Commit Graph

5 Commits

Author SHA1 Message Date
a2de146fb6 Add Qwen3.6 MoE inference support 2026-07-13 20:24:41 +08:00
e04a8ffb18 speculative: EAGLE3 draft head implementation (Phase 25 step 1)
- eagle3.rs: Eagle3Head struct loads AngelSlim/Qwen3-8B_eagle3 safetensors,
  runs a single draft step via fc(concat(h_low, h_mid, h_high)) +
  concat(input_norm(emb), hidden_norm(fused_h)) → 1 midlayer → norm →
  lm_head → argmax in draft_vocab(32000) → d2t → target_vocab.
- qwen3.rs: new decode_core_with_hidden method that mirrors decode_core
  but captures hidden states at 3 configurable layer indices (default
  [11, 23, 35] for the 36-layer Qwen3-8B). Also expose embed_tokens_tensor
  and (in eagle3) map_draft_to_target as public accessors.
- loader.rs: make_tensor now pub(crate) so eagle3 can reuse it.
- bin/check-eagle3.rs: sanity binary that loads target + EAGLE, runs one
  prefill + one decode + one EAGLE step, prints the top-5 EAGLE predictions.
  Verified on dash5 with prompt "The capital of France is":
    target says: " Paris" then "."
    EAGLE top-5: "," / " Paris" / " Madrid" / "." / " Berlin"
  Weights load correctly, d2t mapping works, hidden state hooks are the
  right shape ([1, 4096]), and EAGLE produces thematically-relevant tokens.

The top-1 pick "," doesn't match target's "." at this position, but
that's expected: this test uses hidden states from a single decode step
with no recursive chaining. A full speculative loop still needs the
γ≥2 verify + accept path wired up (next step).
2026-07-01 17:23:22 +08:00
531cd3fe08 style: format Rust workspace 2026-06-18 18:11:58 +08:00
9f1fbbb98b quantization: add FP8 E4M3 W8A16 for gpt-oss MoE expert weights
Store expert gate_up_proj and down_proj weights in FP8 E4M3 (1 byte/elem)
with per-expert FP32 scale factors. At inference, a fused CUDA kernel
dequantizes to BF16 before the existing cuBLAS batched GEMM.

Results on gpt-oss-20b (50-problem GSM8K subset):
  - FP8 TP=1: 47/50 = 94.0% (single RTX 5090, ~25 GB VRAM)
  - BF16 TP=2: 47/50 = 94.0% (requires 2× RTX 5090, ~39 GB total)

No measurable accuracy degradation. Model size: 41.8 GB → 22.7 GB (−46%).

New files:
  - tools/quantize_fp8.py: offline BF16→FP8 conversion script
  - csrc/quantization/dequant_fp8.cu: per-expert-scale dequant kernel
  - crates/xserv-kernels/src/quantization.rs: Rust FFI wrapper
  - tools/eval_gsm8k_batch.sh: GSM8K accuracy evaluation harness

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-06-07 19:33:07 +08:00
e1e75fc7f6 phase 6+7+8: model loading, BPE tokenizer, GPT-2 inference (Milestone ①)
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>
2026-05-21 22:04:00 +08:00