text stringlengths 1 1.02k | class_index int64 0 10.8k | source stringlengths 85 188 |
|---|---|---|
return_token_type_ids=return_token_type_ids,
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Copied from transformers.models.layoutxlm.tokenization_layoutxlm_fast.LayoutXLMTokenizerFast.tokenize
def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
batched_input = [(text, pair)] if pair else [text]
self._tokenizer.encode_speci... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
def batch_encode_plus_boxes(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
boxes: Optional[List[List[List[int]]]] = None,
word_labels: Optional[List[List[i... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
return_offsets_mapping: bool = False,
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
"""
Tokenize and prepare for the model a list of sequences or a list of pairs of sequences. | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
<Tip warning={true}>
This method is deprecated, `__call__` should be used instead.
</Tip>
Args:
batch_text_or_text_pairs (`List[str]`, `List[Tuple[str, str]]`, `List[List[str]]`, `List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also `List[List[int]]`, `List[Tuple[... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
return self._batch_encode_plus_boxes(
batch_text_or_text_pairs=batch_text_or_text_pairs,
is_pair=is_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strateg... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
def _batch_encode_plus_boxes(
self,
batch_text_or_text_pairs: Union[
List[TextInput],
List[TextInputPair],
List[PreTokenizedInput],
],
is_pair: bool = None,
boxes: Optional[List[List[List[int]]]] = None,
word_labels: Optional[List[List[... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
return_length: bool = False,
verbose: bool = True,
**kwargs,
) -> BatchEncoding:
if not isinstance(batch_text_or_text_pairs, list):
raise TypeError(f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})") | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Set the truncation and padding strategy and restore the initial configuration
self.set_truncation_and_padding(
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_length=max_length,
stride=stride,
pad_to_multiple_of=pad_to... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Convert encoding to dict
# `Tokens` has type: Tuple[
# List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
# List[EncodingFast]
# ]
# with nested dimensions corresponding to batch, overflows, sequence le... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
# From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
# (we say ~ because the number of overflow varies with the example in the batch)
#
#... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# If returning overflowing tokens, we need to return a mapping
# from the batch idx to the original sample
if return_overflowing_tokens:
overflow_to_sample_mapping = []
for i, (toks, _) in enumerate(tokens_and_encodings):
overflow_to_sample_mapping += [i] * len(to... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# create the token boxes
token_boxes = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
else:
original_index = batch_index
... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
elif id == self.pad_token_id:
token_boxes_example.append(self.pad_token_box)
else:
raise ValueError("Id not recognized")
token_boxes.append(token_boxes_example) | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
sanitized_tokens["bbox"] = token_boxes | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# optionally, create the labels
if word_labels is not None:
labels = []
for batch_index in range(len(sanitized_tokens["input_ids"])):
if return_overflowing_tokens:
original_index = sanitized_tokens["overflow_to_sample_mapping"][batch_index]
... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Use the real label id for the first token of the word, and padding ids for the remaining tokens
labels_example.append(word_labels[original_index][word_id])
else:
labels_example.append(self.pad_token_label)
... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
sanitized_tokens["labels"] = labels
# finally, remove offsets if the user didn't want them
if not return_offsets_mapping:
del sanitized_tokens["offset_mapping"]
return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors) | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
def _encode_plus_boxes(
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_strategy: PaddingStra... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# make it a batched input
# 2 options:
# 1) only text, in case text must be a list of str
# 2) text + text_pair, in which case text = str and text_pair a list of str
batched_input = [(text, text_pair)] if text_pair else [text]
batched_boxes = [boxes]
batched_word_labels =... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
return_attention_mask=return_attention_mask,
return_overflowing_tokens=return_overflowing_tokens,
return_special_tokens_mask=return_special_tokens_mask,
return_offsets_mapping=return_offsets_mapping,
return_length=return_length,
verbose=verbose,
**... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Return tensor is None, then we can remove the leading batch axis
# Overflowing tokens are returned as a batch of output so we keep them in this case
if return_tensors is None and not return_overflowing_tokens:
batched_output = BatchEncoding(
{
key: value... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
def encode_boxes(
self,
text: Union[TextInput, PreTokenizedInput, EncodedInput],
text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
`tokenize` method) or a list of integers (tokenized string ids using the `convert_tokens_to_ids`
method).
text_pair (`str`, `List[str]` or `List[int]`, *optional*):
Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
return encoded_inputs["input_ids"] | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
def encode_plus_boxes(
self,
text: Union[TextInput, PreTokenizedInput],
text_pair: Optional[PreTokenizedInput] = None,
boxes: Optional[List[List[int]]] = None,
word_labels: Optional[List[List[int]]] = None,
add_special_tokens: bool = True,
padding: Union[bool, str... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
**kwargs,
) -> BatchEncoding:
"""
Tokenize and prepare for the model a sequence or a pair of sequences. | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
<Tip warning={true}>
This method is deprecated, `__call__` should be used instead.
</Tip>
Args:
text (`str`, `List[str]` or (for non-fast tokenizers) `List[int]`):
The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
padding_strategy, truncation_strategy, max_length, kwargs = self._get_padding_truncation_strategies(
padding=padding,
truncation=truncation,
max_length=max_length,
pad_to_multiple_of=pad_to_mu... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
return self._encode_plus_boxes(
text=text,
text_pair=text_pair,
boxes=boxes,
word_labels=word_labels,
add_special_tokens=add_special_tokens,
padding_strategy=padding_strategy,
truncation_strategy=truncation_strategy,
max_len... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Copied from transformers.models.layoutxlm.tokenization_layoutxlm_fast.LayoutXLMTokenizerFast._pad
def _pad(
self,
encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
max_length: Optional[int] = None,
padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
- PaddingStrategy.LONGEST Pad to the longest sequence in the batch
- PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
- PaddingStrategy.DO_NOT_PAD: Do not pad
The tokenizer padding sides are defined in self.padding_side: | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
- 'left': pads on the left of the sequences
- 'right': pads on the right of the sequences
pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
This is especially useful to enable the use of Tensor Core on NVIDIA hardware... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
required_input = encoded_inputs[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
max_length = len(required_input)
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
max_length = ((max_length // pad_... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
if needs_to_be_padded:
difference = max_length - len(required_input)
padding_side = padding_side if padding_side is not None else self.padding_side
if padding_side == "right":
if return_attention_mask:
encoded_inputs["attention_mask"] = encoded_inp... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
encoded_inputs["special_tokens_mask"] = encoded_inputs["special_tokens_mask"] + [1] * difference
encoded_inputs[self.model_input_names[0]] = required_input + [self.pad_token_id] * difference
elif padding_side == "left":
if return_attention_mask:
encoded_in... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
encoded_inputs["special_tokens_mask"] = [1] * difference + encoded_inputs["special_tokens_mask"]
encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
else:
raise ValueError("Invalid padding strategy:" + str(padding_side)) | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
return encoded_inputs
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 to... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
if token_ids_1 is None:
return token_ids_0 + [self.sep_token_id]
sep = [self.sep_token_id]
return token_ids_0 + sep + token_ids_1 + sep
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
# Copied from transformers.models.layoutxlm.tokenization_layoutxlm_fast.LayoutXLMTokenizerFast.save_vocabulary
def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast to... | 3,046 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/tokenization_udop_fast.py |
class UdopTextKwargs(TextKwargs, total=False):
word_labels: Optional[Union[List[int], List[List[int]]]]
boxes: Union[List[List[int]], List[List[List[int]]]] | 3,047 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
class UdopProcessorKwargs(ProcessingKwargs, total=False):
text_kwargs: UdopTextKwargs
_defaults = {
"text_kwargs": {
"add_special_tokens": True,
"padding": False,
"truncation": False,
"stride": 0,
"return_overflowing_tokens": False,
... | 3,048 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
class UdopProcessor(ProcessorMixin):
r"""
Constructs a UDOP processor which combines a LayoutLMv3 image processor and a UDOP tokenizer into a single processor.
[`UdopProcessor`] offers all the functionalities you need to prepare data for the model.
It first uses [`LayoutLMv3ImageProcessor`] to resize,... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
Args:
image_processor (`LayoutLMv3ImageProcessor`):
An instance of [`LayoutLMv3ImageProcessor`]. The image processor is a required input.
tokenizer (`UdopTokenizer` or `UdopTokenizerFast`):
An instance of [`UdopTokenizer`] or [`UdopTokenizerFast`]. The tokenizer is a required inp... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
def __call__(
self,
images: Optional[ImageInput] = None,
text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
# The following is to capture `text_pair` argument that may be passed as a positional argument.
# See transformers.processing_utils... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
bounding boxes along with the additional arguments to [`~UdopTokenizer.__call__`] and returns the output,
together with the prepared `pixel_values`. In case [`UdopImageProcessor`] was initialized with `apply_ocr` set
to `False`, it passes the words (`text`/``text_pair`) and `boxes` specified by the user... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
Alternatively, one can pass `text_target` and `text_pair_target` to prepare the targets of UDOP.
Please refer to the docstring of the above two methods for more information.
"""
# verify input
output_kwargs = self._merge_kwargs(
UdopProcessorKwargs,
tokenizer_ini... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."
)
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueE... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
else:
# use the processor to prepare the inputs of UDOP
# first, apply the image processor
features = self.image_processor(images=images, **output_kwargs["images_kwargs"])
features_words = features.pop("words", None)
features_boxes = features.pop("boxes", None... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(text, str):
text = [text] # add batch dimension (as the image processor always adds a batch dimension)
output_kwargs["text_kwargs"]["... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
# Copied from transformers.models.layoutlmv3.processing_layoutlmv3.LayoutLMv3Processor.get_overflowing_images
def get_overflowing_images(self, images, overflow_to_sample_mapping):
# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
images_with_overflow ... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
# Copied from transformers.models.layoutlmv3.processing_layoutlmv3.LayoutLMv3Processor.batch_decode
def batch_decode(self, *args, **kwargs):
"""
This method forwards all its arguments to PreTrainedTokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please
refer to the docstring of this method... | 3,049 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/processing_udop.py |
class BaseModelOutputWithAttentionMask(ModelOutput):
"""
Class for the model's outputs that may also contain a past key/values (to speed up sequential decoding). Includes
an additional attention mask. | 3,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Args:
last_hidden_state (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Sequence of hidden-states at the output of the last layer of the model. If `past_key_values` is used only
the last hidden-state of the sequences of shape `(batch_size, 1, hidden_size)` is... | 3,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
self-attention blocks and optionally if `config.is_encoder_decoder=True` in the cross-attention blocks)
that can be used (see `past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or
... | 3,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in
the self-attention heads.
cross_attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` and
`config.add_cross_attention=True` is passed or when `confi... | 3,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
last_hidden_state: torch.FloatTensor = None
attention_mask: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
cross_attentions: Optional[Tuple[torch... | 3,050 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopPatchEmbeddings(nn.Module):
"""2D Image to Patch Embeddings"""
def __init__(self, config):
super().__init__()
image_size, patch_size = config.image_size, config.patch_size
num_channels, hidden_size = config.num_channels, config.hidden_size
image_size = image_size if i... | 3,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def forward(self, pixel_values):
batch_size, num_channels, height, width = pixel_values.shape
if height != self.image_size[0] or width != self.image_size[1]:
raise ValueError(
f"Input image size ({height}*{width}) doesn't match model"
f" ({self.image_size[0]}*... | 3,051 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopPreTrainedModel(PreTrainedModel):
"""
An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
models. Based on `T5PreTrainedModel`.
"""
config_class = UdopConfig
base_model_prefix = "transformer"
supports_gradient_checkpoint... | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def _init_weights(self, module):
"""Initialize the weights"""
factor = self.config.initializer_factor # Used for testing weights initialization
if isinstance(module, UdopLayerNorm):
module.weight.data.fill_(factor * 1.0)
elif isinstance(module, nn.Embedding):
mod... | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
factor = self.config.initializer_factor
d_model = self.config.d_model
module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5))
elif isinstance(module, UdopModel):
# Mesh TensorFlow embeddings initialization
# See https://githu... | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# and https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L89
module.wi.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_model) ** -0.5))
if hasattr(module.wi, "bias") and module.wi.bias is not None:
module.wi.bias.... | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
module.wi_1.bias.data.zero_()
module.wo.weight.data.normal_(mean=0.0, std=factor * ((self.config.d_ff) ** -0.5))
if hasattr(module.wo, "bias") and module.wo.bias is not None:
module.wo.bias.data.zero_()
elif isinstance(module, UdopAttention):
# Mesh TensorFlow... | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
module.o.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
if module.has_relative_attention_bias:
module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((d_model) ** -0.5)) | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Copied from transformers.models.prophetnet.modeling_prophetnet.ProphetNetPreTrainedModel._shift_right with ProphetNet->Udop
def _shift_right(self, input_ids):
decoder_start_token_id = self.config.decoder_start_token_id
pad_token_id = self.config.pad_token_id
assert decoder_start_token_id ... | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
assert torch.all(shifted_input_ids >= 0).item(), "Verify that `shifted_input_ids` has only positive values"
return shifted_input_ids | 3,052 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopLayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-6):
"""
Construct a layernorm module in the Udop style. No bias and no subtraction of mean.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance_epsilon = eps
... | 3,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# convert into half-precision if necessary
if self.weight.dtype in [torch.float16, torch.bfloat16]:
hidden_states = hidden_states.to(self.weight.dtype)
return self.weight * hidden_states | 3,053 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopDenseActDense(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
self.wi = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=False)
self.dropout = nn.Dropout(config.dropout_rate)
self.act = A... | 3,054 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopDenseGatedActDense(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
self.wi_0 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wi_1 = nn.Linear(config.d_model, config.d_ff, bias=False)
self.wo = nn.Linear(config.d_ff, config.d_model, bias=Fals... | 3,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# To make 8bit quantization work for google/flan-t5-xxl, self.wo is kept in float32.
# See https://github.com/huggingface/transformers/issues/20287
# we also make sure the weights are not in `int8` in case users will force `_keep_in_fp32_modules` to be `None``
if (
isinstance(self.wo... | 3,055 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopLayerFF(nn.Module):
def __init__(self, config: UdopConfig):
super().__init__()
if config.is_gated_act:
self.DenseReluDense = UdopDenseGatedActDense(config)
else:
self.DenseReluDense = UdopDenseActDense(config)
self.layer_norm = UdopLayerNorm(config.... | 3,056 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopAttention(nn.Module):
def __init__(
self,
config: UdopConfig,
has_relative_attention_bias=False,
layer_idx: Optional[int] = None,
):
super().__init__()
self.is_decoder = config.is_decoder
self.has_relative_attention_bias = has_relative_attention_... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
"when creating this class."
) | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# Mesh TensorFlow initialization to avoid scaling before softmax
self.q = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.k = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.v = nn.Linear(self.d_model, self.inner_dim, bias=False)
self.o = nn.Linear(self.inner_dim, self.... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def prune_heads(self, heads):
if len(heads) == 0:
return
heads, index = find_pruneable_heads_and_indices(
heads, self.n_heads, self.key_value_proj_dim, self.pruned_heads
)
# Prune linear layers
self.q = prune_linear_layer(self.q, index)
self.k = pr... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Translate relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use s... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
Returns:
a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
"""
relative_buckets = 0
if bidirectional:
num_buckets //= 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
relative_position_if_large = max_exact + (
torch.log(relative_position.float() / max_exact)
/ math.log(max_distance / max_exact)
* (num_buckets - max_exact)
).to(torch.... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def compute_bias(self, query_length, key_length, device=None, cache_position=None):
"""Compute binned relative position bias"""
if device is None:
device = self.relative_attention_bias.weight.device
if cache_position is None:
context_position = torch.arange(query_length, ... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
values = self.relative_attention_bias(relative_position_bucket) # shape (query_length, key_length, num_heads)
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def forward(
self,
hidden_states,
mask=None,
key_value_states=None,
position_bias=None,
past_key_value=None,
layer_head_mask=None,
query_length=None,
use_cache=False,
output_attentions=False,
cache_position=None,
):
"""
... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if past_key_value is not None:
is_updated = past_key_value.is_updated.get(self.layer_idx)
if is_cross_attention:
# after the first generated id, we can subsequently re-use all key/value_states from cache
curr_past_key_value = past_key_value.cross_attention_cache
... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
current_states = key_value_states if is_cross_attention else hidden_states
if is_cross_attention and past_key_value is not None and is_updated:
# reuse k,v, cross_attentions
key_states = curr_past_key_value.key_cache[self.layer_idx]
value_states = curr_past_key_value.value_ca... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if past_key_value is not None:
# save all key/value_states to cache to be re-used for fast auto-regressive generation
cache_position = cache_position if not is_cross_attention else None
key_states, value_states = curr_past_key_value.update(
key_states,... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if position_bias is None:
key_length = key_states.shape[-2]
# cache position is 0-indexed so we add 1 to get the real length of queries (aka with past)
real_seq_length = query_length if query_length is not None else cache_position[-1] + 1
if not self.has_relative_attentio... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
if mask is not None:
causal_mask = mask[:, :, :, : key_states.shape[-2]]
position_bias = position_bias + causal_mask
if self.pruned_heads:
mask = torch.ones(position_bias.shape[1])
mask[list(self.pruned_heads)] = 0
position_bias_masked = posit... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
attn_output = attn_output.transpose(1, 2).contiguous()
attn_output = attn_output.view(batch_size, -1, self.inner_dim)
attn_output = self.o(attn_output)
outputs = (attn_output, past_key_value, position_bias)
if output_attentions:
outputs = outputs + (attn_weights,)
r... | 3,057 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopLayerSelfAttention(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.SelfAttention = UdopAttention(
config, has_relative_attention_bias=has_relative_attention_bias, layer_idx=layer_idx
)
... | 3,058 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
output_attentions=False,
cache_position=None,
):
normed_hidden_states = self.layer_norm(hidden_states... | 3,058 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopLayerCrossAttention(nn.Module):
def __init__(self, config, layer_idx: Optional[int] = None):
super().__init__()
self.EncDecAttention = UdopAttention(config, has_relative_attention_bias=False, layer_idx=layer_idx)
self.layer_norm = UdopLayerNorm(config.d_model, eps=config.layer_norm... | 3,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def forward(
self,
hidden_states,
key_value_states,
attention_mask=None,
position_bias=None,
layer_head_mask=None,
past_key_value=None,
use_cache=False,
query_length=None,
output_attentions=False,
cache_position=None,
):
... | 3,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
return outputs | 3,059 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
class UdopBlock(nn.Module):
def __init__(self, config, has_relative_attention_bias=False, layer_idx: Optional[int] = None):
super().__init__()
self.is_decoder = config.is_decoder
self.layer = nn.ModuleList()
self.layer.append(
UdopLayerSelfAttention(
confi... | 3,060 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
def forward(
self,
hidden_states,
attention_mask=None,
position_bias=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
encoder_decoder_position_bias=None,
layer_head_mask=None,
cross_attn_layer_head_mask=None,
past_key_value=No... | 3,060 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
... | 3,060 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
do_cross_attention = self.is_decoder and encoder_hidden_states is not None
if do_cross_attention:
cross_attention_outputs = self.layer[1](
hidden_states,
key_value_states=encoder_hidden_states,
attention_mask=encoder_attention_mask,
pos... | 3,060 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
... | 3,060 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
# clamp inf values to enable fp16 training
if hidden_states.dtype == torch.float16:
clamp_value = torch.where(
torch.isinf(hidden_states).any(),
torch.finfo(hidden_states.dtype).max - 1000,
torch.finfo(hidden_states.dtype).max,
)
... | 3,060 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/udop/modeling_udop.py |
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