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 forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[torch.FloatTensor]] = None,... |
Returns:
Example:
```python
>>> from transformers import PegasusTokenizer, PegasusModel
>>> tokenizer = PegasusTokenizer.from_pretrained("google/pegasus-x-large")
>>> model = PegasusModel.from_pretrained("google/pegasus-x-large")
>>> inputs = tokenizer("Studi... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/pegasus_x/modeling_pegasus_x.py | Apache-2.0 |
def convert_perceiver_checkpoint(pickle_file, pytorch_dump_folder_path, architecture="MLM"):
"""
Copy/paste/tweak model's weights to our Perceiver structure.
"""
# load parameters as FlatMapping data structure
with open(pickle_file, "rb") as f:
checkpoint = pickle.loads(f.read())
state... |
Copy/paste/tweak model's weights to our Perceiver structure.
| convert_perceiver_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/convert_perceiver_haiku_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/convert_perceiver_haiku_to_pytorch.py | Apache-2.0 |
def center_crop(self, image):
"""
Crops `image` to *self.crop_size* using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).
Args:
image (`PIL.Image.Image` or `np.ndarray` or `tor... |
Crops `image` to *self.crop_size* using a center crop. Note that if the image is too small to be cropped to the
size given, it will be padded (so the returned result has the size asked).
Args:
image (`PIL.Image.Image` or `np.ndarray` or `torch.Tensor`):
The image to... | center_crop | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/feature_extraction_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/feature_extraction_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: torch.FloatTensor,
attention_mask: Optional[torch.FloatTensor] = None,
subsampled_output_points: Optional[Dict[str, torch.Tensor]] = None,
head_mask: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hi... |
Returns:
Examples:
```python
>>> from transformers import PerceiverConfig, PerceiverTokenizer, PerceiverFeatureExtractor, PerceiverModel
>>> from transformers.models.perceiver.modeling_perceiver import (
... PerceiverTextPreprocessor,
... PerceiverImage... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor]... |
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/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor]... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the classification/regression loss. Indices should be in `[0, ..., config.num_labels -
1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels >
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor]... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.n... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor]... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.n... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor]... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.n... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
labels: Optional[torch.Tensor]... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the optical flow loss. Indices should be in `[0, ..., config.num_labels - 1]`.
Returns:
Examples:
```python
>>> from transformers import PerceiverForOpticalFlow
>>> impo... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def forward(
self,
inputs: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
subsampled_output_points: Optional[Dict[str, torch.Tensor]] = None,
head_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
`config.n... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def build_position_encoding(
position_encoding_type,
out_channels=None,
project_pos_dim=-1,
trainable_position_encoding_kwargs=None,
fourier_position_encoding_kwargs=None,
):
"""
Builds the position encoding.
Args:
- out_channels: refers to the number of channels of the position enc... |
Builds the position encoding.
Args:
- out_channels: refers to the number of channels of the position encodings.
- project_pos_dim: if specified, will project the position encodings to this dimension.
| build_position_encoding | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def restructure(modality_sizes: ModalitySizeType, inputs: torch.Tensor) -> Mapping[str, torch.Tensor]:
"""
Partitions a [B, N, C] tensor into tensors for each modality.
Args:
modality_sizes
dict specifying the size of the modality
inputs:
input tensor
Returns:
... |
Partitions a [B, N, C] tensor into tensors for each modality.
Args:
modality_sizes
dict specifying the size of the modality
inputs:
input tensor
Returns:
dict mapping name of modality to its associated tensor.
| restructure | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def space_to_depth(frames: torch.Tensor, temporal_block_size: int = 1, spatial_block_size: int = 1) -> torch.Tensor:
"""
Space to depth transform. Rearranges blocks of spatial data, into depth.
This function assumes the channels to be first, but will place the channels last after transformation.
Based... |
Space to depth transform. Rearranges blocks of spatial data, into depth.
This function assumes the channels to be first, but will place the channels last after transformation.
Based on https://discuss.pytorch.org/t/is-there-any-layer-like-tensorflows-space-to-depth-function/3487/15.
| space_to_depth | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def __init__(
self,
num_layers: int = 1,
in_channels: int = 3,
out_channels: int = 64,
use_batchnorm: bool = True,
):
"""
Constructs a Conv2DDownsample model.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of in... |
Constructs a Conv2DDownsample model.
Args:
in_channels (`int`, *optional*, defaults to 3):
The number of input channels.
out_channels (`int`, *optional*, defaults to 64):
The number of conv output channels.
use_batchnorm (`bool`, *optional*, defaul... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def generate_fourier_features(pos, num_bands, max_resolution=(224, 224), concat_pos=True, sine_only=False):
"""
Generate a Fourier frequency position encoding with linear spacing.
Args:
pos (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`):
The Tensor containing the position ... |
Generate a Fourier frequency position encoding with linear spacing.
Args:
pos (`torch.LongTensor` of shape `(batch_size, sequence_length, dim)`):
The Tensor containing the position of n points in d dimensional space.
num_bands (`int`):
The number of frequency bands (K) to use.
... | generate_fourier_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def build_linear_positions(index_dims, output_range=(-1.0, 1.0)):
"""
Generate an array of position indices for an N-D input array.
Args:
index_dims (`List[int]`):
The shape of the index dimensions of the input array.
output_range (`Tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`):... |
Generate an array of position indices for an N-D input array.
Args:
index_dims (`List[int]`):
The shape of the index dimensions of the input array.
output_range (`Tuple[float]`, *optional*, defaults to `(-1.0, 1.0)`):
The min and max values taken by each input index dimension.
... | build_linear_positions | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def _check_or_build_spatial_positions(pos, index_dims, batch_size):
"""
Checks or builds spatial position features (x, y, ...).
Args:
pos (`torch.FloatTensor`):
None, or an array of position features. If None, position features are built. Otherwise, their size is checked.
index_dims (`L... |
Checks or builds spatial position features (x, y, ...).
Args:
pos (`torch.FloatTensor`):
None, or an array of position features. If None, position features are built. Otherwise, their size is checked.
index_dims (`List[int]`):
An iterable giving the spatial/index size of the data t... | _check_or_build_spatial_positions | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def output_size(self):
"""Returns size of positional encodings last dimension."""
num_dims = len(self.max_resolution)
encoding_size = self.num_bands * num_dims
if not self.sine_only:
encoding_size *= 2
if self.concat_pos:
encoding_size += self.num_dimensio... | Returns size of positional encodings last dimension. | output_size | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def _build_network_inputs(self, inputs: torch.Tensor, network_input_is_1d: bool = True):
"""
Construct the final input, including position encoding.
This method expects the inputs to always have channels as last dimension.
"""
batch_size = inputs.shape[0]
index_dims = i... |
Construct the final input, including position encoding.
This method expects the inputs to always have channels as last dimension.
| _build_network_inputs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | Apache-2.0 |
def _build_network_inputs(self, inputs):
"""Construct the final input, including position encoding."""
batch_size = inputs.shape[0]
index_dims = inputs.shape[1:-1]
# Construct the position encoding.
if self.position_encoding_type == "trainable":
pos_enc = self.positi... | Construct the final input, including position encoding. | _build_network_inputs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/modeling_perceiver.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. A sequence has the
following format:
- single sequence:... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks. A sequence has the
following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
token_ids_0 (`List[int]`):
List of... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/perceiver/tokenization_perceiver.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/perceiver/tokenization_perceiver.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 PhoBERT sequence ha... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A PhoBERT 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/phobert/tokenization_phobert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/phobert/tokenization_phobert.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. PhoBERT 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. PhoBERT 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/phobert/tokenization_phobert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/phobert/tokenization_phobert.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optio... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PLBartTokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/plbart/modeling_plbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/plbart/modeling_plbart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
cr... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PLBartTokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/plbart/modeling_plbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/plbart/modeling_plbart.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.FloatTensor] = None,
encoder_attention_mask: Optional[torch.FloatTensor] = None,
head_mask: Optional[torch.Tensor] = None,
c... |
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
provide it.
Indices can be obtained using [`PLBartTokenizer`]. See [`PreT... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/plbart/modeling_plbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/plbart/modeling_plbart.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 PLBART sequence ha... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An PLBART sequence has the following format, where `X` represents the sequence:
- `input_ids` (for encoder) `X [eos, src_lang_code]`
- `decoder_input_i... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/plbart/tokenization_plbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/plbart/tokenization_plbart.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. PLBart does not
make use of token type ids, therefore a l... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. PLBart does not
make use of token type ids, therefore a list of zeros is returned.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List[int]`, *opt... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/plbart/tokenization_plbart.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/plbart/tokenization_plbart.py | Apache-2.0 |
def replace_key_with_offset(key, offset, original_name, new_name):
"""
Replaces the key by subtracting the offset from the original layer number
"""
to_find = original_name.split(".")[0]
key_list = key.split(".")
orig_block_num = int(key_list[key_list.index(to_find) - 2])
layer_num = int(key... |
Replaces the key by subtracting the offset from the original layer number
| replace_key_with_offset | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/poolformer/convert_poolformer_original_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/poolformer/convert_poolformer_original_to_pytorch.py | Apache-2.0 |
def convert_poolformer_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our PoolFormer structure.
"""
# load default PoolFormer configuration
config = PoolFormerConfig()
# set attributes based on model_name
repo_id = "huggingface/la... |
Copy/paste/tweak model's weights to our PoolFormer structure.
| convert_poolformer_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/poolformer/convert_poolformer_original_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/poolformer/convert_poolformer_original_to_pytorch.py | Apache-2.0 |
def convert_prophetnet_checkpoint_to_pytorch(prophetnet_checkpoint_path: str, pytorch_dump_folder_path: str):
"""
Copy/paste/tweak prohpetnet's weights to our prophetnet structure.
"""
if "xprophetnet" in prophetnet_checkpoint_path:
prophet_old = XLMProphetNetForConditionalGenerationOld.from_pre... |
Copy/paste/tweak prohpetnet's weights to our prophetnet structure.
| convert_prophetnet_checkpoint_to_pytorch | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/convert_prophetnet_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def ngram_attention_bias(sequence_length, ngram, device, dtype):
"""
This function computes the bias for the predict stream
"""
left_block = (
torch.ones((ngram, sequence_length, sequence_length), device=device, dtype=dtype) * torch.finfo(dtype).min
)
right_block = left_block.detach().cl... |
This function computes the bias for the predict stream
| ngram_attention_bias | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def compute_relative_buckets(num_buckets, max_distance, relative_positions, is_bidirectional=False):
"""
This function computes individual parts of the relative position buckets. For more detail, see paper.
"""
inv_relative_positions = -relative_positions
rel_positions_bucket = 0
if is_bidirect... |
This function computes individual parts of the relative position buckets. For more detail, see paper.
| compute_relative_buckets | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def compute_all_stream_relative_buckets(num_buckets, max_distance, position_ids):
"""
This function computes both main and predict relative position buckets. For more detail, see paper.
"""
# main stream
main_stream_relative_positions = position_ids.unsqueeze(1).repeat(1, position_ids.size(-1), 1)
... |
This function computes both main and predict relative position buckets. For more detail, see paper.
| compute_all_stream_relative_buckets | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Opti... |
Returns:
Example:
```python
>>> from transformers import ProphetNetTokenizer, ProphetNetEncoder
>>> import torch
>>> tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
>>> model = ProphetNetEncoder.from_pretrained("patrickvon... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decod... |
Returns:
Example:
```python
>>> from transformers import ProphetNetTokenizer, ProphetNetModel
>>> tokenizer = ProphetNetTokenizer.from_pretrained("microsoft/prophetnet-large-uncased")
>>> model = ProphetNetModel.from_pretrained("microsoft/prophetnet-large-uncased")
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
decoder_input_ids: Optional[torch.Tensor] = None,
decoder_attention_mask: Optional[torch.BoolTensor] = None,
head_mask: Optional[torch.Tensor] = None,
decod... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
labe... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_hidden_states: Optional[torch.Tensor] = None,
encoder_attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
cross... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (`... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/modeling_prophetnet.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A ProphetNet
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 ProphetNet
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is ... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/prophetnet/tokenization_prophetnet.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_emb... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/qdqbert/modeling_qdqbert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/qdqbert/modeling_qdqbert.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair
(see `input_ids` docstring). Indices should be in `[0, 1]`:
- 0 indicates sequence B is a continuation ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/qdqbert/modeling_qdqbert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/qdqbert/modeling_qdqbert.py | Apache-2.0 |
def from_pretrained_question_encoder_generator(
cls,
question_encoder_pretrained_model_name_or_path: str = None,
generator_pretrained_model_name_or_path: str = None,
retriever: RagRetriever = None,
**kwargs
) -> PreTrainedModel:
r"""
Instantiates an question e... |
Instantiates an question encoder and a generator from one or two base classes of the library from pretrained
model checkpoints.
The model is set in evaluation mode by default using `model.eval()` (Dropout modules are deactivated). To train
the model, you need to first set it back in tr... | from_pretrained_question_encoder_generator | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.... |
Returns:
Example:
```python
>>> from transformers import RagTokenizer, RagRetriever, RagModel
>>> import torch
>>> tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
>>> retriever = RagRetriever.from_pretrained(
... "facebook/rag-t... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.BoolT... |
exclude_bos_score (`bool`, *optional*):
Only relevant if `labels` is passed. If `True`, the score of the BOS token is disregarded when computing
the loss.
reduce_loss (`bool`, *optional*):
Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | Apache-2.0 |
def generate(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.LongTensor] = None,
context_input_ids: Optional[torch.LongTensor] = None,
context_attention_mask: Optional[torch.LongTensor] = None,
doc_scores: Optional[torch.FloatTensor] = ... |
Implements RAG sequence "thorough" decoding. Read the [`~generation_utils.GenerationMixin.generate`]`
documentation for more information on how to set other generate input parameters.
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
... | generate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | Apache-2.0 |
def _reorder_cache(past, beam_idx):
"""Reorders cache for generation. BART-inspired but we need to take care of the extra dimension for docs"""
def _reorder_stacked(hidden_states, new_order):
n_docs = hidden_states.shape[0] // new_order.shape[0]
hidden_states = hidden_states.vie... | Reorders cache for generation. BART-inspired but we need to take care of the extra dimension for docs | _reorder_cache | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
decoder_input_ids: Optional[torch.LongTensor] = None,
decoder_attention_mask: Optional[torch.... |
do_marginalize (`bool`, *optional*):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
reduce_loss (`bool`, *optional*):
Only relevant if `labels` is passed. If `True`, the NLL loss is reduced using the `tor... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | Apache-2.0 |
def shift_tokens_right(self, input_ids, start_token_id=None):
"""Shift input ids one token to the right, and pad with start_token_id"""
if start_token_id is None:
start_token_id = self.config.decoder_start_token_id
shifted_input_ids = input_ids.new_zeros(input_ids.shape)
shif... | Shift input ids one token to the right, and pad with start_token_id | shift_tokens_right | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_rag.py | Apache-2.0 |
def from_pretrained_question_encoder_generator(
cls,
question_encoder_pretrained_model_name_or_path: str = None,
generator_pretrained_model_name_or_path: str = None,
retriever: RagRetriever = None,
*model_args,
**kwargs
) -> TFPreTrainedModel:
r"""
Ins... |
Instantiates an question encoder and a generator from one or two base classes of the library from pretrained
model checkpoints.
Params:
question_encoder_pretrained_model_name_or_path (`str`, *optional*):
Information necessary to initiate the question encoder. Can be... | from_pretrained_question_encoder_generator | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
encoder_outputs: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_input_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
decoder_attention... |
Returns:
Example:
```python
>>> from transformers import RagTokenizer, RagRetriever, TFRagModel
>>> import torch
>>> tokenizer = RagTokenizer.from_pretrained("facebook/rag-token-base")
>>> retriever = RagRetriever.from_pretrained(
... "facebook/rag... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.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,
encoder_ou... |
do_marginalize (`bool`, *optional*):
If `True`, the logits are marginalized over all documents by making use of
`torch.nn.functional.log_softmax`.
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cros... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | Apache-2.0 |
def generate(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[tf.Tensor] = None,
context_input_ids=None,
context_attention_mask=None,
doc_scores=None,
max_length=None,
min_length=None,
early_stopping=None,
use_c... |
Implements TFRAG token decoding.
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
The sequence used as a prompt for the generation. If `input_ids` is not passed, then
`context_input_ids` has to be provided.
atte... | generate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | Apache-2.0 |
def extend_enc_output(tensor, num_beams=None):
"""
Broadcast tensor with `num_beams` replica, with correct order Input: tensor of shape (batch_size*n_docs ,
d) Output: tensor of shape (batch_size*num_beams*n_docs , d)
"""
# expand batch_size & num_beam dimens... |
Broadcast tensor with `num_beams` replica, with correct order Input: tensor of shape (batch_size*n_docs ,
d) Output: tensor of shape (batch_size*num_beams*n_docs , d)
| extend_enc_output | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.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,
encoder_ou... |
exclude_bos_score (`bool`, *optional*):
Only relevant if `labels` is passed. If `True`, the score of the BOS token is disregarded when computing
the loss.
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing ... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | Apache-2.0 |
def generate(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[tf.Tensor] = None,
context_input_ids=None,
context_attention_mask=None,
doc_scores=None,
do_deduplication=None, # defaults to True
num_return_sequences=None, # def... |
Implements RAG sequence "thorough" decoding. Read the [`~generation_utils.GenerationMixin.generate`]`
documentation for more information on how to set other generate input parameters
Args:
input_ids (`tf.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
... | generate | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/modeling_tf_rag.py | Apache-2.0 |
def postprocess_docs(self, docs, input_strings, prefix, n_docs, return_tensors=None):
r"""
Postprocessing retrieved `docs` and combining them with `input_strings`.
Args:
docs (`dict`):
Retrieved documents.
input_strings (`str`):
Input str... |
Postprocessing retrieved `docs` and combining them with `input_strings`.
Args:
docs (`dict`):
Retrieved documents.
input_strings (`str`):
Input strings decoded by `preprocess_query`.
prefix (`str`):
Prefix added at th... | postprocess_docs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/retrieval_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/retrieval_rag.py | Apache-2.0 |
def retrieve(self, question_hidden_states: np.ndarray, n_docs: int) -> Tuple[np.ndarray, List[dict]]:
"""
Retrieves documents for specified `question_hidden_states`.
Args:
question_hidden_states (`np.ndarray` of shape `(batch_size, vector_size)`):
A batch of query ve... |
Retrieves documents for specified `question_hidden_states`.
Args:
question_hidden_states (`np.ndarray` of shape `(batch_size, vector_size)`):
A batch of query vectors to retrieve with.
n_docs (`int`):
The number of docs retrieved per query.
... | retrieve | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/retrieval_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/retrieval_rag.py | Apache-2.0 |
def __call__(
self,
question_input_ids: List[List[int]],
question_hidden_states: np.ndarray,
prefix=None,
n_docs=None,
return_tensors=None,
) -> BatchEncoding:
"""
Retrieves documents for specified `question_hidden_states`.
Args:
q... |
Retrieves documents for specified `question_hidden_states`.
Args:
question_input_ids: (`List[List[int]]`) batch of input ids
question_hidden_states (`np.ndarray` of shape `(batch_size, vector_size)`:
A batch of query vectors to retrieve with.
prefix:... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rag/retrieval_rag.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rag/retrieval_rag.py | Apache-2.0 |
def span_candidates(masks):
"""
Generate span candidates.
Args:
masks: <bool> [num_retrievals, max_sequence_len]
Returns:
starts: <int32> [num_spans] ends: <int32> [num_spans] span_masks: <int32> [num_retrievals, num_spans]
... |
Generate span candidates.
Args:
masks: <bool> [num_retrievals, max_sequence_len]
Returns:
starts: <int32> [num_spans] ends: <int32> [num_spans] span_masks: <int32> [num_retrievals, num_spans]
whether spans locate in evidence block.
... | span_candidates | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Returns:
... |
Returns:
Example:
```python
>>> from transformers import RealmTokenizer, RealmEmbedder
>>> import torch
>>> tokenizer = RealmTokenizer.from_pretrained("google/realm-cc-news-pretrained-embedder")
>>> model = RealmEmbedder.from_pretrained("google/realm-cc-news-p... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
candidate_input_ids=None,
candidate_attention_mask=None,
candidate_token_type_ids=None,
candidate_inputs_embeds=None,
head_mask=None,
in... |
candidate_input_ids (`torch.LongTensor` of shape `(batch_size, num_candidates, sequence_length)`):
Indices of candidate input sequence tokens in the vocabulary.
Indices can be obtained using [`RealmTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
relevance_score=None,
labels=None,
mlm_mask=None,
output_attentions=None,
output_hidden_stat... |
relevance_score (`torch.FloatTensor` of shape `(batch_size, num_candidates)`, *optional*):
Relevance score derived from RealmScorer, must be specified if you want to compute the masked language
modeling loss.
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | Apache-2.0 |
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
relevance_score=None,
block_mask=None,
start_positions=None,
end_positions=None,
has_answers... |
relevance_score (`torch.FloatTensor` of shape `(searcher_beam_size,)`, *optional*):
Relevance score, which must be specified if you want to compute the logits and marginal log loss.
block_mask (`torch.BoolTensor` of shape `(searcher_beam_size, sequence_length)`, *optional*):
The... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | Apache-2.0 |
def marginal_log_loss(logits, is_correct):
"""Loss based on the negative marginal log-likelihood."""
def mask_to_score(mask, dtype=torch.float32):
return (1.0 - mask.type(dtype)) * torch.finfo(dtype).min
# []
log_numerator = torch.log... | Loss based on the negative marginal log-likelihood. | marginal_log_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | Apache-2.0 |
def forward(
self,
input_ids,
attention_mask=None,
token_type_ids=None,
answer_ids=None,
return_dict=None,
):
r"""
Returns:
Example:
```python
>>> import torch
>>> from transformers import RealmForOpenQA, RealmRetrieve... |
Returns:
Example:
```python
>>> import torch
>>> from transformers import RealmForOpenQA, RealmRetriever, RealmTokenizer
>>> retriever = RealmRetriever.from_pretrained("google/realm-orqa-nq-openqa")
>>> tokenizer = RealmTokenizer.from_pretrained("google/realm-... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/modeling_realm.py | Apache-2.0 |
def batch_encode_candidates(self, text, **kwargs):
r"""
Encode a batch of text or text pair. This method is similar to regular __call__ method but has the following
differences:
1. Handle additional num_candidate axis. (batch_size, num_candidates, text)
2. Always pad the... |
Encode a batch of text or text pair. This method is similar to regular __call__ method but has the following
differences:
1. Handle additional num_candidate axis. (batch_size, num_candidates, text)
2. Always pad the sequences to *max_length*.
3. Must specify *max_le... | batch_encode_candidates | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm.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 REALM sequence has ... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A REALM sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
t... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A REALM 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 REALM 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 `None... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm.py | Apache-2.0 |
def batch_encode_candidates(self, text, **kwargs):
r"""
Encode a batch of text or text pair. This method is similar to regular __call__ method but has the following
differences:
1. Handle additional num_candidate axis. (batch_size, num_candidates, text)
2. Always pad the... |
Encode a batch of text or text pair. This method is similar to regular __call__ method but has the following
differences:
1. Handle additional num_candidate axis. (batch_size, num_candidates, text)
2. Always pad the sequences to *max_length*.
3. Must specify *max_le... | batch_encode_candidates | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/realm/tokenization_realm_fast.py | Apache-2.0 |
def _look_adjacent(self, vectors, num_chunks_before, num_chunks_after):
"""
Used to implement attention between consecutive chunks.
Args:
vectors: array of shape [batch_size, num_attention_heads, n_chunks, chunk_len, ...]
num_chunks_before: chunks before current chunk to... |
Used to implement attention between consecutive chunks.
Args:
vectors: array of shape [batch_size, num_attention_heads, n_chunks, chunk_len, ...]
num_chunks_before: chunks before current chunk to include in attention
num_chunks_after: chunks after current chunk to i... | _look_adjacent | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def _split_seq_length_dim_to(self, vectors, dim_factor_1, dim_factor_2, num_attn_heads, attn_head_size=None):
"""
splits sequence length dim of vectors into `dim_factor_1` and `dim_factor_2` dims
"""
batch_size = vectors.shape[0]
split_dim_shape = (batch_size, num_attn_heads, dim... |
splits sequence length dim of vectors into `dim_factor_1` and `dim_factor_2` dims
| _split_seq_length_dim_to | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def _len_and_dim_norm(self, vectors):
"""
length and attention head size dim normalization
"""
vectors = self._len_norm(vectors)
vectors = vectors * torch.rsqrt(
torch.tensor(self.attention_head_size, device=vectors.device, dtype=vectors.dtype)
)
retur... |
length and attention head size dim normalization
| _len_and_dim_norm | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def _gather_by_expansion(self, vectors, idxs, num_hashes):
"""
expand dims of idxs and vectors for all hashes and gather
"""
expanded_idxs = idxs.unsqueeze(-1).expand(-1, -1, -1, self.attention_head_size)
vectors = vectors.repeat(1, 1, num_hashes, 1)
return torch.gather(v... |
expand dims of idxs and vectors for all hashes and gather
| _gather_by_expansion | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def _init_attention_seed(self):
"""
This function sets a new seed for the attention layer to make dropout deterministic for both forward calls: 1
normal forward call and 1 forward call in backward to recalculate activations.
"""
# randomize seeds
# use cuda generator if ... |
This function sets a new seed for the attention layer to make dropout deterministic for both forward calls: 1
normal forward call and 1 forward call in backward to recalculate activations.
| _init_attention_seed | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def _init_feed_forward_seed(self):
"""
This function sets a new seed for the feed forward layer to make dropout deterministic for both forward calls:
1 normal forward call and 1 forward call in backward to recalculate activations.
"""
# randomize seeds
# use cuda generato... |
This function sets a new seed for the feed forward layer to make dropout deterministic for both forward calls:
1 normal forward call and 1 forward call in backward to recalculate activations.
| _init_feed_forward_seed | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
num_hashes: Optional[in... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the sequence classification/regression loss. Indices should be in `[-100, 0, ...,
config.vocab_size - 1]`. All labels set to `-100` are ignored (masked), the loss is only computed for
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
num_hashes: Optional[in... |
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),
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
inputs_embeds: Optional[torch.Tensor] = None,
num_hashes: Optional[in... |
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/reformer/modeling_reformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/reformer/modeling_reformer.py | Apache-2.0 |
def get_from_to_our_keys(model_name: str) -> Dict[str, str]:
"""
Returns a dictionary that maps from original model's key -> our implementation's keys
"""
# create our model (with small weights)
our_config = RegNetConfig(depths=[2, 7, 17, 1], hidden_sizes=[8, 8, 8, 8], groups_width=8)
if "in1k"... |
Returns a dictionary that maps from original model's key -> our implementation's keys
| get_from_to_our_keys | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/regnet/convert_regnet_seer_10b_to_pytorch.py | Apache-2.0 |
def __call__(self, x: Tensor):
"""
Transfer the weights of `self.src` to `self.dest` by performing a forward pass using `x` as input. Under the
hood we tracked all the operations in both modules.
"""
dest_traced = Tracker(self.dest)(x).parametrized
src_traced = Tracker(se... |
Transfer the weights of `self.src` to `self.dest` by performing a forward pass using `x` as input. Under the
hood we tracked all the operations in both modules.
| __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/regnet/convert_regnet_to_pytorch.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
labels: Optional[torch.LongTensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> ImageClassifierOutputWithNoAttention:
r"""
labels (`torch.Long... |
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
| forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/regnet/modeling_regnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/regnet/modeling_regnet.py | Apache-2.0 |
def call(
self,
pixel_values: tf.Tensor = None,
labels: tf.Tensor = None,
output_hidden_states: bool = None,
return_dict: bool = None,
training=False,
) -> Union[TFSequenceClassifierOutput, Tuple[tf.Tensor]]:
r"""
labels (`tf.Tensor` of shape `(batch_s... |
labels (`tf.Tensor` of shape `(batch_size,)`, *optional*):
Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
| call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/regnet/modeling_tf_regnet.py | Apache-2.0 |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.LongTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
inputs_embe... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rembert/modeling_rembert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rembert/modeling_rembert.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 REMBERT sequence ha... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A REMBERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RemBERT
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 RemBERT
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 `No... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert.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 RemBERT sequence ha... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RemBERT sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py | Apache-2.0 |
def get_special_tokens_mask(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
) -> List[int]:
"""
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens... |
Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer `prepare_for_model` method.
Args:
token_ids_0 (`List[int]`):
List of ids.
token_ids_1 (`List[int]`, *optional*... | get_special_tokens_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. A RemBERT
sequence pair mask has the following format:
... |
Creates a mask from the two sequences passed to be used in a sequence-pair classification task. A RemBERT
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 None... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/rembert/tokenization_rembert_fast.py | Apache-2.0 |
def forward(
self,
input_ids_query: torch.LongTensor,
attention_mask_query: Optional[torch.FloatTensor],
input_ids_doc: torch.LongTensor,
attention_mask_doc: Optional[torch.FloatTensor],
checkpoint_batch_size: int = -1,
) -> torch.FloatTensor:
r"""
Arg... |
Args:
input_ids_query (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary for the queries in a batch.
Indices can be obtained using [`RetriBertTokenizer`]. See [`PreTrainedTokenizer.encode`] and
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/retribert/modeling_retribert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/retribert/modeling_retribert.py | Apache-2.0 |
def create_position_ids_from_input_ids(input_ids, padding_idx):
"""
Replace non-padding symbols with their position numbers. Position numbers begin at padding_idx+1. Padding symbols
are ignored. This is modified from fairseq's `utils.make_positions`.
Args:
input_ids: jnp.ndarray
padding... |
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`.
Args:
input_ids: jnp.ndarray
padding_idx: int
Returns: jnp.ndarray
| create_position_ids_from_input_ids | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/roberta/modeling_flax_roberta.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTensor] = None,
head_mask: Optional[torch.FloatTensor] = None,
... |
encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
the model is configured as a decoder.
encoder_attention_mask (... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/roberta/modeling_roberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/roberta/modeling_roberta.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.
Roberta tokenizer has a special mask token to be usable in the fill-mask pipeline. The mask token will greedily
compris... |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
Roberta 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/roberta/tokenization_roberta_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/roberta/tokenization_roberta_fast.py | Apache-2.0 |
def call(
self,
input_ids: tf.Tensor = None,
token_type_ids: tf.Tensor = None,
inputs_embeds: tf.Tensor = None,
training: bool = False,
) -> tf.Tensor:
"""
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`):... |
Applies embedding based on inputs tensor.
Returns:
final_embeddings (`tf.Tensor`): output embedding tensor.
| call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
inputs_embeds: Optional[Un... |
labels (`tf.Tensor` or `np.ndarray` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the cross entropy classification loss. Indices should be in `[0, ...,
config.vocab_size - 1]`.
| call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/roformer/modeling_tf_roformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/roformer/modeling_tf_roformer.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 RoFormer sequence h... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A RoFormer sequence has the following format:
- single sequence: `[CLS] X [SEP]`
- pair of sequences: `[CLS] A [SEP] B [SEP]`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/roformer/tokenization_roformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/roformer/tokenization_roformer.py | Apache-2.0 |
def create_token_type_ids_from_sequences(
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
) -> List[int]:
"""
Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
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 RoFormer
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/roformer/tokenization_roformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/roformer/tokenization_roformer.py | Apache-2.0 |
def convert_segformer_checkpoint(model_name, checkpoint_path, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our SegFormer structure.
"""
# load default SegFormer configuration
config = SegformerConfig()
encoder_only = False
# set attributes based on model_name
repo... |
Copy/paste/tweak model's weights to our SegFormer structure.
| convert_segformer_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/segformer/convert_segformer_original_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/segformer/convert_segformer_original_to_pytorch.py | Apache-2.0 |
def __call__(
self,
images: ImageInput,
segmentation_maps: ImageInput = None,
return_tensors: Optional[Union[str, TensorType]] = None,
**kwargs
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s) and optional corresponding segme... |
Main method to prepare for the model one or several image(s) and optional corresponding segmentation maps.
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/segformer/feature_extraction_segformer.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/segformer/feature_extraction_segformer.py | Apache-2.0 |
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