code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def run_test_in_subprocess(test_case, target_func, inputs=None, timeout=600):
"""
To run a test in a subprocess. In particular, this can avoid (GPU) memory issue.
Args:
test_case (`unittest.TestCase`):
The test that will run `target_func`.
target_func (`Callable`):
T... |
To run a test in a subprocess. In particular, this can avoid (GPU) memory issue.
Args:
test_case (`unittest.TestCase`):
The test that will run `target_func`.
target_func (`Callable`):
The function implementing the actual testing logic.
inputs (`dict`, *optional*... | run_test_in_subprocess | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/testing_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/testing_utils.py | Apache-2.0 |
def shape_list(tensor: Union[tf.Tensor, np.ndarray]) -> List[int]:
"""
Deal with dynamic shape in tensorflow cleanly.
Args:
tensor (`tf.Tensor` or `np.ndarray`): The tensor we want the shape of.
Returns:
`List[int]`: The shape of the tensor as a list.
"""
if isinstance(tensor, ... |
Deal with dynamic shape in tensorflow cleanly.
Args:
tensor (`tf.Tensor` or `np.ndarray`): The tensor we want the shape of.
Returns:
`List[int]`: The shape of the tensor as a list.
| shape_list | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tf_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tf_utils.py | Apache-2.0 |
def stable_softmax(logits: tf.Tensor, axis: Optional[int] = None, name: Optional[str] = None) -> tf.Tensor:
"""
Stable wrapper that returns the same output as `tf.nn.softmax`, but that works reliably with XLA on CPU. It is
meant as a workaround for the [following issue](https://github.com/tensorflow/tensorf... |
Stable wrapper that returns the same output as `tf.nn.softmax`, but that works reliably with XLA on CPU. It is
meant as a workaround for the [following issue](https://github.com/tensorflow/tensorflow/issues/55682), and will be
removed after it gets fixed. The arguments and outputs are the same as `tf.nn.so... | stable_softmax | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tf_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tf_utils.py | Apache-2.0 |
def split(self, text: str) -> List[str]:
"""
Will look for the words added to the trie within `text`. Output is the original string splitted along the
boundaries of the words found.
This trie will match the longest possible word first !
Example:
```python
>>> t... |
Will look for the words added to the trie within `text`. Output is the original string splitted along the
boundaries of the words found.
This trie will match the longest possible word first !
Example:
```python
>>> trie = Trie()
>>> trie.split("[CLS] This is a... | split | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def _is_whitespace(char):
"""Checks whether `char` is a whitespace character."""
# \t, \n, and \r are technically control characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat ... | Checks whether `char` is a whitespace character. | _is_whitespace | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def _is_control(char):
"""Checks whether `char` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
... | Checks whether `char` is a control character. | _is_control | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def _is_punctuation(char):
"""Checks whether `char` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
... | Checks whether `char` is a punctuation character. | _is_punctuation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def _is_end_of_word(text):
"""Checks whether the last character in text is one of a punctuation, control or whitespace character."""
last_char = text[-1]
return bool(_is_control(last_char) | _is_punctuation(last_char) | _is_whitespace(last_char)) | Checks whether the last character in text is one of a punctuation, control or whitespace character. | _is_end_of_word | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def _is_start_of_word(text):
"""Checks whether the first character in text is one of a punctuation, control or whitespace character."""
first_char = text[0]
return bool(_is_control(first_char) | _is_punctuation(first_char) | _is_whitespace(first_char)) | Checks whether the first character in text is one of a punctuation, control or whitespace character. | _is_start_of_word | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def _insert_one_token_to_ordered_list(token_list: List[str], new_token: str):
"""
Inserts one token to an ordered list if it does not already exist. Note: token_list must be sorted.
"""
insertion_idx = bisect.bisect_left(token_list, new_token)
# Checks if new_token is already in the ordered token_li... |
Inserts one token to an ordered list if it does not already exist. Note: token_list must be sorted.
| _insert_one_token_to_ordered_list | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def _add_tokens(self, new_tokens: Union[List[str], List[AddedToken]], special_tokens: bool = False) -> int:
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary.
... |
Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary.
Args:
new_tokens (`List[str]`or `List[tokenizers.AddedToken]`):
Token(s) to add in vocabul... | _add_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
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... |
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 your training loop.
</Tip>
Args:
pair (`bool`, ... | num_special_tokens_to_add | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def tokenize(self, text: TextInput, **kwargs) -> List[str]:
"""
Converts a string in a sequence of tokens, using the tokenizer.
Split in words for word-based vocabulary or sub-words for sub-word-based vocabularies
(BPE/SentencePieces/WordPieces). Takes care of added tokens.
Arg... |
Converts a string in a sequence of tokens, using the tokenizer.
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... | tokenize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
"""
Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
vocabulary.
Args:
tokens (`str` or `List[str]`): One or several token(s) to... |
Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
vocabulary.
Args:
tokens (`str` or `List[str]`): One or several token(s) to convert to token id(s).
Returns:
`int` or `List[int]`: The token id or list of... | convert_tokens_to_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
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.DO_NOT_PAD,
truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TR... |
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
... | _batch_prepare_for_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def prepare_for_tokenization(
self, text: str, is_split_into_words: bool = False, **kwargs
) -> Tuple[str, Dict[str, Any]]:
"""
Performs any necessary transformations before tokenization.
This method should pop the arguments from kwargs and return the remaining `kwargs` as well. We ... |
Performs any necessary transformations before tokenization.
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`):
... | prepare_for_tokenization | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
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... |
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` or `encode_plus` methods.
Args:
token_ids_0 (`List[int]`):
List of ids of the first sequence.
... | get_special_tokens_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
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 (`... |
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 (`int` or `List[int]`):
The token id (or token ids) to convert to tokens.
skip_special_tokens (`bool`, *optional*, defau... | convert_ids_to_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils.py | Apache-2.0 |
def __getitem__(self, item: Union[int, str]) -> Union[Any, EncodingFast]:
"""
If the key is a string, returns the value of the dict associated to `key` ('input_ids', 'attention_mask',
etc.).
If the key is an integer, get the `tokenizers.Encoding` for batch item with index `key`.
... |
If the key is a string, returns the value of the dict associated to `key` ('input_ids', 'attention_mask',
etc.).
If the key is an integer, get the `tokenizers.Encoding` for batch item with index `key`.
| __getitem__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def tokens(self, batch_index: int = 0) -> List[str]:
"""
Return the list of tokens (sub-parts of the input strings after word/subword splitting and before conversion to
integer indices) at a given batch index (only works for the output of a fast tokenizer).
Args:
batch_index... |
Return the list of tokens (sub-parts of the input strings after word/subword splitting and before conversion to
integer indices) at a given batch index (only works for the output of a fast tokenizer).
Args:
batch_index (`int`, *optional*, defaults to 0): The index to access in the ... | tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def sequence_ids(self, batch_index: int = 0) -> List[Optional[int]]:
"""
Return a list mapping the tokens to the id of their original sentences:
- `None` for special tokens added around or between sequences,
- `0` for tokens corresponding to words in the first sequence,
... |
Return a list mapping the tokens to the id of their original sentences:
- `None` for special tokens added around or between sequences,
- `0` for tokens corresponding to words in the first sequence,
- `1` for tokens corresponding to words in the second sequence when a pair o... | sequence_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def words(self, batch_index: int = 0) -> List[Optional[int]]:
"""
Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.
Args:
batch_index (`int`, *optional*, defaults to 0): The index to access in the batch.
Returns:
... |
Return a list mapping the tokens to their actual word in the initial sentence for a fast tokenizer.
Args:
batch_index (`int`, *optional*, defaults to 0): The index to access in the batch.
Returns:
`List[Optional[int]]`: A list indicating the word corresponding to each ... | words | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def token_to_sequence(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
"""
Get the index of the sequence represented by the given token. In the general use case, this method returns `0`
for a single sequence or the first sequence of a pair, and `1` for the second seque... |
Get the index of the sequence represented by the given token. In the general use case, this method returns `0`
for a single sequence or the first sequence of a pair, and `1` for the second sequence of a pair
Can be called as:
- `self.token_to_sequence(token_index)` if batch size is 1
... | token_to_sequence | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def token_to_word(self, batch_or_token_index: int, token_index: Optional[int] = None) -> int:
"""
Get the index of the word corresponding (i.e. comprising) to an encoded token in a sequence of the batch.
Can be called as:
- `self.token_to_word(token_index)` if batch size is 1
-... |
Get the index of the word corresponding (i.e. comprising) to an encoded token in a sequence of the batch.
Can be called as:
- `self.token_to_word(token_index)` if batch size is 1
- `self.token_to_word(batch_index, token_index)` if batch size is greater than 1
This method is p... | token_to_word | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def word_to_tokens(
self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0
) -> Optional[TokenSpan]:
"""
Get the encoded token span corresponding to a word in a sequence of the batch.
Token spans are returned as a [`~tokenization_utils_base.TokenSp... |
Get the encoded token span corresponding to a word in a sequence of the batch.
Token spans are returned as a [`~tokenization_utils_base.TokenSpan`] with:
- **start** -- Index of the first token.
- **end** -- Index of the token following the last token.
Can be called as:
... | word_to_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def token_to_chars(self, batch_or_token_index: int, token_index: Optional[int] = None) -> CharSpan:
"""
Get the character span corresponding to an encoded token in a sequence of the batch.
Character spans are returned as a [`~tokenization_utils_base.CharSpan`] with:
- **start** -- Inde... |
Get the character span corresponding to an encoded token in a sequence of the batch.
Character spans are returned as a [`~tokenization_utils_base.CharSpan`] with:
- **start** -- Index of the first character in the original string associated to the token.
- **end** -- Index of the char... | token_to_chars | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def char_to_token(
self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0
) -> int:
"""
Get the index of the token in the encoded output comprising a character in the original string for a sequence
of the batch.
Can be called as:
-... |
Get the index of the token in the encoded output comprising a character in the original string for a sequence
of the batch.
Can be called as:
- `self.char_to_token(char_index)` if batch size is 1
- `self.char_to_token(batch_index, char_index)` if batch size is greater or equal... | char_to_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def word_to_chars(
self, batch_or_word_index: int, word_index: Optional[int] = None, sequence_index: int = 0
) -> CharSpan:
"""
Get the character span in the original string corresponding to given word in a sequence of the batch.
Character spans are returned as a CharSpan NamedTuple... |
Get the character span in the original string corresponding to given word in a sequence of the batch.
Character spans are returned as a CharSpan NamedTuple with:
- start: index of the first character in the original string
- end: index of the character following the last character in ... | word_to_chars | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def char_to_word(self, batch_or_char_index: int, char_index: Optional[int] = None, sequence_index: int = 0) -> int:
"""
Get the word in the original string corresponding to a character in the original string of a sequence of the
batch.
Can be called as:
- `self.char_to_word(cha... |
Get the word in the original string corresponding to a character in the original string of a sequence of the
batch.
Can be called as:
- `self.char_to_word(char_index)` if batch size is 1
- `self.char_to_word(batch_index, char_index)` if batch size is greater than 1
Th... | char_to_word | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def convert_to_tensors(
self, tensor_type: Optional[Union[str, TensorType]] = None, prepend_batch_axis: bool = False
):
"""
Convert the inner content to tensors.
Args:
tensor_type (`str` or [`~utils.TensorType`], *optional*):
The type of tensors to use. I... |
Convert the inner content to tensors.
Args:
tensor_type (`str` or [`~utils.TensorType`], *optional*):
The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
`None`, no modification is done.
prepend... | convert_to_tensors | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def to(self, device: Union[str, "torch.device"]) -> "BatchEncoding":
"""
Send all values to device by calling `v.to(device)` (PyTorch only).
Args:
device (`str` or `torch.device`): The device to put the tensors on.
Returns:
[`BatchEncoding`]: The same instance a... |
Send all values to device by calling `v.to(device)` (PyTorch only).
Args:
device (`str` or `torch.device`): The device to put the tensors on.
Returns:
[`BatchEncoding`]: The same instance after modification.
| to | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def add_special_tokens(self, special_tokens_dict: Dict[str, Union[str, AddedToken]]) -> int:
"""
Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If
special tokens are NOT in the vocabulary, they are added to it (indexed starting from the... |
Add a dictionary of special tokens (eos, pad, cls, etc.) to the encoder and link them to class attributes. If
special tokens are NOT in the vocabulary, they are added to it (indexed starting from the last index of the
current vocabulary).
Note,None When adding new tokens to the vocabul... | add_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def add_tokens(
self, new_tokens: Union[str, AddedToken, List[Union[str, AddedToken]]], special_tokens: bool = False
) -> int:
"""
Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of t... |
Add a list of new tokens to the tokenizer class. If the new tokens are not in the vocabulary, they are added to
it with indices starting from length of the current vocabulary and and will be isolated before the tokenization
algorithm is applied. Added tokens and tokens from the vocabulary of th... | add_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def bos_token(self) -> str:
"""
`str`: Beginning of sentence token. Log an error if used while not having been set.
"""
if self._bos_token is None:
if self.verbose:
logger.error("Using bos_token, but it is not set yet.")
return None
return ... |
`str`: Beginning of sentence token. Log an error if used while not having been set.
| bos_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def eos_token(self) -> str:
"""
`str`: End of sentence token. Log an error if used while not having been set.
"""
if self._eos_token is None:
if self.verbose:
logger.error("Using eos_token, but it is not set yet.")
return None
return str(se... |
`str`: End of sentence token. Log an error if used while not having been set.
| eos_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def unk_token(self) -> str:
"""
`str`: Unknown token. Log an error if used while not having been set.
"""
if self._unk_token is None:
if self.verbose:
logger.error("Using unk_token, but it is not set yet.")
return None
return str(self._unk_... |
`str`: Unknown token. Log an error if used while not having been set.
| unk_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def sep_token(self) -> str:
"""
`str`: Separation token, to separate context and query in an input sequence. Log an error if used while not
having been set.
"""
if self._sep_token is None:
if self.verbose:
logger.error("Using sep_token, but it is not s... |
`str`: Separation token, to separate context and query in an input sequence. Log an error if used while not
having been set.
| sep_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def pad_token(self) -> str:
"""
`str`: Padding token. Log an error if used while not having been set.
"""
if self._pad_token is None:
if self.verbose:
logger.error("Using pad_token, but it is not set yet.")
return None
return str(self._pad_... |
`str`: Padding token. Log an error if used while not having been set.
| pad_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def cls_token(self) -> str:
"""
`str`: Classification token, to extract a summary of an input sequence leveraging self-attention along the full
depth of the model. Log an error if used while not having been set.
"""
if self._cls_token is None:
if self.verbose:
... |
`str`: Classification token, to extract a summary of an input sequence leveraging self-attention along the full
depth of the model. Log an error if used while not having been set.
| cls_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def mask_token(self) -> str:
"""
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
"""
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is ... |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def additional_special_tokens(self) -> List[str]:
"""
`List[str]`: All the additional special tokens you may want to use. Log an error if used while not having been
set.
"""
if self._additional_special_tokens is None:
if self.verbose:
logger.error("Usi... |
`List[str]`: All the additional special tokens you may want to use. Log an error if used while not having been
set.
| additional_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def bos_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the beginning of sentence token in the vocabulary. Returns `None` if the token has not
been set.
"""
if self._bos_token is None:
return None
return self.convert_tokens_to_ids(self.bos_token) |
`Optional[int]`: Id of the beginning of sentence token in the vocabulary. Returns `None` if the token has not
been set.
| bos_token_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def eos_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
set.
"""
if self._eos_token is None:
return None
return self.convert_tokens_to_ids(self.eos_token) |
`Optional[int]`: Id of the end of sentence token in the vocabulary. Returns `None` if the token has not been
set.
| eos_token_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def unk_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the unknown token in the vocabulary. Returns `None` if the token has not been set.
"""
if self._unk_token is None:
return None
return self.convert_tokens_to_ids(self.unk_token) |
`Optional[int]`: Id of the unknown token in the vocabulary. Returns `None` if the token has not been set.
| unk_token_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def sep_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the separation token in the vocabulary, to separate context and query in an input
sequence. Returns `None` if the token has not been set.
"""
if self._sep_token is None:
return None
return sel... |
`Optional[int]`: Id of the separation token in the vocabulary, to separate context and query in an input
sequence. Returns `None` if the token has not been set.
| sep_token_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def pad_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the padding token in the vocabulary. Returns `None` if the token has not been set.
"""
if self._pad_token is None:
return None
return self.convert_tokens_to_ids(self.pad_token) |
`Optional[int]`: Id of the padding token in the vocabulary. Returns `None` if the token has not been set.
| pad_token_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def cls_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the classification token in the vocabulary, to extract a summary of an input sequence
leveraging self-attention along the full depth of the model.
Returns `None` if the token has not been set.
"""
if sel... |
`Optional[int]`: Id of the classification token in the vocabulary, to extract a summary of an input sequence
leveraging self-attention along the full depth of the model.
Returns `None` if the token has not been set.
| cls_token_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def mask_token_id(self) -> Optional[int]:
"""
`Optional[int]`: Id of the mask token in the vocabulary, used when training a model with masked-language
modeling. Returns `None` if the token has not been set.
"""
if self._mask_token is None:
return None
return s... |
`Optional[int]`: Id of the mask token in the vocabulary, used when training a model with masked-language
modeling. Returns `None` if the token has not been set.
| mask_token_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def special_tokens_map(self) -> Dict[str, Union[str, List[str]]]:
"""
`Dict[str, Union[str, List[str]]]`: A dictionary mapping special token class attributes (`cls_token`,
`unk_token`, etc.) to their values (`'<unk>'`, `'<cls>'`, etc.).
Convert potential tokens of `tokenizers.AddedToken... |
`Dict[str, Union[str, List[str]]]`: A dictionary mapping special token class attributes (`cls_token`,
`unk_token`, etc.) to their values (`'<unk>'`, `'<cls>'`, etc.).
Convert potential tokens of `tokenizers.AddedToken` type to string.
| special_tokens_map | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def special_tokens_map_extended(self) -> Dict[str, Union[str, AddedToken, List[Union[str, AddedToken]]]]:
"""
`Dict[str, Union[str, tokenizers.AddedToken, List[Union[str, tokenizers.AddedToken]]]]`: A dictionary mapping
special token class attributes (`cls_token`, `unk_token`, etc.) to their val... |
`Dict[str, Union[str, tokenizers.AddedToken, List[Union[str, tokenizers.AddedToken]]]]`: A dictionary mapping
special token class attributes (`cls_token`, `unk_token`, etc.) to their values (`'<unk>'`, `'<cls>'`, etc.).
Don't convert tokens of `tokenizers.AddedToken` type to string so they can... | special_tokens_map_extended | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def all_special_tokens_extended(self) -> List[Union[str, AddedToken]]:
"""
`List[Union[str, tokenizers.AddedToken]]`: All the special tokens (`'<unk>'`, `'<cls>'`, etc.) mapped to class
attributes.
Don't convert tokens of `tokenizers.AddedToken` type to string so they can be used to con... |
`List[Union[str, tokenizers.AddedToken]]`: All the special tokens (`'<unk>'`, `'<cls>'`, etc.) mapped to class
attributes.
Don't convert tokens of `tokenizers.AddedToken` type to string so they can be used to control more finely how
special tokens are tokenized.
| all_special_tokens_extended | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def all_special_ids(self) -> List[int]:
"""
`List[int]`: List the ids of the special tokens(`'<unk>'`, `'<cls>'`, etc.) mapped to class attributes.
"""
all_toks = self.all_special_tokens
all_ids = self.convert_tokens_to_ids(all_toks)
return all_ids |
`List[int]`: List the ids of the special tokens(`'<unk>'`, `'<cls>'`, etc.) mapped to class attributes.
| all_special_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def save_pretrained(
self,
save_directory: Union[str, os.PathLike],
legacy_format: Optional[bool] = None,
filename_prefix: Optional[str] = None,
push_to_hub: bool = False,
**kwargs,
) -> Tuple[str]:
"""
Save the full tokenizer state.
This met... |
Save the full tokenizer state.
This method make sure the full tokenizer can then be re-loaded using the
[`~tokenization_utils_base.PreTrainedTokenizer.from_pretrained`] class method..
Warning,None This won't save modifications you may have applied to the tokenizer after the instantia... | save_pretrained | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def _save_pretrained(
self,
save_directory: Union[str, os.PathLike],
file_names: Tuple[str],
legacy_format: Optional[bool] = None,
filename_prefix: Optional[str] = None,
) -> Tuple[str]:
"""
Save a tokenizer using the slow-tokenizer/legacy format: vocabulary +... |
Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens.
Fast tokenizers can also be saved in a unique JSON file containing {config + vocab + added-tokens} using the
specific [`~tokenization_utils_fast.PreTrainedTokenizerFast._save_pretrained`]
| _save_pretrained | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def encode(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, Trun... |
Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.
Same as doing `self.convert_tokens_to_ids(self.tokenize(text))`.
Args:
text (`str`, `List[str]` or `List[int]`):
The first sequence to be encoded. This can be a string, a list of ... | encode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def _get_padding_truncation_strategies(
self, padding=False, truncation=None, max_length=None, pad_to_multiple_of=None, verbose=True, **kwargs
):
"""
Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy
and pad_to_max_length)... |
Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy
and pad_to_max_length) and behaviors.
| _get_padding_truncation_strategies | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None,
text_target: Union[TextInput, PreTokenizedInput, List[TextInput], Lis... |
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences.
Args:
text (`str`, `List[str]`, `List[List[str]]`, *optional*):
The sequence or batch of sequences to be encoded. Each sequence can be a string or a ... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str,... |
Tokenize and prepare for the model a sequence or a pair of sequences.
<Tip warning={true}>
This method is deprecated, `__call__` should be used instead.
</Tip>
Args:
text (`str`, `List[str]` or `List[int]` (the latter only for not-fast tokenizers)):
... | encode_plus | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
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_tok... |
Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.
<Tip warning={true}>
This method is deprecated, `__call__` should be used instead.
</Tip>
Args:
batch_text_or_text_pairs (`List[str]`, `List[Tuple[str, str]]`, `List[List[str]... | batch_encode_plus | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def pad(
self,
encoded_inputs: Union[
BatchEncoding,
List[BatchEncoding],
Dict[str, EncodedInput],
Dict[str, List[EncodedInput]],
List[Dict[str, EncodedInput]],
],
padding: Union[bool, str, PaddingStrategy] = True,
max_l... |
Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
in the batch.
Padding side (left/right) padding token ids are defined at the tokenizer level (with `self.padding_side`,
`self.pad_token_id` and `self.pad_token_type_id`).
... | pad | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create the token type IDs corresponding to the sequences passed. [What are token type
IDs?](../glossary#token-type-ids)
Should be overridden in... |
Create the token type IDs corresponding to the sequences passed. [What are token type
IDs?](../glossary#token-type-ids)
Should be overridden in a subclass if the model has a special way of building those.
Args:
token_ids_0 (`List[int]`): The first tokenized sequence.
... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def build_inputs_with_special_tokens(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens.
This impleme... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens.
This implementation does not add special tokens and this method should be overridden in a subclass.
Args:
token_ids_0 (`List[int]`): The f... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def prepare_for_model(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
max_length: Optional[int] = None,
... |
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. Pleas... | prepare_for_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def truncate_sequences(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
num_tokens_to_remove: int = 0,
truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
stride: int = 0,
) -> Tuple[List[int], List[int], List[int]]:
"""
... |
Truncates a sequence pair in-place following the strategy.
Args:
ids (`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
`convert_tokens_to_ids` methods.
pair_ids (`List[int]`, *o... | truncate_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
return_attention_mask: Optional[bool] = None,
) -... |
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
Args:
encoded_inputs:
Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
max_length: maximum length of the returned list and opt... | _pad | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def batch_decode(
self,
sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
**kwargs
) -> List[str]:
"""
Convert a list of lists of token ids int... |
Convert a list of lists of token ids into a list of strings by calling decode.
Args:
sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
List of tokenized input ids. Can be obtained using the `__call__` method.
skip_special_toke... | batch_decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def decode(
self,
token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
**kwargs
) -> str:
"""
Converts a sequence of ids in a string, using the tokenizer and ... |
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def clean_up_tokenization(out_string: str) -> str:
"""
Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.
Args:
out_string (`str`): The text to clean up.
Returns:
`str`: The cleaned-up string.
... |
Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.
Args:
out_string (`str`): The text to clean up.
Returns:
`str`: The cleaned-up string.
| clean_up_tokenization | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def _eventual_warn_about_too_long_sequence(self, ids: List[int], max_length: Optional[int], verbose: bool):
"""
Depending on the input and internal state we might trigger a warning about a sequence that is too long for its
corresponding model
Args:
ids (`List[str]`): The ids... |
Depending on the input and internal state we might trigger a warning about a sequence that is too long for its
corresponding model
Args:
ids (`List[str]`): The ids produced by the tokenization
max_length (`int`, *optional*): The max_length desired (does not trigger a wa... | _eventual_warn_about_too_long_sequence | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def as_target_tokenizer(self):
"""
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.
"""
warnings.warn(
"`as_target_tokenizer` is deprecated... |
Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
sequence-to-sequence models that need a slightly different processing for the labels.
| as_target_tokenizer | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def register_for_auto_class(cls, auto_class="AutoTokenizer"):
"""
Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the
library are already mapped with `AutoTokenizer`.
<Tip warning={true}>
This API is experimental and ma... |
Register this class with a given auto class. This should only be used for custom tokenizers as the ones in the
library are already mapped with `AutoTokenizer`.
<Tip warning={true}>
This API is experimental and may have some slight breaking changes in the next releases.
</Tip>... | register_for_auto_class | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def prepare_seq2seq_batch(
self,
src_texts: List[str],
tgt_texts: Optional[List[str]] = None,
max_length: Optional[int] = None,
max_target_length: Optional[int] = None,
padding: str = "longest",
return_tensors: str = None,
truncation: bool = True,
... |
Prepare model inputs for translation. For best performance, translate one sentence at a time.
Arguments:
src_texts (`List[str]`):
List of documents to summarize or source language texts.
tgt_texts (`list`, *optional*):
List of summaries or target... | prepare_seq2seq_batch | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def get_fast_tokenizer_file(tokenization_files: List[str]) -> str:
"""
Get the tokenization file to use for this version of transformers.
Args:
tokenization_files (`List[str]`): The list of available configuration files.
Returns:
`str`: The tokenization file to use.
"""
tokeniz... |
Get the tokenization file to use for this version of transformers.
Args:
tokenization_files (`List[str]`): The list of available configuration files.
Returns:
`str`: The tokenization file to use.
| get_fast_tokenizer_file | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_base.py | Apache-2.0 |
def get_added_vocab(self) -> Dict[str, int]:
"""
Returns the added tokens in the vocabulary as a dictionary of token to index.
Returns:
`Dict[str, int]`: The added tokens.
"""
base_vocab = self._tokenizer.get_vocab(with_added_tokens=False)
full_vocab = self._... |
Returns the added tokens in the vocabulary as a dictionary of token to index.
Returns:
`Dict[str, int]`: The added tokens.
| get_added_vocab | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | Apache-2.0 |
def _convert_encoding(
self,
encoding: EncodingFast,
return_token_type_ids: Optional[bool] = None,
return_attention_mask: Optional[bool] = None,
return_overflowing_tokens: bool = False,
return_special_tokens_mask: bool = False,
return_offsets_mapping: bool = False... |
Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
of encodings, take care of building a batch from overflowing tokens.
Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
... | _convert_encoding | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | Apache-2.0 |
def set_truncation_and_padding(
self,
padding_strategy: PaddingStrategy,
truncation_strategy: TruncationStrategy,
max_length: int,
stride: int,
pad_to_multiple_of: Optional[int],
):
"""
Define the truncation and the padding strategies for fast tokenize... |
Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers
library) and restore the tokenizer settings afterwards.
The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a
padding / tru... | set_truncation_and_padding | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | Apache-2.0 |
def _save_pretrained(
self,
save_directory: Union[str, os.PathLike],
file_names: Tuple[str],
legacy_format: Optional[bool] = None,
filename_prefix: Optional[str] = None,
) -> Tuple[str]:
"""
Save a tokenizer using the slow-tokenizer/legacy format: vocabulary +... |
Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens as well as in a unique JSON
file containing {config + vocab + added-tokens}.
| _save_pretrained | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/tokenization_utils_fast.py | Apache-2.0 |
def get_train_dataloader(self) -> DataLoader:
"""
Returns the training [`~torch.utils.data.DataLoader`].
Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
training if necessary) otherwise.
Subclass and override this me... |
Returns the training [`~torch.utils.data.DataLoader`].
Will use no sampler if `train_dataset` does not implement `__len__`, a random sampler (adapted to distributed
training if necessary) otherwise.
Subclass and override this method if you want to inject some custom behavior.
| get_train_dataloader | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def get_eval_dataloader(self, eval_dataset: Optional[Dataset] = None) -> DataLoader:
"""
Returns the evaluation [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (`torch.utils.data.Dataset`, *o... |
Returns the evaluation [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
eval_dataset (`torch.utils.data.Dataset`, *optional*):
If provided, will override `self.eval_dataset`. If it is a [`~dataset... | get_eval_dataloader | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def get_test_dataloader(self, test_dataset: Dataset) -> DataLoader:
"""
Returns the test [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
test_dataset (`torch.utils.data.Dataset`, *optional*):
... |
Returns the test [`~torch.utils.data.DataLoader`].
Subclass and override this method if you want to inject some custom behavior.
Args:
test_dataset (`torch.utils.data.Dataset`, *optional*):
The test dataset to use. If it is a [`~datasets.Dataset`], columns not acce... | get_test_dataloader | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def create_optimizer_and_scheduler(self, num_training_steps: int):
"""
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass an... |
Setup the optimizer and the learning rate scheduler.
We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
Trainer's init through `optimizers`, or subclass and override this method (or `create_optimizer` and/or
`create_scheduler... | create_optimizer_and_scheduler | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def get_optimizer_cls_and_kwargs(args: TrainingArguments) -> Tuple[Any, Any]:
"""
Returns the optimizer class and optimizer parameters based on the training arguments.
Args:
args (`transformers.training_args.TrainingArguments`):
The training arguments for the trainin... |
Returns the optimizer class and optimizer parameters based on the training arguments.
Args:
args (`transformers.training_args.TrainingArguments`):
The training arguments for the training session.
| get_optimizer_cls_and_kwargs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def create_scheduler(self, num_training_steps: int, optimizer: torch.optim.Optimizer = None):
"""
Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.
Args:
num_training_steps (int): The number ... |
Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or
passed as an argument.
Args:
num_training_steps (int): The number of training steps to do.
| create_scheduler | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def train(
self,
resume_from_checkpoint: Optional[Union[str, bool]] = None,
trial: Union["optuna.Trial", Dict[str, Any]] = None,
ignore_keys_for_eval: Optional[List[str]] = None,
**kwargs,
):
"""
Main training entry point.
Args:
resume_fro... |
Main training entry point.
Args:
resume_from_checkpoint (`str` or `bool`, *optional*):
If a `str`, local path to a saved checkpoint as saved by a previous instance of [`Trainer`]. If a
`bool` and equals `True`, load the last checkpoint in *args.output_dir* a... | train | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def hyperparameter_search(
self,
hp_space: Optional[Callable[["optuna.Trial"], Dict[str, float]]] = None,
compute_objective: Optional[Callable[[Dict[str, float]], float]] = None,
n_trials: int = 20,
direction: str = "minimize",
backend: Optional[Union["str", HPSearchBacke... |
Launch an hyperparameter search using `optuna` or `Ray Tune` or `SigOpt`. The optimized quantity is determined
by `compute_objective`, which defaults to a function returning the evaluation loss when no metric is provided,
the sum of all metrics otherwise.
<Tip warning={true}>
... | hyperparameter_search | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def compute_loss_context_manager(self):
"""
A helper wrapper to group together context managers.
"""
return ContextManagers(
[
self.torchdynamo_smart_context_manager(),
self.autocast_smart_context_manager(),
]
) |
A helper wrapper to group together context managers.
| compute_loss_context_manager | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
"""
Perform a training step on a batch of inputs.
Subclass and override to inject custom behavior.
Args:
model (`nn.Module`):
The model to train.
... |
Perform a training step on a batch of inputs.
Subclass and override to inject custom behavior.
Args:
model (`nn.Module`):
The model to train.
inputs (`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.
... | training_step | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def compute_loss(self, model, inputs, return_outputs=False):
"""
How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for custom behavior.
"""
if self.label_smoother is not None and "labels" in inputs:
... |
How the loss is computed by Trainer. By default, all models return the loss in the first element.
Subclass and override for custom behavior.
| compute_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def save_model(self, output_dir: Optional[str] = None, _internal_call: bool = False):
"""
Will save the model, so you can reload it using `from_pretrained()`.
Will only save from the main process.
"""
if output_dir is None:
output_dir = self.args.output_dir
... |
Will save the model, so you can reload it using `from_pretrained()`.
Will only save from the main process.
| save_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def evaluate(
self,
eval_dataset: Optional[Dataset] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
) -> Dict[str, float]:
"""
Run evaluation and returns metrics.
The calling script will be responsible for providing a method ... |
Run evaluation and returns metrics.
The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
(pass it to the init `compute_metrics` argument).
You can also subclass and override this method to inject custom behavior.
Args:
... | evaluate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def predict(
self, test_dataset: Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "test"
) -> PredictionOutput:
"""
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain label... |
Run prediction and returns predictions and potential metrics.
Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
will also return metrics, like in `evaluate()`.
Args:
test_dataset (`Dataset`):
Dataset... | predict | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def evaluation_loop(
self,
dataloader: DataLoader,
description: str,
prediction_loss_only: Optional[bool] = None,
ignore_keys: Optional[List[str]] = None,
metric_key_prefix: str = "eval",
) -> EvalLoopOutput:
"""
Prediction/evaluation loop, shared by `... |
Prediction/evaluation loop, shared by `Trainer.evaluate()` and `Trainer.predict()`.
Works both with or without labels.
| evaluation_loop | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def prediction_step(
self,
model: nn.Module,
inputs: Dict[str, Union[torch.Tensor, Any]],
prediction_loss_only: bool,
ignore_keys: Optional[List[str]] = None,
) -> Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]:
"""
Perform an ev... |
Perform an evaluation step on `model` using `inputs`.
Subclass and override to inject custom behavior.
Args:
model (`nn.Module`):
The model to evaluate.
inputs (`Dict[str, Union[torch.Tensor, Any]]`):
The inputs and targets of the model.... | prediction_step | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def floating_point_ops(self, inputs: Dict[str, Union[torch.Tensor, Any]]):
"""
For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
... |
For models that inherit from [`PreTrainedModel`], uses that method to compute the number of floating point
operations for every backward + forward pass. If using another model, either implement such a method in the
model or subclass and override this method.
Args:
inputs (`... | floating_point_ops | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def init_git_repo(self, at_init: bool = False):
"""
Initializes a git repo in `self.args.hub_model_id`.
Args:
at_init (`bool`, *optional*, defaults to `False`):
Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is
... |
Initializes a git repo in `self.args.hub_model_id`.
Args:
at_init (`bool`, *optional*, defaults to `False`):
Whether this function is called before any training or not. If `self.args.overwrite_output_dir` is
`True` and `at_init` is `True`, the path to the re... | init_git_repo | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer.py | Apache-2.0 |
def save_to_json(self, json_path: str):
"""Save the content of this instance in JSON format inside `json_path`."""
json_string = json.dumps(dataclasses.asdict(self), indent=2, sort_keys=True) + "\n"
with open(json_path, "w", encoding="utf-8") as f:
f.write(json_string) | Save the content of this instance in JSON format inside `json_path`. | save_to_json | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_callback.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_callback.py | Apache-2.0 |
def load_from_json(cls, json_path: str):
"""Create an instance from the content of `json_path`."""
with open(json_path, "r", encoding="utf-8") as f:
text = f.read()
return cls(**json.loads(text)) | Create an instance from the content of `json_path`. | load_from_json | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_callback.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_callback.py | Apache-2.0 |
def _new_step(self):
"""Internal method that resets the variable for a new step."""
self.should_save = False
self.should_evaluate = False
self.should_log = False | Internal method that resets the variable for a new step. | _new_step | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_callback.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_callback.py | Apache-2.0 |
def torch_pad_and_concatenate(tensor1, tensor2, padding_index=-100):
"""Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary."""
tensor1 = atleast_1d(tensor1)
tensor2 = atleast_1d(tensor2)
if len(tensor1.shape) == 1 or tensor1.shape[1] == tensor2.shape[1]:
... | Concatenates `tensor1` and `tensor2` on first axis, applying padding on the second if necessary. | torch_pad_and_concatenate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
def numpy_pad_and_concatenate(array1, array2, padding_index=-100):
"""Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary."""
array1 = atleast_1d(array1)
array2 = atleast_1d(array2)
if len(array1.shape) == 1 or array1.shape[1] == array2.shape[1]:
return... | Concatenates `array1` and `array2` on first axis, applying padding on the second if necessary. | numpy_pad_and_concatenate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/trainer_pt_utils.py | Apache-2.0 |
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