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import inspect
import pickle
from types import GeneratorType
from typing import Any, Callable, Dict, Iterable, List, Optional, Union
import catalogue
import numpy
import pytest
try:
from pydantic.v1 import BaseModel, PositiveInt, StrictBool, StrictFloat, constr
except ImportError:
from pydantic import BaseModel, PositiveInt, StrictBool, StrictFloat, constr # type: ignore
import thinc.config
from thinc.api import Config, Model, NumpyOps, RAdam
from thinc.config import ConfigValidationError
from thinc.types import Generator, Ragged
from thinc.util import partial
from .util import make_tempdir
EXAMPLE_CONFIG = """
[optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
use_averages = true
[optimizer.learn_rate]
@schedules = "warmup_linear.v1"
initial_rate = 0.1
warmup_steps = 10000
total_steps = 100000
[pipeline]
[pipeline.parser]
name = "parser"
factory = "parser"
[pipeline.parser.model]
@layers = "spacy.ParserModel.v1"
hidden_depth = 1
hidden_width = 64
token_vector_width = 128
[pipeline.parser.model.tok2vec]
@layers = "Tok2Vec.v1"
width = ${pipeline.parser.model:token_vector_width}
[pipeline.parser.model.tok2vec.embed]
@layers = "spacy.MultiFeatureHashEmbed.v1"
width = ${pipeline.parser.model.tok2vec:width}
[pipeline.parser.model.tok2vec.embed.hidden]
@layers = "MLP.v1"
depth = 1
pieces = 3
layer_norm = true
outputs = ${pipeline.parser.model.tok2vec.embed:width}
[pipeline.parser.model.tok2vec.encode]
@layers = "spacy.MaxoutWindowEncoder.v1"
depth = 4
pieces = 3
window_size = 1
[pipeline.parser.model.lower]
@layers = "spacy.ParserLower.v1"
[pipeline.parser.model.upper]
@layers = "thinc.Linear.v1"
"""
OPTIMIZER_CFG = """
[optimizer]
@optimizers = "Adam.v1"
beta1 = 0.9
beta2 = 0.999
use_averages = true
[optimizer.learn_rate]
@schedules = "warmup_linear.v1"
initial_rate = 0.1
warmup_steps = 10000
total_steps = 100000
"""
class my_registry(thinc.config.registry):
cats = catalogue.create("thinc", "tests", "cats", entry_points=False)
class HelloIntsSchema(BaseModel):
hello: int
world: int
class Config:
extra = "forbid"
class DefaultsSchema(BaseModel):
required: int
optional: str = "default value"
class Config:
extra = "forbid"
class ComplexSchema(BaseModel):
outer_req: int
outer_opt: str = "default value"
level2_req: HelloIntsSchema
level2_opt: DefaultsSchema = DefaultsSchema(required=1)
@my_registry.cats.register("catsie.v1")
def catsie_v1(evil: StrictBool, cute: bool = True) -> str:
if evil:
return "scratch!"
else:
return "meow"
@my_registry.cats.register("catsie.v2")
def catsie_v2(evil: StrictBool, cute: bool = True, cute_level: int = 1) -> str:
if evil:
return "scratch!"
else:
if cute_level > 2:
return "meow <3"
return "meow"
good_catsie = {"@cats": "catsie.v1", "evil": False, "cute": True}
ok_catsie = {"@cats": "catsie.v1", "evil": False, "cute": False}
bad_catsie = {"@cats": "catsie.v1", "evil": True, "cute": True}
worst_catsie = {"@cats": "catsie.v1", "evil": True, "cute": False}
def test_make_config_positional_args_dicts():
cfg = {
"hyper_params": {"n_hidden": 512, "dropout": 0.2, "learn_rate": 0.001},
"model": {
"@layers": "chain.v1",
"*": {
"relu1": {"@layers": "Relu.v1", "nO": 512, "dropout": 0.2},
"relu2": {"@layers": "Relu.v1", "nO": 512, "dropout": 0.2},
"softmax": {"@layers": "Softmax.v1"},
},
},
"optimizer": {"@optimizers": "Adam.v1", "learn_rate": 0.001},
}
resolved = my_registry.resolve(cfg)
model = resolved["model"]
X = numpy.ones((784, 1), dtype="f")
model.initialize(X=X, Y=numpy.zeros((784, 1), dtype="f"))
model.begin_update(X)
model.finish_update(resolved["optimizer"])
def test_objects_from_config():
config = {
"optimizer": {
"@optimizers": "my_cool_optimizer.v1",
"beta1": 0.2,
"learn_rate": {
"@schedules": "my_cool_repetitive_schedule.v1",
"base_rate": 0.001,
"repeat": 4,
},
}
}
@thinc.registry.optimizers.register("my_cool_optimizer.v1")
def make_my_optimizer(learn_rate: List[float], beta1: float):
return RAdam(learn_rate, beta1=beta1)
@thinc.registry.schedules("my_cool_repetitive_schedule.v1")
def decaying(base_rate: float, repeat: int) -> List[float]:
return repeat * [base_rate]
optimizer = my_registry.resolve(config)["optimizer"]
assert optimizer.b1(step=optimizer._step, key=(0, "")) == 0.2
assert optimizer.learn_rate(step=optimizer._step, key=(0, "")) == 0.001
def test_handle_generic_model_type():
"""Test that validation can handle checks against arbitrary generic
types in function argument annotations."""
@my_registry.layers("my_transform.v1")
def my_transform(model: Model[int, int]):
model.name = "transformed_model"
return model
cfg = {"@layers": "my_transform.v1", "model": {"@layers": "Linear.v1"}}
model = my_registry.resolve({"test": cfg})["test"]
assert isinstance(model, Model)
assert model.name == "transformed_model"
def test_arg_order_is_preserved():
str_cfg = """
[model]
[model.chain]
@layers = "chain.v1"
[model.chain.*.hashembed]
@layers = "HashEmbed.v1"
nO = 8
nV = 8
[model.chain.*.expand_window]
@layers = "expand_window.v1"
window_size = 1
"""
cfg = Config().from_str(str_cfg)
resolved = my_registry.resolve(cfg)
model = resolved["model"]["chain"]
# Fails when arguments are sorted, because expand_window
# is sorted before hashembed.
assert model.name == "hashembed>>expand_window"
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