Suya OCR API — vLLM-backed, OpenAI-compatible OCR service

FastAPI service wrapping the Surya-OCR-2 model (datalab-to) served through vLLM: legacy /v1/api/ai/* endpoints, an OpenAI-compatible /v1/chat/completions endpoint, a coalescing request batcher, a local OCR CLI, Docker packaging, multilingual example outputs, and quantization/concurrency benchmarks. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-17 10:20:02 +04:00
commit 1a585693be
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+#!/usr/bin/env bash
+# MTP (multi-token-prediction) speculative-decode sweep for the latency report.
+# Serves the BF16 model three ways — no speculation (baseline), MTP with 1
+# speculative token, MTP with 2 — and captures the same page subset through the
+# OCR API for each. The model ships 1 nextn-predict layer, so MTP=1 is the
+# expected-valid setting and MTP=2 is exploratory.
+#
+# Run INSIDE the bench container:  bash scripts/quant/tune_mtp.sh
+# Requires: api.py running on :5002 pointed at :8000; compat libs on path.
+set -uo pipefail
+
+MODEL="${MODEL:-datalab-to/surya-ocr-2}"
+OCR_URL="http://127.0.0.1:5002/v1/api/ai/suya_ocr_vllm/"
+N_PAGES="${N_PAGES:-5}"
+OUT_ROOT="results/quant/mtp"
+LOG=/tmp/tune_mtp.log
+export LD_LIBRARY_PATH="/usr/local/cuda/compat:${LD_LIBRARY_PATH:-}"
+
+mapfile -t ALL < <(sed '/^#/d;/^$/d' eval_set/manifest.txt)
+IMAGES=("${ALL[@]:0:$N_PAGES}")
+
+# config_name | extra vLLM args. The speculative-config JSON is written compact
+# (no spaces) so it survives word-splitting as a single argv token.
+CONFIGS=(
+  "baseline|--enable-prefix-caching"
+  "mtp1|--enable-prefix-caching --speculative-config {\"method\":\"mtp\",\"num_speculative_tokens\":1}"
+  "mtp2|--enable-prefix-caching --speculative-config {\"method\":\"mtp\",\"num_speculative_tokens\":2}"
+)
+
+base_args() {
+  echo "--host 127.0.0.1 --port 8000 --model $MODEL --served-model-name $MODEL \
+--max-model-len 18000 --max-num-seqs 16 --gpu-memory-utilization 0.85 --no-enforce-eager \
+--mm-processor-kwargs {\"min_pixels\":3136,\"max_pixels\":6291456}"
+}
+
+stop_server() {
+  local pid
+  pid=$(ss -ltnp 2>/dev/null | grep ":8000 " | grep -oP 'pid=\K[0-9]+' | head -1)
+  [ -n "$pid" ] && kill -9 "$pid" 2>/dev/null
+  pkill -9 -f "vllm.entrypoints" 2>/dev/null
+  # EngineCore outlives the API server and holds the GPU; kill every remaining
+  # CUDA compute process, then poll until the memory is actually freed.
+  for p in $(nvidia-smi --query-compute-apps=pid --format=csv,noheader 2>/dev/null); do
+    kill -9 "$p" 2>/dev/null
+  done
+  for _ in $(seq 1 30); do
+    used=$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -1)
+    [ "${used:-99999}" -lt 2000 ] && break
+    sleep 2
+  done
+}
+
+for entry in "${CONFIGS[@]}"; do
+  name="${entry%%|*}"
+  extra="${entry#*|}"
+  echo "=== mtp config: $name ($extra) ===" | tee -a "$LOG"
+  stop_server
+  # shellcheck disable=SC2046
+  nohup python3 -m vllm.entrypoints.openai.api_server $(base_args) $extra \
+    > "/tmp/mtp_${name}.log" 2>&1 &
+  ok=0
+  for _ in $(seq 1 96); do
+    if curl -fs http://127.0.0.1:8000/health >/dev/null 2>&1; then ok=1; break; fi
+    if grep -qiE "Engine core initialization failed|ValueError|RuntimeError|Traceback" "/tmp/mtp_${name}.log" 2>/dev/null; then break; fi
+    sleep 5
+  done
+  if [ "$ok" -ne 1 ]; then
+    echo "$name: FAILED to start (see /tmp/mtp_${name}.log)" | tee -a "$LOG"
+    continue
+  fi
+  python3 -m scripts.quant.capture --url "$OCR_URL" --out-dir "$OUT_ROOT/$name" "${IMAGES[@]}" \
+    >> "$LOG" 2>&1
+  echo "$name: captured $(ls "$OUT_ROOT/$name"/*.json 2>/dev/null | wc -l) pages" | tee -a "$LOG"
+done
+stop_server
+echo "MTP_DONE" | tee -a "$LOG"