Checkpoints that ship calibrated k_scale/v_scale tensors (e.g.
unsloth/Qwen3.8-27B-NVFP4, which carries one pair per full-attention
layer) name them ...self_attn.{k,v}_scale, but the runtime parameters
live on the inner attention layer (...self_attn.attn.{k,v}_scale).
Qwen3_5Model.load_weights hands the iterable straight to
AutoWeightsLoader, which performs no scale-name remapping, so the
scales are silently dropped and --kv-cache-dtype fp8 runs at scale 1.0
with only a warning_once that fires even on success (the MTP draft
layer legitimately has no scales).
Route scale-suffixed names through maybe_remap_kv_scale_name first,
mirroring gemma4/gpt_oss. No behavior change for checkpoints without
scales.
Measured on RTX 5090 / Qwen3.8-27B NVFP4 / fp8 KV / MTP k=3 (GSM8K
greedy, n=300): 92.3% at scale 1.0 vs 93.7% with scales loaded
(bf16-KV control 94.5%).
Fixes#54623
Co-Authored-By: Hinata <hinata@idihore.id>
Signed-off-by: MOVZX <movzx@yahoo.com>
- Keep only Qwen3.5, Qwen3.5-MTP and Qwen4Exp model paths; trim the model registry to 41 entries
- Delete idle attention backends (MLA, FlashInfer, ROCm-only, quant backends) and dead GDN CuTeDSL kernels
- Remove inc/quark quant loaders; keep compressed-tensors and Marlin
- Drop the Rust frontend, pooling entrypoints, and idle test suites
- Rebuild kernels for sm_80;sm_86 and strip the extensions
- Verified: full import sweep clean (optional deps only) and a dummy-load Qwen3.5 engine run on RTX 3090 via the Triton GDN path
Co-authored-by: Hinata <hinata@idihore.id>
Signed-off-by: MOVZX <movzx@yahoo.com>
Qwen3_VisionTransformer derived its compute device from
patch_embed.proj.weight and fed pos_embed.weight straight into the
position-embedding interpolation kernel, so the last two anchor weights
had to stay in VRAM for a tower to be streamable at all.
Record the device the tower was built on before the offloader moves
anything, and fall back to it when no weight is on the accelerator.
Move the interpolation table to that device per encode, which costs one
8.4 MiB host-to-device copy for a 456 MiB tower. Towers now leave no
parameters in VRAM between encodes; only small buffers such as the fused
input norm and rotary inv_freq remain.
Also let a tower restrict streaming through tower_offload_children, and
warn when a tower resolves its device property to another device type,
so a tower that reads an anchor weight reports the cause instead of a
Triton pointer error.
Co-authored-by: Hinata <hinata@idihore.id>
Signed-off-by: MOVZX <movzx@yahoo.com>
With VLLM_ABLITERATION set, boot crashed: dynamo traces
abliteration.register from Qwen3NextGatedDeltaNet.__init__, and
buf.copy_(r) fails on the meta device during the compile-time model
construction pass. Disable tracing for register and guard the copy so
an unmaterializable buffer turns projection off with a warning.
Log activation at registration instead of inside project_: project_
runs in the compiled region, and a logger call there aborts dynamo.
The width guard uses the same escape hatch.
Also project the embedding that Qwen3NextMultiTokenPredictor builds
for itself, which the first pass missed, and add tests for the
2560-value Flash-Next direction against a 5120 model and for a
half-width activation.
Co-authored-by: Hinata <hinata@idihore.id>
Signed-off-by: MOVZX <movzx@yahoo.com>
orcarouter/Qwen3.8-27B-Uncensored removes refusal by editing weights. The
Swift release we serve is AutoRound W8A16, so re-editing weights means
re-quantizing. Project one unit direction r out of every residual-stream
writer output instead: x <- x - r (r . x), which equals the weight edit
W' = W - r(r^T W) on every writer feeding that stream (Arditi et al. 2024).
Sites: attention output for both the full-attention and GDN branches, the
MLP output, and token embeddings, in Qwen3NextDecoderLayer.forward and
Qwen3NextModel.embed_input_ids, which Qwen3_5DecoderLayer and Qwen3_5Model
inherit but whose base constructors they skip, so both register there too.
The MTP drafter reuses Qwen3_5DecoderLayer and calls its own
embed_input_ids, so the draft path is covered at the same width, and
_maybe_share_embeddings at PP=1 hands the draft the target embedding
module. Sequence parallelism shards dim 0 only, so the last dim is
hidden_size at every site.
VLLM_ABLITERATION names a JSON file with key refusal_direction, a
safetensors tensor, or a bare name resolved under
/opt/qwen38/abliteration then /data/abliteration. Empty or 0 is off.
register() always creates the buffer _abliteration_r, zeros when off, so
one compiled graph serves ON and OFF and toggling the switch
(declared in envs.py, hence in compile_factors) invalidates the compile
cache. project_() is an in-place rank-1 update with no full-size
temporary, and skips tensors whose width is not hidden_size.
Co-authored-by: Hinata <hinata@idihore.id>
Signed-off-by: MOVZX <movzx@yahoo.com>
MOVZXandHinata
