def from_params(cls, vocab: Vocabulary,
params: Params) -> 'ElmoTokenEmbedder': # type: ignore
# pylint: disable=arguments-differ
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
do_layer_norm = params.pop_bool('do_layer_norm', False)
dropout = params.pop_float("dropout", 0.5)
namespace_to_cache = params.pop("namespace_to_cache", None)
if namespace_to_cache is not None:
vocab_to_cache = list(
vocab.get_token_to_index_vocabulary(namespace_to_cache).keys())
else:
vocab_to_cache = None
projection_dim = params.pop_int("projection_dim", None)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
do_layer_norm=do_layer_norm,
dropout=dropout,
requires_grad=requires_grad,
projection_dim=projection_dim,
vocab_to_cache=vocab_to_cache)
def from_params(cls, vocab: Vocabulary, params: Params) -> 'TokenCharactersEncoder': # type: ignore
# pylint: disable=arguments-differ
embedding_params: Params = params.pop("embedding")
# Embedding.from_params() uses "tokens" as the default namespace, but we need to change
# that to be "token_characters" by default.
embedding_params.setdefault("vocab_namespace", "token_characters")
embedding = Embedding.from_params(vocab, embedding_params)
encoder_params: Params = params.pop("encoder")
encoder = Seq2VecEncoder.from_params(encoder_params)
dropout = params.pop_float("dropout", 0.0)
params.assert_empty(cls.__name__)
return cls(embedding, encoder, dropout)
def from_params(self, params: Params) -> PytorchSeq2VecWrapper:
if not params.pop('batch_first', True):
raise ConfigurationError("Our encoder semantics assumes batch is always first!")
if self._module_class in self.PYTORCH_MODELS:
params['batch_first'] = True
module = self._module_class(**params.as_dict())
return PytorchSeq2VecWrapper(module)
def from_params(cls, params: Params):
input_dim = params.pop_int('input_dim')
num_layers = params.pop_int('num_layers')
hidden_dims = params.pop('hidden_dims')
activations = params.pop('activations')
dropout = params.pop('dropout', 0.0)
if isinstance(activations, list):
activations = [Activation.by_name(name)() for name in activations]
else:
activations = Activation.by_name(activations)()
params.assert_empty(cls.__name__)
return cls(input_dim=input_dim,
num_layers=num_layers,
hidden_dims=hidden_dims,
activations=activations,
dropout=dropout)
def from_params(
cls, vocab: Vocabulary,
params: Params) -> 'BasicTextFieldEmbedder': # type: ignore
# pylint: disable=arguments-differ,bad-super-call
# The original `from_params` for this class was designed in a way that didn't agree
# with the constructor. The constructor wants a 'token_embedders' parameter that is a
# `Dict[str, TokenEmbedder]`, but the original `from_params` implementation expected those
# key-value pairs to be top-level in the params object.
#
# This breaks our 'configuration wizard' and configuration checks. Hence, going forward,
# the params need a 'token_embedders' key so that they line up with what the constructor wants.
# For now, the old behavior is still supported, but produces a DeprecationWarning.
embedder_to_indexer_map = params.pop("embedder_to_indexer_map", None)
if embedder_to_indexer_map is not None:
embedder_to_indexer_map = embedder_to_indexer_map.as_dict(
quiet=True)
allow_unmatched_keys = params.pop_bool("allow_unmatched_keys", False)
token_embedder_params = params.pop('token_embedders', None)
if token_embedder_params is not None:
# New way: explicitly specified, so use it.
token_embedders = {
name: TokenEmbedder.from_params(subparams, vocab=vocab)
for name, subparams in token_embedder_params.items()
}
else:
# Warn that the original behavior is deprecated
warnings.warn(
DeprecationWarning(
"the token embedders for BasicTextFieldEmbedder should now "
"be specified as a dict under the 'token_embedders' key, "
"not as top-level key-value pairs"))
token_embedders = {}
keys = list(params.keys())
for key in keys:
embedder_params = params.pop(key)
token_embedders[key] = TokenEmbedder.from_params(
vocab=vocab, params=embedder_params)
params.assert_empty(cls.__name__)
return cls(token_embedders, embedder_to_indexer_map,
allow_unmatched_keys)
def from_params(cls, params: Params) -> 'Elmo':
# Add files to archive
params.add_file_to_archive('options_file')
params.add_file_to_archive('weight_file')
options_file = params.pop('options_file')
weight_file = params.pop('weight_file')
requires_grad = params.pop('requires_grad', False)
num_output_representations = params.pop('num_output_representations')
do_layer_norm = params.pop_bool('do_layer_norm', False)
dropout = params.pop_float('dropout', 0.5)
params.assert_empty(cls.__name__)
return cls(options_file=options_file,
weight_file=weight_file,
num_output_representations=num_output_representations,
requires_grad=requires_grad,
do_layer_norm=do_layer_norm,
dropout=dropout)
def from_params(cls, vocab: Vocabulary,
params: Params) -> 'BidafEnsemble': # type: ignore
# pylint: disable=arguments-differ
if vocab:
raise ConfigurationError("vocab should be None")
submodels = []
paths = params.pop("submodels")
for path in paths:
submodels.append(load_archive(path).model)
return cls(submodels=submodels)
def datasets_from_params(params: Params) -> Dict[str, Iterable[Instance]]:
"""
Load all the datasets specified by the config.
"""
dataset_reader = DatasetReader.from_params(params.pop('dataset_reader'))
validation_dataset_reader_params = params.pop("validation_dataset_reader",
None)
validation_and_test_dataset_reader: DatasetReader = dataset_reader
if validation_dataset_reader_params is not None:
logger.info(
"Using a separate dataset reader to load validation and test data."
)
validation_and_test_dataset_reader = DatasetReader.from_params(
validation_dataset_reader_params)
train_data_path = params.pop('train_data_path')
logger.info("Reading training data from %s", train_data_path)
train_data = dataset_reader.read(train_data_path)
datasets: Dict[str, Iterable[Instance]] = {"train": train_data}
validation_data_path = params.pop('validation_data_path', None)
if validation_data_path is not None:
logger.info("Reading validation data from %s", validation_data_path)
validation_data = validation_and_test_dataset_reader.read(
validation_data_path)
datasets["validation"] = validation_data
test_data_path = params.pop("test_data_path", None)
if test_data_path is not None:
logger.info("Reading test data from %s", test_data_path)
test_data = validation_and_test_dataset_reader.read(test_data_path)
datasets["test"] = test_data
return datasets
def from_params(cls, vocab: Vocabulary, params: Params) -> 'BiattentiveClassificationNetwork': # type: ignore
# pylint: disable=arguments-differ
embedder_params = params.pop("text_field_embedder")
text_field_embedder = TextFieldEmbedder.from_params(vocab=vocab, params=embedder_params)
embedding_dropout = params.pop("embedding_dropout")
pre_encode_feedforward = FeedForward.from_params(params.pop("pre_encode_feedforward"))
encoder = Seq2SeqEncoder.from_params(params.pop("encoder"))
integrator = Seq2SeqEncoder.from_params(params.pop("integrator"))
integrator_dropout = params.pop("integrator_dropout")
output_layer_params = params.pop("output_layer")
if "activations" in output_layer_params:
output_layer = FeedForward.from_params(output_layer_params)
else:
output_layer = Maxout.from_params(output_layer_params)
elmo = params.pop("elmo", None)
if elmo is not None:
elmo = Elmo.from_params(elmo)
use_input_elmo = params.pop_bool("use_input_elmo", False)
use_integrator_output_elmo = params.pop_bool("use_integrator_output_elmo", False)
initializer = InitializerApplicator.from_params(params.pop('initializer', []))
regularizer = RegularizerApplicator.from_params(params.pop('regularizer', []))
params.assert_empty(cls.__name__)
return cls(vocab=vocab,
text_field_embedder=text_field_embedder,
embedding_dropout=embedding_dropout,
pre_encode_feedforward=pre_encode_feedforward,
encoder=encoder,
integrator=integrator,
integrator_dropout=integrator_dropout,
output_layer=output_layer,
elmo=elmo,
use_input_elmo=use_input_elmo,
use_integrator_output_elmo=use_integrator_output_elmo,
initializer=initializer,
regularizer=regularizer)
def from_params(cls, model_parameters: List, params: Params): # type: ignore
# pylint: disable=arguments-differ
if isinstance(params, str):
optimizer = params
params = Params({})
else:
optimizer = params.pop_choice("type", Optimizer.list_available())
# make the parameter groups if need
groups = params.pop("parameter_groups", None)
if groups:
# The input to the optimizer is list of dict.
# Each dict contains a "parameter group" and groups specific options,
# e.g., {'params': [list of parameters], 'lr': 1e-3, ...}
# Any config option not specified in the additional options (e.g.
# for the default group) is inherited from the top level config.
# see: http://pytorch.org/docs/0.3.0/optim.html?#per-parameter-options
#
# groups contains something like:
#"parameter_groups": [
# [["regex1", "regex2"], {"lr": 1e-3},
# ["regex3"], {"lr": 1e-4}]
#]
#(note that the allennlp config files require double quotes ", and will
# fail (sometimes silently) with single quotes ').
# This is typed as as Any since the dict values other then
# the params key are passed to the Optimizer constructor and
# can be any type it accepts.
# In addition to any parameters that match group specific regex,
# we also need a group for the remaining "default" group.
# Those will be included in the last entry of parameter_groups.
parameter_groups: Any = [{'params': []} for _ in range(len(groups) + 1)]
# add the group specific kwargs
for k in range(len(groups)): # pylint: disable=consider-using-enumerate
parameter_groups[k].update(groups[k][1].as_dict())
regex_use_counts: Dict[str, int] = {}
parameter_group_names: List[set] = [set() for _ in range(len(groups) + 1)]
for name, param in model_parameters:
# Determine the group for this parameter.
group_index = None
for k, group_regexes in enumerate(groups):
for regex in group_regexes[0]:
if regex not in regex_use_counts:
regex_use_counts[regex] = 0
if re.search(regex, name):
if group_index is not None and group_index != k:
raise ValueError("{} was specified in two separate parameter groups".format(name))
group_index = k
regex_use_counts[regex] += 1
if group_index is not None:
parameter_groups[group_index]['params'].append(param)
parameter_group_names[group_index].add(name)
else:
# the default group
parameter_groups[-1]['params'].append(param)
parameter_group_names[-1].add(name)
# log the parameter groups
logger.info("Done constructing parameter groups.")
for k in range(len(groups) + 1):
group_options = {key: val for key, val in parameter_groups[k].items()
if key != 'params'}
logger.info("Group %s: %s, %s", k,
list(parameter_group_names[k]),
group_options)
# check for unused regex
for regex, count in regex_use_counts.items():
if count == 0:
logger.warning("When constructing parameter groups, "
" %s not match any parameter name", regex)
else:
parameter_groups = [param for name, param in model_parameters]
# Log the number of parameters to optimize
num_parameters = 0
for parameter_group in parameter_groups:
if isinstance(parameter_group, dict):
num_parameters += sum(parameter.numel() for parameter in parameter_group["params"])
else:
num_parameters += parameter_group.numel()
logger.info("Number of trainable parameters: %s", num_parameters)
return Optimizer.by_name(optimizer)(parameter_groups, **params.as_dict()) # type: ignore
def train_model(params: Params,
serialization_dir: str,
file_friendly_logging: bool = False,
recover: bool = False) -> Model:
"""
Trains the model specified in the given :class:`Params` object, using the data and training
parameters also specified in that object, and saves the results in ``serialization_dir``.
Parameters
----------
params : ``Params``
A parameter object specifying an AllenNLP Experiment.
serialization_dir : ``str``
The directory in which to save results and logs.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
down tqdm's output to only once every 10 seconds.
recover : ``bool``, optional (default=False)
If ``True``, we will try to recover a training run from an existing serialization
directory. This is only intended for use when something actually crashed during the middle
of a run. For continuing training a model on new data, see the ``fine-tune`` command.
Returns
-------
best_model: ``Model``
The model with the best epoch weights.
"""
prepare_environment(params)
create_serialization_dir(params, serialization_dir, recover)
prepare_global_logging(serialization_dir, file_friendly_logging)
check_for_gpu(params.get('trainer').get('cuda_device', -1))
params.to_file(os.path.join(serialization_dir, CONFIG_NAME))
all_datasets = datasets_from_params(params)
datasets_for_vocab_creation = set(
params.pop("datasets_for_vocab_creation", all_datasets))
for dataset in datasets_for_vocab_creation:
if dataset not in all_datasets:
raise ConfigurationError(
f"invalid 'dataset_for_vocab_creation' {dataset}")
logger.info(
"From dataset instances, %s will be considered for vocabulary creation.",
", ".join(datasets_for_vocab_creation))
vocab = Vocabulary.from_params(
params.pop("vocabulary", {}),
(instance for key, dataset in all_datasets.items()
for instance in dataset if key in datasets_for_vocab_creation))
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
model = Model.from_params(vocab=vocab, params=params.pop('model'))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(vocab)
validation_iterator_params = params.pop("validation_iterator", None)
if validation_iterator_params:
validation_iterator = DataIterator.from_params(
validation_iterator_params)
validation_iterator.index_with(vocab)
else:
validation_iterator = None
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
trainer = Trainer.from_params(model,
serialization_dir,
iterator,
train_data,
validation_data,
trainer_params,
validation_iterator=validation_iterator)
evaluate_on_test = params.pop_bool("evaluate_on_test", False)
params.assert_empty('base train command')
try:
metrics = trainer.train()
except KeyboardInterrupt:
# if we have completed an epoch, try to create a model archive.
if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
logging.info(
"Training interrupted by the user. Attempting to create "
"a model archive using the current best epoch weights.")
archive_model(serialization_dir,
files_to_archive=params.files_to_archive)
raise
# Now tar up results
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
logger.info("Loading the best epoch weights.")
best_model_state_path = os.path.join(serialization_dir, 'best.th')
best_model_state = torch.load(best_model_state_path)
best_model = model
best_model.load_state_dict(best_model_state)
if test_data and evaluate_on_test:
logger.info(
"The model will be evaluated using the best epoch weights.")
test_metrics = evaluate(
best_model,
test_data,
validation_iterator or iterator,
cuda_device=trainer._cuda_devices[0] # pylint: disable=protected-access
)
for key, value in test_metrics.items():
metrics["test_" + key] = value
elif test_data:
logger.info(
"To evaluate on the test set after training, pass the "
"'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")
metrics_json = json.dumps(metrics, indent=2)
with open(os.path.join(serialization_dir, "metrics.json"),
"w") as metrics_file:
metrics_file.write(metrics_json)
logger.info("Metrics: %s", metrics_json)
return best_model
def fine_tune_model(model: Model,
params: Params,
serialization_dir: str,
extend_vocab: bool = False,
file_friendly_logging: bool = False) -> Model:
"""
Fine tunes the given model, using a set of parameters that is largely identical to those used
for :func:`~allennlp.commands.train.train_model`, except that the ``model`` section is ignored,
if it is present (as we are already given a ``Model`` here).
The main difference between the logic done here and the logic done in ``train_model`` is that
here we do not worry about vocabulary construction or creating the model object. Everything
else is the same.
Parameters
----------
archive : ``Archive``
A saved model archive that is the result of running the ``train`` command.
train_data_path : ``str``
Path to the training data to use for fine-tuning.
serialization_dir : ``str``
The directory in which to save results and logs.
validation_data_path : ``str``, optional
Path to the validation data to use while fine-tuning.
extend_vocab: ``bool``, optional (default=False)
If ``True``, we use the new instances to extend your vocabulary.
file_friendly_logging : ``bool``, optional (default=False)
If ``True``, we add newlines to tqdm output, even on an interactive terminal, and we slow
down tqdm's output to only once every 10 seconds.
"""
prepare_environment(params)
if os.path.exists(serialization_dir) and os.listdir(serialization_dir):
raise ConfigurationError(f"Serialization directory ({serialization_dir}) "
f"already exists and is not empty.")
os.makedirs(serialization_dir, exist_ok=True)
prepare_global_logging(serialization_dir, file_friendly_logging)
serialization_params = deepcopy(params).as_dict(quiet=True)
with open(os.path.join(serialization_dir, CONFIG_NAME), "w") as param_file:
json.dump(serialization_params, param_file, indent=4)
if params.pop('model', None):
logger.warning("You passed parameters for the model in your configuration file, but we "
"are ignoring them, using instead the model parameters in the archive.")
vocabulary_params = params.pop('vocabulary', {})
if vocabulary_params.get('directory_path', None):
logger.warning("You passed `directory_path` in parameters for the vocabulary in "
"your configuration file, but it will be ignored. ")
all_datasets = datasets_from_params(params)
vocab = model.vocab
if extend_vocab:
datasets_for_vocab_creation = set(params.pop("datasets_for_vocab_creation", all_datasets))
for dataset in datasets_for_vocab_creation:
if dataset not in all_datasets:
raise ConfigurationError(f"invalid 'dataset_for_vocab_creation' {dataset}")
logger.info("Extending model vocabulary using %s data.", ", ".join(datasets_for_vocab_creation))
vocab.extend_from_instances(vocabulary_params,
(instance for key, dataset in all_datasets.items()
for instance in dataset
if key in datasets_for_vocab_creation))
vocab.save_to_files(os.path.join(serialization_dir, "vocabulary"))
iterator = DataIterator.from_params(params.pop("iterator"))
iterator.index_with(model.vocab)
train_data = all_datasets['train']
validation_data = all_datasets.get('validation')
test_data = all_datasets.get('test')
trainer_params = params.pop("trainer")
no_grad_regexes = trainer_params.pop("no_grad", ())
for name, parameter in model.named_parameters():
if any(re.search(regex, name) for regex in no_grad_regexes):
parameter.requires_grad_(False)
frozen_parameter_names, tunable_parameter_names = \
get_frozen_and_tunable_parameter_names(model)
logger.info("Following parameters are Frozen (without gradient):")
for name in frozen_parameter_names:
logger.info(name)
logger.info("Following parameters are Tunable (with gradient):")
for name in tunable_parameter_names:
logger.info(name)
trainer = Trainer.from_params(model,
serialization_dir,
iterator,
train_data,
validation_data,
trainer_params)
evaluate_on_test = params.pop_bool("evaluate_on_test", False)
params.assert_empty('base train command')
try:
metrics = trainer.train()
except KeyboardInterrupt:
# if we have completed an epoch, try to create a model archive.
if os.path.exists(os.path.join(serialization_dir, _DEFAULT_WEIGHTS)):
logging.info("Fine-tuning interrupted by the user. Attempting to create "
"a model archive using the current best epoch weights.")
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
raise
# Now tar up results
archive_model(serialization_dir, files_to_archive=params.files_to_archive)
if test_data and evaluate_on_test:
test_metrics = evaluate(model, test_data, iterator, cuda_device=trainer._cuda_devices[0]) # pylint: disable=protected-access
for key, value in test_metrics.items():
metrics["test_" + key] = value
elif test_data:
logger.info("To evaluate on the test set after training, pass the "
"'evaluate_on_test' flag, or use the 'allennlp evaluate' command.")
metrics_json = json.dumps(metrics, indent=2)
with open(os.path.join(serialization_dir, "metrics.json"), "w") as metrics_file:
metrics_file.write(metrics_json)
logger.info("Metrics: %s", metrics_json)
return model
def from_params(cls, vocab: Vocabulary,
params: Params) -> 'Embedding': # type: ignore
"""
We need the vocabulary here to know how many items we need to embed, and we look for a
``vocab_namespace`` key in the parameter dictionary to know which vocabulary to use. If
you know beforehand exactly how many embeddings you need, or aren't using a vocabulary
mapping for the things getting embedded here, then you can pass in the ``num_embeddings``
key directly, and the vocabulary will be ignored.
In the configuration file, a file containing pretrained embeddings can be specified
using the parameter ``"pretrained_file"``.
It can be the path to a local file or an URL of a (cached) remote file.
Two formats are supported:
* hdf5 file - containing an embedding matrix in the form of a torch.Tensor;
* text file - an utf-8 encoded text file with space separated fields::
[word] [dim 1] [dim 2] ...
The text file can eventually be compressed with gzip, bz2, lzma or zip.
You can even select a single file inside an archive containing multiple files
using the URI::
"(archive_uri)#file_path_inside_the_archive"
where ``archive_uri`` can be a file system path or a URL. For example::
"(http://nlp.stanford.edu/data/glove.twitter.27B.zip)#glove.twitter.27B.200d.txt"
"""
# pylint: disable=arguments-differ
num_embeddings = params.pop_int('num_embeddings', None)
vocab_namespace = params.pop("vocab_namespace", "tokens")
if num_embeddings is None:
num_embeddings = vocab.get_vocab_size(vocab_namespace)
embedding_dim = params.pop_int('embedding_dim')
pretrained_file = params.pop("pretrained_file", None)
projection_dim = params.pop_int("projection_dim", None)
trainable = params.pop_bool("trainable", True)
padding_index = params.pop_int('padding_index', None)
max_norm = params.pop_float('max_norm', None)
norm_type = params.pop_float('norm_type', 2.)
scale_grad_by_freq = params.pop_bool('scale_grad_by_freq', False)
sparse = params.pop_bool('sparse', False)
params.assert_empty(cls.__name__)
if pretrained_file:
# If we're loading a saved model, we don't want to actually read a pre-trained
# embedding file - the embeddings will just be in our saved weights, and we might not
# have the original embedding file anymore, anyway.
weight = _read_pretrained_embeddings_file(pretrained_file,
embedding_dim, vocab,
vocab_namespace)
else:
weight = None
return cls(num_embeddings=num_embeddings,
embedding_dim=embedding_dim,
projection_dim=projection_dim,
weight=weight,
padding_index=padding_index,
trainable=trainable,
max_norm=max_norm,
norm_type=norm_type,
scale_grad_by_freq=scale_grad_by_freq,
sparse=sparse)
def from_params(cls, params: Params, size: int) -> 'C': # type: ignore
name = params.pop('name')
return cls(size=size, name=name)