# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/siglip2/modular_siglip2.py. # Copyright 2025 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import math import warnings from dataclasses import dataclass from typing import Any, Callable, Optional, Tuple, Union, List import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from torch.nn.init import _calculate_fan_in_and_fan_out from transformers.activations import ACT2FN from transformers.modeling_attn_mask_utils import _prepare_4d_attention_mask from transformers.configuration_utils import PretrainedConfig from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS, PreTrainedModel from transformers.utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, can_return_tuple, logging, replace_return_docstrings, ) from transformers.models.siglip2.configuration_siglip2 import Siglip2Config, Siglip2TextConfig from collections import defaultdict from itertools import accumulate from math import isqrt from typing import Dict logger = logging.get_logger(__name__) import inspect import os from typing import Optional, Tuple import torch import torch.nn.functional as F from transformers.utils import is_flash_attn_2_available, is_flash_attn_greater_or_equal, logging from transformers.integrations.flash_attention import flash_attention_forward as original_flash_attention_forward flash_241 = is_flash_attn_greater_or_equal("2.4.1") deterministic_g = os.environ.get("FLASH_ATTENTION_DETERMINISTIC", "0") == "1" logger = logging.get_logger(__name__) if is_flash_attn_2_available(): from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa from flash_attn import flash_attn_func, flash_attn_varlen_func, flash_attn_varlen_qkvpacked_func _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters) def _flash_attention_forward( query_states: torch.Tensor, key_states: torch.Tensor, value_states: torch.Tensor, attention_mask: torch.Tensor, query_length: int, is_causal: bool, dropout: float = 0.0, position_ids: Optional[torch.Tensor] = None, softmax_scale: Optional[float] = None, sliding_window: Optional[int] = None, use_top_left_mask: bool = False, softcap: Optional[float] = None, deterministic: bool = None, cu_seq_lens_q: Optional[torch.LongTensor] = None, cu_seq_lens_k: Optional[torch.LongTensor] = None, max_length_q: Optional[int] = None, max_length_k: Optional[int] = None, target_dtype: Optional[torch.dtype] = None, **kwargs, ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. Args: query_states (`torch.Tensor`): Input query states to be passed to Flash Attention API key_states (`torch.Tensor`): Input key states to be passed to Flash Attention API value_states (`torch.Tensor`): Input value states to be passed to Flash Attention API attention_mask (`torch.Tensor`): The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the position of padding tokens and 1 for the position of non-padding tokens. dropout (`int`, *optional*): Attention dropout softmax_scale (`float`, *optional*): The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim) use_sliding_windows (`bool`, *optional*): Whether to activate sliding window attention. """ if not use_top_left_mask: causal = is_causal else: # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__. causal = is_causal and query_length != 1 # Assuming 4D tensors, key_states.shape[1] is the key/value sequence length (source length). use_sliding_windows = ( _flash_supports_window_size and sliding_window is not None and key_states.shape[1] > sliding_window ) flash_kwargs = {"window_size": (sliding_window, sliding_window)} if use_sliding_windows else {} if flash_241: if deterministic is None: deterministic = deterministic_g flash_kwargs["deterministic"] = deterministic if softcap is not None: flash_kwargs["softcap"] = softcap attn_output = flash_attn_varlen_func( query_states[0], key_states[0], value_states[0], cu_seqlens_q=cu_seq_lens_q, cu_seqlens_k=cu_seq_lens_k, max_seqlen_q=max_length_q, max_seqlen_k=max_length_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, **flash_kwargs, ) return attn_output from transformers.utils import is_flash_attn_greater_or_equal_2_10 _use_top_left_mask = not is_flash_attn_greater_or_equal_2_10() def flash_attention_forward_for_packing( module: torch.nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor]=None, dropout: float = 0.0, scaling: Optional[float] = None, sliding_window: Optional[int] = None, softcap: Optional[float] = None, seq_len_list: Optional[List[int]] = None, **kwargs, ) -> Tuple[torch.Tensor, None]: # This is before the transpose seq_len = query.shape[2] # FA2 uses non-transposed inputs query = query.transpose(1, 2) key = key.transpose(1, 2) value = value.transpose(1, 2) # In PEFT, usually we cast the layer norms in float32 for training stability reasons # therefore the input hidden states gets silently casted in float32. Hence, we need # cast them back in the correct dtype just to be sure everything works as expected. # This might slowdown training & inference so it is recommended to not cast the LayerNorms # in fp32. (usually our RMSNorm modules handle it correctly) target_dtype = None if query.dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(module.config, "_pre_quantization_dtype"): target_dtype = module.config._pre_quantization_dtype else: target_dtype = next(layer for layer in module.modules() if isinstance(layer, torch.nn.Linear)).weight.dtype # FA2 always relies on the value set in the module, so remove it if present in kwargs to avoid passing it twice kwargs.pop("is_causal", None) cu_seqlens = F.pad(torch.cumsum(torch.tensor(seq_len_list, device=query.device, dtype=torch.int32), dim=0), (1, 0)) cu_seqlens = cu_seqlens.to(torch.int32) max_seq_len = max(seq_len_list) attn_output = _flash_attention_forward( query, key, value, attention_mask, query_length=seq_len, is_causal=module.is_causal, dropout=dropout, softmax_scale=scaling, sliding_window=sliding_window, softcap=softcap, use_top_left_mask=_use_top_left_mask, target_dtype=target_dtype, cu_seq_lens_q=cu_seqlens, cu_seq_lens_k=cu_seqlens, max_length_q=max_seq_len, max_length_k=max_seq_len, **kwargs, ) return attn_output.squeeze(0), None # General docstring _CONFIG_FOR_DOC = "Siglip2Config" class Siglip2VisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`Siglip2VisionModel`]. It is used to instantiate a Siglip2 vision encoder according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the Siglip2 [google/siglip2-base-patch16-naflex](https://huggingface.co/google/siglip2-base-patch16-naflex) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the encoder layers and the pooler layer. intermediate_size (`int`, *optional*, defaults to 3072): Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. num_hidden_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. num_attention_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. num_channels (`int`, *optional*, defaults to 3): Number of channels in the input images. num_patches (`int`, *optional*, defaults to 256): The number of patches in the image with the size of (`patch_size`, `patch_size`). The image is resized to fill maximum of this number of patches, and to preserve the aspect ratio. In case the resulted number of patches is lower, the image is padded in "patch" dimension. patch_size (`int`, *optional*, defaults to 16): The size (resolution) of each patch. hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported. layer_norm_eps (`float`, *optional*, defaults to 1e-06): The epsilon used by the layer normalization layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. Example: ```python >>> from transformers import Siglip2VisionConfig, Siglip2VisionModel >>> # Initializing a Siglip2VisionConfig with google/siglip2-base-patch16-naflex style configuration >>> configuration = Siglip2VisionConfig() >>> # Initializing a Siglip2VisionModel (with random weights) from the google/siglip2-base-patch16-naflex style configuration >>> model = Siglip2VisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "siglip2_vision_model" base_config_key = "vision_config" def __init__( self, hidden_size=1152, intermediate_size=4304, num_hidden_layers=27, num_attention_heads=16, num_channels=3, num_patches=256, patch_size=14, # manully modified hidden_act="gelu_pytorch_tanh", layer_norm_eps=1e-6, attention_dropout=0.0, window_size=14, # full_attention_indexes=[7, 14, 21, 26], use_rope=True, use_windows_attn=True, **kwargs, ): super().__init__(**kwargs) self.hidden_size = hidden_size self.intermediate_size = intermediate_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.num_channels = num_channels self.patch_size = patch_size self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.num_patches = num_patches self.window_size = window_size self.full_attention_indexes = full_attention_indexes self.use_windows_attn = use_windows_attn self.use_rope = use_rope @dataclass class Siglip2VisionOutput(ModelOutput): """ Base class for vision model's outputs that also contains image embeddings of the pooling of the last hidden states. Args: image_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The image embeddings obtained by applying the projection layer to the pooler_output. 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. 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. """ image_embeds: Optional[torch.FloatTensor] = None last_hidden_state: Optional[torch.FloatTensor] = None hidden_states: Optional[Tuple[torch.FloatTensor, ...]] = None attentions: Optional[Tuple[torch.FloatTensor, ...]] = None spatial_shapes: Optional[torch.LongTensor] = None def convert_image_to_patches(image: "torch.Tensor", patch_size: int) -> "torch.Tensor": """ Convert 3D tensor image of shape (num_channels, image_height, image_width) into 2D tensor of patches of shape (num_patches_height * num_patches_width, patch_size * patch_size * num_channels). """ num_channels, image_height, image_width = image.shape num_patches_height = image_height // patch_size num_patches_width = image_width // patch_size patched_image = image.reshape(num_channels, num_patches_height, patch_size, num_patches_width, patch_size) patched_image = patched_image.permute(1, 3, 2, 4, 0) patched_image = patched_image.reshape(num_patches_height * num_patches_width, -1) return patched_image def convert_images_to_patches(image: "torch.Tensor", patch_size: int) -> "torch.Tensor": """ Convert 4D tensor image of shape (batch_size, num_channels, image_height, image_width) into 2D tensor of patches of shape (batch_size, num_patches_height * num_patches_width, patch_size * patch_size * num_channels). """ batch_size, num_channels, image_height, image_width = image.shape assert image_height % patch_size == 0 and image_width % patch_size == 0, f"image_height % patch_size == 0 and image_width % patch_size == 0" num_patches_height = image_height // patch_size num_patches_width = image_width // patch_size patched_image = image.reshape(batch_size, num_channels, num_patches_height, patch_size, num_patches_width, patch_size) patched_image = patched_image.permute(0, 2, 4, 3, 5, 1) # (batch_size, num_patches_height, num_patches_width, patch_size, patch_size, num_channels) patched_image = patched_image.reshape(batch_size * num_patches_height * num_patches_width, -1) return patched_image class Siglip2VisionEmbeddings(nn.Module): def __init__(self, config: Siglip2VisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.patch_size = config.patch_size self.window_size = config.window_size self.patch_embedding = nn.Linear( in_features=config.num_channels * self.patch_size * self.patch_size, out_features=self.embed_dim, ) self.num_patches = config.num_patches self.position_embedding_size = int(self.num_patches**0.5) self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim) def split_patch_embeddings_to_windows_with_meta(self, patch_embeds, batch_hw, window_size): """ Args: patch_embeds: Tensor, shape (1, sum(H_i*W_i), C) batch_hw: List[(H_i, W_i)] window_size: int Returns: windows_tensor: Tensor, shape (total_windows, window_size*window_size, C) win_meta_list: List[dict] with keys: - img_idx: index in batch_hw - patch_hw: original (H, W) - win_xy: (h0, w0) 左上角相对于原图 - win_hw: 原图内有效窗口大小 (h_eff, w_eff) """ # 1. 计算每张图在 flat tensor 中的起始位置 batch_hw = batch_hw.tolist() counts = [H * W for (H, W) in batch_hw] starts = [0] + list(accumulate(counts))[:-1] # 2. 按 (H,W) 分组，同一尺寸一起处理 size2info = defaultdict(list) for img_idx, ((H, W), start) in enumerate(zip(batch_hw, starts)): size2info[(H, W)].append((img_idx, start)) all_windows = [] all_meta = [] # print(size2info) # 3. 对每个尺寸组做 batch unfold + pad for (H, W), info in size2info.items(): H, W = int(H), int(W) B = len(info) C = patch_embeds.shape[-1] img_idxs, img_starts = zip(*info) # 3.1 取出并 reshape 成 (B, C, H, W) imgs = [] for st in img_starts: flat = patch_embeds[0, st: st + H * W] # (H*W, C) imgs.append(flat.transpose(0,1).reshape(C, H, W)) batch_tensor = torch.stack(imgs, dim=0) # (B, C, H, W) # 3.2 计算 pad 大小 (bottom, right)，保证能被 window_size 整除 pad_h = (window_size - H % window_size) % window_size pad_w = (window_size - W % window_size) % window_size # pad 格式： (left, right, top, bottom) for last two dims batch_padded = F.pad(batch_tensor, (0, pad_w, 0, pad_h)) H_pad, W_pad = H + pad_h, W + pad_w n_h = H_pad // window_size n_w = W_pad // window_size n_windows = n_h * n_w # 3.3 batched unfold -> (B, C*ws*ws, n_windows) patches_unf = F.unfold( batch_padded, kernel_size=(window_size, window_size), stride=(window_size, window_size) ) # 3.4 reshape到 (B*n_windows, ws*ws, C) patches = ( patches_unf .view(B, C, window_size * window_size, n_windows) # (B, C, ws*ws, n_win) .permute(0, 3, 2, 1) # (B, n_win, ws*ws, C) .reshape(-1, window_size * window_size, C) # (B*n_win, ws*ws, C) ) all_windows.append(patches) # 3.5 生成 meta：记录原图内有效窗口大小 for b, img_idx in enumerate(img_idxs): for win_id in range(n_windows): i, j = divmod(win_id, n_w) h0, w0 = i * window_size, j * window_size # 在原图内的实际结束坐标 h1 = min(h0 + window_size, H) w1 = min(w0 + window_size, W) all_meta.append({ 'img_idx': img_idx, 'patch_hw': (H, W), 'win_xy': (h0, w0), 'win_hw': (h1 - h0, w1 - w0), # 有效区域大小 }) # 4. 拼接并根据 img_idx + win_xy 排序，恢复输入顺序 sorted_idx = sorted( range(len(all_meta)), key=lambda k: ( all_meta[k]['img_idx'], all_meta[k]['win_xy'][0], all_meta[k]['win_xy'][1] ) ) all_windows = torch.cat(all_windows, dim=0) all_windows = all_windows[sorted_idx] win_meta_list = [all_meta[i] for i in sorted_idx] windows_list = [] for meta, win in zip(win_meta_list, all_windows): h_eff, w_eff = meta['win_hw'] valid_num = h_eff * w_eff # 只保留真正来自原图的 patch tokens if valid_num == window_size * window_size: windows_list.append(win) else: win = win.view(window_size, window_size, -1)[:h_eff, :w_eff, :].reshape(h_eff * w_eff, -1) windows_list.append(win) # shape (valid_num, C) # 如果你需要一个单一 tensor，可以再 cat 一次： all_tokens = torch.cat(windows_list, dim=0).unsqueeze(0) # shape (sum(valid_num), C) # 1. 先重算每张图在原始 flat tensor 中的起始位置 counts = [H * W for H, W in batch_hw] starts = [0] + list(accumulate(counts))[:-1] total_patches = sum(counts) # 2. 构造映射：mapping[orig_idx] = new_idx mapping = [None] * total_patches offset = 0 # all_tokens 维度上的游标 for meta in win_meta_list: img_idx = meta['img_idx'] H, W = meta['patch_hw'] h0, w0 = meta['win_xy'] h_eff, w_eff = meta['win_hw'] base = starts[img_idx] # 对该窗口内所有真正来自原图的 patch token 计算映射 for u in range(h_eff): for v in range(w_eff): # 原始 flat 坐标 orig_idx = base + (h0+u) * W + (w0) + v # 在 all_tokens 里的位置：在该窗口区段里按 row-major 展平 p = u * w_eff + v mapping[orig_idx] = offset + p # 窗口结束后，offset 推进该窗口的有效 token 数 offset += h_eff * w_eff reverse_mapping = torch.tensor(mapping, dtype=torch.long) return all_tokens, win_meta_list, reverse_mapping @staticmethod def resize_positional_embeddings( positional_embeddings: torch.Tensor, spatial_shapes: torch.LongTensor, ) -> torch.Tensor: """ Resize positional embeddings to image-specific size and pad to a fixed size. Args: positional_embeddings (`torch.Tensor`): Position embeddings of shape (height, width, embed_dim) spatial_shapes (`torch.LongTensor`): Spatial shapes of shape (batch_size, 2) to resize the positional embeddings to max_length (`int`): Maximum length of the positional embeddings to pad resized positional embeddings to Returns: `torch.Tensor`: Embeddings of shape (batch_size, max_length, embed_dim) """ batch_size = spatial_shapes.shape[0] embed_dim = positional_embeddings.shape[-1] source_dtype = positional_embeddings.dtype resulted_positional_embeddings = [] # (height, width, embed_dim) -> (1, embed_dim, height, width) for interpolation positional_embeddings = positional_embeddings.permute(2, 0, 1).unsqueeze(0) # Upcast to float32 on CPU because antialias is not supported for bfloat16/float16 on CPU if positional_embeddings.device.type == "cpu": positional_embeddings = positional_embeddings.to(torch.float32) for i in range(batch_size): # (1, dim, height, width) -> (1, dim, target_height, target_width) height, width = spatial_shapes[i] resized_embeddings = F.interpolate( positional_embeddings, size=(height, width), mode="bilinear", align_corners=False, antialias=True, ) # (1, dim, target_height, target_width) -> (target_height * target_width, dim) resized_embeddings = resized_embeddings.reshape(embed_dim, height * width).transpose(0, 1) # Cast to original dtype resized_embeddings = resized_embeddings.to(source_dtype) resulted_positional_embeddings.append(resized_embeddings) return torch.cat(resulted_positional_embeddings, dim=0).unsqueeze(0) def get_spatial_shapes(self, bchw_list: List[torch.Tensor]) -> torch.Tensor: hw_list = [] for shape in bchw_list: b, _, h, w = shape hw_list.extend([(h//self.patch_size, w//self.patch_size)] * b) hw_tensor = torch.tensor(hw_list) return hw_tensor def forward(self, pixel_values: torch.FloatTensor) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor`): Pixel values of shape (batch_size, num_channels, height, width) """ bchw_list = [each.shape for each in pixel_values] pixel_values = torch.cat([convert_images_to_patches(each, self.patch_size) for each in pixel_values], dim=0) # Apply patch embeddings to already patchified pixel values target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) # Get positional resized and padded positional embeddings positional_embeddings = self.position_embedding.weight.reshape( self.position_embedding_size, self.position_embedding_size, -1 ) spatial_shapes = self.get_spatial_shapes(bchw_list) resized_positional_embeddings = self.resize_positional_embeddings( positional_embeddings, spatial_shapes ) # Add positional embeddings to patch embeddings embeddings = patch_embeds + resized_positional_embeddings windows_tensor, win_meta_list, reverse_mapping = self.split_patch_embeddings_to_windows_with_meta(embeddings, spatial_shapes, self.window_size) return windows_tensor, win_meta_list, spatial_shapes, reverse_mapping class Rope2DPosEmb(nn.Module): """ copy from https://huggingface.co/moonshotai/Kimi-VL-A3B-Thinking/blob/main/modeling_kimi_vl.py#L324 2D rotary position embedding with multi-resolution support. This class is intended to be used in the following way: 1. Before training, create an instance of Rope2DPosEmb. This instance will hold the precomputed cis. 2. Before each forward pass, call `get_freqs_cis_by_*` to get the `freqs_cis` tensor for this iteration. 3. During the forward pass, pass the `freqs_cis` tensor to each attention layer, and call `apply` just before each attention operation. The rope is shared across all attention layers and all heads. Refs: - RoFormer: https://arxiv.org/abs/2104.09864 - VisionLLaMA: https://arxiv.org/abs/2403.00522 - https://github.com/Meituan-AutoML/VisionLLaMA/blob/main/dit/models.py Args: dim (int): usually the multi-head attention dimension, should be divisible by 4 (TODO: relax this constraint if needed) max_height (int): the maximum height of the 2D grid max_width (int): the maximum width of the 2D grid theta_base (float): the base of the theta device (str): the device to store the precomputed cis """ def __init__(self, dim: int, max_height: int, max_width: int, theta_base=10000, window_size=14): super().__init__() self.dim = dim assert self.dim % 4 == 0, "dim must be divisible by 4" self.max_height = max_height self.max_width = max_width self.theta_base = theta_base self.window_size = window_size self.freqs_cis = None def extra_repr(self): return f"dim={self.dim}, max_height={self.max_height}, max_width={self.max_width}, theta_base={self.theta_base}" def _precompute_freqs_cis(self, device: torch.device) -> torch.Tensor: """Calculate the cis(freqs) for each position in the 2D grid. Return: complex tensor of shape (max_height, max_width, dim//2) and value: height axis: ret[h, w, 2*i] = cis(h * theta_base**(-4*i/dim)) weight axis: ret[h, w, 2*i+1] = cis(w * theta_base**(-4*i/dim)) with (i in [0, dim//4)) note: `cis` is a mathematical notation defined by cis x = cos x + i sin x, """ N = self.max_height * self.max_width flat_pos = torch.arange(0, N).float().to(device) x_pos = flat_pos % self.max_width y_pos = flat_pos // self.max_width dim_range = ( torch.arange(0, self.dim, 4)[: (self.dim // 4)].float().to(device) ) # C/4 freqs = 1.0 / (self.theta_base ** (dim_range / self.dim)) x_freqs = torch.outer(x_pos, freqs).float() # N, C/4 y_freqs = torch.outer(y_pos, freqs).float() # N, C/4 x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs) # N, C/4 y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs) # N, C/4 # N, C/4, 2 freqs_cis = torch.cat( [x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1 ) # max_height, max_width, C/2 freqs_cis = freqs_cis.reshape(self.max_height, self.max_width, -1) return freqs_cis def get_freqs_cis(self, win_meta_list: List[Dict], device: torch.device) -> torch.Tensor: """ Args: win_meta_list (List[Dict]): window meta list Returns: freqs_cis: tensor of shape (sum(t * height * width), dim//2) """ if self.freqs_cis is None: self.freqs_cis = self._precompute_freqs_cis(device) # assert all xy <512 assert all(win_meta['win_xy'][0] + win_meta['win_hw'][0] < 512 and win_meta['win_xy'][1] + win_meta['win_hw'][1] < 512 for win_meta in win_meta_list) freqs_cis = torch.cat([self.freqs_cis[win_meta['win_xy'][0]:win_meta['win_xy'][0] + win_meta['win_hw'][0], win_meta['win_xy'][1]: win_meta['win_xy'][1] + win_meta['win_hw'][1]].reshape(-1, self.dim // 2) for win_meta in win_meta_list], dim=0) freqs_cis = freqs_cis.unsqueeze(0) return freqs_cis def eager_attention_forward( module: nn.Module, query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, attention_mask: Optional[torch.Tensor], scaling: float, dropout: float = 0.0, **kwargs, ): attn_weights = torch.matmul(query, key.transpose(-1, -2)) * scaling if attention_mask is not None: attn_weights = attn_weights + attention_mask attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query.dtype) attn_weights = nn.functional.dropout(attn_weights, p=dropout, training=module.training) attn_output = torch.matmul(attn_weights, value) attn_output = attn_output.transpose(1, 2).contiguous() return attn_output, attn_weights def _apply_rope_input_validation(x, freqs_cis): assert x.ndim == freqs_cis.ndim + 1, (x.shape, freqs_cis.shape) assert x.shape[:-2] == freqs_cis.shape[:-1], (x.shape, freqs_cis.shape) assert x.shape[-1] == 2 * freqs_cis.shape[-1], (x.shape, freqs_cis.shape) assert freqs_cis.dtype == torch.complex64, freqs_cis.dtype def apply_rope( xq: torch.Tensor, xk: torch.Tensor, freqs_cis: torch.Tensor ) -> tuple[torch.Tensor, torch.Tensor]: """ Args: (The leading dimensions of all inputs should be the same) xq: query, tensor of shape (..., num_heads, head_dim) xk: key, tensor of shape (..., num_heads, head_dim) freqs_cis: tensor of shape (..., head_dim/2), dtype=torch.complex64. It contains the precomputed cis(freqs) for each position in the 2D grid. Returns: xq_out, xk_out: tensors of shape (..., num_heads, head_dim) """ _apply_rope_input_validation(xq, freqs_cis) _apply_rope_input_validation(xk, freqs_cis) freqs_cis = freqs_cis.unsqueeze(-2) # ..., 1, head_dim/2 # ..., num_heads, head_dim/2 xq_ = torch.view_as_complex(xq.float().view(*xq.shape[:-1], -1, 2)) xk_ = torch.view_as_complex(xk.float().view(*xq.shape[:-1], -1, 2)) xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(-2) # ..., num_heads, head_dim return xq_out.type_as(xq), xk_out.type_as(xk) class Siglip2Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__(self, config: Union[Siglip2VisionConfig, Siglip2TextConfig]): super().__init__() self.config = config self.embed_dim = config.hidden_size self.num_heads = config.num_attention_heads self.head_dim = self.embed_dim // self.num_heads if self.head_dim * self.num_heads != self.embed_dim: raise ValueError( f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`:" f" {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout self.is_causal = False self.k_proj = nn.Linear(self.embed_dim, self.embed_dim) self.v_proj = nn.Linear(self.embed_dim, self.embed_dim) self.q_proj = nn.Linear(self.embed_dim, self.embed_dim) self.out_proj = nn.Linear(self.embed_dim, self.embed_dim) self.use_windows_attn = config.use_windows_attn self.use_rope = config.use_rope def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, rope_freqs_cis: Optional[torch.Tensor] = None, win_meta_list: Optional[List[Dict]] = None, windows_attn: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]: """Input shape: Batch x Time x Channel""" batch_size, seq_length, embed_dim = hidden_states.shape queries = self.q_proj(hidden_states) keys = self.k_proj(hidden_states) values = self.v_proj(hidden_states) queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim) # .transpose(1, 2) keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim) # .transpose(1, 2) values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2) if self.use_rope: queries, keys = apply_rope(queries, keys, rope_freqs_cis) queries = queries.transpose(1, 2) keys = keys.transpose(1, 2) attention_interface: Callable = eager_attention_forward if self.config._attn_implementation != "eager": if self.config._attn_implementation == "sdpa" and output_attentions: logger.warning_once( "`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to " 'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' ) if self.config._attn_implementation == "flash_attention_2": from transformers.modeling_utils import AttentionInterface AttentionInterface._global_mapping['flash_attention_2_packing'] = flash_attention_forward_for_packing setattr(AttentionInterface, 'flash_attention_2_packing', flash_attention_forward_for_packing) attention_interface = ALL_ATTENTION_FUNCTIONS['flash_attention_2_packing'] else: attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation] if windows_attn and self.use_windows_attn: seq_len_list = [win_meta['win_hw'][0] * win_meta['win_hw'][1] for win_meta in win_meta_list] else: mapper = defaultdict(lambda: 0) for win_meta in win_meta_list: mapper[win_meta['img_idx']] += win_meta['win_hw'][0] * win_meta['win_hw'][1] seq_len_list = [mapper[i] for i in range(len(mapper))] attention_mask = None attn_output, attn_weights = attention_interface( self, queries, keys, values, attention_mask, is_causal=self.is_causal, scaling=self.scale, dropout=0.0 if not self.training else self.dropout, seq_len_list=seq_len_list, ) attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous() attn_output = self.out_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights class Siglip2MLP(nn.Module): def __init__(self, config): super().__init__() self.config = config self.activation_fn = ACT2FN[config.hidden_act] self.fc1 = nn.Linear(config.hidden_size, config.intermediate_size) self.fc2 = nn.Linear(config.intermediate_size, config.hidden_size) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states = self.fc2(hidden_states) return hidden_states class Siglip2EncoderLayer(nn.Module): def __init__(self, config: Union[Siglip2VisionConfig, Siglip2TextConfig]): super().__init__() self.embed_dim = config.hidden_size self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.self_attn = Siglip2Attention(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = Siglip2MLP(config) def forward( self, hidden_states: torch.Tensor, output_attentions: Optional[bool] = False, rope_freqs_cis: Optional[torch.Tensor] = None, win_meta_list: Optional[List[Dict]] = None, windows_attn: Optional[bool] = False, ) -> Tuple[torch.FloatTensor]: """ Args: hidden_states (`torch.FloatTensor`): Input to the layer of shape `(batch, seq_len, embed_dim)`. attention_mask (`torch.FloatTensor`): Attention mask of shape `(batch, 1, q_len, k_v_seq_len)` where padding elements are indicated by very large negative values. output_attentions (`bool`, *optional*, defaults to `False`): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, attn_weights = self.self_attn( hidden_states=hidden_states, output_attentions=output_attentions, rope_freqs_cis=rope_freqs_cis, win_meta_list=win_meta_list, windows_attn=windows_attn, ) hidden_states = residual + hidden_states residual = hidden_states hidden_states = self.layer_norm2(hidden_states) hidden_states = self.mlp(hidden_states) hidden_states = residual + hidden_states outputs = (hidden_states,) if output_attentions: outputs += (attn_weights,) return outputs class Siglip2Encoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Siglip2EncoderLayer`]. Args: config: Siglip2Config """ def __init__(self, config: Siglip2Config): super().__init__() self.config = config self.rope_2d = Rope2DPosEmb( config.hidden_size // config.num_attention_heads, 512, 512, config.window_size ) self.layers = nn.ModuleList([Siglip2EncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False self.full_attention_indexes = config.full_attention_indexes # Ignore copy @can_return_tuple def forward( self, inputs_embeds, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, win_meta_list: Optional[List[Dict]] = None, spatial_shapes: Optional[torch.Tensor] = None, ) -> BaseModelOutput: r""" Args: inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. 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?](../glossary#attention-mask) output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None rope_freqs_cis = self.rope_2d.get_freqs_cis(win_meta_list=win_meta_list, device=inputs_embeds.device) hidden_states = inputs_embeds for win_idx, encoder_layer in enumerate(self.layers): if win_idx not in self.full_attention_indexes: windows_attn = True else: windows_attn = False if output_hidden_states: encoder_states = encoder_states + (hidden_states,) if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func( encoder_layer.__call__, hidden_states, output_attentions, rope_freqs_cis, win_meta_list, windows_attn ) else: layer_outputs = encoder_layer( hidden_states, output_attentions=output_attentions, rope_freqs_cis=rope_freqs_cis, win_meta_list=win_meta_list, windows_attn=windows_attn ) hidden_states = layer_outputs[0] if output_attentions: all_attentions = all_attentions + (layer_outputs[1],) if output_hidden_states: encoder_states = encoder_states + (hidden_states,) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions, ) def reconstruct_patch_embeddings(last_hidden_state: torch.Tensor, win_meta_list: list[dict], spatial_shapes: torch.Tensor) -> torch.Tensor: idx_map = build_idx_map(win_meta_list, spatial_shapes) last_hidden_state = last_hidden_state[:, idx_map, :] return last_hidden_state SIGLIP2_VISION_INPUTS_DOCSTRING = r""" Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. interpolate_pos_encoding (`bool`, *optional*, defaults to `False`): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class Siglip2VisionTransformer(nn.Module): def __init__(self, config: Siglip2VisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = Siglip2VisionEmbeddings(config) self.encoder = Siglip2Encoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.use_head = True if not hasattr(config, "vision_use_head") else config.vision_use_head if self.use_head: self.head = Siglip2MultiheadAttentionPoolingHead(config) self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" @can_return_tuple @add_start_docstrings_to_model_forward(SIGLIP2_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Siglip2VisionConfig) def forward( self, pixel_values: torch.FloatTensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> BaseModelOutputWithPooling: r""" Returns: """ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) windows_tensor, win_meta_list, spatial_shapes, reverse_mapping = self.embeddings(pixel_values) encoder_outputs: BaseModelOutput = self.encoder( inputs_embeds=windows_tensor, output_attentions=output_attentions, output_hidden_states=output_hidden_states, win_meta_list=win_meta_list, spatial_shapes=spatial_shapes, ) last_hidden_state = encoder_outputs.last_hidden_state last_hidden_state = self.post_layernorm(last_hidden_state) last_hidden_state = last_hidden_state[:, reverse_mapping, :] return Siglip2VisionOutput( last_hidden_state=last_hidden_state, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, spatial_shapes=spatial_shapes, ) def _trunc_normal_(tensor, mean, std, a, b): # Cut & paste from PyTorch official master until it's in a few official releases - RW # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf def norm_cdf(x): # Computes standard normal cumulative distribution function return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 if (mean < a - 2 * std) or (mean > b + 2 * std): warnings.warn( "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " "The distribution of values may be incorrect.", stacklevel=2, ) # Values are generated by using a truncated uniform distribution and # then using the inverse CDF for the normal distribution. # Get upper and lower cdf values l = norm_cdf((a - mean) / std) u = norm_cdf((b - mean) / std) # Uniformly fill tensor with values from [l, u], then translate to # [2l-1, 2u-1]. tensor.uniform_(2 * l - 1, 2 * u - 1) # Use inverse cdf transform for normal distribution to get truncated # standard normal tensor.erfinv_() # Transform to proper mean, std tensor.mul_(std * math.sqrt(2.0)) tensor.add_(mean) # Clamp to ensure it's in the proper range tensor.clamp_(min=a, max=b) def trunc_normal_tf_( tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0 ) -> torch.Tensor: """Fills the input Tensor with values drawn from a truncated normal distribution. The values are effectively drawn from the normal distribution :math:`\\mathcal{N}(\text{mean}, \text{std}^2)` with values outside :math:`[a, b]` redrawn until they are within the bounds. The method used for generating the random values works best when :math:`a \\leq \text{mean} \\leq b`. NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0 and the result is subsequently scaled and shifted by the mean and std args. Args: tensor: an n-dimensional `torch.Tensor` mean: the mean of the normal distribution std: the standard deviation of the normal distribution a: the minimum cutoff value b: the maximum cutoff value """ with torch.no_grad(): _trunc_normal_(tensor, 0, 1.0, a, b) tensor.mul_(std).add_(mean) def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"): fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) if mode == "fan_in": denom = fan_in elif mode == "fan_out": denom = fan_out elif mode == "fan_avg": denom = (fan_in + fan_out) / 2 variance = scale / denom if distribution == "truncated_normal": # constant is stddev of standard normal truncated to (-2, 2) trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978) elif distribution == "normal": with torch.no_grad(): tensor.normal_(std=math.sqrt(variance)) elif distribution == "uniform": bound = math.sqrt(3 * variance) with torch.no_grad(): tensor.uniform_(-bound, bound) else: raise ValueError(f"invalid distribution {distribution}") def lecun_normal_(tensor): variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal") def default_flax_embed_init(tensor): variance_scaling_(tensor, mode="fan_in", distribution="normal") SIGLIP2_START_DOCSTRING = r""" 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](https://pytorch.org/docs/stable/nn.html#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. Parameters: config ([`Siglip2Config`]): 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 [`~PreTrainedModel.from_pretrained`] method to load the model weights. """ SIGLIP2_INPUTS_DOCSTRING = r""" Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#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?](../glossary#attention-mask) 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.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using [`AutoImageProcessor`]. See [`CLIPImageProcessor.__call__`] for details. return_loss (`bool`, *optional*): Whether or not to return the contrastive loss. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. interpolate_pos_encoding (`bool`, *optional*, defaults to `False`): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ class Siglip2PreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = Siglip2Config base_model_prefix = "siglip2" supports_gradient_checkpointing = True _no_split_modules = [ "Siglip2TextEmbeddings", "Siglip2EncoderLayer", "Siglip2VisionEmbeddings", "Siglip2EncoderLayer", "Siglip2MultiheadAttentionPoolingHead", ] _supports_flash_attn_2 = True _supports_sdpa = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, Siglip2VisionEmbeddings): width = ( self.config.vision_config.hidden_size if isinstance(self.config, Siglip2Config) else self.config.hidden_size ) nn.init.normal_(module.position_embedding.weight, std=1 / np.sqrt(width)) elif isinstance(module, nn.Embedding): default_flax_embed_init(module.weight) elif isinstance(module, Siglip2Attention): nn.init.xavier_uniform_(module.q_proj.weight) nn.init.xavier_uniform_(module.k_proj.weight) nn.init.xavier_uniform_(module.v_proj.weight) nn.init.xavier_uniform_(module.out_proj.weight) nn.init.zeros_(module.q_proj.bias) nn.init.zeros_(module.k_proj.bias) nn.init.zeros_(module.v_proj.bias) nn.init.zeros_(module.out_proj.bias) elif isinstance(module, Siglip2MLP): nn.init.xavier_uniform_(module.fc1.weight) nn.init.xavier_uniform_(module.fc2.weight) nn.init.normal_(module.fc1.bias, std=1e-6) nn.init.normal_(module.fc2.bias, std=1e-6) elif isinstance(module, Siglip2MultiheadAttentionPoolingHead): nn.init.xavier_uniform_(module.probe.data) nn.init.xavier_uniform_(module.attention.in_proj_weight.data) nn.init.zeros_(module.attention.in_proj_bias.data) elif isinstance(module, Siglip2Model): logit_scale_init = torch.log(torch.tensor(1.0)) module.logit_scale.data.fill_(logit_scale_init) module.logit_bias.data.zero_() elif isinstance(module, Siglip2ForImageClassification): nn.init.normal_( module.classifier.weight, std=self.config.vision_config.hidden_size**-0.5 * self.config.initializer_factor, ) elif isinstance(module, (nn.Linear, nn.Conv2d)): lecun_normal_(module.weight) if module.bias is not None: nn.init.zeros_(module.bias) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) class Siglip2MultiheadAttentionPoolingHead(nn.Module): """Multihead Attention Pooling.""" def __init__(self, config: Siglip2VisionConfig): super().__init__() self.probe = nn.Parameter(torch.randn(1, 1, config.hidden_size)) self.attention = torch.nn.MultiheadAttention(config.hidden_size, config.num_attention_heads, batch_first=True) self.layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.mlp = Siglip2MLP(config) self.num_heads = config.num_attention_heads def forward(self, hidden_state: torch.Tensor, attention_mask: Optional[torch.Tensor] = None) -> torch.Tensor: batch_size = hidden_state.shape[0] probe = self.probe.repeat(batch_size, 1, 1) if attention_mask is not None: target_len, source_len = probe.shape[1], hidden_state.shape[1] attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_state.dtype, target_len) attention_mask = attention_mask.repeat(1, self.num_heads, target_len, 1) attention_mask = attention_mask.reshape(-1, target_len, source_len) hidden_state = self.attention(probe, hidden_state, hidden_state, attn_mask=attention_mask)[0] residual = hidden_state hidden_state = self.layernorm(hidden_state) hidden_state = residual + self.mlp(hidden_state) return hidden_state[:, 0] @add_start_docstrings( """The vision model from Siglip2 without any head or projection on top.""", SIGLIP2_START_DOCSTRING, ) class Siglip2VisionModel(Siglip2PreTrainedModel): config_class = Siglip2VisionConfig main_input_name = "pixel_values" def __init__(self, config: Siglip2VisionConfig): super().__init__(config) self.vision_model = Siglip2VisionTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding @can_return_tuple @add_start_docstrings_to_model_forward(SIGLIP2_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=Siglip2VisionConfig) def forward( self, pixel_values: torch.FloatTensor, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, ) -> BaseModelOutputWithPooling: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, Siglip2VisionModel >>> model = Siglip2VisionModel.from_pretrained("google/siglip2-base-patch16-224") >>> processor = AutoProcessor.from_pretrained("google/siglip2-base-patch16-224") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled features ```""" return self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, ) __all__ = [ "Siglip2VisionModel", ]