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class VideoLlavaForConditionalGeneration(VideoLlavaPreTrainedModel, GenerationMixin):
def __init__(self, config: VideoLlavaConfig):
super().__init__(config)
self.video_tower = AutoModel.from_config(config.vision_config)
self.image_tower = AutoModel.from_config(config.vision_config)
... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
def get_output_embeddings(self):
return self.language_model.get_output_embeddings()
def set_output_embeddings(self, new_embeddings):
self.language_model.set_output_embeddings(new_embeddings)
def set_decoder(self, decoder):
self.language_model.set_decoder(decoder)
def get_decoder(s... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
# 1. Create a mask to know where special image tokens are
special_image_token_mask = input_ids == special_vision_token
num_special_image_tokens = torch.sum(special_image_token_mask, dim=-1)
# Compute the maximum embed dimension
max_seq_len = (num_special_image_tokens.max() * (num_image_p... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
# 2. Compute the positions where text should be written
# Calculate new positions for text tokens in merged image-text sequence.
# `special_image_token_mask` identifies image tokens. Each image token will be replaced by `nb_text_tokens_per_images - 1` text tokens.
# `torch.cumsum` computes how e... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
# 3. Create the full embedding, already padded to the maximum position
# expand input ids so that the second "merge" with videos does not fail
final_embedding = torch.zeros(
batch_size, max_seq_len, embed_dim, dtype=inputs_embeds.dtype, device=inputs_embeds.device
)
final_att... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
text_to_overwrite.to(target_device),
)
attention_mask = attention_mask.to(target_device) | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
# 4. Fill the embeddings based on the mask. If we have ["hey" "<image>", "how", "are"]
# we need to index copy on [0, 577, 578, 579] for the text and [1:576] for the image features
final_embedding[batch_indices, text_to_overwrite] = inputs_embeds[batch_indices, non_image_indices]
final_attention... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
# 5. Fill the embeddings corresponding to the images. Anything that is still zeros needs filling
image_to_overwrite = torch.full((batch_size, max_seq_len), True, dtype=torch.bool, device=inputs_embeds.device)
image_to_overwrite[batch_indices, text_to_overwrite] = False
if left_padding:
... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
if image_to_overwrite.sum() != visual_features.shape[:-1].numel():
visual_type = "videos" if num_frames == 8 else "images"
num_images //= num_frames
raise ValueError(
f"The input provided to the model are wrong. The number of {visual_type} tokens is {torch.sum(special... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
def get_image_features(
self, pixel_values_images: torch.FloatTensor, vision_feature_layer: int, vision_feature_select_strategy: str
):
"""
Obtains image last hidden states from the vision tower and apply multimodal projection.
Args:
pixel_values_images (`torch.FloatTens... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
image_outputs = self.image_tower(pixel_values_images, output_hidden_states=True)
image_outputs = image_outputs.hidden_states[vision_feature_layer].squeeze(1)
if vision_feature_select_strategy == "default":
image_outputs = image_outputs[:, 1:]
elif vision_feature_select_strategy == "... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
Args:
pixel_values_videos (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
The tensors corresponding to the input videos.
vision_feature_layer (`int`):
The index of the layer to select the vision feature.
Returns:
... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
@add_start_docstrings_to_model_forward(VIDEO_LLAVA_INPUTS_DOCSTRING)
@replace_return_docstrings(output_type=VideoLlavaCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values_images: torch.FloatTensor = None,
pixel_... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
num_logits_to_keep: int = 0,
) -> Union[Tuple, VideoLlavaCausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
>>> def read_video_pyav(container, indices):
... '''
... Decode the video with PyAV decoder.
... Args:
... container (`av.container.input.InputContainer`): PyAV container.
... indices (`List[int]`): List of frame indices to decode.
... Retu... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
>>> model = VideoLlavaForConditionalGeneration.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")
>>> processor = VideoLlavaProcessor.from_pretrained("LanguageBind/Video-LLaVA-7B-hf")
>>> prompt = "USER: <video>\nWhy is this video funny? ASSISTANT:"
>>> video_path = hf_hub_download(repo_id="raus... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
>>> # Generate
>>> generate_ids = model.generate(**inputs, max_length=80)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"USER: Why is this video funny? ASSISTANT: The video is funny because the baby is playing with a Wii remote while s... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
>>> # Generate
>>> generate_ids = model.generate(**inputs, max_length=50)
>>> processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
['USER: How many cats do you see? ASSISTANT: There are two cats visible in the image. (or three, if you count the one ... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
return_dict = return_dict if return_dict is not None els... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
if (pixel_values_images is not None or pixel_values_videos is not None) and inputs_embeds is not None:
raise ValueError(
"You cannot specify both `pixel_values_images`/`pixel_values_videos` and `inputs_embeds` at the same "
"time, and must specify either one"
)
... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
if pixel_values_images is not None:
image_features = self.get_image_features(
pixel_values_images,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_feature_select_strategy,
)
n_image_tokens = (input_ids =... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
inputs_embeds = inputs_embeds.masked_scatter(special_image_mask, image_features) | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
if pixel_values_videos is not None:
video_features, num_frames = self.get_video_features(
pixel_values_videos=pixel_values_videos, vision_feature_layer=vision_feature_layer
)
n_video_tokens = (input_ids == self.config.video_token_index).sum().item()
n_vid... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
outputs = self.language_model(
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
use_cache=use_cache,
output_attentions=output_attentions,
output_hidden_states=output... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
loss = None
if labels is not None:
# Shift so that tokens < n predict n
if attention_mask is not None:
# we use the input attention mask to shift the logits and labels, because it is 2D.
# we also crop attn mask in case it is longer, which happens in Prefi... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
) | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return VideoLlavaCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
model_inputs = self.language_model.prepare_inputs_for_generation(
input_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
cache_position=cache_position,
num_logits_to_keep=num_logits_to_keep,
... | 3,023 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/modeling_video_llava.py |
class VideoLlavaImageProcessor(BaseImageProcessor):
r"""
Constructs a CLIP image processor. | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
Args:
do_resize (`bool`, *optional*, defaults to `True`):
Whether to resize the image's (height, width) dimensions to the specified `size`. Can be overridden by
`do_resize` in the `preprocess` method.
size (`Dict[str, int]` *optional*, defaults to `{"shortest_edge": 224}`):
... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
crop_size (`Dict[str, int]` *optional*, defaults to 224):
Size of the output image after applying `center_crop`. Can be overridden by `crop_size` in the `preprocess`
method.
do_rescale (`bool`, *optional*, defaults to `True`):
Whether to rescale the image by the specified sca... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
Mean to use if normalizing the image. This is a float or list of floats the length of the number of
channels in the image. Can be overridden by the `image_mean` parameter in the `preprocess` method.
image_std (`float` or `List[float]`, *optional*, defaults to `[0.26862954, 0.26130258, 0.27577711]`):... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
model_input_names = ["pixel_values"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_center_crop: bool = True,
crop_size: Dict[str, int] = None,
do_rescale: bool = True,
... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_center_crop = do_center_crop
self.crop_size = crop_size
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
Args:
image (`np.ndarray`):
Image to resize.
size (`Dict[str, int]`):
Size of the output image.
resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BICUBIC`):
Resampling filter to use when resiizing the image.
... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
raise ValueError("Size must contain either 'shortest_edge' or 'height' and 'width'.") | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
output_size = get_resize_output_image_size(
image,
size=size,
default_to_square=default_to_square,
input_data_format=input_data_format,
)
return resize(
image,
size=output_size,
resample=resample,
data_format... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
@filter_out_non_signature_kwargs()
def preprocess(
self,
images: List[ImageInput] = None,
videos: List[VideoInput] = None,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_center_crop: bool = None,
crop_size:... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
Args:
images (`ImageInput`, *optional*):
List of images to preprocess. Expects a single or batch of images with pixel values ranging from 0 to 255. If
passing in images with pixel values between 0 and 1, set `do_rescale=False`.
videos (`VideoInput`, *optional*):
... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
Resampling filter to use if resizing the image. This can be one of the enum `PILImageResampling`. Only
has an effect if `do_resize` is set to `True`.
do_center_crop (`bool`, *optional*, defaults to `self.do_center_crop`):
Whether to center crop the image.
crop_siz... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
Image mean to use for normalization. Only has an effect if `do_normalize` is set to `True`.
image_std (`float` or `List[float]`, *optional*, defaults to `self.image_std`):
Image standard deviation to use for normalization. Only has an effect if `do_normalize` is set to
`True`... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
The channel dimension format for the output image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_las... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
size = get_size_dict(size, param_name="size", default_to_square=False)
resample = resample... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
image_std = image_std if image_std is not None else self.image_std
do_convert_rgb = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
if images is not None:
images = make_list_of_images(images)
if videos is not None:
videos = make_batched_videos(videos)
if (videos is not None and not valid_images(videos)) or (images is not None and not valid_images(images)):
raise ValueError(
"Inval... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
data = {}
if videos is not None:
pixel_values_videos = [
[
self._preprocess_image(
image=frame,
do_resize=do_resize,
size=size,
resample=resample,
... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
if images is not None:
pixel_values_images = [
self._preprocess_image(
image=image,
do_resize=do_resize,
size=size,
resample=resample,
do_rescale=do_rescale,
rescale_factor... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
def _preprocess_image(
self,
image: ImageInput = None,
do_resize: Optional[bool] = None,
size: Optional[Dict[str, int]] = None,
resample: PILImageResampling = None,
do_rescale: Optional[bool] = None,
rescale_factor: Optional[float] = None,
do_normalize: Op... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
crop_size=crop_size,
do_resize=do_resize,
size=size,
resample=resample,
) | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
# PIL RGBA images are converted to RGB
if do_convert_rgb:
image = convert_to_rgb(image)
# All transformations expect numpy arrays.
image = to_numpy_array(image)
if do_rescale and is_scaled_image(image):
logger.warning_once(
"It looks like you are... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
if do_rescale:
image = self.rescale(image=image, scale=rescale_factor, input_data_format=input_data_format)
if do_normalize:
image = self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
image = to_channel_dimension_format(image, data_... | 3,024 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/image_processing_video_llava.py |
class VideoLlavaConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`VideoLlavaForConditionalGeneration`]. It is used to instantiate an
VideoLlava model according to the specified arguments, defining the model architecture. Instantiating a configuration
with t... | 3,025 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py |
Args:
vision_config (`VideoLlavaVisionConfig`, *optional*):
Custom vision config or dict. Defaults to `CLIPVisionConfig` if not indicated.
text_config (`Union[AutoConfig, dict]`, *optional*):
The config object of the text backbone. Can be any of `LlamaConfig` or `MistralConfig`.
... | 3,025 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py |
The feature selection strategy used to select the vision feature from the CLIP backbone.
Can be either "full" to select all features or "default" to select features without `CLS`.
vision_feature_layer (`int`, *optional*, defaults to -2):
The index of the layer to select the vision featur... | 3,025 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py |
Example:
```python
>>> from transformers import VideoLlavaForConditionalGeneration, VideoLlavaConfig, CLIPVisionConfig, LlamaConfig
>>> # Initializing a CLIP-vision config
>>> vision_config = CLIPVisionConfig()
>>> # Initializing a Llama config
>>> text_config = LlamaConfig()
>>> # Initi... | 3,025 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py |
def __init__(
self,
vision_config=None,
text_config=None,
ignore_index=-100,
image_token_index=32000,
video_token_index=32001,
projector_hidden_act="gelu",
vision_feature_select_strategy="default",
vision_feature_layer=-2,
image_seq_length=... | 3,025 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py |
if isinstance(self.vision_config, dict):
if "model_type" not in vision_config:
vision_config["model_type"] = "clip_vision_model"
logger.warning("Key=`model_type` not found in vision config, setting it to `clip_vision_model`")
self.vision_config = CONFIG_MAPPING[vi... | 3,025 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py |
if isinstance(text_config, dict):
if "model_type" not in text_config:
text_config["model_type"] = "llama"
logger.warning("Key=`model_type` not found in text config, setting it to `llama`")
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
... | 3,025 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/video_llava/configuration_video_llava.py |
class CohereLayerNorm(nn.Module):
def __init__(self, hidden_size=None, eps=1e-5, bias=False):
"""The hidden size can be a tuple or an int. The tuple is used for QKNorm to normalize across head_dim"""
super().__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.variance... | 3,026 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class CohereRotaryEmbedding(LlamaRotaryEmbedding):
@torch.no_grad()
def forward(self, x, position_ids):
if "dynamic" in self.rope_type:
self._dynamic_frequency_update(position_ids, device=x.device)
# Core RoPE block
inv_freq_expanded = self.inv_freq[None, :, None].float().ex... | 3,027 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
# Advanced RoPE types (e.g. yarn) apply a post-processing scaling factor, equivalent to scaling attention
cos = cos * self.attention_scaling
sin = sin * self.attention_scaling
return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype) | 3,027 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class CohereMLP(LlamaMLP):
def __init__(self, config):
super().__init__(config)
self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
self.down_proj = nn.Linear(self.intermediate... | 3,028 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class CohereAttention(LlamaAttention):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(self, config: CohereConfig, layer_idx: Optional[int] = None):
super().__init__(config, layer_idx)
self.use_qk_norm = config.use_qk_norm
if self.use_qk_norm:
... | 3,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
def forward(
self,
hidden_states: torch.Tensor,
position_embeddings: Tuple[torch.Tensor, torch.Tensor],
attention_mask: Optional[torch.Tensor],
past_key_value: Optional[Cache] = None,
cache_position: Optional[torch.LongTensor] = None,
**kwargs: Unpack[FlashAttenti... | 3,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
cos, sin = position_embeddings
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
if past_key_value is not None:
... | 3,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
attention_interface: Callable = eager_attention_forward
if self.config._attn_implementation != "eager":
if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
logger.warning_once(
"`torch.nn.functional.scaled_dot_product_attentio... | 3,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
attn_output = attn_output.reshape(*input_shape, -1).contiguous()
attn_output = self.o_proj(attn_output)
return attn_output, attn_weights | 3,029 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class CohereDecoderLayer(nn.Module):
def __init__(self, config: CohereConfig, layer_idx: int):
super().__init__()
self.hidden_size = config.hidden_size
self.self_attn = CohereAttention(config=config, layer_idx=layer_idx)
self.mlp = CohereMLP(config)
self.input_layernorm = Coh... | 3,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Cache] = None,
output_attentions: Optional[bool] = False,
use_cache: Optional[bool] = False,
... | 3,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
query_sequence_length, key_sequence_length)` if default attention is used.
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
output_attentions (`bool`, *optional*):
Whether or not to return the attentions tensors of all attention... | 3,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
with `head_dim` being the embedding dimension of each attention head.
"""
residual = hidden_states | 3,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
hidden_states = self.input_layernorm(hidden_states)
# Self Attention
hidden_states_attention, self_attn_weights = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_value=past_key_value,
... | 3,030 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class CohereModel(LlamaModel):
def __init__(self, config: CohereConfig):
super().__init__(config)
self.layers = nn.ModuleList(
[CohereDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
)
self.rotary_emb = CohereRotaryEmbedding(config=config)... | 3,031 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ... | 3,032 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class CohereForCausalLM(LlamaForCausalLM):
def __init__(self, config):
super().__init__(config)
self.model = CohereModel(config)
self.logit_scale = config.logit_scale
self.tie_word_embeddings = config.tie_word_embeddings | 3,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
def forward(
self,
input_ids: torch.LongTensor = None,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_values: Optional[Union[Cache, List[torch.FloatTensor]]] = None,
inputs_embeds: Optional[torch.FloatTensor] = Non... | 3,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`. | 3,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
num_logits_to_keep (`int`, *optional*):
Calculate logits for the last `num_logits_to_keep` tokens. If `0`, calculate logits for all
`input_ids` (special case). Only last token logits are needed for generation, and calculating them only for that
token can save memory, whic... | 3,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
>> # Generate
>> generate_ids = model.generate(inputs.input_ids, max_length=30)
>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
"Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
```"""
... | 3,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
outputs = self.model(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
inputs_embeds=inputs_embeds,
... | 3,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return CausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outputs.hidden_sta... | 3,033 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/modular_cohere.py |
class CohereTokenizerFast(PreTrainedTokenizerFast):
"""
Construct a Cohere tokenizer. Based on byte-level Byte-Pair-Encoding.
This uses notably ByteFallback and NFC normalization.
```python
>>> from transformers import AutoTokenizer
>>> tokenizer = AutoTokenizer.from_pretrained("CohereForAI/c... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
the model was not pretrained this way, it might yield a decrease in performance.
<Tip>
When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
Args:
vocab_file (`str`, *optional*):
Path to the vocabulary file.
merges_file (`str`, *optional*):
Path to the merges file.
tokenizer_file (`str`, *optional*):
[tokenizers](https://github.com/huggingface/tokenizers) file (generally has a .json extension) that... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.
eos_token (`str` or `tokenizers.AddedToken`, *optional*, defaults to `"<|END_OF_TURN_TOKEN|>"`):
The end of sequence token.
add_bos_token (`bool`, *optional*, defaults to `True`):
... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
padding_side = "left"
model_input_names = ["input_ids", "attention_mask"]
slow_tokenizer_class = None
# No `max_model_input_sizes` | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
def __init__(
self,
vocab_file=None,
merges_file=None,
tokenizer_file=None,
clean_up_tokenization_spaces=False,
unk_token="<UNK>",
bos_token="<BOS_TOKEN>",
eos_token="<|END_OF_TURN_TOKEN|>",
add_bos_token=True,
add_eos_token=False,
... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
self._add_eos_token = add_eos_token
self.update_post_processor()
self.use_default_system_prompt = use_default_system_prompt
self.vocab_file = vocab_file
self.grounded_generation_template = kwargs.pop("grounded_generation_template", None)
self.tool_use_template = kwargs.pop("tool_... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
# TODO @ArthurZucker this can only work one way for now, to update later-on. Tests should also properly
# check this as they were green before.
pre_tok_state = pickle.dumps(self.backend_tokenizer.pre_tokenizer)
decoder_state = pickle.dumps(self.backend_tokenizer.decoder)
if add_prefix_s... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
def _batch_encode_plus(self, *args, **kwargs) -> BatchEncoding:
is_split_into_words = kwargs.get("is_split_into_words", False)
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
def update_post_processor(self):
"""
Updates the underlying post processor with the current `bos_token` and `eos_token`.
"""
bos = self.bos_token
bos_token_id = self.bos_token_id
if bos is None and self.add_bos_token:
raise ValueError("add_bos_token = True but... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
special_tokens = []
if self.add_bos_token:
special_tokens.append((bos, bos_token_id))
if self.add_eos_token:
special_tokens.append((eos, eos_token_id))
self._tokenizer.post_processor = processors.TemplateProcessing(
single=single, pair=pair, special_tokens=spe... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
Once rendered, the prompt instructs the model to generate a list of actions to perform on a set of user supplied tools
to help carry out the user's requests.
Conceptually, this works in the same way as `apply_chat_format`, but takes an additional `tools` parameter.
Converts a chat in the form ... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
Args:
conversation (Union[List[Dict[str, str]]]): A list of dicts
with "role" and "content" keys, representing the chat history so far.
tools (List[Dict]): a list of tools to render into the prompt for the model to choose from.
See an example at the bottom of the ... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
Valid parameter names contain only the characters a-z, A-Z, 0-9, _ and must not begin with a digit.
Parameter specs are as follows:
* description (str): The description of the parameter.
* type (str): the type of the parameter - most effective for py... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
Whether to tokenize the output. If `False`, the output will be a string.
padding (`bool`, defaults to `False`):
Whether to pad sequences to the maximum length. Has no effect if tokenize is `False`.
truncation (`bool`, defaults to `False`):
Whether to truncate sequ... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
- `'np'`: Return NumPy `np.ndarray` objects.
- `'jax'`: Return JAX `jnp.ndarray` objects.
return_dict (`bool`, *optional*, defaults to `False`):
Whether to return a dictionary with named outputs. Has no effect if tokenize is `False`.
**tokenizer_kwargs: Additional... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
Returns:
`str`: A rendered prompt string.
or if tokenize=True:
`List[int]`: A list of token ids representing the tokenized chat so far, including control tokens. This
output is ready to pass to the model, either directly or via methods like `generate()`.
Examples... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
```python
>> tokenizer = CohereTokenizerFast.from_pretrained("CohereForAI/c4ai-command-r-v01")
>> tools = [
{
"name": "internet_search",
"description": "Returns a list of relevant document snippets for a textual query retrieved from the internet",
... | 3,034 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/cohere/tokenization_cohere_fast.py |
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