fix: DPO tool role KeyError (#3217), dataset hash output_dir (#3303), config validators (#3538) [skip ci]

* fix: DPO tool role KeyError, dataset hash output_dir, config validators [skip-e2e]

- Add 'tool' to default role_map_inv in dpo/chat_template.py default() and
  argilla_chat() so datasets with tool-call messages no longer raise
  KeyError: 'tool' (closes #3217)

- Fix generate_dataset_hash_from_config to use canonical tokenizer config +
  overrides content instead of tokenizer.name_or_path when added_tokens_overrides
  is set, preventing cache busting when only output_dir changes (closes #3303)

- Add three Pydantic config validators to AxolotlConfigWCapabilities:
  * save_strategy: 'best' requires metric_for_best_model
  * streaming=True is incompatible with val_set_size > 0
  * lora_target_modules list entries must be valid Python regex patterns

- Tests for all three changes

* review: condense comment in shared.py, swap Mistral model for SmolLM2-135M in test_hash

* chore: lint

* move the validators out of the w/ capabilities schema

---------

Co-authored-by: Wing Lian <wing@axolotl.ai>

src/axolotl/prompt_strategies/dpo/chat_template.py

@@ -29,6 +29,7 @@ def default(cfg, dataset_idx=0, **kwargs):
             "user": ["user"],
             "assistant": ["assistant"],
             "system": ["system"],
+            "tool": ["tool"],
         },
     )
     role_map = {}
@@ -174,6 +175,7 @@ def argilla_chat(cfg, dataset_idx=0, **kwargs):
             "user": ["user"],
             "assistant": ["assistant"],
             "system": ["system"],
+            "tool": ["tool"],
         },
     )
     role_map = {}

src/axolotl/utils/data/shared.py

@@ -516,12 +516,21 @@ def generate_dataset_hash_from_config(
     Returns:
         MD5 hash string representing the configuration.
     """
+    # When added_tokens_overrides is set, tokenizer.name_or_path contains output_dir.
+    # Use the canonical tokenizer config + overrides content so the hash is stable across output_dir changes.
+    if cfg.get("added_tokens_overrides"):
+        tokenizer_fingerprint = f"{cfg.tokenizer_config}+overrides:" + ",".join(
+            f"{k}={v}" for k, v in sorted(cfg.added_tokens_overrides.items())
+        )
+    else:
+        tokenizer_fingerprint = tokenizer_name
+
     config_str = (
         f"{cfg.sequence_len}@{cfg.sample_packing}@{cfg.eval_sample_packing}@"
         f"{cfg.group_by_length}@{cfg.kd_temperature or 1.0}@"
         f"{cfg.dataset_exact_deduplication or False}|"
         f"{'|'.join(sorted([f'{d.path}:{d.type}:{d.shards}:{d.conversation}:{d.split}:{d.temperature or 1.0}' for d in cfg_datasets]))}"
-        f"|{tokenizer_name}"
+        f"|{tokenizer_fingerprint}"
     )
     return str(md5(config_str))
 

src/axolotl/utils/schemas/config.py

@@ -1,5 +1,6 @@
 """Module with Pydantic models for configuration."""
 
+import re
 from typing import Annotated, Any, Literal
 
 from accelerate.utils import is_fp8_available
@@ -1338,6 +1339,39 @@ class AxolotlInputConfig(
             )
         return data
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_save_strategy_best_requires_metric(cls, data):
+        if data.get("save_strategy") == "best" and not data.get(
+            "metric_for_best_model"
+        ):
+            raise ValueError(
+                "save_strategy: 'best' requires metric_for_best_model to be set. "
+                "Please specify the metric to use, e.g. metric_for_best_model: eval_loss"
+            )
+        return data
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_lora_target_modules_regex(cls, data):
+        lora_target_modules = data.get("lora_target_modules")
+        if not isinstance(lora_target_modules, list):
+            return data
+        invalid = []
+        for pattern in lora_target_modules:
+            if not isinstance(pattern, str):
+                continue
+            try:
+                re.compile(pattern)
+            except re.error:
+                invalid.append(pattern)
+        if invalid:
+            raise ValueError(
+                f"lora_target_modules contains invalid regex pattern(s): {invalid}. "
+                "Please provide valid Python regex patterns or plain module name strings."
+            )
+        return data
+
 
 class AxolotlConfigWCapabilities(AxolotlInputConfig):
     """Wrapper to valdiate GPU capabilities with the configured options"""