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def preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
image_grid_pinpoints: List = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
tensor. Both channels-first and channels-last formats are supported.
do_resize (`bool`, *optional*, defaults to `self.do_resize`):
Whether to resize the image.
size ... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.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_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
Whether to rescale the image.
rescale_factor (`flo... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
`True`.
do_pad (`bool`, *optional*, defaults to `self.do_pad`):
Whether to pad the image. If `True`, will pad the patch dimension of the images in the batch to the largest
number of patches in the batch. Padding will be applied to the bottom and right with zeros.
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.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... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
"""
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, default_to_square=False)
image_grid_pinpoints = image_grid_pinpoints if image_grid_pinpoints is not None else self.image_grid_pinpoints
... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
if not valid_images(images):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray."
)
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled images. If the input"
" images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
)
if inpu... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
new_images = []
image_sizes = [get_image_size(image, channel_dim=input_data_format) for image in images]
for image in images:
# convert image into a list of patches
# we intentially use the same data format as the input data format
size_tuple = (
(size... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
# preprocess patches
pixel_values = self._preprocess(
image_patches,
do_resize=do_resize,
size=size_tuple,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_... | 9,943 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/image_processing_llava_onevision.py |
class LlavaOnevisionVideoProcessor(BaseImageProcessor):
r"""
Constructs a LLaVa-Onevisino-Video video processor. Based on [`SiglipImageProcessor`] with incorporation of processing each video frame. | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.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}`):
... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
rescale_factor (`int` or `float`, *optional*, defaults to `1/255`):
Scale factor to use if rescaling the image. Can be overridden by `rescale_factor` in the `preprocess`
method.
do_normalize (`bool`, *optional*, defaults to `True`):
Whether to normalize the image. Can be over... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
number of channels in the image. Can be overridden by the `image_std` parameter in the `preprocess` method.
Can be overridden by the `image_std` parameter in the `preprocess` method.
do_convert_rgb (`bool`, *optional*, defaults to `True`):
Whether to convert the image to RGB.
""" | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
model_input_names = ["pixel_values_videos"]
def __init__(
self,
do_resize: bool = True,
size: Dict[str, int] = None,
resample: PILImageResampling = PILImageResampling.BICUBIC,
do_rescale: bool = True,
rescale_factor: Union[int, float] = 1 / 255,
do_normalize:... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
self.do_resize = do_resize
self.size = size
self.resample = resample
self.do_rescale = do_rescale
self.rescale_factor = rescale_factor
self.do_normalize = do_normalize
self.image_mean = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
self.image_std = im... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
def _preprocess(
self,
images: ImageInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float,... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resampl... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
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 deviati... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:
- `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
- `"channels_last"` or `ChannelDimens... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
# All transformations expect numpy arrays.
images = [to_numpy_array(image) for image in images]
if do_rescale and is_scaled_image(images[0]):
logger.warning_once(
"It looks like you are trying to rescale already rescaled videos. If the input"
" images have pi... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
if do_normalize:
images = [
self.normalize(image=image, mean=image_mean, std=image_std, input_data_format=input_data_format)
for image in images
]
images = [
to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format) ... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
def preprocess(
self,
videos: VideoInput,
do_resize: bool = None,
size: Dict[str, int] = None,
resample: PILImageResampling = None,
do_rescale: bool = None,
rescale_factor: float = None,
do_normalize: bool = None,
image_mean: Optional[Union[float, ... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
size (`Dict[str, int]`, *optional*, defaults to `self.size`):
Size of the image after resizing. Shortest edge of the image is resized to size["shortest_edge"], with
the longest edge resized to keep the input aspect ratio.
resample (`int`, *optional*, defaults to `self.resampl... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
image_mean (`float` or `List[float]`, *optional*, defaults to `self.image_mean`):
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 deviati... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
- `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
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.... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
- `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
- `"none"` or `ChannelDimension.NONE`: image in (height, width) format. | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
"""
do_resize = do_resize if do_resize is not None else self.do_resize
size = size if size is not None else self.size
resample = resample if resample is not None else self.resample
do_rescale = do_rescale if do_rescale is not None else self.do_rescale
rescale_factor = rescale_fac... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
validate_preprocess_arguments(
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
image_mean=image_mean,
image_std=image_std,
do_resize=do_resize,
size=size,
resample=resample,
)
... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
pixel_values = [
self._preprocess(
video,
do_resize=do_resize,
size=size_tuple,
resample=resample,
do_rescale=do_rescale,
rescale_factor=rescale_factor,
do_normalize=do_normalize,
... | 9,944 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/video_processing_llava_onevision.py |
class LlavaOnevisionCausalLMOutputWithPast(ModelOutput):
"""
Base class for LlavaOnevision causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token predicti... | 9,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_s... | 9,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
image_hidden_states (`torch.FloatTensor`, *optional*):
A `torch.FloatTensor` of size (batch_size * num_patches, num_images, sequence_length, hidden_size)`.
image_... | 9,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[List[torch.FloatTensor]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = None
image_hidden_states: Optional[torch.FloatTensor] = None
... | 9,945 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
class LlavaOnevisionMultiModalProjector(nn.Module):
def __init__(self, config: LlavaOnevisionConfig):
super().__init__()
self.linear_1 = nn.Linear(
config.vision_config.hidden_size, config.text_config.hidden_size, bias=config.multimodal_projector_bias
)
self.act = ACT2FN[... | 9,946 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
class LlavaOnevisionPreTrainedModel(PreTrainedModel):
config_class = LlavaOnevisionConfig
base_model_prefix = "model"
supports_gradient_checkpointing = True
_no_split_modules = ["LlavaOnevisionVisionAttention"]
_skip_keys_device_placement = "past_key_values"
_supports_flash_attn_2 = True
_su... | 9,947 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
# Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextPreTrainedModel._init_weights
def _init_weights(self, module):
# important: this ported version of LlavaNext isn't meant for training from scratch - only
# inference and fine-tuning - so the proper init weights code has been r... | 9,947 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
if isinstance(module, (nn.Linear, nn.Conv2d)):
module.weight.data.normal_(mean=0.0, std=std)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(module, nn.Embedding):
module.weight.data.normal_(mean=0.0, std=std)
if module.padding... | 9,947 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
class LlavaOnevisionForConditionalGeneration(LlavaOnevisionPreTrainedModel, GenerationMixin):
def __init__(self, config: LlavaOnevisionConfig):
super().__init__(config)
self.vision_tower = AutoModel.from_config(config.vision_config)
self.multi_modal_projector = LlavaOnevisionMultiModalProje... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
# Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_input_embeddings
def get_input_embeddings(self):
return self.language_model.get_input_embeddings()
# Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.se... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
# Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.set_decoder
def set_decoder(self, decoder):
self.language_model.set_decoder(decoder)
# Copied from transformers.models.llava_next.modeling_llava_next.LlavaNextForConditionalGeneration.get_decoder
def ... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
Args:
image_features (`List[torch.Tensor]` of length num_images, each of shape `(num_patches, image_length, embed_dim)`)
List of image feature tensor, each contains all the visual feature of all patches.
image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
Actu... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
if image_feature.shape[0] > 1:
base_image_feature = image_feature[0]
image_feature = image_feature[1:]
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
if height * width != base_image_feature.shape[0]:
... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
max_num_patches = int(vision_aspect_ratio.strip("anyres_max_"))
channels, curr_height, curr_width = image_feature.shape
ratio = math.sqrt(curr_height * curr_width / (max_num_patches * height**2))
if ratio > 1.1:
image_feature = image_feature[None]
... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
image_feature = image_feature.flatten(1, 2).transpose(0, 1)
image_feature = torch.cat((base_image_feature, image_feature), dim=0)
else:
image_feature = image_feature[0]
if image_newline is not None:
image_feature = torch.cat((image_feature,... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
def apply_pooling(self, image_features):
height = width = self.config.vision_config.image_size // self.config.vision_config.patch_size
batch_frames, seq_len, dim = image_features.shape
image_features = image_features.view(batch_frames, height, width, -1)
image_features = image_features.p... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
def get_image_features(
self,
pixel_values: torch.FloatTensor,
image_sizes: torch.Tensor,
vision_feature_layer: int,
vision_feature_select_strategy: str,
):
"""
Obtains image last hidden states from the vision tower and apply multimodal projection. | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
Args:
pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_patches, channels, height, width)`)
The tensors corresponding to the input images.
image_sizes (`torch.Tensor` of shape `(num_images, 2)`)
Actual image size of each images (H, W).
visio... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
image_size=imsize,
grid_pinpoints=self.config.image_grid_pinpoints,
patch_size=self.config.vision_config.image_size,
)
for imsize in image_sizes
]
if pixel_values.dim() == 5:
# stacked if input is (batch_size, num_patches, num_channels,... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
image_features = self.vision_tower(pixel_values, output_hidden_states=True)
selected_image_feature = image_features.hidden_states[vision_feature_layer]
if vision_feature_select_strategy == "default":
selected_image_feature = selected_image_feature[:, 1:]
elif vision_feature_select_st... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
Args:
pixel_values (`torch.FloatTensor]` of shape `(batch_size, num_frames, channels, height, width)`)
The tensors corresponding to the input video.
vision_feature_layer (`int`):
The index of the layer to select the vision feature.
vision_feature_select... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
selected_video_feature = video_features.hidden_states[vision_feature_layer] | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
if vision_feature_select_strategy == "default":
selected_video_feature = selected_video_feature[:, 1:]
elif vision_feature_select_strategy == "full":
selected_video_feature = selected_video_feature
video_features = self.multi_modal_projector(selected_video_feature)
video... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
@add_start_docstrings(LLAVA_ONEVISION_INPUTS_DOCSTRING)
def forward(
self,
input_ids: torch.LongTensor = None,
pixel_values: torch.FloatTensor = None,
image_sizes: Optional[torch.LongTensor] = None,
pixel_values_videos: torch.FloatTensor = None,
image_sizes_videos: Op... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
cache_position: Optional[torch.LongTensor] = None,
num_logits_to_keep: int = 0,
) -> Union[Tuple, LlavaOnevisionCausalLMOutputWithPast]:
r"""
Args:
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
Labels for computing the masked la... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.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... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
>>> conversation = [
... {
... "role": "user",
... "content": [
... {"type": "text", "text": "What is shown in this image?"},
... {"type": "image"},
... ],
... },
... ]
>>> prompt = processor.apply_ch... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
>>> output = model.generate(**inputs, max_new_tokens=20, do_sample=False)
>>> processor.batch_decode(output, skip_special_tokens=True)[0]
"user\n\nWhat is shown in this image?\nassistant\ncat"
```"""
output_attentions = output_attentions if output_attentions is not None else self.config.... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
vision_aspect_ratio if vision_aspect_ratio is not None else self.config.vision_aspect_ratio
) | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
if (input_ids is None) ^ (inputs_embeds is not None):
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
if (pixel_values is not None or pixel_values_videos is not None) and inputs_embeds is not None:
raise ValueError(
"You cannot specify both... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
# Images are processed with Anyres
if pixel_values is not None:
image_features = self.get_image_features(
pixel_values,
image_sizes,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_feature_select_strateg... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
if n_image_tokens != n_image_features:
raise ValueError(
f"Image features and image tokens do not match: tokens: {n_image_tokens}, features {n_image_features}"
)
special_image_mask = (
(input_ids == self.config.image_token_index)
... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
# Video are simply embedded and further pooled to decrease seq len
if pixel_values_videos is not None:
video_features = self.get_video_features(
pixel_values_videos,
vision_feature_layer=vision_feature_layer,
vision_feature_select_strategy=vision_featu... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
n_video_tokens = (input_ids == self.config.video_token_index).sum().item()
n_video_features = video_features.shape[0]
if n_video_tokens != n_video_features:
raise ValueError(
f"Video features and video tokens do not match: tokens: {n_video_tokens}, features {n... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.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... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.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... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
) | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
if not return_dict:
output = (logits,) + outputs[1:]
return (loss,) + output if loss is not None else output
return LlavaOnevisionCausalLMOutputWithPast(
loss=loss,
logits=logits,
past_key_values=outputs.past_key_values,
hidden_states=outp... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
def prepare_inputs_for_generation(
self,
input_ids,
past_key_values=None,
inputs_embeds=None,
pixel_values=None,
image_sizes=None,
pixel_values_videos=None,
image_sizes_videos=None,
attention_mask=None,
cache_position=None,
num_logi... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
if cache_position[0] == 0:
# If we're in cached decoding stage, pixel values should be None because input ids do not contain special image token anymore
# Otherwise we need pixel values to be passed to model
model_inputs["pixel_values"] = pixel_values
model_inputs["image_... | 9,948 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/llava_onevision/modeling_llava_onevision.py |
class Qwen2AudioCausalLMOutputWithPast(ModelOutput):
"""
Base class for Qwen2Audio causal language model (or autoregressive) outputs.
Args:
loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided):
Language modeling loss (for next-token prediction).
... | 9,949 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
`past_key_values` input) to speed up sequential decoding.
hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_s... | 9,949 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.
attention_mask (`torch.FloatTensor`, *optional*):
Attentions mask, used to update attention mask and position_ids.
"""
loss: Optional[torch.FloatTensor] = None
l... | 9,949 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioAttention(nn.Module):
"""Multi-headed attention from 'Attention Is All You Need' paper"""
def __init__(
self,
embed_dim: int,
num_heads: int,
dropout: float = 0.0,
is_decoder: bool = False,
bias: bool = True,
is_causal: bool = False,
... | 9,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if layer_idx is None and is_decoder:
logger.warning_once(
f"Instantiating a decoder {self.__class__.__name__} without passing `layer_idx` is not recommended and "
"will to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
... | 9,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions:... | 9,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if past_key_value is not None:
is_updated = past_key_value.is_updated.get(self.layer_idx)
if is_cross_attention:
# after the first generated id, we can subsequently re-use all key/value_states from cache
past_key_value.is_updated[self.layer_idx] = True
... | 9,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# use key_value_states if cross attention
current_states = key_value_states if key_value_states is not None else hidden_states
if is_cross_attention and past_key_value and is_updated:
# reuse k,v, cross_attentions
key_states = past_key_value.key_cache[self.layer_idx]
... | 9,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3))
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
attn_weights = attn_weights + causal_mask
attn_weights = nn.functional.so... | 9,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
if attn_output.size() != (bsz, self.num_heads, tgt_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
... | 9,950 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioFlashAttention2(Qwen2AudioAttention):
"""
Qwen2Audio flash attention module. This module inherits from `Qwen2AudioAttention` as the weights of the module stays
untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
flash attent... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
# flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: Optional[torch.Tensor] = None,
output_attentions:... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
raise ValueError("Qwen2AudioFlashAttention2 attention does not support output_attentions") | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = torch.reshape(self.q_proj(hidden_states), (bsz, tgt_len, se... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# use key_value_states if cross attention
current_states = key_value_states if key_value_states is not None else hidden_states
if is_cross_attention and past_key_value and is_updated:
# reuse k,v, cross_attentions
key_states = past_key_value.key_cache[self.layer_idx]
... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]
# We would need to refactor the KV cache to be able to avoid many of these transpose/reshape/view.
key_states = key_states.transpose(1, 2)
value_states = valu... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
input_dtype = query_states.dtype
if input_dtype == torch.float32:
if torch.is_autocast_enabled():
target_dtype = torch.get_autocast_gpu_dtype()
# Handle the case where the model is quantized
elif hasattr(self.config, "_pre_quantization_dtype"):
... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
attn_output = _flash_attention_forward(
query_states,
key_states,
value_states,
causal_mask,
tgt_len,
dropout=self.dropout if self.training else 0.0,
is_causal=self.is_causal,
use_top_left_mask=self._flash_attn_uses_top_left... | 9,951 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioSdpaAttention(Qwen2AudioAttention):
def forward(
self,
hidden_states: torch.Tensor,
key_value_states: Optional[torch.Tensor] = None,
past_key_value: Optional[EncoderDecoderCache] = None,
attention_mask: Optional[torch.Tensor] = None,
layer_head_mask: O... | 9,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
"Qwen2AudioModel is using Qwen2AudioSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True` or `layer_head_mask` not None. Falling back to the manual attention"
' implementation, but specifying the manual implementation will be required from Transf... | 9,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# if key_value_states are provided this layer is used as a cross-attention layer
# for the decoder
is_cross_attention = key_value_states is not None
bsz, tgt_len, _ = hidden_states.size()
# get query proj
query_states = self._shape(self.q_proj(hidden_states), tgt_len, bsz)
... | 9,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# use key_value_states if cross attention
current_states = key_value_states if key_value_states is not None else hidden_states
if is_cross_attention and past_key_value and is_updated:
# reuse k,v, cross_attentions
key_states = past_key_value.key_cache[self.layer_idx]
... | 9,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
causal_mask = attention_mask
if attention_mask is not None: # no matter the length, we just slice it
causal_mask = attention_mask[:, :, :, : key_states.shape[-2]]
# We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional... | 9,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# NOTE: SDPA with memory-efficient backend is currently (torch==2.1.2) bugged when using non-contiguous inputs and a custom attn_mask,
# but we are fine here as `_shape` do call `.contiguous()`. Reference: https://github.com/pytorch/pytorch/issues/112577
attn_output = torch.nn.functional.scaled_dot_prod... | 9,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
# Use the `embed_dim` from the config (stored in the class) rather than `hidden_state` because `attn_output` can be
# partitioned across GPUs when using tensor-parallelism.
attn_output = attn_output.reshape(bsz, tgt_len, self.embed_dim)
attn_output = self.out_proj(attn_output)
return a... | 9,952 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
class Qwen2AudioEncoderLayer(nn.Module):
def __init__(self, config: Qwen2AudioConfig):
super().__init__()
self.embed_dim = config.d_model
self.self_attn = QWEN2AUDIO_ATTENTION_CLASSES[config._attn_implementation](
embed_dim=self.embed_dim,
num_heads=config.encoder_at... | 9,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
def forward(
self,
hidden_states: torch.Tensor,
attention_mask: torch.Tensor,
layer_head_mask: torch.Tensor,
output_attentions: bool = False,
) -> torch.Tensor:
"""
Args:
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq... | 9,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
hidden_states, attn_weights, _ = self.self_attn(
hidden_states=hidden_states,
attention_mask=attention_mask,
layer_head_mask=layer_head_mask,
output_attentions=output_attentions,
)
hidden_states = nn.functional.dropout(hidden_states, p=self.dropout, traini... | 9,953 | /Users/nielsrogge/Documents/python_projecten/transformers/src/transformers/models/qwen2_audio/modeling_qwen2_audio.py |
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