mirror of
https://github.com/ml-explore/mlx-examples.git
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8c2cf665ed
* Convert mlx_lm.lora to use YAML configuration * pre-commit run fixes * Fix loading of config file * Remove invalid YAML from doc * Update command-line options and YAML parameter overriding, per feedback in #503 * Minor wording change * Positional argument * Moved config to a (-c/--config) flag * Removed CLI option defaults (since CLI options take precedence and their defaults are in CONFIG_DEFAULTS) * pre-commit format updates * Fix handling of CLI option defaults * Prevent None values of unspecified CLI options from overwriting values from CONFIG_DEFAULTS * nits --------- Co-authored-by: Awni Hannun <awni@apple.com>
288 lines
8.1 KiB
Python
288 lines
8.1 KiB
Python
import argparse
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import json
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import math
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import re
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import types
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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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import yaml
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from mlx.utils import tree_flatten
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from .tuner.trainer import TrainingArgs, TrainingCallback, evaluate, train
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from .tuner.utils import linear_to_lora_layers
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from .utils import load
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yaml_loader = yaml.SafeLoader
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yaml_loader.add_implicit_resolver(
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"tag:yaml.org,2002:float",
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re.compile(
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"""^(?:
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[-+]?(?:[0-9][0-9_]*)\\.[0-9_]*(?:[eE][-+]?[0-9]+)?
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|[-+]?(?:[0-9][0-9_]*)(?:[eE][-+]?[0-9]+)
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|\\.[0-9_]+(?:[eE][-+][0-9]+)?
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|[-+]?[0-9][0-9_]*(?::[0-5]?[0-9])+\\.[0-9_]*
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|[-+]?\\.(?:inf|Inf|INF)
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|\\.(?:nan|NaN|NAN))$""",
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re.X,
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),
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list("-+0123456789."),
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)
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CONFIG_DEFAULTS = {
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"model": "mlx_model",
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"train": False,
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"data": "data/",
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"seed": 0,
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"lora_layers": 16,
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"batch_size": 4,
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"iters": 1000,
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"val_batches": 25,
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"learning_rate": 1e-5,
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"steps_per_report": 10,
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"steps_per_eval": 200,
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"resume_adapter_file": None,
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"adapter_file": "adapters.npz",
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"save_every": 100,
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"test": False,
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"test_batches": 500,
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"max_seq_length": 2048,
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}
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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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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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help="The maximum number of tokens to generate",
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)
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parser.add_argument("--temp", type=float, help="The sampling temperature")
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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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)
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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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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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help="Number of layers to fine-tune",
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)
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parser.add_argument("--batch-size", type=int, help="Minibatch size.")
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parser.add_argument("--iters", type=int, help="Iterations to train for.")
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parser.add_argument(
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"--val-batches",
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type=int,
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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("--learning-rate", type=float, help="Adam learning rate.")
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parser.add_argument(
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"--steps-per-report",
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type=int,
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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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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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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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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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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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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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help="Maximum sequence length.",
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)
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parser.add_argument(
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"-c",
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"--config",
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default=None,
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help="A YAML configuration file with the training options",
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)
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parser.add_argument("--seed", type=int, 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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def run(args, training_callback: TrainingCallback = None):
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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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# Freeze all layers
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model.freeze()
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# Convert linear layers to lora layers and unfreeze in the process
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linear_to_lora_layers(model, args.lora_layers)
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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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training_callback=training_callback,
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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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raise NotImplementedError(
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"Please use mlx_lm.generate with trained adapter for generation."
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)
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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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config = args.config
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args = vars(args)
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if config:
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print("Loading configuration file", config)
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with open(config, "r") as file:
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config = yaml.load(file, yaml_loader)
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# Prefer parameters from command-line arguments
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for k, v in config.items():
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if not args.get(k, None):
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args[k] = v
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# Update defaults for unspecified parameters
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for k, v in CONFIG_DEFAULTS.items():
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if not args.get(k, None):
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args[k] = v
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run(types.SimpleNamespace(**args))
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