mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
258 lines
7.4 KiB
Python
258 lines
7.4 KiB
Python
import argparse
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import json
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import math
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from pathlib import Path
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import mlx.optimizers as optim
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import numpy as np
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from mlx.utils import tree_flatten
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from .tuner.lora import LoRALinear
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from .tuner.trainer import TrainingArgs, evaluate, train
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from .utils import LORA_SUPPORTED_MODELS, generate, load
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def build_parser():
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parser = argparse.ArgumentParser(description="LoRA or QLoRA finetuning.")
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parser.add_argument(
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"--model",
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default="mlx_model",
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help="The path to the local model directory or Hugging Face repo.",
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)
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# Generation args
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parser.add_argument(
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"--max-tokens",
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"-m",
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type=int,
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default=100,
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help="The maximum number of tokens to generate",
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)
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parser.add_argument(
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"--temp", type=float, default=0.8, help="The sampling temperature"
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)
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parser.add_argument(
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"--prompt",
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"-p",
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type=str,
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help="The prompt for generation",
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default=None,
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)
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# Training args
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parser.add_argument(
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"--train",
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action="store_true",
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help="Do training",
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)
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parser.add_argument(
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"--data",
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type=str,
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default="data/",
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help="Directory with {train, valid, test}.jsonl files",
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)
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parser.add_argument(
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"--lora-layers",
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type=int,
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default=16,
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help="Number of layers to fine-tune",
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)
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parser.add_argument("--batch-size", type=int, default=4, help="Minibatch size.")
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parser.add_argument(
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"--iters", type=int, default=1000, help="Iterations to train for."
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)
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parser.add_argument(
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"--val-batches",
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type=int,
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default=25,
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help="Number of validation batches, -1 uses the entire validation set.",
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)
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parser.add_argument(
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"--learning-rate", type=float, default=1e-5, help="Adam learning rate."
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)
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parser.add_argument(
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"--steps-per-report",
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type=int,
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default=10,
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help="Number of training steps between loss reporting.",
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)
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parser.add_argument(
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"--steps-per-eval",
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type=int,
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default=200,
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help="Number of training steps between validations.",
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)
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parser.add_argument(
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"--resume-adapter-file",
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type=str,
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default=None,
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help="Load path to resume training with the given adapter weights.",
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)
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parser.add_argument(
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"--adapter-file",
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type=str,
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default="adapters.npz",
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help="Save/load path for the trained adapter weights.",
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)
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parser.add_argument(
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"--save-every",
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type=int,
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default=100,
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help="Save the model every N iterations.",
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)
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parser.add_argument(
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"--test",
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action="store_true",
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help="Evaluate on the test set after training",
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)
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parser.add_argument(
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"--test-batches",
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type=int,
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default=500,
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help="Number of test set batches, -1 uses the entire test set.",
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)
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parser.add_argument(
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"--max_seq_length",
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type=int,
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default=2048,
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help="Maximum sequence length.",
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)
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parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")
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return parser
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class Dataset:
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"""
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Light-weight wrapper to hold lines from a jsonl file
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"""
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def __init__(self, path: Path, key: str = "text"):
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if not path.exists():
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self._data = None
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else:
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with open(path, "r") as fid:
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self._data = [json.loads(l) for l in fid]
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self._key = key
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def __getitem__(self, idx: int):
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return self._data[idx][self._key]
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def __len__(self):
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if self._data is None:
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return 0
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return len(self._data)
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def load_dataset(args):
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names = ("train", "valid", "test")
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train, valid, test = (Dataset(Path(args.data) / f"{n}.jsonl") for n in names)
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if args.train and len(train) == 0:
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raise ValueError(
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"Training set not found or empty. Must provide training set for fine-tuning."
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)
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if args.train and len(valid) == 0:
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raise ValueError(
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"Validation set not found or empty. Must provide validation set for fine-tuning."
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)
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if args.test and len(test) == 0:
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raise ValueError(
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"Test set not found or empty. Must provide test set for evaluation."
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)
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return train, valid, test
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if __name__ == "__main__":
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parser = build_parser()
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args = parser.parse_args()
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np.random.seed(args.seed)
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print("Loading pretrained model")
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model, tokenizer = load(args.model)
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if model.__class__ not in LORA_SUPPORTED_MODELS:
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raise ValueError(
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f"Model {model.__class__} not supported. "
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f"Supported models: {LORA_SUPPORTED_MODELS}"
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)
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# Freeze all layers other than LORA linears
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model.freeze()
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for l in model.model.layers[len(model.model.layers) - args.lora_layers :]:
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l.self_attn.q_proj = LoRALinear.from_linear(l.self_attn.q_proj)
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l.self_attn.v_proj = LoRALinear.from_linear(l.self_attn.v_proj)
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if hasattr(l, "block_sparse_moe"):
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l.block_sparse_moe.gate = LoRALinear.from_linear(l.block_sparse_moe.gate)
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p = sum(v.size for _, v in tree_flatten(model.parameters())) / 10**6
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print(f"Total parameters {p:.3f}M")
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p = sum(v.size for _, v in tree_flatten(model.trainable_parameters())) / 10**6
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print(f"Trainable parameters {p:.3f}M")
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print("Loading datasets")
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train_set, valid_set, test_set = load_dataset(args)
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# Resume training the given adapters.
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if args.resume_adapter_file is not None:
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print(f"Loading pretrained adapters from {args.resume_adapter_file}")
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model.load_weights(args.resume_adapter_file, strict=False)
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# init training args
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trainingArgs = TrainingArgs(
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batch_size=args.batch_size,
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iters=args.iters,
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val_batches=args.val_batches,
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steps_per_report=args.steps_per_report,
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steps_per_eval=args.steps_per_eval,
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steps_per_save=args.save_every,
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adapter_file=args.adapter_file,
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max_seq_length=args.max_seq_length,
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)
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if args.train:
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print("Training")
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model.train()
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opt = optim.Adam(learning_rate=args.learning_rate)
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# Train model
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train(
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model=model,
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tokenizer=tokenizer,
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args=trainingArgs,
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optimizer=opt,
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train_dataset=train_set,
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val_dataset=valid_set,
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)
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# Load the LoRA adapter weights which we assume should exist by this point
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if not Path(args.adapter_file).is_file():
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raise ValueError(
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f"Adapter file {args.adapter_file} missing. "
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"Use --train to learn and save the adapters.npz."
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)
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model.load_weights(args.adapter_file, strict=False)
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if args.test:
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print("Testing")
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model.eval()
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test_loss = evaluate(
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model=model,
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dataset=test_set,
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tokenizer=tokenizer,
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batch_size=args.batch_size,
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num_batches=args.test_batches,
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)
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test_ppl = math.exp(test_loss)
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print(f"Test loss {test_loss:.3f}, Test ppl {test_ppl:.3f}.")
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if args.prompt is not None:
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print("Generating")
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model.eval()
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generate(
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model=model,
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tokenizer=tokenizer,
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temp=args.temp,
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max_tokens=args.max_tokens,
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prompt=args.prompt,
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verbose=True,
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)
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