# coding=utf-8 # Copyright 2024 The HuggingFace Inc. team. All rights reserved. # # 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. """ Siglip model configuration""" import os from typing import Union from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) SIGLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = { "google/siglip-base-patch16-224": "https://huggingface.co/google/siglip-base-patch16-224/resolve/main/config.json", } class SiglipTextConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a Siglip text 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 text encoder of the Siglip [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 32000): Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`SiglipModel`]. 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. max_position_embeddings (`int`, *optional*, defaults to 64): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). 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. pad_token_id (`int`, *optional*, defaults to 1): The id of the padding token in the vocabulary. bos_token_id (`int`, *optional*, defaults to 49406): The id of the beginning-of-sequence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 49407): The id of the end-of-sequence token in the vocabulary. Example: ```python >>> from transformers import SiglipTextConfig, SiglipTextModel >>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration >>> configuration = SiglipTextConfig() >>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = SiglipTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "siglip_text_model" def __init__( self, vocab_size=32000, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, max_position_embeddings=64, hidden_act="gelu_pytorch_tanh", layer_norm_eps=1e-6, attention_dropout=0.0, # This differs from `CLIPTokenizer`'s default and from openai/siglip # See https://github.com/huggingface/transformers/pull/24773#issuecomment-1632287538 pad_token_id=1, bos_token_id=49406, eos_token_id=49407, _flash_attn_2_enabled=True, **kwargs, ): super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size 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.max_position_embeddings = max_position_embeddings self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self.attention_dropout = attention_dropout self._flash_attn_2_enabled = _flash_attn_2_enabled @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the text config dict if we are loading from SiglipConfig if config_dict.get("model_type") == "siglip": config_dict = config_dict["text_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class SiglipVisionConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a Siglip 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 Siglip [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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. image_size (`int`, *optional*, defaults to 224): The size (resolution) of each image. 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 SiglipVisionConfig, SiglipVisionModel >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration >>> configuration = SiglipVisionConfig() >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = SiglipVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "siglip_vision_model" def __init__( self, hidden_size=768, intermediate_size=3072, num_hidden_layers=12, num_attention_heads=12, num_channels=3, image_size=224, patch_size=16, hidden_act="gelu_pytorch_tanh", layer_norm_eps=1e-6, attention_dropout=0.0, _flash_attn_2_enabled=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.image_size = image_size self.attention_dropout = attention_dropout self.layer_norm_eps = layer_norm_eps self.hidden_act = hidden_act self._flash_attn_2_enabled = _flash_attn_2_enabled @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": cls._set_token_in_kwargs(kwargs) config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs) # get the vision config dict if we are loading from SiglipConfig if config_dict.get("model_type") == "siglip": config_dict = config_dict["vision_config"] if "model_type" in config_dict and hasattr(cls, "model_type") and config_dict["model_type"] != cls.model_type: logger.warning( f"You are using a model of type {config_dict['model_type']} to instantiate a model of type " f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." ) return cls.from_dict(config_dict, **kwargs) class SiglipConfig(PretrainedConfig): r""" [`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs. Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: text_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`SiglipTextConfig`]. vision_config (`dict`, *optional*): Dictionary of configuration options used to initialize [`SiglipVisionConfig`]. kwargs (*optional*): Dictionary of keyword arguments. Example: ```python >>> from transformers import SiglipConfig, SiglipModel >>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration >>> configuration = SiglipConfig() >>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration >>> model = SiglipModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig >>> from transformers import SiglipTextConfig, SiglipVisionConfig >>> # Initializing a SiglipText and SiglipVision configuration >>> config_text = SiglipTextConfig() >>> config_vision = SiglipVisionConfig() >>> config = SiglipConfig.from_text_vision_configs(config_text, config_vision) ```""" model_type = "siglip" def __init__(self, text_config=None, vision_config=None, **kwargs): super().__init__(**kwargs) if text_config is None: text_config = {} logger.info("`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.") if vision_config is None: vision_config = {} logger.info("`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.") self.text_config = SiglipTextConfig(**text_config) self.vision_config = SiglipVisionConfig(**vision_config) self.initializer_factor = 1.0 @classmethod def from_text_vision_configs(cls, text_config: SiglipTextConfig, vision_config: SiglipVisionConfig, **kwargs): r""" Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision model configuration. Returns: [`SiglipConfig`]: An instance of a configuration object """ return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs) # coding=utf-8 # Copyright 2024 Google AI and The HuggingFace Team. All rights reserved. # # 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. """ PyTorch Siglip model.""" import math import warnings from dataclasses import dataclass from typing import Any, Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F import torch.utils.checkpoint from torch import nn 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.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling from transformers.modeling_utils import PreTrainedModel from transformers.utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, logging, replace_return_docstrings, ) logger = logging.get_logger(__name__) _CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224" SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [ "google/siglip-base-patch16-224", # See all SigLIP models at https://huggingface.co/models?filter=siglip ] if is_flash_attn_2_available(): from flash_attn import flash_attn_func, flash_attn_varlen_func from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa # Copied from transformers.models.llama.modeling_llama._get_unpad_data def _get_unpad_data(attention_mask): seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32) indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten() max_seqlen_in_batch = seqlens_in_batch.max().item() cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0)) return ( indices, cu_seqlens, max_seqlen_in_batch, ) 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 if tensor.dtype in [torch.float16, torch.bfloat16]: # The `erfinv_` op is not (yet?) defined in float16+cpu, bfloat16+gpu og_dtype = tensor.dtype tensor = tensor.to(torch.float32) tensor.erfinv_() tensor = tensor.to(og_dtype) else: 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 if tensor.dtype == torch.float16: # The `clamp_` op is not (yet?) defined in float16+cpu tensor = tensor.to(torch.float32) tensor.clamp_(min=a, max=b) tensor = tensor.to(torch.float16) else: 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 subsquently 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") @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPVisionModelOutput with CLIP->Siglip class SiglipVisionModelOutput(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: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPTextModelOutput with CLIP->Siglip class SiglipTextModelOutput(ModelOutput): """ Base class for text model's outputs that also contains a pooling of the last hidden states. Args: text_embeds (`torch.FloatTensor` of shape `(batch_size, output_dim)` *optional* returned when model is initialized with `with_projection=True`): The text 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. """ text_embeds: Optional[torch.FloatTensor] = None last_hidden_state: torch.FloatTensor = None hidden_states: Optional[Tuple[torch.FloatTensor]] = None attentions: Optional[Tuple[torch.FloatTensor]] = None @dataclass # Copied from transformers.models.clip.modeling_clip.CLIPOutput with CLIP->Siglip class SiglipOutput(ModelOutput): """ Args: loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `return_loss` is `True`): Contrastive loss for image-text similarity. logits_per_image:(`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeds` and `text_embeds`. This represents the image-text similarity scores. logits_per_text:(`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeds` and `image_embeds`. This represents the text-image similarity scores. text_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`]. image_embeds(`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`]. text_model_output(`BaseModelOutputWithPooling`): The output of the [`SiglipTextModel`]. vision_model_output(`BaseModelOutputWithPooling`): The output of the [`SiglipVisionModel`]. """ loss: Optional[torch.FloatTensor] = None logits_per_image: torch.FloatTensor = None logits_per_text: torch.FloatTensor = None text_embeds: torch.FloatTensor = None image_embeds: torch.FloatTensor = None text_model_output: BaseModelOutputWithPooling = None vision_model_output: BaseModelOutputWithPooling = None def to_tuple(self) -> Tuple[Any]: return tuple( self[k] if k not in ["text_model_output", "vision_model_output"] else getattr(self, k).to_tuple() for k in self.keys() ) class SiglipVisionEmbeddings(nn.Module): def __init__(self, config: SiglipVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, padding="valid", ) self.num_patches_per_side = self.image_size // self.patch_size self.num_patches = self.num_patches_per_side**2 self.num_positions = self.num_patches self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) def forward(self, pixel_values: torch.FloatTensor, patch_attention_mask: torch.BoolTensor) -> torch.Tensor: batch_size = pixel_values.size(0) patch_embeds = self.patch_embedding(pixel_values) embeddings = patch_embeds.flatten(2).transpose(1, 2) max_im_h, max_im_w = pixel_values.size(2), pixel_values.size(3) max_nb_patches_h, max_nb_patches_w = max_im_h // self.patch_size, max_im_w // self.patch_size boundaries = torch.arange(1 / self.num_patches_per_side, 1.0, 1 / self.num_patches_per_side) position_ids = torch.full( size=( batch_size, max_nb_patches_h * max_nb_patches_w, ), fill_value=0, ) for batch_idx, p_attn_mask in enumerate(patch_attention_mask): nb_patches_h = p_attn_mask[:, 0].sum() nb_patches_w = p_attn_mask[0].sum() fractional_coords_h = torch.arange(0, 1 - 1e-6, 1 / nb_patches_h) fractional_coords_w = torch.arange(0, 1 - 1e-6, 1 / nb_patches_w) bucket_coords_h = torch.bucketize(fractional_coords_h, boundaries, right=True) bucket_coords_w = torch.bucketize(fractional_coords_w, boundaries, right=True) pos_ids = (bucket_coords_h[:, None] * self.num_patches_per_side + bucket_coords_w).flatten() position_ids[batch_idx][p_attn_mask.view(-1).cpu()] = pos_ids position_ids = position_ids.to(self.position_embedding.weight.device) embeddings = embeddings + self.position_embedding(position_ids) return embeddings # Copied from transformers.models.clip.modeling_clip.CLIPTextEmbeddings with CLIP->Siglip class SiglipTextEmbeddings(nn.Module): def __init__(self, config: SiglipTextConfig): super().__init__() embed_dim = config.hidden_size self.token_embedding = nn.Embedding(config.vocab_size, embed_dim) self.position_embedding = nn.Embedding(config.max_position_embeddings, embed_dim) # position_ids (1, len position emb) is contiguous in memory and exported when serialized self.register_buffer( "position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)), persistent=False ) def forward( self, input_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, ) -> torch.Tensor: seq_length = input_ids.shape[-1] if input_ids is not None else inputs_embeds.shape[-2] if position_ids is None: position_ids = self.position_ids[:, :seq_length] if inputs_embeds is None: inputs_embeds = self.token_embedding(input_ids) position_embeddings = self.position_embedding(position_ids) embeddings = inputs_embeds + position_embeddings return embeddings class SiglipAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" # Copied from transformers.models.clip.modeling_clip.CLIPAttention.__init__ def __init__(self, config): 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.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) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = False, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: """Input shape: Batch x Time x Channel""" batch_size, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) query_states = query_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(batch_size, q_len, self.num_heads, self.head_dim).transpose(1, 2) k_v_seq_len = key_states.shape[-2] attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) * self.scale if attn_weights.size() != (batch_size, self.num_heads, q_len, k_v_seq_len): raise ValueError( f"Attention weights should be of size {(batch_size, self.num_heads, q_len, k_v_seq_len)}, but is" f" {attn_weights.size()}" ) if attention_mask is not None: if attention_mask.size() != (batch_size, 1, q_len, k_v_seq_len): raise ValueError( f"Attention mask should be of size {(batch_size, 1, q_len, k_v_seq_len)}, but is {attention_mask.size()}" ) attn_weights = attn_weights + attention_mask # upcast attention to fp32 attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (batch_size, self.num_heads, q_len, self.head_dim): raise ValueError( f"`attn_output` should be of size {(batch_size, self.num_heads, q_len, self.head_dim)}, but is" f" {attn_output.size()}" ) attn_output = attn_output.transpose(1, 2).contiguous() attn_output = attn_output.reshape(batch_size, q_len, self.embed_dim) attn_output = self.out_proj(attn_output) return attn_output, attn_weights class SiglipFlashAttention2(SiglipAttention): """ Llama flash attention module. This module inherits from `LlamaAttention` as the weights of the module stays untouched. The only required change would be on the forward pass where it needs to correctly call the public API of flash attention and deal with padding tokens in case the input contains any of them. """ def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.is_causal = False # Hack to make sure we don't use a causal mask def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_value: Optional[Tuple[torch.Tensor]] = None, output_attentions: bool = False, use_cache: bool = False, **kwargs, ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: output_attentions = False bsz, q_len, _ = hidden_states.size() query_states = self.q_proj(hidden_states) key_states = self.k_proj(hidden_states) value_states = self.v_proj(hidden_states) # Flash attention requires the input to have the shape # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) key_states = key_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) value_states = value_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) kv_seq_len = key_states.shape[-2] if past_key_value is not None: kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx) # cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) # query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids) # if past_key_value is not None: # cache_kwargs = {"sin": sin, "cos": cos} # Specific to RoPE models # key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. query_states = query_states.transpose(1, 2) key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) dropout_rate = self.dropout if self.training else 0.0 # 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. (LlamaRMSNorm handles it correctly) input_dtype = query_states.dtype if input_dtype == torch.float32: if torch.is_autocast_enabled(): target_dtype = torch.get_autocast_gpu_dtype() # Handle the case where the model is quantized elif hasattr(self.config, "_pre_quantization_dtype"): target_dtype = self.config._pre_quantization_dtype else: target_dtype = self.q_proj.weight.dtype logger.warning_once( "The input hidden states seems to be silently casted in float32, this might be related to the fact" " you have upcasted embedding or layer norm layers in float32. We will cast back the input in" f" {target_dtype}." ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) attn_output = self._flash_attention_forward( query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate ) attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous() attn_output = self.out_proj(attn_output) if not output_attentions: attn_weights = None return attn_output, attn_weights def _flash_attention_forward( self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None ): """ 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) """ # 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 = self.is_causal and query_length != 1 # Contains at least one padding token in the sequence if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( query_states, key_states, value_states, attention_mask, query_length ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens attn_output_unpad = flash_attn_varlen_func( query_states, key_states, value_states, cu_seqlens_q=cu_seqlens_q, cu_seqlens_k=cu_seqlens_k, max_seqlen_q=max_seqlen_in_batch_q, max_seqlen_k=max_seqlen_in_batch_k, dropout_p=dropout, softmax_scale=softmax_scale, causal=causal, ) attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: attn_output = flash_attn_func( query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal ) return attn_output def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length): indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape key_layer = index_first_axis( key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) value_layer = index_first_axis( value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k ) if query_length == kv_seq_len: query_layer = index_first_axis( query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k ) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( batch_size + 1, dtype=torch.int32, device=query_layer.device ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, key_layer, value_layer, indices_q, (cu_seqlens_q, cu_seqlens_k), (max_seqlen_in_batch_q, max_seqlen_in_batch_k), ) # Copied from transformers.models.clip.modeling_clip.CLIPMLP with CLIP->Siglip class SiglipMLP(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 # Copied from transformers.models.clip.modeling_clip.CLIPEncoderLayer with CLIP->Siglip class SiglipEncoderLayer(nn.Module): def __init__(self, config: SiglipConfig): super().__init__() self.embed_dim = config.hidden_size self.self_attn = ( SiglipAttention(config) if not getattr(config, "_flash_attn_2_enabled", False) else SiglipFlashAttention2(config) ) self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = SiglipMLP(config) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, output_attentions: 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, attention_mask=attention_mask, output_attentions=output_attentions, ) 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 SiglipPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = SiglipConfig base_model_prefix = "siglip" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, SiglipVisionEmbeddings): width = ( self.config.vision_config.hidden_size if isinstance(self.config, SiglipConfig) 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, SiglipAttention): nn.init.normal_(module.q_proj.weight) nn.init.normal_(module.k_proj.weight) nn.init.normal_(module.v_proj.weight) nn.init.normal_(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, SiglipMLP): nn.init.normal_(module.fc1.weight) nn.init.normal_(module.fc2.weight) nn.init.normal_(module.fc1.bias, std=1e-6) nn.init.normal_(module.fc2.bias, std=1e-6) elif isinstance(module, SiglipMultiheadAttentionPoolingHead): nn.init.normal_(module.probe.data) nn.init.normal_(module.attention.in_proj_weight.data) nn.init.zeros_(module.attention.in_proj_bias.data) elif isinstance(module, SiglipModel): logit_scale_init = torch.tensor(0.0) module.logit_scale.data.fill_(logit_scale_init) module.logit_bias.data.zero_() 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) SIGLIP_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 ([`SiglipConfig`]): 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. """ SIGLIP_TEXT_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) 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. """ SIGLIP_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. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ SIGLIP_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. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ # Copied from transformers.models.clip.modeling_clip.CLIPEncoder with CLIP->Siglip class SiglipEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`SiglipEncoderLayer`]. Args: config: SiglipConfig """ def __init__(self, config: SiglipConfig): super().__init__() self.config = config self.layers = nn.ModuleList([SiglipEncoderLayer(config) for _ in range(config.num_hidden_layers)]) self.gradient_checkpointing = False # Ignore copy def forward( self, inputs_embeds, attention_mask: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, 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 ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict encoder_states = () if output_hidden_states else None all_attentions = () if output_attentions else None hidden_states = inputs_embeds for encoder_layer in self.layers: 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, attention_mask, output_attentions, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, output_attentions=output_attentions, ) 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,) if not return_dict: return tuple(v for v in [hidden_states, encoder_states, all_attentions] if v is not None) return BaseModelOutput( last_hidden_state=hidden_states, hidden_states=encoder_states, attentions=all_attentions ) class SiglipTextTransformer(nn.Module): def __init__(self, config: SiglipTextConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = SiglipTextEmbeddings(config) self.encoder = SiglipEncoder(config) self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.head = nn.Linear(embed_dim, embed_dim) @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, 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 ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict if input_ids is None: raise ValueError("You have to specify input_ids") input_shape = input_ids.size() input_ids = input_ids.view(-1, input_shape[-1]) hidden_states = self.embeddings(input_ids=input_ids, position_ids=position_ids) # note: SigLIP's text model does not use a causal mask, unlike the original CLIP model. # expand attention_mask if attention_mask is not None: # [batch_size, seq_len] -> [batch_size, 1, tgt_seq_len, src_seq_len] attention_mask = _prepare_4d_attention_mask(attention_mask, hidden_states.dtype) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.final_layer_norm(last_hidden_state) # Assuming "sticky" EOS tokenization, last token is always EOS. pooled_output = last_hidden_state[:, -1, :] pooled_output = self.head(pooled_output) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) @add_start_docstrings( """The text model from SigLIP without any head or projection on top.""", SIGLIP_START_DOCSTRING, ) class SiglipTextModel(SiglipPreTrainedModel): config_class = SiglipTextConfig _no_split_modules = ["SiglipTextEmbeddings", "SiglipEncoderLayer"] def __init__(self, config: SiglipTextConfig): super().__init__(config) self.text_model = SiglipTextTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.text_model.embeddings.token_embedding def set_input_embeddings(self, value): self.text_model.embeddings.token_embedding = value @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipTextConfig) def forward( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from transformers import AutoTokenizer, SiglipTextModel >>> model = SiglipTextModel.from_pretrained("google/siglip-base-patch16-224") >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") >>> # important: make sure to set padding="max_length" as that's how the model was trained >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") >>> outputs = model(**inputs) >>> last_hidden_state = outputs.last_hidden_state >>> pooled_output = outputs.pooler_output # pooled (EOS token) states ```""" return_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) class SiglipVisionTransformer(nn.Module): def __init__(self, config: SiglipVisionConfig): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = SiglipVisionEmbeddings(config) self.encoder = SiglipEncoder(config) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.head = SiglipMultiheadAttentionPoolingHead(config) @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig) def forward( self, pixel_values, patch_attention_mask: Optional[torch.BoolTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, 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 ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict batch_size = pixel_values.size(0) if patch_attention_mask is None: patch_attention_mask = torch.ones( size=( batch_size, pixel_values.size(2) // self.config.patch_size, pixel_values.size(3) // self.config.patch_size, ), dtype=torch.bool, device=pixel_values.device, ) hidden_states = self.embeddings(pixel_values=pixel_values, patch_attention_mask=patch_attention_mask) patch_attention_mask = patch_attention_mask.view(batch_size, -1) # The call to `_upad_input` in `_flash_attention_forward` is expensive # So when the `patch_attention_mask` is full of 1s (i.e. attending to the whole sequence), # avoiding passing the attention_mask, which is equivalent to attending to the full sequence if not torch.any(~patch_attention_mask): attention_mask=None else: attention_mask = ( _prepare_4d_attention_mask(patch_attention_mask, hidden_states.dtype) if not self.config._flash_attn_2_enabled else patch_attention_mask ) encoder_outputs = self.encoder( inputs_embeds=hidden_states, attention_mask=attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) last_hidden_state = encoder_outputs[0] last_hidden_state = self.post_layernorm(last_hidden_state) pooled_output = self.head( hidden_state=last_hidden_state, attention_mask=patch_attention_mask, ) if not return_dict: return (last_hidden_state, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, ) class SiglipMultiheadAttentionPoolingHead(nn.Module): """Multihead Attention Pooling.""" def __init__(self, config: SiglipVisionConfig): 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 = SiglipMLP(config) def forward(self, hidden_state, attention_mask): batch_size = hidden_state.shape[0] probe = self.probe.repeat(batch_size, 1, 1) hidden_state = self.attention( query=probe, key=hidden_state, value=hidden_state, key_padding_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 SigLIP without any head or projection on top.""", SIGLIP_START_DOCSTRING, ) class SiglipVisionModel(SiglipPreTrainedModel): config_class = SiglipVisionConfig main_input_name = "pixel_values" def __init__(self, config: SiglipVisionConfig): super().__init__(config) self.vision_model = SiglipVisionTransformer(config) # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.vision_model.embeddings.patch_embedding @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=BaseModelOutputWithPooling, config_class=SiglipVisionConfig) def forward( self, pixel_values, patch_attention_mask: Optional[torch.BoolTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, BaseModelOutputWithPooling]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, SiglipVisionModel >>> model = SiglipVisionModel.from_pretrained("google/siglip-base-patch16-224") >>> processor = AutoProcessor.from_pretrained("google/siglip-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_dict = return_dict if return_dict is not None else self.config.use_return_dict return self.vision_model( pixel_values=pixel_values, patch_attention_mask=patch_attention_mask, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) @add_start_docstrings(SIGLIP_START_DOCSTRING) class SiglipModel(SiglipPreTrainedModel): config_class = SiglipConfig def __init__(self, config: SiglipConfig): super().__init__(config) if not isinstance(config.text_config, SiglipTextConfig): raise ValueError( "config.text_config is expected to be of type SiglipTextConfig but is of type" f" {type(config.text_config)}." ) if not isinstance(config.vision_config, SiglipVisionConfig): raise ValueError( "config.vision_config is expected to be of type SiglipVisionConfig but is of type" f" {type(config.vision_config)}." ) text_config = config.text_config vision_config = config.vision_config self.text_model = SiglipTextTransformer(text_config) self.vision_model = SiglipVisionTransformer(vision_config) self.logit_scale = nn.Parameter(torch.randn(1)) self.logit_bias = nn.Parameter(torch.randn(1)) # Initialize weights and apply final processing self.post_init() @add_start_docstrings_to_model_forward(SIGLIP_TEXT_INPUTS_DOCSTRING) def get_text_features( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: text_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The text embeddings obtained by applying the projection layer to the pooled output of [`SiglipTextModel`]. Examples: ```python >>> from transformers import AutoTokenizer, AutoModel >>> import torch >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") >>> tokenizer = AutoTokenizer.from_pretrained("google/siglip-base-patch16-224") >>> # important: make sure to set padding="max_length" as that's how the model was trained >>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding="max_length", return_tensors="pt") >>> with torch.no_grad(): ... text_features = model.get_text_features(**inputs) ```""" # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = text_outputs[1] return pooled_output @add_start_docstrings_to_model_forward(SIGLIP_VISION_INPUTS_DOCSTRING) def get_image_features( self, pixel_values: Optional[torch.FloatTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> torch.FloatTensor: r""" Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`SiglipVisionModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AutoModel >>> import torch >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") >>> processor = AutoProcessor.from_pretrained("google/siglip-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") >>> with torch.no_grad(): ... image_features = model.get_image_features(**inputs) ```""" # Use SiglipModel's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) pooled_output = vision_outputs[1] return pooled_output @add_start_docstrings_to_model_forward(SIGLIP_INPUTS_DOCSTRING) @replace_return_docstrings(output_type=SiglipOutput, config_class=SiglipConfig) def forward( self, input_ids: Optional[torch.LongTensor] = None, pixel_values: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, return_loss: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SiglipOutput]: r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, AutoModel >>> import torch >>> model = AutoModel.from_pretrained("google/siglip-base-patch16-224") >>> processor = AutoProcessor.from_pretrained("google/siglip-base-patch16-224") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> texts = ["a photo of 2 cats", "a photo of 2 dogs"] >>> # important: we pass `padding=max_length` since the model was trained with this >>> inputs = processor(text=texts, images=image, padding="max_length", return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> logits_per_image = outputs.logits_per_image >>> probs = torch.sigmoid(logits_per_image) # these are the probabilities >>> print(f"{probs[0][0]:.1%} that image 0 is '{texts[0]}'") 31.9% that image 0 is 'a photo of 2 cats' ```""" # Use SigLIP model's config for some fields (if specified) instead of those of vision & text components. output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) return_dict = return_dict if return_dict is not None else self.config.use_return_dict vision_outputs = self.vision_model( pixel_values=pixel_values, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) text_outputs = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, ) image_embeds = vision_outputs[1] text_embeds = text_outputs[1] # normalized features image_embeds = image_embeds / image_embeds.norm(p=2, dim=-1, keepdim=True) text_embeds = text_embeds / text_embeds.norm(p=2, dim=-1, keepdim=True) # cosine similarity as logits logits_per_text = torch.matmul(text_embeds, image_embeds.t()) * self.logit_scale.exp() + self.logit_bias logits_per_image = logits_per_text.t() loss = None if return_loss: raise NotImplementedError("SigLIP loss to be implemented") if not return_dict: output = (logits_per_image, logits_per_text, text_embeds, image_embeds, text_outputs, vision_outputs) return ((loss,) + output) if loss is not None else output return SiglipOutput( loss=loss, logits_per_image=logits_per_image, logits_per_text=logits_per_text, text_embeds=text_embeds, image_embeds=image_embeds, text_model_output=text_outputs, vision_model_output=vision_outputs, ) def get_siglip_vision_model(_flash_attn_2_enabled=True, **kwargs): siglip_vision_config = { "hidden_size": 1152, "image_size": 448, "intermediate_size": 4304, "model_type": "siglip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 27, "patch_size": 14, } model_config = SiglipVisionConfig(**siglip_vision_config, _flash_attn_2_enabled=_flash_attn_2_enabled, **kwargs) vision_model = SiglipVisionModel(model_config).vision_model return vision_model