Merge pull request #10844 from polm/feature/coref-torch-guard

Add guards around torch import for coref

spacy/ml/models/__init__.py

@@ -1,5 +1,3 @@
-from .coref import * #noqa
-from .span_predictor import * #noqa
 from .entity_linker import *  # noqa
 from .multi_task import *  # noqa
 from .parser import *  # noqa
@@ -7,3 +5,10 @@ from .spancat import *  # noqa
 from .tagger import *  # noqa
 from .textcat import *  # noqa
 from .tok2vec import *  # noqa
+
+# some models require Torch
+from thinc.util import has_torch
+if has_torch:
+    from .coref import * #noqa
+    from .span_predictor import * #noqa
+

spacy/ml/models/coref.py

@@ -1,14 +1,12 @@
 from typing import List, Tuple
-import torch
 
 from thinc.api import Model, chain
 from thinc.api import PyTorchWrapper, ArgsKwargs
 from thinc.types import Floats2d, Ints2d, Ints1d
-from thinc.util import xp2torch, torch2xp
+from thinc.util import torch, xp2torch, torch2xp
 
 from ...tokens import Doc
 from ...util import registry
-from .coref_util import add_dummy
 
 
 @registry.architectures("spacy.Coref.v1")
@@ -186,6 +184,23 @@ class CorefScorer(torch.nn.Module):
         return coref_scores, top_indices
 
 
+EPSILON = 1e-7
+# Note this function is kept here to keep a torch dep out of coref_util.
+def add_dummy(tensor: torch.Tensor, eps: bool = False):
+    """Prepends zeros (or a very small value if eps is True)
+    to the first (not zeroth) dimension of tensor.
+    """
+    kwargs = dict(device=tensor.device, dtype=tensor.dtype)
+    shape: List[int] = list(tensor.shape)
+    shape[1] = 1
+    if not eps:
+        dummy = torch.zeros(shape, **kwargs)  # type: ignore
+    else:
+        dummy = torch.full(shape, EPSILON, **kwargs)  # type: ignore
+    output = torch.cat((dummy, tensor), dim=1)
+    return output
+
+
 class AnaphoricityScorer(torch.nn.Module):
     """Calculates anaphoricity scores by passing the inputs into a FFNN"""
 

spacy/ml/models/coref_util.py

@@ -2,15 +2,12 @@ from thinc.types import Ints2d
 from spacy.tokens import Doc
 from typing import List, Tuple, Callable, Any, Set, Dict
 from ...util import registry
-import torch
 
 # type alias to make writing this less tedious
 MentionClusters = List[List[Tuple[int, int]]]
 
 DEFAULT_CLUSTER_PREFIX = "coref_clusters"
 
-EPSILON = 1e-7
-
 class GraphNode:
     def __init__(self, node_id: int):
         self.id = node_id
@@ -25,20 +22,6 @@ class GraphNode:
         return str(self.id)
 
 
-def add_dummy(tensor: torch.Tensor, eps: bool = False):
-    """ Prepends zeros (or a very small value if eps is True)
-    to the first (not zeroth) dimension of tensor.
-    """
-    kwargs = dict(device=tensor.device, dtype=tensor.dtype)
-    shape: List[int] = list(tensor.shape)
-    shape[1] = 1
-    if not eps:
-        dummy = torch.zeros(shape, **kwargs)          # type: ignore
-    else:
-        dummy = torch.full(shape, EPSILON, **kwargs)  # type: ignore
-    output = torch.cat((dummy, tensor), dim=1)
-    return output
-
 def get_sentence_ids(doc):
     out = []
     sent_id = -1

spacy/ml/models/span_predictor.py

@@ -1,10 +1,9 @@
 from typing import List, Tuple
-import torch
 
 from thinc.api import Model, chain, tuplify
 from thinc.api import PyTorchWrapper, ArgsKwargs
 from thinc.types import Floats2d, Ints1d
-from thinc.util import xp2torch, torch2xp
+from thinc.util import torch, xp2torch, torch2xp
 
 from ...tokens import Doc
 from ...util import registry