forked from jsl/video_editing_poc
perf(vision): add resident llama-server backend (model loads once)
The CLI backend reloads the ~3GB model per frame (~2min/frame). Add a SERVER mode: when LLAMACPP_SERVER_BIN is set, start llama-server once, POST base64 frames to its OpenAI /v1/chat/completions endpoint on loopback, stop it at the end. Same answers, ~3x faster on a full clip (measured: 3 frames 181s incl. one-time load vs ~6-8min). CLI mode (LLAMACPP_MTMD_BIN) remains the simple fallback. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
JSLMPR
parent
1ea6640da0
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@ -3,91 +3,164 @@
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Drop-in replacement for tools/vision_caption.py: SAME manifest/output contract, so the Java
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HighlightVisionDirector is unchanged — only the configured script path differs. Uses a stronger local VLM
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(e.g. Qwen2.5-VL-3B-Instruct) through llama.cpp's multimodal CLI (`llama-mtmd-cli --mmproj`), which runs on
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this Intel x86 CPU via AVX SIMD and sidesteps the torch==2.2.2 / transformers 4.x version trap entirely.
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(e.g. Qwen2.5-VL-3B-Instruct) through llama.cpp, which runs on this Intel x86 CPU via AVX SIMD and sidesteps
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the torch==2.2.2 / transformers 4.x version trap entirely.
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Manifest JSON: [{"id": "...", "image": "/abs/path.jpg", "question": "..."}]
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Output JSON: [{"id": "...", "answer": "..."}]
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Fully offline. Paths come from env vars (so the Java side needs no backend-specific args):
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LLAMACPP_MTMD_BIN path to the llama.cpp `llama-mtmd-cli` binary (required)
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Fully offline. Two backends, selected by env:
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SERVER mode (fast — model loaded ONCE): set LLAMACPP_SERVER_BIN to the `llama-server` binary.
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CLI mode (simple — reloads per frame): set LLAMACPP_MTMD_BIN to the `llama-mtmd-cli` binary.
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(server mode wins if both are set.)
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LLAMACPP_VLM_MODEL path to the VLM GGUF weights, e.g. Qwen2.5-VL-3B-Instruct-Q4_K_M.gguf (required)
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LLAMACPP_VLM_MMPROJ path to the matching multimodal projector GGUF (mmproj-*.gguf) (required)
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LLAMACPP_VLM_NTOKENS max tokens to generate per answer (optional, default 64)
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LLAMACPP_SERVER_PORT loopback port for server mode (optional, default 8123)
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Per-frame invocation keeps the worker simple and serverless (no loopback). The model is memory-mapped, so
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after the first call the OS page cache keeps repeated loads cheap. For heavy batches a persistent
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`llama-server` backend would be faster — a documented future optimisation, not required here.
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CPU is forced (`-ngl 0 --no-mmproj-offload`): this machine's integrated GPU times out on the vision encoder
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(Metal command-buffer timeout). Server mode keeps the model resident and POSTs base64 frames to the
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OpenAI-compatible /v1/chat/completions endpoint on 127.0.0.1 — far faster than reloading per frame.
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"""
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from __future__ import annotations
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import argparse
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import base64
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import json
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import os
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import re
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import subprocess
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import sys
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import time
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import urllib.error
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import urllib.request
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def _clean(text: str) -> str:
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# The CLI echoes the chat-templated conversation; the real answer is the LAST assistant turn.
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if not text:
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return ""
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text = text.replace("\x1b[0m", "")
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marker = "assistant"
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idx = text.rfind(marker)
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# CLI mode echoes the chat-templated conversation; take the LAST assistant turn. (Server mode returns
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# clean content already, in which case there is no 'assistant' marker and the whole string is kept.)
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idx = text.rfind("assistant")
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if idx >= 0:
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text = text[idx + len(marker):]
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text = re.sub(r"<\|[^>]*\|>", " ", text) # strip chat control tokens like <|im_end|>
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text = text.replace("[end of text]", " ")
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text = text[idx + len("assistant"):]
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text = re.sub(r"<\|[^>]*\|>", " ", text).replace("[end of text]", " ")
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lines = [ln.strip() for ln in text.splitlines() if ln.strip()]
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# Drop any leaked log/timing lines; keep the natural-language answer.
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lines = [ln for ln in lines
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if not re.match(r"^(llama_|main:|mtmd_|clip_|ggml_|build:|load|encoding|decoding|\d[\d.:]*\s)",
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ln, re.IGNORECASE)]
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return " ".join(lines).strip()
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def _caption(binary: str, model: str, mmproj: str, image: str, question: str, ntokens: int) -> str:
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# --- CLI mode (per-frame reload) --------------------------------------------------------------------------
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def _caption_cli(binary: str, model: str, mmproj: str, image: str, question: str, ntokens: int) -> str:
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command = [
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binary, "-m", model, "--mmproj", mmproj, "--image", image,
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"-p", question, "--temp", "0", "-n", str(ntokens), "-t", "4",
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# Force CPU: this machine's integrated GPU times out (Metal command-buffer) on the vision encoder.
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"-ngl", "0", "--no-mmproj-offload",
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]
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proc = subprocess.run(command, capture_output=True, text=True, timeout=900)
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if proc.returncode != 0:
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sys.stderr.write("vision_caption_llamacpp: cli failed (%s): %s\n" % (proc.returncode, proc.stderr[-500:]))
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sys.stderr.write("vision_caption_llamacpp(cli): failed (%s): %s\n" % (proc.returncode, proc.stderr[-500:]))
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return ""
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return _clean(proc.stdout)
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# --- SERVER mode (model loaded once) ----------------------------------------------------------------------
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def _start_server(binary: str, model: str, mmproj: str, port: int):
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command = [
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binary, "-m", model, "--mmproj", mmproj, "--host", "127.0.0.1", "--port", str(port),
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"-ngl", "0", "--no-mmproj-offload", "-t", "4", "-c", "4096",
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]
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proc = subprocess.Popen(command, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
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# Wait for readiness (model load on CPU can take a while).
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deadline = time.time() + 300
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while time.time() < deadline:
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if proc.poll() is not None:
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raise RuntimeError("llama-server exited during startup (code %s)" % proc.returncode)
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try:
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with urllib.request.urlopen("http://127.0.0.1:%d/health" % port, timeout=3) as resp:
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if resp.status == 200:
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return proc
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except Exception:
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time.sleep(2)
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proc.terminate()
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raise RuntimeError("llama-server did not become healthy within timeout")
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def _caption_server(port: int, image: str, question: str, ntokens: int) -> str:
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with open(image, "rb") as fh:
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b64 = base64.b64encode(fh.read()).decode("ascii")
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payload = {
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"messages": [{"role": "user", "content": [
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{"type": "text", "text": question},
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{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64," + b64}},
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]}],
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"temperature": 0,
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"max_tokens": ntokens,
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}
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req = urllib.request.Request(
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"http://127.0.0.1:%d/v1/chat/completions" % port,
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data=json.dumps(payload).encode("utf-8"),
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headers={"Content-Type": "application/json"},
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)
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with urllib.request.urlopen(req, timeout=900) as resp:
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body = json.loads(resp.read().decode("utf-8"))
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return _clean(body["choices"][0]["message"]["content"])
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def main() -> int:
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parser = argparse.ArgumentParser(description="Local VLM frame captioner (llama.cpp / GGUF)")
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parser.add_argument("--manifest", required=True)
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parser.add_argument("--output", required=True)
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args = parser.parse_args()
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binary = os.environ.get("LLAMACPP_MTMD_BIN", "")
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model = os.environ.get("LLAMACPP_VLM_MODEL", "")
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mmproj = os.environ.get("LLAMACPP_VLM_MMPROJ", "")
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ntokens = int(os.environ.get("LLAMACPP_VLM_NTOKENS", "64"))
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missing = [name for name, val in
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(("LLAMACPP_MTMD_BIN", binary), ("LLAMACPP_VLM_MODEL", model), ("LLAMACPP_VLM_MMPROJ", mmproj))
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if not val or not os.path.exists(val)]
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if missing:
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sys.stderr.write("vision_caption_llamacpp: missing/invalid env paths: %s\n" % ", ".join(missing))
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server_bin = os.environ.get("LLAMACPP_SERVER_BIN", "")
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cli_bin = os.environ.get("LLAMACPP_MTMD_BIN", "")
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port = int(os.environ.get("LLAMACPP_SERVER_PORT", "8123"))
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for name, val in (("LLAMACPP_VLM_MODEL", model), ("LLAMACPP_VLM_MMPROJ", mmproj)):
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if not val or not os.path.exists(val):
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sys.stderr.write("vision_caption_llamacpp: missing/invalid %s\n" % name)
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return 2
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use_server = bool(server_bin and os.path.exists(server_bin))
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if not use_server and not (cli_bin and os.path.exists(cli_bin)):
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sys.stderr.write("vision_caption_llamacpp: set LLAMACPP_SERVER_BIN or LLAMACPP_MTMD_BIN to a real path\n")
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return 2
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with open(args.manifest, "r", encoding="utf-8") as handle:
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items = json.load(handle)
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server = None
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results = []
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try:
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if use_server:
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server = _start_server(server_bin, model, mmproj, port)
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for item in items:
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answer = ""
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try:
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answer = _caption(binary, model, mmproj, item["image"], item["question"], ntokens)
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if use_server:
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answer = _caption_server(port, item["image"], item["question"], ntokens)
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else:
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answer = _caption_cli(cli_bin, model, mmproj, item["image"], item["question"], ntokens)
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except Exception as exc: # a single bad frame must never fail the batch
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sys.stderr.write("vision_caption_llamacpp: frame %s failed: %s\n" % (item.get("id"), exc))
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results.append({"id": item.get("id"), "answer": answer})
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finally:
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if server is not None:
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server.terminate()
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try:
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server.wait(timeout=10)
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except Exception:
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server.kill()
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os.makedirs(os.path.dirname(os.path.abspath(args.output)), exist_ok=True)
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with open(args.output, "w", encoding="utf-8") as handle:
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