第一次提交

compute/engines/__init__.py

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+"""Training engine adapters package."""

compute/engines/llama_factory/__init__.py

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+"""LLaMA-Factory engine adapter package."""

compute/engines/llama_factory/adapter.py

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+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+
+@dataclass(frozen=True)
+class LlamaFactoryCommand:
+    command: list[str]
+    work_dir: str
+    env: dict[str, str]
+
+
+def validate_config(config: dict[str, Any]) -> list[str]:
+    errors: list[str] = []
+    if not config.get("base_model") and not config.get("model_name_or_path"):
+        errors.append("base_model or model_name_or_path is required")
+    if not config.get("dataset") and not config.get("dataset_dir"):
+        errors.append("dataset or dataset_dir is required")
+    learning_rate = float(config.get("learning_rate", 0.0002))
+    if learning_rate <= 0:
+        errors.append("learning_rate must be greater than zero")
+    epochs = int(config.get("n_epochs", config.get("num_train_epochs", 1)))
+    if epochs <= 0:
+        errors.append("n_epochs must be greater than zero")
+    return errors
+
+
+def build_command(config: dict[str, Any], llama_factory_home: str = "/app/LLaMA-Factory") -> LlamaFactoryCommand:
+    errors = validate_config(config)
+    if errors:
+        raise ValueError("; ".join(errors))
+
+    model_path = config.get("base_model") or config.get("model_name_or_path")
+    dataset = config.get("dataset") or config.get("dataset_dir")
+    output_dir = config.get("output_dir") or f"/data/yg-ft/outputs/{config.get('name', 'training-job')}"
+    command = [
+        "llamafactory-cli",
+        "train",
+        "--stage",
+        str(config.get("stage", "sft")).lower(),
+        "--do_train",
+        "true",
+        "--model_name_or_path",
+        str(model_path),
+        "--dataset",
+        str(dataset),
+        "--template",
+        str(config.get("template", "qwen")),
+        "--finetuning_type",
+        str(config.get("train_method", config.get("finetuning_type", "lora"))),
+        "--output_dir",
+        str(output_dir),
+        "--per_device_train_batch_size",
+        str(config.get("batch_size", 2)),
+        "--learning_rate",
+        str(config.get("learning_rate", 0.0002)),
+        "--num_train_epochs",
+        str(config.get("n_epochs", 3)),
+        "--save_steps",
+        str(config.get("save_steps", 50)),
+    ]
+    quantization_bit = int(config.get("quantization_bit", 0) or 0)
+    if quantization_bit in {4, 8}:
+        command.extend(["--quantization_bit", str(quantization_bit)])
+    return LlamaFactoryCommand(command=command, work_dir=str(Path(llama_factory_home)), env={})
+
+
+def parse_log_line(line: str) -> dict[str, float] | None:
+    if "loss" not in line or "learning_rate" not in line:
+        return None
+    result: dict[str, float] = {}
+    for key in ["loss", "grad_norm", "learning_rate", "epoch"]:
+        match = re.search(rf"['\"]?{key}['\"]?\s*:\s*([-+]?\d+(?:\.\d+)?(?:[eE][-+]?\d+)?)", line)
+        if match:
+            result[key] = float(match.group(1))
+    return result or None
+