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#!/usr/bin/env python
# coding: utf-8
import datetime
from rknn.api import RKNN
from sys import exit
ONNX_MODEL = "RWKV-x070-World-0.1B-v2.8-20241210-ctx4096.onnx"
RKNN_MODEL = ONNX_MODEL.replace(".onnx", ".rknn")
DATASET = ""
QUANTIZE = False
detailed_performance_log = True
timedate_iso = datetime.datetime.now().isoformat()
rknn = RKNN(verbose=True)
rknn.config(
# mean_values=[x * 255 for x in [0.485, 0.456, 0.406]],
# std_values=[x * 255 for x in [0.229, 0.224, 0.225]],
quantized_dtype="w8a8",
quantized_algorithm="normal",
quantized_method="channel",
quantized_hybrid_level=0,
target_platform="rk3588",
quant_img_RGB2BGR=False,
float_dtype="float16",
optimization_level=3,
custom_string=f"converted at {timedate_iso}",
remove_weight=False,
compress_weight=False,
inputs_yuv_fmt=None,
single_core_mode=False,
dynamic_input=None,
model_pruning=False,
op_target=None,
quantize_weight=False,
remove_reshape=False,
sparse_infer=False,
enable_flash_attention=False,
# ้่็ๅๆฐ
# disable_rules=[],
# sram_prefer=False,
# nbuf_prefer=False,
# check_data=[],
)
ret = rknn.load_onnx(model=ONNX_MODEL)
ret = rknn.build(do_quantization=QUANTIZE, dataset=DATASET, rknn_batch_size=None)
ret = rknn.export_rknn(RKNN_MODEL)
# ret = rknn.init_runtime(target='rk3588',device_id='cbb956772bf5dac9',core_mask=RKNN.NPU_CORE_0,perf_debug=detailed_performance_log)
# rknn.eval_perf()
# ret = rknn.accuracy_analysis(inputs=['../embeddings.npy','../state.npy','../scale_ratio.npy'], target='rk3588', device_id=device_id)
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