Don't use numpy directly for similarity (#3362)

* Don't use numpy directly for similarity

* Contributor agreement

.github/contributors/danielkingai2.md

@@ -0,0 +1,106 @@
+# spaCy contributor agreement
+
+This spaCy Contributor Agreement (**"SCA"**) is based on the
+[Oracle Contributor Agreement](http://www.oracle.com/technetwork/oca-405177.pdf).
+The SCA applies to any contribution that you make to any product or project
+managed by us (the **"project"**), and sets out the intellectual property rights
+you grant to us in the contributed materials. The term **"us"** shall mean
+[ExplosionAI UG (haftungsbeschränkt)](https://explosion.ai/legal). The term
+**"you"** shall mean the person or entity identified below.
+
+If you agree to be bound by these terms, fill in the information requested
+below and include the filled-in version with your first pull request, under the
+folder [`.github/contributors/`](/.github/contributors/). The name of the file
+should be your GitHub username, with the extension `.md`. For example, the user
+example_user would create the file `.github/contributors/example_user.md`.
+
+Read this agreement carefully before signing. These terms and conditions
+constitute a binding legal agreement.
+
+## Contributor Agreement
+
+1. The term "contribution" or "contributed materials" means any source code,
+object code, patch, tool, sample, graphic, specification, manual,
+documentation, or any other material posted or submitted by you to the project.
+
+2. With respect to any worldwide copyrights, or copyright applications and
+registrations, in your contribution:
+
+    * you hereby assign to us joint ownership, and to the extent that such
+    assignment is or becomes invalid, ineffective or unenforceable, you hereby
+    grant to us a perpetual, irrevocable, non-exclusive, worldwide, no-charge,
+    royalty-free, unrestricted license to exercise all rights under those
+    copyrights. This includes, at our option, the right to sublicense these same
+    rights to third parties through multiple levels of sublicensees or other
+    licensing arrangements;
+
+    * you agree that each of us can do all things in relation to your
+    contribution as if each of us were the sole owners, and if one of us makes
+    a derivative work of your contribution, the one who makes the derivative
+    work (or has it made will be the sole owner of that derivative work;
+
+    * you agree that you will not assert any moral rights in your contribution
+    against us, our licensees or transferees;
+
+    * you agree that we may register a copyright in your contribution and
+    exercise all ownership rights associated with it; and
+
+    * you agree that neither of us has any duty to consult with, obtain the
+    consent of, pay or render an accounting to the other for any use or
+    distribution of your contribution.
+
+3. With respect to any patents you own, or that you can license without payment
+to any third party, you hereby grant to us a perpetual, irrevocable,
+non-exclusive, worldwide, no-charge, royalty-free license to:
+
+    * make, have made, use, sell, offer to sell, import, and otherwise transfer
+    your contribution in whole or in part, alone or in combination with or
+    included in any product, work or materials arising out of the project to
+    which your contribution was submitted, and
+
+    * at our option, to sublicense these same rights to third parties through
+    multiple levels of sublicensees or other licensing arrangements.
+
+4. Except as set out above, you keep all right, title, and interest in your
+contribution. The rights that you grant to us under these terms are effective
+on the date you first submitted a contribution to us, even if your submission
+took place before the date you sign these terms.
+
+5. You covenant, represent, warrant and agree that:
+
+    * Each contribution that you submit is and shall be an original work of
+    authorship and you can legally grant the rights set out in this SCA;
+
+    * to the best of your knowledge, each contribution will not violate any
+    third party's copyrights, trademarks, patents, or other intellectual
+    property rights; and
+
+    * each contribution shall be in compliance with U.S. export control laws and
+    other applicable export and import laws. You agree to notify us if you
+    become aware of any circumstance which would make any of the foregoing
+    representations inaccurate in any respect. We may publicly disclose your
+    participation in the project, including the fact that you have signed the SCA.
+
+6. This SCA is governed by the laws of the State of California and applicable
+U.S. Federal law. Any choice of law rules will not apply.
+
+7. Please place an “x” on one of the applicable statement below. Please do NOT
+mark both statements:
+
+    * [ ] I am signing on behalf of myself as an individual and no other person
+    or entity, including my employer, has or will have rights with respect to my
+    contributions.
+
+    * [ ] I am signing on behalf of my employer or a legal entity and I have the
+    actual authority to contractually bind that entity.
+
+## Contributor Details
+
+| Field                          | Entry                |
+|------------------------------- | -------------------- |
+| Name                           | Daniel King          |
+| Company name (if applicable)   | Allen Institute for Artificial Intelligence |
+| Title or role (if applicable)  | Predoctoral Young Investigator |
+| Date                           | 03/06/2019           |
+| GitHub username                | danielkingai2        |
+| Website (optional)             |                      |

spacy/lexeme.pyx

@@ -8,6 +8,7 @@ cimport numpy as np
 np.import_array()
 from libc.string cimport memset
 import numpy
+from thinc.neural.util import get_array_module
 
 from .typedefs cimport attr_t, flags_t
 from .attrs cimport IS_ALPHA, IS_ASCII, IS_DIGIT, IS_LOWER, IS_PUNCT, IS_SPACE
@@ -123,7 +124,10 @@ cdef class Lexeme:
                 return 1.0
         if self.vector_norm == 0 or other.vector_norm == 0:
             return 0.0
-        return (numpy.dot(self.vector, other.vector) /
+
+        vector = self.vector
+        xp = get_array_module(vector)
+        return (xp.dot(self.vector, other.vector) /
                 (self.vector_norm * other.vector_norm))
 
     def to_bytes(self):

spacy/tokens/doc.pyx

@@ -321,7 +321,10 @@ cdef class Doc:
 
         if self.vector_norm == 0 or other.vector_norm == 0:
             return 0.0
-        return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
+
+        vector = self.vector
+        xp = get_array_module(vector)
+        return xp.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
 
     property has_vector:
         """A boolean value indicating whether a word vector is associated with
@@ -356,10 +359,7 @@ cdef class Doc:
                                            dtype='f')
                 return self._vector
             elif self.vocab.vectors.data.size > 0:
-                vector = numpy.zeros((self.vocab.vectors_length,), dtype='f')
-                for token in self.c[:self.length]:
-                    vector += self.vocab.get_vector(token.lex.orth)
-                self._vector = vector / len(self)
+                self._vector = sum(t.vector for t in self) / len(self)
                 return self._vector
             elif self.tensor.size > 0:
                 self._vector = self.tensor.mean(axis=0)

spacy/tokens/span.pyx

@@ -6,6 +6,7 @@ cimport numpy as np
 import numpy
 import numpy.linalg
 from libc.math cimport sqrt
+from thinc.neural.util import get_array_module
 
 from .doc cimport token_by_start, token_by_end, get_token_attr, _get_lca_matrix
 from .token cimport TokenC
@@ -214,7 +215,10 @@ cdef class Span:
                 return 1.0
         if self.vector_norm == 0.0 or other.vector_norm == 0.0:
             return 0.0
-        return numpy.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
+
+        vector = self.vector
+        xp = get_array_module(vector)
+        return xp.dot(self.vector, other.vector) / (self.vector_norm * other.vector_norm)
 
     cpdef np.ndarray to_array(self, object py_attr_ids):
         """Given a list of M attribute IDs, export the tokens to a numpy

spacy/tokens/token.pyx

@@ -9,6 +9,7 @@ from cython.view cimport array as cvarray
 cimport numpy as np
 np.import_array()
 import numpy
+from thinc.neural.util import get_array_module
 
 from ..typedefs cimport hash_t
 from ..lexeme cimport Lexeme
@@ -165,7 +166,10 @@ cdef class Token:
         other_norm = other.vector_norm
         if self_norm == 0 or other_norm == 0:
             return 0.0
-        return (numpy.dot(self.vector, other.vector) /
+
+        vector = self.vector
+        xp = get_array_module(self.vector)
+        return (xp.dot(self.vector, other.vector) /
                 (self_norm * other_norm))
 
     property lex_id: