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https://github.com/explosion/spaCy.git
synced 2025-07-18 12:12:20 +03:00
remove useless extra prefix and device from spanpredictor
This commit is contained in:
Kádár Ákos
parent
1c5dabcb47
commit
83ac0477c8
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@ -55,14 +55,14 @@ def build_wl_coref_model(
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)
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coref_model = tok2vec >> coref_scorer
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# XXX just ignore this until the coref scorer is integrated
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span_predictor = PyTorchWrapper(
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SpanPredictor(
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# span_predictor = PyTorchWrapper(
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# SpanPredictor(
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# TODO this was hardcoded to 1024, check
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hidden_size,
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sp_embedding_size,
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),
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convert_inputs=convert_span_predictor_inputs
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)
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# hidden_size,
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# sp_embedding_size,
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# ),
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# convert_inputs=convert_span_predictor_inputs
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# )
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# TODO combine models so output is uniform (just one forward pass)
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# It may be reasonable to have an option to disable span prediction,
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# and just return words as spans.
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@ -88,7 +88,6 @@ def build_span_predictor(
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)
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# TODO use proper parameter for prefix
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head_info = build_get_head_metadata(
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"span_coref_head_clusters",
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"coref_head_clusters"
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)
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model = (tok2vec & head_info) >> span_predictor
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@ -217,13 +216,10 @@ def _clusterize(
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return sorted(clusters)
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def build_get_head_metadata(update_prefix, predict_prefix):
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def build_get_head_metadata(prefix):
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# TODO this name is awful, fix it
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model = Model("HeadDataProvider",
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attrs={
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"update_prefix": update_prefix,
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"predict_prefix": predict_prefix
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},
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attrs={'prefix': prefix},
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forward=head_data_forward)
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return model
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@ -233,10 +229,7 @@ def head_data_forward(model, docs, is_train):
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"""
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sent_ids = []
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head_ids = []
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if is_train:
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prefix = model.attrs["update_prefix"]
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else:
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prefix = model.attrs["predict_prefix"]
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prefix = model.attrs["prefix"]
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for doc in docs:
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sids = model.ops.asarray2i(get_sentence_ids(doc))
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sent_ids.append(sids)
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@ -511,7 +504,7 @@ class RoughScorer(torch.nn.Module):
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class SpanPredictor(torch.nn.Module):
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def __init__(self, input_size: int, hidden_size: int, dist_emb_size: int, device):
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def __init__(self, input_size: int, hidden_size: int, dist_emb_size: int):
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super().__init__()
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# input size = single token size
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# 64 = probably distance emb size
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@ -551,13 +544,13 @@ class SpanPredictor(torch.nn.Module):
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torch.Tensor: span start/end scores, [n_heads, n_words, 2]
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"""
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# Obtain distance embedding indices, [n_heads, n_words]
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relative_positions = (heads_ids.unsqueeze(1) - torch.arange(words.shape[0], device=words.device).unsqueeze(0))
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relative_positions = (heads_ids.unsqueeze(1) - torch.arange(words.shape[0]).unsqueeze(0))
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# make all valid distances positive
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emb_ids = relative_positions + 63
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# "too_far"
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emb_ids[(emb_ids < 0) + (emb_ids > 126)] = 127
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# Obtain "same sentence" boolean mask, [n_heads, n_words]
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sent_id = torch.tensor(sent_id, device=words.device)
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sent_id = torch.tensor(sent_id)
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heads_ids = heads_ids.long()
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same_sent = (sent_id[heads_ids].unsqueeze(1) == sent_id.unsqueeze(0))
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# To save memory, only pass candidates from one sentence for each head
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@ -571,18 +564,18 @@ class SpanPredictor(torch.nn.Module):
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self.emb(emb_ids[rows, cols]),
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), dim=1)
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lengths = same_sent.sum(dim=1)
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padding_mask = torch.arange(0, lengths.max().item(), device=words.device).unsqueeze(0)
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padding_mask = torch.arange(0, lengths.max().item()).unsqueeze(0)
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padding_mask = (padding_mask < lengths.unsqueeze(1)) # [n_heads, max_sent_len]
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# [n_heads, max_sent_len, input_size * 2 + distance_emb_size]
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# This is necessary to allow the convolution layer to look at several
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# word scores
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padded_pairs = torch.zeros(*padding_mask.shape, pair_matrix.shape[-1], device=words.device)
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padded_pairs = torch.zeros(*padding_mask.shape, pair_matrix.shape[-1])
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padded_pairs[padding_mask] = pair_matrix
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res = self.ffnn(padded_pairs) # [n_heads, n_candidates, last_layer_output]
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res = self.conv(res.permute(0, 2, 1)).permute(0, 2, 1) # [n_heads, n_candidates, 2]
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scores = torch.full((heads_ids.shape[0], words.shape[0], 2), float('-inf'), device=words.device)
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scores = torch.full((heads_ids.shape[0], words.shape[0], 2), float('-inf'))
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scores[rows, cols] = res[padding_mask]
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# Make sure that start <= head <= end during inference
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@ -417,7 +417,6 @@ DEFAULT_SPAN_PREDICTOR_MODEL = Config().from_str(default_span_predictor_config)[
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default_config={
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"model": DEFAULT_SPAN_PREDICTOR_MODEL,
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"input_prefix": "coref_head_clusters",
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"target_prefix": "span_head_target_clusters",
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"output_prefix": "coref_clusters",
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},
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default_score_weights={"span_predictor_f": 1.0, "span_predictor_p": None, "span_predictor_r": None},
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@ -427,7 +426,6 @@ def make_span_predictor(
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name: str,
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model,
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input_prefix: str = "coref_head_clusters",
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target_prefix: str = "span_head_target_clusters",
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output_prefix: str = "coref_clusters",
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) -> "SpanPredictor":
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"""Create a SpanPredictor component."""
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@ -446,14 +444,12 @@ class SpanPredictor(TrainablePipe):
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name: str = "span_predictor",
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*,
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input_prefix: str = "coref_head_clusters",
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target_prefix: str = "span_coref_head_clusters",
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output_prefix: str = "coref_clusters",
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) -> None:
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self.vocab = vocab
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self.model = model
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self.name = name
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self.input_prefix = input_prefix
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self.target_prefix = target_prefix
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self.output_prefix = output_prefix
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self.cfg = {}
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@ -519,8 +515,9 @@ class SpanPredictor(TrainablePipe):
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for doc, eg in zip(docs, examples):
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# replicates the EntityLinker's behaviour and
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# copies annotations over https://bit.ly/3iweDcW
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# takes 'coref_head_clusters' from the reference.
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for key, sg in eg.reference.spans.items():
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if key.startswith(self.target_prefix):
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if key.startswith(self.input_prefix):
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doc.spans[key] = [doc[span.start:span.end] for span in sg]
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span_scores, backprop = self.model.begin_update([doc])
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loss, d_scores = self.get_loss([eg], span_scores)
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@ -573,7 +570,7 @@ class SpanPredictor(TrainablePipe):
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for eg in examples:
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# get gold data
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gold = doc2clusters(eg.predicted, self.target_prefix)
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gold = doc2clusters(eg.predicted, self.input_prefix)
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# flatten the gold data
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starts = []
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ends = []
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@ -614,7 +611,6 @@ class SpanPredictor(TrainablePipe):
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doc = ex.predicted
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assert len(doc) > 2, "Coreference requires at least two tokens"
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doc.spans[f"{self.input_prefix}_0"] = [doc[0:1], doc[1:2]]
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doc.spans[f"{self.target_prefix}_0"] = [doc[0:1], doc[1:2]]
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X.append(ex.predicted)
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Y.append(ex.reference)
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