# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """Implementation of SiglipVisionModel intended to be only used within a vision language model.""" from collections.abc import Iterable import torch from torch import nn from torch.nn import functional as F from transformers import Siglip2VisionConfig from transformers.configuration_utils import PretrainedConfig from vllm.distributed import divide, get_tensor_model_parallel_world_size from vllm.model_executor.layers.activation import get_act_fn from vllm.model_executor.layers.attention.mm_encoder_attention import MMEncoderAttention from vllm.model_executor.layers.conv import Conv2dLayer from vllm.model_executor.layers.linear import ( ColumnParallelLinear, LinearBase, QKVParallelLinear, ReplicatedLinear, RowParallelLinear, ) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.rotary_embedding.common import ( ApplyRotaryEmb, ) from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.platforms import current_platform from .vision import is_vit_use_data_parallel class VisionRotaryEmbedding(nn.Module): def __init__(self, dim: int, theta: float = 10000.0) -> None: super().__init__() inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) def forward(self, seqlen: int) -> torch.Tensor: seq = torch.arange( seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype ) freqs = torch.outer(seq, self.inv_freq) return freqs class Siglip2VisionEmbeddings(nn.Module): def __init__(self, config: PretrainedConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.patch_size = config.patch_size self.image_size = config.image_size self.num_patches = config.num_patches self.preserve_original_pe = config.preserve_original_pe self.hidden_stride = config.hidden_stride # siglip2 naflex if self.num_patches > 0: self.patch_embedding = ReplicatedLinear( input_size=config.num_channels * self.patch_size * self.patch_size, output_size=self.embed_dim, return_bias=False, ) if self.preserve_original_pe: self.position_embedding_size = int(self.num_patches**0.5) self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim) else: self.patch_embedding = Conv2dLayer( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, padding="valid", ) if self.preserve_original_pe: self.num_patches = (self.image_size // self.patch_size) ** 2 self.position_embedding_size = self.image_size // self.patch_size self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim) def forward( self, pixel_values: torch.FloatTensor, grid_thws: torch.LongTensor | None = None, ) -> torch.Tensor: """ Args: pixel_values (`torch.FloatTensor`): Pixel values of shape ( num_patches, num_channels * temporal_patch_size * patch_size * patch_size ) grid_thws: (`torch.LongTensor`): grid shape (num_patches, 3) """ # Apply patch embeddings to already patchified pixel values target_dtype = self.patch_embedding.weight.dtype if isinstance(self.patch_embedding, LinearBase): patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) elif isinstance(self.patch_embedding, Conv2dLayer): pixel_values = pixel_values.view( -1, self.config.num_channels * self.config.temporal_patch_size, self.patch_size, self.patch_size, ) patch_embeds = self.patch_embedding(pixel_values.to(dtype=target_dtype)) patch_embeds = patch_embeds.reshape(-1, self.embed_dim) if self.preserve_original_pe: assert grid_thws is not None pos_embed_new = torch.zeros_like(patch_embeds) positional_embeddings = ( self.position_embedding.weight.reshape( self.position_embedding_size, self.position_embedding_size, -1 ) .unsqueeze(0) .permute(0, 3, 1, 2) ) cnt = 0 for t, h, w in grid_thws: volume = t * h * w pe = F.interpolate( positional_embeddings, size=(h, w), mode="bicubic", align_corners=False, ) pe = pe.permute(0, 2, 3, 1).reshape(1, h * w, -1) pe = pe[0].repeat(t, 1) pe = pe.reshape( t, h // self.hidden_stride, self.hidden_stride, w // self.hidden_stride, self.hidden_stride, -1, ) pe = pe.permute(0, 1, 3, 2, 4, 5).reshape(volume, -1) pos_embed_new[cnt : cnt + volume] = pe cnt += volume patch_embeds = patch_embeds + pos_embed_new return patch_embeds def apply_rotary_pos_emb( q: torch.Tensor, k: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, is_flash_attn_backend: bool, apply_rotary_emb: ApplyRotaryEmb, ) -> tuple[torch.Tensor, torch.Tensor]: cos = cos.chunk(2, dim=-1)[0].contiguous() sin = sin.chunk(2, dim=-1)[0].contiguous() if is_flash_attn_backend and current_platform.is_cuda(): apply_rotary_emb_func = apply_rotary_emb.forward_cuda elif is_flash_attn_backend and current_platform.is_rocm(): apply_rotary_emb_func = apply_rotary_emb.forward_hip else: apply_rotary_emb_func = apply_rotary_emb.forward_native q_embed = apply_rotary_emb_func(q, cos, sin) k_embed = apply_rotary_emb_func(k, cos, sin) return q_embed, k_embed class Siglip2Attention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, config: Siglip2VisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): 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 " f"(got `embed_dim`: {self.embed_dim} and " f"`num_heads`: {self.num_heads})." ) self.scale = self.head_dim**-0.5 self.dropout = config.attention_dropout use_data_parallel = is_vit_use_data_parallel() self.qkv_proj = QKVParallelLinear( hidden_size=self.embed_dim, head_size=self.head_dim, total_num_heads=self.num_heads, quant_config=quant_config, prefix=f"{prefix}.qkv_proj", disable_tp=use_data_parallel, ) self.out_proj = RowParallelLinear( input_size=self.embed_dim, output_size=self.embed_dim, quant_config=quant_config, prefix=f"{prefix}.out_proj", disable_tp=use_data_parallel, ) self.tp_size = ( 1 if use_data_parallel else get_tensor_model_parallel_world_size() ) self.num_heads_per_partition = divide(self.num_heads, self.tp_size) self.use_rope = config.use_rope self.attn = MMEncoderAttention( num_heads=self.num_heads_per_partition, head_size=self.head_dim, scale=self.scale, prefix=f"{prefix}.attn", ) self.apply_rotary_emb = ApplyRotaryEmb( enforce_enable=True, enable_fp32_compute=True, ) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, position_embeddings: tuple[torch.Tensor, torch.Tensor] | None = None, ) -> tuple[torch.Tensor, torch.Tensor | None]: """Input shape: Batch x Time x Channel""" seq_length, embed_dim = hidden_states.shape qkv_states, _ = self.qkv_proj(hidden_states) queries, keys, values = qkv_states.chunk(3, dim=-1) queries = queries.view(seq_length, self.num_heads_per_partition, self.head_dim) keys = keys.view(seq_length, self.num_heads_per_partition, self.head_dim) values = values.view(seq_length, self.num_heads_per_partition, self.head_dim) if self.use_rope: cos, sin = position_embeddings queries, keys = apply_rotary_pos_emb( queries.unsqueeze(0), keys.unsqueeze(0), cos, sin, self.attn.is_flash_attn_backend, self.apply_rotary_emb, ) queries = queries.squeeze(0) keys = keys.squeeze(0) max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max() attn_output = self.attn( query=queries.unsqueeze(0), key=keys.unsqueeze(0), value=values.unsqueeze(0), cu_seqlens=cu_seqlens, max_seqlen=max_seqlen, ) attn_output = attn_output.reshape( seq_length, self.num_heads_per_partition * self.head_dim ) attn_output, _ = self.out_proj(attn_output) return attn_output class Siglip2MLP(nn.Module): def __init__( self, config: Siglip2VisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.config = config use_data_parallel = is_vit_use_data_parallel() self.activation_fn = get_act_fn(config.hidden_act) self.fc1 = ColumnParallelLinear( config.hidden_size, config.intermediate_size, quant_config=quant_config, prefix=f"{prefix}.fc1", disable_tp=use_data_parallel, ) self.fc2 = RowParallelLinear( config.intermediate_size, config.hidden_size, quant_config=quant_config, prefix=f"{prefix}.fc2", disable_tp=use_data_parallel, ) def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: hidden_states, _ = self.fc1(hidden_states) hidden_states = self.activation_fn(hidden_states) hidden_states, _ = self.fc2(hidden_states) return hidden_states class Siglip2EncoderLayer(nn.Module): def __init__( self, config: Siglip2VisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.embed_dim = config.hidden_size self.layer_norm1 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.self_attn = Siglip2Attention( config, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.layer_norm2 = nn.LayerNorm(self.embed_dim, eps=config.layer_norm_eps) self.mlp = Siglip2MLP( config, quant_config=quant_config, prefix=f"{prefix}.mlp", ) def forward( self, hidden_states: torch.Tensor, cu_seqlens: torch.Tensor, position_embeddings: torch.Tensor, ) -> tuple[torch.FloatTensor]: """ Args: hidden_states: Input tensor of shape (batch, seq_len, embed_dim). cu_seqlens: Cumulative sequence lengths tensor. position_embeddings: Position embeddings tensor. """ residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states = self.self_attn( hidden_states=hidden_states, cu_seqlens=cu_seqlens, position_embeddings=position_embeddings, ) 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 return hidden_states class Siglip2Encoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`Siglip2EncoderLayer`]. Args: config: PretrainedConfig """ def __init__( self, config: Siglip2VisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.config = config self.layers = nn.ModuleList( [ Siglip2EncoderLayer( config, quant_config=quant_config, prefix=f"{prefix}.layers.{idx}", ) for idx in range(config.num_hidden_layers) ] ) self.rotary_pos_emb = VisionRotaryEmbedding( config.hidden_size // config.num_attention_heads // 2 ) self.patch_size = config.patch_size self.hidden_stride = config.hidden_stride self.window_size = config.window_size self.spatial_merge_unit = config.hidden_stride * config.hidden_stride if config.fullatt_block_indexes is None: self.fullatt_block_indexes = None else: self.fullatt_block_indexes = [ int(i) for i in config.fullatt_block_indexes.split("|") ] # copied from qwen2.5_vl def rot_pos_emb(self, grid_thw): pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) hpos_ids = hpos_ids.reshape( h // self.hidden_stride, self.hidden_stride, w // self.hidden_stride, self.hidden_stride, ) hpos_ids = hpos_ids.permute(0, 2, 1, 3) hpos_ids = hpos_ids.flatten() wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) wpos_ids = wpos_ids.reshape( h // self.hidden_stride, self.hidden_stride, w // self.hidden_stride, self.hidden_stride, ) wpos_ids = wpos_ids.permute(0, 2, 1, 3) wpos_ids = wpos_ids.flatten() pos_ids.append(torch.stack([hpos_ids, wpos_ids], dim=-1).repeat(t, 1)) pos_ids = torch.cat(pos_ids, dim=0) max_grid_size = grid_thw[:, 1:].max() rotary_pos_emb_full = self.rotary_pos_emb(max_grid_size) rotary_pos_emb = rotary_pos_emb_full[pos_ids].flatten(1) return rotary_pos_emb def get_window_index(self, grid_thw): window_index: list = [] cu_window_seqlens: list = [0] window_index_id = 0 # patch (after merge) number in each window vit_merger_window_size = ( self.window_size // self.hidden_stride // self.patch_size ) for grid_t, grid_h, grid_w in grid_thw: llm_grid_h, llm_grid_w = ( grid_h // self.hidden_stride, # number of patch after merge grid_w // self.hidden_stride, ) index = torch.arange(grid_t * llm_grid_h * llm_grid_w).reshape( grid_t, llm_grid_h, llm_grid_w ) pad_h = vit_merger_window_size - llm_grid_h % vit_merger_window_size pad_w = vit_merger_window_size - llm_grid_w % vit_merger_window_size num_windows_h = (llm_grid_h + pad_h) // vit_merger_window_size num_windows_w = (llm_grid_w + pad_w) // vit_merger_window_size index_padded = F.pad(index, (0, pad_w, 0, pad_h), "constant", -100) index_padded = index_padded.reshape( grid_t, num_windows_h, vit_merger_window_size, num_windows_w, vit_merger_window_size, ) index_padded = index_padded.permute(0, 1, 3, 2, 4).reshape( grid_t, num_windows_h * num_windows_w, vit_merger_window_size, vit_merger_window_size, ) seqlens = (index_padded != -100).sum([2, 3]).reshape(-1) index_padded = index_padded.reshape(-1) index_new = index_padded[index_padded != -100] window_index.append(index_new + window_index_id) cu_seqlens_tmp = ( seqlens.cumsum(0) * self.spatial_merge_unit + cu_window_seqlens[-1] ) cu_window_seqlens.extend(cu_seqlens_tmp.tolist()) window_index_id += (grid_t * llm_grid_h * llm_grid_w).item() window_index = torch.cat(window_index, dim=0) return window_index, cu_window_seqlens def forward( self, inputs_embeds: torch.Tensor, grid_thws: torch.Tensor, ) -> torch.Tensor: r""" Args: inputs_embeds: Input tensor of shape (batch_size, sequence_length, hidden_size). Embedded representation of the input tokens. grid_thws: Grid tensor of shape (num_patches, 3) containing grid dimensions. Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. """ rotary_pos_emb = self.rot_pos_emb(grid_thws) window_index, cu_window_seqlens = self.get_window_index(grid_thws) cu_window_seqlens = torch.tensor( cu_window_seqlens, device=inputs_embeds.device, dtype=grid_thws.dtype if torch.jit.is_tracing() else torch.int32, ) cu_window_seqlens = torch.unique_consecutive(cu_window_seqlens) seq_len, _ = inputs_embeds.size() inputs_embeds = inputs_embeds.reshape( seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1 ) inputs_embeds = inputs_embeds[window_index, :, :] inputs_embeds = inputs_embeds.reshape(seq_len, -1) rotary_pos_emb = rotary_pos_emb.reshape( seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1 ) rotary_pos_emb = rotary_pos_emb[window_index, :, :] rotary_pos_emb = rotary_pos_emb.reshape(seq_len, -1) emb = torch.cat((rotary_pos_emb, rotary_pos_emb), dim=-1) position_embeddings = (emb.cos(), emb.sin()) cu_seqlens = torch.repeat_interleave( grid_thws[:, 1] * grid_thws[:, 2], grid_thws[:, 0] ).cumsum( dim=0, # Select dtype based on the following factors: # - FA2 requires that cu_seqlens_q must have dtype int32 # - torch.onnx.export requires that cu_seqlens_q must have # same dtype as grid_thw # See https://github.com/huggingface/transformers/pull/34852 # for more information dtype=grid_thws.dtype if torch.jit.is_tracing() else torch.int32, ) cu_seqlens = torch.cat([cu_seqlens.new_zeros(1), cu_seqlens]) reverse_indices = torch.argsort(window_index) hidden_states = inputs_embeds for index, block in enumerate(self.layers): if not self.fullatt_block_indexes or index in self.fullatt_block_indexes: cu_seqlens_tmp = cu_seqlens else: cu_seqlens_tmp = cu_window_seqlens hidden_states = block(hidden_states, cu_seqlens_tmp, position_embeddings) hidden_states = hidden_states.reshape( seq_len // self.spatial_merge_unit, self.spatial_merge_unit, -1 ) hidden_states = hidden_states[reverse_indices, :].reshape(seq_len, -1) return hidden_states class Siglip2VisionTransformer(nn.Module): def __init__( self, config: Siglip2VisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = Siglip2VisionEmbeddings(config) self.encoder = Siglip2Encoder( config, quant_config=quant_config, prefix=f"{prefix}.encoder", ) self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) def forward( self, pixel_values: torch.FloatTensor, grid_thws: torch.LongTensor, ) -> torch.Tensor: r""" spatial_shapes (`torch.LongTensor` of shape `(batch_size, 2)`): Tensor containing the spatial dimensions (height, width) of the input images. """ hidden_states = self.embeddings(pixel_values, grid_thws) last_hidden_state = self.encoder(hidden_states, grid_thws) last_hidden_state = self.post_layernorm(last_hidden_state) return last_hidden_state class Siglip2NavitModel(torch.nn.Module): def __init__( self, config: Siglip2VisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() self.vision_model = Siglip2VisionTransformer( config, quant_config=quant_config, prefix=f"{prefix}.vision_model", ) def forward( self, pixel_values: torch.FloatTensor, grid_thws: torch.LongTensor, ) -> torch.Tensor: return self.vision_model( pixel_values=pixel_values, grid_thws=grid_thws, ) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: stacked_params_mapping = [ # (param_name, shard_name, shard_id) ("qkv_proj", "q_proj", "q"), ("qkv_proj", "k_proj", "k"), ("qkv_proj", "v_proj", "v"), ] params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() for name, loaded_weight in weights: for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name not in name: continue name = name.replace(weight_name, param_name) param = params_dict[name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) break else: param = params_dict[name] weight_loader = getattr(param, "weight_loader", default_weight_loader) weight_loader(param, loaded_weight) loaded_params.add(name) return loaded_params