mirror of https://github.com/explosion/spaCy.git
91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
"""Thinc layer to do simpler transition-based parsing, NER, etc."""
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from typing import Dict, Optional
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from thinc.api import Ops, Model
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from thinc.types import Padded, Floats3d
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def IOB() -> Model[Padded, Padded]:
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return Model(
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"biluo",
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forward,
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init=init,
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dims={"nO": None},
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attrs={"get_num_actions": get_num_actions},
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)
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def init(model: Model, X: Optional[Padded] = None, Y: Optional[Padded] = None) -> None:
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if X is not None and Y is not None:
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if X.data.shape != Y.data.shape:
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# TODO: Fix error
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raise ValueError("Mismatched shapes (TODO: Fix message)")
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model.set_dim("nO", X.data.shape[2])
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elif X is not None:
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model.set_dim("nO", X.data.shape[2])
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elif Y is not None:
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model.set_dim("nO", Y.data.shape[2])
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elif model.get_dim("nO") is None:
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raise ValueError("Dimension unset for BILUO: nO")
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def forward(model: Model[Padded, Padded], Xp: Padded, is_train: bool):
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n_labels = (model.get_dim("nO") - 1) // 2
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n_tokens, n_docs, n_actions = Xp.data.shape
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# At each timestep, we make a validity mask of shape (n_docs, n_actions)
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# to indicate which actions are valid next for each sequence. To construct
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# the mask, we have a state of shape (2, n_actions) and a validity table of
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# shape (2, n_actions+1, n_actions). The first dimension of the state indicates
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# whether it's the last token, the second dimension indicates the previous
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# action, plus a special 'null action' for the first entry.
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valid_transitions = _get_transition_table(model.ops, n_labels)
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prev_actions = model.ops.alloc1i(n_docs)
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# Initialize as though prev action was O
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prev_actions.fill(n_actions - 1)
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Y = model.ops.alloc3f(*Xp.data.shape)
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masks = model.ops.alloc3f(*Y.shape)
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for t in range(Xp.data.shape[0]):
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masks[t] = valid_transitions[prev_actions]
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# Don't train the out-of-bounds sequences.
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masks[t, Xp.size_at_t[t] :] = 0
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# Valid actions get 0*10e8, invalid get -1*10e8
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Y[t] = Xp.data[t] + ((masks[t] - 1) * 10e8)
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prev_actions = Y[t].argmax(axis=-1)
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def backprop_biluo(dY: Padded) -> Padded:
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# Masking the gradient seems to do poorly here. But why?
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# dY.data *= masks
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return dY
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return Padded(Y, Xp.size_at_t, Xp.lengths, Xp.indices), backprop_biluo
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def get_num_actions(n_labels: int) -> int:
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# One BEGIN action per label
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# One IN action per label
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# One LAST action per label
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# One UNIT action per label
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# One OUT action
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return n_labels * 2 + 1
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def _get_transition_table(
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ops: Ops, n_labels: int, _cache: Dict[int, Floats3d] = {}
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) -> Floats3d:
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n_actions = get_num_actions(n_labels)
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if n_actions in _cache:
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return ops.asarray(_cache[n_actions])
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table = ops.alloc2f(n_actions, n_actions)
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B_start, B_end = (0, n_labels)
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I_start, I_end = (B_end, B_end + n_labels)
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O_action = I_end
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B_range = ops.xp.arange(B_start, B_end)
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I_range = ops.xp.arange(I_start, I_end)
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# B and O are always valid
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table[:, B_start:B_end] = 1
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table[:, O_action] = 1
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# I can only follow a matching B
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table[B_range, I_range] = 1
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_cache[n_actions] = table
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return table
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