dsv4.1: vision tower and image preprocessing (#39668)
Co-authored-by: BBuf <1182563586@qq.com> Co-authored-by: Yuhao Yang <47235274+yhyang201@users.noreply.github.com>
This commit is contained in:
Liangsheng Yin
co-authored by
BBuf
Yuhao Yang
BBuf
Yuhao Yang
parent
408d2334c3
commit
c89c63fa38
@@ -628,12 +628,17 @@ class ModelConfig:
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or hasattr(self.hf_config, "audio_config")
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)
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)
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has_dsv41_vision = (
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self.hf_config.model_type == "deepseek_v41"
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and self.hf_config.vision_n_layers > 0
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)
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self.is_multimodal = (
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enable_multimodal
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and not self.is_lm_only
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and (
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is_multimodal_model(self.hf_config.architectures)
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or has_multimodal_subconfig
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or has_dsv41_vision
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)
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)
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self.is_audio_model = enable_multimodal and is_audio_model(
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@@ -652,6 +657,8 @@ class ModelConfig:
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self.is_multimodal
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and getattr(self.hf_config, "vision_config", None) is not None
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)
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if self.is_multimodal and has_dsv41_vision:
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self.is_image_understandable_model = True
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# Models expose audio_config at different nesting levels:
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# - top-level audio_config: e.g. Qwen2Audio
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@@ -0,0 +1,151 @@
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"""DeepSeek-V4.1 vision tower and aligner."""
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from functools import lru_cache
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import torch
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import torch.nn.functional as F
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from torch import nn
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from sglang.srt.layers.attention.vision import (
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VisionAttention,
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VisionAttentionMetadata,
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prepare_vision_attention_metadata,
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)
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from sglang.srt.layers.layernorm import RMSNorm
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def _rms_norm(dim: int) -> RMSNorm:
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# The fused CUDA kernels do not take an fp32 weight with a bf16 input.
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return RMSNorm(dim, eps=1e-6, weight_dtype=torch.float32, force_native=True)
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@lru_cache(8)
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def get_vision_cos_sin(n_h: int, n_w: int, dim: int, theta: float):
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inv_freq = 1.0 / (theta ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
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hpos = torch.arange(n_h).unsqueeze(1).expand(n_h, n_w)
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wpos = torch.arange(n_w).unsqueeze(0).expand(n_h, n_w)
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freqs = torch.stack([hpos, wpos], dim=-1).reshape(-1, 2, 1).float() * inv_freq
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freqs = freqs.flatten(1)
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return freqs.cos().unsqueeze(1), freqs.sin().unsqueeze(1)
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def apply_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
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dtype = x.dtype
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x1, x2 = x.float().chunk(2, dim=-1)
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return torch.cat([x1 * cos - x2 * sin, x2 * cos + x1 * sin], dim=-1).to(dtype)
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class PatchEmbed(nn.Module):
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def __init__(self, args):
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super().__init__()
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self.proj = nn.Linear(3 * args.vision_patch_size**2, args.vision_dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.proj(x.flatten(1))
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def apply_vision_rotary(q, k, position_embeddings, x_shape):
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# The reference pairs the two halves of each head, with FP32 arithmetic.
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cos, sin = position_embeddings
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return apply_rotary(q, cos, sin), apply_rotary(k, cos, sin)
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class Attention(VisionAttention):
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def __init__(self, args):
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super().__init__(
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embed_dim=args.vision_dim,
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num_heads=args.vision_n_heads,
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projection_size=args.vision_dim,
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use_qkv_parallel=True,
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use_data_parallel=True,
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customized_position_embedding_applier=apply_vision_rotary,
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)
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def forward(
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self,
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x: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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metadata: VisionAttentionMetadata,
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) -> torch.Tensor:
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return (
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super()
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.forward(
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x,
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position_embeddings=(cos, sin),
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forward_metadata=metadata,
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)
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.squeeze(0)
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)
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class MLP(nn.Module):
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def __init__(self, args):
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super().__init__()
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self.w1 = nn.Linear(args.vision_dim, 2 * args.vision_inter_dim, bias=False)
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self.w2 = nn.Linear(args.vision_inter_dim, args.vision_dim, bias=False)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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gate, up = self.w1(x).chunk(2, dim=-1)
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return self.w2(F.silu(gate) * up)
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class Block(nn.Module):
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def __init__(self, args):
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super().__init__()
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self.norm1 = _rms_norm(args.vision_dim)
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self.attn = Attention(args)
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self.norm2 = _rms_norm(args.vision_dim)
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self.mlp = MLP(args)
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def forward(
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self,
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x: torch.Tensor,
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cos: torch.Tensor,
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sin: torch.Tensor,
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metadata: VisionAttentionMetadata,
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) -> torch.Tensor:
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x = x + self.attn(self.norm1(x), cos, sin, metadata)
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return x + self.mlp(self.norm2(x))
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class ViT(nn.Module):
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"""DeepSeek ViT: full bidirectional attention over one image with 2D RoPE."""
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def __init__(self, args):
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super().__init__()
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self.rope_dim = args.vision_dim // args.vision_n_heads // 2
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self.rope_theta = args.vision_rope_theta
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self.patch_embed = PatchEmbed(args)
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self.blocks = nn.ModuleList([Block(args) for _ in range(args.vision_n_layers)])
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self.norm = _rms_norm(args.vision_dim)
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def forward(self, patches: torch.Tensor, n_h: int, n_w: int) -> torch.Tensor:
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x = self.patch_embed(patches)
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cos, sin = get_vision_cos_sin(n_h, n_w, self.rope_dim, self.rope_theta)
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cos, sin = cos.to(x.device), sin.to(x.device)
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# Passing the known length avoids device-to-host length discovery per layer.
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metadata = prepare_vision_attention_metadata(
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torch.tensor([0, x.shape[0]], dtype=torch.int32),
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x.device,
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max_seqlen=x.shape[0],
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)
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for block in self.blocks:
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x = block(x, cos, sin, metadata)
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return self.norm(x)
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class Aligner(nn.Module):
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def __init__(self, args):
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super().__init__()
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self.downsample_ratio = args.vision_downsample_ratio
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in_dim = args.vision_dim * self.downsample_ratio**2
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self.w1 = nn.Linear(in_dim, args.dim)
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self.w2 = nn.Linear(args.dim, args.dim)
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def forward(self, x: torch.Tensor, n_h: int, n_w: int) -> torch.Tensor:
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r = self.downsample_ratio
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x = x.view(n_h, n_w, -1).permute(2, 0, 1)
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x = F.pad(x, (0, -n_w % r, 0, -n_h % r))
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x = F.unfold(x.unsqueeze(0), r, stride=r).squeeze(0).transpose(0, 1)
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return self.w2(F.gelu(self.w1(x)))
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@@ -0,0 +1,200 @@
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"""Image preprocessing.
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An image becomes a `n_vit_h x n_vit_w` patch grid for the ViT and a `n_llm_h x n_llm_w` token grid
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after the 3x3 aligner downsample, which the LLM sees as
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[IMAGE_START] + ([IMAGE] * n_llm_w + [IMAGE_NEW_LINE]) * n_llm_h + [IMAGE_END]
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Every one of those positions carries `image_token_id` in `input_ids`; only the token type tells them
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apart. The IMAGE slots are filled with aligner rows in reading order.
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"""
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import math
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import numpy as np
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import torch
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import torch.nn.functional as F
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from PIL import Image, ImageOps
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IMAGE_START, IMAGE, IMAGE_NEW_LINE, IMAGE_END = range(4)
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GPU_PLAN_KEY = "dsv41_gpu_plan"
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def num_image_tokens(n_llm_h: int, n_llm_w: int) -> int:
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return n_llm_h * (n_llm_w + 1) + 2
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def llm_grid(best_height: int, best_width: int, patch_size: int, downsample_ratio: int):
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"""Token grid the aligner produces from a patch grid of this pixel size."""
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return math.ceil((best_height // patch_size) / downsample_ratio), math.ceil(
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(best_width // patch_size) / downsample_ratio
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)
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def solve_resize_ratio(height, width, patch_size, downsample_ratio, max_n_token):
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"""Largest aspect-preserving pixel size whose token grid still fits in max_n_token."""
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r = height / width
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max_w_float = math.sqrt((max_n_token - 2) / r + 0.25) - 0.5
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max_h_float = max_w_float * r
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cell = patch_size * downsample_ratio
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if max_w_float < 1.0: # very tall: collapse to a single column
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return (max_n_token - 2) // 2 * cell, cell
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if max_h_float < 1.0: # very wide: collapse to a single row
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return cell, (max_n_token - 3) * cell
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beta = min(
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math.floor(max_w_float) * cell / width, math.floor(max_h_float) * cell / height
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)
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return math.floor(height * beta / patch_size) * patch_size, math.floor(
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width * beta / patch_size
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) * patch_size
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def safe_resize(
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height, width, best_height, best_width, patch_size, downsample_ratio, max_n_token
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):
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"""Shrink the pixel size until the image costs at most max_n_token LLM tokens."""
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n_llm_h, n_llm_w = llm_grid(best_height, best_width, patch_size, downsample_ratio)
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if num_image_tokens(n_llm_h, n_llm_w) > max_n_token:
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best_height, best_width = solve_resize_ratio(
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height, width, patch_size, downsample_ratio, max_n_token
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)
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n_llm_h, n_llm_w = llm_grid(
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best_height, best_width, patch_size, downsample_ratio
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)
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assert num_image_tokens(n_llm_h, n_llm_w) <= max_n_token
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return n_llm_h, n_llm_w, best_height, best_width
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def plan_image_grid(width: int, height: int, args):
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"""Resize plan for an image of the given original size; a pure function of its arguments."""
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p = args.vision_patch_size
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if (
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args.vision_max_wh_ratio is not None
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and width > height * args.vision_max_wh_ratio
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):
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width = height * args.vision_max_wh_ratio
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if 0 < width * height < args.vision_min_pixels:
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ratio = (args.vision_min_pixels / (width * height)) ** 0.5
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width = int(width * ratio)
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height = int(height * ratio)
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best_width = math.ceil(width / p) * p
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best_height = math.ceil(height / p) * p
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return safe_resize(
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height,
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width,
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best_height,
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best_width,
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p,
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args.vision_downsample_ratio,
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args.vision_max_n_token,
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)
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def to_rgb(image: Image.Image) -> Image.Image:
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"""The same RGB conversion for every preprocessing backend."""
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return image.convert("RGB")
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def patchify_image(image, args):
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p = args.vision_patch_size
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image = to_rgb(image)
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n_llm_h, n_llm_w, best_height, best_width = plan_image_grid(
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image.width, image.height, args
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)
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n_vit_h, n_vit_w = best_height // p, best_width // p
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if (
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args.vision_max_wh_ratio is not None
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and image.width >= args.vision_max_wh_ratio * image.height
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):
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image = image.resize((best_width, best_height))
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else:
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image = ImageOps.pad(image, (best_width, best_height), color=(127, 127, 127))
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x = torch.from_numpy(np.asarray(image, dtype=np.float32)).permute(2, 0, 1) / 255
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x = ((x - 0.5) / 0.5).to(torch.bfloat16)
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patches = (
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x.reshape(3, n_vit_h, p, n_vit_w, p)
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.permute(1, 3, 0, 2, 4)
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.reshape(n_vit_h * n_vit_w, 3, p, p)
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)
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return patches, n_vit_h, n_vit_w, n_llm_h, n_llm_w
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def image_token_types(n_llm_h: int, n_llm_w: int) -> torch.Tensor:
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types = [IMAGE_START]
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types += ([IMAGE] * n_llm_w + [IMAGE_NEW_LINE]) * n_llm_h
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types.append(IMAGE_END)
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return torch.tensor(types, dtype=torch.int64)
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def prepare_image(image, args):
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image = to_rgb(image)
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lh, lw, height, width = plan_image_grid(image.width, image.height, args)
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stretch = (
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args.vision_max_wh_ratio is not None
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and image.width >= args.vision_max_wh_ratio * image.height
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)
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resize_h, resize_w = height, width
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if not stretch:
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if image.width / image.height > width / height:
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resize_h = round(image.height / image.width * width)
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elif image.width / image.height < width / height:
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resize_w = round(image.width / image.height * height)
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plan = {
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"height": height,
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"width": width,
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"resize_h": resize_h,
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"resize_w": resize_w,
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"top": round((height - resize_h) / 2),
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"left": round((width - resize_w) / 2),
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"patch_size": args.vision_patch_size,
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}
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return np.array(image, dtype=np.uint8), plan, lh, lw
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def patchify_image_rust(image, args, *, resize_patchify):
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pixels, plan, lh, lw = prepare_image(image, args)
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bits = resize_patchify(
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pixels,
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(plan["height"], plan["width"]),
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(plan["resize_h"], plan["resize_w"]),
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(plan["top"], plan["left"]),
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plan["patch_size"],
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)
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p = plan["patch_size"]
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h, w = plan["height"] // p, plan["width"] // p
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patches = torch.from_numpy(bits).view(torch.bfloat16).view(h * w, 3, p, p)
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return patches, h, w, lh, lw
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def prepare_image_gpu(image, args):
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pixels, plan, lh, lw = prepare_image(image, args)
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return torch.from_numpy(pixels).permute(2, 0, 1).contiguous(), plan, lh, lw
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def materialize_image_gpu(pixels: torch.Tensor, plan: dict) -> torch.Tensor:
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"""Resize, pad, normalize and patchify on the input tensor's device."""
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x = pixels.unsqueeze(0).float()
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target = (plan["resize_h"], plan["resize_w"])
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# PIL resizes separably, rounding and clamping to uint8 after each pass;
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# fusing the two passes into one float resize diverges on high-contrast images.
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for size in ((x.shape[-2], target[1]), target):
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if x.shape[-2:] != size:
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x = (
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F.interpolate(
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x, size=size, mode="bicubic", align_corners=False, antialias=True
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)
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.round()
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.clamp_(0, 255)
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)
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top, left = plan["top"], plan["left"]
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x = F.pad(
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x,
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(left, plan["width"] - target[1] - left, top, plan["height"] - target[0] - top),
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value=127,
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)
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x = ((x / 255 - 0.5) / 0.5).to(torch.bfloat16)
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p = plan["patch_size"]
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h, w = plan["height"] // p, plan["width"] // p
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return x.reshape(3, h, p, w, p).permute(1, 3, 0, 2, 4).reshape(h * w, 3, p, p)
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@@ -0,0 +1,127 @@
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"""DeepSeek-V4.1 image preprocessing, preserving raw token IDs for Engram."""
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import asyncio
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import logging
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from functools import partial
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import torch
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from sglang.srt.environ import envs
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from sglang.srt.managers.schedule_batch import (
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Modality,
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MultimodalDataItem,
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MultimodalProcessorOutput,
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)
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from sglang.srt.models.deepseek_v4 import DeepseekV4ForCausalLM
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from sglang.srt.multimodal.deepseek_v41_image_processing import (
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GPU_PLAN_KEY,
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image_token_types,
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patchify_image,
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patchify_image_rust,
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prepare_image_gpu,
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)
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from sglang.srt.multimodal.processors.base_processor import (
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BaseMultimodalProcessor,
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MultimodalSpecialTokens,
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)
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from sglang.srt.runtime_context import get_mm
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from sglang.srt.rust_extensions import load_rust_extension
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logger = logging.getLogger(__name__)
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class DeepseekV41ImageProcessor(BaseMultimodalProcessor):
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models = [DeepseekV4ForCausalLM]
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preserve_processor_input_ids = True
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prefer_tokenized_input = True
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gpu_image_decode = False
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def __init__(self, hf_config, server_args, _processor, *args, **kwargs):
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||||
self.preprocess_backend = "cpu"
|
||||
self.cpu_patchify = patchify_image
|
||||
backend = get_mm().image_processor_backend
|
||||
if backend == "pil" or get_mm().disable_fast_image_processor:
|
||||
self.preprocess_backend = "cpu"
|
||||
elif envs.SGLANG_ENCODER_IMAGE_PROCESSOR_USE_GPU.get():
|
||||
self.preprocess_backend = "gpu"
|
||||
elif backend == "auto":
|
||||
# Resolved before super().__init__(), which fingerprints the backend;
|
||||
# a load failure falls back to PIL, later image errors do not.
|
||||
try:
|
||||
extension = load_rust_extension(
|
||||
"sglang.srt.rust_extensions._multimodal"
|
||||
)
|
||||
resize_patchify = extension.dsv41.resize_patchify
|
||||
except (ImportError, OSError, RuntimeError, AttributeError) as error:
|
||||
logger.warning(
|
||||
"V4.1 Rust image processor unavailable; using PIL: %s", error
|
||||
)
|
||||
else:
|
||||
self.preprocess_backend = "rust"
|
||||
self.cpu_patchify = partial(
|
||||
patchify_image_rust, resize_patchify=resize_patchify
|
||||
)
|
||||
super().__init__(hf_config, server_args, _processor, *args, **kwargs)
|
||||
self.image_token_id = hf_config.image_token_id
|
||||
self.mm_tokens = MultimodalSpecialTokens(
|
||||
image_token=_processor.convert_ids_to_tokens(self.image_token_id),
|
||||
image_token_id=self.image_token_id,
|
||||
).build(_processor)
|
||||
|
||||
def preprocess_fingerprint_payload(self):
|
||||
payload = super().preprocess_fingerprint_payload()
|
||||
payload["dsv41_preprocess_backend"] = self.preprocess_backend
|
||||
return payload
|
||||
|
||||
async def process_mm_data_async(
|
||||
self, image_data, input_text, request_obj, *args, **kwargs
|
||||
):
|
||||
base = await self.load_mm_data(
|
||||
input_text, image_data=image_data, multimodal_tokens=self.mm_tokens
|
||||
)
|
||||
ids = (
|
||||
input_text
|
||||
if isinstance(input_text, list)
|
||||
else self._processor.encode(input_text)
|
||||
)
|
||||
if ids.count(self.image_token_id) != len(base.images):
|
||||
raise ValueError("Image placeholders and images must match")
|
||||
images = iter(base.images)
|
||||
tokens, items = [], []
|
||||
for token in ids:
|
||||
if token != self.image_token_id:
|
||||
tokens.append(token)
|
||||
continue
|
||||
image = next(images)
|
||||
plan = None
|
||||
if self.preprocess_backend == "gpu":
|
||||
patches, plan, lh, lw = await asyncio.to_thread(
|
||||
prepare_image_gpu, image, self.hf_config
|
||||
)
|
||||
h = plan["height"] // plan["patch_size"]
|
||||
w = plan["width"] // plan["patch_size"]
|
||||
else:
|
||||
patches, h, w, lh, lw = await asyncio.to_thread(
|
||||
self.cpu_patchify, image, self.hf_config
|
||||
)
|
||||
if self.keep_mm_features_on_device:
|
||||
patches = patches.to(torch.device("cuda", self.server_args.base_gpu_id))
|
||||
metadata = {"n_vit_h": h, "n_vit_w": w}
|
||||
if plan is not None:
|
||||
metadata[GPU_PLAN_KEY] = plan
|
||||
count = len(image_token_types(lh, lw))
|
||||
start = len(tokens)
|
||||
tokens.extend([self.image_token_id] * count)
|
||||
items.append(
|
||||
MultimodalDataItem(
|
||||
modality=Modality.IMAGE,
|
||||
feature=patches,
|
||||
offsets=[(start, start + count - 1)],
|
||||
model_specific_data=metadata,
|
||||
)
|
||||
)
|
||||
return MultimodalProcessorOutput(
|
||||
input_ids=tokens,
|
||||
mm_items=self._prepare_mm_items_for_transport(items),
|
||||
im_token_id=self.image_token_id,
|
||||
)
|
||||
@@ -49,6 +49,7 @@ from .tokenizer import (
|
||||
_fix_added_tokens_encoding,
|
||||
_fix_special_tokens_pattern,
|
||||
_install_tokenizer_warnings_filter,
|
||||
get_tokenizer,
|
||||
)
|
||||
|
||||
_IMAGE_PROCESSOR_BACKENDS = {"auto", "torchvision", "pil"}
|
||||
@@ -253,6 +254,10 @@ def get_processor(
|
||||
revision=revision,
|
||||
**kwargs,
|
||||
)
|
||||
if config.model_type == "deepseek_v41" and config.vision_n_layers > 0:
|
||||
return get_tokenizer(
|
||||
tokenizer_name, trust_remote_code=trust_remote_code, revision=revision
|
||||
)
|
||||
is_ocr2 = _is_deepseek_ocr2_model(config)
|
||||
if _is_deepseek_ocr_model(config) or is_ocr2:
|
||||
config.model_type = "deepseek-ocr"
|
||||
@@ -355,8 +360,6 @@ def get_processor(
|
||||
# AutoProcessor may internally create a TokenizersBackend tokenizer
|
||||
# (same issue as get_tokenizer). Replace it with a properly loaded one.
|
||||
if type(tokenizer).__name__ == _TOKENIZERS_BACKEND:
|
||||
from .tokenizer import get_tokenizer
|
||||
|
||||
logger.warning(
|
||||
"Processor tokenizer for %s is TokenizersBackend, "
|
||||
"reloading via get_tokenizer",
|
||||
|
||||
@@ -154,6 +154,9 @@ def test_overrides_take_the_worker_pools_processor_clone():
|
||||
# explicitly so that adding a processor forces a decision instead of silently
|
||||
# leaving it at one-worker speed.
|
||||
_NO_WORKER_POOL_ROUTE = {
|
||||
# Runs its own image preprocessing to keep the raw token ids the Engram
|
||||
# hasher needs; the shared chain would re-tokenize them.
|
||||
"deepseek_v41.py",
|
||||
"dots_note_omni.py",
|
||||
"inkling.py",
|
||||
"lightonocr.py",
|
||||
|
||||
Reference in New Issue
Block a user