Add runtime error in train.py if yaml config is improperly formatted …

…with extraneous or missing values (#506)

## Description
This PR enables us to sanity check our train yaml configuration files before we run the full training pipeline. This enables us to catch errors in the YAML config before a training run starts. If a yaml config is improperly formatted with extraneous or missing values, a `NameError` will be thrown. 

## Unit Test: 
Added unit tests to make sure we raise a `NameError` and/or warn the user if the yaml is incorrectly formatted. Warnings are used if the parameter has an optional default value. 

## Integration Test:
Before and after training runs show the same loss curves throughout training. 
<img width="333" alt="Screenshot 2023-08-15 at 10 57 55 AM" src="https://github.com/mosaicml/llm-foundry/assets/13524881/a0329a77-c54b-4d0b-961b-058e35021664">


**mpt-125m training branch master**: https://wandb.ai/mosaic-ml/chuck-runs/runs/2jq1uy86
**mpt-125m training branch chuck/add_yaml_sanity_check_train**: https://wandb.ai/mosaic-ml/chuck-runs/runs/n8wlqwec

All runs at: https://wandb.ai/mosaic-ml/chuck-runs?workspace=user-chuck-tang98 

---------

Co-authored-by: Daniel King <[email protected]>

llmfoundry/utils/config_utils.py

@@ -4,7 +4,7 @@
 import contextlib
 import math
 import warnings
-from typing import Dict, Optional, Union
+from typing import Any, Dict, Optional, Union
 
 from composer.utils import dist
 from omegaconf import DictConfig
@@ -13,6 +13,26 @@
 from llmfoundry.models.utils import init_empty_weights
 
 
+def pop_config(cfg: DictConfig,
+               key: str,
+               must_exist: bool = True,
+               default_value: Any = None) -> Any:
+    """Pop a value from the main config file and return it.
+
+    If the key does not exist, return the default_value or raise a RuntimeError
+    depending on the must_exist flag.
+    """
+    value = cfg.pop(key, None)
+    if value is not None:
+        return value
+    elif must_exist:
+        raise NameError(
+            f'The {key} parameter is missing and must exist for execution. Please check your yaml.'
+        )
+    else:
+        return default_value
+
+
 def calculate_batch_size_info(global_batch_size: int,
                               device_microbatch_size: Union[int, str]):
     if global_batch_size % dist.get_world_size() != 0:
@@ -90,6 +110,11 @@ def process_init_device(model_cfg: DictConfig, fsdp_config: Optional[Dict]):
 
 
 def log_config(cfg: DictConfig):
+    """Logs the current config and updates the wandb and mlflow configs.
+
+    This function can be called multiple times to update the wandb and MLflow
+    config with different variables.
+    """
     print(om.to_yaml(cfg))
     if 'wandb' in cfg.get('loggers', {}):
         try: