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out_model_dir = "./onnx/sim"
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if not os.path.exists(out_model_dir):
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os.makedirs(out_model_dir)
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out_model_path = in_model_path.split("/")[-1][:-5] + ".sim.onnx"
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out_model_path = os.path.join(out_model_dir, out_model_path)
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print(out_model_path)
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if os.path.isdir(out_model_path):
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out_model_path = os.path.join(out_model_path, os.path.basename(in_model_path))
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onnx_model = onnx.load(in_model_path)
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print(f"load model from {in_model_path} success")
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size_th_bytes = args.size_th_kb * 1024
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onnx_model, removed_inits = compress_onnx_model(onnx_model, size_th_bytes=size_th_bytes)
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print(f"compress model success")
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onnx_model = set_onnx_input_shape(onnx_model, args.input_shape)
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tensor_size_threshold = f"{args.size_th_kb}KB"
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skipped_optimizers = args.skip.split(";")
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onnx_model, check = simplify(onnx_model, skipped_optimizers=skipped_optimizers,
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tensor_size_threshold=tensor_size_threshold)
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if not check:
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raise ValueError(f"simplify compressed model {in_model_path} failed")
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print(f"simplify model success")
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onnx_model = uncompress_onnx_model(onnx_model, removed_inits)
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print(f"uncompress model success")
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save_extern = True if args.save_extern_data else False
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onnx.save(onnx_model, out_model_path, save_as_external_data=save_extern)
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del onnx_model, removed_inits
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import gc
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gc.collect()
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quantize_onnx(args, out_model_path)
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def quantize_onnx(args, in_model_path):
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out_model_dir = "./onnx/quant"
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if not os.path.exists(out_model_dir):
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os.makedirs(out_model_dir)
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out_model_name = in_model_path.split("/")[-1][:-5] + ".onnx"
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out_model_path = os.path.join(out_model_dir, out_model_name)
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onnx_model = onnx.load(in_model_path)
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print(f"load model from {in_model_path} success")
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if args.quantize != "none":
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from optimum.onnxruntime.configuration import AutoQuantizationConfig
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from optimum.onnxruntime import ORTQuantizer
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if args.quantize == "avx2":
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dqconfig = AutoQuantizationConfig.avx2(
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is_static=False,
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per_channel=False,
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use_symmetric_activations=True,
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)
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elif args.quantize == "avx512":
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dqconfig = AutoQuantizationConfig.avx512(
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is_static=False,
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per_channel=False,
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use_symmetric_activations=True,
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)
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elif args.quantize == "avx512_vnni":
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dqconfig = AutoQuantizationConfig.avx512_vnni(
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is_static=False,
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per_channel=False,
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use_symmetric_activations=True,
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)
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print(f"Quantizing the model with {args.quantize}...")
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dir_path = os.path.dirname(args.in_model_path)
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quantizer = ORTQuantizer.from_pretrained(dir_path)
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save_extern = True if args.save_extern_data else False
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model_quantized_path = quantizer.quantize(
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save_dir=out_model_path.replace(".onnx", f".{args.quantize}.onnx"),
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quantization_config=dqconfig,
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use_external_data_format=save_extern,
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)
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print(f"Quantized model saved to {model_quantized_path}")
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else:
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print("No quantization performed. Pass...")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description='export chatglm2',
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)
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parser.add_argument('-m', '--in_model_path', required=True, type=str)
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parser.add_argument('-o', '--out_model_path', required=False, type=str, default="")
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parser.add_argument('--size_th_kb', required=False, type=int, default="1024")
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parser.add_argument('--save_extern_data', required=False, type=int, default=1)
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parser.add_argument('--input_shape', required=False, type=str, default="")
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parser.add_argument('--skip', required=False, type=str, default="")
|
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