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 _concatenate_3_blocks(x: torch.Tensor, block_dim: int, sequence_dim: int, pad_value: int = 0) -> torch.Tensor:
"""Concatenate three consecutive blocks for each input block for local attentiont.
For more information, see: https://arxiv.org/pdf/2112.07916.pdf.
"""
num_blocks = x.shape[block_dim]
... | Concatenate three consecutive blocks for each input block for local attentiont.
For more information, see: https://arxiv.org/pdf/2112.07916.pdf.
| _concatenate_3_blocks | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | Apache-2.0 |
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the LongT5 style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps |
Construct a layernorm module in the LongT5 style. No bias and no subtraction of mean.
| __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | Apache-2.0 |
def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
"""
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate rel... |
Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_positi... | _relative_position_bucket | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | Apache-2.0 |
def project(hidden_states, proj_layer, key_value_states, past_key_value):
"""projects hidden states correctly to key/query states"""
if key_value_states is None:
# self-attn
# (batch_size, n_heads, seq_length, dim_per_head)
hidden_states = shape(pr... | projects hidden states correctly to key/query states | project | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = N... |
Returns:
Example:
```python
>>> from transformers import T5Tokenizer, LongT5Model
>>> tokenizer = T5Tokenizer.from_pretrained("google/long-t5-local-base")
>>> model = LongT5Model.from_pretrained("google/long-t5-local-base")
>>> # Let's try a very long encoder... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.FloatTensor] = N... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
labe... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.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,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_... |
Returns:
Example:
```python
>>> from transformers import AutoTokenizer, LongT5ForConditionalGeneration
>>> tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
>>> model = LongT5EncoderModel.from_pretrained("google/long-t5-local-base")
>>> in... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_longt5.py | Apache-2.0 |
def get_extended_attention_mask(
self, word_attention_mask: torch.LongTensor, entity_attention_mask: Optional[torch.LongTensor]
):
"""
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
word_attention_mask (`torch.Long... |
Makes broadcastable attention and causal masks so that future and masked tokens are ignored.
Arguments:
word_attention_mask (`torch.LongTensor`):
Attention mask for word tokens with ones indicating tokens to attend to, zeros for tokens to ignore.
entity_attentio... | get_extended_attention_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/luke/modeling_luke.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/luke/modeling_luke.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,
entity_ids: Optional[torch.LongTensor] = 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/luke/modeling_luke.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/luke/modeling_luke.py | Apache-2.0 |
def __call__(
self,
text: Union[TextInput, List[TextInput]],
text_pair: Optional[Union[TextInput, List[TextInput]]] = None,
entity_spans: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = None,
entity_spans_pair: Optional[Union[EntitySpanInput, List[EntitySpanInput]]] = N... |
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences, depending on the task you want to prepare them for.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be enco... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | Apache-2.0 |
def _batch_prepare_for_model(
self,
batch_ids_pairs: List[Tuple[List[int], None]],
batch_entity_ids_pairs: List[Tuple[Optional[List[int]], Optional[List[int]]]],
batch_entity_token_spans_pairs: List[Tuple[Optional[List[Tuple[int, int]]], Optional[List[Tuple[int, int]]]]],
add_spe... |
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/models/luke/tokenization_luke.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | Apache-2.0 |
def prepare_for_model(
self,
ids: List[int],
pair_ids: Optional[List[int]] = None,
entity_ids: Optional[List[int]] = None,
pair_entity_ids: Optional[List[int]] = None,
entity_token_spans: Optional[List[Tuple[int, int]]] = None,
pair_entity_token_spans: Optional[Li... |
Prepares a sequence of input id, entity id and entity span, or a pair of sequences of inputs ids, entity ids,
entity spans 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 (wi... | prepare_for_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.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`) .. note:: If th... | pad | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | Apache-2.0 |
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
max_entity_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
pad_to_multiple_of: Optional[int] = None,
retu... |
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 op... | _pad | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/luke/tokenization_luke.py | Apache-2.0 |
def resize_num_qa_labels(self, num_labels):
"""
Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
will add newly initialized weights. Reducing the size will remove weights from the end
Args:
num_labels (`int`, *optio... |
Build a resized question answering linear layer Module from a provided new linear layer. Increasing the size
will add newly initialized weights. Reducing the size will remove weights from the end
Args:
num_labels (`int`, *optional*):
New number of labels in the line... | resize_num_qa_labels | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/lxmert/modeling_lxmert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lxmert/modeling_lxmert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
visual_feats: Optional[torch.FloatTensor] = None,
visual_pos: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
visual_attention_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/lxmert/modeling_lxmert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lxmert/modeling_lxmert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
visual_feats: Optional[torch.FloatTensor] = None,
visual_pos: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
visual_attention_mask: Optional[torch.FloatTensor] = None... |
labels: (`Torch.Tensor` of shape `(batch_size)`, *optional*):
A one-hot representation of the correct answer
| forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/lxmert/modeling_lxmert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lxmert/modeling_lxmert.py | Apache-2.0 |
def call(
self,
input_ids=None,
visual_feats=None,
visual_pos=None,
attention_mask=None,
visual_attention_mask=None,
token_type_ids=None,
inputs_embeds=None,
masked_lm_labels=None,
obj_labels=None,
matched_label=None,
ans=No... |
masked_lm_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/lxmert/modeling_tf_lxmert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lxmert/modeling_tf_lxmert.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 Lxmert sequence has... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Lxmert 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/lxmert/tokenization_lxmert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lxmert/tokenization_lxmert.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 Lxmert
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 Lxmert
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 `Non... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/lxmert/tokenization_lxmert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lxmert/tokenization_lxmert.py | Apache-2.0 |
def create_position_ids_from_input_ids(input_ids, padding_idx, past_key_values_length=0):
"""
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`.
"""
# The series of cas... |
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`.
| create_position_ids_from_input_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Opti... |
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 [`M2M100Tokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross... |
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 [`M2M100Tokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/modeling_m2m_100.py | Apache-2.0 |
def _convert_id_to_token(self, index: int) -> str:
"""Converts an index (integer) in a token (str) using the decoder."""
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(index, self.unk_token) | Converts an index (integer) in a token (str) using the decoder. | _convert_id_to_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.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 sequence has... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An MBART sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encoder) `X [eos, src_lang_code]`
- `decoder_input_id... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | Apache-2.0 |
def _build_translation_inputs(self, raw_inputs, src_lang: Optional[str], tgt_lang: Optional[str], **extra_kwargs):
"""Used by translation pipeline, to prepare inputs for the generate function"""
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and ... | Used by translation pipeline, to prepare inputs for the generate function | _build_translation_inputs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | Apache-2.0 |
def set_src_lang_special_tokens(self, src_lang: str) -> None:
"""Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
lang_token = self.get_lang_token(src_lang)
self.cur_lang_id = self.lang_token_to_id[lang_token]
self.prefix_tokens = [self.c... | Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]. | set_src_lang_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | Apache-2.0 |
def set_tgt_lang_special_tokens(self, tgt_lang: str) -> None:
"""Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]."""
lang_token = self.get_lang_token(tgt_lang)
self.cur_lang_id = self.lang_token_to_id[lang_token]
self.prefix_tokens = [se... | Reset the special tokens to the target language setting. No prefix and suffix=[eos, tgt_lang_code]. | set_tgt_lang_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/m2m_100/tokenization_m2m_100.py | Apache-2.0 |
def write_model_card(self, model_dict, dry_run=False) -> str:
"""
Construct card from data parsed from YAML and the model's name. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
"""
model_dir_url = f"{TATOEBA_MODELS_URL}/{model_di... |
Construct card from data parsed from YAML and the model's name. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
| write_model_card | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_tatoeba_to_pytorch.py | Apache-2.0 |
def find_pretrained_model(src_lang: str, tgt_lang: str) -> List[str]:
"""Find models that can accept src_lang as input and return tgt_lang as output."""
prefix = "Helsinki-NLP/opus-mt-"
model_list = list_models()
model_ids = [x.modelId for x in model_list if x.modelId.startswith("Helsinki-NLP")]
src... | Find models that can accept src_lang as input and return tgt_lang as output. | find_pretrained_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | Apache-2.0 |
def convert_hf_name_to_opus_name(hf_model_name):
"""
Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME.
"""
hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
if hf_model_name in GROUP_TO_OPUS_NAME:
opus_w_prefix = GROUP_TO_... |
Relies on the assumption that there are no language codes like pt_br in models that are not in GROUP_TO_OPUS_NAME.
| convert_hf_name_to_opus_name | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | Apache-2.0 |
def write_model_card(
hf_model_name: str,
repo_root=DEFAULT_REPO,
save_dir=Path("marian_converted"),
dry_run=False,
extra_metadata={},
) -> str:
"""
Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync model_card_dir
s3://models.huggingface... |
Copy the most recent model's readme section from opus, and add metadata. upload command: aws s3 sync model_card_dir
s3://models.huggingface.co/bert/Helsinki-NLP/ --dryrun
| write_model_card | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | Apache-2.0 |
def _parse_readme(lns):
"""Get link and metadata from opus model card equivalent."""
subres = {}
for ln in [x.strip() for x in lns]:
if not ln.startswith("*"):
continue
ln = ln[1:].strip()
for k in ["download", "dataset", "models", "model", "pre-processing"]:
... | Get link and metadata from opus model card equivalent. | _parse_readme | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/convert_marian_to_pytorch.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 MarianTokenizer, FlaxMarianMTModel
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> text = "M... | encode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_flax_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_flax_marian.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 MarianTokenizer, FlaxMarianMTModel
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/op... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_flax_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_flax_marian.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 MarianTokenizer, FlaxMarianMTModel
>>> model = FlaxMarianMTModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/op... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_flax_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_flax_marian.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: O... |
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 [`MarianTokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.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 [`MarianTokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
decoder_hea... |
Returns:
Example:
```python
>>> from transformers import MarianTokenizer, MarianModel
>>> tokenizer = MarianTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> model = MarianModel.from_pretrained("Helsinki-NLP/opus-mt-en-de")
>>> inputs = tokenizer("S... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.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 [`MarianTokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_marian.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 [`MarianTokenizer`]. See [`PreTrainedT... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_tf_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_tf_marian.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 [`MarianTokenizer`]. See [`PreTrainedT... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_tf_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_tf_marian.py | Apache-2.0 |
def adjust_logits_during_generation(
self, logits, cur_len, max_length, forced_bos_token_id, forced_eos_token_id, **kwargs
):
"""Never predict pad_token_id. Predict </s> when max_length is reached."""
vocab_range = tf.constant(range(self.config.vocab_size))
logits = tf.where(vocab_ra... | Never predict pad_token_id. Predict </s> when max_length is reached. | adjust_logits_during_generation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/modeling_tf_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/modeling_tf_marian.py | Apache-2.0 |
def remove_language_code(self, text: str):
"""Remove language codes like >>fr<< before sentencepiece"""
match = self.language_code_re.match(text)
code: list = [match.group(0)] if match else []
return code, self.language_code_re.sub("", text) | Remove language codes like >>fr<< before sentencepiece | remove_language_code | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.py | Apache-2.0 |
def convert_tokens_to_string(self, tokens: List[str]) -> str:
"""Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise"""
if self._decode_use_source_tokenizer:
return self.spm_source.DecodePieces(tokens)
else:
return self.spm_target.DecodePiece... | Uses source spm if _decode_use_source_tokenizer is True, and target spm otherwise | convert_tokens_to_string | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.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 appending eos_token_id."""
if token_ids_1 is None:
return token_ids_0 + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic fo... | Build model inputs from a sequence by appending eos_token_id. | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.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]:
"""Get list where entries are [1] if a token is [eos] or [pad] else 0."""
if already_has_special_tokens:
return self._special_token_m... | Get list where entries are [1] if a token is [eos] or [pad] else 0. | get_special_tokens_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/marian/tokenization_marian.py | Apache-2.0 |
def __call__(self, html_strings) -> BatchFeature:
"""
Main method to prepare for the model one or several HTML strings.
Args:
html_strings (`str`, `List[str]`):
The HTML string or batch of HTML strings from which to extract nodes and corresponding xpaths.
Re... |
Main method to prepare for the model one or several HTML strings.
Args:
html_strings (`str`, `List[str]`):
The HTML string or batch of HTML strings from which to extract nodes and corresponding xpaths.
Returns:
[`BatchFeature`]: A [`BatchFeature`] with ... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/feature_extraction_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/feature_extraction_markuplm.py | Apache-2.0 |
def forward(
self,
input_ids=None,
xpath_tags_seq=None,
xpath_subs_seq=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
... |
Returns:
Examples:
```python
>>> from transformers import MarkupLMProcessor, MarkupLMModel
>>> processor = MarkupLMProcessor.from_pretrained("microsoft/markuplm-base")
>>> model = MarkupLMModel.from_pretrained("microsoft/markuplm-base")
>>> html_string = "<ht... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/modeling_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/modeling_markuplm.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: O... |
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/markuplm/modeling_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/modeling_markuplm.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: O... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, A... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/modeling_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/modeling_markuplm.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
xpath_tags_seq: Optional[torch.Tensor] = None,
xpath_subs_seq: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: O... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence 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
`confi... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/modeling_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/modeling_markuplm.py | Apache-2.0 |
def __call__(
self,
html_strings=None,
nodes=None,
xpaths=None,
node_labels=None,
questions=None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStrategy] = False,
truncation: Union[bool, str, TruncationStrategy] = None,
... |
This method first forwards the `html_strings` argument to [`~MarkupLMFeatureExtractor.__call__`]. Next, it
passes the `nodes` and `xpaths` along with the additional arguments to [`~MarkupLMTokenizer.__call__`] and
returns the output.
Optionally, one can also provide a `text` argument w... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/processing_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/processing_markuplm.py | Apache-2.0 |
def get_pairs(word):
"""
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
strings).
"""
pairs = set()
prev_char = word[0]
for char in word[1:]:
pairs.add((prev_char, char))
prev_char = char
return pairs |
Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
strings).
| get_pairs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | Apache-2.0 |
def get_xpath_seq(self, xpath):
"""
Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
tag IDs and corresponding subscripts, taking into account max depth.
"""
xpath_tags_list = []
xpath_subs_list = []
xp... |
Given the xpath expression of one particular node (like "/html/body/div/li[1]/div/span[2]"), return a list of
tag IDs and corresponding subscripts, taking into account max depth.
| get_xpath_seq | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.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 RoBERTa sequence ha... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoBERTa 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/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.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]:
"""
Args:
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
s... |
Args:
Retrieve 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` method.
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/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.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. RoBERTa does not
make use of token type ids, therefore a ... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa 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]`, *op... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | Apache-2.0 |
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
xpaths: Union[List[List[int]], List[List[List[int]]]] = None,
node_labels: Optional[Union[List[i... |
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with node-level xpaths and optional labels.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | Apache-2.0 |
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingSt... |
Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
`__call__` should be used instead.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The first sequence to be encoded. This can be a string, a list of str... | encode_plus | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | Apache-2.0 |
def prepare_for_model(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
xpaths: Optional[List[List[int]]] = None,
node_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, Pad... |
Prepares a sequence or a pair of sequences 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. Please Note, for *text_pair* dif... | prepare_for_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | Apache-2.0 |
def truncate_sequences(
self,
ids: List[int],
xpath_tags_seq: List[List[int]],
xpath_subs_seq: List[List[int]],
pair_ids: Optional[List[int]] = None,
pair_xpath_tags_seq: Optional[List[List[int]]] = None,
pair_xpath_subs_seq: Optional[List[List[int]]] = None,
... |
Args:
Truncates a sequence pair in-place following the strategy.
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.
xpath_tags_seq (`List[List... | truncate_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.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,
) -... |
Args:
Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
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 opti... | _pad | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm.py | Apache-2.0 |
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
xpaths: Union[List[List[int]], List[List[List[int]]]] = None,
node_labels: Optional[Union[List[i... |
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with nodes, xpaths and optional labels.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences to be encoded. Each seq... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/markuplm/tokenization_markuplm_fast.py | Apache-2.0 |
def from_backbone_and_decoder_configs(
cls, backbone_config: PretrainedConfig, decoder_config: PretrainedConfig, **kwargs
):
"""Instantiate a [`MaskFormerConfig`] (or a derived class) from a pre-trained backbone model configuration and DETR model
configuration.
Args:
... | Instantiate a [`MaskFormerConfig`] (or a derived class) from a pre-trained backbone model configuration and DETR model
configuration.
Args:
backbone_config ([`PretrainedConfig`]):
The backbone configuration.
decoder_config ([`PretrainedConfig`]):
... | from_backbone_and_decoder_configs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/configuration_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/configuration_maskformer.py | Apache-2.0 |
def __init__(self, to_track: Dict):
"""This class "tracks" a python dictionary by keeping track of which item is accessed.
Args:
to_track (Dict): The dictionary we wish to track
"""
self.to_track = to_track
self._seen: Set[str] = set() | This class "tracks" a python dictionary by keeping track of which item is accessed.
Args:
to_track (Dict): The dictionary we wish to track
| __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/convert_maskformer_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def _resize_with_size_divisibility(self, image, size, target=None, max_size=None):
"""
Resize the image to the given size. Size can be min_size (scalar) or (width, height) tuple. If size is an int,
smaller edge of the image will be matched to this number.
If given, also resize the targe... |
Resize the image to the given size. Size can be min_size (scalar) or (width, height) tuple. If size is an int,
smaller edge of the image will be matched to this number.
If given, also resize the target accordingly.
| _resize_with_size_divisibility | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | Apache-2.0 |
def __call__(
self,
images: ImageInput,
segmentation_maps: ImageInput = None,
pad_and_return_pixel_mask: Optional[bool] = True,
instance_id_to_semantic_id: Optional[Union[List[Dict[int, int]], Dict[int, int]]] = None,
return_tensors: Optional[Union[str, TensorType]] = Non... |
Main method to prepare for the model one or several image(s) and optional annotations. Images are by default
padded up to the largest image in a batch, and a pixel mask is created that indicates which pixels are
real/which are padding.
MaskFormer addresses semantic segmentation with a ... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | Apache-2.0 |
def encode_inputs(
self,
pixel_values_list: List["np.ndarray"],
segmentation_maps: ImageInput = None,
pad_and_return_pixel_mask: bool = True,
instance_id_to_semantic_id: Optional[Union[List[Dict[int, int]], Dict[int, int]]] = None,
return_tensors: Optional[Union[str, Tens... |
Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
MaskFormer addresses semantic segmentation with a mask classification paradigm, thus input segmentation maps
will be converted to lists of binary masks and their respective labels. Let's see an example, assu... | encode_inputs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | Apache-2.0 |
def post_process_segmentation(
self, outputs: "MaskFormerForInstanceSegmentationOutput", target_size: Tuple[int, int] = None
) -> "torch.Tensor":
"""
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image segmentation predictions. Only
supports PyTorch.
... |
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image segmentation predictions. Only
supports PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentationOutput`]):
The outputs from [`MaskFormerForInstanceSegmentation`].
target_si... | post_process_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | Apache-2.0 |
def post_process_semantic_segmentation(
self, outputs, target_sizes: Optional[List[Tuple[int, int]]] = None
) -> "torch.Tensor":
"""
Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs... |
Converts the output of [`MaskFormerForInstanceSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple[int, int]]`, *optional*, defau... | post_process_semantic_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | Apache-2.0 |
def post_process_instance_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
target_sizes: Optional[List[Tuple[int, int]]] = None,
return_coco_annotation: Optional[bool] = False,
) -> Lis... |
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into instance segmentation predictions. Only
supports PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentation`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to... | post_process_instance_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | Apache-2.0 |
def post_process_panoptic_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_sizes: Optional[List[Tuple[int, int]]] = None,
) -> List[... |
Converts the output of [`MaskFormerForInstanceSegmentationOutput`] into image panoptic segmentation
predictions. Only supports PyTorch.
Args:
outputs ([`MaskFormerForInstanceSegmentationOutput`]):
The outputs from [`MaskFormerForInstanceSegmentation`].
t... | post_process_panoptic_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/feature_extraction_maskformer.py | Apache-2.0 |
def upsample_like(pixel_values: Tensor, like: Tensor, mode: str = "bilinear") -> Tensor:
"""
An utility function that upsamples `pixel_values` to match the dimension of `like`.
Args:
pixel_values (`torch.Tensor`):
The tensor we wish to upsample.
like (`torch.Tensor`):
... |
An utility function that upsamples `pixel_values` to match the dimension of `like`.
Args:
pixel_values (`torch.Tensor`):
The tensor we wish to upsample.
like (`torch.Tensor`):
The tensor we wish to use as size target.
mode (str, *optional*, defaults to `"bilinea... | upsample_like | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def dice_loss(inputs: Tensor, labels: Tensor, num_masks: int) -> Tensor:
r"""
Compute the DICE loss, similar to generalized IOU for masks as follows:
$$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$
In practice, since `labels` is a binary mask, (only 0s and 1s), dice can ... |
Compute the DICE loss, similar to generalized IOU for masks as follows:
$$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x \cap y }{x \cup y + 1}} $$
In practice, since `labels` is a binary mask, (only 0s and 1s), dice can be computed as follow
$$ \mathcal{L}_{\text{dice}(x, y) = 1 - \frac{2 * x * ... | dice_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def sigmoid_focal_loss(
inputs: Tensor, labels: Tensor, num_masks: int, alpha: float = 0.25, gamma: float = 2
) -> Tensor:
r"""
Focal loss proposed in [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) originally used in
RetinaNet. The loss is computed as follows:
$$ \mathcal... |
Focal loss proposed in [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002) originally used in
RetinaNet. The loss is computed as follows:
$$ \mathcal{L}_{\text{focal loss} = -(1 - p_t)^{\gamma}\log{(p_t)} $$
where \\(CE(p_t) = -\log{(p_t)}}\\), CE is the standard Cross Entropy L... | sigmoid_focal_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def pair_wise_dice_loss(inputs: Tensor, labels: Tensor) -> Tensor:
"""
A pair wise version of the dice loss, see `dice_loss` for usage.
Args:
inputs (`torch.Tensor`):
A tensor representing a mask
labels (`torch.Tensor`):
A tensor with the same shape as inputs. Stores... |
A pair wise version of the dice loss, see `dice_loss` for usage.
Args:
inputs (`torch.Tensor`):
A tensor representing a mask
labels (`torch.Tensor`):
A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
(... | pair_wise_dice_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def pair_wise_sigmoid_focal_loss(inputs: Tensor, labels: Tensor, alpha: float = 0.25, gamma: float = 2.0) -> Tensor:
r"""
A pair wise version of the focal loss, see `sigmoid_focal_loss` for usage.
Args:
inputs (`torch.Tensor`):
A tensor representing a mask.
labels (`torch.Tensor... |
A pair wise version of the focal loss, see `sigmoid_focal_loss` for usage.
Args:
inputs (`torch.Tensor`):
A tensor representing a mask.
labels (`torch.Tensor`):
A tensor with the same shape as inputs. Stores the binary classification labels for each element in inputs
... | pair_wise_sigmoid_focal_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(self, cost_class: float = 1.0, cost_mask: float = 1.0, cost_dice: float = 1.0):
"""Creates the matcher
Params:
cost_class (float, *optional*, defaults to 1.0):
This is the relative weight of the classification error in the matching cost.
cost_mask (f... | Creates the matcher
Params:
cost_class (float, *optional*, defaults to 1.0):
This is the relative weight of the classification error in the matching cost.
cost_mask (float, *optional*, defaults to 1.0):
This is the relative weight of the focal loss of th... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def forward(self, masks_queries_logits, class_queries_logits, mask_labels, class_labels) -> List[Tuple[Tensor]]:
"""Performs the matching
Params:
masks_queries_logits (`torch.Tensor`):
A tensor` of dim `batch_size, num_queries, num_labels` with the
classifi... | Performs the matching
Params:
masks_queries_logits (`torch.Tensor`):
A tensor` of dim `batch_size, num_queries, num_labels` with the
classification logits.
class_queries_logits (`torch.Tensor`):
A tensor` of dim `batch_size, num_queries,... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(
self,
num_labels: int,
matcher: MaskFormerHungarianMatcher,
weight_dict: Dict[str, float],
eos_coef: float,
):
"""
The MaskFormer Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we compute
hungarian a... |
The MaskFormer Loss. The loss is computed very similar to DETR. The process happens in two steps: 1) we compute
hungarian assignment between ground truth masks and the outputs of the model 2) we supervise each pair of
matched ground-truth / prediction (supervise class and mask)
Args:
... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def loss_labels(
self, class_queries_logits: Tensor, class_labels: List[Tensor], indices: Tuple[np.array]
) -> Dict[str, Tensor]:
"""Compute the losses related to the labels using cross entropy.
Args:
class_queries_logits (`torch.Tensor`):
A tensor of shape `batc... | Compute the losses related to the labels using cross entropy.
Args:
class_queries_logits (`torch.Tensor`):
A tensor of shape `batch_size, num_queries, num_labels`
class_labels (`List[torch.Tensor]`):
List of class labels of shape `(labels)`.
i... | loss_labels | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def loss_masks(
self, masks_queries_logits: Tensor, mask_labels: List[Tensor], indices: Tuple[np.array], num_masks: int
) -> Dict[str, Tensor]:
"""Compute the losses related to the masks using focal and dice loss.
Args:
masks_queries_logits (`torch.Tensor`):
A te... | Compute the losses related to the masks using focal and dice loss.
Args:
masks_queries_logits (`torch.Tensor`):
A tensor of shape `batch_size, num_queries, height, width`
mask_labels (`torch.Tensor`):
List of mask labels of shape `(labels, height, width)`... | loss_masks | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def forward(
self,
masks_queries_logits: Tensor,
class_queries_logits: Tensor,
mask_labels: List[Tensor],
class_labels: List[Tensor],
auxiliary_predictions: Optional[Dict[str, Tensor]] = None,
) -> Dict[str, Tensor]:
"""
This performs the loss computat... |
This performs the loss computation.
Args:
masks_queries_logits (`torch.Tensor`):
A tensor of shape `batch_size, num_queries, height, width`
class_queries_logits (`torch.Tensor`):
A tensor of shape `batch_size, num_queries, num_labels`
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def get_num_masks(self, class_labels: torch.Tensor, device: torch.device) -> torch.Tensor:
"""
Computes the average number of target masks across the batch, for normalization purposes.
"""
num_masks = sum([len(classes) for classes in class_labels])
num_masks_pt = torch.as_tensor(... |
Computes the average number of target masks across the batch, for normalization purposes.
| get_num_masks | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(self, in_features: int, out_features: int, kernel_size: int = 3, padding: int = 1):
"""
A basic module that executes conv - norm - in sequence used in MaskFormer.
Args:
in_features (`int`):
The number of input features (channels).
out_feature... |
A basic module that executes conv - norm - in sequence used in MaskFormer.
Args:
in_features (`int`):
The number of input features (channels).
out_features (`int`):
The number of outputs features (channels).
| __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(self, in_features: int, lateral_features: int):
"""
A Feature Pyramid Network Layer (FPN) layer. It creates a feature map by aggregating features from the previous
and backbone layer. Due to the spatial mismatch, the tensor coming from the previous layer is upsampled.
Args:... |
A Feature Pyramid Network Layer (FPN) layer. It creates a feature map by aggregating features from the previous
and backbone layer. Due to the spatial mismatch, the tensor coming from the previous layer is upsampled.
Args:
in_features (`int`):
The number of input fe... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(self, in_features: int, lateral_widths: List[int], feature_size: int = 256):
"""
Feature Pyramid Network, given an input tensor and a set of feature map of different feature/spatial size, it
creates a list of feature maps with the same feature size.
Args:
in_fea... |
Feature Pyramid Network, given an input tensor and a set of feature map of different feature/spatial size, it
creates a list of feature maps with the same feature size.
Args:
in_features (`int`):
The number of input features (channels).
lateral_widths (`... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(self, *args, feature_size: int = 256, mask_feature_size: int = 256, **kwargs):
"""
Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
Segmentation](https://arxiv.org/abs/2107.06278). It first runs the backbone's feature into a Feature Pyramid... |
Pixel Decoder Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
Segmentation](https://arxiv.org/abs/2107.06278). It first runs the backbone's feature into a Feature Pyramid
Network creating a list of feature maps. Then, it projects the last one to the correct `mask_s... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(self, input_dim: int, hidden_dim: int, output_dim: int, num_layers: int = 3):
"""
A classic Multi Layer Perceptron (MLP).
Args:
input_dim (`int`):
The input dimensions.
hidden_dim (`int`):
The hidden dimensions.
ou... |
A classic Multi Layer Perceptron (MLP).
Args:
input_dim (`int`):
The input dimensions.
hidden_dim (`int`):
The hidden dimensions.
output_dim (`int`):
The output dimensions.
num_layers (int, *optional*, defa... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def __init__(self, config: MaskFormerConfig):
"""
Pixel Level Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
Segmentation](https://arxiv.org/abs/2107.06278). It runs the input image through a backbone and a pixel
decoder, generating an image feature map and... |
Pixel Level Module proposed in [Per-Pixel Classification is Not All You Need for Semantic
Segmentation](https://arxiv.org/abs/2107.06278). It runs the input image through a backbone and a pixel
decoder, generating an image feature map and pixel embeddings.
Args:
config ([`M... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def forward(
self,
pixel_values: Tensor,
mask_labels: Optional[List[Tensor]] = None,
class_labels: Optional[List[Tensor]] = None,
pixel_mask: Optional[Tensor] = None,
output_auxiliary_logits: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
... |
mask_labels (`List[torch.Tensor]`, *optional*):
List of mask labels of shape `(num_labels, height, width)` to be fed to a model
class_labels (`List[torch.LongTensor]`, *optional*):
list of target class labels of shape `(num_labels, height, width)` to be fed to a model. They iden... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/maskformer/modeling_maskformer.py | Apache-2.0 |
def shift_tokens_right(input_ids: jnp.ndarray, pad_token_id: int) -> jnp.ndarray:
"""
Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
have a single `decoder_start_token_id` in contrast to other Bart-like models.
"""
prev_output_token... |
Shift input ids one token to the right, and wrap the last non pad token (the <LID> token) Note that MBart does not
have a single `decoder_start_token_id` in contrast to other Bart-like models.
| shift_tokens_right | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.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 MBartTokenizer, FlaxMBartForConditionalGeneration
>>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
>>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-c... | encode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.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
>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration
>>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
>>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-c... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.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
>>> from transformers import MBartTokenizer, FlaxMBartForConditionalGeneration
>>> model = FlaxMBartForConditionalGeneration.from_pretrained("facebook/mbart-large-cc25")
>>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-c... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_flax_mbart.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 [`MBartTokenizer`]. See [`PreTr... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_mbart.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 [`MBartTokenizer`]. See [`PreTr... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_mbart.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 [`MBartTokenizer`]. See [`PreTr... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/mbart/modeling_mbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/mbart/modeling_mbart.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
inputs_embeds: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[boo... |
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 |
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