Kuantisasi Model dan Deployment

Artikel ini memperkenalkan cara mengkuantisasi model KataGo untuk mengurangi kebutuhan sumber daya, serta solusi deployment di berbagai platform.

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Gambaran Teknik Kuantisasi

Mengapa Perlu Kuantisasi?

Presisi	Ukuran	Kecepatan	Kehilangan Presisi
FP32	100%	Baseline	0%
FP16	50%	+50%	~0%
INT8	25%	+100%	<1%

Tipe Kuantisasi

Post-Training Quantization (PTQ)
├── Sederhana dan cepat
├── Tidak perlu pelatihan ulang
└── Mungkin ada kehilangan presisi

Quantization-Aware Training (QAT)
├── Presisi lebih tinggi
├── Perlu pelatihan ulang
└── Lebih kompleks

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FP16 Half Precision

Konsep

Mengkonversi floating point 32-bit ke 16-bit:

# Konversi FP32 → FP16
model_fp16 = model.half()

# Inferensi
with torch.cuda.amp.autocast():
    output = model_fp16(input.half())

Konfigurasi KataGo

# config.cfg
useFP16 = true           # Aktifkan inferensi FP16
useFP16Storage = true    # Penyimpanan hasil antara FP16

Dampak Performa

Seri GPU	Akselerasi FP16
GTX 10xx	Tidak ada (tanpa Tensor Core)
RTX 20xx	+30-50%
RTX 30xx	+50-80%
RTX 40xx	+80-100%

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Kuantisasi INT8

Alur Kuantisasi

import torch.quantization as quant

# 1. Siapkan model
model.eval()
model.qconfig = quant.get_default_qconfig('fbgemm')

# 2. Persiapan kuantisasi
model_prepared = quant.prepare(model)

# 3. Kalibrasi (menggunakan data representatif)
with torch.no_grad():
    for data in calibration_loader:
        model_prepared(data)

# 4. Konversi ke model terkuantisasi
model_quantized = quant.convert(model_prepared)

Data Kalibrasi

def create_calibration_dataset(num_samples=1000):
    """Buat dataset kalibrasi"""
    samples = []

    # Sampel dari pertandingan aktual
    for game in random_games(num_samples):
        position = random_position(game)
        features = encode_state(position)
        samples.append(features)

    return samples

Catatan

• Kuantisasi INT8 membutuhkan data kalibrasi
• Beberapa layer mungkin tidak cocok untuk kuantisasi
• Perlu menguji kehilangan presisi

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Deployment TensorRT

Alur Konversi

import tensorrt as trt

def convert_to_tensorrt(onnx_path, engine_path):
    logger = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(logger)
    network = builder.create_network(
        1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
    )
    parser = trt.OnnxParser(network, logger)

    # Parse model ONNX
    with open(onnx_path, 'rb') as f:
        parser.parse(f.read())

    # Atur opsi optimasi
    config = builder.create_builder_config()
    config.max_workspace_size = 1 << 30  # 1GB

    # Aktifkan FP16
    config.set_flag(trt.BuilderFlag.FP16)

    # Bangun engine
    engine = builder.build_engine(network, config)

    # Simpan
    with open(engine_path, 'wb') as f:
        f.write(engine.serialize())

Menggunakan Engine TensorRT

def inference_with_tensorrt(engine_path, input_data):
    # Muat engine
    with open(engine_path, 'rb') as f:
        engine = trt.Runtime(logger).deserialize_cuda_engine(f.read())

    context = engine.create_execution_context()

    # Alokasi memori
    d_input = cuda.mem_alloc(input_data.nbytes)
    d_output = cuda.mem_alloc(output_size)

    # Salin input
    cuda.memcpy_htod(d_input, input_data)

    # Eksekusi inferensi
    context.execute_v2([int(d_input), int(d_output)])

    # Ambil output
    output = np.empty(output_shape, dtype=np.float32)
    cuda.memcpy_dtoh(output, d_output)

    return output

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Ekspor ONNX

PyTorch → ONNX

import torch.onnx

def export_to_onnx(model, output_path):
    model.eval()

    # Buat contoh input
    dummy_input = torch.randn(1, 22, 19, 19)

    # Ekspor
    torch.onnx.export(
        model,
        dummy_input,
        output_path,
        input_names=['input'],
        output_names=['policy', 'value', 'ownership'],
        dynamic_axes={
            'input': {0: 'batch_size'},
            'policy': {0: 'batch_size'},
            'value': {0: 'batch_size'},
            'ownership': {0: 'batch_size'}
        },
        opset_version=13
    )

Validasi Model ONNX

import onnx
import onnxruntime as ort

# Validasi struktur model
model = onnx.load("model.onnx")
onnx.checker.check_model(model)

# Uji inferensi
session = ort.InferenceSession("model.onnx")
output = session.run(None, {'input': input_data})

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Deployment Multi-Platform

Deployment Server

# docker-compose.yml
version: '3'
services:
  katago:
    image: katago/katago:latest
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]
    volumes:
      - ./models:/models
      - ./config:/config
    command: >
      katago analysis
      -model /models/kata-b18c384.bin.gz
      -config /config/analysis.cfg

Integrasi Aplikasi Desktop

# Embed KataGo ke aplikasi Python
import subprocess
import json

class KataGoProcess:
    def __init__(self, katago_path, model_path):
        self.process = subprocess.Popen(
            [katago_path, 'analysis', '-model', model_path],
            stdin=subprocess.PIPE,
            stdout=subprocess.PIPE,
            text=True
        )

    def analyze(self, moves):
        query = {
            'id': 'query1',
            'moves': moves,
            'rules': 'chinese',
            'komi': 7.5,
            'boardXSize': 19,
            'boardYSize': 19
        }
        self.process.stdin.write(json.dumps(query) + '\n')
        self.process.stdin.flush()

        response = self.process.stdout.readline()
        return json.loads(response)

Deployment Perangkat Mobile

iOS (Core ML)

import coremltools as ct

# Konversi ke Core ML
mlmodel = ct.convert(
    model,
    inputs=[ct.TensorType(shape=(1, 22, 19, 19))],
    minimum_deployment_target=ct.target.iOS15
)

mlmodel.save("KataGo.mlmodel")

Android (TensorFlow Lite)

import tensorflow as tf

# Konversi ke TFLite
converter = tf.lite.TFLiteConverter.from_saved_model(model_path)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]

tflite_model = converter.convert()

with open('katago.tflite', 'wb') as f:
    f.write(tflite_model)

Sistem Embedded

Raspberry Pi

# Gunakan backend Eigen (CPU saja)
./katago gtp -model kata-b10c128.bin.gz -config rpi.cfg

# rpi.cfg - Konfigurasi optimasi Raspberry Pi
numSearchThreads = 4
maxVisits = 100
nnMaxBatchSize = 1

NVIDIA Jetson

# Gunakan backend CUDA
./katago gtp -model kata-b18c384.bin.gz -config jetson.cfg

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Perbandingan Performa

Performa Berbagai Metode Deployment

Metode Deployment	Hardware	Playouts/detik
CUDA FP32	RTX 3080	~3000
CUDA FP16	RTX 3080	~5000
TensorRT FP16	RTX 3080	~6500
OpenCL	M1 Pro	~1500
Core ML	M1 Pro	~1800
TFLite	Pixel 7	~50
Eigen	RPi 4	~15

Perbandingan Ukuran Model

Format	Ukuran b18c384
Asli (.bin.gz)	~140 MB
ONNX FP32	~280 MB
ONNX FP16	~140 MB
TensorRT FP16	~100 MB
TFLite FP16	~140 MB

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Checklist Deployment

• Pilih presisi kuantisasi yang sesuai
• Siapkan data kalibrasi (INT8)
• Ekspor ke format target
• Verifikasi kehilangan presisi dapat diterima
• Uji performa platform target
• Optimasi penggunaan memori
• Buat alur deployment otomatis

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Bacaan Lanjutan

• Backend GPU dan Optimasi — Optimasi performa dasar
• Evaluasi dan Benchmark — Verifikasi performa setelah deployment
• Integrasi ke Proyek Anda — Contoh integrasi API