text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
def build_in_name_scope(self):
with tf.name_scope(self.name):
self.build(input_shape=None)
@property
def framework(self) -> str:
"""
:str: Identifies that this is a TensorFlow model.
"""
return "tf"
def build(self, input_shape=None):
pass # This... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def __init__(self, config, *inputs, **kwargs):
super().__init__(*inputs, **kwargs)
if not isinstance(config, PretrainedConfig):
raise TypeError(
f"Parameter config in `{self.__class__.__name__}(config)` should be an instance of class "
"`PretrainedConfig`. To ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
@functools.wraps(keras.Model.fit)
def fit(self, *args, **kwargs):
args, kwargs = convert_batch_encoding(*args, **kwargs)
return super().fit(*args, **kwargs)
@functools.wraps(keras.Model.train_on_batch)
def train_on_batch(self, *args, **kwargs):
args, kwargs = convert_batch_encoding(... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
@functools.wraps(keras.Model.evaluate)
def evaluate(self, *args, **kwargs):
args, kwargs = convert_batch_encoding(*args, **kwargs)
return super().evaluate(*args, **kwargs)
@classmethod
def from_config(cls, config, **kwargs):
if isinstance(config, PretrainedConfig):
retur... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
head_mask (`tf.Tensor` with shape `[num_heads]` or `[num_hidden_layers x num_heads]`, *optional*):
The mask indicating if we should keep the heads or not (1.0 for keep, 0.0 for discard).
num_hidden_layers (`int`):
The number of hidden layers in the model.
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def _convert_head_mask_to_5d(self, head_mask, num_hidden_layers):
"""-> [num_hidden_layers x batch x num_heads x seq_length x seq_length]"""
if head_mask.shape.rank == 1:
head_mask = head_mask[None, None, :, None, None]
head_mask = tf.repeat(head_mask, repeats=num_hidden_layers, ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
@tf.function
def serving(self, inputs):
"""
Args:
Method used for serving the model. Does not have a specific signature, but will be specialized as concrete
functions when saving with `save_pretrained`.
inputs (`Dict[str, tf.Tensor]`):
The input of the sav... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
@property
def input_signature(self) -> Dict[str, tf.TensorSpec]:
"""
This property should return a dict mapping input names to tf.TensorSpec objects, representing the expected
shape and dtype for model inputs. It is used for both serving and for generating dummy inputs.
"""
m... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
pixel_values_shape = [None, None, None, None]
if hasattr(self.config, "vision_config"):
vision_config = self.config.vision_config
else:
vision_config = self.config
if hasattr(vision_config, "num_channels"):
pixel_values_shape[1] = visio... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
"Could not infer input image shape from config, please override input_signature to specify input shapes."
)
sig["pixel_values"] = tf.TensorSpec(pixel_values_shape, tf.float32, name="pixel_values")
if "input_features" in model_inputs:
raise NotImplementedError("Audio model... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def serving_output(self, output):
"""
Prepare the output of the saved model. Can be overridden if specific serving modifications are required.
"""
if not isinstance(output, ModelOutput):
return output
for key in output:
if key.endswith("hidden_states") and... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
output[key] = tf.convert_to_tensor(output[key])
except (ValueError, tf.errors.InvalidArgumentError):
pass # Layers may not have the same dimensions
return output | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
@classmethod
def can_generate(cls) -> bool:
"""
Returns whether this model can generate sequences with `.generate()`.
Returns:
`bool`: Whether this model can generate sequences with `.generate()`.
"""
# Detects whether `prepare_inputs_for_generation` has been ove... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if main_layer is not self:
return main_layer.get_input_embeddings()
else:
raise NotImplementedError
def _save_checkpoint(self, checkpoint_dir, epoch):
if not os.path.isdir(checkpoint_dir):
os.mkdir(checkpoint_dir)
# We avoid tf.train.checkpoint or saving ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def prepare_tf_dataset(
self,
dataset: "datasets.Dataset", # noqa:F821
batch_size: int = 8,
shuffle: bool = True,
tokenizer: Optional["PreTrainedTokenizerBase"] = None,
collate_fn: Optional[Callable] = None,
collate_fn_args: Optional[Dict[str, Any]] = None,
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
dataset (`Any`):
A [~`datasets.Dataset`] to be wrapped as a `tf.data.Dataset`.
batch_size (`int`, *optional*, defaults to 8):
The size of batches to return.
shuffle (`bool`, defaults to `True`):
Whether to return samples from the ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
collate_fn_args (`Dict[str, Any]`, *optional*):
A dict of arguments to pass to the `collate_fn` alongside the list of samples.
drop_remainder (`bool`, *optional*):
Whether to drop the final batch, if the batch_size does not evenly divide the dataset length. Defaults
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Returns:
`Dataset`: A `tf.data.Dataset` which is ready to pass to the Keras API.
"""
requires_backends(self, ["datasets"])
import datasets
if collate_fn is None:
if tokenizer is None:
collate_fn = DefaultDataCollator(return_tensors="np")
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if not isinstance(dataset, datasets.Dataset):
raise TypeError("Dataset argument should be a datasets.Dataset!")
model_inputs = list(inspect.signature(self.call).parameters)
model_labels = find_labels(self.__class__)
if "cols_to_retain" in list(inspect.signature(dataset._get_output_si... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if feature not in model_inputs and feature not in ("label_ids", "label")
]
dataset = dataset.remove_columns(unwanted_columns)
output_signature, _ = dataset._get_output_signature(
dataset, batch_size=None, collate_fn=collate_fn, collate_fn_args=collate_fn_args
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Backwards compatibility for older versions of datasets. Previously, if `columns` or `label_cols`
# were a single element list, the returned element spec would be a single element. Now, passing [feature]
# will return a dict structure {"feature": feature}, and passing a single string will return a sing... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def compile(
self,
optimizer="rmsprop",
loss="auto_with_warning",
metrics=None,
loss_weights=None,
weighted_metrics=None,
run_eagerly=None,
steps_per_execution=None,
**kwargs,
):
"""
This is a thin wrapper that sets the model's ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
"get the internal loss without printing this info string."
)
loss = "auto"
if loss == "auto":
loss = dummy_loss
self._using_dummy_loss = True
else:
self._using_dummy_loss = False
parent_args = list(inspect.signature(keras.Model.compile)... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
weighted_metrics=weighted_metrics,
run_eagerly=run_eagerly,
experimental_steps_per_execution=steps_per_execution,
**kwargs,
) | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def compute_loss(self, *args, **kwargs):
if hasattr(keras.Model, "compute_loss"):
# This will be true in TF 2.8 or greater
return super().compute_loss(*args, **kwargs)
else:
warnings.warn(
"The old compute_loss method is deprecated as it conflicts with... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def get_label_to_output_name_mapping(self):
arg_names = list(inspect.signature(self.call).parameters)
if self._label_to_output_map is not None:
return self._label_to_output_map
elif "start_positions" in arg_names:
return {"start_positions": "start_logits", "end_positions"... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def train_step(self, data):
"""
A modification of Keras's default `train_step` that correctly handles matching outputs to labels for our models
and supports directly training on the loss output head. In addition, it ensures input keys are copied to the
labels where appropriate. It will a... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# We hardcode the most common renamings; models with weirder names can set `self._label_to_output_map`
arg_names = list(inspect.signature(self.call).parameters)
label_kwargs = find_labels(self.__class__)
label_to_output = self.get_label_to_output_name_mapping()
output_to_label = {val: ke... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# When using a dummy loss, we ensure that separate labels are copied to the correct model arguments,
# if those keys are not already present in the input dict
if self._using_dummy_loss and y is not None:
# If y is a tensor and the model only has one label-like input, map y to that input
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
elif output_to_label.get(key, None) in arg_names and key not in x:
x[output_to_label[key]] = val
if y is None:
y = {key: val for key, val in x.items() if key in label_kwargs}
if not y and not self._using_dummy_loss:
raise ValueError("Could not find... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if isinstance(y, dict):
# Rename labels at this point to match output heads
y = {label_to_output.get(key, key): val for key, val in y.items()}
# Run forward pass.
with tf.GradientTape() as tape:
if self._using_dummy_loss and "return_loss" in arg_names:
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# This next block matches outputs to label keys. Tensorflow's standard method for doing this
# can get very confused if any of the keys contain nested values (e.g. lists/tuples of Tensors)
if isinstance(y, dict) and len(y) == 1:
if list(y.keys())[0] in y_pred.keys():
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
y_pred = y_pred.to_tuple()[1:]
else:
y_pred = y_pred.to_tuple()
y_pred = y_pred[: len(y)] # Remove unused fields in case those cause problems
else:
# If the labels are a single tensor, match them to the first non-loss tensor in the output
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if loss is None:
loss = self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)
# Run backwards pass.
self.optimizer.minimize(loss, self.trainable_variables, tape=tape)
self.compiled_metrics.update_state(y, y_pred, sample_weight)
# Collect metric... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def test_step(self, data):
"""
A modification of Keras's default `train_step` that correctly handles matching outputs to labels for our models
and supports directly training on the loss output head. In addition, it ensures input keys are copied to the
labels where appropriate. It will al... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
x, y, sample_weight = keras.utils.unpack_x_y_sample_weight(data)
# If the inputs are mutable dictionaries, make a shallow copy of them because we will modify
# them during input/label pre-processing. This avoids surprising the user by wrecking their data.
# In addition, modifying mutable Python ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# When using a dummy loss, we ensure that separate labels are copied to the correct model arguments,
# if those keys are not already present in the input dict
if self._using_dummy_loss and y is not None:
arg_names = list(inspect.signature(self.call).parameters)
# If y is a tensor... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
x[key] = val
elif output_to_label.get(key, None) in arg_names and key not in x:
x[output_to_label[key]] = val
if y is None:
y = {key: val for key, val in x.items() if key in label_kwargs}
if not y and not self._using_dummy_loss:
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if isinstance(y, dict):
# Rename labels at this point to match output heads
y = {label_to_output.get(key, key): val for key, val in y.items()}
# Run forward pass.
if self._using_dummy_loss and "return_loss" in arg_names:
y_pred = self(x, return_loss=True, training=Fa... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# This next block matches outputs to label keys. Tensorflow's standard method for doing this
# can get very confused if any of the keys contain nested values (e.g. lists/tuples of Tensors)
if isinstance(y, dict) and len(y) == 1:
if list(y.keys())[0] in y_pred.keys():
y_pred =... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
y_pred = y_pred.to_tuple()
y_pred = y_pred[: len(y)] # Remove unused fields in case those cause problems
else:
# If the labels are a single tensor, match them to the first non-loss tensor in the output
if list(y_pred.keys())[0] == "loss":
y_pred = y_pred[1]
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if loss is None:
loss = self.compiled_loss(y, y_pred, sample_weight, regularization_losses=self.losses)
self.compiled_metrics.update_state(y, y_pred, sample_weight)
# Collect metrics to return
return_metrics = {}
for metric in self.metrics:
result = metric.result... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def create_model_card(
self,
output_dir,
model_name: str,
language: Optional[str] = None,
license: Optional[str] = None,
tags: Optional[str] = None,
finetuned_from: Optional[str] = None,
tasks: Optional[str] = None,
dataset_tags: Optional[Union[str... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
output_dir (`str` or `os.PathLike`):
The folder in which to create the model card.
model_name (`str`, *optional*):
The name of the model.
language (`str`, *optional*):
The language of the model (if applicable)
license ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
One or several task identifiers, to be included in the metadata of the model card.
dataset_tags (`str` or `List[str]`, *optional*):
One or several dataset tags, to be included in the metadata of the model card.
dataset (`str` or `List[str]`, *optional*):
One or se... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
training_summary = TrainingSummary.from_keras(
self,
keras_history=self.history,
language=language,
license=license,
tags=tags,
model_name=model_name,
finetuned_from=finetuned_from,
tasks=tasks,
dataset_tags=data... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
try:
main_layer.set_input_embeddings(value)
except AttributeError:
logger.info("Building the model")
self.build_in_name_scope()
main_layer.set_input_embeddings(value)
def get_output_embeddings(self) -> Union[None, keras.layers.Layer]:
"""
Retu... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
value (`tf.Variable`):
The new weights mapping hidden states to vocabulary.
"""
if self.get_lm_head() is not None:
lm_head = self.get_lm_head()
try:
lm_head.set_output_embeddings(value)
except AttributeError:
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def get_prefix_bias_name(self) -> Union[None, str]:
"""
Get the concatenated _prefix name of the bias from the model name to the parent layer
Return:
`str`: The _prefix name of the bias.
"""
warnings.warn("The method get_prefix_bias_name is deprecated. Please use `ge... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def set_bias(self, value):
"""
Set all the bias in the LM head.
Args:
value (`Dict[tf.Variable]`):
All the new bias attached to an LM head.
"""
if self.get_lm_head() is not None:
lm_head = self.get_lm_head()
try:
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Takes care of tying weights embeddings afterwards if the model class has a `tie_weights()` method.
Arguments:
new_num_tokens (`int`, *optional*):
The number of new tokens in the embedding matrix. Increasing the size will add newly initialized
vectors at the end. Redu... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if new_num_tokens is None or new_num_tokens == self.config.vocab_size:
return self._get_word_embedding_weight(self.get_input_embeddings())
model_embeds = self._resize_token_embeddings(new_num_tokens)
# Update base model and current model config
self.config.vocab_size = new_num_toke... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Return:
`keras.layers.Embedding`: Pointer to the input tokens of the model.
"""
if new_num_tokens is None or new_num_tokens == self.config.vocab_size:
return self.get_input_embeddings()
model_embeds = self._v2_resize_token_embeddings(new_num_tokens)
# Update bas... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
embeds = getattr(embedding_layer, "decoder", None)
if embeds is not None:
return embeds
# The reason why the attributes don't exist might be
# because the model is not built, so retry getting
# the argument after building the model
model.build_in_name_scope()
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# if word embeddings are not tied, make sure that lm head bias is resized as well
if self.get_bias() is not None:
old_lm_head_bias = self.get_bias()
new_lm_head_bias = self._get_resized_lm_head_bias(old_lm_head_bias, new_num_tokens)
self.set_bias(new_lm_head_bias)
#... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def _v2_resize_token_embeddings(self, new_num_tokens):
old_embeddings = self.get_input_embeddings()
new_embeddings = self._v2_get_resized_embeddings(old_embeddings, new_num_tokens)
self.set_input_embeddings(new_embeddings)
# If word embeddings are not tied, make sure that lm head bias i... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# If word embeddings are not tied, make sure that lm head decoder is resized as well.
tied_weights = self.get_input_embeddings() == self.get_output_embeddings()
if self.get_output_embeddings() is not None and not tied_weights:
old_lm_head_decoder = self._get_word_embedding_weight(self.get_ou... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
old_lm_head_bias (`tf.Variable`):
Old lm head bias to be resized.
new_num_tokens (`int`, *optional*):
New number of tokens in the linear matrix.
Increasing the size will add newly initialized vectors at the end. Reducing the size will remove... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# initialize new bias
if tf.math.greater(size_diff, 0):
padding_shape = [[0, size_diff]] if first_dim is None else [[0, 0], [0, size_diff]]
current_bias = tf.pad(weight.value(), tf.convert_to_tensor(padding_shape), constant_values=-1)
num_tokens_to_copy = min(... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
new_bias = self.add_weight(
shape=final_shape,
initializer="zeros",
trainable=True,
name=weight.name.split(":")[0],
)
init_bias = tf.where(bias_mask, current_bias, new_bias.value())
new_bias.assign(init_bias)
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
old_lm_head_bias (`Dict[str, tf.Variable]`):
Old lm head bias to be resized.
new_num_tokens (`int`):
New number of tokens in the linear matrix. Increasing the size will add newly initialized vectors at
the end. Reducing the size will remove v... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Copy the old bias values to the new bias
if old_num_tokens > new_num_tokens:
new_bias = weight.value()[..., :new_num_tokens]
else:
padding_shape = [[0, size_diff]] if first_dim is None else [[0, 0], [0, size_diff]]
new_bias = tf.pad(weight.value(... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
vectors from the end. If not provided or `None`, just returns None
Return:
`tf.Variable`: Pointer to the resized decoder or None if the output embeddings are different from the input
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if old_lm_head_decoder is not None and not is_input_output_equals:
old_embedding_dim = shape_list(old_lm_head_decoder)[1]
decoder_mask, current_decoder = init_copy_embeddings(old_lm_head_decoder, new_num_tokens)
new_lm_head_decoder = self.add_weight(
shape=(new_num_to... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Args:
old_embeddings (`tf.Variable`):
Old embeddings to be resized.
new_num_tokens (`int`, *optional*):
New number of tokens in the embedding matrix.
Increasing the size will add newly initialized vectors at the end. Reducing the size will remove
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Return:
`tf.Variable`: Pointer to the resized Embedding Module or the old Embedding Module if `new_num_tokens` is
`None`
"""
# TODO (joao): flagged for replacement (by `_v2_get_resized_embeddings`) due to embeddings refactor
old_embedding_dim = shape_list(old_embeddings)[... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def _v2_get_resized_embeddings(
self, old_embeddings: keras.layers.Embedding, new_num_tokens: int
) -> keras.layers.Embedding:
"""
Build a resized Embedding layer from a provided Embedding layer. Increasing the size will add newly initialized
vectors at the end. Reducing the size wil... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Get the initialization range for the embeddings
init_range = 0.02 # default value
potential_initialization_variable_names = [
"initializer_range", # most common
"initializer_factor", # e.g. T5
"init_std", # e.g BART
]
for var_name in potential_in... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Copy the old embeddings to the new embeddings
if old_embeddings.input_dim >= new_num_tokens:
init_embeddings = old_embeddings.embeddings[:new_num_tokens]
else:
init_embeddings = tf.concat(
[old_embeddings.embeddings, new_embeddings.embeddings[old_embeddings.inpu... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
def save_pretrained(
self,
save_directory,
saved_model=False,
version=1,
push_to_hub=False,
signatures=None,
max_shard_size: Union[int, str] = "5GB",
create_pr: bool = False,
safe_serialization: bool = False,
token: Optional[Union[str, bool... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Arguments:
save_directory (`str`):
Directory to which to save. Will be created if it doesn't exist.
saved_model (`bool`, *optional*, defaults to `False`):
If the model has to be saved in saved model format as well or not.
version (`int`, *optional*, de... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Model's signature used for serving. This will be passed to the `signatures` argument of model.save().
max_shard_size (`int` or `str`, *optional*, defaults to `"10GB"`):
The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size
lower t... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
<Tip warning={true}>
If a single weight of the model is bigger than `max_shard_size`, it will be in its own checkpoint shard
which will be bigger than `max_shard_size`.
</Tip> | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
create_pr (`bool`, *optional*, defaults to `False`):
Whether or not to create a PR with the uploaded files or directly commit.
safe_serialization (`bool`, *optional*, defaults to `False`):
Whether to save the model using `safetensors` or the traditional TensorFlow way (that u... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
)
if token is not None:
raise ValueError(
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if push_to_hub:
commit_message = kwargs.pop("commit_message", None)
repo_id = kwargs.pop("repo_id", save_directory.split(os.path.sep)[-1])
repo_id = self._create_repo(repo_id, **kwargs)
files_timestamps = self._get_files_timestamps(save_directory) | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if saved_model:
# If `torch_dtype` is in the config with a torch dtype class as the value, we need to change it to string.
# (Although TF doesn't care about this attribute, we can't just remove it or set it to `None`.)
if getattr(self.config, "torch_dtype", None) is not None and not ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
int64_serving = self.serving.get_concrete_function(int64_spec)
signatures = {"serving_default": serving_default, "int64_serving": int64_serving}
else:
signatures = serving_default
saved_model_dir = os.path.join(save_directory, "saved_model", str(versio... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Save configuration file
self.config.architectures = [self.__class__.__name__[2:]]
# If we have a custom model, we copy the file defining it in the folder and set the attributes so it can be
# loaded from the Hub.
if self._auto_class is not None:
custom_object_save(self, sa... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Clean the folder from a previous save
for filename in os.listdir(save_directory):
full_filename = os.path.join(save_directory, filename)
# If we have a shard file that is not going to be replaced, we delete it, but only from the main process
# in distributed settings to avo... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if index is None:
if safe_serialization:
state_dict = {strip_model_name_and_prefix(w.name): w.value() for w in self.weights}
safe_save_file(state_dict, output_model_file, metadata={"format": "tf"})
else:
self.save_weights(output_model_file)
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
f"index located at {save_index_file}."
)
for shard_file, shard in shards.items():
if safe_serialization:
shard_state_dict = {strip_model_name... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
layer_name = "/".join(layer.name.split("/")[1:])
param_dset = shard_file.create_dataset(
layer_name, layer.numpy().shape, dtype=layer.numpy().dtype
)
param_dset[:] = layer.numpy()
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if push_to_hub:
self._upload_modified_files(
save_directory,
repo_id,
files_timestamps,
commit_message=commit_message,
token=token,
)
@classmethod
def from_pretrained(
cls,
pretrained_model_n... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
The warning *Weights from XXX not initialized from pretrained model* means that the weights of XXX do not come
pretrained with the rest of the model. It is up to you to train those weights with a downstream fine-tuning
task.
The warning *Weights from XXX not used in YYY* means that the layer XX... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
- A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
- A path to a *directory* containing model weights saved using
[`~TFPreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
- A path or url to a *PyTorch... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
All remaining positional arguments will be passed to the underlying model's `__init__` method.
config (`Union[PretrainedConfig, str]`, *optional*):
Can be either: | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
- an instance of a class derived from [`PretrainedConfig`],
- a string valid as input to [`~PretrainedConfig.from_pretrained`].
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when: | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
- The model is a model provided by the library (loaded with the *model id* string of a pretrained
model).
- The model was saved using [`~TFPreTrainedModel.save_pretrained`] and is reloaded by supplying the
save directory.
- The model is... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
as the weights of the model (if for instance, you are instantiating a model with 10 labels from a
checkpoint with 3 labels).
cache_dir (`str`, *optional*):
Path to a directory in which a downloaded pretrained model configuration should be cached if the
standar... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
`{'http': 'foo.bar:3128', 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
output_loading_info(`bool`, *optional*, defaults to `False`): Whether ot not to also return a
dictionary containing missing keys, unexpected keys and error messages.
local_fil... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
git-based system for storing models and other artifacts on huggingface.co, so `revision` can be any
identifier allowed by git. | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
<Tip>
To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`.
</Tip> | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
mirror (`str`, *optional*):
Mirror source to accelerate downloads in China. If you are from China and have an accessibility
problem, you can set this option to resolve it. Note that we do not guarantee the timeliness or safety.
Please refer to the mirror site for more inf... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
Whether or not to use `safetensors` checkpoints. Defaults to `None`. If not specified and `safetensors`
is not installed, it will be set to `False`.
kwargs (remaining dictionary of keyword arguments, *optional*):
Can be used to update the configuration object (after it being ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
- If a configuration is provided with `config`, `**kwargs` will be directly passed to the
underlying model's `__init__` method (we assume all relevant updates to the configuration have
already been done)
- If a configuration is not provided, `kwargs` will ... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
>>> # Download model and configuration from huggingface.co and cache.
>>> model = TFBertModel.from_pretrained("google-bert/bert-base-uncased")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model = TFBertModel.from_pretrained("./tes... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
proxies = kwargs.pop("proxies", None)
output_loading_info = kwargs.pop("output_loading_info", False)
use_auth_token = kwargs.pop("use_auth_token", None)
trust_remote_code = kwargs.pop("trust_remote_code", None)
_ = kwargs.pop("mirror", None)
load_weight_prefix = kwargs.pop("load_... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
# Not relevant for TF models
_ = kwargs.pop("adapter_kwargs", None)
if use_auth_token is not None:
warnings.warn(
"The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.",
FutureWarning,
... | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True
if use_safetensors is None and not is_safetensors_available():
use_safetensors = False | 281 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_tf_utils.py |
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