explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-25 17:36:30 +03:00
Code Issues Packages Projects Releases Wiki Activity

Fix 'debug model' for transformers + generalize (#7973)

Browse Source 
* add overrides to docs

* fix debug model with transformer

* assume training data is set in config
This commit is contained in:
Sofie Van Landeghem 2021-05-06 10:43:32 +02:00 committed by GitHub
parent cc5aeaed29
commit 02a6a5fea0
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 26 additions and 43 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

68
spacy/cli/debug_model.py
View File

@ -1,5 +1,6 @@
from typing import Dict, Any, Optional, Iterable
from pathlib import Path
import itertools
from spacy.training import Example
from spacy.util import resolve_dot_names
@ -73,23 +74,24 @@ def debug_model_cli(
msg.info(f"Fixing random seed: {seed}")
fix_random_seed(seed)
pipe = nlp.get_pipe(component)
if not hasattr(pipe, "model"):
msg.fail(
f"The component '{component}' does not specify an object that holds a Model.",
exits=1,
)
model = pipe.model
debug_model(config, T, nlp, model, print_settings=print_settings)
debug_model(config, T, nlp, pipe, print_settings=print_settings)
def debug_model(
config,
resolved_train_config,
nlp,
model: Model,
pipe,
*,
print_settings: Optional[Dict[str, Any]] = None,
):
if not hasattr(pipe, "model"):
msg.fail(
f"The component '{pipe}' does not specify an object that holds a Model.",
exits=1,
)
model = pipe.model
if not isinstance(model, Model):
msg.fail(
f"Requires a Thinc Model to be analysed, but found {type(model)} instead.",
@ -105,8 +107,6 @@ def debug_model(
_print_model(model, print_settings)
# STEP 1: Initializing the model and printing again
X = _get_docs()
# The output vector might differ from the official type of the output layer
with data_validation(False):
try:
dot_names = [resolved_train_config["train_corpus"]]
@ -114,15 +114,17 @@ def debug_model(
(train_corpus,) = resolve_dot_names(config, dot_names)
nlp.initialize(lambda: train_corpus(nlp))
msg.info("Initialized the model with the training corpus.")
examples = list(itertools.islice(train_corpus(nlp), 5))
except ValueError:
try:
_set_output_dim(nO=7, model=model)
with show_validation_error():
nlp.initialize(lambda: [Example.from_dict(x, {}) for x in X])
examples = [Example.from_dict(x, {}) for x in _get_docs()]
nlp.initialize(lambda: examples)
msg.info("Initialized the model with dummy data.")
except Exception:
msg.fail(
"Could not initialize the model: you'll have to provide a valid train_corpus argument in the config file.",
"Could not initialize the model: you'll have to provide a valid 'train_corpus' argument in the config file.",
exits=1,
)
@ -133,26 +135,23 @@ def debug_model(
# STEP 2: Updating the model and printing again
optimizer = Adam(0.001)
set_dropout_rate(model, 0.2)
# ugly hack to deal with Tok2Vec listeners
tok2vec = None
if model.has_ref("tok2vec") and model.get_ref("tok2vec").name == "tok2vec-listener":
tok2vec = nlp.get_pipe("tok2vec")
# ugly hack to deal with Tok2Vec/Transformer listeners
upstream_component = None
if model.has_ref("tok2vec") and "tok2vec-listener" in model.get_ref("tok2vec").name:
upstream_component = nlp.get_pipe("tok2vec")
if model.has_ref("tok2vec") and "transformer-listener" in model.get_ref("tok2vec").name:
upstream_component = nlp.get_pipe("transformer")
goldY = None
for e in range(3):
if tok2vec:
tok2vec.update([Example.from_dict(x, {}) for x in X])
Y, get_dX = model.begin_update(X)
if goldY is None:
goldY = _simulate_gold(Y)
dY = get_gradient(goldY, Y, model.ops)
get_dX(dY)
model.finish_update(optimizer)
if upstream_component:
upstream_component.update(examples)
pipe.update(examples)
if print_settings.get("print_after_training"):
msg.divider(f"STEP 2 - after training")
_print_model(model, print_settings)
# STEP 3: the final prediction
prediction = model.predict(X)
prediction = model.predict([ex.predicted for ex in examples])
if print_settings.get("print_prediction"):
msg.divider(f"STEP 3 - prediction")
msg.info(str(prediction))
@ -160,19 +159,6 @@ def debug_model(
msg.good(f"Succesfully ended analysis - model looks good.")
def get_gradient(goldY, Y, ops):
return ops.asarray(Y) - ops.asarray(goldY)
def _simulate_gold(element, counter=1):
if isinstance(element, Iterable):
for i in range(len(element)):
element[i] = _simulate_gold(element[i], counter + i)
return element
else:
return 1 / counter
def _sentences():
return [
"Apple is looking at buying U.K. startup for $1 billion",
@ -209,11 +195,7 @@ def _print_model(model, print_settings):
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)}")
msg.info(f" - dim {name}: {node.maybe_get_dim(name)}")
if parameters:
for name in node.param_names:
if node.has_param(name):

1
website/docs/api/cli.md
View File

@ -768,6 +768,7 @@ $ python -m spacy debug model ./config.cfg tagger -l "5,15" -DIM -PAR -P0 -P1 -P
| `--print-step3`, `-P3` | Print final predictions. ~~bool (flag)~~ |
| `--gpu-id`, `-g` | GPU ID or `-1` for CPU. Defaults to `-1`. ~~int (option)~~ |
| `--help`, `-h` | Show help message and available arguments. ~~bool (flag)~~ |
| overrides | Config parameters to override. Should be options starting with `--` that correspond to the config section and value to override, e.g. `--paths.train ./train.spacy`. ~~Any (option/flag)~~ |
| **PRINTS** | Debugging information. |
## train {#train tag="command"}