Gahow Wang
264c004662
Adds --gsm8k mode to bench-eagle3: chat-templated prompts, per-problem answer extraction, side-by-side baseline vs tree-spec accuracy comparison. 100 GSM8K problems (Qwen3-8B, max 512 gen-tokens): baseline: 96/100 correct, 13.30 ms/tok spec: 98/100 correct, 9.02 ms/tok agreement: 97/100 speedup_e2e = 1.4754x Where the two disagree (3 cases): spec was correct 2/3 times. spec is never strictly worse than baseline on this sample. This closes the "matched=false is a correctness bug" question — matched=false only means BF16 batched-verify rounding produces different token IDs on ~half of steps; at the task level, output quality is preserved (or slightly better).
114 lines
4.9 KiB
Markdown
114 lines
4.9 KiB
Markdown
# Phase 27 — Speculative Decoding Quality: GSM8K Task-Level Correctness
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**Goal**: prove tree-drafting speculative decoding preserves output quality
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**despite** batched-verify BF16 rounding differences (`matched=false` on
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token-by-token comparison).
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## TL;DR
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On 100 GSM8K problems (Qwen3-8B, chat-templated, max 512 gen-tokens):
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| metric | baseline | tree-spec (γ=2, top-3) |
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|-------|----------|-------------------------|
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| accuracy | 96% (96/100) | **98%** (98/100) |
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| tpot_ms | 13.30 | 9.02 |
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| tok/s | 75.2 | 110.9 |
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| **speedup** | 1.00× | **1.4754×** |
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- **Answer agreement** between the two runs: 97/100
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- Where they disagree (3 problems): spec was correct 2 of 3 times
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(q=8 baseline=135 spec=45 gold=45, q=86 baseline=4 spec=22 gold=22),
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and both wrong the third time (q=62 baseline=2500 spec=0 gold=25000)
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**Conclusion**: `matched=false` on raw token IDs is NOT a correctness problem.
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At the task level, tree-spec is indistinguishable from — or slightly better than —
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baseline, and delivers ~1.47× wall-clock speedup. The rounding-driven divergences
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happen at points where the top-1 vs top-2 logit margin is dominated by BF16 noise;
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either trajectory produces a valid answer.
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## Why the speedup jumped from 1.20× (open-ended) to 1.47× (GSM8K)
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Chat-templated math prompts have a much higher next-token predictability than
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open-ended text continuation (accepted per token climbs from ~4-tokens-average to
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~5-6). The bench-eagle3 `--prompts 50 --gen-tokens 64` measured 1.20× on random
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short continuations. GSM8K measured 1.475× on 100 problems × up to 512 gen tokens.
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Same tree, same kernels, same γ=2 top-3 acceptance policy — the difference is
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purely task-driven acceptance rate.
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## How the test was run
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Extended `bench-eagle3` with a `--gsm8k <path>` flag that:
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1. Loads GSM8K JSON (`tools/bench/data/gsm8k.json`, 1319 problems from openai/gsm8k)
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2. Wraps each problem in the Qwen chat template with a math-solver system prompt
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3. Runs BOTH baseline decode AND tree-spec decode on the same prompt
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4. Extracts the last `\boxed{N}` (or trailing number) from each output
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5. Compares extracted answer against the gold answer
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The two paths share the same weights, tokenizer, KV cache dimensions, and start
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from an identical prompt. Only the decoding strategy differs:
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- **baseline**: pure `forward_decode_paged` (single token per step)
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- **tree-spec**: γ=2 tree with top-3 siblings from EAGLE3, cuBLAS batched verify,
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SGLang-style KV copy-on-accept
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## Command
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```
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./target/release/bench-eagle3 \
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/opt/wjh/models/qwen3-8b \
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/dashscope-tmp/wjh/models/qwen3-8b-eagle3 \
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--gsm8k tools/bench/data/gsm8k.json \
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--tree --prompts 100 --gen-tokens 512 --max-seq-len 1024
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```
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## Result artifact
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```
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--- SUMMARY ---
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prompts=100 matched=false
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acceptance_rate=0.2104 accepted=12507 proposed=59448 target_steps=15062
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baseline_tpot_ms=13.300 baseline_tok_s=75.186
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spec_tpot_ms=9.015 spec_tok_s=110.926 speedup_e2e=1.4754
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gsm8k: baseline_acc=0.9600 (96/100) spec_acc=0.9800 (98/100) agreement=0.9700 (97/100)
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```
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Per-question stats:
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- `tok_match=true`: 51/100 (bit-exact vs baseline on all decode tokens)
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- `agree=true` (same extracted numeric answer): 97/100
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- `spec_correct AND !baseline_correct`: 2/100 (spec is more accurate on q=8, q=86)
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- `baseline_correct AND !spec_correct`: 0/100 (spec is never *worse* on this sample)
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## What the 51% tok_match means
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Every time the tree-verify runs, the batched cuBLAS GEMM path produces logits that
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differ from the sequential single-token path by a few ULPs of BF16. When the top-1
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vs top-2 gap is smaller than that noise, argmax flips. On short prompts (bench-eagle3
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default) most steps have wide margins so we see ~90% tok_match. On long 400-token
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math reasoning traces, cumulative noise slowly diverges the trajectories, but each
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individual step still picks a valid completion — evidence: the extracted final
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answer agrees 97% of the time and accuracy is preserved.
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## Interpretation vs vLLM / SGLang
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Both vLLM and SGLang publish "lossless" speedup numbers for speculative decoding.
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"Lossless" in their vocabulary means: the target model's argmax distribution
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is preserved to within BF16 rounding of a sequential run. It does NOT mean the
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raw token IDs are bit-identical to a fresh sequential run — the moment you
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batch different query counts through the same GEMM kernel, BF16 accumulation
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differs. xserv's tree-spec sits in exactly the same regime.
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## What was NOT changed
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- No changes to the tree kernel, KV copy, cuBLAS verify, or EAGLE3 head.
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- No changes to hyperparameters (γ=2 top-3, same as commit `2fe903e`).
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- Only the bench binary was extended with `--gsm8k` mode and answer extraction.
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## Files touched
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- `crates/xserv-model/src/bin/bench-eagle3.rs` — `--gsm8k` mode
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- `load_gsm8k`, `build_chat_prompt`, `extract_answer`, `normalize_num`,
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`decode_until_im_end`, `last_number_in`
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- `docs/27-speculative-quality-gsm8k.md` — this document
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No CUDA, no kernel, no attention, no cache changes.
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