407 lines
18 KiB
Python
407 lines
18 KiB
Python
# coding=utf-8
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# Copyright 2025 the HuggingFace Inc. team. All rights reserved.
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# Written by Orr Zohar
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Optional, Union
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import torch
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import torch.utils.checkpoint
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from torch import nn
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from ...cache_utils import Cache, DynamicCache
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from ...modeling_flash_attention_utils import FlashAttentionKwargs
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from ...processing_utils import Unpack
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from ...utils import auto_docstring, can_return_tuple, logging
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from ..idefics3.configuration_idefics3 import Idefics3Config, Idefics3VisionConfig
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from ..idefics3.image_processing_idefics3 import Idefics3ImageProcessor
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from ..idefics3.image_processing_idefics3_fast import Idefics3ImageProcessorFast
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from ..idefics3.modeling_idefics3 import (
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Idefics3BaseModelOutputWithPast,
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Idefics3ForConditionalGeneration,
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Idefics3Model,
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Idefics3PreTrainedModel,
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Idefics3VisionTransformer,
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)
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logger = logging.get_logger(__name__)
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class SmolVLMVisionConfig(Idefics3VisionConfig):
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r"""
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This is the configuration class to store the configuration of a [`SmolVLMVisionModel`]. It is used to instantiate a
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SmolVLM vision encoder according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the SigLIP checkpoint
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[google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) used in SmolVLM
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[HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct).
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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hidden_size (`int`, *optional*, defaults to 1152):
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Dimensionality of the encoder layers and the pooler layer.
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intermediate_size (`int`, *optional*, defaults to 3072):
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Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 12):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_channels (`int`, *optional*, defaults to 3):
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Number of channels in the input images.
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image_size (`int`, *optional*, defaults to 224):
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The size (resolution) of each image.
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patch_size (`int`, *optional*, defaults to 32):
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The size (resolution) of each patch.
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hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
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The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
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`"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
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layer_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio for the attention probabilities.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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Example:
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```python
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>>> from transformers.models.smolvlm.modeling_smolvlm import SmolVLMVisionTransformer
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>>> from transformers.models.smolvlm.configuration_smolvlm import SmolVLMVisionConfig
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>>> # Initializing a SmolVLMVisionConfig with google/siglip-so400m-patch14-384 style configuration
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>>> configuration = SmolVLMVisionConfig()
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>>> # Initializing a SmolVLMVisionTransformer (with random weights) from the google/siglip-so400m-patch14-384 style configuration
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>>> model = SmolVLMVisionTransformer(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "smolvlm_vision"
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pass
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class SmolVLMPreTrainedModel(Idefics3PreTrainedModel):
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pass
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class SmolVLMVisionTransformer(Idefics3VisionTransformer):
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pass
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class SmolVLMConfig(Idefics3Config):
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r"""
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This is the configuration class to store the configuration of a [`SmolVLMModel`]. It is used to instantiate a
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SmolVLM model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the model of the SmolVLM
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[HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should cache the key/value pairs of the attention mechanism. Only
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relevant if `config.is_decoder=True`.
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image_token_id (`int`, *optional*, defaults to 128257):
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The id of the "image" token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether or not to tie the word embeddings with the token embeddings.
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vision_config (`IdeficsVisionConfig` or `dict`, *optional*, defaults to `IdeficsVisionConfig`):
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Custom vision config or dict for the vision tower
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text_config (`PretrainedConfig` or `dict`, *optional*, defaults to `LlamaConfig`):
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Custom text config or dict for the text model
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scale_factor (`int`, *optional*, defaults to 2):
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The scale factor for the image encoder.
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pad_token_id (`int`, *optional*, defaults to 128002):
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The id of the padding token.
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Example:
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```python
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>>> from transformers import SmolVLMModel, SmolVLMConfig
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>>> # Initializing configuration
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>>> configuration = SmolVLMConfig()
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>>> # Initializing a model from the configuration
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>>> model = SmolVLMModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "smolvlm"
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pass
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class SmolVLMImageProcessor(Idefics3ImageProcessor):
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pass
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class SmolVLMImageProcessorFast(Idefics3ImageProcessorFast):
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pass
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class SmolVLMBaseModelOutputWithPast(Idefics3BaseModelOutputWithPast):
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pass
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class SmolVLMModel(Idefics3Model):
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"""
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A subclass of Idefics3Model. We do *not* remove or block the call to inputs_merger
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in forward. Instead, we override inputs_merger here with custom logic.
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"""
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def inputs_merger(
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self, input_ids: torch.LongTensor, inputs_embeds: torch.Tensor, image_hidden_states: torch.Tensor
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):
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_, patch_size, _ = image_hidden_states.shape
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if input_ids is None:
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image_mask = inputs_embeds == self.get_input_embeddings()(
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torch.tensor(self.config.image_token_id, dtype=torch.long, device=inputs_embeds.device)
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)
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image_mask = image_mask[..., 0] # slice off the hidden dim
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else:
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image_mask = input_ids == self.config.image_token_id
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num_image_tokens = image_mask.sum(dim=1)
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if not torch.all(num_image_tokens % patch_size == 0):
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raise ValueError("At least one sample has <image> tokens not divisible by patch_size.")
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blocks_per_sample = num_image_tokens // patch_size
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offsets = torch.nn.functional.pad(blocks_per_sample.cumsum(dim=0), (1, 0), value=0)
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block_offset = offsets[:-1]
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row_cum = image_mask.cumsum(dim=-1)
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chunk_idx = (row_cum - 1) // patch_size
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local_idx = (row_cum - 1) % patch_size
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block_idx = block_offset.unsqueeze(1) + chunk_idx
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image_embeds = torch.zeros_like(inputs_embeds)
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image_embeds[image_mask] = image_hidden_states[block_idx[image_mask], local_idx[image_mask], :]
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merged_embeds = torch.where(image_mask.unsqueeze(-1), image_embeds, inputs_embeds)
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return merged_embeds
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def get_image_features(self, pixel_values: torch.FloatTensor, pixel_attention_mask: torch.LongTensor = None):
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"""
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Encodes images into continuous embeddings that can be forwarded to the language model.
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Args:
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pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
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The tensors corresponding to the input images.
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pixel_attention_mask (`torch.LongTensor`, *optional*):
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The attention mask indicating padded regions in the image.
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"""
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batch_size, num_images, num_channels, height, width = pixel_values.shape
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pixel_values = pixel_values.view(batch_size * num_images, *pixel_values.shape[2:])
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# Remove padding images - padding images are full 0.
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nb_values_per_image = pixel_values.shape[1:].numel()
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real_images_inds = (pixel_values == 0.0).sum(dim=(-1, -2, -3)) != nb_values_per_image
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if not any(real_images_inds):
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# no images, leave one empty image.
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real_images_inds[0] = True
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pixel_values = pixel_values[real_images_inds].contiguous()
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# Handle the vision attention mask
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if pixel_attention_mask is None:
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pixel_attention_mask = torch.ones(
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size=[pixel_values.shape[i] for i in (0, 2, 3)],
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dtype=torch.bool,
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device=pixel_values.device,
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)
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else:
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# Remove padding images from the mask
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pixel_attention_mask = pixel_attention_mask.view(batch_size * num_images, *pixel_attention_mask.shape[2:])
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pixel_attention_mask = pixel_attention_mask[real_images_inds].contiguous()
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patch_size = self.config.vision_config.patch_size
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patches_subgrid = pixel_attention_mask.unfold(dimension=1, size=patch_size, step=patch_size)
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patches_subgrid = patches_subgrid.unfold(dimension=2, size=patch_size, step=patch_size)
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patch_attention_mask = (patches_subgrid.sum(dim=(-1, -2)) > 0).bool()
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# Get sequence from the vision encoder
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image_hidden_states = self.vision_model(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask)
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image_hidden_states = image_hidden_states.last_hidden_state
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# Modality projection & resampling
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image_hidden_states = self.connector(image_hidden_states)
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return image_hidden_states
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@can_return_tuple
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@auto_docstring(
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custom_intro="""
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Inputs fed to the model can have an arbitrary number of images. To account for this, pixel_values fed to
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the model have image padding -> (batch_size, max_num_images, 3, max_heights, max_widths) where
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max_num_images is the maximum number of images among the batch_size samples in the batch.
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Padding images are not needed beyond padding the pixel_values at the entrance of the model.
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For efficiency, we only pass through the vision_model's forward the real images by
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discarding the padding images i.e. pixel_values of size (image_batch_size, 3, height, width) where
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image_batch_size would be 7 when num_images_per_sample=[1, 3, 1, 2] and max_num_images would be 3.
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"""
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)
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def forward(
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self,
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input_ids: Optional[torch.LongTensor] = None,
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attention_mask: Optional[torch.Tensor] = None,
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position_ids: Optional[torch.LongTensor] = None,
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past_key_values: Optional[Cache] = None,
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inputs_embeds: Optional[torch.FloatTensor] = None,
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pixel_values: Optional[torch.FloatTensor] = None,
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pixel_attention_mask: Optional[torch.BoolTensor] = None,
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image_hidden_states: Optional[torch.FloatTensor] = None,
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use_cache: Optional[bool] = None,
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output_attentions: Optional[bool] = None,
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output_hidden_states: Optional[bool] = None,
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return_dict: Optional[bool] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Union[tuple, SmolVLMBaseModelOutputWithPast]:
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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output_hidden_states = (
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output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
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)
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use_cache = use_cache if use_cache is not None else self.config.use_cache
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict
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if self.training and self.text_model.gradient_checkpointing and use_cache:
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logger.warning_once(
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"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
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)
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use_cache = False
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# retrieve input_ids and inputs_embeds
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if input_ids is not None:
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batch_size, seq_length = input_ids.shape
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elif inputs_embeds is not None:
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batch_size, seq_length, _ = inputs_embeds.shape
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else:
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raise ValueError("You have to specify either input_ids or inputs_embeds")
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if use_cache and past_key_values is None:
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past_key_values = DynamicCache()
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if inputs_embeds is None:
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inputs_embeds = self.text_model.get_input_embeddings()(input_ids).to(input_ids.device)
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# START VISUAL INPUTS INTEGRATION
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if pixel_values is not None and image_hidden_states is not None:
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raise ValueError("You cannot specify both pixel_values and image_hidden_states at the same time")
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if pixel_values is not None:
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image_hidden_states = self.get_image_features(pixel_values, pixel_attention_mask).to(inputs_embeds.device)
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elif image_hidden_states is not None:
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image_hidden_states = image_hidden_states.to(dtype=self.dtype, device=inputs_embeds.device)
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if image_hidden_states is not None:
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# When we generate, we don't want to replace the potential image_token_id that we generated by images
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# that simply don't exist
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inputs_embeds = self.inputs_merger(
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input_ids=input_ids,
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inputs_embeds=inputs_embeds,
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image_hidden_states=image_hidden_states,
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)
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outputs = self.text_model(
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inputs_embeds=inputs_embeds,
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attention_mask=attention_mask,
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position_ids=position_ids,
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past_key_values=past_key_values,
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use_cache=use_cache,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=True,
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cache_position=cache_position,
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**kwargs,
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)
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return SmolVLMBaseModelOutputWithPast(
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last_hidden_state=outputs.last_hidden_state,
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past_key_values=outputs.past_key_values,
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hidden_states=outputs.hidden_states,
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attentions=outputs.attentions,
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image_hidden_states=image_hidden_states,
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)
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class SmolVLMForConditionalGeneration(Idefics3ForConditionalGeneration):
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def __init__(self, config):
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super().__init__(config)
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self.model = SmolVLMModel(config)
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self.lm_head = nn.Linear(config.text_config.hidden_size, config.text_config.vocab_size, bias=False)
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self.post_init()
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def forward(self, **super_kwargs):
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r"""
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pixel_attention_mask (`torch.Tensor` of shape `(batch_size, image_size, image_size)`, *optional*):
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Mask to avoid performing attention on padding pixel indices.
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image_hidden_states (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`):
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The hidden states of the image encoder after modality projection.
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
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Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
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config.vocab_size]` or `model.image_token_id`. Tokens with indices set to `model.image_token_id` are
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ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
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Example:
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```python
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>>> import requests
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>>> import torch
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>>> from PIL import Image
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>>> from io import BytesIO
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>>> from transformers import AutoProcessor, AutoModelForImageTextToText
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>>> from transformers.image_utils import load_image
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>>> # Note that passing the image urls (instead of the actual pil images) to the processor is also possible
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>>> image1 = load_image("https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg")
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>>> image2 = load_image("https://cdn.britannica.com/59/94459-050-DBA42467/Skyline-Chicago.jpg")
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>>> image3 = load_image("https://cdn.britannica.com/68/170868-050-8DDE8263/Golden-Gate-Bridge-San-Francisco.jpg")
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>>> processor = AutoProcessor.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct")
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>>> model = AutoModelForImageTextToText.from_pretrained("HuggingFaceTB/SmolVLM2-2.2B-Instruct", torch_dtype=torch.bfloat16, device_map="auto")
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>>> # Create inputs
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>>> messages = [
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... {
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... "role": "user",
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... "content": [
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... {"type": "video", "path": path/to/video},
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... {"type": "text", "text": "What is happening in this video?"},
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... ]
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... }
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... ]
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>>> inputs = processor.apply_chat_template([messages], add_generation_prompt=True)
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>>> # Generate
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>>> generated_ids = model.generate(**inputs, max_new_tokens=256)
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>>> generated_texts = processor.batch_decode(generated_ids, skip_special_tokens=True)
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>>> print(generated_texts)
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```"""
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super().forward(**super_kwargs)
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__all__ = [
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"SmolVLMVisionConfig",
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"SmolVLMConfig",
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"SmolVLMImageProcessor",
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"SmolVLMImageProcessorFast",
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"SmolVLMForConditionalGeneration",
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"SmolVLMPreTrainedModel",
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"SmolVLMModel",
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"SmolVLMVisionTransformer",
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]
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