# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # coding=utf-8 # Copyright 2025 The HunYuan team. # Copyright 2025 The vLLM team. # Copyright 2025 EleutherAI and the HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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. """Inference-only HunYuan-VL model compatible with HuggingFace weights.""" from collections.abc import Callable, Iterable, Mapping, Sequence from functools import partial from typing import Annotated, Any, Literal, TypeAlias import torch import torch.nn as nn import torch.nn.functional as F from transformers import BatchFeature from vllm.config import VllmConfig from vllm.config.multimodal import BaseDummyOptions from vllm.distributed import parallel_state from vllm.distributed import utils as dist_utils from vllm.logger import init_logger 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.layernorm import RMSNorm from vllm.model_executor.layers.linear import ( ColumnParallelLinear, QKVParallelLinear, RowParallelLinear, ) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.model_executor.models.module_mapping import MultiModelKeys from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import ( ImageItem, ModalityData, MultiModalDataDict, MultiModalFeatureSpec, MultiModalFieldConfig, MultiModalKwargsItems, ) from vllm.multimodal.parse import ( DictEmbeddingItems, ImageSize, ModalityDataItems, MultiModalDataItems, MultiModalDataParser, ) from vllm.multimodal.processing import ( BaseDummyInputsBuilder, BaseMultiModalProcessor, BaseProcessingInfo, PromptReplacement, PromptUpdate, ) from vllm.sequence import IntermediateTensors from vllm.transformers_utils.configs.hunyuan_vl import ( HunYuanVLConfig, HunYuanVLVisionConfig, ) from vllm.transformers_utils.processors.hunyuan_vl import HunYuanVLProcessor from vllm.transformers_utils.processors.hunyuan_vl_image import smart_resize from vllm.utils.tensor_schema import TensorSchema, TensorShape from .interfaces import ( MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP, SupportsQuant, SupportsXDRoPE, ) from .utils import ( AutoWeightsLoader, WeightsMapper, init_vllm_registered_model, maybe_prefix, ) from .vision import is_vit_use_data_parallel logger = init_logger(__name__) # === Vision Inputs === # class HunYuanVLImagePixelInputs(TensorSchema): """ Dimensions: - np: Number of patches - ni: Number of images - cps: Number of channels * patch_size * patch_size """ type: Literal["pixel_values"] pixel_values: Annotated[ torch.Tensor, TensorShape("np", "cps"), ] image_grid_thw: Annotated[ torch.Tensor, TensorShape("ni", 3), ] class HunYuanVLImageEmbeddingInputs(TensorSchema): """ Dimensions: - nf: Number of image features - hs: Hidden size - ni: Number of images """ type: Literal["image_embeds"] image_embeds: Annotated[ torch.Tensor, TensorShape("nf", "hs"), ] image_grid_thw: Annotated[ torch.Tensor, TensorShape("ni", 3), ] HunYuanVLImageInputs: TypeAlias = ( HunYuanVLImagePixelInputs | HunYuanVLImageEmbeddingInputs ) # === Vision Encoder === # class HunYuanVisionMLP(nn.Module): def __init__( self, in_features: int, hidden_features: int, bias: bool = True, act_fn: Callable[[torch.Tensor], torch.Tensor] = F.gelu, quant_config: QuantizationConfig | None = None, prefix: str = "", ): super().__init__() use_data_parallel = is_vit_use_data_parallel() self.dense_h_to_4h = ColumnParallelLinear( in_features, hidden_features, bias=bias, quant_config=quant_config, prefix=f"{prefix}.dense_h_to_4h", disable_tp=use_data_parallel, ) self.dense_4h_to_h = RowParallelLinear( hidden_features, in_features, bias=bias, quant_config=quant_config, prefix=f"{prefix}.dense_4h_to_h", disable_tp=use_data_parallel, ) self.act_fn = act_fn def forward(self, x: torch.Tensor): x_up, _ = self.dense_h_to_4h(x) x_down, _ = self.dense_4h_to_h(self.act_fn(x_up)) return x_down class HunYuanVisionAttention(nn.Module): def __init__( self, embed_dim: int, num_heads: int, projection_size: int, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() # Per attention head and per partition values. use_data_parallel = is_vit_use_data_parallel() self.tp_size = ( 1 if use_data_parallel else parallel_state.get_tensor_model_parallel_world_size() ) self.hidden_size_per_attention_head = dist_utils.divide( projection_size, num_heads ) self.num_attention_heads_per_partition = dist_utils.divide( num_heads, self.tp_size ) self.qkv = QKVParallelLinear( hidden_size=embed_dim, head_size=self.hidden_size_per_attention_head, total_num_heads=num_heads, total_num_kv_heads=num_heads, bias=True, quant_config=quant_config, prefix=f"{prefix}.qkv", disable_tp=use_data_parallel, ) self.o_proj = RowParallelLinear( input_size=projection_size, output_size=embed_dim, quant_config=quant_config, prefix=f"{prefix}.o_proj", disable_tp=use_data_parallel, ) self.scale = self.hidden_size_per_attention_head**-0.5 self.attn = MMEncoderAttention( self.num_attention_heads_per_partition, self.hidden_size_per_attention_head, self.scale, prefix=f"{prefix}.attn", ) def forward( self, x: torch.Tensor, ) -> torch.Tensor: qkv, _ = self.qkv(x) q, k, v = qkv.chunk(3, dim=-1) out = self.attn(q, k, v) output, _ = self.o_proj(out) return output class HunYuanVisionBlock(nn.Module): def __init__( self, dim: int, num_heads: int, mlp_hidden_dim: int, act_fn: Callable[[torch.Tensor], torch.Tensor] = F.gelu, norm_layer: Callable[[int], nn.Module] | None = None, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() if norm_layer is None: norm_layer = partial(nn.LayerNorm, eps=1e-6) self.input_layernorm = norm_layer(dim) self.post_attention_layernorm = norm_layer(dim) self.self_attn = HunYuanVisionAttention( embed_dim=dim, num_heads=num_heads, projection_size=dim, quant_config=quant_config, prefix=f"{prefix}.self_attn", ) self.mlp = HunYuanVisionMLP( dim, mlp_hidden_dim, act_fn=act_fn, bias=True, quant_config=quant_config, prefix=f"{prefix}.mlp", ) def forward( self, x: torch.Tensor, ) -> torch.Tensor: x = x + self.self_attn(self.input_layernorm(x)) x = x + self.mlp(self.post_attention_layernorm(x)) return x class HunYuanVisionPatchEmbed(nn.Module): def __init__(self, config: HunYuanVLVisionConfig): super().__init__() self.config = config self.embed_dim = config.hidden_size self.patch_size = config.patch_size self.num_channels = config.num_channels self.spatial_merge_size = config.spatial_merge_size self.interpolate_mode = config.interpolate_mode 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=True, ) self.max_num_patches = (config.max_image_size // self.patch_size) ** 2 self.num_positions = self.max_num_patches + 1 self.position_edge = int(self.num_positions**0.5) # first token is cls token, skip it self.position_embedding = nn.Embedding(self.num_positions, self.embed_dim) self.patch_pos_embed = None def forward( self, pixel_values: torch.Tensor, grid_thw: list[list[int]] ) -> torch.Tensor: num_patches = pixel_values.size(0) pixel_values = pixel_values.reshape( num_patches, self.num_channels, self.patch_size, self.patch_size ) patch_embeds = self.patch_embedding(pixel_values) patch_embeds = patch_embeds.squeeze(-1).squeeze(-1).unsqueeze(0) if self.patch_pos_embed is None: patch_pos_shape = ( 1, self.position_edge, self.position_edge, self.embed_dim, ) self.patch_pos_embed = ( self.position_embedding.weight[1:, :] .reshape(patch_pos_shape) .permute(0, 3, 1, 2) .float() ) patch_pos_embed_list = [] for grid in grid_thw: _, h0, w0 = grid # we add a small number to avoid floating point error in the interpolation # see discussion at https://github.com/facebookresearch/dino/issues/8 h0, w0 = h0 + 0.1, w0 + 0.1 patch_pos_embed = nn.functional.interpolate( self.patch_pos_embed, scale_factor=(h0 / self.position_edge, w0 / self.position_edge), mode=self.interpolate_mode, align_corners=False, ) patch_pos_embed = ( patch_pos_embed.reshape(self.embed_dim, -1) .transpose(0, 1) .unsqueeze(0) .to(patch_embeds.dtype) ) patch_pos_embed_list.append(patch_pos_embed) patch_pos_embed = torch.cat(patch_pos_embed_list, dim=1) embeddings = patch_embeds + patch_pos_embed return embeddings class HunYuanVisionPatchMerger(nn.Module): def __init__( self, in_channels, out_channels, spatial_merge_size=2, rms_norm_eps=1e-5, prefix="", ): super().__init__() self.spatial_merge_size = spatial_merge_size embed_std = out_channels**-0.5 self.proj = nn.Sequential( nn.Conv2d( in_channels, in_channels * 2, kernel_size=spatial_merge_size, stride=spatial_merge_size, ), nn.GELU(), nn.Conv2d(in_channels * 2, in_channels * 4, kernel_size=1), ) self.mlp = nn.Linear(in_channels * 4, out_channels) self.image_newline = nn.Parameter(torch.randn(in_channels * 4) * embed_std) self.image_begin = nn.Parameter(torch.randn(out_channels) * embed_std) self.image_end = nn.Parameter(torch.randn(out_channels) * embed_std) self.image_sep = nn.Parameter(torch.randn(out_channels) * embed_std) self.before_rms = RMSNorm(in_channels, eps=rms_norm_eps) self.after_rms = RMSNorm(out_channels, eps=rms_norm_eps) def forward(self, x, size=(16, 16)): x = self.before_rms(x) h, w = size dtype = x.dtype x = x.permute(0, 2, 1).reshape(x.shape[0], -1, h, w) x = self.proj(x) # b,c,h,w b, c, h, w = x.shape x = torch.cat( [x, self.image_newline.reshape(1, c, 1, 1).expand(b, c, h, 1).to(dtype)], dim=-1, ) x = x.reshape(b, c, -1).permute(0, 2, 1) x = self.mlp(x) begin = self.image_begin.reshape(1, 1, -1).expand(b, 1, x.shape[-1]).to(dtype) end = self.image_end.reshape(1, 1, -1).expand(b, 1, x.shape[-1]).to(dtype) x = torch.cat([begin, x, end], dim=1) return self.after_rms(x) class HunYuanVisionTransformer(nn.Module): def __init__( self, vision_config: HunYuanVLVisionConfig, quant_config: QuantizationConfig | None = None, prefix: str = "", ) -> None: super().__init__() num_hidden_layers = vision_config.num_hidden_layers self.hidden_size = vision_config.hidden_size self.num_heads = vision_config.num_attention_heads self.spatial_merge_size = vision_config.spatial_merge_size from vllm.compilation.backends import set_model_tag with set_model_tag("HunYuanVisionPatchEmbed"): self.embeddings = HunYuanVisionPatchEmbed(vision_config) norm_layer = partial(nn.LayerNorm, eps=vision_config.rms_norm_eps) with set_model_tag("HunYuanVisionBlock"): self.layers = nn.ModuleList( [ HunYuanVisionBlock( dim=vision_config.hidden_size, num_heads=vision_config.num_attention_heads, mlp_hidden_dim=vision_config.intermediate_size, act_fn=get_act_fn(vision_config.hidden_act), norm_layer=norm_layer, quant_config=quant_config, prefix=f"{prefix}.layers.{layer_idx}", ) for layer_idx in range(num_hidden_layers) ] ) with set_model_tag("HunYuanVisionPatchMerger"): self.perceive = HunYuanVisionPatchMerger( vision_config.hidden_size, vision_config.out_hidden_size, spatial_merge_size=vision_config.spatial_merge_size, rms_norm_eps=vision_config.rms_norm_eps, prefix=f"{prefix}.perceive", ) @property def dtype(self) -> torch.dtype: return self.embeddings.patch_embedding.weight.dtype @property def device(self) -> torch.device: return self.embeddings.patch_embedding.weight.device def forward( self, x: torch.Tensor, grid_thw: list[list[int]], ) -> torch.Tensor: # patchify seq_len = x.size(0) cu_seqlens: list = [0] hidden_states = x.to(device=self.device, dtype=self.dtype) # embeddings = patch_embeds + patch_pos_embed hidden_states = self.embeddings(hidden_states, grid_thw) for t, h, w in grid_thw: t, h, w = int(t), int(h), int(w) cu_seqlens.append(h * w) cu_seqlens = torch.tensor(cu_seqlens, dtype=torch.int32) cu_seqlens = torch.cumsum(cu_seqlens, dim=0, dtype=torch.int32) cu_seqlens = cu_seqlens.to(device=self.device, non_blocking=True) hidden_states = hidden_states.reshape(seq_len, -1) hidden_states = hidden_states.unsqueeze(0) # build per-image lengths once split_lengths = [int(h) * int(w) for (_, h, w) in grid_thw] for layer in self.layers: # hidden_states: (1, T_total, D) parts = hidden_states.split(split_lengths, dim=1) # list of (1, L_i, D) parts = [layer(p) for p in parts] hidden_states = torch.cat(parts, dim=1) # adapter split_lengths = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() split_items = hidden_states.split(split_lengths, dim=1) image_embeds_list = [] for grid, split_item in zip(grid_thw, split_items): image_embeds_list.append( self.perceive(split_item.contiguous(), size=grid[1:]).squeeze(0) ) return image_embeds_list def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: stacked_params_mapping = [ # (param_name, shard_name, shard_id) (".qkv", ".q_proj", "q"), (".qkv", ".k_proj", "k"), (".qkv", ".v_proj", "v"), ] params_dict = dict(self.named_parameters(remove_duplicate=False)) 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 def _hunyuan_vl_field_config(hf_inputs: Mapping[str, torch.Tensor]): image_grid_thw = hf_inputs.get("image_grid_thw", torch.empty((0, 3))) image_grid_sizes = image_grid_thw.prod(-1) return dict( pixel_values=MultiModalFieldConfig.flat_from_sizes("image", image_grid_sizes), image_embeds=MultiModalFieldConfig.flat_from_sizes("image", image_grid_sizes), image_grid_thw=MultiModalFieldConfig.batched("image", keep_on_cpu=True), ) class HunYuanVLMultiModalDataParser(MultiModalDataParser): def _parse_image_data( self, data: dict[str, torch.Tensor] | ModalityData[ImageItem], ) -> ModalityDataItems[Any, Any] | None: if isinstance(data, dict): return DictEmbeddingItems( data, modality="image", required_fields={"image_embeds", "image_grid_thw"}, fields_factory=_hunyuan_vl_field_config, ) return super()._parse_image_data(data) class HunYuanVLProcessingInfo(BaseProcessingInfo): def get_hf_config(self): return self.ctx.get_hf_config(HunYuanVLConfig) def get_hf_processor( self, **kwargs: object, ) -> HunYuanVLProcessor: return self.ctx.get_hf_processor( HunYuanVLProcessor, use_fast=kwargs.pop("use_fast", True), **kwargs, ) def get_image_processor( self, **kwargs: object, ) -> HunYuanVLProcessor: return self.get_hf_processor(**kwargs).image_processor def get_supported_mm_limits(self) -> Mapping[str, int | None]: return {"image": None} def get_mm_max_tokens_per_item( self, seq_len: int, mm_counts: Mapping[str, int], ) -> Mapping[str, int]: max_image_tokens = self.get_max_image_tokens() # TODO: support video max_video_tokens = 0 return {"image": max_image_tokens, "video": max_video_tokens} def _get_vision_info( self, *, image_width: int, image_height: int, num_frames: int = 1, do_resize: bool = True, image_processor: HunYuanVLProcessor | None, ) -> tuple[ImageSize, int]: if image_processor is None: image_processor = self.get_image_processor() hf_config = self.get_hf_config() vision_config = hf_config.vision_config patch_size = vision_config.patch_size spatial_merge_size = vision_config.spatial_merge_size if do_resize: resized_height, resized_width = smart_resize( height=image_height, width=image_width, factor=patch_size * spatial_merge_size, min_pixels=image_processor.min_pixels, max_pixels=image_processor.max_pixels, ) preprocessed_size = ImageSize(width=resized_width, height=resized_height) else: preprocessed_size = ImageSize(width=image_width, height=image_height) grid_t = 1 grid_h = preprocessed_size.height // patch_size grid_w = preprocessed_size.width // patch_size num_vision_tokens = ( grid_t * grid_h // spatial_merge_size * (grid_w // spatial_merge_size + 1) + 2 ) return preprocessed_size, num_vision_tokens def get_num_image_tokens( self, *, image_width: int, image_height: int, image_processor: HunYuanVLProcessor | None, ) -> int: _, num_image_tokens = self._get_vision_info( image_width=image_width, image_height=image_height, image_processor=image_processor, ) return num_image_tokens def get_image_size_with_most_features(self) -> ImageSize: max_image_size, _ = self._get_vision_info( image_width=512, image_height=8192, image_processor=None, ) return max_image_size def get_max_image_tokens(self) -> int: target_width, target_height = self.get_image_size_with_most_features() return self.get_num_image_tokens( image_width=target_width, image_height=target_height, image_processor=None, ) class HunYuanVLDummyInputsBuilder(BaseDummyInputsBuilder[HunYuanVLProcessingInfo]): def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str: num_images = mm_counts.get("image", 0) hf_processor = self.info.get_hf_processor() image_token: str = hf_processor.image_token return image_token * num_images def get_dummy_mm_data( self, seq_len: int, mm_counts: Mapping[str, int], mm_options: Mapping[str, BaseDummyOptions] | None = None, ) -> MultiModalDataDict: num_images = mm_counts.get("image", 1) target_width, target_height = self.info.get_image_size_with_most_features() return { "image": self._get_dummy_images( width=target_width, height=target_height, num_images=num_images ), } class HunYuanVLMultiModalProcessor(BaseMultiModalProcessor[HunYuanVLProcessingInfo]): def _get_data_parser(self) -> MultiModalDataParser: return HunYuanVLMultiModalDataParser() def _call_hf_processor( self, prompt: str, mm_data: Mapping[str, object], mm_kwargs: Mapping[str, object], tok_kwargs: Mapping[str, object], ) -> BatchFeature: return self.info.ctx.call_hf_processor( self.info.get_hf_processor(**mm_kwargs), dict(text=prompt, **mm_data), dict(**mm_kwargs, **tok_kwargs), ) def _get_prompt_updates( self, mm_items: MultiModalDataItems, hf_processor_mm_kwargs: Mapping[str, Any], out_mm_kwargs: MultiModalKwargsItems, ) -> Sequence[PromptUpdate]: hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs) image_processor = self.info.get_image_processor(**hf_processor_mm_kwargs) placeholder = { "image": hf_processor.image_token_id, } merge_size = image_processor.merge_size def get_replacement_hunyuan_vl(item_idx: int, modality: str): out_item = out_mm_kwargs[modality][item_idx] grid_thw = out_item[f"{modality}_grid_thw"].data assert isinstance(grid_thw, torch.Tensor) _, grid_h, grid_w = grid_thw num_tokens = (int(grid_h) // merge_size) * ( int(grid_w) // merge_size + 1 ) + 2 return [placeholder[modality]] * num_tokens return [ PromptReplacement( modality=modality, target=[placeholder[modality]], replacement=partial(get_replacement_hunyuan_vl, modality=modality), ) for modality in ("image",) ] def _get_mm_fields_config( self, hf_inputs: BatchFeature, hf_processor_mm_kwargs: Mapping[str, object], ) -> Mapping[str, MultiModalFieldConfig]: return _hunyuan_vl_field_config(hf_inputs) @MULTIMODAL_REGISTRY.register_processor( HunYuanVLMultiModalProcessor, info=HunYuanVLProcessingInfo, dummy_inputs=HunYuanVLDummyInputsBuilder, ) class HunYuanVLForConditionalGeneration( nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP, SupportsQuant, SupportsXDRoPE, ): # To ensure correct weight loading and mapping. hf_to_vllm_mapper = WeightsMapper( orig_to_new_prefix={ # mapping for new names in checkpoint saved after transformers v4.52 "vit.vit.": "visual.", "vit.": "visual.", "model.": "language_model.model.", } ) supports_encoder_tp_data = True def get_xdrope_input_positions( self, input_tokens: list[int], mm_features: list[MultiModalFeatureSpec], ) -> torch.Tensor: kwargs = MultiModalFeatureSpec.gather_kwargs( mm_features, {"image_grid_thw"}, ) image_grid_thw = [item.tolist() for item in kwargs.get("image_grid_thw", [])] hf_config = self.config image_start_token_id = hf_config.image_start_token_id spatial_merge_size = hf_config.vision_config.spatial_merge_size xd_num = len(hf_config.rope_scaling["xdrope_section"]) input_tokens_tensor = torch.tensor(input_tokens) image_start_indices = torch.argwhere( input_tokens_tensor == image_start_token_id ).squeeze(1) p_index = torch.arange(len(input_tokens_tensor)) w_index = torch.arange(len(input_tokens_tensor)) h_index = torch.arange(len(input_tokens_tensor)) t_index = torch.arange(len(input_tokens_tensor)) for image_index in range(len(image_start_indices)): # +1 : first image_token, +2: for xdrope positions pos = image_start_indices[image_index] + 2 t, h, w = image_grid_thw[image_index] _, llm_grid_h, llm_grid_w = ( t, h // spatial_merge_size, w // spatial_merge_size, ) token_num = (llm_grid_w + 1) * llm_grid_h w_index[pos : pos + token_num].copy_( torch.arange(0, llm_grid_w + 1) .reshape(1, -1) .expand(llm_grid_h, -1) .reshape(-1) ) h_index[pos : pos + token_num].copy_( torch.arange(0, llm_grid_h) .reshape(-1, 1) .expand(-1, llm_grid_w + 1) .reshape(-1) ) t_index[pos : pos + token_num] = image_index if xd_num == 4: llm_positions = torch.stack([p_index, w_index, h_index, t_index]) elif xd_num == 3: llm_positions = torch.stack([w_index, h_index, t_index]) return llm_positions @classmethod def get_placeholder_str(cls, modality: str, i: int) -> str | None: if modality.startswith("image"): return "<｜hy_place▁holder▁no▁100｜><｜hy_place▁holder▁no▁102｜><｜hy_place▁holder▁no▁101｜>" # noqa: E501 raise ValueError("Only image modality is supported") def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config: HunYuanVLConfig = vllm_config.model_config.hf_config self.config = config with self._mark_tower_model(vllm_config, {"image"}): self.visual = HunYuanVisionTransformer( config.vision_config, quant_config=vllm_config.quant_config, prefix=maybe_prefix(prefix, "visual"), ) with self._mark_language_model(vllm_config): self.language_model = init_vllm_registered_model( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model.model"), architectures=[ "HunYuanDenseV1ForCausalLM", "HunYuanMoEV1ForCausalLM", ], ) self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors ) def _parse_and_validate_image_input( self, **kwargs: object ) -> HunYuanVLImageInputs | None: pixel_values = kwargs.pop("pixel_values", None) image_embeds = kwargs.pop("image_embeds", None) image_grid_thw = kwargs.pop("image_grid_thw", None) if pixel_values is None and image_embeds is None: return None # TODO: refine if isinstance(pixel_values, list): pixel_values = torch.cat(pixel_values, dim=0) if len(pixel_values.shape) == 3: last_dim = pixel_values.shape[-1] pixel_values = pixel_values.reshape(-1, last_dim) image_grid_thw = image_grid_thw.reshape(-1, 3) if pixel_values is not None: return HunYuanVLImagePixelInputs( type="pixel_values", pixel_values=pixel_values, image_grid_thw=image_grid_thw, ) if image_embeds is not None: return HunYuanVLImageEmbeddingInputs( type="image_embeds", image_embeds=image_embeds, image_grid_thw=image_grid_thw, ) def _process_image_input( self, image_input: HunYuanVLImageInputs ) -> tuple[torch.Tensor, ...]: grid_thw = image_input["image_grid_thw"] assert grid_thw.ndim == 2 grid_thw_list = grid_thw.tolist() if image_input["type"] == "image_embeds": image_embeds = image_input["image_embeds"].type(self.visual.dtype) else: pixel_values = image_input["pixel_values"] # TODO: use_data_parallel (split image_embeds in visual) image_embeds = self.visual(pixel_values, grid_thw=grid_thw_list) return image_embeds def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict: mm_input_by_modality = {} # Preserve the order of modalities if there are multiple of them # from the order of kwargs. for input_key in kwargs: if ( input_key in ("pixel_values", "image_embeds") and "image" not in mm_input_by_modality ): mm_input_by_modality["image"] = self._parse_and_validate_image_input( **kwargs ) return mm_input_by_modality def embed_multimodal(self, **kwargs: object) -> MultiModalEmbeddings: mm_input_by_modality = self._parse_and_validate_multimodal_inputs(**kwargs) if not mm_input_by_modality: return [] # The result multimodal_embeddings is tuple of tensors, with each # tensor correspoending to a multimodal data item (image or video). multimodal_embeddings: tuple[torch.Tensor, ...] = () # NOTE: It is important to iterate over the keys in this dictionary # to preserve the order of the modalities. for modality in mm_input_by_modality: multimodal_input = mm_input_by_modality[modality] if modality == "image": image_embeddings = self._process_image_input(multimodal_input) multimodal_embeddings += tuple(image_embeddings) return multimodal_embeddings def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, intermediate_tensors: IntermediateTensors | None, inputs_embeds: torch.Tensor | None, **kwargs: object, ) -> torch.Tensor | IntermediateTensors: if intermediate_tensors is not None: inputs_embeds = None hidden_states = self.language_model( input_ids=input_ids, positions=positions, intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds, ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, ) -> torch.Tensor | None: return self.language_model.compute_logits(hidden_states) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: loader = AutoWeightsLoader( self, skip_prefixes=(["lm_head."] if self.config.tie_word_embeddings else None), ) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper) def get_mm_mapping(self) -> MultiModelKeys: """ Get the module prefix in multimodal models """ return MultiModelKeys.from_string_field( language_model="language_model.model", connector="visual.perceive", tower_model="visual", )