# Adapted from # https://github.com/huggingface/transformers/blob/19e6e80e10118f855137b90740936c0b11ac397f/src/transformers/models/qwen2_vl/modeling_qwen2_vl.py # Copyright 2024 The Qwen team. # Copyright 2023 The vLLM team. # Copyright 2022 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 Qwen2-VL model compatible with HuggingFace weights.""" from functools import cached_property, partial from typing import (Any, Iterable, List, Literal, Mapping, Optional, Set, Tuple, Type, TypedDict, Union) import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from PIL import Image from transformers import BatchFeature from transformers.models.qwen2_vl import (Qwen2VLImageProcessor, Qwen2VLProcessor) from transformers.models.qwen2_vl.configuration_qwen2_vl import ( Qwen2VLConfig, Qwen2VLVisionConfig) from transformers.models.qwen2_vl.image_processing_qwen2_vl import smart_resize from vllm.attention import AttentionMetadata from vllm.config import VllmConfig from vllm.distributed import parallel_state from vllm.distributed import utils as dist_utils from vllm.inputs import InputContext from vllm.logger import init_logger from vllm.model_executor import SamplingMetadata from vllm.model_executor.layers.activation import QuickGELU from vllm.model_executor.layers.linear import (ColumnParallelLinear, RowParallelLinear) from vllm.model_executor.layers.quantization import QuantizationConfig from vllm.model_executor.layers.quantization.gptq import GPTQConfig from vllm.model_executor.layers.quantization.gptq_marlin import ( GPTQMarlinConfig) from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler from vllm.model_executor.model_loader.weight_utils import default_weight_loader from vllm.multimodal import MULTIMODAL_REGISTRY from vllm.multimodal.inputs import MultiModalDataDict, NestedTensors from vllm.multimodal.processing import (BaseMultiModalProcessor, MultiModalDataItems, ProcessorInputs, PromptReplacement) from vllm.platforms import _Backend from vllm.sequence import IntermediateTensors from vllm.transformers_utils.config import uses_mrope from vllm.utils import is_list_of from .interfaces import SupportsLoRA, SupportsMultiModal, SupportsPP from .utils import (AutoWeightsLoader, WeightsMapper, get_vit_attn_backend, init_vllm_registered_model, maybe_prefix) logger = init_logger(__name__) # === Vision Inputs === # class Qwen2VLImagePixelInputs(TypedDict): type: Literal["pixel_values"] pixel_values: torch.Tensor """Shape: `(num_patches, num_channels * patch_size * patch_size)` """ image_grid_thw: torch.Tensor """Shape: `(num_images, 3)` This should be in `(grid_t, grid_h, grid_w)` format. """ class Qwen2VLImageEmbeddingInputs(TypedDict): type: Literal["image_embeds"] image_embeds: torch.Tensor """Supported types: - List[`torch.Tensor`]: A list of tensors holding all images' features. Each tensor holds an image's features. - `torch.Tensor`: A tensor holding all images' features (concatenation of all images' feature tensors). Tensor shape: `(num_image_features, hidden_size)` - `num_image_features` varies based on the number and resolution of the images. - `hidden_size` must match the hidden size of language model backbone. """ image_grid_thw: torch.Tensor """Shape: `(num_images, 3)` This should be in `(grid_t, grid_h, grid_w)` format. """ Qwen2VLImageInputs = Union[Qwen2VLImagePixelInputs, Qwen2VLImageEmbeddingInputs] class Qwen2VLVideoPixelInputs(TypedDict): type: Literal["pixel_values_videos"] pixel_values_videos: torch.Tensor """Shape: `(num_patches, num_channels * temporal_patch_size * patch_size * patch_size)` """ video_grid_thw: torch.Tensor """Shape: `(num_videos, 3)` This should be in `(grid_t, grid_h, grid_w)` format. """ class Qwen2VLVideoEmbeddingInputs(TypedDict): type: Literal["video_embeds"] video_embeds: torch.Tensor """Supported types: - List[`torch.Tensor`]: A list of tensors holding all videos' features. Each tensor holds an video's features. - `torch.Tensor`: A tensor holding all videos' features (concatenation of all videos' feature tensors). Tensor shape: `(num_image_features, hidden_size)` - `num_image_features` varies based on the number and resolution of the videos. - `hidden_size` must match the hidden size of language model backbone. """ video_grid_thw: torch.Tensor """Shape: `(num_videos, 3)` This should be in `(grid_t, grid_h, grid_w)` format. """ Qwen2VLVideoInputs = Union[Qwen2VLVideoPixelInputs, Qwen2VLVideoEmbeddingInputs] # === Vision Encoder === # class Qwen2VisionMLP(nn.Module): def __init__( self, in_features: int, hidden_features: int, act_layer: Type[nn.Module] = QuickGELU, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ): super().__init__() self.fc1 = ColumnParallelLinear(in_features, hidden_features, quant_config=quant_config, prefix=f"{prefix}.fc1") self.act = act_layer() self.fc2 = RowParallelLinear(hidden_features, in_features, quant_config=quant_config, prefix=f"{prefix}.fc2") def forward(self, x: torch.Tensor) -> torch.Tensor: x_parallel, _ = self.fc1(x) x_parallel = self.act(x_parallel) x, _ = self.fc2(x_parallel) return x def rotate_half(x: torch.Tensor, interleaved: bool = False) -> torch.Tensor: if not interleaved: x1, x2 = x.chunk(2, dim=-1) return torch.cat((-x2, x1), dim=-1) else: x1, x2 = x[..., ::2], x[..., 1::2] return rearrange(torch.stack((-x2, x1), dim=-1), "... d two -> ... (d two)", two=2) def apply_rotary_emb_torch(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor, interleaved: bool = False) -> torch.Tensor: """ x: (batch_size, seqlen, nheads, headdim) cos, sin: (seqlen, rotary_dim / 2) or (batch_size, seqlen, rotary_dim / 2) """ ro_dim = cos.shape[-1] * 2 assert ro_dim <= x.shape[-1] cos = repeat( cos, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)") sin = repeat( sin, "... d -> ... 1 (2 d)" if not interleaved else "... d -> ... 1 (d 2)") return torch.cat( [ x[..., :ro_dim] * cos + rotate_half(x[..., :ro_dim], interleaved) * sin, x[..., ro_dim:] ], dim=-1, ) def apply_rotary_pos_emb_vision(t: torch.Tensor, freqs: torch.Tensor) -> torch.Tensor: t_ = t.float() cos = freqs.cos() sin = freqs.sin() output = apply_rotary_emb_torch(t_, cos, sin).type_as(t) return output class Qwen2VisionAttention(nn.Module): def __init__( self, embed_dim: Optional[int] = None, num_heads: Optional[int] = None, projection_size: Optional[int] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() # Per attention head and per partition values. world_size = 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, world_size) self.qkv = ColumnParallelLinear(input_size=embed_dim, output_size=3 * projection_size, quant_config=quant_config, prefix=f"{prefix}.qkv") self.proj = RowParallelLinear(input_size=projection_size, output_size=embed_dim, quant_config=quant_config, prefix=f"{prefix}.proj") # Detect attention implementation. self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True) if self.attn_backend not in { _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS }: raise RuntimeError( f"Qwen2-VL does not support {self.attn_backend} backend now.") def forward( self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor = None, ) -> torch.Tensor: # [s, b, c] --> [s, b, head * 3 * head_dim] x, _ = self.qkv(x) # [s, b, head * 3 * head_dim] --> [s, b, head, 3 * head_dim] new_x_shape = x.size()[:-1] + ( self.num_attention_heads_per_partition, 3 * self.hidden_size_per_attention_head, ) x = x.view(*new_x_shape) # [s, b, head, 3 * head_dim] --> 3 [s, b, head, head_dim] q, k, v = dist_utils.split_tensor_along_last_dim(x, 3) batch_size = q.shape[1] q, k, v = (rearrange(x, "s b ... -> b s ...").contiguous() for x in (q, k, v)) if rotary_pos_emb is not None: q = apply_rotary_pos_emb_vision(q, rotary_pos_emb) k = apply_rotary_pos_emb_vision(k, rotary_pos_emb) if self.attn_backend == _Backend.FLASH_ATTN: # from vllm_flash_attn.flash_attn_interface import ( # flash_attn_varlen_func) from flash_attn import flash_attn_varlen_func q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]) max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() output = flash_attn_varlen_func(q, k, v, cu_seqlens_q=cu_seqlens, cu_seqlens_k=cu_seqlens, max_seqlen_q=max_seqlen, max_seqlen_k=max_seqlen, dropout_p=0, causal=False) context_layer = rearrange(output, "(b s) ... -> b s ...", b=batch_size) elif self.attn_backend == _Backend.TORCH_SDPA: seq_length = q.size(1) q, k, v = (rearrange(x, "b s h d -> b h s d") for x in [q, k, v]) attention_mask = torch.zeros([1, seq_length, seq_length], device=q.device, dtype=torch.bool) for i in range(1, len(cu_seqlens)): attention_mask[..., cu_seqlens[i - 1]:cu_seqlens[i], cu_seqlens[i - 1]:cu_seqlens[i]] = True output = F.scaled_dot_product_attention(q, k, v, attention_mask, dropout_p=0.0) context_layer = rearrange(output, "b h s d -> b s h d ") elif self.attn_backend == _Backend.XFORMERS: from xformers import ops as xops from xformers.ops.fmha.attn_bias import BlockDiagonalMask seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens, kv_seqlen=None) context_layer = xops.memory_efficient_attention_forward( q, k, v, attn_bias=attn_bias, p=0, scale=None) context_layer = rearrange(context_layer, "b s h d -> s b (h d)").contiguous() output, _ = self.proj(context_layer) return output class Qwen2VisionBlock(nn.Module): def __init__( self, dim: int, num_heads: int, mlp_ratio: float, act_layer: Type[nn.Module] = QuickGELU, norm_layer: Type[nn.Module] = None, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() if norm_layer is None: norm_layer = partial(nn.LayerNorm, eps=1e-6) self.norm1 = norm_layer(dim) self.norm2 = norm_layer(dim) mlp_hidden_dim = int(dim * mlp_ratio) self.attn = Qwen2VisionAttention(embed_dim=dim, num_heads=num_heads, projection_size=dim, quant_config=quant_config, prefix=f"{prefix}.attn") self.mlp = Qwen2VisionMLP(dim, mlp_hidden_dim, act_layer=act_layer, quant_config=quant_config, prefix=f"{prefix}.mlp") def forward(self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor) -> torch.Tensor: x = x + self.attn(self.norm1(x), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb) x = x + self.mlp(self.norm2(x)) return x class Qwen2VisionPatchEmbed(nn.Module): def __init__( self, patch_size: int = 14, temporal_patch_size: int = 2, in_chans: int = 3, embed_dim: int = 1152, ) -> None: super().__init__() self.patch_size = patch_size self.temporal_patch_size = temporal_patch_size self.embed_dim = embed_dim kernel_size = [temporal_patch_size, patch_size, patch_size] self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=kernel_size, stride=kernel_size, bias=False) def forward(self, x: torch.Tensor) -> torch.Tensor: L, C = x.shape x = x.view(L, -1, self.temporal_patch_size, self.patch_size, self.patch_size) x = self.proj(x).view(L, self.embed_dim) return x class Qwen2VisionPatchMerger(nn.Module): def __init__( self, d_model: int, context_dim: int, norm_layer: Type[nn.Module] = None, spatial_merge_size: int = 2, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() self.hidden_size = context_dim * (spatial_merge_size**2) if norm_layer is None: norm_layer = partial(nn.LayerNorm, eps=1e-6) self.ln_q = norm_layer(context_dim) self.mlp = nn.ModuleList([ ColumnParallelLinear(self.hidden_size, self.hidden_size, bias=True, quant_config=quant_config, prefix=f"{prefix}.mlp.0"), nn.GELU(), RowParallelLinear(self.hidden_size, d_model, bias=True, quant_config=quant_config, prefix=f"{prefix}.mlp.2"), ]) def forward(self, x: torch.Tensor) -> torch.Tensor: x = self.ln_q(x) x = x.view(-1, self.hidden_size) mlp_fc1, mlp_act, mlp_fc2 = self.mlp x_parallel, _ = mlp_fc1(x) x_parallel = mlp_act(x_parallel) out, _ = mlp_fc2(x_parallel) return out class Qwen2VisionRotaryEmbedding(nn.Module): def __init__(self, dim: int, theta: float = 10000.0) -> None: super().__init__() self.dim = dim self.theta = theta inv_freq = 1.0 / (theta **(torch.arange(0, dim, 2, dtype=torch.float) / dim)) self.register_buffer("inv_freq", inv_freq, persistent=False) self._seq_len_cached = 0 self._freqs_cached = None def update_freqs_cache(self, seqlen: int) -> None: if seqlen > self._seq_len_cached: seqlen *= 2 self._seq_len_cached = seqlen self.inv_freq = 1.0 / (self.theta**(torch.arange( 0, self.dim, 2, dtype=torch.float, device=self.inv_freq.device) / self.dim)) seq = torch.arange(seqlen, device=self.inv_freq.device, dtype=self.inv_freq.dtype) freqs = torch.outer(seq, self.inv_freq) self._freqs_cached = freqs def forward(self, seqlen: int) -> torch.Tensor: self.update_freqs_cache(seqlen) return self._freqs_cached[:seqlen] class Qwen2VisionTransformer(nn.Module): def __init__( self, vision_config: Qwen2VLVisionConfig, norm_eps: float = 1e-6, quant_config: Optional[QuantizationConfig] = None, prefix: str = "", ) -> None: super().__init__() patch_size: int = vision_config.patch_size temporal_patch_size: int = vision_config.temporal_patch_size spatial_merge_size: int = vision_config.spatial_merge_size in_chans: int = vision_config.in_chans hidden_size: int = vision_config.hidden_size embed_dim: int = vision_config.embed_dim depth: int = vision_config.depth num_heads: int = vision_config.num_heads mlp_ratio: float = vision_config.mlp_ratio self.spatial_merge_size = spatial_merge_size self.num_heads = num_heads self.embed_dim = embed_dim self.patch_embed = Qwen2VisionPatchEmbed( patch_size=patch_size, temporal_patch_size=temporal_patch_size, in_chans=in_chans, embed_dim=embed_dim, ) norm_layer = partial(nn.LayerNorm, eps=norm_eps) head_dim = embed_dim // num_heads self.rotary_pos_emb = Qwen2VisionRotaryEmbedding(head_dim // 2) self.blocks = nn.ModuleList([ Qwen2VisionBlock(dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, norm_layer=norm_layer, quant_config=quant_config, prefix=f"{prefix}.blocks.{layer_idx}") for layer_idx in range(depth) ]) self.merger = Qwen2VisionPatchMerger( d_model=hidden_size, context_dim=embed_dim, norm_layer=norm_layer, quant_config=quant_config, prefix=f"{prefix}.merger", ) @property def dtype(self) -> torch.dtype: return self.patch_embed.proj.weight.dtype @property def device(self) -> torch.device: return self.patch_embed.proj.weight.device def rot_pos_emb(self, grid_thw: torch.Tensor) -> torch.Tensor: pos_ids = [] for t, h, w in grid_thw: hpos_ids = torch.arange(h).unsqueeze(1).expand(-1, w) wpos_ids = torch.arange(w).unsqueeze(0).expand(h, -1) hpos_ids = hpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ).permute(0, 2, 1, 3).flatten() wpos_ids = wpos_ids.reshape( h // self.spatial_merge_size, self.spatial_merge_size, w // self.spatial_merge_size, self.spatial_merge_size, ).permute(0, 2, 1, 3).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 forward( self, x: torch.Tensor, grid_thw: torch.Tensor, ) -> torch.Tensor: # patchify x = x.to(device=self.device, dtype=self.dtype) x = self.patch_embed(x) # compute position embedding rotary_pos_emb = self.rot_pos_emb(grid_thw) # compute cu_seqlens cu_seqlens = torch.repeat_interleave(grid_thw[:, 1] * grid_thw[:, 2], grid_thw[:, 0]).cumsum( dim=0, dtype=torch.int32) cu_seqlens = F.pad(cu_seqlens, (1, 0), "constant", 0) # transformers x = x.unsqueeze(1) for blk in self.blocks: x = blk(x, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb) # adapter x = self.merger(x) return x 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(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: if name.endswith("qkv.weight"): visual_num_heads = self.num_heads visual_embed_dim = self.embed_dim head_size = visual_embed_dim // visual_num_heads loaded_weight = loaded_weight.view(3, visual_num_heads, head_size, visual_embed_dim) loaded_weight = loaded_weight.transpose(0, 1) loaded_weight = loaded_weight.reshape(-1, visual_embed_dim) elif name.endswith("qkv.bias"): visual_num_heads = self.num_heads visual_embed_dim = self.embed_dim head_size = visual_embed_dim // visual_num_heads loaded_weight = loaded_weight.view(3, visual_num_heads, head_size) loaded_weight = loaded_weight.transpose(0, 1) loaded_weight = loaded_weight.reshape(-1) 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 # === Vision input helpers === # def _get_vision_info( vision_config: Qwen2VLVisionConfig, height: int, width: int, min_pixels: int, max_pixels: int, do_resize: bool = True, data_type_key: str = "image", mm_count: int = 1, ): """Get information (resized height / width and number of vision tokens) of input image / video frame.""" patch_size = vision_config.patch_size merge_size = vision_config.spatial_merge_size temporal_patch_size = vision_config.temporal_patch_size if do_resize: resized_height, resized_width = smart_resize( height=height, width=width, factor=patch_size * merge_size, min_pixels=min_pixels, max_pixels=max_pixels, ) else: resized_height, resized_width = height, width if data_type_key == "image": grid_t = mm_count else: assert data_type_key == "video" grid_t = max(mm_count // temporal_patch_size, 1) grid_h = resized_height // patch_size grid_w = resized_width // patch_size vision_tokens = grid_t * grid_h * grid_w llm_num_vision_tokens = vision_tokens // (merge_size**2) return resized_height, resized_width, llm_num_vision_tokens def _get_image_processor(hf_processor: Qwen2VLProcessor): image_processor = hf_processor.image_processor # type: ignore assert isinstance(image_processor, Qwen2VLImageProcessor) return image_processor def get_max_qwen2_vl_mm_tokens(ctx: InputContext, data_type_key: str, *, min_pixels: Optional[int] = None, max_pixels: Optional[int] = None) -> int: hf_config = ctx.get_hf_config(Qwen2VLConfig) vision_config = hf_config.vision_config hf_processor = ctx.get_hf_processor(Qwen2VLProcessor) image_processor = _get_image_processor(hf_processor) _, _, max_llm_image_tokens = _get_vision_info( vision_config, height=9999999, width=9999999, min_pixels=min_pixels or image_processor.min_pixels, max_pixels=max_pixels or image_processor.max_pixels, data_type_key=data_type_key, ) return max_llm_image_tokens get_max_qwen2_vl_image_tokens = partial(get_max_qwen2_vl_mm_tokens, data_type_key="image") get_max_qwen2_vl_video_tokens = partial(get_max_qwen2_vl_mm_tokens, data_type_key="video") class Qwen2VLMultiModalDataItems(MultiModalDataItems): @staticmethod def from_dict(data: MultiModalDataDict) -> "MultiModalDataItems": """ Normalize :class:`MultiModalDataDict` to :class:`MultiModalDataItems`. """ multi_data = Qwen2VLMultiModalDataItems() for k, v in data.items(): # TODO: Make a separate modality for embedding inputs # to avoid confusion # yapf: disable if k == "video": # Special case since even a single item can be a list multi_data[k] = ( # type: ignore[index] v if (isinstance(v, (dict, torch.Tensor)) # type: ignore[assignment] or is_list_of(v, list)) else [v] ) elif k in ("image", "audio"): multi_data[k] = ( # type: ignore[index] v if isinstance(v, (dict, torch.Tensor, list)) else [v] ) else: multi_data[k] = v if isinstance(v, list) else [v] # type: ignore[index] # yapf: enable return multi_data def get_item_counts(self) -> Mapping[str, int]: return { m: ( len(items[f"{m}_grid_thw"]) # type: ignore if isinstance(items, dict) else len(items)) for m, items in self.items() } class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor): def _get_mm_items( self, mm_data: MultiModalDataDict, ) -> MultiModalDataItems: return Qwen2VLMultiModalDataItems.from_dict(mm_data) def _get_hf_processor( self, *, min_pixels: Optional[int] = None, max_pixels: Optional[int] = None, ) -> Qwen2VLProcessor: hf_processor = self.ctx.get_hf_processor(Qwen2VLProcessor) image_processor = _get_image_processor(hf_processor) if min_pixels: image_processor.min_pixels = min_pixels if max_pixels: image_processor.max_pixels = max_pixels if max_pixels or min_pixels: image_processor.size = { "min_pixels": image_processor.min_pixels, "max_pixels": image_processor.max_pixels, } return hf_processor def _get_processor_data( self, mm_items: MultiModalDataItems, ) -> tuple[dict[str, Any], dict[str, Any]]: processor_data = dict[str, Any]() passthrough_data = dict[str, Any]() for k, v in mm_items.items(): # TODO: Make a separate modality for embedding inputs # to avoid confusion if k in ("image", "video", "audio"): if isinstance(v, dict): # Pass through embedding inputs (dict) passthrough_data.update(v) elif isinstance(v, torch.Tensor) and v.ndim == 3: # Pass through embedding inputs (single) passthrough_data[f"{k}_embeds"] = [v] elif (is_list_of(v, torch.Tensor) and len(v) > 0 and v[0].ndim == 2): # Pass through embedding inputs (multi) passthrough_data[f"{k}_embeds"] = v else: # Map keys to plural form, e.g.: image -> images processor_data[f"{k}s"] = v else: processor_data[k] = v return processor_data, passthrough_data def _get_prompt_replacements( self, mm_items: MultiModalDataItems, hf_inputs: BatchFeature, mm_processor_kwargs: Mapping[str, object], ) -> list[PromptReplacement]: hf_processor = self._get_hf_processor() image_processor = _get_image_processor(hf_processor) # NOTE: Only Qwen2VLProcessor in transformers 4.47.0 has # image_token and video_token registered placeholder = { "image": hf_processor.image_token, "video": hf_processor.video_token, } merge_length = image_processor.merge_size**2 def get_replacement_qwen2vl(item_idx: int, modality: str): grid_thw = hf_inputs[f"{modality}_grid_thw"][item_idx] num_tokens = grid_thw.prod() // merge_length return placeholder[modality] * num_tokens return [ PromptReplacement( modality=modality, target=placeholder[modality], replacement=partial(get_replacement_qwen2vl, modality=modality), ) for modality in ("image", "video") ] def _get_dummy_mm_inputs( self, mm_counts: Mapping[str, int], ) -> ProcessorInputs: num_images = mm_counts["image"] hf_processor = self._get_hf_processor() image_token: str = hf_processor.image_token image_processor = _get_image_processor(hf_processor) data = {} resized_height, resized_width = smart_resize( height=9999999, width=9999999, factor=image_processor.patch_size * image_processor.merge_size, min_pixels=image_processor.min_pixels, max_pixels=image_processor.max_pixels, ) dummy_image = Image.new("RGB", (resized_width, resized_height), color=0) data["image"] = [dummy_image] * num_images return ProcessorInputs( prompt_text=image_token * num_images, mm_data=data, mm_processor_kwargs={}, ) @MULTIMODAL_REGISTRY.register_max_image_tokens(get_max_qwen2_vl_image_tokens) @MULTIMODAL_REGISTRY.register_max_multimodal_tokens( "video", get_max_qwen2_vl_video_tokens) @MULTIMODAL_REGISTRY.register_processor(Qwen2VLMultiModalProcessor) class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP): packed_modules_mapping = { "qkv_proj": [ "q_proj", "k_proj", "v_proj", ], "gate_up_proj": [ "gate_proj", "up_proj", ], } # LoRA specific attributes # TODO Support LoRA for the visual encoder in the future. supported_lora_modules = [ "qkv_proj", "o_proj", "gate_up_proj", "down_proj", ] embedding_modules = {} embedding_padding_modules = [] # To ensure correct weight loading and mapping. hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={ "lm_head.": "language_model.lm_head.", "model.": "language_model.model.", }) def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config: Qwen2VLConfig = vllm_config.model_config.hf_config cache_config = vllm_config.cache_config quant_config = vllm_config.quant_config multimodal_config = vllm_config.model_config.multimodal_config assert not cache_config.enable_prefix_caching, \ "Qwen2-VL currently does not support prefix caching" self.config = config self.multimodal_config = multimodal_config self.visual = Qwen2VisionTransformer( config.vision_config, norm_eps=getattr(config, "rms_norm_eps", 1e-6), quant_config=self._maybe_ignore_quant_config(quant_config), prefix=maybe_prefix(prefix, "visual"), ) self.language_model = init_vllm_registered_model( vllm_config=vllm_config, prefix=maybe_prefix(prefix, "language_model"), architectures=["Qwen2ForCausalLM"], ) self.make_empty_intermediate_tensors = ( self.language_model.make_empty_intermediate_tensors) @cached_property def sampler(self): if hasattr(self.language_model, "sampler"): return self.language_model.sampler return get_sampler() def _maybe_ignore_quant_config(self, quant_config: QuantizationConfig): # GPTQ configs do not have a list of ignored modules, however AutoGPTQ # seems to avoid vision encoder sections for some models. # See: https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct-GPTQ-Int4 if isinstance(quant_config, (GPTQConfig, GPTQMarlinConfig)): return None return quant_config def _validate_and_reshape_mm_tensor(self, mm_input: Union[torch.Tensor, List[torch.Tensor]], name: str) -> torch.Tensor: if not isinstance(mm_input, (torch.Tensor, list)): raise ValueError(f"Incorrect type of {name}. " f"Got type: {type(mm_input)}") if isinstance(mm_input, torch.Tensor): if mm_input.ndim == 2: return mm_input if mm_input.ndim != 3: raise ValueError(f"{name} should be 2D or batched 3D tensor. " f"Got ndim: {mm_input.ndim}") return torch.concat(list(mm_input)) else: return torch.concat(mm_input) def _parse_and_validate_image_input( self, **kwargs: object) -> Optional[Qwen2VLImageInputs]: 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 if pixel_values is not None: pixel_values = self._validate_and_reshape_mm_tensor( pixel_values, "image pixel values") image_grid_thw = self._validate_and_reshape_mm_tensor( image_grid_thw, "image grid_thw") if not isinstance(pixel_values, (torch.Tensor, list)): raise ValueError("Incorrect type of image pixel values. " f"Got type: {type(pixel_values)}") return Qwen2VLImagePixelInputs(type="pixel_values", pixel_values=pixel_values, image_grid_thw=image_grid_thw) if image_embeds is not None: image_embeds = self._validate_and_reshape_mm_tensor( image_embeds, "image embeds") image_grid_thw = self._validate_and_reshape_mm_tensor( image_grid_thw, "image grid_thw") if not isinstance(image_embeds, torch.Tensor): raise ValueError("Incorrect type of image embeddings. " f"Got type: {type(image_embeds)}") return Qwen2VLImageEmbeddingInputs(type="image_embeds", image_embeds=image_embeds, image_grid_thw=image_grid_thw) def _parse_and_validate_video_input( self, **kwargs: object) -> Optional[Qwen2VLVideoInputs]: pixel_values_videos = kwargs.pop("pixel_values_videos", None) video_embeds = kwargs.pop("video_embeds", None) video_grid_thw = kwargs.pop("video_grid_thw", None) if pixel_values_videos is None and video_embeds is None: return None if pixel_values_videos is not None: pixel_values_videos = self._validate_and_reshape_mm_tensor( pixel_values_videos, "video pixel values") video_grid_thw = self._validate_and_reshape_mm_tensor( video_grid_thw, "video grid_thw") return Qwen2VLVideoPixelInputs( type="pixel_values_videos", pixel_values_videos=pixel_values_videos, video_grid_thw=video_grid_thw, ) if video_embeds is not None: video_embeds = self._validate_and_reshape_mm_tensor( video_embeds, "video embeds") video_grid_thw = self._validate_and_reshape_mm_tensor( video_grid_thw, "video grid_thw") if not isinstance(video_embeds, torch.Tensor): raise ValueError("Incorrect type of video embeddings. " f"Got type: {type(video_embeds)}") return Qwen2VLVideoEmbeddingInputs(type="video_embeds", video_embeds=video_embeds, video_grid_thw=video_grid_thw) def _process_image_input(self, image_input: Qwen2VLImageInputs) -> torch.Tensor: if image_input["type"] == "image_embeds": return image_input["image_embeds"].type(self.visual.dtype) pixel_values = image_input["pixel_values"].type(self.visual.dtype) image_embeds = self.visual(pixel_values, grid_thw=image_input["image_grid_thw"]) return image_embeds def _process_video_input(self, video_input: Qwen2VLVideoInputs) -> torch.Tensor: if video_input["type"] == "video_embeds": return video_input["video_embeds"].type(self.visual.dtype) pixel_values_videos = video_input["pixel_values_videos"].type( self.visual.dtype) video_embeds = self.visual(pixel_values_videos, grid_thw=video_input["video_grid_thw"]) return video_embeds def _merge_multimodal_embeddings( self, input_ids: torch.Tensor, inputs_embeds: torch.Tensor, multimodal_embeddings: torch.Tensor, placeholder_token_id: int, ) -> torch.Tensor: mask = (input_ids == placeholder_token_id) inputs_embeds[mask, :] = multimodal_embeddings return inputs_embeds def get_multimodal_embeddings( self, **kwargs) -> Optional[List[Tuple[NestedTensors, str]]]: image_input = self._parse_and_validate_image_input(**kwargs) video_input = self._parse_and_validate_video_input(**kwargs) if image_input is None and video_input is None: return None # We make a tuple of each embedding with its modality string. This is a # temporary workaround for models to handle mixed modalities when # get_multimodal_embeddings and get_input_embeddings are called # separately. # TODO(ywang96): Add support for mixed-modality inference for v1. multimodal_embeddings: List[Tuple[NestedTensors, str]] = [] if image_input is not None: image_embeds = self._process_image_input(image_input) multimodal_embeddings.append((image_embeds, "image")) if video_input is not None: video_embeds = self._process_video_input(video_input) multimodal_embeddings.append((video_embeds, "video")) return multimodal_embeddings def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[List[Tuple[NestedTensors, str]]] = None, ) -> torch.Tensor: inputs_embeds = self.language_model.get_input_embeddings(input_ids) if multimodal_embeddings is not None: for embeddings, modality in multimodal_embeddings: if modality == "image": inputs_embeds = self._merge_multimodal_embeddings( input_ids, inputs_embeds, embeddings, placeholder_token_id=self.config.image_token_id, ) if modality == "video": inputs_embeds = self._merge_multimodal_embeddings( input_ids, inputs_embeds, embeddings, placeholder_token_id=self.config.video_token_id, ) return inputs_embeds def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, kv_caches: List[torch.Tensor], attn_metadata: AttentionMetadata, intermediate_tensors: Optional[IntermediateTensors] = None, inputs_embeds: Optional[torch.Tensor] = None, **kwargs: object, ) -> Union[torch.Tensor, IntermediateTensors]: """Run forward pass for Qwen2-VL. Args: input_ids: Flattened (concatenated) input_ids corresponding to a batch. positions: Flattened (concatenated) position ids corresponding to a batch. **NOTE**: If mrope is enabled (default setting for Qwen2-VL opensource models), the shape will be `(3, seq_len)`, otherwise it will be `(seq_len,). pixel_values: Pixel values to be fed to a model. `None` if no images are passed. image_grid_thw: Tensor `(n_images, 3)` of image 3D grid in LLM. `None` if no images are passed. pixel_values_videos: Pixel values of videos to be fed to a model. `None` if no videos are passed. video_grid_thw: Tensor `(n_videos, 3)` of video 3D grid in LLM. `None` if no videos are passed. """ if intermediate_tensors is not None: inputs_embeds = None # NOTE: In v1, inputs_embeds is always generated at model runner, this # condition is for v0 compatibility. elif inputs_embeds is None: multimodal_embeddings = self.get_multimodal_embeddings(**kwargs) # We need to check for usage of mrope here in case there is # multimodal data. # TODO (ywang96): move this to model runner in V1. if multimodal_embeddings is not None and uses_mrope(self.config): assert positions.ndim == 2 and positions.size(0) == 3, ( "multimodal section rotary embedding requires " f"(3, seq_len) positions, but got {positions.size()}") inputs_embeds = self.get_input_embeddings(input_ids, multimodal_embeddings) input_ids = None hidden_states = self.language_model.model( input_ids=input_ids, positions=positions, kv_caches=kv_caches, attn_metadata=attn_metadata, intermediate_tensors=intermediate_tensors, inputs_embeds=inputs_embeds, ) return hidden_states def compute_logits( self, hidden_states: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[torch.Tensor]: return self.language_model.compute_logits(hidden_states, sampling_metadata) def sample( self, logits: torch.Tensor, sampling_metadata: SamplingMetadata, ) -> Optional[SamplerOutput]: return self.language_model.sample(logits, sampling_metadata) def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]) -> Set[str]: loader = AutoWeightsLoader(self) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)