# Copied and adapted from: https://github.com/hao-ai-lab/FastVideo # SPDX-License-Identifier: Apache-2.0 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/models/clip.py # Adapted from transformers: https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py """Minimal implementation of CLIPVisionModel intended to be only used within a vision language model.""" from collections.abc import Iterable from typing import Optional import torch import torch.nn as nn from sglang.multimodal_gen.configs.models.encoders import ( BaseEncoderOutput, CLIPTextConfig, CLIPVisionConfig, ) from sglang.multimodal_gen.runtime.distributed import divide, get_tp_world_size from sglang.multimodal_gen.runtime.layers.activation import get_act_fn from sglang.multimodal_gen.runtime.layers.attention import LocalAttention from sglang.multimodal_gen.runtime.layers.linear import ( ColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from sglang.multimodal_gen.runtime.layers.quantization import QuantizationConfig # TODO: support quantization # from vllm.model_executor.layers.quantization import QuantizationConfig from sglang.multimodal_gen.runtime.loader.weight_utils import default_weight_loader from sglang.multimodal_gen.runtime.models.encoders.base import ImageEncoder, TextEncoder from sglang.multimodal_gen.runtime.models.encoders.vision import ( resolve_visual_encoder_outputs, ) from sglang.multimodal_gen.runtime.platforms import ( AttentionBackendEnum, current_platform, ) from sglang.multimodal_gen.runtime.utils.logging_utils import init_logger logger = init_logger(__name__) # Adapted from https://github.com/huggingface/transformers/blob/v4.39.0/src/transformers/models/clip/modeling_clip.py#L164 # noqa class CLIPVisionEmbeddings(nn.Module): def __init__(self, config: CLIPVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.image_size = config.image_size self.patch_size = config.patch_size assert self.image_size % self.patch_size == 0 self.class_embedding = nn.Parameter(torch.randn(self.embed_dim)) self.patch_embedding = nn.Conv2d( in_channels=config.num_channels, out_channels=self.embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=False, ) self.num_patches = (self.image_size // self.patch_size) ** 2 self.num_positions = self.num_patches + 1 self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.register_buffer( "position_ids", torch.arange(self.num_positions).expand((1, -1)), persistent=False, ) def forward(self, pixel_values: torch.Tensor) -> torch.Tensor: batch_size = pixel_values.shape[0] target_dtype = self.patch_embedding.weight.dtype patch_embeds = self.patch_embedding( pixel_values.to(dtype=target_dtype) ) # shape = [*, width, grid, grid] patch_embeds = patch_embeds.flatten(2).transpose(1, 2) class_embeds = self.class_embedding.expand(batch_size, 1, -1) embeddings = torch.cat([class_embeds, patch_embeds], dim=1) embeddings = embeddings + self.position_embedding(self.position_ids) return embeddings class CLIPTextEmbeddings(nn.Module): def __init__(self, config: CLIPTextConfig): super().__init__() self.config = config 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: torch.LongTensor | None = None, position_ids: torch.LongTensor | None = None, inputs_embeds: torch.FloatTensor | None = None, ) -> torch.Tensor: if input_ids is not None: seq_length = input_ids.shape[-1] elif inputs_embeds is not None: seq_length = inputs_embeds.shape[-2] else: raise ValueError("Either input_ids or inputs_embeds must be provided.") max_position_embedding = self.position_embedding.weight.shape[0] if seq_length > max_position_embedding: raise ValueError( f"Sequence length must be less than max_position_embeddings (got `sequence length`: " f"{seq_length} and max_position_embeddings: {max_position_embedding}" ) 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 CLIPAttention(nn.Module): """Multi-headed attention from 'Attention Is All You Need' paper""" def __init__( self, config: CLIPVisionConfig | CLIPTextConfig, 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( "embed_dim must be divisible by num_heads " f"(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.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", ) self.out_proj = RowParallelLinear( input_size=self.embed_dim, output_size=self.embed_dim, quant_config=quant_config, prefix=f"{prefix}.out_proj", ) self.tp_size = get_tp_world_size() self.num_heads_per_partition = divide(self.num_heads, self.tp_size) self.attn = LocalAttention( self.num_heads_per_partition, self.head_dim, self.num_heads_per_partition, softmax_scale=self.scale, causal=True, supported_attention_backends=config._supported_attention_backends, ) def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): return ( tensor.view(bsz, seq_len, self.num_heads, self.head_dim) .transpose(1, 2) .contiguous() ) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, ): """Input shape: Batch x Time x Channel""" qkv_states, _ = self.qkv_proj(hidden_states) query_states, key_states, value_states = qkv_states.chunk(3, dim=-1) # use flash_attn_func query_states = query_states.reshape( query_states.shape[0], query_states.shape[1], self.num_heads_per_partition, self.head_dim, ) key_states = key_states.reshape( key_states.shape[0], key_states.shape[1], self.num_heads_per_partition, self.head_dim, ) value_states = value_states.reshape( value_states.shape[0], value_states.shape[1], self.num_heads_per_partition, self.head_dim, ) if self.attn.backend == AttentionBackendEnum.TORCH_SDPA: query_states = query_states.transpose(1, 2) # [B, H, S, D] key_states = key_states.transpose(1, 2) value_states = value_states.transpose(1, 2) if current_platform.is_rocm() or current_platform.is_musa(): # ROCm: Using both is_causal=True and attn_mask causes NaN. # Use is_causal=True alone (padding mask not needed for CLIP # since pooler_output comes from EOS token before padding). # XXX (MUSA): Torch SDPA on MUSA currently does not support # using both `attn_mask` and `is_causal=True` simultaneously. attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=None, is_causal=True, scale=self.scale, ) else: if attention_mask is not None: # SDPA requires [B, 1, 1, S] or [B, S, S] format mask if attention_mask.dim() == 2: attn_mask = attention_mask[:, None, None, :].to( dtype=query_states.dtype ) attn_mask = (1.0 - attn_mask) * torch.finfo( query_states.dtype ).min else: attn_mask = attention_mask else: attn_mask = None attn_output = torch.nn.functional.scaled_dot_product_attention( query_states, key_states, value_states, attn_mask=attn_mask, is_causal=attention_mask is None, scale=self.scale, ) attn_output = attn_output.transpose(1, 2) else: # Use LocalAttention (doesn't support attention_mask, but maintains compatibility) attn_output = self.attn(query_states, key_states, value_states) attn_output = attn_output.reshape( attn_output.shape[0], attn_output.shape[1], self.num_heads_per_partition * self.head_dim, ) attn_output, _ = self.out_proj(attn_output) return attn_output, None class CLIPMLP(nn.Module): def __init__( self, config: CLIPVisionConfig | CLIPTextConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() self.config = config self.activation_fn = get_act_fn(config.hidden_act) self.fc1 = ColumnParallelLinear( config.hidden_size, config.intermediate_size, bias=True, quant_config=quant_config, prefix=f"{prefix}.fc1", ) self.fc2 = RowParallelLinear( config.intermediate_size, config.hidden_size, bias=True, quant_config=quant_config, prefix=f"{prefix}.fc2", ) 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 CLIPEncoderLayer(nn.Module): def __init__( self, config: CLIPTextConfig | CLIPVisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() self.self_attn = CLIPAttention( config, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.layer_norm1 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.mlp = CLIPMLP(config, quant_config=quant_config, prefix=f"{prefix}.mlp") self.layer_norm2 = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) def forward( self, hidden_states: torch.Tensor, attention_mask: torch.Tensor | None = None, ) -> torch.Tensor: residual = hidden_states hidden_states = self.layer_norm1(hidden_states) hidden_states, _ = self.self_attn( hidden_states=hidden_states, attention_mask=attention_mask, ) 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 CLIPEncoder(nn.Module): """ Transformer encoder consisting of `config.num_hidden_layers` self attention layers. Each layer is a [`CLIPEncoderLayer`]. Args: config: CLIPConfig """ def __init__( self, config: CLIPVisionConfig | CLIPTextConfig, quant_config: QuantizationConfig | None = None, num_hidden_layers_override: int | None = None, prefix: str = "", ) -> None: super().__init__() self.config = config if num_hidden_layers_override is None: num_hidden_layers = config.num_hidden_layers else: num_hidden_layers = num_hidden_layers_override self.layers = nn.ModuleList( [ CLIPEncoderLayer( config=config, quant_config=quant_config, prefix=f"{prefix}.layers.{layer_idx}", ) for layer_idx in range(num_hidden_layers) ] ) def forward( self, inputs_embeds: torch.Tensor, return_all_hidden_states: bool, attention_mask: torch.Tensor | None = None, ) -> torch.Tensor | list[torch.Tensor]: hidden_states_pool = [inputs_embeds] hidden_states = inputs_embeds for idx, encoder_layer in enumerate(self.layers): hidden_states = encoder_layer( hidden_states, attention_mask=attention_mask, ) if return_all_hidden_states: hidden_states_pool.append(hidden_states) # If we have multiple feature sample layers, we return all hidden # states in order and grab the ones we need by index. if return_all_hidden_states: return hidden_states_pool return [hidden_states] class CLIPTextTransformer(nn.Module): def __init__( self, config: CLIPTextConfig, quant_config: QuantizationConfig | None = None, num_hidden_layers_override: int | None = None, prefix: str = "", ): super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = CLIPTextEmbeddings(config) self.encoder = CLIPEncoder( config, quant_config=quant_config, num_hidden_layers_override=num_hidden_layers_override, prefix=prefix, ) self.final_layer_norm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) # For `pooled_output` computation self.eos_token_id = config.eos_token_id def forward( self, input_ids: torch.Tensor | None, position_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, output_hidden_states: bool | None = None, ) -> BaseEncoderOutput: output_hidden_states = ( output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states ) 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) # CLIP's text model uses causal mask, prepare it here. # https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324 # causal_attention_mask = _create_4d_causal_attention_mask( # input_shape, hidden_states.dtype, device=hidden_states.device # ) # # expand attention_mask # if attention_mask is not None and not self._use_flash_attention_2: # raise NotImplementedError("attention_mask is not supported for CLIPTextTransformer") # # [bsz, seq_len] -> [bsz, 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, return_all_hidden_states=output_hidden_states, attention_mask=attention_mask, ) last_hidden_state = encoder_outputs[-1] last_hidden_state = self.final_layer_norm(last_hidden_state) if self.eos_token_id == 2: # The `eos_token_id` was incorrect before PR #24773: Let's keep what have been done here. # A CLIP model with such `eos_token_id` in the config can't work correctly with extra new tokens added # ------------------------------------------------------------ # text_embeds.shape = [batch_size, sequence_length, transformer.width] # take features from the eot embedding (eot_token is the highest number in each sequence) # casting to torch.int for onnx compatibility: argmax doesn't support int64 inputs with opset 14 pooled_output = last_hidden_state[ torch.arange( last_hidden_state.shape[0], device=last_hidden_state.device ), input_ids.to(dtype=torch.int, device=last_hidden_state.device).argmax( dim=-1 ), ] else: # The config gets updated `eos_token_id` from PR #24773 (so the use of exta new tokens is possible) pooled_output = last_hidden_state[ torch.arange( last_hidden_state.shape[0], device=last_hidden_state.device ), # We need to get the first position of `eos_token_id` value (`pad_token_ids` might equal to `eos_token_id`) # Note: we assume each sequence (along batch dim.) contains an `eos_token_id` (e.g. prepared by the tokenizer) ( input_ids.to(dtype=torch.int, device=last_hidden_state.device) == self.eos_token_id ) .int() .argmax(dim=-1), ] return BaseEncoderOutput( last_hidden_state=last_hidden_state, pooler_output=pooled_output, hidden_states=encoder_outputs, # attentions=encoder_outputs.attentions, ) class CLIPTextModel(TextEncoder): def __init__( self, config: CLIPTextConfig, ) -> None: super().__init__(config) self.text_model = CLIPTextTransformer( config=config, quant_config=config.quant_config, prefix=config.prefix ) def forward( self, input_ids: torch.Tensor | None, position_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, output_hidden_states: bool | None = None, **kwargs, ) -> BaseEncoderOutput: outputs: BaseEncoderOutput = self.text_model( input_ids=input_ids, attention_mask=attention_mask, position_ids=position_ids, output_hidden_states=output_hidden_states, ) return outputs def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: # Define mapping for stacked parameters 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: # Handle q_proj, k_proj, v_proj -> qkv_proj mapping for param_name, weight_name, shard_id in stacked_params_mapping: if weight_name in name: # Replace the weight name with the parameter name model_param_name = name.replace(weight_name, param_name) if model_param_name in params_dict: param = params_dict[model_param_name] weight_loader = param.weight_loader weight_loader(param, loaded_weight, shard_id) loaded_params.add(model_param_name) break else: # Use default weight loader for all other parameters if name in params_dict: 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 class CLIPVisionTransformer(nn.Module): def __init__( self, config: CLIPVisionConfig, quant_config: QuantizationConfig | None = None, num_hidden_layers_override: int | None = None, require_post_norm: bool | None = None, prefix: str = "", ) -> None: super().__init__() self.config = config embed_dim = config.hidden_size self.embeddings = CLIPVisionEmbeddings(config) # NOTE: This typo of "layrnorm" is not fixed on purpose to match # the original transformers code and name of the model weights. self.pre_layrnorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) self.encoder = CLIPEncoder( config=config, quant_config=quant_config, num_hidden_layers_override=num_hidden_layers_override, prefix=f"{prefix}.encoder", ) num_hidden_layers = config.num_hidden_layers if len(self.encoder.layers) > config.num_hidden_layers: raise ValueError( f"The original encoder only has {num_hidden_layers} " f"layers, but you requested {len(self.encoder.layers)} layers." ) # If possible, skip post_layernorm to conserve memory if require_post_norm is None: require_post_norm = len(self.encoder.layers) == num_hidden_layers if require_post_norm: self.post_layernorm = nn.LayerNorm(embed_dim, eps=config.layer_norm_eps) else: self.post_layernorm = None def forward( self, pixel_values: torch.Tensor, output_hidden_states: Optional[bool] = None, feature_sample_layers: list[int] | None = None, ) -> BaseEncoderOutput: hidden_states = self.embeddings(pixel_values) hidden_states = self.pre_layrnorm(hidden_states) return_all_hidden_states = output_hidden_states or ( feature_sample_layers is not None ) # Produces either the last layer output or all of the hidden states, # depending on if we have feature_sample_layers or not encoder_outputs = self.encoder( inputs_embeds=hidden_states, return_all_hidden_states=return_all_hidden_states, ) if not return_all_hidden_states: encoder_outputs = encoder_outputs[0] # Handle post-norm (if applicable) and stacks feature layers if needed encoder_outputs = resolve_visual_encoder_outputs( encoder_outputs, feature_sample_layers, self.post_layernorm, self.config.num_hidden_layers, ) if return_all_hidden_states: return BaseEncoderOutput(hidden_states=encoder_outputs) return BaseEncoderOutput(last_hidden_state=encoder_outputs) class CLIPVisionModel(ImageEncoder): config_class = CLIPVisionConfig main_input_name = "pixel_values" packed_modules_mapping = {"qkv_proj": ["q_proj", "k_proj", "v_proj"]} def __init__(self, config: CLIPVisionConfig) -> None: super().__init__(config) self.vision_model = CLIPVisionTransformer( config=config, quant_config=config.quant_config, num_hidden_layers_override=config.num_hidden_layers_override, require_post_norm=config.require_post_norm, prefix=f"{config.prefix}.vision_model", ) def forward( self, pixel_values: torch.Tensor, feature_sample_layers: list[int] | None = None, output_hidden_states: Optional[bool] = None, **kwargs, ) -> BaseEncoderOutput: base_encoder_output = self.vision_model( pixel_values, output_hidden_states=output_hidden_states, feature_sample_layers=feature_sample_layers, ) return base_encoder_output @property def device(self): return next(self.parameters()).device # (TODO) Add prefix argument for filtering out weights to be loaded # ref: https://github.com/vllm-project/vllm/pull/7186#discussion_r1734163986 def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: params_dict = dict(self.named_parameters()) loaded_params: set[str] = set() layer_count = len(self.vision_model.encoder.layers) for name, loaded_weight in weights: if name.startswith("visual_projection"): continue # post_layernorm is not needed in CLIPVisionModel if ( name.startswith("vision_model.post_layernorm") and self.vision_model.post_layernorm is None ): continue # omit layers when num_hidden_layers_override is set if name.startswith("vision_model.encoder.layers"): layer_idx = int(name.split(".")[3]) if layer_idx >= layer_count: continue for ( param_name, weight_name, shard_id, ) in self.config.arch_config.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 class BertModel(CLIPTextModel): pass EntryClass = [CLIPTextModel, CLIPVisionModel]