code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
causal_attention_mask: torch.Tensor,
output_attentions: Optional[bool] = False,
) -> Tuple[torch.FloatTensor]:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | Apache-2.0 |
def forward(
self,
inputs_embeds,
attention_mask: Optional[torch.Tensor] = None,
causal_attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) ... |
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bo... |
Returns:
Examples:
```python
>>> from transformers import CLIPTokenizer, CLIPTextModel
>>> model = CLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tokeniz... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> Union[Tuple, BaseModelOutputWithPooling]:
r"""
Returns:
... |
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, CLIPVisionModel
>>> model = CLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = CLIPProcessor.from_pretra... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | Apache-2.0 |
def get_text_features(
self,
input_ids: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: O... |
Returns:
text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by
applying the projection layer to the pooled output of [`CLIPTextModel`].
Examples:
```python
>>> from transformers import CLIPTokenizer, CLIPModel
... | get_text_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | Apache-2.0 |
def get_image_features(
self,
pixel_values: Optional[torch.FloatTensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) -> torch.FloatTensor:
r"""
Returns:
image_f... |
Returns:
image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by
applying the projection layer to the pooled output of [`CLIPVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import reques... | get_image_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
pixel_values: Optional[torch.FloatTensor] = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
return_loss: Optional[bool] = None,
output_attentions... |
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, CLIPModel
>>> model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = CLIPProcessor.from_pretrained("openai... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_clip.py | Apache-2.0 |
def get_text_features(
self,
input_ids,
attention_mask=None,
position_ids=None,
params: dict = None,
dropout_rng: jax.random.PRNGKey = None,
train=False,
):
r"""
Args:
input_ids (`numpy.ndarray` of shape `(batch_size, sequence_lengt... |
Args:
input_ids (`numpy.ndarray` 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 [`CLIPTokenizer`]. See [`PreTraine... | get_text_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_flax_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_flax_clip.py | Apache-2.0 |
def get_image_features(
self, pixel_values, params: dict = None, dropout_rng: jax.random.PRNGKey = None, train=False
):
r"""
Args:
pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default... |
Args:
pixel_values (`numpy.ndarray` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained
using [`CLIPFeatureExtractor`]. See [`CLIPFeatureExtractor.__call__`] for d... | get_image_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_flax_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_flax_clip.py | Apache-2.0 |
def call(self, pixel_values: tf.Tensor) -> tf.Tensor:
"""`pixel_values` is expected to be of NCHW format."""
batch_size, num_channels, height, width = shape_list(pixel_values)
# When running on CPU, `tf.nn.conv2d` doesn't support `NCHW` format.
# So change the input format from `NCHW` ... | `pixel_values` is expected to be of NCHW format. | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | Apache-2.0 |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
causal_attention_mask: tf.Tensor,
output_attentions: bool,
training: bool = False,
) -> Tuple[tf.Tensor]:
"""
Args:
hidden_states (`tf.Tensor`): input to the layer of shap... |
Args:
hidden_states (`tf.Tensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
attention_mask (`tf.Tensor`): attention mask of size
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
causal_attenti... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,... |
Returns:
Examples:
```python
>>> from transformers import CLIPTokenizer, TFCLIPTextModel
>>> model = TFCLIPTextModel.from_pretrained("openai/clip-vit-base-patch32")
>>> tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32")
>>> inputs = tok... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[TFModelInputType] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple... |
Returns:
Examples:
```python
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, TFCLIPVisionModel
>>> model = TFCLIPVisionModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor = CLIPProcessor.from_pr... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | Apache-2.0 |
def get_text_features(
self,
input_ids: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[... |
Returns:
text_features (`tf.Tensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying
the projection layer to the pooled output of [`TFCLIPTextModel`].
Examples:
```python
>>> from transformers import CLIPTokenizer, TFCLIPModel
... | get_text_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | Apache-2.0 |
def get_image_features(
self,
pixel_values: Optional[TFModelInputType] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> tf.Tensor:
r"""
Retur... |
Returns:
image_features (`tf.Tensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying
the projection layer to the pooled output of [`TFCLIPVisionModel`].
Examples:
```python
>>> from PIL import Image
>>> import requests
... | get_image_features | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
pixel_values: Optional[TFModelInputType] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
position_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
return_loss: Optional[bool] = None,
... |
Returns:
Examples:
```python
>>> import tensorflow as tf
>>> from PIL import Image
>>> import requests
>>> from transformers import CLIPProcessor, TFCLIPModel
>>> model = TFCLIPModel.from_pretrained("openai/clip-vit-base-patch32")
>>> processor... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/modeling_tf_clip.py | Apache-2.0 |
def __call__(self, text=None, images=None, return_tensors=None, **kwargs):
"""
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to e... |
Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
and `kwargs` arguments to CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode
the text. To prepare the image(s), this method forwards the `images` and... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/processing_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/processing_clip.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 CLIP sequence has t... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CLIP sequence has the following format:
- single sequence: `<|startoftext|> X <|endoftext|>`
Pairs of sequences are not the expected use case, but t... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/tokenization_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/tokenization_clip.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. CLIP does not make use of token type ids, therefore a list of
zeros is returned.
Args:
... |
Create a mask from the two sequences passed. CLIP 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]`, *optional*):
Optional second list of IDs... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/clip/tokenization_clip.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/clip/tokenization_clip.py | Apache-2.0 |
def duplicate_interleave(m):
"""
A simple version of `torch.repeat_interleave` for duplicating a matrix while interleaving the copy.
"""
dim0 = m.shape[0]
m = m.view(-1, 1) # flatten the matrix
m = m.repeat(1, 2) # repeat all elements into the 2nd dimension
m = m.view(dim0, -1) # reshape ... |
A simple version of `torch.repeat_interleave` for duplicating a matrix while interleaving the copy.
| duplicate_interleave | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/codegen/modeling_codegen.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/codegen/modeling_codegen.py | Apache-2.0 |
def _reorder_cache(past: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor) -> Tuple[Tuple[torch.Tensor]]:
"""
This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
[`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_val... |
This function is used to re-order the `past_key_values` cache if [`~PretrainedModel.beam_search`] or
[`~PretrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
beam_idx at every generation step.
| _reorder_cache | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/codegen/modeling_codegen.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/codegen/modeling_codegen.py | Apache-2.0 |
def decode(
self,
token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
skip_special_tokens: bool = False,
clean_up_tokenization_spaces: bool = True,
truncate_before_pattern: Optional[List[str]] = None,
**kwargs
) -> str:
"""
Con... |
Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
tokens and clean up tokenization spaces.
Similar to doing `self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))`.
Args:
token_ids (`Union[int, List[int... | decode | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/codegen/tokenization_codegen.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/codegen/tokenization_codegen.py | Apache-2.0 |
def convert_conditional_detr_checkpoint(model_name, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our CONDITIONAL_DETR structure.
"""
# load default config
config = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
conf... |
Copy/paste/tweak model's weights to our CONDITIONAL_DETR structure.
| convert_conditional_detr_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/convert_conditional_detr_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def center_to_corners_format(x):
"""
Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
(x_0, y_0, x_1, y_1).
"""
center_x, center_y, width, height = x.unbind(-1)
b = [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x +... |
Converts a PyTorch tensor of bounding boxes of center format (center_x, center_y, width, height) to corners format
(x_0, y_0, x_1, y_1).
| center_to_corners_format | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def corners_to_center_format(x):
"""
Converts a NumPy array of bounding boxes of shape (number of bounding boxes, 4) of corners format (x_0, y_0, x_1,
y_1) to center format (center_x, center_y, width, height).
"""
x_transposed = x.T
x0, y0, x1, y1 = x_transposed[0], x_transposed[1], x_transposed... |
Converts a NumPy array of bounding boxes of shape (number of bounding boxes, 4) of corners format (x_0, y_0, x_1,
y_1) to center format (center_x, center_y, width, height).
| corners_to_center_format | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def masks_to_boxes(masks):
"""
Compute the bounding boxes around the provided panoptic segmentation masks.
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
"""
if masks.si... |
Compute the bounding boxes around the provided panoptic segmentation masks.
The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
Returns a [N, 4] tensor, with the boxes in corner (xyxy) format.
| masks_to_boxes | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def binary_mask_to_rle(mask):
"""
Args:
Converts given binary mask of shape (height, width) to the run-length encoding (RLE) format.
mask (`torch.Tensor` or `numpy.array`):
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
seg... |
Args:
Converts given binary mask of shape (height, width) to the run-length encoding (RLE) format.
mask (`torch.Tensor` or `numpy.array`):
A binary mask tensor of shape `(height, width)` where 0 denotes background and 1 denotes the target
segment_id or class_id.
Returns:
... | binary_mask_to_rle | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def convert_segmentation_to_rle(segmentation):
"""
Converts given segmentation map of shape (height, width) to the run-length encoding (RLE) format.
Args:
segmentation (`torch.Tensor` or `numpy.array`):
A segmentation map of shape `(height, width)` where each value denotes a segment or ... |
Converts given segmentation map of shape (height, width) to the run-length encoding (RLE) format.
Args:
segmentation (`torch.Tensor` or `numpy.array`):
A segmentation map of shape `(height, width)` where each value denotes a segment or class id.
Returns:
`List[List]`: A list of... | convert_segmentation_to_rle | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def remove_low_and_no_objects(masks, scores, labels, object_mask_threshold, num_labels):
"""
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
`labels`.
Args:
masks (`torch.Tensor`):
A tensor of shape `(num_queries, hei... |
Binarize the given masks using `object_mask_threshold`, it returns the associated values of `masks`, `scores` and
`labels`.
Args:
masks (`torch.Tensor`):
A tensor of shape `(num_queries, height, width)`.
scores (`torch.Tensor`):
A tensor of shape `(num_queries)`.
... | remove_low_and_no_objects | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def prepare_coco_detection(self, image, target, return_segmentation_masks=False):
"""
Convert the target in COCO format into the format expected by ConditionalDETR.
"""
w, h = image.size
image_id = target["image_id"]
image_id = np.asarray([image_id], dtype=np.int64)
... |
Convert the target in COCO format into the format expected by ConditionalDETR.
| prepare_coco_detection | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def _resize(self, image, size, target=None, max_size=None):
"""
Resize the image to the given size. Size can be min_size (scalar) or (w, h) tuple. If size is an int, smaller
edge of the image will be matched to this number.
If given, also resize the target accordingly.
"""
... |
Resize the image to the given size. Size can be min_size (scalar) or (w, h) tuple. If size is an int, smaller
edge of the image will be matched to this number.
If given, also resize the target accordingly.
| _resize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def _normalize(self, image, mean, std, target=None):
"""
Normalize the image with a certain mean and std.
If given, also normalize the target bounding boxes based on the size of the image.
"""
image = self.normalize(image, mean=mean, std=std)
if target is None:
... |
Normalize the image with a certain mean and std.
If given, also normalize the target bounding boxes based on the size of the image.
| _normalize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def __call__(
self,
images: ImageInput,
annotations: Union[List[Dict], List[List[Dict]]] = None,
return_segmentation_masks: Optional[bool] = False,
masks_path: Optional[pathlib.Path] = None,
pad_and_return_pixel_mask: Optional[bool] = True,
return_tensors: Optiona... |
Main method to prepare for the model one or several image(s) and optional annotations. Images are by default
padded up to the largest image in a batch, and a pixel mask is created that indicates which pixels are
real/which are padding.
<Tip warning={true}>
NumPy arrays and PyT... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def pad_and_create_pixel_mask(
self, pixel_values_list: List["torch.Tensor"], return_tensors: Optional[Union[str, TensorType]] = None
):
"""
Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
Args:
pixel_values_list (`List[torch.Tensor... |
Pad images up to the largest image in a batch and create a corresponding `pixel_mask`.
Args:
pixel_values_list (`List[torch.Tensor]`):
List of images (pixel values) to be padded. Each image should be a tensor of shape (C, H, W).
return_tensors (`str` or [`~utils... | pad_and_create_pixel_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def post_process(self, outputs, target_sizes):
"""
Args:
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
outputs ([`ConditionalDetrObjectDetectionOutput`]):
Raw outputs of the model.... |
Args:
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
outputs ([`ConditionalDetrObjectDetectionOutput`]):
Raw outputs of the model.
target_sizes (`torch.Tensor` of shape `(batch... | post_process | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def post_process_object_detection(
self, outputs, threshold: float = 0.5, target_sizes: Union[TensorType, List[Tuple]] = None
):
"""
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
Args:
... |
Converts the output of [`ConditionalDetrForObjectDetection`] into the format expected by the COCO api. Only
supports PyTorch.
Args:
outputs ([`DetrObjectDetectionOutput`]):
Raw outputs of the model.
threshold (`float`, *optional*):
Score ... | post_process_object_detection | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def post_process_semantic_segmentation(self, outputs, target_sizes: List[Tuple[int, int]] = None):
"""
Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
... |
Converts the output of [`ConditionalDetrForSegmentation`] into semantic segmentation maps. Only supports
PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
target_sizes (`List[Tuple[int, int]]`, *optional*, defaults to... | post_process_semantic_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def post_process_instance_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
target_sizes: Optional[List[Tuple[int, int]]] = None,
return_coco_annotation: Optional[bool] = False,
) -> Lis... |
Converts the output of [`ConditionalDetrForSegmentation`] into instance segmentation predictions. Only supports
PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
Raw outputs of the model.
threshold (`float`, *optional*, defaults to 0.5):
... | post_process_instance_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def post_process_panoptic_segmentation(
self,
outputs,
threshold: float = 0.5,
mask_threshold: float = 0.5,
overlap_mask_area_threshold: float = 0.8,
label_ids_to_fuse: Optional[Set[int]] = None,
target_sizes: Optional[List[Tuple[int, int]]] = None,
) -> List[... |
Converts the output of [`ConditionalDetrForSegmentation`] into image panoptic segmentation predictions. Only
supports PyTorch.
Args:
outputs ([`ConditionalDetrForSegmentation`]):
The outputs from [`ConditionalDetrForSegmentation`].
threshold (`float`, *o... | post_process_panoptic_segmentation | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/feature_extraction_conditional_detr.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
position_embeddings: torch.Tensor = None,
output_attentions: bool = False,
):
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch,... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_embeddings: Optional[torch.Tensor] = None,
query_position_embeddings: Optional[torch.Tensor] = None,
query_sine_embed: Optional[torch.Tensor] = None,
encoder_hi... |
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(seq_len, batch, embed_dim)`
attention_mask (`torch.FloatTensor`): attention mask of size
`(batch, 1, target_len, source_len)` where padding elements are indicated by very large negative
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(
self,
inputs_embeds=None,
attention_mask=None,
position_embeddings=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, seque... |
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
Flattened feature map (output of the backbone + projection layer) that is passed to the encoder.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *op... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(
self,
inputs_embeds=None,
attention_mask=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
position_embeddings=None,
query_position_embeddings=None,
output_attentions=None,
output_hidden_states=None,
return_dict=No... |
Args:
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`):
The query embeddings that are passed into the decoder.
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
Mask to avoid pe... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(
self,
pixel_values,
pixel_mask=None,
decoder_attention_mask=None,
encoder_outputs=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
... |
Returns:
Examples:
```python
>>> from transformers import AutoFeatureExtractor, AutoModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=T... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(
self,
pixel_values,
pixel_mask=None,
decoder_attention_mask=None,
encoder_outputs=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
... |
labels (`List[Dict]` of len `(batch_size,)`, *optional*):
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
following 2 keys: 'class_labels' and 'boxes' (the class labels and bounding boxes of an image in the batch
respe... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(
self,
pixel_values,
pixel_mask=None,
decoder_attention_mask=None,
encoder_outputs=None,
inputs_embeds=None,
decoder_inputs_embeds=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
... |
labels (`List[Dict]` of len `(batch_size,)`, *optional*):
Labels for computing the bipartite matching loss, DICE/F-1 loss and Focal loss. List of dicts, each
dictionary containing at least the following 3 keys: 'class_labels', 'boxes' and 'masks' (the class labels,
bounding ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def dice_loss(inputs, targets, num_boxes):
"""
Compute the DICE loss, similar to generalized IOU for masks
Args:
inputs: A float tensor of arbitrary shape.
The predictions for each example.
targets: A float tensor with the same shape as inputs. Stores the binary
... |
Compute the DICE loss, similar to generalized IOU for masks
Args:
inputs: A float tensor of arbitrary shape.
The predictions for each example.
targets: A float tensor with the same shape as inputs. Stores the binary
classification label for each element in inpu... | dice_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def sigmoid_focal_loss(inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2):
"""
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
Args:
inputs (`torch.FloatTensor` of arbitrary shape):
The predictions for each example.
targets (`torch.F... |
Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
Args:
inputs (`torch.FloatTensor` of arbitrary shape):
The predictions for each example.
targets (`torch.FloatTensor` with the same shape as `inputs`)
A tensor storing the binary classificatio... | sigmoid_focal_loss | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def loss_labels(self, outputs, targets, indices, num_boxes):
"""
Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor
of dim [nb_target_boxes]
"""
if "logits" not in outputs:
raise KeyError("No logits were found... |
Classification loss (Binary focal loss) targets dicts must contain the key "class_labels" containing a tensor
of dim [nb_target_boxes]
| loss_labels | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def loss_cardinality(self, outputs, targets, indices, num_boxes):
"""
Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
"""
logit... |
Compute the cardinality error, i.e. the absolute error in the number of predicted non-empty boxes.
This is not really a loss, it is intended for logging purposes only. It doesn't propagate gradients.
| loss_cardinality | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def loss_boxes(self, outputs, targets, indices, num_boxes):
"""
Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.
Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
are expected in format... |
Compute the losses related to the bounding boxes, the L1 regression loss and the GIoU loss.
Targets dicts must contain the key "boxes" containing a tensor of dim [nb_target_boxes, 4]. The target boxes
are expected in format (center_x, center_y, w, h), normalized by the image size.
| loss_boxes | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def loss_masks(self, outputs, targets, indices, num_boxes):
"""
Compute the losses related to the masks: the focal loss and the dice loss.
Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w].
"""
if "pred_masks" not in outputs:
... |
Compute the losses related to the masks: the focal loss and the dice loss.
Targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w].
| loss_masks | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(self, outputs, targets):
"""
This performs the loss computation.
Args:
outputs (`dict`, *optional*):
Dictionary of tensors, see the output specification of the model for the format.
targets (`List[dict]`, *optional*):
List of... |
This performs the loss computation.
Args:
outputs (`dict`, *optional*):
Dictionary of tensors, see the output specification of the model for the format.
targets (`List[dict]`, *optional*):
List of dicts, such that `len(targets) == batch_size`. ... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def forward(self, outputs, targets):
"""
Args:
outputs (`dict`):
A dictionary that contains at least these entries:
* "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
* "pred_boxes": Tensor of dim [b... |
Args:
outputs (`dict`):
A dictionary that contains at least these entries:
* "logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
* "pred_boxes": Tensor of dim [batch_size, num_queries, 4] with the predicted bo... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def box_area(boxes: Tensor) -> Tensor:
"""
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
Args:
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
Boxes for which the area will be computed. They are expected to be in (x1,... |
Computes the area of a set of bounding boxes, which are specified by its (x1, y1, x2, y2) coordinates.
Args:
boxes (`torch.FloatTensor` of shape `(number_of_boxes, 4)`):
Boxes for which the area will be computed. They are expected to be in (x1, y1, x2, y2) format with `0 <= x1
... | box_area | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | Apache-2.0 |
def generalized_box_iou(boxes1, boxes2):
"""
Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
Returns:
`torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
"""
# degenerate boxes gives inf / nan res... |
Generalized IoU from https://giou.stanford.edu/. The boxes should be in [x0, y0, x1, y1] (corner) format.
Returns:
`torch.FloatTensor`: a [N, M] pairwise matrix, where N = len(boxes1) and M = len(boxes2)
| generalized_box_iou | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/conditional_detr/modeling_conditional_detr.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 ConvBERT sequence h... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A ConvBERT 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/convbert/tokenization_convbert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/convbert/tokenization_convbert.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 ConvBERT
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 ConvBERT
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/convbert/tokenization_convbert.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/convbert/tokenization_convbert.py | Apache-2.0 |
def convert_convnext_checkpoint(checkpoint_url, pytorch_dump_folder_path):
"""
Copy/paste/tweak model's weights to our ConvNext structure.
"""
# define ConvNext configuration based on URL
config, expected_shape = get_convnext_config(checkpoint_url)
# load original state_dict from URL
state_... |
Copy/paste/tweak model's weights to our ConvNext structure.
| convert_convnext_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/convnext/convert_convnext_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/convnext/convert_convnext_to_pytorch.py | Apache-2.0 |
def __call__(
self, images: ImageInput, return_tensors: Optional[Union[str, TensorType]] = None, **kwargs
) -> BatchFeature:
"""
Main method to prepare for the model one or several image(s).
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images w... |
Main method to prepare for the model one or several image(s).
<Tip warning={true}>
NumPy arrays and PyTorch tensors are converted to PIL images when resizing, so the most efficient is to pass
PIL images.
</Tip>
Args:
images (`PIL.Image.Image`, `np.ndarray... | __call__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/convnext/feature_extraction_convnext.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/convnext/feature_extraction_convnext.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[TFModelInputType] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: bool = False,
) -> Union[TFBaseModelOutputWithPooling, Tuple[tf.Tensor]]:
r"""
Returns:
Example... |
Returns:
Examples:
```python
>>> from transformers import ConvNextFeatureExtractor, TFConvNextModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[TFModelInputType] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[Union[np.ndarray, tf.Tensor]] = None,
training: Optional[bool] = False,
) -> Union[TFSequenceClassifierOu... |
labels (`tf.Tensor` or `np.ndarray` 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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/convnext/modeling_tf_convnext.py | Apache-2.0 |
def __init__(
self,
vocab_file,
do_lower_case=False,
remove_space=True,
keep_accents=False,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
sep_token="<sep>",
pad_token="<pad>",
cls_token="<cls>",
mask_token="<mask>",
... |
Construct a CPM tokenizer. Based on [Jieba](https://pypi.org/project/jieba/) and
[SentencePiece](https://github.com/google/sentencepiece).
This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should
refer to this superclass for more inform... | __init__ | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cpm/tokenization_cpm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cpm/tokenization_cpm.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 XLNet sequence has... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. An XLNet sequence has the following format:
- single sequence: `X <sep> <cls>`
- pair of sequences: `A <sep> B <sep> <cls>`
Args:
... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cpm/tokenization_cpm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cpm/tokenization_cpm.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. An XLNet
sequence pair mask has the following format:
... |
Create a mask from the two sequences passed to be used in a sequence-pair classification task. An XLNet
sequence pair mask has the following format:
```
0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
| first sequence | second sequence |
```
If `token_ids_1` is `Non... | create_token_type_ids_from_sequences | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cpm/tokenization_cpm.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cpm/tokenization_cpm.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTens... |
Returns:
Example:
```python
>>> from transformers import CTRLTokenizer, CTRLModel
>>> import torch
>>> tokenizer = CTRLTokenizer.from_pretrained("ctrl")
>>> model = CTRLModel.from_pretrained("ctrl")
>>> # CTRL was trained with control codes as the fir... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/ctrl/modeling_ctrl.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/ctrl/modeling_ctrl.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTens... |
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set
`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-10... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/ctrl/modeling_ctrl.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/ctrl/modeling_ctrl.py | Apache-2.0 |
def forward(
self,
input_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
attention_mask: Optional[torch.FloatTensor] = None,
token_type_ids: Optional[torch.LongTensor] = None,
position_ids: Optional[torch.LongTens... |
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/ctrl/modeling_ctrl.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/ctrl/modeling_ctrl.py | Apache-2.0 |
def call(
self,
input_ids: Optional[TFModelInputType] = None,
past_key_values: Optional[Tuple[Tuple[Union[np.ndarray, tf.Tensor]]]] = None,
attention_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
token_type_ids: Optional[Union[np.ndarray, tf.Tensor]] = None,
positi... |
labels (`tf.Tensor` 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/ctrl/modeling_tf_ctrl.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/ctrl/modeling_tf_ctrl.py | Apache-2.0 |
def embeddings(idx):
"""
The function helps in renaming embedding layer weights.
Args:
idx: stage number in original model
"""
embed = []
embed.append(
(
f"cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight",
f"stage{idx}.patch_em... |
The function helps in renaming embedding layer weights.
Args:
idx: stage number in original model
| embeddings | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def attention(idx, cnt):
"""
The function helps in renaming attention block layers weights.
Args:
idx: stage number in original model
cnt: count of blocks in each stage
"""
attention_weights = []
attention_weights.append(
(
f"cvt.encoder.stages.{idx}.layers.{... |
The function helps in renaming attention block layers weights.
Args:
idx: stage number in original model
cnt: count of blocks in each stage
| attention | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def final():
"""
Function helps in renaming final classification layer
"""
head = []
head.append(("layernorm.weight", "norm.weight"))
head.append(("layernorm.bias", "norm.bias"))
head.append(("classifier.weight", "head.weight"))
head.append(("classifier.bias", "head.bias"))
return he... |
Function helps in renaming final classification layer
| final | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def convert_cvt_checkpoint(cvt_model, image_size, cvt_file_name, pytorch_dump_folder):
"""
Fucntion to convert the microsoft cvt checkpoint to huggingface checkpoint
"""
img_labels_file = "imagenet-1k-id2label.json"
num_labels = 1000
repo_id = "huggingface/label-files"
num_labels = num_labe... |
Fucntion to convert the microsoft cvt checkpoint to huggingface checkpoint
| convert_cvt_checkpoint | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[tf.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFBaseModelOutputWithCLSToken, Tuple[tf.Tensor]]:
r"""
Returns:
Exa... |
Returns:
Examples:
```python
>>> from transformers import AutoFeatureExtractor, TFCvtModel
>>> from PIL import Image
>>> import requests
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cvt/modeling_tf_cvt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cvt/modeling_tf_cvt.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[tf.Tensor] = None,
labels: Optional[tf.Tensor] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
training: Optional[bool] = False,
) -> Union[TFImageClassifierOutputWithNoAttention, Tuple[tf... |
labels (`tf.Tensor` or `np.ndarray` 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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/cvt/modeling_tf_cvt.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/cvt/modeling_tf_cvt.py | Apache-2.0 |
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be r... |
Calling this function will disable the gradient computation for the feature encoder so that its parameter will
not be updated during training.
| freeze_feature_extractor | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | Apache-2.0 |
def forward(
self,
input_values: Optional[torch.Tensor],
attention_mask: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[torch.Tensor] = None... |
labels (`torch.LongTensor` of shape `(batch_size, target_length)`, *optional*):
Labels for connectionist temporal classification. Note that `target_length` has to be smaller or equal to
the sequence length of the output logits. Indices are selected in `[-100, 0, ..., config.vocab_size -... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | Apache-2.0 |
def freeze_feature_extractor(self):
"""
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
not be updated during training.
"""
warnings.warn(
"The method `freeze_feature_extractor` is deprecated and will be ... |
Calling this function will disable the gradient computation for the feature encoder so that its parameters will
not be updated during training.
| freeze_feature_extractor | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | Apache-2.0 |
def freeze_base_model(self):
"""
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
"""
for param in self.data2vec_audio.parameters():
... |
Calling this function will disable the gradient computation for the base model so that its parameters will not
be updated during training. Only the classification head will be updated.
| freeze_base_model | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | Apache-2.0 |
def _get_tdnn_output_lengths(self, input_lengths: Union[torch.LongTensor, int]):
"""
Computes the output length of the TDNN layers
"""
def _conv_out_length(input_length, kernel_size, stride):
# 1D convolutional layer output length formula taken
# from https://pyt... |
Computes the output length of the TDNN layers
| _get_tdnn_output_lengths | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_audio.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/data2vec/modeling_data2vec_text.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_text.py | Apache-2.0 |
def forward(
self,
pixel_values: Optional[torch.Tensor] = None,
head_mask: Optional[torch.Tensor] = None,
labels: Optional[torch.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = No... |
labels (`torch.LongTensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_vision.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_data2vec_vision.py | Apache-2.0 |
def dummy_inputs(self) -> Dict[str, tf.Tensor]:
"""
Dummy inputs to build the network. Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
"""
VISION_DUMMY_INPUTS = tf.random.uniform(
shape=(3, self.config.num_channels, self.config.image_size, self.config.image_siz... |
Dummy inputs to build the network. Returns:
`Dict[str, tf.Tensor]`: The dummy inputs.
| dummy_inputs | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[TFModelInputType] = None,
head_mask: Optional[Union[np.ndarray, tf.Tensor]] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
labels: Optional[U... |
labels (`tf.Tensor` or `np.ndarray` 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
... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | Apache-2.0 |
def get_kernels(ind, outd) -> List:
"""Returns a List [(kernel_offset_start,kernel_length)] defining all the pooling kernels for a 1-D adaptive
pooling layer that takes an input of dimension `ind` and yields an output of dimension `outd`"""
def start_index(a, b, c):
... | Returns a List [(kernel_offset_start,kernel_length)] defining all the pooling kernels for a 1-D adaptive
pooling layer that takes an input of dimension `ind` and yields an output of dimension `outd` | get_kernels | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | Apache-2.0 |
def call(
self,
pixel_values: Optional[tf.Tensor] = None,
head_mask: Optional[tf.Tensor] = None,
labels: Optional[tf.Tensor] = None,
output_attentions: Optional[bool] = None,
output_hidden_states: Optional[bool] = None,
return_dict: Optional[bool] = None,
) ->... |
labels (`tf.Tensor` of shape `(batch_size, height, width)`, *optional*):
Ground truth semantic segmentation maps for computing the loss. Indices should be in `[0, ...,
config.num_labels - 1]`. If `config.num_labels > 1`, a classification loss is computed (Cross-Entropy).
Return... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/data2vec/modeling_tf_data2vec_vision.py | Apache-2.0 |
def forward(self, x):
"""
Call the module
Args:
x (`torch.tensor`): The input tensor to apply dropout
"""
if self.training and self.drop_prob > 0:
return XDropout.apply(x, self.get_context())
return x |
Call the module
Args:
x (`torch.tensor`): The input tensor to apply dropout
| forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/modeling_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/modeling_deberta.py | Apache-2.0 |
def build_relative_position(query_size, key_size, device):
"""
Build relative position according to the query and key
We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
\\(P_k\\) is range from (0, key_size), The relative positions from quer... |
Build relative position according to the query and key
We assume the absolute position of query \(P_q\) is range from (0, query_size) and the absolute position of key
\(P_k\) is range from (0, key_size), The relative positions from query to key is \(R_{q \rightarrow k} = P_q -
P_k\)
Args:
... | build_relative_position | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/modeling_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/modeling_deberta.py | Apache-2.0 |
def forward(
self,
hidden_states,
attention_mask,
output_attentions=False,
query_states=None,
relative_pos=None,
rel_embeddings=None,
):
"""
Call the module
Args:
hidden_states (`torch.FloatTensor`):
Input s... |
Call the module
Args:
hidden_states (`torch.FloatTensor`):
Input states to the module usually the output from previous layer, it will be the Q,K and V in
*Attention(Q,K,V)*
attention_mask (`torch.ByteTensor`):
An attention mask m... | forward | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/modeling_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/modeling_deberta.py | Apache-2.0 |
def xdropout(self, inputs):
"""
Applies dropout to the inputs, as vanilla dropout, but also scales the remaining elements up by 1/drop_prob.
"""
mask = tf.cast(
1
- tf.compat.v1.distributions.Bernoulli(probs=1.0 - self.drop_prob).sample(sample_shape=shape_list(inp... |
Applies dropout to the inputs, as vanilla dropout, but also scales the remaining elements up by 1/drop_prob.
| xdropout | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | Apache-2.0 |
def build_relative_position(query_size, key_size):
"""
Build relative position according to the query and key
We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
\\(P_k\\) is range from (0, key_size), The relative positions from query to key... |
Build relative position according to the query and key
We assume the absolute position of query \(P_q\) is range from (0, query_size) and the absolute position of key
\(P_k\) is range from (0, key_size), The relative positions from query to key is \(R_{q \rightarrow k} = P_q -
P_k\)
Args:
... | build_relative_position | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | Apache-2.0 |
def call(
self,
hidden_states: tf.Tensor,
attention_mask: tf.Tensor,
query_states: tf.Tensor = None,
relative_pos: tf.Tensor = None,
rel_embeddings: tf.Tensor = None,
output_attentions: bool = False,
training: bool = False,
) -> Tuple[tf.Tensor]:
... |
Call the module
Args:
hidden_states (`tf.Tensor`):
Input states to the module usually the output from previous layer, it will be the Q,K and V in
*Attention(Q,K,V)*
attention_mask (`tf.Tensor`):
An attention mask matrix of shape ... | call | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/modeling_tf_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/modeling_tf_deberta.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 DeBERTa sequence ha... |
Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A DeBERTa sequence has the following format:
- single sequence: [CLS] X [SEP]
- pair of sequences: [CLS] A [SEP] B [SEP]
Args:
tok... | build_inputs_with_special_tokens | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta.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` or `encode_plus` methods.
Args:
token_ids_0 (`List[int]`):
List of IDs.
token_ids_1 (`List... | get_special_tokens_mask | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta.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 DeBERTa
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 DeBERTa
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/deberta/tokenization_deberta.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta.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.
Deberta tokenizer has a special mask token to be used in the fill-mask pipeline. The mask token will greedily
comprise ... |
`str`: Mask token, to use when training a model with masked-language modeling. Log an error if used while not
having been set.
Deberta tokenizer has a special mask token to be used 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/deberta/tokenization_deberta_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py | Apache-2.0 |
def mask_token(self, value):
"""
Overriding the default behavior of the mask token to have it eat the space before it.
"""
# Mask token behave like a normal word, i.e. include the space before it
# So we set lstrip to True
value = AddedToken(value, lstrip=True, rstrip=Fal... |
Overriding the default behavior of the mask token to have it eat the space before it.
| mask_token | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py | Apache-2.0 |
def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):
"""
Build relative position according to the query and key
We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
\\(P_k\\) is range from (0, key_size), The rel... |
Build relative position according to the query and key
We assume the absolute position of query \(P_q\) is range from (0, query_size) and the absolute position of key
\(P_k\) is range from (0, key_size), The relative positions from query to key is \(R_{q \rightarrow k} = P_q -
P_k\)
Args:
... | build_relative_position | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta_v2/modeling_deberta_v2.py | Apache-2.0 |
def build_relative_position(query_size, key_size, bucket_size=-1, max_position=-1):
"""
Build relative position according to the query and key
We assume the absolute position of query \\(P_q\\) is range from (0, query_size) and the absolute position of key
\\(P_k\\) is range from (0, key_size), The rel... |
Build relative position according to the query and key
We assume the absolute position of query \(P_q\) is range from (0, query_size) and the absolute position of key
\(P_k\) is range from (0, key_size), The relative positions from query to key is \(R_{q \rightarrow k} = P_q -
P_k\)
Args:
... | build_relative_position | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta_v2/modeling_tf_deberta_v2.py | Apache-2.0 |
def _tokenize(self, text: str) -> List[str]:
"""Take as input a string and return a list of strings (tokens) for words/sub-words"""
if self.do_lower_case:
text = text.lower()
return self._tokenizer.tokenize(text) | Take as input a string and return a list of strings (tokens) for words/sub-words | _tokenize | python | FMInference/FlexLLMGen | benchmark/third_party/transformers/src/transformers/models/deberta_v2/tokenization_deberta_v2.py | https://github.com/FMInference/FlexLLMGen/blob/master/benchmark/third_party/transformers/src/transformers/models/deberta_v2/tokenization_deberta_v2.py | Apache-2.0 |
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