Gahow Wang
9f1fbbb98b
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>
108 lines
3.2 KiB
Bash
Executable File
108 lines
3.2 KiB
Bash
Executable File
#!/bin/bash
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# GSM8K evaluation via repeated xserv-chat invocations.
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# Usage: eval_gsm8k_batch.sh <model-dir> <limit> [gpu_id] [tp]
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set -uo pipefail
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export PATH=/usr/local/cuda/bin:$PATH
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source ~/.cargo/env 2>/dev/null || true
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export LD_LIBRARY_PATH=/usr/local/cuda/lib64:${LD_LIBRARY_PATH:-}
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MODEL_DIR="${1:?Usage: $0 <model-dir> <limit> [gpu_id] [tp]}"
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LIMIT="${2:-50}"
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GPU="${3:-0}"
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TP="${4:-1}"
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export CUDA_VISIBLE_DEVICES=$GPU
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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XSERV_CHAT="$SCRIPT_DIR/../target/release/xserv-chat"
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DATA="$SCRIPT_DIR/bench/data/gsm8k.json"
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SYSTEM='Solve the problem step by step. Put your final numeric answer inside \\boxed{}.'
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echo "=== GSM8K Eval: model=$MODEL_DIR, limit=$LIMIT, gpu=$GPU, tp=$TP ==="
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TMPDIR=$(mktemp -d)
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trap "rm -rf $TMPDIR" EXIT
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# Generate problem files
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python3 -c "
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import json
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problems = json.load(open('$DATA'))[:$LIMIT]
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for i, p in enumerate(problems):
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with open(f'$TMPDIR/{i:04d}.txt', 'w') as f:
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f.write(p['problem'].replace(chr(10), ' '))
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with open(f'$TMPDIR/{i:04d}.gold', 'w') as f:
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f.write(p['answer'])
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print(f'{len(problems)} problems prepared')
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"
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TOTAL=$(ls "$TMPDIR"/*.txt 2>/dev/null | wc -l)
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CORRECT=0
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SCORED=0
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START_TIME=$(date +%s)
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TP_FLAG=""
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if [ "$TP" -gt 1 ]; then
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TP_FLAG="--tp $TP"
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fi
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for f in $(ls "$TMPDIR"/*.txt | sort); do
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IDX=$(basename "$f" .txt)
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GOLD=$(cat "$TMPDIR/${IDX}.gold")
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QUESTION=$(cat "$f")
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# Run single-question xserv-chat
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RAW_OUT=$(echo "$QUESTION" | timeout 120 "$XSERV_CHAT" "$MODEL_DIR" \
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--max-tokens 512 --max-seq-len 1024 \
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--system "$SYSTEM" --no-color $TP_FLAG 2>/dev/null || true)
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# Extract predicted answer
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PRED=$(echo "$RAW_OUT" | python3 -c "
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import re, sys
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text = sys.stdin.read()
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# Extract everything after 'assistant>'
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if 'assistant>' in text:
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text = text.split('assistant>', 1)[1]
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if 'user>' in text:
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text = text[:text.rindex('user>')]
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boxed = re.findall(r'\\\\boxed\s*\{([^{}]*)\}', text)
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if boxed:
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nums = re.findall(r'-?\d+(?:,\d{3})*(?:\.\d+)?', boxed[-1])
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if nums:
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s = nums[-1].replace(',','')
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f = float(s)
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print(str(int(f)) if f == int(f) else f'{f:g}')
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sys.exit(0)
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nums = re.findall(r'-?\d+(?:,\d{3})*(?:\.\d+)?', text)
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if nums:
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s = nums[-1].replace(',','')
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f = float(s)
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print(str(int(f)) if f == int(f) else f'{f:g}')
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else:
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print('NONE')
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" 2>/dev/null || echo "NONE")
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# Normalize gold
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GOLD_NORM=$(python3 -c "
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s='$GOLD'.replace(',','').strip()
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f=float(s)
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print(str(int(f)) if f==int(f) else f'{f:g}')
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" 2>/dev/null || echo "$GOLD")
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SCORED=$((SCORED + 1))
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if [ "$PRED" = "$GOLD_NORM" ]; then
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CORRECT=$((CORRECT + 1))
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echo "[✓] $IDX gold=$GOLD_NORM pred=$PRED"
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elif [ "$PRED" = "NONE" ]; then
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echo "[E] $IDX gold=$GOLD_NORM pred=NONE (no output)"
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else
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echo "[✗] $IDX gold=$GOLD_NORM pred=$PRED"
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fi
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done
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END_TIME=$(date +%s)
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ELAPSED=$((END_TIME - START_TIME))
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echo "------------------------------------------------------------------------"
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python3 -c "print(f'Results: $CORRECT/$SCORED correct = {$CORRECT/$SCORED*100:.1f}% accuracy')"
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echo "Wall time: ${ELAPSED}s (avg $((ELAPSED / TOTAL))s/problem)"
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echo "=== Done ==="
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