extract module for working with cfg

scripts/finetune.py

@@ -19,6 +19,7 @@
 
 from axolotl.logging_config import configure_logging
 from axolotl.utils.bench import log_gpu_memory_usage
+from axolotl.utils.config import normalize_config, validate_config
 from axolotl.utils.data import load_prepare_datasets, load_pretraining_dataset
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import barrier, is_main_process
@@ -29,7 +30,6 @@
     process_datasets_for_packing,
     setup_trainer,
 )
-from axolotl.utils.validation import validate_config
 from axolotl.utils.wandb import setup_wandb_env_vars
 
 project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
@@ -44,27 +44,6 @@
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 
 
-def choose_device(cfg):
-    def get_device():
-        try:
-            if torch.cuda.is_available():
-                return f"cuda:{cfg.local_rank}"
-
-            if torch.backends.mps.is_available():
-                return "mps"
-
-            raise SystemError("No CUDA/mps device found")
-        except Exception:  # pylint: disable=broad-exception-caught
-            return "cpu"
-
-    cfg.device = get_device()
-    if cfg.device_map != "auto":
-        if cfg.device.startswith("cuda"):
-            cfg.device_map = {"": cfg.local_rank}
-        else:
-            cfg.device_map = {"": cfg.device}
-
-
 def get_multi_line_input() -> Optional[str]:
     print("Give me an instruction (Ctrl + D to finish): ")
     instruction = ""
@@ -194,31 +173,9 @@ def train(
 
     validate_config(cfg)
 
-    # setup some derived config / hyperparams
-    cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (
-        cfg.batch_size // cfg.micro_batch_size
-    )
-    cfg.batch_size = (
-        cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
-    )
-    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
-    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
-    choose_device(cfg)
-    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
-    if cfg.ddp:
-        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
-        cfg.batch_size = cfg.batch_size * cfg.world_size
+    normalize_config(cfg)
 
     setup_wandb_env_vars(cfg)
-    if cfg.device == "mps":
-        cfg.load_in_8bit = False
-        cfg.tf32 = False
-        if cfg.bf16:
-            cfg.fp16 = True
-        cfg.bf16 = False
-
-    if cfg.tf32:
-        torch.backends.cuda.matmul.allow_tf32 = True
 
     # load the tokenizer first
     tokenizer_config = cfg.tokenizer_config or cfg.base_model_config

src/axolotl/utils/validation.py → src/axolotl/utils/config.py

@@ -1,12 +1,60 @@
-"""Module for validating config files"""
+"""Module for working with config dicts"""
 
 import logging
+import os
 
 import torch
 
 LOG = logging.getLogger("axolotl")
 
 
+def choose_device(cfg):
+    def get_device():
+        try:
+            if torch.cuda.is_available():
+                return f"cuda:{cfg.local_rank}"
+
+            if torch.backends.mps.is_available():
+                return "mps"
+
+            raise SystemError("No CUDA/mps device found")
+        except Exception:  # pylint: disable=broad-exception-caught
+            return "cpu"
+
+    cfg.device = get_device()
+    if cfg.device_map != "auto":
+        if cfg.device.startswith("cuda"):
+            cfg.device_map = {"": cfg.local_rank}
+        else:
+            cfg.device_map = {"": cfg.device}
+
+
+def normalize_config(cfg):
+    # setup some derived config / hyperparams
+    cfg.gradient_accumulation_steps = cfg.gradient_accumulation_steps or (
+        cfg.batch_size // cfg.micro_batch_size
+    )
+    cfg.batch_size = (
+        cfg.batch_size or cfg.micro_batch_size * cfg.gradient_accumulation_steps
+    )
+    cfg.world_size = int(os.environ.get("WORLD_SIZE", 1))
+    cfg.local_rank = int(os.environ.get("LOCAL_RANK", 0))
+    choose_device(cfg)
+    cfg.ddp = cfg.ddp if cfg.ddp is not None else cfg.world_size != 1
+    if cfg.ddp:
+        cfg.device_map = {"": int(os.environ.get("LOCAL_RANK", 0))}
+        cfg.batch_size = cfg.batch_size * cfg.world_size
+
+    if cfg.device == "mps":
+        cfg.load_in_8bit = False
+        cfg.tf32 = False
+        if cfg.bf16:
+            cfg.fp16 = True
+        cfg.bf16 = False
+    else:
+        torch.backends.cuda.matmul.allow_tf32 = cfg.tf32 or False
+
+
 def validate_config(cfg):
     if cfg.max_packed_sequence_len and cfg.sample_packing:
         raise ValueError(

tests/test_validation.py

@@ -6,8 +6,8 @@
 
 import pytest
 
+from axolotl.utils.config import validate_config
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.validation import validate_config
 
 
 class ValidationTest(unittest.TestCase):