mirror of
https://github.com/unslothai/unsloth.git
synced 2026-04-28 03:19:57 +00:00
refactor
This commit is contained in:
Dan Saunders
parent
4ef25032c1
commit
22f9a65772
3 changed files with 164 additions and 206 deletions
228
cli.py
228
cli.py
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@ -1,4 +1,3 @@
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import json
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import logging
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import sys
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import time
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@ -6,7 +5,8 @@ from pathlib import Path
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from typing import Optional, List
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import typer
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import yaml
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from cli.config import Config, load_config
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app = typer.Typer(
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help="Command-line interface for Unsloth training, chat, and export.",
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@ -23,65 +23,6 @@ def configure_logging(verbose: bool):
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)
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def _load_config(config_path: Optional[Path]) -> dict:
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if not config_path:
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return {}
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path = Path(config_path)
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if not path.exists():
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raise typer.BadParameter(f"Config file not found: {config_path}")
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text = path.read_text(encoding="utf-8")
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if path.suffix.lower() in {".yaml", ".yml"}:
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return yaml.safe_load(text) or {}
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else:
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return json.loads(text or "{}")
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def _flatten_config(cfg: dict) -> dict:
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"""
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Flatten nested config sections into a single dict.
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Expected sections:
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data: dataset, local_dataset, format_type
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training: training_type, max_seq_length, load_in_4bit, output_dir, etc.
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lora: lora_r, lora_alpha, lora_dropout, target_modules, etc.
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vision: finetune_vision_layers, finetune_language_layers, etc.
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logging: enable_wandb, wandb_project, wandb_token, enable_tensorboard, etc.
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"""
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if not isinstance(cfg, dict):
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return {}
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flattened = {}
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# Handle top-level 'model' key
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if "model" in cfg:
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flattened["model"] = cfg["model"]
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sections = ["data", "training", "lora", "vision", "logging"]
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for section in sections:
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if section in cfg and isinstance(cfg[section], dict):
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flattened.update(cfg[section])
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return flattened
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def _merge_config(cfg: dict, defaults: dict, overrides: dict) -> dict:
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"""
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Merge CLI overrides with config and defaults.
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CLI override wins, then config value, then default.
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"""
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merged = {}
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for key, default in defaults.items():
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cli_val = overrides.get(key, None)
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if cli_val is not None:
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merged[key] = cli_val
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elif key in cfg and cfg[key] is not None:
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merged[key] = cfg[key]
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else:
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merged[key] = default
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return merged
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@app.command()
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def train(
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model: Optional[str] = typer.Option(
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@ -198,99 +139,22 @@ def train(
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"""
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Launch training using the existing Unsloth training backend.
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"""
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cfg = _load_config(config)
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cfg = _flatten_config(cfg)
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try:
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cfg = load_config(config)
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except FileNotFoundError as e:
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typer.echo(f"Error: {e}", err=True)
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raise typer.Exit(code=2)
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# Defaults (match previous behavior)
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defaults = {
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"model": None,
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"training_type": "lora",
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"max_seq_length": 2048,
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"load_in_4bit": True,
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"output_dir": Path("./outputs"),
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"dataset": None,
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"local_dataset": None,
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"format_type": "auto",
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"num_epochs": 3,
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"learning_rate": 2e-4,
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"batch_size": 2,
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"gradient_accumulation_steps": 4,
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"warmup_steps": 5,
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"max_steps": 0,
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"save_steps": 0,
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"weight_decay": 0.01,
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"random_seed": 3407,
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"packing": False,
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"train_on_completions": False,
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"lora_r": 64,
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"lora_alpha": 16,
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"lora_dropout": 0.0,
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"gradient_checkpointing": True,
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"target_modules": "q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj",
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"vision_all_linear": False,
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"finetune_vision_layers": True,
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"finetune_language_layers": True,
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"finetune_attention_modules": True,
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"finetune_mlp_modules": True,
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"use_rslora": False,
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"use_loftq": False,
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"enable_wandb": False,
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"wandb_project": "unsloth-training",
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"enable_tensorboard": False,
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"tensorboard_dir": "runs",
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}
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# Apply CLI overrides
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cli_args = {k: v for k, v in locals().items() if k not in ("config", "verbose", "hf_token", "cfg")}
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cfg.apply_overrides(**cli_args)
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overrides = {
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"training_type": training_type,
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"max_seq_length": max_seq_length,
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"load_in_4bit": load_in_4bit,
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"output_dir": output_dir,
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"dataset": dataset,
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"local_dataset": local_dataset,
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"format_type": format_type,
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"num_epochs": num_epochs,
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"learning_rate": learning_rate,
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"batch_size": batch_size,
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"gradient_accumulation_steps": gradient_accumulation_steps,
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"warmup_steps": warmup_steps,
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"max_steps": max_steps,
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"save_steps": save_steps,
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"weight_decay": weight_decay,
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"random_seed": random_seed,
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"packing": packing,
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"train_on_completions": train_on_completions,
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"lora_r": lora_r,
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"lora_alpha": lora_alpha,
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"lora_dropout": lora_dropout,
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"gradient_checkpointing": gradient_checkpointing,
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"target_modules": target_modules,
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"vision_all_linear": vision_all_linear,
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"finetune_vision_layers": finetune_vision_layers,
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"finetune_language_layers": finetune_language_layers,
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"finetune_attention_modules": finetune_attention_modules,
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"finetune_mlp_modules": finetune_mlp_modules,
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"use_rslora": use_rslora,
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"use_loftq": use_loftq,
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"enable_wandb": enable_wandb,
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"wandb_project": wandb_project,
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"wandb_token": wandb_token,
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"enable_tensorboard": enable_tensorboard,
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"tensorboard_dir": tensorboard_dir,
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}
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merged = _merge_config(cfg, defaults, overrides)
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model_val = merged.get("model")
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if not model_val:
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# Validate required fields
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if not cfg.model:
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typer.echo("Error: provide --model or set model in --config", err=True)
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raise typer.Exit(code=2)
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# Convert specific types
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output_dir_val = Path(merged["output_dir"])
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dataset_val = merged.get("dataset")
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local_dataset_val = merged.get("local_dataset")
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if not dataset_val and not local_dataset_val:
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if not cfg.data.dataset and not cfg.data.local_dataset:
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typer.echo(
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"Error: provide --dataset or --local-dataset (or via --config)", err=True
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)
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@ -304,86 +168,38 @@ def train(
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trainer = UnslothTrainer()
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model_config = ModelConfig.from_ui_selection(
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dropdown_value=model_val, search_value=None, hf_token=hf_token, is_lora=False
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dropdown_value=cfg.model, search_value=None, hf_token=hf_token, is_lora=False
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)
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if not model_config:
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typer.echo("Could not resolve model config", err=True)
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raise typer.Exit(code=1)
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is_vision = model_config.is_vision
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use_lora = cfg.training.training_type.lower() == "lora"
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if not trainer.load_model(
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model_name=model_config.identifier,
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max_seq_length=merged["max_seq_length"],
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load_in_4bit=merged["load_in_4bit"]
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if merged["training_type"].lower() == "lora"
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else False,
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max_seq_length=cfg.training.max_seq_length,
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load_in_4bit=cfg.training.load_in_4bit if use_lora else False,
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hf_token=hf_token,
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):
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typer.echo("Model load failed", err=True)
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raise typer.Exit(code=1)
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use_lora = merged["training_type"].lower() == "lora"
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# Match UI behavior for target modules:
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# - Text: use parsed target modules list
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# - Vision: if vision_all_linear, use ["all-linear"]; otherwise empty list
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target_modules_list = [
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m.strip() for m in merged["target_modules"].split(",") if m.strip()
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]
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if use_lora and is_vision:
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if merged["vision_all_linear"]:
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target_modules_list = ["all-linear"]
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else:
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target_modules_list = []
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if not trainer.prepare_model_for_training(
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use_lora=use_lora,
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finetune_vision_layers=merged["finetune_vision_layers"],
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finetune_language_layers=merged["finetune_language_layers"],
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finetune_attention_modules=merged["finetune_attention_modules"],
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finetune_mlp_modules=merged["finetune_mlp_modules"],
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target_modules=target_modules_list,
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lora_r=merged["lora_r"],
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lora_alpha=merged["lora_alpha"],
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lora_dropout=merged["lora_dropout"],
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use_gradient_checkpointing=merged["gradient_checkpointing"],
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use_rslora=merged["use_rslora"],
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use_loftq=merged["use_loftq"],
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):
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if not trainer.prepare_model_for_training(**cfg.model_kwargs(use_lora, is_vision)):
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typer.echo("Model preparation failed", err=True)
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raise typer.Exit(code=1)
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ds = trainer.load_and_format_dataset(
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dataset_source=dataset_val or "",
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format_type=merged["format_type"],
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local_datasets=local_dataset_val,
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dataset_source=cfg.data.dataset or "",
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format_type=cfg.data.format_type,
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local_datasets=cfg.data.local_dataset,
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)
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if ds is None:
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typer.echo("Dataset load failed", err=True)
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raise typer.Exit(code=1)
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started = trainer.start_training(
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dataset=ds,
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output_dir=str(output_dir_val),
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num_epochs=merged["num_epochs"],
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learning_rate=merged["learning_rate"],
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batch_size=merged["batch_size"],
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gradient_accumulation_steps=merged["gradient_accumulation_steps"],
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warmup_steps=merged["warmup_steps"],
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max_steps=merged["max_steps"],
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save_steps=merged["save_steps"],
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weight_decay=merged["weight_decay"],
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random_seed=merged["random_seed"],
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packing=merged["packing"],
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train_on_completions=merged["train_on_completions"],
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enable_wandb=merged["enable_wandb"],
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wandb_project=merged["wandb_project"],
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wandb_token=merged.get("wandb_token"),
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enable_tensorboard=merged["enable_tensorboard"],
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tensorboard_dir=merged["tensorboard_dir"],
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max_seq_length=merged["max_seq_length"],
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)
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started = trainer.start_training(dataset=ds, **cfg.training_kwargs())
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if not started:
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typer.echo("Training failed to start", err=True)
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