Move set_{gpu_allocator,seed}_from_config to spacy.util

spacy/training/initialize.py

@@ -1,6 +1,5 @@
 from typing import Union, Dict, Optional, Any, IO, TYPE_CHECKING
-from thinc.api import Config, fix_random_seed, set_gpu_allocator
+from thinc.api import Config, ConfigValidationError
-from thinc.api import ConfigValidationError
 from pathlib import Path
 import srsly
 import numpy
@@ -19,6 +18,7 @@ from ..schemas import ConfigSchemaDistill, ConfigSchemaTraining
 from ..util import registry, load_model_from_config, resolve_dot_names, logger
 from ..util import load_model, ensure_path, get_sourced_components
 from ..util import OOV_RANK, DEFAULT_OOV_PROB
+from ..util import set_gpu_allocator_from_config, set_seed_from_config
 
 if TYPE_CHECKING:
     from ..language import Language  # noqa: F401
@@ -27,8 +27,8 @@ if TYPE_CHECKING:
 def init_nlp(config: Config, *, use_gpu: int = -1) -> "Language":
     raw_config = config
     config = raw_config.interpolate()
-    _set_seed_from_config(config)
+    set_seed_from_config(config)
-    _set_gpu_allocator_from_config(config, use_gpu)
+    set_gpu_allocator_from_config(config, use_gpu)
     # Use original config here before it's resolved to functions
     sourced = get_sourced_components(config)
     nlp = load_model_from_config(raw_config, auto_fill=True)
@@ -106,8 +106,8 @@ def init_nlp_student(
     """
     raw_config = config
     config = raw_config.interpolate()
-    _set_seed_from_config(config)
+    set_seed_from_config(config)
-    _set_gpu_allocator_from_config(config, use_gpu)
+    set_gpu_allocator_from_config(config, use_gpu)
 
     # Use original config here before it's resolved to functions
     sourced = get_sourced_components(config)
@@ -422,18 +422,3 @@ def ensure_shape(vectors_loc):
         yield from lines2
         lines2.close()
     lines.close()
-
-
-def _set_gpu_allocator_from_config(config: Config, use_gpu: int):
-    if "gpu_allocator" not in config["training"]:
-        raise ValueError(Errors.E1015.format(value="[training] gpu_allocator"))
-    allocator = config["training"]["gpu_allocator"]
-    if use_gpu >= 0 and allocator:
-        set_gpu_allocator(allocator)
-
-
-def _set_seed_from_config(config: Config):
-    if "seed" not in config["training"]:
-        raise ValueError(Errors.E1015.format(value="[training] seed"))
-    if config["training"]["seed"] is not None:
-        fix_random_seed(config["training"]["seed"])

spacy/training/loop.py

@@ -2,7 +2,7 @@ from typing import List, Callable, Tuple, Dict, Iterable, Union, Any, IO
 from typing import Optional, TYPE_CHECKING
 from pathlib import Path
 from timeit import default_timer as timer
-from thinc.api import Optimizer, Config, constant, fix_random_seed, set_gpu_allocator
+from thinc.api import Optimizer, Config, constant
 from wasabi import Printer
 import random
 import sys
@@ -15,6 +15,7 @@ from ..errors import Errors
 from ..tokens.doc import Doc
 from .. import ty
 from ..util import resolve_dot_names, registry, logger
+from ..util import set_gpu_allocator_from_config, set_seed_from_config
 
 if TYPE_CHECKING:
     from ..language import Language  # noqa: F401
@@ -53,11 +54,8 @@ def distill(
     msg = Printer(no_print=True)
     # Create iterator, which yields out info after each optimization step.
     config = student.config.interpolate()
-    if config["training"]["seed"] is not None:
+    set_seed_from_config(config)
-        fix_random_seed(config["training"]["seed"])
+    set_gpu_allocator_from_config(config, use_gpu)
-    allocator = config["training"]["gpu_allocator"]
-    if use_gpu >= 0 and allocator:
-        set_gpu_allocator(allocator)
     T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
     D = registry.resolve(config["distillation"], schema=ConfigSchemaDistill)
     dot_names = [D["corpus"], T["dev_corpus"]]
@@ -175,11 +173,8 @@ def train(
     msg = Printer(no_print=True)
     # Create iterator, which yields out info after each optimization step.
     config = nlp.config.interpolate()
-    if config["training"]["seed"] is not None:
+    set_seed_from_config(config)
-        fix_random_seed(config["training"]["seed"])
+    set_gpu_allocator_from_config(config, use_gpu)
-    allocator = config["training"]["gpu_allocator"]
-    if use_gpu >= 0 and allocator:
-        set_gpu_allocator(allocator)
     T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
     dot_names = [T["train_corpus"], T["dev_corpus"]]
     train_corpus, dev_corpus = resolve_dot_names(config, dot_names)

spacy/util.py

@@ -11,6 +11,7 @@ from pathlib import Path
 import thinc
 from thinc.api import NumpyOps, get_current_ops, Adam, Config, Optimizer
 from thinc.api import ConfigValidationError, Model, constant as constant_schedule
+from thinc.api import fix_random_seed, set_gpu_allocator
 import functools
 import itertools
 import numpy
@@ -1790,3 +1791,22 @@ def find_available_port(start: int, host: str, auto_select: bool = False) -> int
     # if we get here, the port changed
     warnings.warn(Warnings.W124.format(host=host, port=start, serve_port=port))
     return port
+
+
+def set_gpu_allocator_from_config(config: Config, use_gpu: int):
+    """Change the global GPU allocator based to the value in
+    the configuration."""
+    if "gpu_allocator" not in config["training"]:
+        raise ValueError(Errors.E1015.format(value="[training] gpu_allocator"))
+    allocator = config["training"]["gpu_allocator"]
+    if use_gpu >= 0 and allocator:
+        set_gpu_allocator(allocator)
+
+
+def set_seed_from_config(config: Config):
+    """Set the random number generator seed to the value in
+    the configuration."""
+    if "seed" not in config["training"]:
+        raise ValueError(Errors.E1015.format(value="[training] seed"))
+    if config["training"]["seed"] is not None:
+        fix_random_seed(config["training"]["seed"])