Parse the model's `pre_tokenizer` section to extract its Split regex
instead of hardcoding the GPT-2 pattern. The gpt-oss-20b model uses
a GPT-4-style regex that produces different word boundaries, causing a
1-token prompt mismatch vs HuggingFace (136 → 135 tokens, now aligned).
Unsupported lookahead `(?!\S)` is stripped — it only affects trailing
whitespace edge cases. Falls back to the old GPT-2/Qwen heuristic if
the model regex fails to compile or is absent.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Track an ordered eos_token_ids list (not just one id) and add is_eos().
gpt-oss/harmony ends the assistant turn on <|return|> and also treats
<|call|> and <|endoftext|> as terminators (generation_config.json
eos_token_id = [200002, 199999, 200012]); single-eos families are
unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- paged_kv_cache: new block-paged KV cache; adds a pinned-host swap pool with
a second BlockAllocator, per-sequence Location {Gpu,Cpu}, and lossless
swap_out/swap_in (block-granular D2H/H2D) for vLLM-style preemption.
bytes_per_block helper exposes per-block cost for VRAM-based sizing.
- decode_graph: CUDA-graph decode path.
- qwen3/gpt2/kv_cache: paged prefill/decode forward + related updates.
- tokenizer/bins: BPE updates, new xserv-chat CLI, bench-qwen3 tweaks.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
Qwen3 model (qwen3.rs):
- RMSNorm + QK normalization (per-head q_norm/k_norm)
- GQA: 32 Q heads, 8 KV heads, repeat_kv for attention
- SwiGLU FFN: gate_proj → SiLU → * up_proj → down_proj
- RoPE with transpose for [1,H,S,D] ↔ [S,H,D] layout
- BF16 forward pass, [out,in] weight layout via linear_t
- No attention bias (attention_bias=false)
Tokenizer fixes:
- Fixed unicode_to_byte: shifted bytes now use correct inverse lookup table
- MergeEntry supports both string and array formats
- Both GPT-2 and Qwen3 tokenizers work correctly (English + Chinese)
KVCache refactored:
- Dtype-agnostic: stores raw bytes per-head, works for F32 and BF16
- append_kv_tensor/get_kv_tensors use Tensor directly
CLI updated:
- Auto-detects model type from config.json (gpt2 vs qwen3)
- Supports both GPT-2 (F32) and Qwen3 (BF16)
Verified: Qwen3-8B generates coherent English and Chinese on single RTX 5090.
61/61 tests pass, GPT-2 performance no regression.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Phase 6 — Model Loading (xserv-model):
- safetensors parser with single/sharded file support
- ModelConfig with dual naming (GPT-2 n_embd/n_head + modern HF naming)
- Weight loading flow: safetensors → mmap → CPU Tensor → GPU
Phase 7 — BPE Tokenizer (xserv-tokenizer):
- Full BPE encode/decode from tokenizer.json
- GPT-2 byte-to-unicode mapping (printable ASCII identity + shifted bytes)
- Pre-tokenization regex, special token handling
- Chat template support structure
Phase 8 — GPT-2 Complete Inference:
- GPT-2 model definition: wte, wpe, 12 transformer blocks, ln_f
- Forward pass: embedding → (LayerNorm → MHA → residual → LayerNorm → MLP → residual) × 12 → LN → logits
- QKV split with correct [batch, heads, seq, dim] layout (fixed reshape bug)
- Greedy sampling from last-position logits
- Interactive CLI: xserv-cli <model-dir> [--max-tokens N]
Verified: GPT-2 124M generates coherent English text on RTX 5090.
"The future of AI is uncertain. The future of AI is uncertain..."
"Once upon a time, the world was a place of great beauty..."
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
