text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
# very important for fast and slow equivalence!
is_special = token in self.all_special_tokens or special_tokens
token = AddedToken(
token, rstrip=False, lstrip=False, normalized=not is_special, special=is_special
)
elif spec... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
current_vocab[token.content] = token_index
added_tokens += 1
else:
token_index = current_vocab[token.content] | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
if token.special and str(token) not in self.all_special_tokens:
self._special_tokens_map["additional_special_tokens"].append(token)
# the setter automatically updates the reverse map
self._added_tokens_decoder[token_index] = token
self._added_tokens_encoder[token.cont... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def num_special_tokens_to_add(self, pair: bool = False) -> int:
"""
Returns the number of added tokens when encoding a sequence with special tokens.
<Tip>
This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put
this inside yo... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies
(BPE/SentencePieces/WordPieces). Takes care of added tokens.
Args:
text (`str`):
The sequence to be encoded.
**kwargs (additional keyword arguments):
Passed alon... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
if hasattr(self, "do_lower_case") and self.do_lower_case:
# convert non-special tokens to lowercase. Might be super slow as well?
escaped_special_toks = [re.escape(s_tok) for s_tok in (self.all_special_tokens)]
escaped_special_toks += [
re.escape(s_tok.content)
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
# ["This is something", "<special_token_1>", " else"]
for i, token in enumerate(tokens):
if token in no_split_token:
tok_extended = self._added_tokens_decoder.get(self._added_tokens_encoder[token], None)
left = tokens[i - 1] if i > 0 else None
right =... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
tokens[i - 1] += token
tokens[i] = ""
elif tok_extended.single_word and right and right[0] != " ":
tokens[i + 1] = token + tokens[i + 1]
tokens[i] = ""
else:
raise ValueError(
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
# ["This", " is", " something", "<special_token_1>", "else"]
return tokenized_text | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def _tokenize(self, text, **kwargs):
"""
Converts a string into a sequence of tokens (string), using the tokenizer. Split in words for word-based
vocabulary or sub-words for sub-word-based vocabularies (BPE/SentencePieces/WordPieces).
Do NOT take care of added tokens.
"""
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
ids = []
for token in tokens:
ids.append(self._convert_token_to_id_with_added_voc(token))
return ids
def _convert_token_to_id_with_added_voc(self, token):
if token is None:
return None
if token in self._added_tokens_encoder:
return self._added_to... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def _encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
truncation_stra... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kwargs)
return self.convert_tokens_to_ids(tokens)
elif isinstance(text, (list, tuple)) and len(text) > 0 and isinstance(text[0], str):
if is_split_into_words:
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
)
else:
raise ValueError(
f"Input {text} is not valid. Should be a string, a list/tuple of strings or a list/tuple of"
" integers."
) | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast. "
"More info... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
return self.prepare_for_model(
first_ids,
pair_ids=second_ids,
add_special_tokens=add_special_tokens,
padding=padding_strategy.value,
truncation=truncation_strategy.value,
max_length=max_length,
stride=stride,
pad_to_multipl... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def _batch_encode_plus(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
List[PreTokenizedInputPair],
List[EncodedInput],
List[EncodedInputPair],
],
add_special_to... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
split_special_tokens: bool = False,
**kwargs,
) -> BatchEncoding:
def get_input_ids(text):
if isinstance(text, str):
tokens = self.tokenize(text, **kwargs)
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
"Input is not valid. Should be a string, a list/tuple of strings or a list/tuple of integers."
) | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
if return_offsets_mapping:
raise NotImplementedError(
"return_offset_mapping is not available when using Python tokenizers. "
"To use this feature, change your tokenizer to one deriving from "
"transformers.PreTrainedTokenizerFast."
)
inpu... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
batch_outputs = self._batch_prepare_for_model(
input_ids,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
@add_end_docstrings(ENCODE_KWARGS_DOCSTRING, ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
def _batch_prepare_for_model(
self,
batch_ids_pairs: List[Union[PreTokenizedInputPair, Tuple[List[int], None]]],
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
manages a moving window (with user defined stride) for overflowing tokens | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
Args:
batch_ids_pairs: list of tokenized input ids or input ids pairs
""" | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
batch_outputs = {}
for first_ids, second_ids in batch_ids_pairs:
outputs = self.prepare_for_model(
first_ids,
second_ids,
add_special_tokens=add_special_tokens,
padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
prepend_batch_axis=False,
verbose=verbose,
split_special_tokens=split_special_tokens,
) | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
for key, value in outputs.items():
if key not in batch_outputs:
batch_outputs[key] = []
batch_outputs[key].append(value)
batch_outputs = self.pad(
batch_outputs,
padding=padding_strategy.value,
max_length=max_length,
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We test the
`kwargs` at the end of the encoding process to be sure all the arguments have been used.
Args:
text (`str`):
The text to prepare.
is_split_into_words (`bool`, ... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def get_special_tokens_mask(
self, token_ids_0: List, token_ids_1: Optional[List] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
Returns:
A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
"""
if already_has_special_tokens:
if token_ids_1 is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence o... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def convert_ids_to_tokens(
self, ids: Union[int, List[int]], skip_special_tokens: bool = False
) -> Union[str, List[str]]:
"""
Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
added tokens.
Args:
ids (`... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
Returns:
`str` or `List[str]`: The decoded token(s).
"""
if isinstance(ids, int):
if ids in self._added_tokens_decoder:
return self._added_tokens_decoder[ids].content
else:
return self._convert_id_to_token(ids)
tokens = []
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
def _decode(
self,
token_ids: Union[int, List[int]],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = None,
spaces_between_special_tokens: bool = True,
**kwargs,
) -> str:
self._decode_use_source_tokenizer = kwargs.pop("use_source_tokeni... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
legacy_added_tokens = set(self._added_tokens_encoder.keys()) - set(self.all_special_tokens) | {
token for token in self.additional_special_tokens if self.convert_tokens_to_ids(token) >= self.vocab_size
}
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build s... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
current_sub_text = []
sub_texts.append(token)
else:
current_sub_text.append(token)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(current_sub_text)) | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
if spaces_between_special_tokens:
text = " ".join(sub_texts)
else:
text = "".join(sub_texts)
clean_up_tokenization_spaces = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
... | 57 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/tokenization_utils.py |
class OnnxConverterArgumentParser(ArgumentParser):
"""
Wraps all the script arguments supported to export transformers models to ONNX IR
"""
def __init__(self):
super().__init__("ONNX Converter") | 58 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_graph_to_onnx.py |
self.add_argument(
"--pipeline",
type=str,
choices=SUPPORTED_PIPELINES,
default="feature-extraction",
)
self.add_argument(
"--model",
type=str,
required=True,
help="Model's id or path (ex: google-bert/bert-ba... | 58 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_graph_to_onnx.py |
help="Allow exporting model >= than 2Gb",
)
self.add_argument(
"--quantize",
action="store_true",
help="Quantize the neural network to be run with int8",
)
self.add_argument("output") | 58 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/convert_graph_to_onnx.py |
class Conv1D(nn.Module):
"""
1D-convolutional layer as defined by Radford et al. for OpenAI GPT (and also used in GPT-2).
Basically works like a linear layer but the weights are transposed.
Args:
nf (`int`): The number of output features.
nx (`int`): The number of input features.
"... | 59 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/pytorch_utils.py |
class Seq2SeqTrainer(Trainer):
@deprecate_kwarg("tokenizer", new_name="processing_class", version="5.0.0", raise_if_both_names=True)
def __init__(
self,
model: Union["PreTrainedModel", nn.Module] = None,
args: "TrainingArguments" = None,
data_collator: Optional["DataCollator"] = ... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
):
super().__init__(
model=model,
args=args,
data_collator=data_collator,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
proc... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# Override self.model.generation_config if a GenerationConfig is specified in args.
# Priority: args.generation_config > model.generation_config > default GenerationConfig.
if self.args.generation_config is not None:
gen_config = self.load_generation_config(self.args.generation_config)
... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# GenerationConfig provided, nothing to do
if isinstance(gen_config_arg, GenerationConfig):
gen_config = deepcopy(gen_config_arg)
else:
# str or Path
pretrained_model_name = Path(gen_config_arg) if isinstance(gen_config_arg, str) else gen_config_arg
config... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# Strict validation to fail early. `GenerationConfig.save_pretrained()`, run at the end of training, throws
# an exception if there are warnings at validation time.
try:
with warnings.catch_warnings(record=True) as caught_warnings:
gen_config.validate()
if len(cau... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
def evaluate(
self,
eval_dataset: Optional[Dataset] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
**gen_kwargs,
) -> Dict[str, float]:
"""
Run evaluation and returns metrics.
The calling script will be responsible f... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
Args:
eval_dataset (`Dataset`, *optional*):
Pass a dataset if you wish to override `self.eval_dataset`. If it is an [`~datasets.Dataset`], columns
not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__`
method.
... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.
gen_kwargs:
Additional `generate` specific kwargs. | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
Returns:
A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
dictionary also contains the epoch number which comes from the training state.
"""
gen_kwargs = gen_kwargs.copy() | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
# training args
if (
gen_kwargs.get("max_length") is None
and gen_kwargs.get("max_new_tokens") is None
and self.args.generation_max_length is not None
):... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
def predict(
self,
test_dataset: Dataset,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "test",
**gen_kwargs,
) -> "PredictionOutput":
"""
Run prediction and returns predictions and potential metrics.
Depending on the dataset and y... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
Args:
test_dataset (`Dataset`):
Dataset to run the predictions on. If it is a [`~datasets.Dataset`], columns not accepted by the
`model.forward()` method are automatically removed. Has to implement the method `__len__`
ignore_keys (`List[str]`, *optional*):
... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
Number of beams for beam search that will be used when predicting with the generate method. 1 means no
beam search.
gen_kwargs:
Additional `generate` specific kwargs. | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
<Tip>
If your predictions or labels have different sequence lengths (for instance because you're doing dynamic
padding in a token classification task) the predictions will be padded (on the right) to allow for
concatenation into one array. The padding index is -100.
</Tip>
Ret... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
# training args
if (
gen_kwargs.get("max_length") is None
and gen_kwargs.get("max_new_tokens") is None
and self.args.generation_max_length is not None
):... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
def prediction_step(
self,
model: nn.Module,
inputs: Dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[List[str]] = None,
**gen_kwargs,
) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
argument `labels`. Check your model's documentation for all accepted arguments.
prediction_loss_only (`bool`):
Whether or not to return the loss only.
gen_kwarg... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# Priority (handled in generate):
# non-`None` gen_kwargs > model.generation_config > default GenerationConfig()
if len(gen_kwargs) == 0 and hasattr(self, "_gen_kwargs"):
gen_kwargs = self._gen_kwargs.copy()
if "num_beams" in gen_kwargs and gen_kwargs["num_beams"] is None:
... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
generation_inputs = inputs.copy()
# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
# (otherwise, it would continue generating from the padded `decoder_input_ids`)
if (
"labels" in generation_inputs
and "decode... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e97... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
# Retrieves GenerationConfig from model.generation_config
gen_config = self.model.generation_config
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_config.max_length:
generated_tokens = self._pad_tensors_to_max_len(gener... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
with torch.no_grad():
if has_labels:
with self.compute_loss_context_manager():
outputs = model(**inputs)
if self.label_smoother is not None:
loss = self.label_smoother(outputs, inputs["labels"]).mean().detach()
else:
... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
return loss, generated_tokens, labels
def _pad_tensors_to_max_len(self, tensor, max_length):
if self.processing_class is not None and hasattr(self.processing_class, "pad_token_id"):
# If PAD token is not defined at least EOS token has to be defined
pad_token_id = (
s... | 60 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/trainer_seq2seq.py |
class FlaxPreTrainedModel(PushToHubMixin, FlaxGenerationMixin):
r"""
Base class for all models.
[`FlaxPreTrainedModel`] takes care of storing the configuration of the models and handles methods for loading,
downloading and saving models.
Class attributes (overridden by derived classes):
-... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
def __init__(
self,
config: PretrainedConfig,
module: nn.Module,
input_shape: Tuple = (1, 1),
seed: int = 0,
dtype: jnp.dtype = jnp.float32,
_do_init: bool = True,
):
if config is None:
raise ValueError("config cannot be None")
if ... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
if _do_init:
# randomly initialized parameters
random_params = self.init_weights(self.key, input_shape)
params_shape_tree = jax.eval_shape(lambda params: params, random_params)
else:
init_fn = partial(self.init_weights, input_shape=input_shape)
params_... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
def init_weights(self, rng: jax.random.PRNGKey, input_shape: Tuple, params: FrozenDict = None) -> Dict:
raise NotImplementedError(f"init method has to be implemented for {self}")
def enable_gradient_checkpointing(self):
raise NotImplementedError(f"gradient checkpointing method has to be implemented... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
@property
def params(self) -> Union[Dict, FrozenDict]:
if not self._is_initialized:
raise ValueError(
"`params` cannot be accessed from model when the model is created with `_do_init=False`. "
"You must call `init_weights` manually and store the params outside of ... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
if isinstance(params, FrozenDict):
params = unfreeze(params)
param_keys = set(flatten_dict(params).keys())
if len(self.required_params - param_keys) > 0:
raise ValueError(
"Some parameters are missing. Make sure that `params` include the following "
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
if mask is None:
return jax.tree_util.tree_map(conditional_cast, params)
flat_params = flatten_dict(params)
flat_mask, _ = jax.tree_util.tree_flatten(mask)
for masked, key in zip(flat_mask, sorted(flat_params.keys())):
if masked:
flat_params[key] = condi... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
Arguments:
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
mask (`Union[Dict, FrozenDict]`):
A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params
you want to cast, and should be `... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
>>> model = FlaxBertModel.from_pretrained("google-bert/bert-base-cased")
>>> flat_params = traverse_util.flatten_dict(model.params)
>>> mask = {
... path: (path[-2] != ("LayerNorm", "bias") and path[-2:] != ("LayerNorm", "scale"))
... for path in flat_params
... }
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
Arguments:
params (`Union[Dict, FrozenDict]`):
A `PyTree` of model parameters.
mask (`Union[Dict, FrozenDict]`):
A `PyTree` with same structure as the `params` tree. The leaves should be booleans, `True` for params
you want to cast, and should be `... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
def to_fp16(self, params: Union[Dict, FrozenDict], mask: Any = None):
r"""
Cast the floating-point `parmas` to `jax.numpy.float16`. This returns a new `params` tree and does not cast the
`params` in place.
This method can be used on GPU to explicitly convert the model parameters to floa... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
>>> # load model
>>> model = FlaxBertModel.from_pretrained("google-bert/bert-base-cased")
>>> # By default, the model params will be in fp32, to cast these to float16
>>> model.params = model.to_fp16(model.params)
>>> # If you want don't want to cast certain parameters (for example layer... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
@classmethod
def load_flax_weights(cls, resolved_archive_file):
try:
if resolved_archive_file.endswith(".safetensors"):
state = safe_load_file(resolved_archive_file)
state = unflatten_dict(state, sep=".")
else:
with open(resolved_archiv... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
except (UnicodeDecodeError, ValueError):
raise EnvironmentError(f"Unable to convert {resolved_archive_file} to Flax deserializable object. ") | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
return state
@classmethod
def load_flax_sharded_weights(cls, shard_files):
"""
This is the same as [`flax.serialization.from_bytes`]
(https:lax.readthedocs.io/en/latest/_modules/flax/serialization.html#from_bytes) but for a sharded checkpoint.
This load is performed efficiently... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
for shard_file in shard_files:
# load using msgpack utils
try:
with open(shard_file, "rb") as state_f:
state = from_bytes(cls, state_f.read())
except (UnpicklingError, msgpack.exceptions.ExtraData) as e:
with open(shard_file) as f:
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
state = flatten_dict(state, sep="/")
state_sharded_dict.update(state)
del state
gc.collect()
# the state dict is unflattened to the match the format of model.params
return unflatten_dict(state_sharded_dict, sep="/")
@classmethod
def can_generate(cls) -> bool... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
@classmethod
def from_pretrained(
cls,
pretrained_model_name_or_path: Union[str, os.PathLike],
dtype: jnp.dtype = jnp.float32,
*model_args,
config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
cache_dir: Optional[Union[str, os.PathLike]] = None,
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
The warning *Weights from XXX not used in YYY* means that the layer XXX is not used by YYY, therefore those
weights are discarded.
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`):
Can be either:
- A string, the *model id* of a pretrain... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given `dtype`.
**Note that this only specifies the dtype of the computation and does not influence the dtype of model
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
Configuration for the model to use instead of an automatically loaded configuration. Configuration can
be automatically loaded when: | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_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 [`~PreTrainedModel.save_pretrained`] and is reloaded by supplying the
save directory.
- The model is l... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
Whether or not to raise an error if some of the weights from the checkpoint do not have the same size
as the weights of the model (if for instance, you are instantiating a model with 10 labels from a
check... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
local_files_only(`bool`, *optional*, defaults to `False`):
Whether or not to only look at local files (i.e., do not try to download the model).
token (`str` or `bool`, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, or not specified, wi... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
<Tip>
To test a pull request you made on the Hub, you can pass `revision="refs/pr/<pr_number>"`.
</Tip>
subfolder (`str`, *optional*, defaults to `""`):
In case the relevant files are located inside a subfolder of the model repo on huggingface.co, you can
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_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 ... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
>>> # Download model and configuration from huggingface.co and cache.
>>> model = FlaxBertModel.from_pretrained("google-bert/bert-base-cased")
>>> # Model was saved using *save_pretrained('./test/saved_model/')* (for example purposes, not runnable).
>>> model = FlaxBertModel.from_pretrained("./t... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
from_auto_class = kwargs.pop("_from_auto", False)
_do_init = kwargs.pop("_do_init", True)
subfolder = kwargs.pop("subfolder", "")
commit_hash = kwargs.pop("_commit_hash", None) | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
# Not relevant for Flax 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,
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
if is_offline_mode() and not local_files_only:
logger.info("Offline mode: forcing local_files_only=True")
local_files_only = True | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
# Load config if we don't provide a configuration
if not isinstance(config, PretrainedConfig):
config_path = config if config is not None else pretrained_model_name_or_path
config, model_kwargs = cls.config_class.from_pretrained(
config_path,
cache_dir=cac... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
# Add the dtype to model_kwargs
model_kwargs["dtype"] = dtype
# This variable will flag if we're loading a sharded checkpoint. In this case the archive file is just the
# index of the files.
is_sharded = False | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
# Load model
if pretrained_model_name_or_path is not None:
pretrained_model_name_or_path = str(pretrained_model_name_or_path)
is_local = os.path.isdir(pretrained_model_name_or_path)
if os.path.isdir(pretrained_model_name_or_path):
if os.path.isfile(os.path.joi... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)
):
# Load from a safetensors checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_NAME)
elif from_pt and os.path.isfile(os.path.join(pretrained_model_name_... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
elif is_safetensors_available() and os.path.isfile(
os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_INDEX_NAME)
):
# Load from a sharded safetensors checkpoint
archive_file = os.path.join(pretrained_model_name_or_path, SAFE_WEIGHTS_IND... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
f"Error no file named {FLAX_WEIGHTS_NAME} or {WEIGHTS_NAME} found in directory "
f"{pretrained_model_name_or_path}."
)
elif os.path.isfile(os.path.join(subfolder, pretrained_model_name_or_path)):
archive_file = pretrained_model_name_or_path
... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
try:
# Load from URL or cache if already cached
cached_file_kwargs = {
"cache_dir": cache_dir,
"force_download": force_download,
"proxies": proxies,
"resume_download": resume_download,... | 61 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/modeling_flax_utils.py |
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