spaCy/spacy/cli/debug_model.py
Ines Montani 0ab483037c Make debug commands subcommands of spacy debug
Also handle backwards-compatibility so the old commands don't break
2020-07-12 13:53:41 +02:00

169 lines
5.9 KiB
Python

from pathlib import Path
from wasabi import msg
from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam
from ._util import Arg, Opt, debug_cli
from .. import util
from ..lang.en import English
@debug_cli.command("model")
def debug_model_cli(
# fmt: off
config_path: Path = Arg(..., help="Path to config file", exists=True),
layers: str = Opt("", "--layers", "-l", help="Comma-separated names of pipeline components to train"),
dimensions: bool = Opt(False, "--dimensions", "-DIM", help="Show dimensions"),
parameters: bool = Opt(False, "--parameters", "-PAR", help="Show parameters"),
gradients: bool = Opt(False, "--gradients", "-GRAD", help="Show gradients"),
attributes: bool = Opt(False, "--attributes", "-ATTR", help="Show attributes"),
P0: bool = Opt(False, "--print-step0", "-P0", help="Print model before training"),
P1: bool = Opt(False, "--print-step1", "-P1", help="Print model after initialization"),
P2: bool = Opt(False, "--print-step2", "-P2", help="Print model after training"),
P3: bool = Opt(True, "--print-step3", "-P3", help="Print final predictions"),
use_gpu: int = Opt(-1, "--use-gpu", "-g", help="Use GPU"),
seed: int = Opt(None, "--seed", "-s", help="Use GPU"),
# fmt: on
):
"""
Analyze a Thinc model implementation. Includes checks for internal structure
and activations during training.
"""
print_settings = {
"dimensions": dimensions,
"parameters": parameters,
"gradients": gradients,
"attributes": attributes,
"layers": [int(x.strip()) for x in layers.split(",")] if layers else [],
"print_before_training": P0,
"print_after_init": P1,
"print_after_training": P2,
"print_prediction": P3,
}
if seed is not None:
msg.info(f"Fixing random seed: {seed}")
fix_random_seed(seed)
if use_gpu >= 0:
msg.info(f"Using GPU: {use_gpu}")
require_gpu(use_gpu)
else:
msg.info(f"Using CPU")
debug_model(
config_path, print_settings=print_settings,
)
def debug_model(config_path: Path, *, print_settings=None):
if print_settings is None:
print_settings = {}
model = util.load_config(config_path, create_objects=True)["model"]
# STEP 0: Printing before training
msg.info(f"Analysing model with ID {model.id}")
if print_settings.get("print_before_training"):
msg.info(f"Before training:")
_print_model(model, print_settings)
# STEP 1: Initializing the model and printing again
model.initialize(X=_get_docs(), Y=_get_output(model.ops.xp))
if print_settings.get("print_after_init"):
msg.info(f"After initialization:")
_print_model(model, print_settings)
# STEP 2: Updating the model and printing again
optimizer = Adam(0.001)
set_dropout_rate(model, 0.2)
for e in range(3):
Y, get_dX = model.begin_update(_get_docs())
dY = get_gradient(model, Y)
get_dX(dY)
model.finish_update(optimizer)
if print_settings.get("print_after_training"):
msg.info(f"After training:")
_print_model(model, print_settings)
# STEP 3: the final prediction
prediction = model.predict(_get_docs())
if print_settings.get("print_prediction"):
msg.info(f"Prediction:", str(prediction))
def get_gradient(model, Y):
goldY = _get_output(model.ops.xp)
return Y - goldY
def _sentences():
return [
"Apple is looking at buying U.K. startup for $1 billion",
"Autonomous cars shift insurance liability toward manufacturers",
"San Francisco considers banning sidewalk delivery robots",
"London is a big city in the United Kingdom.",
]
def _get_docs():
nlp = English()
return list(nlp.pipe(_sentences()))
def _get_output(xp):
return xp.asarray(
[
xp.asarray([i + 10, i + 20, i + 30], dtype="float32")
for i, _ in enumerate(_get_docs())
]
)
def _print_model(model, print_settings):
layers = print_settings.get("layers", "")
parameters = print_settings.get("parameters", False)
dimensions = print_settings.get("dimensions", False)
gradients = print_settings.get("gradients", False)
attributes = print_settings.get("attributes", False)
for i, node in enumerate(model.walk()):
if not layers or i in layers:
msg.info(f"Layer {i}: model ID {node.id}: '{node.name}'")
if dimensions:
for name in node.dim_names:
if node.has_dim(name):
msg.info(f" - dim {name}: {node.get_dim(name)}")
else:
msg.info(f" - dim {name}: {node.has_dim(name)}")
if parameters:
for name in node.param_names:
if node.has_param(name):
print_value = _print_matrix(node.get_param(name))
msg.info(f" - param {name}: {print_value}")
else:
msg.info(f" - param {name}: {node.has_param(name)}")
if gradients:
for name in node.param_names:
if node.has_grad(name):
print_value = _print_matrix(node.get_grad(name))
msg.info(f" - grad {name}: {print_value}")
else:
msg.info(f" - grad {name}: {node.has_grad(name)}")
if attributes:
attrs = node.attrs
for name, value in attrs.items():
msg.info(f" - attr {name}: {value}")
def _print_matrix(value):
if value is None or isinstance(value, bool):
return value
result = str(value.shape) + " - sample: "
sample_matrix = value
for d in range(value.ndim - 1):
sample_matrix = sample_matrix[0]
sample_matrix = sample_matrix[0:5]
result = result + str(sample_matrix)
return result