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Merge pull request #10141 from danieldk/bool-mask

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Improve unseen label masking
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Daniël de Kok 2022-01-27 08:32:15 +01:00 committed by GitHub
commit b68d7a1ebf
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1 changed files with 11 additions and 11 deletions
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spacy/ml/tb_framework.py
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@ -150,7 +150,7 @@ def forward(model, docs_moves: Tuple[List[Doc], TransitionSystem], is_train: boo
all_statevecs = []
all_scores = []
next_states = [s for s in states if not s.is_final()]
unseen_mask = _get_unseen_mask(model)
seen_mask = _get_seen_mask(model)
ids = numpy.zeros((len(states), nF), dtype="i")
arange = model.ops.xp.arange(nF)
while next_states:
@ -168,9 +168,10 @@ def forward(model, docs_moves: Tuple[List[Doc], TransitionSystem], is_train: boo
# 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)
scores[:, seen_mask] = model.ops.xp.nanmin(scores)
# Transition the states, filtering out any that are finished.
next_states = moves.transition_states(next_states, scores)
cpu_scores = model.ops.to_numpy(scores)
next_states = moves.transition_states(next_states, cpu_scores)
all_scores.append(scores)
if is_train:
# Remember intermediate results for the backprop.
@ -191,7 +192,7 @@ def forward(model, docs_moves: Tuple[List[Doc], TransitionSystem], is_train: boo
for clas in set(model.attrs["unseen_classes"]):
if (d_scores[:, clas] < 0).any():
model.attrs["unseen_classes"].remove(clas)
d_scores *= unseen_mask
d_scores *= seen_mask == False
# 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))
@ -240,7 +241,7 @@ def _forward_reference(
all_scores = []
all_tokfeats = []
next_states = [s for s in states if not s.is_final()]
unseen_mask = _get_unseen_mask(model)
seen_mask = _get_seen_mask(model)
ids = numpy.zeros((len(states), nF), dtype="i")
while next_states:
ids = ids[: len(next_states)]
@ -258,7 +259,7 @@ def _forward_reference(
# 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)
scores[:, seen_mask] = 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)
@ -285,7 +286,7 @@ def _forward_reference(
for clas in set(model.attrs["unseen_classes"]):
if (d_scores[:, clas] < 0).any():
model.attrs["unseen_classes"].remove(clas)
d_scores *= unseen_mask
d_scores *= seen_mask == False
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.
@ -314,11 +315,10 @@ def _forward_reference(
return (states, all_scores), backprop_parser
def _get_unseen_mask(model: Model) -> Floats1d:
mask = model.ops.alloc1f(model.get_dim("nO"))
mask.fill(1)
def _get_seen_mask(model: Model) -> Floats1d:
mask = model.ops.xp.zeros(model.get_dim("nO"), dtype="bool")
for class_ in model.attrs.get("unseen_classes", set()):
mask[class_] = 0
mask[class_] = True
return mask