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Ernie 4.5 VL MoE

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This model was released on 2025-06-30 and added to Hugging Face Transformers on TBD.

PyTorch FlashAttention SDPA Tensor parallelism

Ernie 4.5 VL MoE

Overview

The Ernie 4.5 VL MoE model was released in the Ernie 4.5 Model Family release by baidu. This family of models contains multiple different architectures and model sizes. The Vision-Language series in specific is composed of a novel multimodal heterogeneous structure, sharing paremeters across modalities and dedicating parameters to specific modalities. This becomes especially apparent in the Mixture of Expert (MoE) which is composed of

  • Dedicated Text Experts
  • Dedicated Vision Experts
  • Shared Experts

This architecture has the advantage to enhance multimodal understanding without compromising, and even improving, performance on text-related tasks. An more detailed breakdown is given in the Technical Report.

Other models from the family can be found at Ernie 4.5 and at Ernie 4.5 MoE.

Usage

The example below demonstrates how to generate text based on an image with Pipeline or the AutoModel class.

Pipeline
AutoModel 
from transformers import pipeline

pipe = pipeline(
    task="image-text-to-text",
    model="baidu/ERNIE-4.5-VL-28B-A3B-PT",
    device_map="auto",
    revision="refs/pr/10",
)
message = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What kind of dog is this?"},
            {
                "type": "image",
                "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
            },
        ],
    }
]
print(pipe(text=message, max_new_tokens=20, return_full_text=False))

Using Ernie 4.5 VL MoE with video input is similar to using it with image input. The model can process video data and generate text based on the content of the video.

from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "baidu/ERNIE-4.5-VL-28B-A3B-PT",
    dtype="auto",
    device_map="auto",  # Use tp_plan="auto" instead to enable Tensor Parallelism!
    revision="refs/pr/10",
)
processor = AutoProcessor.from_pretrained("baidu/ERNIE-4.5-VL-28B-A3B-PT", revision="refs/pr/10")
message = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Please describe what you can see during this video."},
            {
                "type": "video",
                "url": "https://huggingface.co/datasets/raushan-testing-hf/videos-test/resolve/main/tiny_video.mp4",
            },
        ],
    }
]

inputs = processor.apply_chat_template(
    message,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt"
).to(model.device)

generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)

Ernie4_5_VL_MoeConfig

class transformers.Ernie4_5_VL_MoeConfig

< >

( text_config = None vision_config = None image_start_token_id = 101304 image_end_token_id = 101305 image_token_id = 100295 video_start_token_id = 101306 video_end_token_id = 101307 video_token_id = 103367 **kwargs )

Parameters

  • text_config (Union[PreTrainedConfig, dict], optional, defaults to Ernie4_5_VL_MoeTextConfig) — The config object or dictionary of the text backbone.
  • vision_config (Union[PreTrainedConfig, dict], optional, defaults to Ernie4_5_VL_MoeVisionConfig) — The config object or dictionary of the vision backbone.
  • image_start_token_id (int, optional, defaults to 101304) — The image token index to encode the start of image.
  • image_end_token_id (int, optional, defaults to 101305) — The image token index to encode the end of image.
  • image_token_id (int, optional, defaults to 100295) — The image token index to encode the image prompt.
  • video_start_token_id (int, optional, defaults to 101306) — The video token index to encode the start of video.
  • video_end_token_id (int, optional, defaults to 101307) — The video token index to encode the end of video.
  • video_token_id (int, optional, defaults to 103367) — The video token index to encode the video prompt.

This is the configuration class to store the configuration of a Ernie4_5_VL_MoeModel. It is used to instantiate a Ernie4.5-VL MoE model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of Ernie 4.5 VL 28B A3B baidu/ERNIE-4.5-VL-28B-A3B-PT.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

>>> from transformers import Ernie4_5_VL_MoeForConditionalGeneration, Ernie4_5_VL_MoeConfig

>>> # Initializing a Ernie4_5_VL_Moe style configuration
>>> configuration = Ernie4_5_VL_MoeConfig()

>>> # Initializing a model from the Ernie 4.5 VL 28B A3B configuration
>>> model = Ernie4_5_VL_MoeForConditionalGeneration(configuration)

>>> # Accessing the model configuration
>>> configuration = model.config

Ernie4_5_VL_MoeTextConfig

class transformers.Ernie4_5_VL_MoeTextConfig

< >

( vocab_size = 103424 hidden_size = 2560 intermediate_size = 12288 num_hidden_layers = 28 num_attention_heads = 20 num_key_value_heads = 4 hidden_act = 'silu' max_position_embeddings = 131072 initializer_range = 0.02 rms_norm_eps = 1e-05 use_cache = True use_bias = False tie_word_embeddings = True rope_parameters = None mlp_layer_types = None moe_intermediate_size = None moe_k = 6 moe_num_experts = 64 moe_num_shared_experts = 2 moe_norm_min = 1e-12 output_router_logits = False router_aux_loss_coef = 0.001 **kwargs )

Parameters

  • vocab_size (int, optional, defaults to 103424) — Vocabulary size of the Ernie 4.5 VL model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling Ernie4_5_VL_MoeTextModel
  • hidden_size (int, optional, defaults to 2560) — Dimension of the hidden representations.
  • intermediate_size (int, optional, defaults to 12288) — Dimension of the MLP representations.
  • num_hidden_layers (int, optional, defaults to 28) — Number of hidden layers in the Transformer encoder.
  • num_attention_heads (int, optional, defaults to 20) — Number of attention heads for each attention layer in the Transformer encoder.
  • num_key_value_heads (int, optional, defaults to 4) — This is the number of key_value heads that should be used to implement Grouped Query Attention. If num_key_value_heads=num_attention_heads, the model will use Multi Head Attention (MHA), if num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details, check out this paper. If it is not specified, will default to 4.
  • hidden_act (str or function, optional, defaults to "silu") — The non-linear activation function (function or string) in the decoder.
  • max_position_embeddings (int, optional, defaults to 131072) — The maximum sequence length that this model might ever be used with.
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
  • rms_norm_eps (float, optional, defaults to 1e-05) — The epsilon used by the rms normalization layers.
  • use_cache (bool, optional, defaults to True) — Whether or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.
  • use_bias (bool, optional, defaults to False) — Whether to use a bias in any of the projections including mlp and attention for example.
  • tie_word_embeddings (bool, optional, defaults to True) — Whether the model’s input and output word embeddings should be tied.
  • rope_parameters (RopeParameters, optional) — Dictionary containing the configuration parameters for the RoPE embeddings. The dictionaty should contain a value for rope_theta and optionally parameters used for scaling in case you want to use RoPE with longer max_position_embeddings.
  • mlp_layer_types (list, optional) — MLP (Moe vs Dense) pattern for each layer.
  • moe_intermediate_size (list[int], optional, defaults to [1536, 512]) — Intermediate size of the routed experts; differs between text (first) and image (second) experts.
  • moe_k (int, optional, defaults to 6) — Number of selected experts.
  • moe_num_experts (int, optional, defaults to 64) — Number of routed experts.
  • moe_num_shared_experts (int, optional, defaults to 2) — The number of experts that are shared for all MoE forwards.
  • moe_norm_min (float, optional, defaults to 1e-12) — Minimum division value during routing normalization.
  • output_router_logits (bool, optional, defaults to False) — Whether or not the router logits should be returned by the model. Enabling this will also allow the model to output the auxiliary loss, including load balancing loss and router z-loss.
  • router_aux_loss_coef (float, optional, defaults to 0.001) — The aux loss factor for the total loss.

This is the configuration class to store the configuration of a Ernie4_5_VL_MoeTextModel. It is used to instantiate a the text model portion of the complete Ernie4.5-VL Moe model according to the specified arguments, defining the model architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Ernie4_5_VL_MoeVisionConfig

class transformers.Ernie4_5_VL_MoeVisionConfig

< >

( depth = 32 hidden_size = 1280 hidden_act = 'quick_gelu' intermediate_size = 5120 num_heads = 16 in_channels = 3 patch_size = 14 spatial_merge_size = 2 temporal_merge_size = 2 rms_norm_eps = 1e-06 initializer_range = 0.02 **kwargs )

Parameters

  • depth (int, optional, defaults to 32) — Number of layers (depth) in the model.
  • hidden_size (int, optional, defaults to 1280) — Dimensionality of the encoder layers and the pooler layer.
  • hidden_act (str or function, optional, defaults to "quick_gelu") — The non-linear activation function (function or string) in the encoder and pooler.
  • intermediate_size (int, optional, defaults to 5120) — Dimensionality of the “intermediate” (i.e., feed-forward) layer in the Transformer encoder.
  • num_heads (int, optional, defaults to 16) — Number of attention heads for each attention layer in the Transformer encoder.
  • in_channels (int, optional, defaults to 3) — The number of input channels.
  • patch_size (int, optional, defaults to 14) — The size (resolution) of each patch.
  • spatial_merge_size (int, optional, defaults to 2) — The size used for merging spatial dimensions.
  • temporal_merge_size (int, optional, defaults to 2) — The size used for merge along the temporal dimension.
  • rms_norm_eps (float, optional, defaults to 1e-06) — The epsilon used by the rms normalization layers.
  • initializer_range (float, optional, defaults to 0.02) — The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

This is the configuration class to store the configuration of the Ernie4_5_VL_MoeVisionTransformerPretrainedModel. It is used to instantiate the vision models portion of the complete Ernie4.5-VL Moe model according to the specified arguments, defining the model architecture.

Configuration objects inherit from PretrainedConfig and can be used to control the model outputs. Read the documentation from PretrainedConfig for more information.

Ernie4_5_VL_MoeImageProcessor

class transformers.Ernie4_5_VL_MoeImageProcessor

< >

( do_resize: bool = True size: typing.Optional[dict[str, int]] = None resample: Resampling = <Resampling.BICUBIC: 3> do_rescale: bool = True rescale_factor: typing.Union[int, float] = 0.00392156862745098 do_normalize: bool = True image_mean: typing.Union[float, list[float], NoneType] = None image_std: typing.Union[float, list[float], NoneType] = None do_convert_rgb: bool = True patch_size: int = 14 temporal_patch_size: typing.Optional[int] = None merge_size: int = 2 **kwargs )

Parameters

  • do_resize (bool, optional, defaults to True) — Whether to resize the image’s (height, width) dimensions.
  • size (dict[str, int], optional, defaults to {"shortest_edge" -- 56 * 56, "longest_edge": 28 * 28 * 6177}): Size of the image after resizing. shortest_edge and longest_edge keys must be present.
  • resample (PILImageResampling, optional, defaults to Resampling.BICUBIC) — Resampling filter to use when resizing the image.
  • do_rescale (bool, optional, defaults to True) — Whether to rescale the image by the specified scale rescale_factor.
  • rescale_factor (int or float, optional, defaults to 1/255) — Scale factor to use if rescaling the image.
  • do_normalize (bool, optional, defaults to True) — Whether to normalize the image.
  • image_mean (float or list[float], optional, defaults to [0.48145466, 0.4578275, 0.40821073]) — Mean to use if normalizing the image. This is a float or list of floats for each channel in the image.
  • image_std (float or list[float], optional, defaults to [0.26862954, 0.26130258, 0.27577711]) — Standard deviation to use if normalizing the image. This is a float or list of floats for each channel in the image.
  • do_convert_rgb (bool, optional, defaults to True) — Whether to convert the image to RGB.
  • patch_size (int, optional, defaults to 14) — The spatial patch size of the vision encoder.
  • temporal_patch_size (int, optional) — The temporal patch size of the vision encoder. Unused in the image processor, only used for videos.
  • merge_size (int, optional, defaults to 2) — The merge size of the vision encoder to llm encoder.

Constructs a Ernie 4.5 VL image processor that dynamically resizes images based on the original images.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] do_resize: typing.Optional[bool] = None size: typing.Optional[dict[str, int]] = None resample: typing.Optional[PIL.Image.Resampling] = None do_rescale: typing.Optional[bool] = None rescale_factor: typing.Optional[float] = None do_normalize: typing.Optional[bool] = None image_mean: typing.Union[float, list[float], NoneType] = None image_std: typing.Union[float, list[float], NoneType] = None patch_size: typing.Optional[int] = None temporal_patch_size: typing.Optional[int] = None merge_size: typing.Optional[int] = None do_convert_rgb: typing.Optional[bool] = None return_tensors: typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None data_format: typing.Optional[transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'> input_data_format: typing.Union[str, transformers.image_utils.ChannelDimension, NoneType] = None )

Parameters

  • images (ImageInput) — Image 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.
  • do_resize (bool, optional, defaults to self.do_resize) — Whether to resize the image.
  • 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.resample) — 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 (float, optional, defaults to self.rescale_factor) — Rescale factor to rescale the image by if do_rescale is set to True.
  • do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the image.
  • 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 deviation to use for normalization. Only has an effect if do_normalize is set to True.
  • patch_size (int, optional, defaults to self.patch_size) — The spatial patch size of the vision encoder.
  • temporal_patch_size (int, optional, defaults to self.temporal_patch_size) — The temporal patch size of the vision encoder.
  • merge_size (int, optional, defaults to self.merge_size) — The merge size of the vision encoder to llm encoder.
  • do_convert_rgb (bool, optional, defaults to self.do_convert_rgb) — Whether to convert the image to RGB.
  • return_tensors (str or TensorType, optional) — The type of tensors to return. Can be one of:
    • Unset: Return a list of np.ndarray.
    • TensorType.PYTORCH or 'pt': Return a batch of type torch.Tensor.
    • TensorType.NUMPY or 'np': Return a batch of type np.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.FIRST: image in (num_channels, height, width) format.
    • "channels_last" or ChannelDimension.LAST: image in (height, width, num_channels) format.
    • Unset: Use the channel dimension format of the input image.
  • input_data_format (ChannelDimension or str, optional) — 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 ChannelDimension.LAST: image in (height, width, num_channels) format.
    • "none" or ChannelDimension.NONE: image in (height, width) format.

Ernie4_5_VL_MoeImageProcessorFast

class transformers.Ernie4_5_VL_MoeImageProcessorFast

< >

( **kwargs: typing_extensions.Unpack[transformers.models.ernie4_5_vl_moe.image_processing_ernie4_5_vl_moe.Ernie4_5_VL_MoeImageProcessorKwargs] )

Constructs a fast Ernie4 5 Vl Moe image processor.

preprocess

< >

( images: typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), list['PIL.Image.Image'], list[numpy.ndarray], list['torch.Tensor']] **kwargs: typing_extensions.Unpack[transformers.models.ernie4_5_vl_moe.image_processing_ernie4_5_vl_moe.Ernie4_5_VL_MoeImageProcessorKwargs] ) → <class 'transformers.image_processing_base.BatchFeature'>

Parameters

  • images (Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, list, list, list]) — Image 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.
  • do_convert_rgb (bool, optional) — Whether to convert the image to RGB.
  • do_resize (bool, optional) — Whether to resize the image.
  • size (Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]) — Describes the maximum input dimensions to the model.
  • crop_size (Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]) — Size of the output image after applying center_crop.
  • resample (Annotated[Union[PILImageResampling, int, NoneType], None]) — 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) — Whether to rescale the image.
  • rescale_factor (float, optional) — Rescale factor to rescale the image by if do_rescale is set to True.
  • do_normalize (bool, optional) — Whether to normalize the image.
  • image_mean (Union[float, list[float], tuple[float, ...], NoneType]) — Image mean to use for normalization. Only has an effect if do_normalize is set to True.
  • image_std (Union[float, list[float], tuple[float, ...], NoneType]) — Image standard deviation to use for normalization. Only has an effect if do_normalize is set to True.
  • do_pad (bool, optional) — Whether to pad the image. Padding is done either to the largest size in the batch or to a fixed square size per image. The exact padding strategy depends on the model.
  • pad_size (Annotated[Union[int, list[int], tuple[int, ...], dict[str, int], NoneType], None]) — The size in {"height": int, "width" int} to pad the images to. Must be larger than any image size provided for preprocessing. If pad_size is not provided, images will be padded to the largest height and width in the batch. Applied only when do_pad=True.
  • do_center_crop (bool, optional) — Whether to center crop the image.
  • data_format (Union[~image_utils.ChannelDimension, str, NoneType]) — Only ChannelDimension.FIRST is supported. Added for compatibility with slow processors.
  • input_data_format (Union[~image_utils.ChannelDimension, str, NoneType]) — 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 ChannelDimension.LAST: image in (height, width, num_channels) format.
    • "none" or ChannelDimension.NONE: image in (height, width) format.
  • device (Annotated[Union[str, torch.device, NoneType], None]) — The device to process the images on. If unset, the device is inferred from the input images.
  • return_tensors (Annotated[Union[str, ~utils.generic.TensorType, NoneType], None]) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
  • disable_grouping (bool, optional) — Whether to disable grouping of images by size to process them individually and not in batches. If None, will be set to True if the images are on CPU, and False otherwise. This choice is based on empirical observations, as detailed here: https://github.com/huggingface/transformers/pull/38157
  • image_seq_length (int, optional) — The number of image tokens to be used for each image in the input. Added for backward compatibility but this should be set as a processor attribute in future models.
  • patch_size (int, optional, defaults to 14) — The spatial patch size of the vision encoder.
  • temporal_patch_size (int, optional) — The temporal patch size of the vision encoder. Unused in the image processor, only used for videos.
  • merge_size (int, optional, defaults to 2) — The merge size of the vision encoder to llm encoder.

Returns

<class 'transformers.image_processing_base.BatchFeature'>

  • data (dict) — Dictionary of lists/arrays/tensors returned by the call method (‘pixel_values’, etc.).
  • tensor_type (Union[None, str, TensorType], optional) — You can give a tensor_type here to convert the lists of integers in PyTorch/Numpy Tensors at initialization.

Ernie4_5_VL_MoeVideoProcessor

class transformers.Ernie4_5_VL_MoeVideoProcessor

< >

( **kwargs: typing_extensions.Unpack[transformers.models.ernie4_5_vl_moe.video_processing_ernie4_5_vl_moe.Ernie4_5_VL_MoeVideoProcessorInitKwargs] )

Parameters

  • do_resize (bool, optional, defaults to self.do_resize) — Whether to resize the video’s (height, width) dimensions to the specified size. Can be overridden by the do_resize parameter in the preprocess method.
  • size (dict, optional, defaults to self.size) — Size of the output video after resizing. Can be overridden by the size parameter in the preprocess method.
  • size_divisor (int, optional, defaults to self.size_divisor) — The size by which to make sure both the height and width can be divided.
  • default_to_square (bool, optional, defaults to self.default_to_square) — Whether to default to a square video when resizing, if size is an int.
  • resample (PILImageResampling, optional, defaults to self.resample) — Resampling filter to use if resizing the video. Only has an effect if do_resize is set to True. Can be overridden by the resample parameter in the preprocess method.
  • do_center_crop (bool, optional, defaults to self.do_center_crop) — Whether to center crop the video to the specified crop_size. Can be overridden by do_center_crop in the preprocess method.
  • crop_size (dict[str, int] optional, defaults to self.crop_size) — Size of the output video after applying center_crop. Can be overridden by crop_size in the preprocess method.
  • do_rescale (bool, optional, defaults to self.do_rescale) — Whether to rescale the video by the specified scale rescale_factor. Can be overridden by the do_rescale parameter in the preprocess method.
  • rescale_factor (int or float, optional, defaults to self.rescale_factor) — Scale factor to use if rescaling the video. Only has an effect if do_rescale is set to True. Can be overridden by the rescale_factor parameter in the preprocess method.
  • do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the video. Can be overridden by the do_normalize parameter in the preprocess method. Can be overridden by the do_normalize parameter in the preprocess method.
  • image_mean (float or list[float], optional, defaults to self.image_mean) — Mean to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. Can be overridden by the image_mean parameter in the preprocess method. Can be overridden by the image_mean parameter in the preprocess method.
  • image_std (float or list[float], optional, defaults to self.image_std) — Standard deviation to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. 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 self.image_std) — Whether to convert the video to RGB.
  • video_metadata (VideoMetadata, optional) — Metadata of the video containing information about total duration, fps and total number of frames.
  • do_sample_frames (int, optional, defaults to self.do_sample_frames) — Whether to sample frames from the video before processing or to process the whole video.
  • num_frames (int, optional, defaults to self.num_frames) — Maximum number of frames to sample when do_sample_frames=True.
  • fps (int or float, optional, defaults to self.fps) — Target frames to sample per second when do_sample_frames=True.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
  • data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) — The channel dimension format for the output video. Can be one of:

    • "channels_first" or ChannelDimension.FIRST: video in (num_channels, height, width) format.
    • "channels_last" or ChannelDimension.LAST: video in (height, width, num_channels) format.
    • Unset: Use the channel dimension format of the input video.
  • input_data_format (ChannelDimension or str, optional) — The channel dimension format for the input video. If unset, the channel dimension format is inferred from the input video. Can be one of:

    • "channels_first" or ChannelDimension.FIRST: video in (num_channels, height, width) format.
    • "channels_last" or ChannelDimension.LAST: video in (height, width, num_channels) format.
    • "none" or ChannelDimension.NONE: video in (height, width) format.
  • device (torch.device, optional) — The device to process the videos on. If unset, the device is inferred from the input videos.
  • return_metadata (bool, optional) — Whether to return video metadata or not.
  • patch_size (int, optional, defaults to 14) — The spacial patch size of the vision encoder.
  • temporal_patch_size (int, optional, defaults to 2) — The temporal patch size of the vision encoder.
  • merge_size (int, optional, defaults to 2) — The merge size of the vision encoder to llm encoder.
  • min_frames (int, optional, defaults to 16) — The minimum number of frames that can be sampled.
  • max_frames (int, optional, defaults to 180) — The maximum number of frames that can be sampled.
  • draw_on_frames (bool, optional, defaults to True) — Whether to draw timestamps on each frame or not. This does not work with torch.compile but resembles the performance of the original model.
  • font (str, optional, defaults to “Roboto-Regular.ttf”) — The associated font name for drawing on frames. Defaults to “Roboto-Regular.ttf” and is expected to be saved along the processor as separate file.

Constructs a fast Ernie 4.5 VL image processor that dynamically resizes videos based on the original videos.

preprocess

< >

( videos: typing.Union[list['PIL.Image.Image'], numpy.ndarray, ForwardRef('torch.Tensor'), list[numpy.ndarray], list['torch.Tensor'], list[list['PIL.Image.Image']], list[list[numpy.ndarray]], list[list['torch.Tensor']], transformers.video_utils.URL, list[transformers.video_utils.URL], list[list[transformers.video_utils.URL]], transformers.video_utils.Path, list[transformers.video_utils.Path], list[list[transformers.video_utils.Path]]] **kwargs: typing_extensions.Unpack[transformers.processing_utils.VideosKwargs] )

Parameters

  • do_resize (bool, optional, defaults to self.do_resize) — Whether to resize the video’s (height, width) dimensions to the specified size. Can be overridden by the do_resize parameter in the preprocess method.
  • size (dict, optional, defaults to self.size) — Size of the output video after resizing. Can be overridden by the size parameter in the preprocess method.
  • size_divisor (int, optional, defaults to self.size_divisor) — The size by which to make sure both the height and width can be divided.
  • default_to_square (bool, optional, defaults to self.default_to_square) — Whether to default to a square video when resizing, if size is an int.
  • resample (PILImageResampling, optional, defaults to self.resample) — Resampling filter to use if resizing the video. Only has an effect if do_resize is set to True. Can be overridden by the resample parameter in the preprocess method.
  • do_center_crop (bool, optional, defaults to self.do_center_crop) — Whether to center crop the video to the specified crop_size. Can be overridden by do_center_crop in the preprocess method.
  • crop_size (dict[str, int] optional, defaults to self.crop_size) — Size of the output video after applying center_crop. Can be overridden by crop_size in the preprocess method.
  • do_rescale (bool, optional, defaults to self.do_rescale) — Whether to rescale the video by the specified scale rescale_factor. Can be overridden by the do_rescale parameter in the preprocess method.
  • rescale_factor (int or float, optional, defaults to self.rescale_factor) — Scale factor to use if rescaling the video. Only has an effect if do_rescale is set to True. Can be overridden by the rescale_factor parameter in the preprocess method.
  • do_normalize (bool, optional, defaults to self.do_normalize) — Whether to normalize the video. Can be overridden by the do_normalize parameter in the preprocess method. Can be overridden by the do_normalize parameter in the preprocess method.
  • image_mean (float or list[float], optional, defaults to self.image_mean) — Mean to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. Can be overridden by the image_mean parameter in the preprocess method. Can be overridden by the image_mean parameter in the preprocess method.
  • image_std (float or list[float], optional, defaults to self.image_std) — Standard deviation to use if normalizing the video. This is a float or list of floats the length of the number of channels in the video. 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 self.image_std) — Whether to convert the video to RGB.
  • video_metadata (VideoMetadata, optional) — Metadata of the video containing information about total duration, fps and total number of frames.
  • do_sample_frames (int, optional, defaults to self.do_sample_frames) — Whether to sample frames from the video before processing or to process the whole video.
  • num_frames (int, optional, defaults to self.num_frames) — Maximum number of frames to sample when do_sample_frames=True.
  • fps (int or float, optional, defaults to self.fps) — Target frames to sample per second when do_sample_frames=True.
  • return_tensors (str or TensorType, optional) — Returns stacked tensors if set to `pt, otherwise returns a list of tensors.
  • data_format (ChannelDimension or str, optional, defaults to ChannelDimension.FIRST) — The channel dimension format for the output video. Can be one of:
    • "channels_first" or ChannelDimension.FIRST: video in (num_channels, height, width) format.
    • "channels_last" or ChannelDimension.LAST: video in (height, width, num_channels) format.
    • Unset: Use the channel dimension format of the input video.
  • input_data_format (ChannelDimension or str, optional) — The channel dimension format for the input video. If unset, the channel dimension format is inferred from the input video. Can be one of:
    • "channels_first" or ChannelDimension.FIRST: video in (num_channels, height, width) format.
    • "channels_last" or ChannelDimension.LAST: video in (height, width, num_channels) format.
    • "none" or ChannelDimension.NONE: video in (height, width) format.
  • device (torch.device, optional) — The device to process the videos on. If unset, the device is inferred from the input videos.
  • return_metadata (bool, optional) — Whether to return video metadata or not.

Ernie4_5_VL_MoeProcessor

class transformers.Ernie4_5_VL_MoeProcessor

< >

( image_processor = None tokenizer = None video_processor = None chat_template = None **kwargs )

Parameters

  • image_processor (Ernie4_5_VL_MoeImageProcessor, optional) — The image processor is a required input.
  • tokenizer (LlamaTokenizerFast, optional) — The tokenizer is a required input.
  • video_processor (Ernie4_5_VL_MoeVideoProcessor, optional) — The video processor is a required input.
  • chat_template (str, optional) — A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string.

Constructs a Ernie 4.5 VL processor which wraps a Ernie 4.5 VL image processor and a Llama tokenizer into a single processor. Ernie4_5_VL_MoeProcessor offers all the functionalities of Ernie4_5_VL_MoeImageProcessor and LlamaTokenizerFast. See the __call__() and decode() for more information.

save_pretrained

< >

( save_directory push_to_hub: bool = False **kwargs )

We additionally save a copy of the font to the save_directory (if we found a file there)

Ernie4_5_VL_MoeTextModel

class transformers.Ernie4_5_VL_MoeTextModel

< >

( config: Ernie4_5_VL_MoeTextConfig )

Parameters

  • config (Ernie4_5_VL_MoeTextConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Ernie4 5 Vl Moe Text Model outputting raw hidden-states without any specific head on to.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( input_ids: typing.Optional[torch.LongTensor] = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None moe_mm_token_type_ids: typing.Optional[torch.IntTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None cache_position: typing.Optional[torch.LongTensor] = None **kwargs: typing_extensions.Unpack[transformers.modeling_flash_attention_utils.FlashAttentionKwargs] ) → transformers.modeling_outputs.MoeModelOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • moe_mm_token_type_ids (torch.IntTensor of shape (batch_size, sequence_length), optional) — The same as mm_token_type_ids while additionally considering start/end image/video tokens as respective vision tokens.
  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — 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_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

transformers.modeling_outputs.MoeModelOutputWithPast or tuple(torch.FloatTensor)

A transformers.modeling_outputs.MoeModelOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (Ernie4_5_VL_MoeConfig) and inputs.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • router_logits (tuple(torch.FloatTensor), optional, returned when output_router_probs=True and config.add_router_probs=True is passed or when config.output_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).

    Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary loss for Mixture of Experts models.

The Ernie4_5_VL_MoeTextModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Ernie4_5_VL_MoeVisionTransformerPretrainedModel

class transformers.Ernie4_5_VL_MoeVisionTransformerPretrainedModel

< >

( config )

Parameters

The bare Ernie4 5 Vl Moe Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( hidden_states: Tensor grid_thw: Tensor **kwargs )

Parameters

  • hidden_states (torch.Tensor) — input to the layer of shape (batch, seq_len, embed_dim) grid_thw (torch.LongTensorof shape(num_images, 3)`): The temporal, height and width dimensions of feature shape for each image. Each row contains [t, h, w] values.

The Ernie4_5_VL_MoeVisionTransformerPretrainedModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Ernie4_5_VL_MoeVariableResolutionResamplerModel

class transformers.Ernie4_5_VL_MoeVariableResolutionResamplerModel

< >

( config: Ernie4_5_VL_MoeConfig )

forward

< >

( hidden_states grid_thw )

Ernie4_5_VL_MoeModel

class transformers.Ernie4_5_VL_MoeModel

< >

( config: Ernie4_5_VL_MoeConfig )

Parameters

  • config (Ernie4_5_VL_MoeConfig) — Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the from_pretrained() method to load the model weights.

The bare Ernie4 5 Vl Moe Model outputting raw hidden-states without any specific head on top.

This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods the library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads etc.)

This model is also a PyTorch torch.nn.Module subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior.

forward

< >

( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None mm_token_type_ids: typing.Optional[torch.IntTensor] = None moe_mm_token_type_ids: typing.Optional[torch.IntTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None use_cache: typing.Optional[bool] = None pixel_values: typing.Optional[torch.Tensor] = None pixel_values_videos: typing.Optional[torch.FloatTensor] = None image_grid_thw: typing.Optional[torch.LongTensor] = None video_grid_thw: typing.Optional[torch.LongTensor] = None rope_deltas: typing.Optional[torch.LongTensor] = None cache_position: typing.Optional[torch.LongTensor] = None **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.MoeModelOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • mm_token_type_ids (torch.IntTensor of shape (batch_size, sequence_length), optional) — Token type ids matching each modality to a different value in the input sequence, i.e. text (0), image (1), video (2).
  • moe_mm_token_type_ids (torch.IntTensor of shape (batch_size, sequence_length), optional) — The same as mm_token_type_ids while additionally considering start/end image/video tokens as respective vision tokens.
  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — 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_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using image_processor_class. See image_processor_class.__call__ for details (processor_class uses image_processor_class for processing images).
  • pixel_values_videos (torch.FloatTensor of shape (batch_size, num_frames, num_channels, frame_size, frame_size), optional) — The tensors corresponding to the input video. Pixel values for videos can be obtained using video_processor_class. See video_processor_class.__call__ for details (processor_class uses video_processor_class for processing videos).
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • video_grid_thw (torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM.
  • rope_deltas (torch.LongTensor of shape (batch_size, ), optional) — The rope index difference between sequence length and multimodal rope.
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.

Returns

transformers.modeling_outputs.MoeModelOutputWithPast or tuple(torch.FloatTensor)

A transformers.modeling_outputs.MoeModelOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (None) and inputs.

  • last_hidden_state (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size)) — Sequence of hidden-states at the output of the last layer of the model.

  • past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

    Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if config.is_encoder_decoder=True in the cross-attention blocks) that can be used (see past_key_values input) to speed up sequential decoding.

  • hidden_states (tuple(torch.FloatTensor), optional, returned when output_hidden_states=True is passed or when config.output_hidden_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

  • router_logits (tuple(torch.FloatTensor), optional, returned when output_router_probs=True and config.add_router_probs=True is passed or when config.output_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).

    Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary loss for Mixture of Experts models.

The Ernie4_5_VL_MoeModel forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Ernie4_5_VL_MoeForConditionalGeneration

class transformers.Ernie4_5_VL_MoeForConditionalGeneration

< >

( config )

forward

< >

( input_ids: LongTensor = None attention_mask: typing.Optional[torch.Tensor] = None position_ids: typing.Optional[torch.LongTensor] = None mm_token_type_ids: typing.Optional[torch.IntTensor] = None moe_mm_token_type_ids: typing.Optional[torch.IntTensor] = None past_key_values: typing.Optional[transformers.cache_utils.Cache] = None inputs_embeds: typing.Optional[torch.FloatTensor] = None labels: typing.Optional[torch.LongTensor] = None use_cache: typing.Optional[bool] = None output_router_logits: typing.Optional[bool] = None pixel_values: typing.Optional[torch.Tensor] = None pixel_values_videos: typing.Optional[torch.FloatTensor] = None image_grid_thw: typing.Optional[torch.LongTensor] = None video_grid_thw: typing.Optional[torch.LongTensor] = None rope_deltas: typing.Optional[torch.LongTensor] = None cache_position: typing.Optional[torch.LongTensor] = None logits_to_keep: typing.Union[int, torch.Tensor] = 0 **kwargs: typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs] ) → transformers.modeling_outputs.MoeCausalLMOutputWithPast or tuple(torch.FloatTensor)

Parameters

  • input_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.

    Indices can be obtained using AutoTokenizer. See PreTrainedTokenizer.encode() and PreTrainedTokenizer.call() for details.

    What are input IDs?

  • attention_mask (torch.Tensor of shape (batch_size, sequence_length), optional) — Mask to avoid performing attention on padding token indices. Mask values selected in [0, 1]:

    • 1 for tokens that are not masked,
    • 0 for tokens that are masked.

    What are attention masks?

  • position_ids (torch.LongTensor of shape (batch_size, sequence_length), optional) — Indices of positions of each input sequence tokens in the position embeddings. Selected in the range [0, config.n_positions - 1].

    What are position IDs?

  • mm_token_type_ids (torch.IntTensor of shape (batch_size, sequence_length), optional) — Token type ids matching each modality to a different value in the input sequence, i.e. text (0), image (1), video (2).
  • moe_mm_token_type_ids (torch.IntTensor of shape (batch_size, sequence_length), optional) — The same as mm_token_type_ids while additionally considering start/end image/video tokens as respective vision tokens.
  • past_key_values (~cache_utils.Cache, optional) — Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the past_key_values returned by the model at a previous stage of decoding, when use_cache=True or config.use_cache=True.

    Only Cache instance is allowed as input, see our kv cache guide. If no past_key_values are passed, DynamicCache will be initialized by default.

    The model will output the same cache format that is fed as input.

    If past_key_values are used, the user is expected to input only unprocessed input_ids (those that don’t have their past key value states given to this model) of shape (batch_size, unprocessed_length) instead of all input_ids of shape (batch_size, sequence_length).

  • inputs_embeds (torch.FloatTensor of shape (batch_size, sequence_length, hidden_size), optional) — 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_ids indices into associated vectors than the model’s internal embedding lookup matrix.
  • labels (torch.LongTensor of shape (batch_size, sequence_length), optional) — Labels for computing the masked language modeling loss. Indices should either be in [0, ..., 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].
  • use_cache (bool, optional) — If set to True, past_key_values key value states are returned and can be used to speed up decoding (see past_key_values).
  • output_router_logits (bool, optional) — Whether or not to return the logits of all the routers. They are useful for computing the router loss, and should not be returned during inference.
  • pixel_values (torch.Tensor of shape (batch_size, num_channels, image_size, image_size), optional) — The tensors corresponding to the input images. Pixel values can be obtained using image_processor_class. See image_processor_class.__call__ for details (processor_class uses image_processor_class for processing images).
  • pixel_values_videos (torch.FloatTensor of shape (batch_size, num_frames, num_channels, frame_size, frame_size), optional) — The tensors corresponding to the input video. Pixel values for videos can be obtained using video_processor_class. See video_processor_class.__call__ for details (processor_class uses video_processor_class for processing videos).
  • image_grid_thw (torch.LongTensor of shape (num_images, 3), optional) — The temporal, height and width of feature shape of each image in LLM.
  • video_grid_thw (torch.LongTensor of shape (num_videos, 3), optional) — The temporal, height and width of feature shape of each video in LLM.
  • rope_deltas (torch.LongTensor of shape (batch_size, ), optional) — The rope index difference between sequence length and multimodal rope.
  • cache_position (torch.LongTensor of shape (sequence_length), optional) — Indices depicting the position of the input sequence tokens in the sequence. Contrarily to position_ids, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length.
  • logits_to_keep (Union[int, torch.Tensor], optional, defaults to 0) — If an int, compute logits for the last 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, which becomes pretty significant for long sequences or large vocabulary size. If a torch.Tensor, must be 1D corresponding to the indices to keep in the sequence length dimension. This is useful when using packed tensor format (single dimension for batch and sequence length).

Returns

transformers.modeling_outputs.MoeCausalLMOutputWithPast or tuple(torch.FloatTensor)

A transformers.modeling_outputs.MoeCausalLMOutputWithPast or a tuple of torch.FloatTensor (if return_dict=False is passed or when config.return_dict=False) comprising various elements depending on the configuration (None) and inputs.

  • loss (torch.FloatTensor of shape (1,), optional, returned when labels is provided) — Language modeling loss (for next-token prediction).

  • logits (torch.FloatTensor of shape (batch_size, sequence_length, config.vocab_size)) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).

  • aux_loss (torch.FloatTensor, optional, returned when labels is provided) — aux_loss for the sparse modules.

  • router_logits (tuple(torch.FloatTensor), optional, returned when output_router_probs=True and config.add_router_probs=True is passed or when config.output_router_probs=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, sequence_length, num_experts).

    Raw router logtis (post-softmax) that are computed by MoE routers, these terms are used to compute the auxiliary loss for Mixture of Experts models.

  • past_key_values (Cache, optional, returned when use_cache=True is passed or when config.use_cache=True) — It is a Cache instance. For more details, see our kv cache guide.

    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_states=True) — Tuple of torch.FloatTensor (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each layer) of shape (batch_size, sequence_length, hidden_size).

    Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.

  • attentions (tuple(torch.FloatTensor), optional, returned when output_attentions=True is passed or when config.output_attentions=True) — Tuple of torch.FloatTensor (one for each layer) of shape (batch_size, num_heads, sequence_length, sequence_length).

    Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads.

The Ernie4_5_VL_MoeForConditionalGeneration forward method, overrides the __call__ special method.

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the pre and post processing steps while the latter silently ignores them.

Update on GitHub

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