validation fixes 20240923 (#1925)

* validation fixes 20240923

* fix run name for wandb and defaults for chat template fields

* fix gradio inference with llama chat template

src/axolotl/cli/__init__.py

@@ -30,6 +30,7 @@ from axolotl.common.cli import TrainerCliArgs, load_model_and_tokenizer
 from axolotl.integrations.base import PluginManager
 from axolotl.logging_config import configure_logging
 from axolotl.train import TrainDatasetMeta
+from axolotl.utils.chat_templates import chat_templates
 from axolotl.utils.config import (
     normalize_cfg_datasets,
     normalize_config,
@@ -234,7 +235,8 @@ def do_inference_gradio(
 
     model, tokenizer = load_model_and_tokenizer(cfg=cfg, cli_args=cli_args)
     prompter = cli_args.prompter
-    default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    # default_tokens = {"unk_token": "<unk>", "bos_token": "<s>", "eos_token": "</s>"}
+    default_tokens: Dict[str, str] = {}
 
     for token, symbol in default_tokens.items():
         # If the token isn't already specified in the config, add it
@@ -242,10 +244,13 @@ def do_inference_gradio(
             tokenizer.add_special_tokens({token: symbol})
 
     prompter_module = None
+    chat_template_str = None
     if prompter:
         prompter_module = getattr(
             importlib.import_module("axolotl.prompters"), prompter
         )
+    elif cfg.chat_template:
+        chat_template_str = chat_templates(cfg.chat_template)
 
     model = model.to(cfg.device, dtype=cfg.torch_dtype)
 
@@ -259,7 +264,24 @@ def do_inference_gradio(
             )
         else:
             prompt = instruction.strip()
-        batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
+
+        if chat_template_str:
+            batch = tokenizer.apply_chat_template(
+                [
+                    {
+                        "role": "user",
+                        "content": prompt,
+                    }
+                ],
+                return_tensors="pt",
+                add_special_tokens=True,
+                add_generation_prompt=True,
+                chat_template=chat_template_str,
+                tokenize=True,
+                return_dict=True,
+            )
+        else:
+            batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()
         with torch.no_grad():
@@ -282,6 +304,7 @@ def do_inference_gradio(
             streamer = TextIteratorStreamer(tokenizer)
             generation_kwargs = {
                 "inputs": batch["input_ids"].to(cfg.device),
+                "attention_mask": batch["attention_mask"].to(cfg.device),
                 "generation_config": generation_config,
                 "streamer": streamer,
             }

src/axolotl/core/trainer_builder.py

@@ -1417,6 +1417,8 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         report_to = []
         if self.cfg.use_wandb:
             report_to.append("wandb")
+            if self.cfg.wandb_name:
+                training_arguments_kwargs["run_name"] = self.cfg.wandb_name
         if self.cfg.use_mlflow:
             report_to.append("mlflow")
         if self.cfg.use_tensorboard:
@@ -1574,6 +1576,12 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
         training_args = self.hook_post_create_training_args(training_args)
 
+        # unset run_name so wandb sets up experiment names
+        if self.cfg.use_wandb and training_args.run_name == training_args.output_dir:
+            training_args.run_name = (  # pylint: disable=attribute-defined-outside-init
+                None
+            )
+
         data_collator_kwargs = {
             "padding": True,  # True/"longest" is the default
         }

src/axolotl/prompt_strategies/chat_template.py

@@ -375,8 +375,8 @@ def load(tokenizer, cfg, ds_cfg: Optional[Dict[str, Any]] = None):
     prompter_params = {
         "tokenizer": tokenizer,
         "chat_template": chat_templates(ds_cfg.get("chat_template", "chatml")),
-        "message_field_role": ds_cfg.get("message_field_role", "from"),
+        "message_field_role": ds_cfg.get("message_field_role", "role"),
-        "message_field_content": ds_cfg.get("message_field_content", "value"),
+        "message_field_content": ds_cfg.get("message_field_content", "content"),
         "message_field_training": ds_cfg.get("message_field_training", None),
         "message_field_training_detail": ds_cfg.get(
             "message_field_training_detail",

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

@@ -1017,12 +1017,20 @@ class AxolotlInputConfig(
         return neftune_noise_alpha
 
     @model_validator(mode="after")
-    def check(self):
+    def check_rl_beta(self):
         if self.dpo_beta and not self.rl_beta:
             self.rl_beta = self.dpo_beta
             del self.dpo_beta
         return self
 
+    @model_validator(mode="after")
+    def check_simpo_warmup(self):
+        if self.rl == "simpo" and self.warmup_ratio:
+            raise ValueError(
+                "warmup_ratio is not supported with the simpo trainer. Please use `warmup_steps` instead"
+            )
+        return self
+
     @model_validator(mode="before")
     @classmethod
     def check_frozen(cls, data):