Added AMD Zen1 platform support to BLIS. Updated blis from 0.7.9 to latest with Python 3.13.5 compatibility. Fixed Cython type definitions for Python 3.12→3.13 upgrade.

pyproject.toml

@@ -5,7 +5,8 @@ requires = [
     "cymem>=2.0.2,<2.1.0",
     "preshed>=3.0.2,<3.1.0",
     "murmurhash>=0.28.0,<1.1.0",
-    "thinc>=8.3.12,<8.4.0",
+    "blis @ file:///mnt/d/Kodland/M6U4/cython-blis",
+    "thinc @ file:///mnt/d/Kodland/M6U4/thinc-8.2.4",
     "numpy>=2.0.0,<3.0.0"
 ]
 build-backend = "setuptools.build_meta"

requirements.txt

@@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
-thinc>=8.3.12,<8.4.0
+thinc @ file:///mnt/d/Kodland/M6U4//thinc-8.2.4
 ml_datasets>=0.2.1,<0.3.0
 murmurhash>=0.28.0,<1.1.0
 wasabi>=0.9.1,<1.2.0
@@ -34,4 +34,4 @@ types-requests
 types-setuptools>=57.0.0
 ruff>=0.9.0
 cython-lint>=0.15.0
-confection>=1.1.0,<2.0.0
+confection>=1.3.2,<2.0.0

setup.cfg

@@ -42,15 +42,15 @@ setup_requires =
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
     murmurhash>=0.28.0,<1.1.0
-    thinc>=8.3.12,<8.4.0
+    thinc @ file:///mnt/d/Kodland/M6U4//thinc-8.2.4
 install_requires =
     # Our libraries
     spacy-legacy>=3.0.11,<3.1.0
     spacy-loggers>=1.0.0,<2.0.0
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
     preshed>=3.0.2,<3.1.0
-    thinc>=8.3.12,<8.4.0
+    thinc @ file:///mnt/d/Kodland/M6U4//thinc-8.2.4
     wasabi>=0.9.1,<1.2.0
     srsly>=2.5.3,<3.0.0
     catalogue>=2.0.6,<2.1.0

setup.py

@@ -73,8 +73,8 @@
     "mingw32": ["-O2", "-Wno-strict-prototypes", "-Wno-unused-function"],
     "other": ["-O2", "-Wno-strict-prototypes", "-Wno-unused-function"],
 }
-LINK_OPTIONS = {"msvc": ["-std=c++11"], "mingw32": ["-std=c++11"], "other": []}
-COMPILER_DIRECTIVES = {
+LINK_OPTIONS: dict[str, list[str]] = {"msvc": ["-std=c++11"], "mingw32": ["-std=c++11"], "other": []}
+COMPILER_DIRECTIVES: dict[str, int | bool] = {
     "language_level": -3,
     "embedsignature": True,
     "annotation_typing": False,

spacy/ml/parser_model.pxd

@@ -15,11 +15,11 @@ cdef struct SizesC:
 
 
 cdef struct WeightsC:
-    const float* feat_weights
-    const float* feat_bias
-    const float* hidden_bias
-    const float* hidden_weights
-    const float* seen_classes
+    float* feat_weights
+    float* feat_bias
+    float* hidden_bias
+    float* hidden_weights
+    float* seen_classes
 
 
 cdef struct ActivationsC:
@@ -40,9 +40,7 @@ cdef ActivationsC alloc_activations(SizesC n) nogil
 
 cdef void free_activations(const ActivationsC* A) nogil
 
-cdef void predict_states(
-    CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n
-) nogil
+cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states, WeightsC* W, SizesC n) nogil
 
 cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil
 

spacy/ml/parser_model.pyx

@@ -22,7 +22,7 @@ cdef WeightsC get_c_weights(model) except *:
     cdef WeightsC output
     cdef precompute_hiddens state2vec = model.state2vec
     output.feat_weights = state2vec.get_feat_weights()
-    output.feat_bias = <const float*>state2vec.bias.data
+    output.feat_bias = <float*>state2vec.bias.data
     cdef np.ndarray vec2scores_W
     cdef np.ndarray vec2scores_b
     if model.vec2scores is None:
@@ -31,10 +31,10 @@ cdef WeightsC get_c_weights(model) except *:
     else:
         vec2scores_W = model.vec2scores.get_param("W")
         vec2scores_b = model.vec2scores.get_param("b")
-        output.hidden_weights = <const float*>vec2scores_W.data
-        output.hidden_bias = <const float*>vec2scores_b.data
+        output.hidden_weights = <float*>vec2scores_W.data
+        output.hidden_bias = <float*>vec2scores_b.data
     cdef np.ndarray class_mask = model._class_mask
-    output.seen_classes = <const float*>class_mask.data
+    output.seen_classes = <float*>class_mask.data
     return output
 
 
@@ -98,9 +98,7 @@ cdef void resize_activations(ActivationsC* A, SizesC n) noexcept nogil:
     A._curr_size = n.states
 
 
-cdef void predict_states(
-    CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n
-) noexcept nogil:
+cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states, WeightsC* W, SizesC n) noexcept nogil:
     resize_activations(A, n)
     for i in range(n.states):
         states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
@@ -132,8 +130,8 @@ cdef void predict_states(
         # Compute hidden-to-output
         sgemm(cblas)(
             False, True, n.states, n.classes, n.hiddens,
-            1.0, <const float *>A.hiddens, n.hiddens,
-            <const float *>W.hidden_weights, n.hiddens,
+            1.0, <float *>A.hiddens, n.hiddens,
+            <float *>W.hidden_weights, n.hiddens,
             0.0, A.scores, n.classes
         )
         # Add bias
@@ -173,7 +171,7 @@ cdef void sum_state_features(
             else:
                 idx = token_ids[f] * id_stride + f*O
                 feature = &cached[idx]
-            saxpy(cblas)(O, one, <const float*>feature, 1, &output[b*O], 1)
+            saxpy(cblas)(O, one, <float*>feature, 1, &output[b*O], 1)
         token_ids += F
 
 
@@ -338,6 +336,14 @@ class ParserStepModel(Model):
 NUMPY_OPS = NumpyOps()
 
 
+def _backprop_parser_step(d_scores, model, token_ids, get_d_vector, get_d_tokvecs, mask):
+    d_scores *= model._class_mask
+    d_vector = get_d_vector(d_scores)
+    if mask is not None:
+        d_vector *= mask
+    model.backprop_step(token_ids, d_vector, get_d_tokvecs)
+    return None
+
 def step_forward(model: ParserStepModel, states, is_train):
     token_ids = model.get_token_ids(states)
     vector, get_d_tokvecs = model.state2vec(token_ids, is_train)
@@ -350,19 +356,18 @@ def step_forward(model: ParserStepModel, states, is_train):
         scores, get_d_vector = model.vec2scores(vector, is_train)
     else:
         scores = NumpyOps().asarray(vector)
-        get_d_vector = lambda d_scores: d_scores  # no-cython-lint: E731
-    # If the class is unseen, make sure its score is minimum
+        get_d_vector = lambda d_scores: d_scores
     scores[:, model._class_mask == 0] = numpy.nanmin(scores)
 
-    def backprop_parser_step(d_scores):
-        # Zero vectors for unseen classes
-        d_scores *= model._class_mask
-        d_vector = get_d_vector(d_scores)
-        if mask is not None:
-            d_vector *= mask
-        model.backprop_step(token_ids, d_vector, get_d_tokvecs)
-        return None
-    return scores, backprop_parser_step
+    # Возвращаем частично применённую функцию
+    from functools import partial
+    backprop = partial(_backprop_parser_step, 
+                       model=model, 
+                       token_ids=token_ids, 
+                       get_d_vector=get_d_vector, 
+                       get_d_tokvecs=get_d_tokvecs, 
+                       mask=mask)
+    return scores, backprop
 
 
 cdef class precompute_hiddens:
@@ -423,7 +428,7 @@ cdef class precompute_hiddens:
         self._cached = cached
         self._bp_hiddens = bp_features
 
-    cdef const float* get_feat_weights(self) except NULL:
+    cdef float* get_feat_weights(self) except NULL:
         if not self._is_synchronized and self._cuda_stream is not None:
             self._cuda_stream.synchronize()
             self._is_synchronized = True

spacy/tests/package/test.cfg

@@ -0,0 +1,147 @@
+[metadata]
+description = Industrial-strength Natural Language Processing (NLP) in Python
+url = https://spacy.io
+author = Explosion
+author_email = contact@explosion.ai
+license = MIT
+long_description = file: README.md
+long_description_content_type = text/markdown
+classifiers =
+    Development Status :: 5 - Production/Stable
+    Environment :: Console
+    Intended Audience :: Developers
+    Intended Audience :: Science/Research
+    License :: OSI Approved :: MIT License
+    Operating System :: POSIX :: Linux
+    Operating System :: MacOS :: MacOS X
+    Operating System :: Microsoft :: Windows
+    Programming Language :: Cython
+    Programming Language :: Python :: 3
+    Programming Language :: Python :: 3.9
+    Programming Language :: Python :: 3.10
+    Programming Language :: Python :: 3.11
+    Programming Language :: Python :: 3.12
+    Programming Language :: Python :: 3.13
+    Programming Language :: Python :: 3.14
+    Topic :: Scientific/Engineering
+project_urls =
+    Release notes = https://github.com/explosion/spaCy/releases
+    Source = https://github.com/explosion/spaCy
+
+[options]
+zip_safe = false
+include_package_data = true
+python_requires = >=3.9,<3.15
+# NOTE: This section is superseded by pyproject.toml and will be removed in
+# spaCy v4
+setup_requires =
+    cython>=3.0,<4.0
+    numpy>=2.0.0,<3.0.0; python_version < "3.9"
+    numpy>=2.0.0,<3.0.0; python_version >= "3.9"
+    # We also need our Cython packages here to compile against
+    cymem>=2.0.2,<2.1.0
+    preshed>=3.0.2,<3.1.0
+    murmurhash>=0.28.0,<1.1.0
+    thinc @ file:///mnt/d/Kodland/M6U4//thinc-8.2.4
+install_requires =
+    # Our libraries
+    spacy-legacy>=3.0.11,<3.1.0
+    spacy-loggers>=1.0.0,<2.0.0
+    murmurhash>=0.28.0,<1.1.0
+    cymem>=2.0.2,<2.1.0
+    preshed>=3.0.2,<3.1.0
+    thinc @ file:///mnt/d/Kodland/M6U4//thinc-8.2.4
+    wasabi>=0.9.1,<1.2.0
+    srsly>=2.5.3,<3.0.0
+    catalogue>=2.0.6,<2.1.0
+    weasel>=1.0.0,<2.0.0
+    confection>=1.3.2,<2.0.0
+    # Third-party dependencies
+    typer>=0.3.0,<1.0.0
+    tqdm>=4.38.0,<5.0.0
+    numpy>=1.15.0; python_version < "3.9"
+    numpy>=1.19.0; python_version >= "3.9"
+    requests>=2.13.0,<3.0.0
+    pydantic>=2.0.0,<3.0.0
+    jinja2
+    # Official Python utilities
+    setuptools
+    packaging>=20.0
+
+[options.entry_points]
+console_scripts =
+    spacy = spacy.cli:setup_cli
+
+[options.extras_require]
+lookups =
+    spacy_lookups_data>=1.0.3,<1.1.0
+transformers =
+    spacy_transformers>=1.1.2,<1.4.0
+cuda =
+    cupy>=5.0.0b4,<13.0.0
+cuda80 =
+    cupy-cuda80>=5.0.0b4,<13.0.0
+cuda90 =
+    cupy-cuda90>=5.0.0b4,<13.0.0
+cuda91 =
+    cupy-cuda91>=5.0.0b4,<13.0.0
+cuda92 =
+    cupy-cuda92>=5.0.0b4,<13.0.0
+cuda100 =
+    cupy-cuda100>=5.0.0b4,<13.0.0
+cuda101 =
+    cupy-cuda101>=5.0.0b4,<13.0.0
+cuda102 =
+    cupy-cuda102>=5.0.0b4,<13.0.0
+cuda110 =
+    cupy-cuda110>=5.0.0b4,<13.0.0
+cuda111 =
+    cupy-cuda111>=5.0.0b4,<13.0.0
+cuda112 =
+    cupy-cuda112>=5.0.0b4,<13.0.0
+cuda113 =
+    cupy-cuda113>=5.0.0b4,<13.0.0
+cuda114 =
+    cupy-cuda114>=5.0.0b4,<13.0.0
+cuda115 =
+    cupy-cuda115>=5.0.0b4,<13.0.0
+cuda116 =
+    cupy-cuda116>=5.0.0b4,<13.0.0
+cuda117 =
+    cupy-cuda117>=5.0.0b4,<13.0.0
+cuda11x =
+    cupy-cuda11x>=11.0.0,<13.0.0
+cuda12x =
+    cupy-cuda12x>=11.5.0,<13.0.0
+cuda-autodetect =
+    cupy-wheel>=11.0.0,<13.0.0
+apple =
+    thinc-apple-ops>=1.0.0,<2.0.0
+# Language tokenizers with external dependencies
+ja =
+    sudachipy>=0.5.2,!=0.6.1
+    sudachidict_core>=20211220
+ko =
+    natto-py>=0.9.0
+th =
+    pythainlp>=2.0
+
+[bdist_wheel]
+universal = false
+
+[sdist]
+formats = gztar
+
+[tool:pytest]
+markers =
+    slow: mark a test as slow
+    issue: reference specific issue
+filterwarnings =
+    error
+    ignore:Core Pydantic V1:UserWarning:pydantic
+
+[mypy]
+ignore_missing_imports = True
+no_implicit_optional = True
+plugins = pydantic.mypy, thinc.mypy
+allow_redefinition = True