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revert commits that should have been on different, local branch
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@ -6,7 +6,7 @@ requires = [
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"cymem>=2.0.2,<2.1.0",
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"preshed>=3.0.2,<3.1.0",
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"murmurhash>=0.28.0,<1.1.0",
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"thinc>=8.0.0a21,<8.0.0a30",
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"thinc>=8.0.0a19,<8.0.0a30",
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"blis>=0.4.0,<0.5.0",
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"pytokenizations",
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"smart_open>=2.0.0,<3.0.0"
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@ -1,7 +1,7 @@
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# Our libraries
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cymem>=2.0.2,<2.1.0
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preshed>=3.0.2,<3.1.0
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thinc>=8.0.0a21,<8.0.0a30
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thinc>=8.0.0a19,<8.0.0a30
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blis>=0.4.0,<0.5.0
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ml_datasets>=0.1.1
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murmurhash>=0.28.0,<1.1.0
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@ -34,13 +34,13 @@ setup_requires =
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cymem>=2.0.2,<2.1.0
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preshed>=3.0.2,<3.1.0
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murmurhash>=0.28.0,<1.1.0
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thinc>=8.0.0a21,<8.0.0a30
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thinc>=8.0.0a19,<8.0.0a30
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install_requires =
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# Our libraries
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murmurhash>=0.28.0,<1.1.0
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cymem>=2.0.2,<2.1.0
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preshed>=3.0.2,<3.1.0
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thinc>=8.0.0a21,<8.0.0a30
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thinc>=8.0.0a19,<8.0.0a30
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blis>=0.4.0,<0.5.0
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wasabi>=0.7.1,<1.1.0
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srsly>=2.1.0,<3.0.0
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@ -2,7 +2,7 @@ from typing import Dict, Any, Optional
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from pathlib import Path
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from wasabi import msg
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from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam, Config
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from thinc.api import Model, DATA_VALIDATION
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from thinc.api import Model
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import typer
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from ._util import Arg, Opt, debug_cli, show_validation_error, parse_config_overrides
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@ -16,7 +16,7 @@ def debug_model_cli(
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# fmt: off
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ctx: typer.Context, # This is only used to read additional arguments
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config_path: Path = Arg(..., help="Path to config file", exists=True),
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pipe_name: str = Arg(..., help="Name of the pipe of which the model should be analysed"),
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section: str = Arg(..., help="Section that defines the model to be analysed"),
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layers: str = Opt("", "--layers", "-l", help="Comma-separated names of layer IDs to print"),
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dimensions: bool = Opt(False, "--dimensions", "-DIM", help="Show dimensions"),
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parameters: bool = Opt(False, "--parameters", "-PAR", help="Show parameters"),
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@ -53,20 +53,20 @@ def debug_model_cli(
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cfg = Config().from_disk(config_path)
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with show_validation_error():
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try:
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nlp, config = util.load_model_from_config(cfg, overrides=config_overrides)
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_, config = util.load_model_from_config(cfg, overrides=config_overrides)
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except ValueError as e:
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msg.fail(str(e), exits=1)
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seed = config.get("training", {}).get("seed", None)
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seed = config["pretraining"]["seed"]
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if seed is not None:
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msg.info(f"Fixing random seed: {seed}")
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fix_random_seed(seed)
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component = nlp.get_pipe(pipe_name)
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component = dot_to_object(config, section)
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if hasattr(component, "model"):
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model = component.model
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else:
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msg.fail(
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f"The component '{pipe_name}' does not specify an object that holds a Model.",
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f"The section '{section}' does not specify an object that holds a Model.",
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exits=1,
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)
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debug_model(model, print_settings=print_settings)
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@ -90,9 +90,7 @@ def debug_model(model: Model, *, print_settings: Optional[Dict[str, Any]] = None
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# STEP 1: Initializing the model and printing again
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Y = _get_output(model.ops.xp)
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_set_output_dim(nO=Y.shape[-1], model=model)
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DATA_VALIDATION.set(False) # The output vector might differ from the official type of the output layer
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model.initialize(X=_get_docs(), Y=Y)
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DATA_VALIDATION.set(True)
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if print_settings.get("print_after_init"):
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msg.info(f"After initialization:")
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_print_model(model, print_settings)
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