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 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 LayoutLM
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 LayoutLM
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 `N... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlm/tokenization_layoutlm.py | Apache-2.0 |
def apply_tesseract(image: Image.Image, lang: Optional[str], tesseract_config: Optional[str]):
"""Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes."""
# apply OCR
data = pytesseract.image_to_data(image, lang=lang, output_type="dict", config=tesseract_confi... | Applies Tesseract OCR on a document image, and returns recognized words + normalized bounding boxes. | apply_tesseract | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py | Apache-2.0 |
def __call__(
self, images: ImageInput, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s).
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.I... |
Main method to prepare for the model one or several image(s).
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.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 relative position to ... |
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_position - query_positi... | relative_position_bucket | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
image: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids... |
Return:
Examples:
```python
>>> from transformers import LayoutLMv2Processor, LayoutLMv2Model, set_seed
>>> from PIL import Image
>>> import torch
>>> from datasets import load_dataset
>>> set_seed(88)
>>> processor = LayoutLMv2Processor.from_... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
image: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids... |
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/layoutlmv2/modeling_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
image: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids... |
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:
Example:
```python
>>> from transformers import LayoutLMv2Process... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
image: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids... |
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/layoutlmv2/modeling_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/modeling_layoutlmv2.py | Apache-2.0 |
def __call__(
self,
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels:... |
This method first forwards the `images` argument to [`~LayoutLMv2FeatureExtractor.__call__`]. In case
[`LayoutLMv2FeatureExtractor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
bounding boxes along with the additional arguments to [`~LayoutLMv2Tokenizer.__ca... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/processing_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/processing_layoutlmv2.py | Apache-2.0 |
def __call__(
self,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels: Optional[Union[List[in... |
Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
sequences with word-level normalized bounding boxes and optional labels.
Args:
text (`str`, `List[str]`, `List[List[str]]`):
The sequence or batch of sequences ... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py | Apache-2.0 |
def _batch_prepare_for_model(
self,
batch_text_or_text_pairs,
is_pair: bool = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_... |
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/layoutlmv2/tokenization_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py | Apache-2.0 |
def encode_plus(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, PaddingStr... |
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/layoutlmv2/tokenization_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py | Apache-2.0 |
def prepare_for_model(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, Padd... |
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/layoutlmv2/tokenization_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py | Apache-2.0 |
def truncate_sequences(
self,
ids: List[int],
token_boxes: List[List[int]],
pair_ids: Optional[List[int]] = None,
pair_token_boxes: Optional[List[List[int]]] = None,
labels: Optional[List[int]] = None,
num_tokens_to_remove: int = 0,
truncation_strategy: Un... |
Truncates a sequence pair in-place following the strategy.
Args:
ids (`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
`convert_tokens_to_ids` methods.
token_boxes (`List[List[i... | truncate_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv2/tokenization_layoutlmv2.py | Apache-2.0 |
def generate_dummy_inputs(
self,
processor: "ProcessorMixin",
batch_size: int = -1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
num_channels: int = 3,
image_width: int = 40,
image_height: int = 40,
) -... |
Generate inputs to provide to the ONNX exporter for the specific framework
Args:
processor ([`ProcessorMixin`]):
The processor associated with this model configuration.
batch_size (`int`, *optional*, defaults to -1):
The batch size to export the ... | generate_dummy_inputs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/configuration_layoutlmv3.py | Apache-2.0 |
def __call__(
self, images: ImageInput, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s).
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.I... |
Main method to prepare for the model one or several image(s).
Args:
images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
The image or batch of images to be prepared. Each image can be a PIL image, NumPy... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/feature_extraction_layoutlmv3.py | Apache-2.0 |
def create_position_ids_from_input_ids(self, 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`.
"""
# 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/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | Apache-2.0 |
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
"""
input_shape = inputs_embeds.size()[:-1]
sequence_length = input_shape[1]
position_ids ... |
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
| create_position_ids_from_inputs_embeds | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | Apache-2.0 |
def cogview_attention(self, attention_scores, alpha=32):
"""
https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation
(PB-Relax). A replacement of the original nn.Softmax(dim=-1)(attention_scores). Seems the new attention_probs
will result i... | ERROR: type should be string, got "\n https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation\n (PB-Relax). A replacement of the original nn.Softmax(dim=-1)(attention_scores). Seems the new attention_probs\n will result in a slower speed and a little bias. Can use torch.allclose(standard_attention_probs,\n cogview_attention_probs, atol=1e-08) for comparison. The smaller atol (e.g., 1e-08), the better.\n " | cogview_attention | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | Apache-2.0 |
def init_visual_bbox(self, image_size=(14, 14), max_len=1000):
"""
Create the bounding boxes for the visual (patch) tokens.
"""
visual_bbox_x = torch.div(
torch.arange(0, max_len * (image_size[1] + 1), max_len), image_size[1], rounding_mode="trunc"
)
visual_bb... |
Create the bounding boxes for the visual (patch) tokens.
| init_visual_bbox | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | Apache-2.0 |
def forward(
self,
input_ids=None,
bbox=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
pixel_values=None,
output_attentions=None,
output_hidden_states=None,
return_dict... |
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModel
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = AutoModel.from_pretrained("microsof... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_m... |
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/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.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,
... |
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/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.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,
... |
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, AutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> import torch
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_layoutlmv3.py | Apache-2.0 |
def create_position_ids_from_inputs_embeds(self, inputs_embds: tf.Tensor) -> tf.Tensor:
"""
We are provided embeddings directly. We cannot infer which are padded, so just generate sequential position
ids.
"""
input_shape = tf.shape(inputs_embds)
sequence_length = input_sh... |
We are provided embeddings directly. We cannot infer which are padded, so just generate sequential position
ids.
| create_position_ids_from_inputs_embeds | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | Apache-2.0 |
def create_position_ids_from_input_ids(self, input_ids: tf.Tensor) -> tf.Tensor:
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_token_index + 1.
"""
mask = tf.cast(tf.not_equal(input_ids, self.padding_token_index), input_ids.dtype)
... |
Replace non-padding symbols with their position numbers. Position numbers begin at padding_token_index + 1.
| create_position_ids_from_input_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | Apache-2.0 |
def cogview_attention(self, attention_scores: tf.Tensor, alpha: Union[float, int] = 32):
"""
https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation
(PB-Relax). A replacement of the original tf.keras.layers.Softmax(axis=-1)(attention_scores). Seems... | ERROR: type should be string, got "\n https://arxiv.org/abs/2105.13290 Section 2.4 Stabilization of training: Precision Bottleneck Relaxation\n (PB-Relax). A replacement of the original tf.keras.layers.Softmax(axis=-1)(attention_scores). Seems the new\n attention_probs will result in a slower speed and a little bias. Can use\n tf.debugging.assert_near(standard_attention_probs, cogview_attention_probs, atol=1e-08) for comparison. The\n smaller atol (e.g., 1e-08), the better.\n " | cogview_attention | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | Apache-2.0 |
def call(
self,
input_ids: Optional[tf.Tensor] = None,
bbox: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
token_type_ids: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
... |
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, TFAutoModel
>>> from datasets import load_dataset
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr=False)
>>> model = TFAutoModel.from_pretrained("micr... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | Apache-2.0 |
def call(
self,
input_ids: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
token_type_ids: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
inputs_embeds: Optional[tf.Tensor] = Non... |
Returns:
Examples:
```python
>>> from transformers import AutoProcessor, TFAutoModelForSequenceClassification
>>> from datasets import load_dataset
>>> import tensorflow as tf
>>> processor = AutoProcessor.from_pretrained("microsoft/layoutlmv3-base", apply_ocr... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | Apache-2.0 |
def call(
self,
input_ids: Optional[tf.Tensor] = None,
bbox: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
token_type_ids: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
... |
labels (`tf.Tensor` 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, TFAutoMo... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | Apache-2.0 |
def call(
self,
input_ids: Optional[tf.Tensor] = None,
attention_mask: Optional[tf.Tensor] = None,
token_type_ids: Optional[tf.Tensor] = None,
position_ids: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
inputs_embeds: Optional[tf.Tensor] = Non... |
start_positions (`tf.Tensor` 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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/modeling_tf_layoutlmv3.py | Apache-2.0 |
def __call__(
self,
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels:... |
This method first forwards the `images` argument to [`~LayoutLMv3FeatureExtractor.__call__`]. In case
[`LayoutLMv3FeatureExtractor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
bounding boxes along with the additional arguments to [`~LayoutLMv3Tokenizer.__ca... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/processing_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/processing_layoutlmv3.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:
to... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.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/layoutlmv3/tokenization_layoutlmv3.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/tokenization_layoutlmv3.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]:
"""
Args:
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 id... |
Args:
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.
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/layoutlmv3/tokenization_layoutlmv3_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutlmv3/tokenization_layoutlmv3_fast.py | Apache-2.0 |
def __call__(
self,
images,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
boxes: Union[List[List[int]], List[List[List[int]]]] = None,
word_labels:... |
This method first forwards the `images` argument to [`~LayoutLMv2FeatureExtractor.__call__`]. In case
[`LayoutLMv2FeatureExtractor`] was initialized with `apply_ocr` set to `True`, it passes the obtained words and
bounding boxes along with the additional arguments to [`~LayoutXLMTokenizer.__cal... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutxlm/processing_layoutxlm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutxlm/processing_layoutxlm.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 XLM-RoBERTa sequen... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLM-RoBERTa sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.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. XLM-RoBERTa does
not make use of token type ids, therefor... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-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]`,... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.py | Apache-2.0 |
def prepare_for_model(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[int]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str, Padd... |
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.
Word-level `boxes` are ... | prepare_for_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.py | Apache-2.0 |
def truncate_sequences(
self,
ids: List[int],
token_boxes: List[List[int]],
pair_ids: Optional[List[int]] = None,
pair_token_boxes: Optional[List[List[int]]] = None,
labels: Optional[List[int]] = None,
num_tokens_to_remove: int = 0,
truncation_strategy: Un... |
Truncates a sequence pair in-place following the strategy.
Args:
ids (`List[int]`):
Tokenized input ids of the first sequence. Can be obtained from a string by chaining the `tokenize` and
`convert_tokens_to_ids` methods.
token_boxes (`List[List[i... | truncate_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/layoutxlm/tokenization_layoutxlm.py | Apache-2.0 |
def forward(
self,
hidden_states,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
"""
[`LEDEncoderSelfAttention`] expects *len(hidden_states)* ... |
[`LEDEncoderSelfAttention`] expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
*attention_window* happens in [`LEDEncoderModel.forward`] to avoid redoing the padding on each layer.
The *attention_mask* is changed in [`LEDEncoderModel.forward`] from 0, 1, 2 to:
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def _pad_and_transpose_last_two_dims(hidden_states_padded, padding):
"""pads rows and then flips rows and columns"""
hidden_states_padded = nn.functional.pad(
hidden_states_padded, padding
) # padding value is not important because it will be overwritten
hidden_states_padded... | pads rows and then flips rows and columns | _pad_and_transpose_last_two_dims | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def _pad_and_diagonalize(chunked_hidden_states):
"""
shift every row 1 step right, converting columns into diagonals.
Example:
```python
chunked_hidden_states: [
0.4983,
2.6918,
-0.0071,
1.0492,
-1.8348,
0.... |
shift every row 1 step right, converting columns into diagonals.
Example:
```python
chunked_hidden_states: [
0.4983,
2.6918,
-0.0071,
1.0492,
-1.8348,
0.7672,
0.2986,
0.0285,
-0... | _pad_and_diagonalize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def _chunk(hidden_states, window_overlap, onnx_export=False):
"""convert into overlapping chunks. Chunk size = 2w, overlap size = w"""
if not onnx_export:
# non-overlapping chunks of size = 2w
hidden_states = hidden_states.view(
hidden_states.size(0),
... | convert into overlapping chunks. Chunk size = 2w, overlap size = w | _chunk | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def _sliding_chunks_query_key_matmul(self, query: torch.Tensor, key: torch.Tensor, window_overlap: int):
"""
Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrai... |
Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained LEDEncoder) with an
overlap of size window_overlap
| _sliding_chunks_query_key_matmul | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def _sliding_chunks_matmul_attn_probs_value(
self, attn_probs: torch.Tensor, value: torch.Tensor, window_overlap: int
):
"""
Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
same shape as `attn_probs`
"""
ba... |
Same as _sliding_chunks_query_key_matmul but for attn_probs and value tensors. Returned tensor will be of the
same shape as `attn_probs`
| _sliding_chunks_matmul_attn_probs_value | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def _get_global_attn_indices(is_index_global_attn):
"""compute global attn indices required throughout forward pass"""
# helper variable
num_global_attn_indices = is_index_global_attn.long().sum(dim=1)
# max number of global attn indices in batch
max_num_global_attn_indices = nu... | compute global attn indices required throughout forward pass | _get_global_attn_indices | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
"""
Args:
hidden_... |
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/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.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 *(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/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def _pad_to_window_size(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor,
inputs_embeds: torch.Tensor,
pad_token_id: int,
):
"""A helper function to pad tokens and mask to work with implementation of Longformer self-attention."""
# padding
... | A helper function to pad tokens and mask to work with implementation of Longformer self-attention. | _pad_to_window_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
input_ids (`t... |
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 [`LEDTokenizer`]. See [`PreTrai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
global_attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
cross_attn_head_mask=None,
past_key_values=None,
inputs_embeds=None,
use_ca... |
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 [`LEDTokenizer`]. See [`PreTrai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_led.py | Apache-2.0 |
def call(
self,
inputs,
training=False,
):
"""
LongformerSelfAttention expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
*attention_window* happens in LongformerModel.forward to avoid redoing the padding on each layer.
The *attenti... |
LongformerSelfAttention expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
*attention_window* happens in LongformerModel.forward to avoid redoing the padding on each layer.
The *attention_mask* is changed in [`LongformerModel.forward`] from 0, 1, 2 to:
-... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | Apache-2.0 |
def _sliding_chunks_query_key_matmul(self, query, key, window_overlap):
"""
Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
o... |
Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
overlap of size window_overlap
| _sliding_chunks_query_key_matmul | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | Apache-2.0 |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
layer_head_mask: tf.Tensor,
is_index_masked: tf.Tensor,
is_index_global_attn: tf.Tensor,
is_global_attn: bool,
training=False,
):
"""
Args:
hidden_states (... |
Args:
hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`tf.Tensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
layer_head_mas... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | Apache-2.0 |
def call(
self,
hidden_states,
attention_mask: Optional[tf.Tensor] = None,
encoder_hidden_states: Optional[tf.Tensor] = None,
encoder_attention_mask: Optional[tf.Tensor] = None,
layer_head_mask: Optional[tf.Tensor] = None,
encoder_layer_head_mask: Optional[tf.Tens... |
Args:
hidden_states (`tf.Tensor`): input to the layer of shape *(seq_len, batch, embed_dim)*
attention_mask (`tf.Tensor`): attention mask of size
*(batch, 1, tgt_len, src_len)* where padding elements are indicated by very large negative values.
encoder_hidden... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | Apache-2.0 |
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
global_attention_mask=None,
head_mask=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
training=False,
):
"""
Args:
... |
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 [`LEDTokenizer`]. See [`PreTrainedToke... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | Apache-2.0 |
def _pad_to_window_size(
self,
input_ids,
attention_mask,
inputs_embeds,
pad_token_id,
):
"""A helper function to pad tokens and mask to work with implementation of Longformer selfattention."""
# padding
attention_window = (
self.attention_... | A helper function to pad tokens and mask to work with implementation of Longformer selfattention. | _pad_to_window_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | Apache-2.0 |
def call(
self,
input_ids=None,
inputs_embeds=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
head_mask=None,
encoder_head_mask=None,
past_key_values=None,
use_cache=None,
output_attentions=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 [`LEDTokenizer`]. See [`PreTrainedTokenizer.encode`] an... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask:... |
Returns:
Examples:
```python
>>> from transformers import LEDTokenizer, TFLEDForConditionalGeneration
>>> import tensorflow as tf
>>> mname = "allenai/led-base-16384"
>>> tokenizer = LEDTokenizer.from_pretrained(mname)
>>> TXT = "My friends are <mask> ... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/modeling_tf_led.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 LED 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 LED 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/led/tokenization_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/tokenization_led.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. LED 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. LED 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/led/tokenization_led.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/tokenization_led.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.
LED 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.
LED 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/led/tokenization_led_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/tokenization_led_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 LED.
"""
# 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 LED.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/led/tokenization_led_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/led/tokenization_led_fast.py | Apache-2.0 |
def create_position_ids_from_input_ids(self, input_ids, padding_idx):
"""
Args:
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`.
x: torch.Tens... |
Args:
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`.
x: torch.Tensor x:
Returns: torch.Tensor
| create_position_ids_from_input_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | Apache-2.0 |
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
Args:
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
"""
input_shape = inpu... |
Args:
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.:
inputs_embeds: torch.Tensor
Returns: torch.Tensor
| create_position_ids_from_inputs_embeds | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
bbox: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
head_mask: Optional[torch.T... |
Returns:
Examples:
```python
>>> from transformers import AutoTokenizer, AutoModel
>>> from datasets import load_dataset
>>> tokenizer = AutoTokenizer.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base")
>>> model = AutoModel.from_pretrained("SCUT-DLVCLab/lil... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask:... |
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/lilt/modeling_lilt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_m... |
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 AutoTokenizer, A... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
bbox: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_m... |
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/lilt/modeling_lilt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/lilt/modeling_lilt.py | Apache-2.0 |
def _get_question_end_index(input_ids, sep_token_id):
"""
Computes the index of the first occurrence of `sep_token_id`.
"""
sep_token_indices = (input_ids == sep_token_id).nonzero()
batch_size = input_ids.shape[0]
assert sep_token_indices.shape[1] == 2, "`input_ids` should have two dimensions"... |
Computes the index of the first occurrence of `sep_token_id`.
| _get_question_end_index | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | Apache-2.0 |
def _compute_global_attention_mask(input_ids, sep_token_id, before_sep_token=True):
"""
Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is
True` else after `sep_token_id`.
"""
question_end_index = _get_question_end_index(input_ids, sep_tok... |
Computes global attention mask by putting attention on all tokens before `sep_token_id` if `before_sep_token is
True` else after `sep_token_id`.
| _compute_global_attention_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | Apache-2.0 |
def create_position_ids_from_inputs_embeds(self, inputs_embeds):
"""
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor inputs_embeds:
Returns: torch.Tensor
"""
in... |
We are provided embeddings directly. We cannot infer which are padded so just generate sequential position ids.
Args:
inputs_embeds: torch.Tensor inputs_embeds:
Returns: torch.Tensor
| create_position_ids_from_inputs_embeds | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | Apache-2.0 |
def forward(
self,
hidden_states,
attention_mask=None,
layer_head_mask=None,
is_index_masked=None,
is_index_global_attn=None,
is_global_attn=None,
output_attentions=False,
):
"""
[`LongformerSelfAttention`] expects *len(hidden_states)* ... |
[`LongformerSelfAttention`] expects *len(hidden_states)* to be multiple of *attention_window*. Padding to
*attention_window* happens in [`LongformerModel.forward`] to avoid redoing the padding on each layer.
The *attention_mask* is changed in [`LongformerModel.forward`] from 0, 1, 2 to:
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids:... |
Returns:
Examples:
```python
>>> import torch
>>> from transformers import LongformerModel, LongformerTokenizer
>>> model = LongformerModel.from_pretrained("allenai/longformer-base-4096")
>>> tokenizer = LongformerTokenizer.from_pretrained("allenai/longformer... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids:... |
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/longformer/modeling_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
global_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.Tensor] = None,
position_ids:... |
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/longformer/modeling_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_longformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
global_attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Opt... |
start_positions (`tf.Tensor` 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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/modeling_tf_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/modeling_tf_longformer.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 Longformer sequence... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A Longformer sequence has the following format:
- single sequence: `<s> X </s>`
- pair of sequences: `<s> A </s></s> B </s>`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/tokenization_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/tokenization_longformer.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. Longformer does
not make use of token type ids, therefore... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. Longformer 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]`, ... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/tokenization_longformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/tokenization_longformer.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.
Longformer tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will
greedily comp... |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
Longformer 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/longformer/tokenization_longformer_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/tokenization_longformer_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 Longformer.
"""
# Mask token behave like a normal word, i.e. incl... |
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 Longformer.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longformer/tokenization_longformer_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longformer/tokenization_longformer_fast.py | Apache-2.0 |
def _pad_to_multiple(x: jnp.ndarray, block_len: int, axis: int, pad_value: int = 0) -> jnp.ndarray:
"""Pad an array so that a sequence length will be a multiple of `block_len`"""
pad_len = -x.shape[axis] % block_len
pad = [(0, 0)] * x.ndim
pad[axis] = (0, pad_len)
x = jnp.pad(x, pad_width=pad, mode=... | Pad an array so that a sequence length will be a multiple of `block_len` | _pad_to_multiple | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _split_into_blocks(x: jnp.ndarray, block_len: int, axis: int) -> jnp.ndarray:
"""Split an input array into blocks of a given `block_len` along the given `axis`. If the dimension length
is not a multiple of `block_len`, it will be padded first with selected `pad_value`.
"""
# pad tensor to multiple o... | Split an input array into blocks of a given `block_len` along the given `axis`. If the dimension length
is not a multiple of `block_len`, it will be padded first with selected `pad_value`.
| _split_into_blocks | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _concatenate_3_blocks(x: jnp.ndarray, block_axis: int, sequence_axis: int, pad_value: int = 0) -> jnp.ndarray:
"""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_axis]
... | 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_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _make_3block_relative_position_ids(block_len: int) -> jnp.ndarray:
"""Makes 3-blocked relative position ids for local attention."""
position_ids = jnp.arange(3 * block_len, dtype=jnp.int32)
center_position_ids = position_ids[block_len:-block_len]
relative_position_ids = position_ids[None, :] - cente... | Makes 3-blocked relative position ids for local attention. | _make_3block_relative_position_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _mask_local_attention_mask(local_attention_mask: np.ndarray, block_len: int) -> jnp.ndarray:
"""Mask local attention mask to enforce that tokens are not allowed to attend tokens farther than ``local_radius."""
relative_position_ids = _make_3block_relative_position_ids(block_len)
locality_mask = jnp.abs(... | Mask local attention mask to enforce that tokens are not allowed to attend tokens farther than ``local_radius. | _mask_local_attention_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _get_local_attention_mask(attention_mask: np.ndarray, block_len: int) -> jnp.ndarray:
"""Prepare attention mask to be applied for a local attention."""
# [batch_size, num_blocks, block_len]
_blocked_attention_mask = _split_into_blocks(attention_mask, block_len, axis=1)
# [batch_size, num_block, 3 * ... | Prepare attention mask to be applied for a local attention. | _get_local_attention_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _make_global_fixed_block_ids(attention_mask: np.ndarray, global_block_size: int) -> Tuple[jnp.ndarray, np.ndarray]:
"""Obtain the "fixed block" global id corresponding to each input token.
This implementation is a simlified version of the original Flaxformr implementation adopted from:
https://github.c... | Obtain the "fixed block" global id corresponding to each input token.
This implementation is a simlified version of the original Flaxformr implementation adopted from:
https://github.com/google/flaxformer/blob/main/flaxformer/architectures/longt5/long_attention.py.
In our scenario, as we use this strategy... | _make_global_fixed_block_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _make_side_relative_position_ids(attention_mask: np.ndarray, global_block_size: int) -> np.ndarray:
"""Create the relative position tensor for local -> global attention."""
block_ids, global_segment_ids = _make_global_fixed_block_ids(attention_mask, global_block_size)
global_seq_len = global_segment_ids... | Create the relative position tensor for local -> global attention. | _make_side_relative_position_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _create_global_aggregates(hidden_states: np.ndarray, block_ids: np.ndarray, global_seq_len: int) -> np.ndarray:
"""Compute individual block aggregates by summing over individual blocks."""
# (batch..., seq_len, global_seq_len))
one_hot_block_ids = jax.nn.one_hot(block_ids, global_seq_len)
return jnp... | Compute individual block aggregates by summing over individual blocks. | _create_global_aggregates | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def __call__(self, hidden_states):
"""
Construct a layernorm module in the LongT5 style; No bias and no subtraction of mean.
"""
# layer norm should always be calculated in float32
variance = jnp.power(hidden_states.astype("f4"), 2).mean(axis=-1, keepdims=True)
hidden_sta... |
Construct a layernorm module in the LongT5 style; No bias and no subtraction of mean.
| __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_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_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def __call__(
self,
hidden_states,
attention_mask=None,
key_value_states=None,
position_bias=None,
use_cache=False,
output_attentions=False,
deterministic=True,
init_cache=False,
):
"""
Self-attention (if key_value_states is Non... |
Self-attention (if key_value_states is None) or attention over source sentence (provided by key_value_states).
| __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def encode(
self,
input_ids: jnp.ndarray,
attention_mask: Optional[jnp.ndarray] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
train: bool = False,
params: dict = None,
... |
Returns:
Example:
```python
>>> from transformers import T5Tokenizer, FlaxLongT5ForConditionalGeneration
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/long-t5-local-base")
>>> tex... | encode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.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,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: ... |
Returns:
Example:
```python
>>> from transformers import T5Tokenizer, FlaxLongT5ForConditionalGeneration
>>> import jax.numpy as jnp
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/l... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.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,
past_key_values: dict = None,
output_attentions: Optional[bool] = None,
output_hidden_states: ... |
Returns:
Example:
```python
>>> from transformers import T5Tokenizer, FlaxLongT5ForConditionalGeneration
>>> import jax.numpy as jnp
>>> tokenizer = T5Tokenizer.from_pretrained("t5-base")
>>> model = FlaxLongT5ForConditionalGeneration.from_pretrained("google/l... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/longt5/modeling_flax_longt5.py | Apache-2.0 |
def _pad_to_multiple(x: torch.Tensor, block_len: int, dim: int, pad_value: int = 0) -> torch.Tensor:
"""Pad a tensor so that a sequence length will be a multiple of `block_len`"""
pad_len = -x.shape[dim] % block_len
# Handle cases when an empty input sequence is given
if not all(x.shape):
new_sh... | Pad a tensor so that a sequence length will be a multiple of `block_len` | _pad_to_multiple | 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 _split_into_blocks(x: torch.Tensor, block_len: int, dim: int) -> torch.Tensor:
"""Split an input tensor into blocks of a given `block_len` along the given `dim`. If the dimension length
is not a multiple of `block_len`, it will be padded first with selected `pad_value`.
"""
# pad tensor to multiple ... | Split an input tensor into blocks of a given `block_len` along the given `dim`. If the dimension length
is not a multiple of `block_len`, it will be padded first with selected `pad_value`.
| _split_into_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 |
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