mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 17:31:18 +08:00
31ddbd7806
* feat: add example for deepseek coder * chore: remove hardcoded rope_scaling_factor * feat: add quantization support * chore: update readme * chore: clean up the rope scalling factor param in create cos sin theta * feat: add repetition_penalty * style /consistency changes to ease future integration * nits in README * one more typo --------- Co-authored-by: Awni Hannun <awni@apple.com>
99 lines
2.6 KiB
Python
99 lines
2.6 KiB
Python
# Copyright © 2023 Apple Inc.
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import argparse
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import copy
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import json
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import shutil
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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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import torch
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from mistral import Mistral, ModelArgs
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from mlx.utils import tree_flatten, tree_map, tree_unflatten
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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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config.pop("sliding_window", None)
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model = Mistral(ModelArgs(**config))
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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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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Convert Mistral weights to MLX.")
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parser.add_argument(
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"--torch-path",
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type=str,
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default="mistral-7B-v0.1",
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help="The path to the PyTorch model.",
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)
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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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torch_path = Path(args.torch_path)
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state = torch.load(str(torch_path / "consolidated.00.pth"))
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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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weights = {k: v.to(torch.float16).numpy() for k, v in state.items()}
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with open(torch_path / "params.json", "r") as f:
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config = json.loads(f.read())
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if args.quantize:
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print("[INFO] Quantizing")
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weights, config = quantize(weights, config, args)
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# Save weights
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np.savez(str(mlx_path / "weights.npz"), **weights)
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# Copy tokenizer
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shutil.copyfile(
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str(torch_path / "tokenizer.model"),
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str(mlx_path / "tokenizer.model"),
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)
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# Save config.json with model_type
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with open(mlx_path / "config.json", "w") as f:
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config["model_type"] = "mistral"
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json.dump(config, f, indent=4)
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