Pydantic 2.x cfg (#1239)

* WIP conversion to use pydantic for config validation

* wip, more fields, add capabilities

* wip

* update pydantic validation to match existing tests

* tweak requirements

* setup deprecated paams pydantic model

* more validations

* wrap up rest of the validations

* flesh out the rest of the options from the readme into pydantic

* fix model validators as class methods

remember to return in validator
missing return
add missing relora attributes
fix test for DictDefault change
fix sys template for mistral from fastchat change in PR 2872
fix test for batch size warning

* more missing attributes for cfg

* updates from PR feedback

* fix validation for datasets and pretrain datasets

* fix test for lora check

src/axolotl/cli/__init__.py

@@ -24,11 +24,13 @@ from art import text2art
 from huggingface_hub import HfApi
 from huggingface_hub.utils import LocalTokenNotFoundError
 from transformers import GenerationConfig, TextIteratorStreamer, TextStreamer
+from transformers.utils import is_torch_bf16_gpu_available
 
 from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
 from axolotl.utils.config import (
+    GPUCapabilities,
     normalize_cfg_datasets,
     normalize_config,
     validate_config,
@@ -328,7 +330,6 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
     # load the config from the yaml file
     with open(config, encoding="utf-8") as file:
         cfg: DictDefault = DictDefault(yaml.safe_load(file))
-    cfg.axolotl_config_path = config
     # 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 = cfg.keys()
@@ -341,7 +342,21 @@ def load_cfg(config: Union[str, Path] = Path("examples/"), **kwargs):
             else:
                 cfg[k] = kwargs[k]
 
-    validate_config(cfg)
+    cfg.axolotl_config_path = config
+
+    try:
+        device_props = torch.cuda.get_device_properties("cuda")
+        gpu_version = "sm_" + str(device_props.major) + str(device_props.minor)
+    except:  # pylint: disable=bare-except # noqa: E722
+        gpu_version = None
+
+    capabilities = GPUCapabilities(
+        bf16=is_torch_bf16_gpu_available(),
+        n_gpu=os.environ.get("WORLD_SIZE", 1),
+        compute_capability=gpu_version,
+    )
+
+    cfg = validate_config(cfg, capabilities=capabilities)
 
     prepare_optim_env(cfg)