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https://github.com/ml-explore/mlx-examples.git
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3cf436b529
* testing quantization * conversion + quantization working * one config processor * quantization in mistral / nits in llama * args for quantization * llama / mistral conversion in good shape * phi2 quantized * mixtral * qwen conversion
93 lines
2.3 KiB
Python
93 lines
2.3 KiB
Python
import argparse
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import copy
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import json
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from pathlib import Path
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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from mlx.utils import tree_flatten, tree_map, tree_unflatten
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from phi2 import ModelArgs, Phi2
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from transformers import AutoModelForCausalLM
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def quantize(weights, config, args):
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quantized_config = copy.deepcopy(config)
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# Load the model:
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model = Phi2(ModelArgs())
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weights = tree_map(mx.array, weights)
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model.update(tree_unflatten(list(weights.items())))
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# Quantize the model:
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nn.QuantizedLinear.quantize_module(model, args.q_group_size, args.q_bits)
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# Update the config:
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quantized_config["quantization"] = {
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"group_size": args.q_group_size,
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"bits": args.q_bits,
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}
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quantized_weights = dict(tree_flatten(model.parameters()))
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return quantized_weights, quantized_config
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def replace_key(key: str) -> str:
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if "wte.weight" in key:
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key = "wte.weight"
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if ".mlp" in key:
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key = key.replace(".mlp", "")
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return key
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def convert():
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parser = argparse.ArgumentParser(description="Convert Phi-2 weights to MLX")
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parser.add_argument(
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"--mlx-path",
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type=str,
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default="mlx_model",
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help="The path to save the MLX model.",
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)
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parser.add_argument(
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"-q",
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"--quantize",
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help="Generate a quantized model.",
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action="store_true",
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)
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parser.add_argument(
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"--q_group_size",
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help="Group size for quantization.",
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type=int,
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default=64,
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)
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parser.add_argument(
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"--q_bits",
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help="Bits per weight for quantization.",
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type=int,
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default=4,
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)
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args = parser.parse_args()
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mlx_path = Path(args.mlx_path)
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mlx_path.mkdir(parents=True, exist_ok=True)
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/phi-2", torch_dtype="auto", trust_remote_code=True
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)
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state_dict = model.state_dict()
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weights = {replace_key(k): v.numpy() for k, v in state_dict.items()}
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params = {}
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if args.quantize:
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print("[INFO] Quantizing")
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weights, params = quantize(weights, params, args)
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np.savez(str(mlx_path / "weights.npz"), **weights)
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with open(mlx_path / "config.json", "w") as fid:
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params["model_type"] = "phi2"
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json.dump(params, fid, indent=4)
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if __name__ == "__main__":
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convert()
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