Experts now stay MXFP4-packed on GPU (~10GB whole model, fits one 32GB
card) instead of dequantized to ~38GB BF16. loader::load_model_dir_split
returns BF16 tensors + raw U8 (_blocks/_scales) in one pass; GptOss slices
each expert's MXFP4 bytes to a GpuBuffer at load, and expert_forward
dequantizes the selected expert to a BF16 scratch (dequant_mxfp4) right
before its GEMM — no per-token CPU->GPU upload, no 38GB BF16 dir.
Verified: gptoss-logits on the original MXFP4 dir
(/opt/wjh/models/gpt-oss-20b) gives logits byte-identical to the BF16 path
— top-1 token 12650 = " Paris" @ 15.3125, full top-10 unchanged — running
on a single GPU. Build green on dash5 (release).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
YaRN RoPE was the missing piece — gpt-oss uses rope_type "yarn" (factor 32,
beta_fast 32, beta_slow 1, orig_max 4096); a plain theta RoPE garbled
attention. Added yarn_rope_cache (host-computed inv_freq + mscale, built
into a RopeCache directly). Experts kept CPU-resident and uploaded per-use
(the dequantized BF16 model is ~36GB, won't fit one 32GB card).
Verified: "The capital of France is" -> top-1 token 12366 = " Paris"
(logit 19.75), matching the llama.cpp oracle's behavior. This exercises the
full MoE path: top-4 router (softmax-after-topk), interleaved clamped
(up+1)*glu experts, attention sinks, sliding window, MXFP4->BF16 weights,
YaRN RoPE, head_dim 64, q/k/v/o biases.
Correctness-first (host attention + per-token MoE); GPU attention-with-sinks
kernel, KV cache, faster MoE, and PP-for-memory come next to run AIME/GSM8K
at speed.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
GptOss model in xserv's own style (not derived from llama.cpp): BF16
loader for the dequantized weights, naive sink-attention + per-token
top-k MoE FFN on host for correctness-first, GPU matmuls via our kernels.
Reuses the Qwen3 forward pattern (rotate_half RoPE θ=150000, head_dim 64,
no q/k norm) and adds q/k/v/o + expert biases, clamped (up+1)*glu experts,
attention sinks, alternating sliding window. gptoss-logits bin dumps
next-token logits for fixed token ids to compare with the llama.cpp oracle.
WIP: compiles pending fixes; numerical alignment vs llama.cpp is the next
step. Then paged-cache + PP wiring + AIME/GSM8K.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
