Fix mypy typing

scripts/alpaca_json_to_jsonl.py

@@ -3,7 +3,7 @@
 import os
 import sys
 
-from typing import Optional
+from typing import Optional, Union
 from pathlib import Path
 
 import fire
@@ -35,6 +35,7 @@ def main(
     """
 
     file_reader = FileReader()
+    writer: Union[StdoutWriter, FileWriter]
     if to_stdout or output is None:
         writer = StdoutWriter()
     else:

scripts/extract_lora.py

@@ -0,0 +1,163 @@
+# import logging
+# import os
+# import random
+# import signal
+# import sys
+# from pathlib import Path
+
+# import fire
+# import torch
+# import yaml
+# from addict import Dict
+
+# from peft import set_peft_model_state_dict, get_peft_model_state_dict
+
+# # add src to the pythonpath so we don't need to pip install this
+# project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
+# src_dir = os.path.join(project_root, "src")
+# sys.path.insert(0, src_dir)
+
+# from axolotl.utils.data import load_prepare_datasets
+# from axolotl.utils.models import load_model
+# from axolotl.utils.trainer import setup_trainer
+# from axolotl.utils.wandb import setup_wandb_env_vars
+
+# logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
+
+
+# def choose_device(cfg):
+#     def get_device():
+#         if torch.cuda.is_available():
+#             return "cuda"
+#         else:
+#             try:
+#                 if torch.backends.mps.is_available():
+#                     return "mps"
+#             except:
+#                 return "cpu"
+
+#     cfg.device = get_device()
+#     if cfg.device == "cuda":
+#         cfg.device_map = {"": cfg.local_rank}
+#     else:
+#         cfg.device_map = {"": cfg.device}
+
+
+# def choose_config(path: Path):
+#     yaml_files = [file for file in path.glob("*.yml")]
+
+#     if not yaml_files:
+#         raise ValueError(
+#             "No YAML config files found in the specified directory. Are you using a .yml extension?"
+#         )
+
+#     print("Choose a YAML file:")
+#     for idx, file in enumerate(yaml_files):
+#         print(f"{idx + 1}. {file}")
+
+#     chosen_file = None
+#     while chosen_file is None:
+#         try:
+#             choice = int(input("Enter the number of your choice: "))
+#             if 1 <= choice <= len(yaml_files):
+#                 chosen_file = yaml_files[choice - 1]
+#             else:
+#                 print("Invalid choice. Please choose a number from the list.")
+#         except ValueError:
+#             print("Invalid input. Please enter a number.")
+
+#     return chosen_file
+
+
+# def save_latest_checkpoint_as_lora(
+#     config: Path = Path("configs/"),
+#     prepare_ds_only: bool = False,
+#     **kwargs,
+# ):
+#     if Path(config).is_dir():
+#         config = choose_config(config)
+
+#     # load the config from the yaml file
+#     with open(config, "r") as f:
+#         cfg: Dict = Dict(lambda: None, yaml.load(f, Loader=yaml.Loader))
+#     # if there are any options passed in the cli, if it is something that seems valid from the yaml,
+#     # then overwrite the value
+#     cfg_keys = dict(cfg).keys()
+#     for k in kwargs:
+#         if k in cfg_keys:
+#             # handle booleans
+#             if isinstance(cfg[k], bool):
+#                 cfg[k] = bool(kwargs[k])
+#             else:
+#                 cfg[k] = kwargs[k]
+
+#     # setup some derived config / hyperparams
+#     cfg.gradient_accumulation_steps = cfg.batch_size // cfg.micro_batch_size
+#     cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
+#     cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
+#     assert cfg.local_rank == 0, "Run this with only one device!"
+
+#     choose_device(cfg)
+#     cfg.ddp = False
+
+#     if cfg.device == "mps":
+#         cfg.load_in_8bit = False
+#         cfg.tf32 = False
+#         if cfg.bf16:
+#             cfg.fp16 = True
+#         cfg.bf16 = False
+
+#     # Load the model and tokenizer
+#     logging.info("loading model, tokenizer, and lora_config...")
+#     model, tokenizer, lora_config = load_model(
+#         cfg.base_model,
+#         cfg.base_model_config,
+#         cfg.model_type,
+#         cfg.tokenizer_type,
+#         cfg,
+#         adapter=cfg.adapter,
+#         inference=True,
+#     )
+
+#     model.config.use_cache = False
+
+#     if torch.__version__ >= "2" and sys.platform != "win32":
+#         logging.info("Compiling torch model")
+#         model = torch.compile(model)
+
+#     possible_checkpoints = [str(cp) for cp in Path(cfg.output_dir).glob("checkpoint-*")]
+#     if len(possible_checkpoints) > 0:
+#         sorted_paths = sorted(
+#             possible_checkpoints, key=lambda path: int(path.split("-")[-1])
+#         )
+#         resume_from_checkpoint = sorted_paths[-1]
+#     else:
+#         raise FileNotFoundError("Checkpoints folder not found")
+
+#     pytorch_bin_path = os.path.join(resume_from_checkpoint, "pytorch_model.bin")
+
+#     assert os.path.exists(pytorch_bin_path), "Bin not found"
+
+#     logging.info(f"Loading {pytorch_bin_path}")
+#     adapters_weights = torch.load(pytorch_bin_path, map_location="cpu")
+
+#     # d = get_peft_model_state_dict(model)
+#     print(model.load_state_dict(adapters_weights))
+#     # with open('b.log', "w") as f:
+#     #     f.write(str(d.keys()))
+#     assert False
+
+#     print((adapters_weights.keys()))
+#     with open("a.log", "w") as f:
+#         f.write(str(adapters_weights.keys()))
+#     assert False
+
+#     logging.info("Setting peft model state dict")
+#     set_peft_model_state_dict(model, adapters_weights)
+
+#     logging.info(f"Set Completed!!! Saving pre-trained model to {cfg.output_dir}")
+#     model.save_pretrained(cfg.output_dir)
+
+
+# if __name__ == "__main__":
+#     fire.Fire(save_latest_checkpoint_as_lora)