# 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. """ # Copied from HuggingFaceM4/siglip-so400m-14-980-flash-attn2-navit and add tgt_sizes import os 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.configuration_utils import PretrainedConfig from transformers.utils import ( ModelOutput, add_start_docstrings, add_start_docstrings_to_model_forward, is_flash_attn_2_available, logging, replace_return_docstrings, ) from transformers.utils import logging logger = logging.get_logger(__name__) 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, **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 @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) _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 ] try: 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 except: pass # 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 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, tgt_sizes: Optional[torch.IntTensor]=None) -> 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): if tgt_sizes is not None: nb_patches_h = tgt_sizes[batch_idx][0] nb_patches_w = tgt_sizes[batch_idx][1] else: 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 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: SiglipVisionConfig): super().__init__() self.embed_dim = config.hidden_size self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" self.self_attn = ( SiglipAttention(config) if not self._use_flash_attention_2 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 = SiglipVisionConfig base_model_prefix = "siglip" supports_gradient_checkpointing = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, SiglipVisionEmbeddings): width = 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, (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 ([`SiglipVisionConfig`]): 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_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. """ # 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: SiglipVisionConfig): 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 ) @add_start_docstrings( """The vision model from SigLIP without any head or projection on top.""", SIGLIP_START_DOCSTRING ) class SiglipVisionTransformer(SiglipPreTrainedModel): config_class = SiglipVisionConfig main_input_name = "pixel_values" _supports_flash_attn_2 = True def __init__(self, config: SiglipVisionConfig): super().__init__(config) 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._use_flash_attention_2 = config._attn_implementation == "flash_attention_2" # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self) -> nn.Module: return self.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, tgt_sizes: Optional[torch.IntTensor] = 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, tgt_sizes=tgt_sizes) 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._use_flash_attention_2 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) if not return_dict: return (last_hidden_state, None) + encoder_outputs[1:] return BaseModelOutputWithPooling( last_hidden_state=last_hidden_state, pooler_output=None, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, )