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
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|---|---|---|---|---|---|---|---|
def call(
self,
input_ids: TFModelInputType = None,
inputs_embeds: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
encoder_hidden_states: Optional[tf.Tensor] = None,
encoder_attention_mask: Optional... |
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`MBartTokenizer`]. See [`PreTrainedTo... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_tf_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_tf_mbart.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 a mask from the two sequences passed to be used in a sequence-pair classification task. mBART does not
make use of token type ids, therefore a li... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. mBART does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *opti... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/tokenization_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/tokenization_mbart.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. The special tokens de... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. The special tokens depend on calling set_lang.
An MBART sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encod... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/tokenization_mbart_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/tokenization_mbart_fast.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. An MBART-50 sequence ... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An MBART-50 sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encoder) `[src_lang_code] X [eos]`
- `labels`: (fo... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50.py | Apache-2.0 |
def set_src_lang_special_tokens(self, src_lang: str) -> None:
"""Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos]."""
self.cur_lang_code_id = self.lang_code_to_id[src_lang]
self.prefix_tokens = [self.cur_lang_code_id]
self.suffix_tokens = [self... | Reset the special tokens to the source lang setting. prefix=[src_lang_code] and suffix=[eos]. | set_src_lang_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50.py | Apache-2.0 |
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
"""Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos]."""
self.cur_lang_code_id = self.lang_code_to_id[tgt_lang]
self.prefix_tokens = [self.cur_lang_code_id]
self.suffix_tokens = [... | Reset the special tokens to the target language setting. prefix=[tgt_lang_code] and suffix=[eos]. | set_tgt_lang_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50.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. The special tokens de... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. The special tokens depend on calling set_lang.
An MBART-50 sequence has the following format, where `X` represents the sequence:
- `input_ids` (for en... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50_fast.py | Apache-2.0 |
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
"""Reset the special tokens to the target language setting. prefix=[src_lang_code] and suffix=[eos]."""
self.cur_lang_code_id = self.convert_tokens_to_ids(tgt_lang)
self.prefix_tokens = [self.cur_lang_code_id]
self.suffix_toke... | Reset the special tokens to the target language setting. prefix=[src_lang_code] and suffix=[eos]. | set_tgt_lang_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart50/tokenization_mbart50_fast.py | Apache-2.0 |
def _extract_mfsc_features(self, one_waveform: np.array) -> np.ndarray:
"""
Extracts MFSC Features for one waveform vector (unbatched). Adapted from Flashlight's C++ MFSC code.
"""
if self.win_function == "hamming_window":
window = torch.hamming_window(window_length=self.samp... |
Extracts MFSC Features for one waveform vector (unbatched). Adapted from Flashlight's C++ MFSC code.
| _extract_mfsc_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mctct/feature_extraction_mctct.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mctct/feature_extraction_mctct.py | Apache-2.0 |
def __call__(
self,
raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
padding: Union[bool, str, PaddingStrategy] = False,
max_length: Optional[int] = None,
truncation: bool = False,
pad_to_multiple_of: Optional[int] = None,
return_at... |
Main method to featurize and prepare for the model one or several sequence(s). sequences. It returns the
log-mel spectrogram of the input audio, as implemented in the original Flashlight MFSC feature extraction code.
Args:
raw_speech (`torch.Tensor`, `np.ndarray`, `List[float]`, `L... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mctct/feature_extraction_mctct.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mctct/feature_extraction_mctct.py | Apache-2.0 |
def __call__(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
[`~MCTCTFeatureExtractor.__call__`] and returns its output. If used in the context
[`~MCTCTProcessor.as_target_processor`] this method forwards all its arg... |
When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
[`~MCTCTFeatureExtractor.__call__`] and returns its output. If used in the context
[`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to AutoTokenizer's
[`~AutoTokeniz... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mctct/processing_mctct.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mctct/processing_mctct.py | Apache-2.0 |
def pad(self, *args, **kwargs):
"""
When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
[`~MCTCTFeatureExtractor.pad`] and returns its output. If used in the context
[`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to ... |
When used in normal mode, this method forwards all its arguments to MCTCTFeatureExtractor's
[`~MCTCTFeatureExtractor.pad`] and returns its output. If used in the context
[`~MCTCTProcessor.as_target_processor`] this method forwards all its arguments to PreTrainedTokenizer's
[`~PreTrained... | pad | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mctct/processing_mctct.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mctct/processing_mctct.py | Apache-2.0 |
def as_target_processor(self):
"""
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning MCTCT.
"""
warnings.warn(
"`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
... |
Temporarily sets the tokenizer for processing the input. Useful for encoding the labels when fine-tuning MCTCT.
| as_target_processor | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mctct/processing_mctct.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mctct/processing_mctct.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_bert/modeling_megatron_bert.py | Apache-2.0 |
def recursive_print(name, val, spaces=0):
"""
Recursively print the structure of a checkpoint. This function is taken from `convert_megatron_gpt2_checkpoint.py`
Args:
name (str): the name of the current tensor parameter
val (Tuple(int)): the shape of the current tensor parameter
spa... |
Recursively print the structure of a checkpoint. This function is taken from `convert_megatron_gpt2_checkpoint.py`
Args:
name (str): the name of the current tensor parameter
val (Tuple(int)): the shape of the current tensor parameter
spaces (int): the number of spaces to print before t... | recursive_print | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | Apache-2.0 |
def megatron_to_transformers_fix_query_key_value_ordering(
param, checkpoint_version, num_splits, num_heads, hidden_size
):
"""
Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] for compatibility with later versions
of NVIDIA Megatron-LM. The inverse operation is performed ins... |
Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :] for compatibility with later versions
of NVIDIA Megatron-LM. The inverse operation is performed inside Megatron-LM to read checkpoints:
https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209 If param is the... | megatron_to_transformers_fix_query_key_value_ordering | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | Apache-2.0 |
def transformers_to_megatron_fix_query_key_value_ordering(
param, checkpoint_version, num_splits, num_heads, hidden_size
):
"""
Permutes layout of param tensor to the one compatible with respective NVIDIA Megatron-LM chekpoint versions. Input
is [num_splits * num_heads * hidden_size, :] and output is [n... |
Permutes layout of param tensor to the one compatible with respective NVIDIA Megatron-LM chekpoint versions. Input
is [num_splits * num_heads * hidden_size, :] and output is [num_heads * hidden_size * num_splits, :] for version
1.0 and [num_heads * num_splits * hidden_size, :] for version 2.0 and later. If... | transformers_to_megatron_fix_query_key_value_ordering | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | Apache-2.0 |
def get_megatron_sharded_states(args, tp_size, pp_size, pp_rank):
"""
Get sharded checkpoints from NVIDIA Megatron-LM checkpoint based on the provided tensor parallel size, pipeline
parallel size and pipeline parallel rank.
Args:
args (argparse.Namespace): the arguments to the script
tp... |
Get sharded checkpoints from NVIDIA Megatron-LM checkpoint based on the provided tensor parallel size, pipeline
parallel size and pipeline parallel rank.
Args:
args (argparse.Namespace): the arguments to the script
tp_size (int): the tensor parallel size
pp_size (int): the pipeline... | get_megatron_sharded_states | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | Apache-2.0 |
def convert_checkpoint_from_megatron_to_transformers(args):
"""
Convert NVIDIA Megatron-LM checkpoint to HuggingFace Transformers checkpoint. This handles Megatron checkpoints
with different tensor parallelism and pipeline parallelism sizes. It saves the converted checkpoint into shards
using HuggingFac... |
Convert NVIDIA Megatron-LM checkpoint to HuggingFace Transformers checkpoint. This handles Megatron checkpoints
with different tensor parallelism and pipeline parallelism sizes. It saves the converted checkpoint into shards
using HuggingFace Transformers checkpoint sharding functionality. This greatly exte... | convert_checkpoint_from_megatron_to_transformers | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | Apache-2.0 |
def convert_checkpoint_from_transformers_to_megatron(args):
"""
Convert a checkpoint from HuggingFace Transformers to Megatron-LM. This allows converted checkpoints with variable
tensor parallelism and pipeline parallelism sizes. It takes as input a checkpoint from HuggingFace Transformers
which can hav... |
Convert a checkpoint from HuggingFace Transformers to Megatron-LM. This allows converted checkpoints with variable
tensor parallelism and pipeline parallelism sizes. It takes as input a checkpoint from HuggingFace Transformers
which can have multiple shards.
Args:
args (argparse.Namespace): th... | convert_checkpoint_from_transformers_to_megatron | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/megatron_gpt2/checkpoint_reshaping_and_interoperability.py | Apache-2.0 |
def forward(
self,
input_modal,
input_ids=None,
modal_start_tokens=None,
modal_end_tokens=None,
attention_mask=None,
token_type_ids=None,
modal_token_type_ids=None,
position_ids=None,
modal_position_ids=None,
head_mask=None,
... |
Returns:
Examples:
```python
# For example purposes. Not runnable.
transformer = BertModel.from_pretrained("bert-base-uncased")
encoder = ImageEncoder(args)
mmbt = MMBTModel(config, transformer, encoder)
``` | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mmbt/modeling_mmbt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mmbt/modeling_mmbt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring) Indices should be in `[0, 1]`.
- 0 indicates sequence B is a continuation o... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_mobilebert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
Return:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import MobileBertTokenizer, TFMobileBertForPreTraining
>>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased")
>>> model = TFMobileBertForPreTraining.from_pr... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
labels (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (masked), the
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
Return:
Examples:
```python
>>> import tensorflow as tf
>>> from transformers import MobileBertTokenizer, TFMobileBertForNextSentencePrediction
>>> tokenizer = MobileBertTokenizer.from_pretrained("google/mobilebert-uncased")
>>> model = TFMobileBertForNextSent... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilebert/modeling_tf_mobilebert.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. A MobileBERT sequence... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A MobileBERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilebert/tokenization_mobilebert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilebert/tokenization_mobilebert.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 a mask from the two sequences passed to be used in a sequence-pair classification task. A MobileBERT
sequence pair mask has the following format:... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A MobileBERT
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is ... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilebert/tokenization_mobilebert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilebert/tokenization_mobilebert.py | Apache-2.0 |
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple] = None):
"""
Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`MobileViTForSemanticSegmentation`]):
... |
Converts the output of [`MobileViTForSemanticSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`MobileViTForSemanticSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple]`, *optional*):
... | post_process_semantic_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilevit/feature_extraction_mobilevit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilevit/feature_extraction_mobilevit.py | Apache-2.0 |
def make_divisible(value: int, divisor: int = 8, min_value: Optional[int] = None) -> int:
"""
Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
original TensorFlow repo. It can be seen here:
https://github.com/tensorflow/models/blob/master/resear... |
Ensure that all layers have a channel count that is divisible by `divisor`. This function is taken from the
original TensorFlow repo. It can be seen here:
https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py
| make_divisible | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | Apache-2.0 |
def _prune_heads(self, heads_to_prune):
"""Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
"""
for layer_index, heads in heads_to_prune.items():
mobilevit_layer = self.encoder.layer[layer_inde... | Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel
| _prune_heads | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, ImageClassifierOutputWithNoAttention]:
r"""
labels (`torch... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
`config.n... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[tuple, SemanticSegmenterOutput]:
r"""
labels (`torch.LongTensor` ... |
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_mobilevit.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[tf.Tensor] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[tf.Tensor] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[tuple, TFImageClassifierOutputWithNoAttention]:
... |
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss). If
`config.num_labe... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[tf.Tensor] = None,
labels: Optional[tf.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[tuple, TFSemanticSegmenterOutputWithNoAttention]:
... |
labels (`tf.Tensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
Return... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mobilevit/modeling_tf_mobilevit.py | Apache-2.0 |
def create_position_ids_from_input_ids(input_ids, padding_idx):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`. :param torch.Tensor x: :return torch.Tensor:
"""
... |
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`. :param torch.Tensor x: :return torch.Tensor:
| create_position_ids_from_input_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mpnet/modeling_mpnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mpnet/modeling_mpnet.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. A MPNet sequence has ... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A MPNet sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
toke... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet.py | Apache-2.0 |
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = 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... |
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` methods.
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional... | get_special_tokens_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet.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]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not
make use of token type ids, therefore a l... |
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *opt... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet.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.
MPNet tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise ... |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
MPNet tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise the space before the *<mask>*.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet_fast.py | Apache-2.0 |
def mask_token(self, value):
"""
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on MPNet.
"""
# Mask token behave like a normal word, i.e. include t... |
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on MPNet.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet_fast.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]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not
make use of token type ids, therefore a l... |
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. MPNet does not
make use of token type ids, therefore a list of zeros is returned
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *opti... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mpnet/tokenization_mpnet_fast.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.FloatTensor,
attention_mask: torch.FloatTensor,
layer_head_mask: torch.FloatTensor,
self_attn_prompt: torch.FloatTensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor, Optional[torch.FloatTensor]]:
... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
cross_attn_l... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optio... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`MvpTokenizer`]. See [`PreTrai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cr... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`MvpTokenizer`]. See [`PreTrai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
c... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`MvpTokenizer`]. See [`PreTrai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/modeling_mvp.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. A MVP sequence has th... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A MVP sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
token_... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp.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 a mask from the two sequences passed to be used in a sequence-pair classification task. MVP does not
make use of token type ids, therefore a list... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. MVP does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *option... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp.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.
MVP tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise th... |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
MVP tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
comprise the space before the *<mask>*.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py | Apache-2.0 |
def mask_token(self, value):
"""
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on Mvp.
"""
# Mask token behave like a normal word, i.e. include the... |
Overriding the default behavior of the mask token to have it eat the space before it.
This is needed to preserve backward compatibility with all the previously used models based on Mvp.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mvp/tokenization_mvp_fast.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torc... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should be in `[-100, 0, ...,
config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are ignored (maske... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/nezha/modeling_nezha.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/nezha/modeling_nezha.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
labels: Optional[torc... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/nezha/modeling_nezha.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/nezha/modeling_nezha.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. An NLLB sequence has ... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An NLLB sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encoder) `X [eos, src_lang_code]`
- `decoder_input_ids... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/nllb/tokenization_nllb.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/nllb/tokenization_nllb.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 a mask from the two sequences passed to be used in a sequence-pair classification task. nllb does not
make use of token type ids, therefore a lis... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. nllb does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *optio... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/nllb/tokenization_nllb.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/nllb/tokenization_nllb.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. The special tokens de... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. The special tokens depend on calling set_lang.
An NLLB sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encode... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/nllb/tokenization_nllb_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/nllb/tokenization_nllb_fast.py | Apache-2.0 |
def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
"""Load tf pre-trained weights in a pytorch model (from NumPy arrays here)"""
import re
import numpy as np
if ".ckpt" in openai_checkpoint_folder_path:
openai_checkpoint_folder_path = os.path.dirname(openai_checkp... | Load tf pre-trained weights in a pytorch model (from NumPy arrays here) | load_tf_weights_in_openai_gpt | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/openai/modeling_openai.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/openai/modeling_openai.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
mc_token_ids (`torch.LongTensor` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`.
labels (`torch.LongTensor` of shape... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/openai/modeling_openai.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/openai/modeling_openai.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Uni... |
mc_token_ids (`tf.Tensor` or `Numpy array` of shape `(batch_size, num_choices)`, *optional*, default to index of the last token of the input):
Index of the classification token in each input sequence. Selected in the range `[0, input_ids.size(-1) -
1]`.
Return:
Example... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/openai/modeling_tf_openai.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/openai/modeling_tf_openai.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
past_key_value: Optional[Tuple[torch.Tensor... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
use_cache:... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
past_key_values: Optional[List[torch.FloatTensor]] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
labels: Op... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = N... |
start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/opt/modeling_opt.py | Apache-2.0 |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
layer_head_mask: Optional[tf.Tensor] = None,
past_key_value: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
training: Optional[bool] = False,
out... |
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`tf.Tensor`, *optional*): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
la... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/opt/modeling_tf_opt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/opt/modeling_tf_opt.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
inputs_embeds: Optional[Union[np.ndarray, tf.Tensor]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
past_key_values: Optional[T... |
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrainedToke... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/opt/modeling_tf_opt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/opt/modeling_tf_opt.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mas... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`OPTTokenizer`]. See [`PreTrai... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/opt/modeling_tf_opt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/opt/modeling_tf_opt.py | Apache-2.0 |
def from_text_vision_configs(cls, text_config: Dict, vision_config: Dict, **kwargs):
r"""
Instantiate a [`OwlViTConfig`] (or a derived class) from owlvit text model configuration and owlvit vision
model configuration.
Returns:
[`OwlViTConfig`]: An instance of a configuration... |
Instantiate a [`OwlViTConfig`] (or a derived class) from owlvit text model configuration and owlvit vision
model configuration.
Returns:
[`OwlViTConfig`]: An instance of a configuration object
| from_text_vision_configs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/configuration_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/configuration_owlvit.py | Apache-2.0 |
def center_to_corners_format(x):
"""
Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
(left, top, right, bottom).
"""
x_center, y_center, width, height = x.unbind(-1)
boxes = [(x_center - 0.5 * width), (y_center - 0.5 * height), (... |
Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
(left, top, right, bottom).
| center_to_corners_format | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py | Apache-2.0 |
def post_process(self, outputs, target_sizes):
"""
Converts the output of [`OwlViTForObjectDetection`] into the format expected by the COCO api.
Args:
outputs ([`OwlViTObjectDetectionOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of sha... |
Converts the output of [`OwlViTForObjectDetection`] into the format expected by the COCO api.
Args:
outputs ([`OwlViTObjectDetectionOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of shape `(batch_size, 2)`):
Tensor containing t... | post_process | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py | Apache-2.0 |
def __call__(
self,
images: Union[
Image.Image, np.ndarray, "torch.Tensor", List[Image.Image], List[np.ndarray], List["torch.Tensor"] # noqa
],
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
) -> BatchFeature:
"""
Main method to... |
Main method to prepare for the model one or several image(s).
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/feature_extraction_owlvit.py | Apache-2.0 |
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) ... |
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`).
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid performing attention on padding token indices. Mask values selected in `[0... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def forward(
self,
input_ids: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
... |
Returns:
Examples:
```python
>>> from transformers import OwlViTProcessor, OwlViTTextModel
>>> model = OwlViTTextModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
>>> inputs = proce... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
... |
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import OwlViTProcessor, OwlViTVisionModel
>>> model = OwlViTVisionModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = OwlViTProcessor.from_p... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
... |
Returns:
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
applying the projection layer to the pooled output of [`OwlViTTextModel`].
Examples:
```python
>>> from transformers import OwlViTProcessor, OwlViTMo... | get_text_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
return_projected: Optional[bool] = True,
) -> torch.FloatTensor:
... |
Returns:
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
applying the projection layer to the pooled output of [`OwlViTVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import reque... | get_image_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
return_loss: Optional[bool] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Op... |
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import OwlViTProcessor, OwlViTModel
>>> model = OwlViTModel.from_pretrained("google/owlvit-base-patch32")
>>> processor = OwlViTProcessor.from_pretrained("g... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def box_predictor(
self,
image_feats: torch.FloatTensor,
feature_map: torch.FloatTensor,
) -> torch.FloatTensor:
"""
Args:
image_feats:
Features extracted from the image, returned by the `image_text_embedder` method.
feature_map:
... |
Args:
image_feats:
Features extracted from the image, returned by the `image_text_embedder` method.
feature_map:
A spatial re-arrangement of image_features, also returned by the `image_text_embedder` method.
Returns:
pred_boxes:
... | box_predictor | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def class_predictor(
self,
image_feats: torch.FloatTensor,
query_embeds: torch.FloatTensor,
query_mask: torch.Tensor,
) -> Tuple[torch.FloatTensor]:
"""
Args:
image_feats:
Features extracted from the `image_text_embedder`.
query... |
Args:
image_feats:
Features extracted from the `image_text_embedder`.
query_embeds:
Text query embeddings.
query_mask:
Must be provided with query_embeddings. A mask indicating which query embeddings are valid.
| class_predictor | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def forward(
self,
input_ids: torch.Tensor,
pixel_values: torch.FloatTensor,
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> OwlViTOb... |
Returns:
Examples:
```python
>>> import requests
>>> from PIL import Image
>>> import torch
>>> from transformers import OwlViTProcessor, OwlViTForObjectDetection
>>> processor = OwlViTProcessor.from_pretrained("google/owlvit-base-patch32")
>>> ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/modeling_owlvit.py | Apache-2.0 |
def __call__(self, text=None, images=None, padding="max_length", return_tensors="np", **kwargs):
"""
Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and
`kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` i... |
Main method to prepare for the model one or several text(s) and image(s). This method forwards the `text` and
`kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode:
the text. To prepare the image(s), this method forwards the `images` and `kw... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/owlvit/processing_owlvit.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/owlvit/processing_owlvit.py | Apache-2.0 |
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
position_ids: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
t... |
Returns:
Example:
```python
>>> from transformers import PegasusTokenizer, FlaxPegasusForConditionalGeneration
>>> model = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-large")
>>> tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-larg... | encode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_flax_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_flax_pegasus.py | Apache-2.0 |
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_atten... |
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import PegasusTokenizer, FlaxPegasusForConditionalGeneration
>>> model = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-large")
>>> tokenizer = PegasusTokenizer.... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_flax_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_flax_pegasus.py | Apache-2.0 |
def decode(
self,
decoder_input_ids,
encoder_outputs,
encoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_attention_mask: Optional[jnp.ndarray] = None,
decoder_position_ids: Optional[jnp.ndarray] = None,
past_key_values: dict = None,
output_atten... |
Returns:
Example:
```python
>>> import jax.numpy as jnp
>>> from transformers import PegasusTokenizer, FlaxPegasusForConditionalGeneration
>>> model = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-large")
>>> tokenizer = PegasusTokenizer.... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_flax_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_flax_pegasus.py | Apache-2.0 |
def resize_position_embeddings(self, new_num_position_embeddings: int):
"""
Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
config.max_position_embeddings`.
Arguments:
new_num_position_embeddings (`int`):
The number of new p... |
Resizes position embeddings matrix of the model if `new_num_position_embeddings !=
config.max_position_embeddings`.
Arguments:
new_num_position_embeddings (`int`):
The number of new position embeddings. If position embeddings are learned, increasing the size will ad... | resize_position_embeddings | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`torch.LongTensor` of shape `(batch_si... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`Pre... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_h... |
Returns:
Example:
```python
>>> from transformers import PegasusTokenizer, PegasusModel
>>> tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-large")
>>> model = PegasusModel.from_pretrained("google/pegasus-large")
>>> inputs = tokenizer("Studies h... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
c... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`Pre... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_pegasus.py | Apache-2.0 |
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
"""
Args:
input_ids (`tf.Tensor` of sha... |
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrained... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | Apache-2.0 |
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
position_ids=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
use_cache=None,
... |
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`PreTrained... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/modeling_tf_pegasus.py | Apache-2.0 |
def _convert_token_to_id(self, token: str) -> int:
"""Converts a token (str) to an id using the vocab."""
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
sp_id = self.sp_model.pi... | Converts a token (str) to an id using the vocab. | _convert_token_to_id | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus.py | Apache-2.0 |
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) to a token (str) using the vocab."""
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
... | Converts an index (integer) to a token (str) using the vocab. | _convert_id_to_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus.py | Apache-2.0 |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""
Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating
and adding special tokens. A PEGASUS sequence has the following format, where `X` represents th... |
Build model inputs from a sequence or a pair of sequences for sequence classification tasks by concatenating
and adding special tokens. A PEGASUS sequence has the following format, where `X` represents the sequence:
- single sequence: `X </s>`
- pair of sequences: `A B </s>` (not inten... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus.py | Apache-2.0 |
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
"""
Build model inputs from a sequence by adding eos to the end. no bos token is added to the front.
- single sequence: `X </s>`
- pair of sequences: `A B </s>` (not intended use)
Args:
... |
Build model inputs from a sequence by adding eos to the end. no bos token is added to the front.
- single sequence: `X </s>`
- pair of sequences: `A B </s>` (not intended use)
Args:
token_ids_0 (`List[int]`):
List of IDs to which the special tokens will be ... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus/tokenization_pegasus_fast.py | Apache-2.0 |
def compute_global_attention_representations(
self, global_q, global_k, global_v, local_k, local_v, mask, dim: DimensionInfo
):
"""Compute attention representations for global tokens.
Global tokens will attend to both global tokens as well as all input sequence tokens. Because the input
... | Compute attention representations for global tokens.
Global tokens will attend to both global tokens as well as all input sequence tokens. Because the input
sequence tokens are arranged in blocks for local attention, we unblock them and compute attention.
Args:
global_q (`torch.Flo... | compute_global_attention_representations | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | Apache-2.0 |
def compute_local_attention_representations(
self, global_k, global_v, local_q, local_k, local_v, mask, dim: DimensionInfo
):
"""Compute attention representations for local tokens.
Local tokens will attend to both global tokens as well as all other tokens within the same local block. Hence,... | Compute attention representations for local tokens.
Local tokens will attend to both global tokens as well as all other tokens within the same local block. Hence,
we need to tile and concatenate the global tokens to every local block
Args:
global_k (`torch.FloatTensor`) of shape [b... | compute_local_attention_representations | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
global_hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_l... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
global_hidden_states (`torch.FloatTensor`): global token hidden states
*(seq_len, num_global_tokens, embed_dim)*
attention_mask (`torch.FloatTensor`): attenti... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`torch.FloatTensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
past_key_values=None,
inputs_embeds=None,
use_cache=None,
output_attentions=None,
output_hidden_states=None,
return_dic... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PegasusTokenizer`]. See [`Pre... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | Apache-2.0 |
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