parser"
 extra_state_tokens = false
 hidden_width = 64
 maxout_pieces = 2
-use_upper = true
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"
@@ -122,6 +121,7 @@ def make_parser(
         scorer=scorer,
     )
 
+
 @Language.factory(
     "beam_parser",
     assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],
@@ -227,6 +227,7 @@ def parser_score(examples, **kwargs):
 
     DOCS: https://spacy.io/api/dependencyparser#score
     """
+
     def has_sents(doc):
         return doc.has_annotation("SENT_START")
 
@@ -234,8 +235,11 @@ def parser_score(examples, **kwargs):
         dep = getattr(token, attr)
         dep = token.vocab.strings.as_string(dep).lower()
         return dep
+
     results = {}
-    results.update(Scorer.score_spans(examples, "sents", has_annotation=has_sents, **kwargs))
+    results.update(
+        Scorer.score_spans(examples, "sents", has_annotation=has_sents, **kwargs)
+    )
     kwargs.setdefault("getter", dep_getter)
     kwargs.setdefault("ignore_labels", ("p", "punct"))
     results.update(Scorer.score_deps(examples, "dep", **kwargs))
@@ -248,11 +252,12 @@ def make_parser_scorer():
     return parser_score
 
 
-cdef class DependencyParser(Parser):
+class DependencyParser(Parser):
     """Pipeline component for dependency parsing.
 
     DOCS: https://spacy.io/api/dependencyparser
     """
+
     TransitionSystem = ArcEager
 
     def __init__(
@@ -272,8 +277,7 @@ cdef class DependencyParser(Parser):
         incorrect_spans_key=None,
         scorer=parser_score,
     ):
-        """Create a DependencyParser.
+        """Create a DependencyParser."""
-        """
         super().__init__(
             vocab,
             model,

spacy/pipeline/ner.py

@@ -4,22 +4,22 @@ from typing import Optional, Iterable, Callable
 from thinc.api import Model, Config
 
 from ._parser_internals.transition_system import TransitionSystem
-from .transition_parser cimport Parser
+from .transition_parser import Parser
-from ._parser_internals.ner cimport BiluoPushDown
+from ._parser_internals.ner import BiluoPushDown
 
 from ..language import Language
 from ..scorer import get_ner_prf, PRFScore
+from ..training import validate_examples
 from ..util import registry
 
 
 default_model_config = """
 [model]
-@architectures = "spacy.TransitionBasedParser.v2"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "ner"
 extra_state_tokens = false
 hidden_width = 64
 maxout_pieces = 2
-use_upper = true
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v2"
@@ -44,8 +44,12 @@ DEFAULT_NER_MODEL = Config().from_str(default_model_config)["model"]
         "incorrect_spans_key": None,
         "scorer": {"@scorers": "spacy.ner_scorer.v1"},
     },
-    default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
+    default_score_weights={
-
+        "ents_f": 1.0,
+        "ents_p": 0.0,
+        "ents_r": 0.0,
+        "ents_per_type": None,
+    },
 )
 def make_ner(
     nlp: Language,
@@ -98,6 +102,7 @@ def make_ner(
         scorer=scorer,
     )
 
+
 @Language.factory(
     "beam_ner",
     assigns=["doc.ents", "token.ent_iob", "token.ent_type"],
@@ -111,7 +116,12 @@ def make_ner(
         "incorrect_spans_key": None,
         "scorer": None,
     },
-    default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None},
+    default_score_weights={
+        "ents_f": 1.0,
+        "ents_p": 0.0,
+        "ents_r": 0.0,
+        "ents_per_type": None,
+    },
 )
 def make_beam_ner(
     nlp: Language,
@@ -185,11 +195,12 @@ def make_ner_scorer():
     return ner_score
 
 
-cdef class EntityRecognizer(Parser):
+class EntityRecognizer(Parser):
     """Pipeline component for named entity recognition.
 
     DOCS: https://spacy.io/api/entityrecognizer
     """
+
     TransitionSystem = BiluoPushDown
 
     def __init__(
@@ -207,8 +218,7 @@ cdef class EntityRecognizer(Parser):
         incorrect_spans_key=None,
         scorer=ner_score,
     ):
-        """Create an EntityRecognizer.
+        """Create an EntityRecognizer."""
-        """
         super().__init__(
             vocab,
             model,
@@ -242,8 +252,11 @@ cdef class EntityRecognizer(Parser):
     def labels(self):
         # Get the labels from the model by looking at the available moves, e.g.
         # B-PERSON, I-PERSON, L-PERSON, U-PERSON
-        labels = set(move.split("-")[1] for move in self.move_names
+        labels = set(
-                     if move[0] in ("B", "I", "L", "U"))
+            move.split("-")[1]
+            for move in self.move_names
+            if move[0] in ("B", "I", "L", "U")
+        )
         return tuple(sorted(labels))
 
     def scored_ents(self, beams):

spacy/pipeline/transition_parser.pxd

@@ -1,19 +0,0 @@
-from cymem.cymem cimport Pool
-
-from ..vocab cimport Vocab
-from .trainable_pipe cimport TrainablePipe
-from ._parser_internals.transition_system cimport Transition, TransitionSystem
-from ._parser_internals._state cimport StateC
-from ..ml.parser_model cimport WeightsC, ActivationsC, SizesC
-
-
-cdef class Parser(TrainablePipe):
-    cdef public object _rehearsal_model
-    cdef readonly TransitionSystem moves
-    cdef public object _multitasks
-
-    cdef void _parseC(self, StateC** states,
-            WeightsC weights, SizesC sizes) nogil
-
-    cdef void c_transition_batch(self, StateC** states, const float* scores,
-            int nr_class, int batch_size) nogil

spacy/pipeline/transition_parser.pyx

@@ -7,30 +7,29 @@ from libcpp.vector cimport vector
 from libc.string cimport memset, memcpy
 from libc.stdlib cimport calloc, free
 import random
+import contextlib
 
 import srsly
-from thinc.api import set_dropout_rate, CupyOps
+from thinc.api import set_dropout_rate, CupyOps, get_array_module
 from thinc.extra.search cimport Beam
 import numpy.random
 import numpy
 import warnings
 
 from ._parser_internals.stateclass cimport StateClass
-from ..ml.parser_model cimport alloc_activations, free_activations
-from ..ml.parser_model cimport predict_states, arg_max_if_valid
-from ..ml.parser_model cimport WeightsC, ActivationsC, SizesC, cpu_log_loss
-from ..ml.parser_model cimport get_c_weights, get_c_sizes
 from ..tokens.doc cimport Doc
 from .trainable_pipe import TrainablePipe
 from ._parser_internals cimport _beam_utils
 from ._parser_internals import _beam_utils
+from ..vocab cimport Vocab
+from ._parser_internals.transition_system cimport TransitionSystem
 
 from ..training import validate_examples, validate_get_examples
 from ..errors import Errors, Warnings
 from .. import util
 
 
-cdef class Parser(TrainablePipe):
+class Parser(TrainablePipe):
     """
     Base class of the DependencyParser and EntityRecognizer.
     """
@@ -129,8 +128,9 @@ cdef class Parser(TrainablePipe):
     @property
     def move_names(self):
         names = []
+        cdef TransitionSystem moves = self.moves
         for i in range(self.moves.n_moves):
-            name = self.moves.move_name(self.moves.c[i].move, self.moves.c[i].label)
+            name = self.moves.move_name(moves.c[i].move, moves.c[i].label)
             # Explicitly removing the internal "U-" token used for blocking entities
             if name != "U-":
                 names.append(name)
@@ -219,9 +219,6 @@ cdef class Parser(TrainablePipe):
 
         stream: The sequence of documents to process.
         batch_size (int): Number of documents to accumulate into a working set.
-        error_handler (Callable[[str, List[Doc], Exception], Any]): Function that
-            deals with a failing batch of documents. The default function just reraises
-            the exception.
 
         YIELDS (Doc): Documents, in order.
         """
@@ -243,79 +240,27 @@ cdef class Parser(TrainablePipe):
     def predict(self, docs):
         if isinstance(docs, Doc):
             docs = [docs]
+        self._ensure_labels_are_added(docs)
         if not any(len(doc) for doc in docs):
             result = self.moves.init_batch(docs)
             return result
-        if self.cfg["beam_width"] == 1:
+        with _change_attrs(self.model, beam_width=self.cfg["beam_width"], beam_density=self.cfg["beam_density"]):
-            return self.greedy_parse(docs, drop=0.0)
+            states_or_beams, _ = self.model.predict((docs, self.moves))
-        else:
+        return states_or_beams
-            return self.beam_parse(
-                docs,
-                drop=0.0,
-                beam_width=self.cfg["beam_width"],
-                beam_density=self.cfg["beam_density"]
-            )
 
     def greedy_parse(self, docs, drop=0.):
-        cdef vector[StateC*] states
+        # TODO: Deprecated
-        cdef StateClass state
+        self._resize()
-        self._ensure_labels_are_added(docs)
+        with _change_attrs(self.model, beam_width=1):
-        set_dropout_rate(self.model, drop)
+            states, _ = self.model.predict((docs, self.moves))
-        batch = self.moves.init_batch(docs)
+        return states
-        model = self.model.predict(docs)
-        weights = get_c_weights(model)
-        for state in batch:
-            if not state.is_final():
-                states.push_back(state.c)
-        sizes = get_c_sizes(model, states.size())
-        with nogil:
-            self._parseC(&states[0],
-                weights, sizes)
-        model.clear_memory()
-        del model
-        return batch
 
     def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.):
-        cdef Beam beam
+        # TODO: Deprecated
-        cdef Doc doc
+        self._resize()
-        self._ensure_labels_are_added(docs)
+        with _change_attrs(self.model, beam_width=self.cfg["beam_width"], beam_density=self.cfg["beam_density"]):
-        batch = _beam_utils.BeamBatch(
+            beams, _ = self.model.predict((docs, self.moves))
-            self.moves,
+        return beams
-            self.moves.init_batch(docs),
-            None,
-            beam_width,
-            density=beam_density
-        )
-        model = self.model.predict(docs)
-        while not batch.is_done:
-            states = batch.get_unfinished_states()
-            if not states:
-                break
-            scores = model.predict(states)
-            batch.advance(scores)
-        model.clear_memory()
-        del model
-        return list(batch)
-
-    cdef void _parseC(self, StateC** states,
-            WeightsC weights, SizesC sizes) nogil:
-        cdef int i, j
-        cdef vector[StateC*] unfinished
-        cdef ActivationsC activations = alloc_activations(sizes)
-        while sizes.states >= 1:
-            predict_states(&activations,
-                states, &weights, sizes)
-            # Validate actions, argmax, take action.
-            self.c_transition_batch(states,
-                activations.scores, sizes.classes, sizes.states)
-            for i in range(sizes.states):
-                if not states[i].is_final():
-                    unfinished.push_back(states[i])
-            for i in range(unfinished.size()):
-                states[i] = unfinished[i]
-            sizes.states = unfinished.size()
-            unfinished.clear()
-        free_activations(&activations)
 
     def set_annotations(self, docs, states_or_beams):
         cdef StateClass state
@@ -327,35 +272,6 @@ cdef class Parser(TrainablePipe):
             for hook in self.postprocesses:
                 hook(doc)
 
-    def transition_states(self, states, float[:, ::1] scores):
-        cdef StateClass state
-        cdef float* c_scores = &scores[0, 0]
-        cdef vector[StateC*] c_states
-        for state in states:
-            c_states.push_back(state.c)
-        self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0])
-        return [state for state in states if not state.c.is_final()]
-
-    cdef void c_transition_batch(self, StateC** states, const float* scores,
-            int nr_class, int batch_size) nogil:
-        # n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
-        with gil:
-            assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
-        is_valid = <int*>calloc(self.moves.n_moves, sizeof(int))
-        cdef int i, guess
-        cdef Transition action
-        for i in range(batch_size):
-            self.moves.set_valid(is_valid, states[i])
-            guess = arg_max_if_valid(&scores[i*nr_class], is_valid, nr_class)
-            if guess == -1:
-                # This shouldn't happen, but it's hard to raise an error here,
-                # and we don't want to infinite loop. So, force to end state.
-                states[i].force_final()
-            else:
-                action = self.moves.c[guess]
-                action.do(states[i], action.label)
-        free(is_valid)
-
     def update(self, examples, *, drop=0., sgd=None, losses=None):
         cdef StateClass state
         if losses is None:
@@ -367,166 +283,88 @@ cdef class Parser(TrainablePipe):
         )
         for multitask in self._multitasks:
             multitask.update(examples, drop=drop, sgd=sgd)
+        # We need to take care to act on the whole batch, because we might be
+        # getting vectors via a listener.
         n_examples = len([eg for eg in examples if self.moves.has_gold(eg)])
         if n_examples == 0:
             return losses
         set_dropout_rate(self.model, drop)
-        # The probability we use beam update, instead of falling back to
+        docs = [eg.x for eg in examples if len(eg.x)]
-        # a greedy update
+        (states, scores), backprop_scores = self.model.begin_update((docs, self.moves))
-        beam_update_prob = self.cfg["beam_update_prob"]
+        if sum(s.shape[0] for s in scores) == 0:
-        if self.cfg['beam_width'] >= 2 and numpy.random.random() < beam_update_prob:
-            return self.update_beam(
-                examples,
-                beam_width=self.cfg["beam_width"],
-                sgd=sgd,
-                losses=losses,
-                beam_density=self.cfg["beam_density"]
-            )
-        max_moves = self.cfg["update_with_oracle_cut_size"]
-        if max_moves >= 1:
-            # Chop sequences into lengths of this many words, to make the
-            # batch uniform length.
-            max_moves = int(random.uniform(max_moves // 2, max_moves * 2))
-            states, golds, _ = self._init_gold_batch(
-                examples,
-                max_length=max_moves
-            )
-        else:
-            states, golds, _ = self.moves.init_gold_batch(examples)
-        if not states:
             return losses
-        model, backprop_tok2vec = self.model.begin_update([eg.x for eg in examples])
+        d_scores = self.get_loss((states, scores), examples)
- 
+        backprop_scores((states, d_scores))
-        all_states = list(states)
-        states_golds = list(zip(states, golds))
-        n_moves = 0
-        while states_golds:
-            states, golds = zip(*states_golds)
-            scores, backprop = model.begin_update(states)
-            d_scores = self.get_batch_loss(states, golds, scores, losses)
-            # Note that the gradient isn't normalized by the batch size
-            # here, because our "samples" are really the states...But we
-            # can't normalize by the number of states either, as then we'd
-            # be getting smaller gradients for states in long sequences.
-            backprop(d_scores)
-            # Follow the predicted action
-            self.transition_states(states, scores)
-            states_golds = [(s, g) for (s, g) in zip(states, golds) if not s.is_final()]
-            if max_moves >= 1 and n_moves >= max_moves:
-                break
-            n_moves += 1
-
-        backprop_tok2vec(golds)
         if sgd not in (None, False):
             self.finish_update(sgd)
+        losses[self.name] += (d_scores**2).sum()
         # Ugh, this is annoying. If we're working on GPU, we want to free the
         # memory ASAP. It seems that Python doesn't necessarily get around to
         # removing these in time if we don't explicitly delete? It's confusing.
-        del backprop
+        del backprop_scores
-        del backprop_tok2vec
-        model.clear_memory()
-        del model
         return losses
 
+    def get_loss(self, states_scores, examples):
+        states, scores = states_scores
+        scores = self.model.ops.xp.vstack(scores)
+        costs = self._get_costs_from_histories(
+            examples,
+            [list(state.history) for state in states]
+        )
+        xp = get_array_module(scores)
+        best_costs = costs.min(axis=1, keepdims=True)
+        gscores = scores.copy()
+        min_score = scores.min() - 1000
+        assert costs.shape == scores.shape, (costs.shape, scores.shape)
+        gscores[costs > best_costs] = min_score
+        max_ = scores.max(axis=1, keepdims=True)
+        gmax = gscores.max(axis=1, keepdims=True)
+        exp_scores = xp.exp(scores - max_)
+        exp_gscores = xp.exp(gscores - gmax)
+        Z = exp_scores.sum(axis=1, keepdims=True)
+        gZ = exp_gscores.sum(axis=1, keepdims=True)
+        d_scores = exp_scores / Z
+        d_scores -= (costs <= best_costs) * (exp_gscores / gZ)
+        return d_scores
+
+    def _get_costs_from_histories(self, examples, histories):
+        cdef TransitionSystem moves = self.moves
+        cdef StateClass state
+        cdef int clas
+        cdef int nF = self.model.get_dim("nF")
+        cdef int nO = moves.n_moves
+        cdef int nS = sum([len(history) for history in histories])
+        cdef Pool mem = Pool()
+        is_valid = <int*>mem.alloc(nO, sizeof(int))
+        c_costs = <float*>mem.alloc(nO, sizeof(float))
+        states = moves.init_batch([eg.x for eg in examples])
+        batch = []
+        for eg, s, h in zip(examples, states, histories):
+            if not s.is_final():
+                gold = moves.init_gold(s, eg)
+                batch.append((eg, s, h, gold))
+        output = []
+        while batch:
+            costs = numpy.zeros((len(batch), nO), dtype="f")
+            for i, (eg, state, history, gold) in enumerate(batch):
+                clas = history.pop(0)
+                moves.set_costs(is_valid, c_costs, state.c, gold)
+                action = moves.c[clas]
+                action.do(state.c, action.label)
+                state.c.history.push_back(clas)
+                for j in range(nO):
+                    costs[i, j] = c_costs[j]
+            output.append(costs)
+            batch = [(eg, s, h, g) for eg, s, h, g in batch if len(h) != 0]
+        return self.model.ops.xp.vstack(output)
+
     def rehearse(self, examples, sgd=None, losses=None, **cfg):
         """Perform a "rehearsal" update, to prevent catastrophic forgetting."""
-        if losses is None:
+        raise NotImplementedError
-            losses = {}
-        for multitask in self._multitasks:
-            if hasattr(multitask, 'rehearse'):
-                multitask.rehearse(examples, losses=losses, sgd=sgd)
-        if self._rehearsal_model is None:
-            return None
-        losses.setdefault(self.name, 0.)
-        validate_examples(examples, "Parser.rehearse")
-        docs = [eg.predicted for eg in examples]
-        states = self.moves.init_batch(docs)
-        # This is pretty dirty, but the NER can resize itself in init_batch,
-        # if labels are missing. We therefore have to check whether we need to
-        # expand our model output.
-        self._resize()
-        # Prepare the stepwise model, and get the callback for finishing the batch
-        set_dropout_rate(self._rehearsal_model, 0.0)
-        set_dropout_rate(self.model, 0.0)
-        tutor, _ = self._rehearsal_model.begin_update(docs)
-        model, backprop_tok2vec = self.model.begin_update(docs)
-        n_scores = 0.
-        loss = 0.
-        while states:
-            targets, _ = tutor.begin_update(states)
-            guesses, backprop = model.begin_update(states)
-            d_scores = (guesses - targets) / targets.shape[0]
-            # If all weights for an output are 0 in the original model, don't
-            # supervise that output. This allows us to add classes.
-            loss += (d_scores**2).sum()
-            backprop(d_scores)
-            # Follow the predicted action
-            self.transition_states(states, guesses)
-            states = [state for state in states if not state.is_final()]
-            n_scores += d_scores.size
-        # Do the backprop
-        backprop_tok2vec(docs)
-        if sgd is not None:
-            self.finish_update(sgd)
-        losses[self.name] += loss / n_scores
-        del backprop
-        del backprop_tok2vec
-        model.clear_memory()
-        tutor.clear_memory()
-        del model
-        del tutor
-        return losses
 
     def update_beam(self, examples, *, beam_width,
             drop=0., sgd=None, losses=None, beam_density=0.0):
-        states, golds, _ = self.moves.init_gold_batch(examples)
+        raise NotImplementedError
-        if not states:
-            return losses
-        # Prepare the stepwise model, and get the callback for finishing the batch
-        model, backprop_tok2vec = self.model.begin_update(
-            [eg.predicted for eg in examples])
-        loss = _beam_utils.update_beam(
-            self.moves,
-            states,
-            golds,
-            model,
-            beam_width,
-            beam_density=beam_density,
-        )
-        losses[self.name] += loss
-        backprop_tok2vec(golds)
-        if sgd is not None:
-            self.finish_update(sgd)
-
-    def get_batch_loss(self, states, golds, float[:, ::1] scores, losses):
-        cdef StateClass state
-        cdef Pool mem = Pool()
-        cdef int i
-
-        # n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
-        assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
-
-        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
-        unseen_classes = self.model.attrs["unseen_classes"]
-        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.c, gold)
-            for j in range(self.moves.n_moves):
-                if costs[j] <= 0.0 and j in unseen_classes:
-                    unseen_classes.remove(j)
-            cpu_log_loss(c_d_scores,
-                costs, is_valid, &scores[i, 0], d_scores.shape[1])
-            c_d_scores += d_scores.shape[1]
-        # Note that we don't normalize this. See comment in update() for why.
-        if losses is not None:
-            losses.setdefault(self.name, 0.)
-            losses[self.name] += (d_scores**2).sum()
-        return d_scores
 
     def set_output(self, nO):
         self.model.attrs["resize_output"](self.model, nO)
@@ -565,7 +403,7 @@ cdef class Parser(TrainablePipe):
             for example in islice(get_examples(), 10):
                 doc_sample.append(example.predicted)
         assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
-        self.model.initialize(doc_sample)
+        self.model.initialize((doc_sample, self.moves))
         if nlp is not None:
             self.init_multitask_objectives(get_examples, nlp.pipeline)
 
@@ -622,44 +460,18 @@ cdef class Parser(TrainablePipe):
                     raise ValueError(Errors.E149) from None
         return self
 
-    def _init_gold_batch(self, examples, max_length):
+
-        """Make a square batch, of length equal to the shortest transition
+@contextlib.contextmanager
-        sequence or a cap. A long
+def _change_attrs(model, **kwargs):
-        doc will get multiple states. Let's say we have a doc of length 2*N,
+    """Temporarily modify a thinc model's attributes."""
-        where N is the shortest doc. We'll make two states, one representing
+    unset = object()
-        long_doc[:N], and another representing long_doc[N:]."""
+    old_attrs = {}
-        cdef:
+    for key, value in kwargs.items():
-            StateClass start_state
+        old_attrs[key] = model.attrs.get(key, unset)
-            StateClass state
+        model.attrs[key] = value
-            Transition action
+    yield model
-        all_states = self.moves.init_batch([eg.predicted for eg in examples])
+    for key, value in old_attrs.items():
-        states = []
+        if value is unset:
-        golds = []
+            model.attrs.pop(key)
-        to_cut = []
-        for state, eg in zip(all_states, examples):
-            if self.moves.has_gold(eg) and not state.is_final():
-                gold = self.moves.init_gold(state, eg)
-                if len(eg.x) < max_length:
-                    states.append(state)
-                    golds.append(gold)
         else:
-                    oracle_actions = self.moves.get_oracle_sequence_from_state(
+            model.attrs[key] = value
-                        state.copy(), gold)
-                    to_cut.append((eg, state, gold, oracle_actions))
-        if not to_cut:
-            return states, golds, 0
-        cdef int clas
-        for eg, state, gold, oracle_actions in to_cut:
-            for i in range(0, len(oracle_actions), max_length):
-                start_state = state.copy()
-                for clas in oracle_actions[i:i+max_length]:
-                    action = self.moves.c[clas]
-                    action.do(state.c, action.label)
-                    if state.is_final():
-                        break
-                if self.moves.has_gold(eg, start_state.B(0), state.B(0)):
-                    states.append(start_state)
-                    golds.append(gold)
-                if state.is_final():
-                    break
-        return states, golds, max_length

spacy/tests/parser/test_add_label.py

@@ -123,6 +123,7 @@ def test_ner_labels_added_implicitly_on_predict():
     assert "D" in ner.labels
 
 
+@pytest.mark.skip(reason="Not yet supported")
 def test_ner_labels_added_implicitly_on_beam_parse():
     nlp = Language()
     ner = nlp.add_pipe("beam_ner")
@@ -134,6 +135,7 @@ def test_ner_labels_added_implicitly_on_beam_parse():
     assert "D" in ner.labels
 
 
+@pytest.mark.skip(reason="greedy_parse is deprecated")
 def test_ner_labels_added_implicitly_on_greedy_parse():
     nlp = Language()
     ner = nlp.add_pipe("beam_ner")

spacy/tests/parser/test_ner.py

@@ -13,6 +13,7 @@ from spacy.pipeline._parser_internals.ner import BiluoPushDown
 from spacy.training import Example, iob_to_biluo
 from spacy.tokens import Doc, Span
 from spacy.vocab import Vocab
+from thinc.api import fix_random_seed
 import logging
 
 from ..util import make_tempdir
@@ -180,6 +181,7 @@ def test_issue4267():
         assert token.ent_iob == 2
 
 
+@pytest.mark.xfail(reason="no beam parser yet")
 @pytest.mark.issue(4313)
 def test_issue4313():
     """This should not crash or exit with some strange error code"""
@@ -391,7 +393,7 @@ def test_train_empty():
         train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
     ner = nlp.add_pipe("ner", last=True)
     ner.add_label("PERSON")
-    nlp.initialize()
+    nlp.initialize(get_examples=lambda: train_examples)
     for itn in range(2):
         losses = {}
         batches = util.minibatch(train_examples, size=8)
@@ -518,11 +520,11 @@ def test_block_ner():
     assert [token.ent_type_ for token in doc] == expected_types
 
 
-@pytest.mark.parametrize("use_upper", [True, False])
+def test_overfitting_IO():
-def test_overfitting_IO(use_upper):
+    fix_random_seed(1)
     # Simple test to try and quickly overfit the NER component
     nlp = English()
-    ner = nlp.add_pipe("ner", config={"model": {"use_upper": use_upper}})
+    ner = nlp.add_pipe("ner", config={"model": {}})
     train_examples = []
     for text, annotations in TRAIN_DATA:
         train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
@@ -533,7 +535,7 @@ def test_overfitting_IO(use_upper):
     for i in range(50):
         losses = {}
         nlp.update(train_examples, sgd=optimizer, losses=losses)
-    assert losses["ner"] < 0.00001
+    assert losses["ner"] < 0.001
 
     # test the trained model
     test_text = "I like London."
@@ -554,7 +556,6 @@ def test_overfitting_IO(use_upper):
         assert ents2[0].label_ == "LOC"
         # Ensure that the predictions are still the same, even after adding a new label
         ner2 = nlp2.get_pipe("ner")
-        assert ner2.model.attrs["has_upper"] == use_upper
         ner2.add_label("RANDOM_NEW_LABEL")
         doc3 = nlp2(test_text)
         ents3 = doc3.ents
@@ -596,6 +597,7 @@ def test_overfitting_IO(use_upper):
     assert ents[1].kb_id == 0
 
 
+@pytest.mark.xfail(reason="no beam parser yet")
 def test_beam_ner_scores():
     # Test that we can get confidence values out of the beam_ner pipe
     beam_width = 16
@@ -631,6 +633,7 @@ def test_beam_ner_scores():
             assert 0 - eps <= score <= 1 + eps
 
 
+@pytest.mark.xfail(reason="no beam parser yet")
 def test_beam_overfitting_IO(neg_key):
     # Simple test to try and quickly overfit the Beam NER component
     nlp = English()

spacy/tests/parser/test_nn_beam.py

@@ -118,6 +118,7 @@ def test_beam_advance_too_few_scores(beam, scores):
         beam.advance(scores[:-1])
 
 
+@pytest.mark.xfail(reason="no beam parser yet")
 def test_beam_parse(examples, beam_width):
     nlp = Language()
     parser = nlp.add_pipe("beam_parser")
@@ -128,6 +129,7 @@ def test_beam_parse(examples, beam_width):
     parser(doc)
 
 
+@pytest.mark.xfail(reason="no beam parser yet")
 @hypothesis.given(hyp=hypothesis.strategies.data())
 def test_beam_density(moves, examples, beam_width, hyp):
     beam_density = float(hyp.draw(hypothesis.strategies.floats(0.0, 1.0, width=32)))

spacy/tests/parser/test_parse.py

@@ -5,9 +5,11 @@ from thinc.api import Adam
 from spacy import registry, util
 from spacy.attrs import DEP, NORM
 from spacy.lang.en import English
-from spacy.tokens import Doc
 from spacy.training import Example
+from spacy.tokens import Doc
 from spacy.vocab import Vocab
+from spacy import util, registry
+from thinc.api import fix_random_seed
 
 from ...pipeline import DependencyParser
 from ...pipeline.dep_parser import DEFAULT_PARSER_MODEL
@@ -58,6 +60,8 @@ PARTIAL_DATA = [
     ),
 ]
 
+PARSERS = ["parser"]  # TODO: Test beam_parser when ready
+
 eps = 0.1
 
 
@@ -318,7 +322,7 @@ def test_parser_constructor(en_vocab):
     DependencyParser(en_vocab, model)
 
 
-@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
+@pytest.mark.parametrize("pipe_name", PARSERS)
 def test_incomplete_data(pipe_name):
     # Test that the parser works with incomplete information
     nlp = English()
@@ -344,8 +348,9 @@ def test_incomplete_data(pipe_name):
     assert doc[2].head.i == 1
 
 
-@pytest.mark.parametrize("pipe_name", ["parser", "beam_parser"])
+@pytest.mark.parametrize("pipe_name", PARSERS)
 def test_overfitting_IO(pipe_name):
+    fix_random_seed(0)
     # Simple test to try and quickly overfit the dependency parser (normal or beam)
     nlp = English()
     parser = nlp.add_pipe(pipe_name)
@@ -354,6 +359,7 @@ def test_overfitting_IO(pipe_name):
         train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
         for dep in annotations.get("deps", []):
             parser.add_label(dep)
+    # train_examples = train_examples[:1]
     optimizer = nlp.initialize()
     # run overfitting
     for i in range(200):
@@ -395,6 +401,7 @@ def test_overfitting_IO(pipe_name):
     assert_equal(batch_deps_1, no_batch_deps)
 
 
+@pytest.mark.xfail(reason="no beam parser yet")
 def test_beam_parser_scores():
     # Test that we can get confidence values out of the beam_parser pipe
     beam_width = 16
@@ -433,6 +440,7 @@ def test_beam_parser_scores():
             assert 0 - eps <= head_score <= 1 + eps
 
 
+@pytest.mark.xfail(reason="no beam parser yet")
 def test_beam_overfitting_IO():
     # Simple test to try and quickly overfit the Beam dependency parser
     nlp = English()

spacy/tests/pipeline/test_tok2vec.py

@@ -255,7 +255,7 @@ cfg_string_multi = """
     factory = "ner"
 
     [components.ner.model]
-    @architectures = "spacy.TransitionBasedParser.v2"
+    @architectures = "spacy.TransitionBasedParser.v3"
 
     [components.ner.model.tok2vec]
     @architectures = "spacy.Tok2VecListener.v1"

spacy/tests/serialize/test_serialize_config.py

@@ -122,33 +122,11 @@ width = ${components.tok2vec.model.width}
 
 parser_config_string_upper = """
 [model]
-@architectures = "spacy.TransitionBasedParser.v2"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "parser"
 extra_state_tokens = false
 hidden_width = 66
 maxout_pieces = 2
-use_upper = true
-
-[model.tok2vec]
-@architectures = "spacy.HashEmbedCNN.v1"
-pretrained_vectors = null
-width = 333
-depth = 4
-embed_size = 5555
-window_size = 1
-maxout_pieces = 7
-subword_features = false
-"""
-
-
-parser_config_string_no_upper = """
-[model]
-@architectures = "spacy.TransitionBasedParser.v2"
-state_type = "parser"
-extra_state_tokens = false
-hidden_width = 66
-maxout_pieces = 2
-use_upper = false
 
 [model.tok2vec]
 @architectures = "spacy.HashEmbedCNN.v1"
@@ -179,7 +157,6 @@ def my_parser():
         extra_state_tokens=True,
         hidden_width=65,
         maxout_pieces=5,
-        use_upper=True,
     )
     return parser
 
@@ -285,15 +262,14 @@ def test_serialize_custom_nlp():
         nlp.to_disk(d)
         nlp2 = spacy.load(d)
         model = nlp2.get_pipe("parser").model
-        model.get_ref("tok2vec")
+        assert model.get_ref("tok2vec") is not None
-        # check that we have the correct settings, not the default ones
+        assert model.has_param("lower_W")
-        assert model.get_ref("upper").get_dim("nI") == 65
+        assert model.has_param("upper_W")
-        assert model.get_ref("lower").get_dim("nI") == 65
+        assert model.has_param("lower_b")
+        assert model.has_param("upper_b")
 
 
-@pytest.mark.parametrize(
+@pytest.mark.parametrize("parser_config_string", [parser_config_string_upper])
-    "parser_config_string", [parser_config_string_upper, parser_config_string_no_upper]
-)
 def test_serialize_parser(parser_config_string):
     """Create a non-default parser config to check nlp serializes it correctly"""
     nlp = English()
@@ -306,11 +282,11 @@ def test_serialize_parser(parser_config_string):
         nlp.to_disk(d)
         nlp2 = spacy.load(d)
         model = nlp2.get_pipe("parser").model
-        model.get_ref("tok2vec")
+        assert model.get_ref("tok2vec") is not None
-        # check that we have the correct settings, not the default ones
+        assert model.has_param("lower_W")
-        if model.attrs["has_upper"]:
+        assert model.has_param("upper_W")
-            assert model.get_ref("upper").get_dim("nI") == 66
+        assert model.has_param("lower_b")
-        assert model.get_ref("lower").get_dim("nI") == 66
+        assert model.has_param("upper_b")
 
 
 def test_config_nlp_roundtrip():
@@ -457,9 +433,7 @@ def test_config_auto_fill_extra_fields():
     load_model_from_config(nlp.config)
 
 
-@pytest.mark.parametrize(
+@pytest.mark.parametrize("parser_config_string", [parser_config_string_upper])
-    "parser_config_string", [parser_config_string_upper, parser_config_string_no_upper]
-)
 def test_config_validate_literal(parser_config_string):
     nlp = English()
     config = Config().from_str(parser_config_string)

spacy/tests/test_misc.py

@@ -5,10 +5,7 @@ from pathlib import Path
 from spacy.about import __version__ as spacy_version
 from spacy import util
 from spacy import prefer_gpu, require_gpu, require_cpu
-from spacy.ml._precomputable_affine import PrecomputableAffine
+from spacy.util import dot_to_object, SimpleFrozenList, import_file, to_ternary_int
-from spacy.ml._precomputable_affine import _backprop_precomputable_affine_padding
-from spacy.util import dot_to_object, SimpleFrozenList, import_file
-from spacy.util import to_ternary_int
 from thinc.api import Config, Optimizer, ConfigValidationError
 from thinc.api import set_current_ops
 from spacy.training.batchers import minibatch_by_words
@@ -81,32 +78,33 @@ def test_util_get_package_path(package):
     assert isinstance(path, Path)
 
 
-def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
+# @pytest.mark.skip(reason="No precomputable affine")
-    model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP).initialize()
+# def test_PrecomputableAffine(nO=4, nI=5, nF=3, nP=2):
-    assert model.get_param("W").shape == (nF, nO, nP, nI)
+#     model = PrecomputableAffine(nO=nO, nI=nI, nF=nF, nP=nP).initialize()
-    tensor = model.ops.alloc((10, nI))
+#     assert model.get_param("W").shape == (nF, nO, nP, nI)
-    Y, get_dX = model.begin_update(tensor)
+#     tensor = model.ops.alloc((10, nI))
-    assert Y.shape == (tensor.shape[0] + 1, nF, nO, nP)
+#     Y, get_dX = model.begin_update(tensor)
-    dY = model.ops.alloc((15, nO, nP))
+#     assert Y.shape == (tensor.shape[0] + 1, nF, nO, nP)
-    ids = model.ops.alloc((15, nF))
+#     dY = model.ops.alloc((15, nO, nP))
-    ids[1, 2] = -1
+#     ids = model.ops.alloc((15, nF))
-    dY[1] = 1
+#     ids[1, 2] = -1
-    assert not model.has_grad("pad")
+#     dY[1] = 1
-    d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
+#     assert not model.has_grad("pad")
-    assert d_pad[0, 2, 0, 0] == 1.0
+#     d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
-    ids.fill(0.0)
+#     assert d_pad[0, 2, 0, 0] == 1.0
-    dY.fill(0.0)
+#     ids.fill(0.0)
-    dY[0] = 0
+#     dY.fill(0.0)
-    ids[1, 2] = 0
+#     dY[0] = 0
-    ids[1, 1] = -1
+#     ids[1, 2] = 0
-    ids[1, 0] = -1
+#     ids[1, 1] = -1
-    dY[1] = 1
+#     ids[1, 0] = -1
-    ids[2, 0] = -1
+#     dY[1] = 1
-    dY[2] = 5
+#     ids[2, 0] = -1
-    d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
+#     dY[2] = 5
-    assert d_pad[0, 0, 0, 0] == 6
+#     d_pad = _backprop_precomputable_affine_padding(model, dY, ids)
-    assert d_pad[0, 1, 0, 0] == 1
+#     assert d_pad[0, 0, 0, 0] == 6
-    assert d_pad[0, 2, 0, 0] == 0
+#     assert d_pad[0, 1, 0, 0] == 1
+#     assert d_pad[0, 2, 0, 0] == 0
 
 
 def test_prefer_gpu():

spacy/training/example.pyx

@@ -1,5 +1,4 @@
 from collections.abc import Iterable as IterableInstance
-import warnings
 import numpy
 from murmurhash.mrmr cimport hash64
 

website/docs/api/architectures.md

@@ -552,18 +552,17 @@ for a Tok2Vec layer.
 
 ## Parser & NER architectures {#parser}
 
-### spacy.TransitionBasedParser.v2 {#TransitionBasedParser source="spacy/ml/models/parser.py"}
+### spacy.TransitionBasedParser.v3 {#TransitionBasedParser source="spacy/ml/models/parser.py"}
 
 > #### Example Config
 >
 > ```ini
 > [model]
-> @architectures = "spacy.TransitionBasedParser.v2"
+> @architectures = "spacy.TransitionBasedParser.v3"
 > state_type = "ner"
 > extra_state_tokens = false
 > hidden_width = 64
 > maxout_pieces = 2
-> use_upper = true
 >
 > [model.tok2vec]
 > @architectures = "spacy.HashEmbedCNN.v2"
@@ -594,13 +593,12 @@ consists of either two or three subnetworks:
   as action scores directly.
 
 | Name                 | Description                                                                                                                                                       |
-| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| -------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------- |
 | `tok2vec`            | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~                                                                        |
 | `state_type`         | Which task to extract features for. Possible values are "ner" and "parser". ~~str~~                                                                               |
 | `extra_state_tokens` | Whether to use an expanded feature set when extracting the state tokens. Slightly slower, but sometimes improves accuracy slightly. Defaults to `False`. ~~bool~~ |
 | `hidden_width`       | The width of the hidden layer. ~~int~~                                                                                                                            |
-| `maxout_pieces`      | How many pieces to use in the state prediction layer. Recommended values are `1`, `2` or `3`. If `1`, the maxout non-linearity is replaced with a [`Relu`](https://thinc.ai/docs/api-layers#relu) non-linearity if `use_upper` is `True`, and no non-linearity if `False`. ~~int~~                                                                                      |
+| `maxout_pieces`      | How many pieces to use in the state prediction layer. Recommended values are `1`, `2` or `3`. ~~int~~                                                             |
-| `use_upper`          | Whether to use an additional hidden layer after the state vector in order to predict the action scores. It is recommended to set this to `False` for large pretrained models such as transformers, and `True` for smaller networks. The upper layer is computed on CPU, which becomes a bottleneck on larger GPU-based models, where it's also less necessary. ~~bool~~ |
 | `nO`                 | The number of actions the model will predict between. Usually inferred from data at the beginning of training, or loaded from disk. ~~int~~                       |
 | **CREATES**          | The model using the architecture. ~~Model[List[Docs], List[List[Floats2d]]]~~                                                                                     |
 

website/docs/usage/embeddings-transformers.md

@@ -141,7 +141,7 @@ factory = "tok2vec"
 factory = "ner"
 
 [components.ner.model]
-@architectures = "spacy.TransitionBasedParser.v1"
+@architectures = "spacy.TransitionBasedParser.v3"
 
 [components.ner.model.tok2vec]
 @architectures = "spacy.Tok2VecListener.v1"
@@ -158,7 +158,7 @@ same. This makes them fully independent and doesn't require an upstream
 factory = "ner"
 
 [components.ner.model]
-@architectures = "spacy.TransitionBasedParser.v1"
+@architectures = "spacy.TransitionBasedParser.v3"
 
 [components.ner.model.tok2vec]
 @architectures = "spacy.Tok2Vec.v2"
@@ -482,7 +482,7 @@ sneakily delegates to the `Transformer` pipeline component.
 factory = "ner"
 
 [nlp.pipeline.ner.model]
-@architectures = "spacy.TransitionBasedParser.v1"
+@architectures = "spacy.TransitionBasedParser.v3"
 state_type = "ner"
 extra_state_tokens = false
 hidden_width = 128