# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project # 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 collections.abc import Iterable, Mapping, Sequence from functools import partial from typing import Any, Callable, Literal, Optional, TypedDict, Union import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange, repeat from transformers import AutoConfig, 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 transformers.models.qwen2_vl.video_processing_qwen2_vl import ( Qwen2VLVideoProcessor) from vllm.config import VllmConfig from vllm.distributed import parallel_state, tensor_model_parallel_all_gather from vllm.distributed import utils as dist_utils 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.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, MultiModalFieldConfig, MultiModalKwargs, VideoItem) from vllm.multimodal.parse import (DictEmbeddingItems, ImageSize, ModalityDataItems, MultiModalDataItems, MultiModalDataParser) from vllm.multimodal.processing import (BaseMultiModalProcessor, BaseProcessingInfo, PromptReplacement, PromptUpdate) from vllm.multimodal.profiling import BaseDummyInputsBuilder from vllm.platforms import _Backend, current_platform from vllm.sequence import IntermediateTensors from vllm.transformers_utils.config import uses_mrope from vllm.transformers_utils.tokenizer import AnyTokenizer from .interfaces import (MultiModalEmbeddings, SupportsLoRA, SupportsMultiModal, SupportsPP) from .utils import (AutoWeightsLoader, WeightsMapper, init_vllm_registered_model, maybe_prefix, merge_multimodal_embeddings) from .vision import get_vit_attn_backend logger = init_logger(__name__) # For profile run _MAX_FRAMES_PER_VIDEO = 16 # === 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() apply_rotary_emb = apply_rotary_emb_torch if current_platform.is_cuda(): from vllm.vllm_flash_attn.layers.rotary import apply_rotary_emb output = apply_rotary_emb(t_, cos, sin).type_as(t) return output class Qwen2VisionAttention(nn.Module): def __init__( self, embed_dim: int, num_heads: int, projection_size: int, 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.tp_size = world_size self.tp_rank = parallel_state.get_tensor_model_parallel_rank() 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, _Backend.ROCM_AITER_FA }: raise RuntimeError( f"Qwen2-VL does not support {self.attn_backend} backend now.") self.is_flash_attn_backend = self.attn_backend in { _Backend.FLASH_ATTN, _Backend.ROCM_AITER_FA } def split_qkv(self, qkv: torch.Tensor) -> tuple[torch.Tensor, ...]: # [s, b, 3 * head * head_dim] seq_len, bs, _ = qkv.shape if self.tp_size > 1: qkv = tensor_model_parallel_all_gather(qkv) # [s, b, 3 * head * head_dim] -> 3 * [s, b, head * head_dim] q, k, v = qkv.chunk(3, dim=2) # 3 * [s, b, head * head_dim] if self.tp_size > 1: splitter = partial(dist_utils.split_tensor_along_last_dim, num_partitions=self.tp_size) q = splitter(q)[self.tp_rank] k = splitter(k)[self.tp_rank] v = splitter(v)[self.tp_rank] # 3 * [s, b, head * head_dim] -> 3 * [s, b, head, head_dim] new_shape = (seq_len, bs, self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) q, k, v = (x.view(*new_shape) for x in (q, k, v)) return q, k, v def forward( self, x: torch.Tensor, cu_seqlens: torch.Tensor, rotary_pos_emb: torch.Tensor, max_seqlen: Optional[int] = None, # Only used for Flash Attention seqlens: Optional[list[int]] = None, # Only used for xFormers ) -> torch.Tensor: # [s, b, c] --> [s, b, 3 * head * head_dim] x, _ = self.qkv(x) # [s, b, 3 * head * head_dim] -> 3 * [s, b, head, head_dim] q, k, v = self.split_qkv(x) 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.is_flash_attn_backend: # from vllm_flash_attn.flash_attn_interface import ( # flash_attn_varlen_func) if self.attn_backend == _Backend.ROCM_AITER_FA: from aiter import flash_attn_varlen_func else: from flash_attn import flash_attn_varlen_func q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]) 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=0.0, causal=False) context_layer = rearrange(output, "(b s) ... -> b s ...", b=batch_size) elif self.attn_backend == _Backend.TORCH_SDPA: # Execute attention entry by entry for speed & less VRAM. outputs = [] for i in range(1, len(cu_seqlens)): start_idx = cu_seqlens[i - 1] end_idx = cu_seqlens[i] q_i = q[:, start_idx:end_idx] k_i = k[:, start_idx:end_idx] v_i = v[:, start_idx:end_idx] q_i, k_i, v_i = (rearrange(x, "b s h d -> b h s d") for x in [q_i, k_i, v_i]) output_i = F.scaled_dot_product_attention(q_i, k_i, v_i, dropout_p=0.0) output_i = rearrange(output_i, "b h s d -> b s h d ") outputs.append(output_i) context_layer = torch.cat(outputs, dim=1) elif self.attn_backend == _Backend.XFORMERS: from xformers import ops as xops from xformers.ops.fmha.attn_bias import BlockDiagonalMask attn_bias = BlockDiagonalMask.from_seqlens(q_seqlen=seqlens, kv_seqlen=None, device=q.device) 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: Optional[Callable[[int], 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, max_seqlen: Optional[int] = None, # Only used for Flash Attention seqlens: Optional[list[int]] = None, # Only used for xFormers ) -> torch.Tensor: x = x + self.attn( self.norm1(x), cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb, max_seqlen=max_seqlen, seqlens=seqlens, ) 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_channels: 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_channels, 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: Optional[Callable[[int], 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 = vision_config.patch_size temporal_patch_size = vision_config.temporal_patch_size spatial_merge_size = vision_config.spatial_merge_size in_channels = vision_config.in_channels hidden_size = vision_config.hidden_size embed_dim = vision_config.embed_dim depth = vision_config.depth num_heads = vision_config.num_heads mlp_ratio = 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_channels=in_channels, 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", ) self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True) @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 compute_attn_mask_seqlen( self, cu_seqlens: torch.Tensor ) -> tuple[Optional[int], Optional[list[int]]]: max_seqlen, seqlens = None, None if (self.attn_backend == _Backend.FLASH_ATTN or self.attn_backend == _Backend.ROCM_AITER_FA): max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item() elif self.attn_backend == _Backend.XFORMERS: seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist() return max_seqlen, seqlens 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) # pre-compute seqlens for attn mask to reduce cuMemcpy operations max_seqlen, seqlens = self.compute_attn_mask_seqlen(cu_seqlens) for blk in self.blocks: x = blk( x, cu_seqlens=cu_seqlens, rotary_pos_emb=rotary_pos_emb, max_seqlen=max_seqlen, seqlens=seqlens, ) # 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: 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 _qwen2vl_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) video_grid_thw = hf_inputs.get("video_grid_thw", torch.empty((0, 3))) video_grid_sizes = video_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"), pixel_values_videos=MultiModalFieldConfig.flat_from_sizes( "video", video_grid_sizes), video_embeds=MultiModalFieldConfig.flat_from_sizes( "video", video_grid_sizes), video_grid_thw=MultiModalFieldConfig.batched("video"), ) class Qwen2VLMultiModalDataParser(MultiModalDataParser): def _parse_image_data( self, data: Union[dict[str, torch.Tensor], ModalityData[ImageItem]], ) -> Optional[ModalityDataItems[Any, Any]]: if isinstance(data, dict): return DictEmbeddingItems( data, modality="image", required_fields={"image_embeds", "image_grid_thw"}, fields_factory=_qwen2vl_field_config, ) return super()._parse_image_data(data) def _parse_video_data( self, data: Union[dict[str, torch.Tensor], ModalityData[VideoItem]], ) -> Optional[ModalityDataItems[Any, Any]]: if isinstance(data, dict): return DictEmbeddingItems( data, modality="video", required_fields={"video_embeds", "video_grid_thw"}, fields_factory=_qwen2vl_field_config, ) return super()._parse_video_data(data) class Qwen2VLProcessingInfo(BaseProcessingInfo): def get_hf_config(self): return self.ctx.get_hf_config(Qwen2VLConfig) def get_hf_processor(self, **kwargs: object) -> Qwen2VLProcessor: return self.ctx.get_hf_processor( Qwen2VLProcessor, use_fast=kwargs.pop("use_fast", True), **kwargs, ) def get_image_processor(self, **kwargs: object) -> Qwen2VLImageProcessor: return self.get_hf_processor(**kwargs).image_processor def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]: return {"image": None, "video": 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() max_video_tokens = self.get_max_video_tokens(seq_len, mm_counts) 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: Optional[Qwen2VLImageProcessor], ) -> 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 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=image_height, width=image_width, factor=patch_size * 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) # NOTE: Frames are padded to be divisible by `temporal_patch_size` # https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L294 padded_num_frames = num_frames + num_frames % temporal_patch_size grid_t = max(padded_num_frames // temporal_patch_size, 1) grid_h = preprocessed_size.height // patch_size grid_w = preprocessed_size.width // patch_size num_patches = grid_t * grid_h * grid_w num_vision_tokens = num_patches // (merge_size**2) return preprocessed_size, num_vision_tokens def get_num_image_tokens( self, *, image_width: int, image_height: int, image_processor: Optional[Qwen2VLImageProcessor], ) -> 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_num_video_tokens( self, *, image_width: int, image_height: int, num_frames: int, image_processor: Optional[Qwen2VLImageProcessor], ) -> int: _, num_video_tokens = self._get_vision_info( image_width=image_width, image_height=image_height, num_frames=num_frames, image_processor=image_processor, ) return num_video_tokens def get_image_size_with_most_features(self) -> ImageSize: max_image_size, _ = self._get_vision_info( image_width=9999999, image_height=9999999, 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, ) def _get_max_video_frames(self, max_tokens: int) -> int: target_width, target_height = self.get_image_size_with_most_features() num_frames = 0 while True: next_num_frames = num_frames + 1 next_max_tokens = self.get_num_video_tokens( image_width=target_width, image_height=target_height, num_frames=next_num_frames, image_processor=None, ) if next_max_tokens > max_tokens: break num_frames = next_num_frames return num_frames def get_num_frames_with_most_features( self, seq_len: int, mm_counts: Mapping[str, int], ) -> int: max_images = mm_counts.get("image", 0) max_videos = mm_counts.get("video", 0) max_image_tokens = self.get_max_image_tokens() * max_images max_total_frames = self._get_max_video_frames(seq_len - max_image_tokens) max_frames_per_video = min(max_total_frames // max(max_videos, 1), _MAX_FRAMES_PER_VIDEO) return max(max_frames_per_video, 1) def get_max_video_tokens( self, seq_len: int, mm_counts: Mapping[str, int], ) -> int: target_width, target_height = self.get_image_size_with_most_features() return self.get_num_video_tokens( image_width=target_width, image_height=target_height, num_frames=self.get_num_frames_with_most_features( seq_len, mm_counts), image_processor=None, ) class Qwen2VLDummyInputsBuilder(BaseDummyInputsBuilder[Qwen2VLProcessingInfo]): def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str: num_images = mm_counts.get("image", 0) num_videos = mm_counts.get("video", 0) hf_processor = self.info.get_hf_processor() image_token: str = hf_processor.image_token video_token: str = hf_processor.video_token return image_token * num_images + video_token * num_videos def get_dummy_mm_data( self, seq_len: int, mm_counts: Mapping[str, int], ) -> MultiModalDataDict: num_images = mm_counts.get("image", 0) num_videos = mm_counts.get("video", 0) target_width, target_height = \ self.info.get_image_size_with_most_features() target_num_frames = \ self.info.get_num_frames_with_most_features(seq_len, mm_counts) return { "image": self._get_dummy_images(width=target_width, height=target_height, num_images=num_images), "video": self._get_dummy_videos( width=target_width, height=target_height, num_frames=target_num_frames, num_videos=num_videos, ) } class Qwen2VLMultiModalProcessor(BaseMultiModalProcessor[Qwen2VLProcessingInfo] ): def _get_data_parser(self) -> MultiModalDataParser: return Qwen2VLMultiModalDataParser() def _get_prompt_updates( self, mm_items: MultiModalDataItems, hf_processor_mm_kwargs: Mapping[str, Any], out_mm_kwargs: MultiModalKwargs, ) -> Sequence[PromptUpdate]: hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs) image_processor = self.info.get_image_processor( **hf_processor_mm_kwargs) tokenizer = self.info.get_tokenizer() vocab = tokenizer.get_vocab() placeholder = { "image": vocab[hf_processor.image_token], "video": vocab[hf_processor.video_token], } merge_length = image_processor.merge_size**2 def get_replacement_qwen2vl(item_idx: int, modality: str): grid_thw = out_mm_kwargs[f"{modality}_grid_thw"][item_idx] assert isinstance(grid_thw, torch.Tensor) num_tokens = int(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_mm_fields_config( self, hf_inputs: BatchFeature, hf_processor_mm_kwargs: Mapping[str, object], ) -> Mapping[str, MultiModalFieldConfig]: return _qwen2vl_field_config(hf_inputs) @MULTIMODAL_REGISTRY.register_processor(Qwen2VLMultiModalProcessor, info=Qwen2VLProcessingInfo, dummy_inputs=Qwen2VLDummyInputsBuilder) class Qwen2VLForConditionalGeneration(nn.Module, SupportsMultiModal, SupportsLoRA, SupportsPP): # 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 "model.language_model.": "language_model.model.", "model.visual.": "visual.", # mapping for original checkpoint "lm_head.": "language_model.lm_head.", "model.": "language_model.model.", }) @classmethod def get_placeholder_str(cls, modality: str, i: int) -> Optional[str]: if modality.startswith("image"): return "<|vision_start|><|image_pad|><|vision_end|>" if modality.startswith("video"): return "<|vision_start|><|video_pad|><|vision_end|>" raise ValueError("Only image or video modality is supported") def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): super().__init__() config: Qwen2VLConfig = vllm_config.model_config.hf_config quant_config = vllm_config.quant_config multimodal_config = vllm_config.model_config.multimodal_config self.config = config self.multimodal_config = multimodal_config if multimodal_config.get_limit_per_prompt("image") or \ multimodal_config.get_limit_per_prompt("video"): 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"), ) else: self.visual = None 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) 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: object, 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} " f"(shape={mm_input.shape})") 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) -> tuple[torch.Tensor, ...]: grid_thw = image_input["image_grid_thw"] assert grid_thw.ndim == 2 if image_input["type"] == "image_embeds": image_embeds = image_input["image_embeds"] else: pixel_values = image_input["pixel_values"] image_embeds = self.visual(pixel_values, grid_thw=grid_thw) # Split concatenated embeddings for each image item. merge_size = self.visual.spatial_merge_size sizes = grid_thw.prod(-1) // merge_size // merge_size return image_embeds.split(sizes.tolist()) def _process_video_input( self, video_input: Qwen2VLVideoInputs) -> tuple[torch.Tensor, ...]: grid_thw = video_input["video_grid_thw"] assert grid_thw.ndim == 2 if video_input["type"] == "video_embeds": video_embeds = video_input["video_embeds"] else: pixel_values_videos = video_input["pixel_values_videos"] video_embeds = self.visual(pixel_values_videos, grid_thw=grid_thw) # Split concatenated embeddings for each video item. merge_size = self.visual.spatial_merge_size sizes = grid_thw.prod(-1) // merge_size // merge_size return video_embeds.split(sizes.tolist()) def _parse_and_validate_multimodal_inputs(self, **kwargs: object) -> dict: modalities = {} # 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 "images" not in modalities: modalities["images"] = self._parse_and_validate_image_input( **kwargs) if input_key in ("pixel_values_videos", "video_embeds") and "videos" not in modalities: modalities["videos"] = self._parse_and_validate_video_input( **kwargs) return modalities def get_language_model(self) -> torch.nn.Module: return self.language_model def get_multimodal_embeddings(self, **kwargs: object) -> MultiModalEmbeddings: modalities = self._parse_and_validate_multimodal_inputs(**kwargs) if not modalities: 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 modalities: if modality == "images": image_input = modalities["images"] vision_embeddings = self._process_image_input(image_input) multimodal_embeddings += vision_embeddings if modality == "videos": video_input = modalities["videos"] video_embeddings = self._process_video_input(video_input) multimodal_embeddings += video_embeddings return multimodal_embeddings def get_input_embeddings( self, input_ids: torch.Tensor, multimodal_embeddings: Optional[MultiModalEmbeddings] = None, ) -> torch.Tensor: inputs_embeds = self.language_model.get_input_embeddings(input_ids) if multimodal_embeddings is not None \ and len(multimodal_embeddings) != 0: inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, multimodal_embeddings, [self.config.image_token_id, self.config.video_token_id]) return inputs_embeds def get_input_embeddings_v0( self, input_ids: torch.Tensor, image_input: Optional[Qwen2VLImagePixelInputs] = None, video_input: Optional[Qwen2VLVideoPixelInputs] = None, ) -> torch.Tensor: inputs_embeds = self.get_input_embeddings(input_ids) if image_input is not None: image_embeds = self._process_image_input(image_input) inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, image_embeds, placeholder_token_id=self.config.image_token_id, ) if video_input is not None: video_embeds = self._process_video_input(video_input) inputs_embeds = merge_multimodal_embeddings( input_ids, inputs_embeds, video_embeds, placeholder_token_id=self.config.video_token_id, ) return inputs_embeds def forward( self, input_ids: torch.Tensor, positions: torch.Tensor, 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 from # `get_multimodal_embeddings` and `get_input_embeddings`, this # condition is only for v0 compatibility. elif inputs_embeds is None: 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: inputs_embeds = None else: if 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_v0( input_ids, image_input=image_input, video_input=video_input) input_ids = None hidden_states = self.language_model.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, sampling_metadata: SamplingMetadata, ) -> Optional[torch.Tensor]: return self.language_model.compute_logits(hidden_states, sampling_metadata) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: skip_prefixes = [] if self.visual is None: skip_prefixes.extend(["visual."]) loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes) 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", connector="visual.merger.", tower_model="visual.", ) class Tarsier2MultiModalProcessor(Qwen2VLMultiModalProcessor): pass class Tarsier2ImageProcessor(Qwen2VLImageProcessor): def __init__( self, size: Optional[dict[str, int]] = None, **kwargs, ) -> None: if size is not None and "min_pixels" in size and "max_pixels" in size: # Remap if Tarsier2-specific format is provided remapped_size = { "shortest_edge": size["min_pixels"], "longest_edge": size["max_pixels"] } super().__init__(size=remapped_size, **kwargs) else: super().__init__(size=size, **kwargs) class Tarsier2Processor(Qwen2VLProcessor): def __init__( self, vision_config: dict, tokenizer: AnyTokenizer, **kwargs, ): self.image_processor = Tarsier2ImageProcessor(**vision_config) super().__init__( image_processor=self.image_processor, tokenizer=tokenizer, video_processor=Qwen2VLVideoProcessor(**vision_config), chat_template=None, **kwargs) class Tarsier2ProcessingInfo(Qwen2VLProcessingInfo): def get_hf_config(self) -> Qwen2VLConfig: model_path = self.ctx.model_config.model original_config = AutoConfig.from_pretrained(model_path) config_dict = original_config.to_dict() correct_config = Qwen2VLConfig.from_dict(config_dict) return correct_config def get_hf_processor(self, **kwargs: object) -> Tarsier2Processor: return Tarsier2Processor( vision_config=self.ctx.get_hf_image_processor_config(), tokenizer=self.get_tokenizer(), **kwargs, ) def get_image_processor(self) -> Tarsier2ImageProcessor: return Tarsier2ImageProcessor( **self.ctx.get_hf_image_processor_config()) @MULTIMODAL_REGISTRY.register_processor(Tarsier2MultiModalProcessor, info=Tarsier2ProcessingInfo, dummy_inputs=Qwen2VLDummyInputsBuilder) class Tarsier2ForConditionalGeneration(Qwen2VLForConditionalGeneration): hf_to_vllm_mapper = WeightsMapper(orig_to_new_prefix={ "vision_tower.": "visual.", }) def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""): # Tarsier2 uses llava as model_type, which will create a Qwen2VLConfig # as text_config, we need to reconstruct Qwen2VLConfig from LlavaConfig. config = vllm_config.model_config.hf_config qwen2vl_config = config.text_config qwen2vl_config.architectures = config.architectures vllm_config.model_config.hf_config = qwen2vl_config super().__init__(vllm_config=vllm_config, prefix=prefix) def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]: skip_prefixes = [] if self.visual is None: skip_prefixes.extend(["visual."]) loader = AutoWeightsLoader(self, skip_prefixes=skip_prefixes) return loader.load_weights(weights, mapper=self.hf_to_vllm_mapper)