fix for protected model_ namespace w pydantic (#1345)

README.md

@@ -546,7 +546,7 @@ base_model_ignore_patterns:
 # You can set that here, or leave this empty to default to base_model
 base_model_config: ./llama-7b-hf
 # You can specify to choose a specific model revision from huggingface hub
-model_revision:
+revision_of_model:
 # Optional tokenizer configuration path in case you want to use a different tokenizer
 # than the one defined in the base model
 tokenizer_config:
@@ -573,7 +573,7 @@ is_qwen_derived_model:
 is_mistral_derived_model:
 
 # optional overrides to the base model configuration
-model_config_overrides:
+overrides_of_model_config:
   # RoPE Scaling https://github.com/huggingface/transformers/pull/24653
   rope_scaling:
     type: # linear | dynamic

src/axolotl/utils/config/__init__.py

@@ -124,7 +124,7 @@ def normalize_config(cfg):
         (hasattr(model_config, "model_type") and model_config.model_type == "llama")
         or cfg.is_llama_derived_model
         or "llama" in cfg.base_model.lower()
-        or (cfg.model_type and "llama" in cfg.model_type.lower())
+        or (cfg.type_of_model and "llama" in cfg.type_of_model.lower())
     )
 
     # figure out if the model is falcon
@@ -140,7 +140,7 @@ def normalize_config(cfg):
         )
         or cfg.is_falcon_derived_model
         or "falcon" in cfg.base_model.lower()
-        or (cfg.model_type and "rwforcausallm" in cfg.model_type.lower())
+        or (cfg.type_of_model and "rwforcausallm" in cfg.type_of_model.lower())
     )
 
     cfg.is_mistral_derived_model = (
@@ -153,7 +153,7 @@ def normalize_config(cfg):
         )
         or cfg.is_mistral_derived_model
         or "mistral" in cfg.base_model.lower().split("/")[-1]
-        or (cfg.model_type and "mistral" in cfg.model_type.lower())
+        or (cfg.type_of_model and "mistral" in cfg.type_of_model.lower())
     )
 
     cfg.is_qwen_derived_model = (
@@ -379,11 +379,11 @@ def legacy_validate_config(cfg):
             "hub_model_id is set without any models being saved. To save a model, set either save_steps or saves_per_epoch."
         )
 
-    if cfg.gptq and cfg.model_revision:
+    if cfg.gptq and cfg.revision_of_model:
         raise ValueError(
-            "model_revision is not supported for GPTQ models. "
+            "revision_of_model is not supported for GPTQ models. "
             + "Please download the model from HuggingFace Hub manually for correct branch, "
-            + "point to its path, and remove model_revision from the config."
+            + "point to its path, and remove revision_of_model from the config."
         )
 
     # if cfg.sample_packing and cfg.sdp_attention:

src/axolotl/utils/config/models/input/v0_4_1/__init__.py

@@ -47,6 +47,16 @@ def validate_noisy_embedding_alpha(cls, noisy_embedding_alpha):
         return noisy_embedding_alpha
 
 
+class RemappedParameters(BaseModel):
+    """parameters that have been remapped to other names"""
+
+    overrides_of_model_config: Optional[Dict[str, Any]] = Field(
+        default=None, alias="model_config"
+    )
+    type_of_model: Optional[str] = Field(default=None, alias="model_type")
+    revision_of_model: Optional[str] = Field(default=None, alias="model_revision")
+
+
 class PretrainingDataset(BaseModel):
     """pretraining dataset configuration subset"""
 
@@ -234,12 +244,8 @@ class ModelInputConfig(BaseModel):
     tokenizer_type: Optional[str] = Field(
         default=None, metadata={"help": "transformers tokenizer class"}
     )
-    model_type: Optional[str] = Field(default=None)
-    model_revision: Optional[str] = None
     trust_remote_code: Optional[bool] = None
 
-    model_config_overrides: Optional[Dict[str, Any]] = None
-
     @field_validator("trust_remote_code")
     @classmethod
     def hint_trust_remote_code(cls, trust_remote_code):
@@ -362,11 +368,17 @@ class AxolotlInputConfig(
     HyperparametersConfig,
     WandbConfig,
     MLFlowConfig,
+    RemappedParameters,
     DeprecatedParameters,
     BaseModel,
 ):
     """wrapper of all config options"""
 
+    class Config:
+        """Config for alias"""
+
+        populate_by_name = True
+
     strict: Optional[bool] = Field(default=False)
     resume_from_checkpoint: Optional[str] = None
     auto_resume_from_checkpoints: Optional[bool] = None
@@ -550,11 +562,11 @@ def check_pretraining_w_group_by_length(cls, data):
     @model_validator(mode="before")
     @classmethod
     def check_gptq_w_revision(cls, data):
-        if data.get("gptq") and data.get("model_revision"):
+        if data.get("gptq") and data.get("revision_of_model"):
             raise ValueError(
-                "model_revision is not supported for GPTQ models. "
+                "revision_of_model is not supported for GPTQ models. "
                 + "Please download the model from HuggingFace Hub manually for correct branch, "
-                + "point to its path, and remove model_revision from the config."
+                + "point to its path, and remove revision_of_model from the config."
             )
         return data
 

src/axolotl/utils/models.py

@@ -86,8 +86,8 @@ def load_model_config(cfg):
         model_config_name = cfg.tokenizer_config
     trust_remote_code = cfg.trust_remote_code is True
     config_kwargs = {}
-    if cfg.model_revision:
-        config_kwargs["revision"] = cfg.model_revision
+    if cfg.revision_of_model:
+        config_kwargs["revision"] = cfg.revision_of_model
 
     try:
         model_config = AutoConfig.from_pretrained(
@@ -104,8 +104,8 @@ def load_model_config(cfg):
             )
         raise err
 
-    if cfg.model_config_overrides:
-        for key, val in cfg.model_config_overrides.items():
+    if cfg.overrides_of_model_config:
+        for key, val in cfg.overrides_of_model_config.items():
             setattr(model_config, key, val)
 
     check_model_config(cfg, model_config)
@@ -272,7 +272,7 @@ def load_model(
     Load a model for a given configuration and tokenizer.
     """
     base_model = cfg.base_model
-    model_type = cfg.model_type
+    model_type = cfg.type_of_model
     model_config = load_model_config(cfg)
 
     # TODO refactor as a kwarg
@@ -426,8 +426,8 @@ def load_model(
     if is_deepspeed_zero3_enabled():
         del model_kwargs["device_map"]
 
-    if cfg.model_revision:
-        model_kwargs["revision"] = cfg.model_revision
+    if cfg.revision_of_model:
+        model_kwargs["revision"] = cfg.revision_of_model
     if cfg.gptq:
         if not hasattr(model_config, "quantization_config"):
             LOG.warning("model config does not contain quantization_config information")

tests/test_validation.py

@@ -3,6 +3,7 @@
 
 import logging
 import os
+import warnings
 from typing import Optional
 
 import pytest
@@ -14,6 +15,8 @@
 from axolotl.utils.models import check_model_config
 from axolotl.utils.wandb_ import setup_wandb_env_vars
 
+warnings.filterwarnings("error")
+
 
 @pytest.fixture(name="minimal_cfg")
 def fixture_cfg():
@@ -190,6 +193,45 @@ def test_lr_as_float(self, minimal_cfg):
 
         assert new_cfg.learning_rate == 0.00005
 
+    def test_model_config_remap(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "model_config": {"model_type": "mistral"},
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg.overrides_of_model_config["model_type"] == "mistral"
+
+    def test_model_type_remap(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "model_type": "AutoModelForCausalLM",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg.type_of_model == "AutoModelForCausalLM"
+
+    def test_model_revision_remap(self, minimal_cfg):
+        cfg = (
+            DictDefault(
+                {
+                    "model_revision": "main",
+                }
+            )
+            | minimal_cfg
+        )
+
+        new_cfg = validate_config(cfg)
+        assert new_cfg.revision_of_model == "main"
+
     def test_qlora(self, minimal_cfg):
         base_cfg = (
             DictDefault(