fix the lora yaml for l3

.github/workflows/base.yml

@@ -32,11 +32,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.2.1
             torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
-          - cuda: "121"
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.3.0
-            torch_cuda_arch_list: "7.0 7.5 8.0 8.6 8.7 8.9 9.0+PTX"
     steps:
       - name: Checkout
         uses: actions/checkout@v3

.github/workflows/main.yml

@@ -30,11 +30,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.2.1
             axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.3.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -91,11 +86,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.2.1
             axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.3.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.github/workflows/nightlies.yml

@@ -29,11 +29,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.2.1
             axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.3.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout
@@ -91,11 +86,6 @@ jobs:
             python_version: "3.11"
             pytorch: 2.2.1
             axolotl_extras:
-          - cuda: 121
-            cuda_version: 12.1.0
-            python_version: "3.11"
-            pytorch: 2.3.0
-            axolotl_extras:
     runs-on: axolotl-gpu-runner
     steps:
       - name: Checkout

.gitignore

@@ -133,7 +133,6 @@ venv/
 ENV/
 env.bak/
 venv.bak/
-venv3.10/
 
 # Spyder project settings
 .spyderproject

docs/config.qmd

@@ -138,7 +138,7 @@ test_datasets:
     data_files:
       - /workspace/data/eval.jsonl
 
-# use RL training: 'dpo', 'ipo', 'kto_pair', 'orpo', 'sppo_hard'
+# use RL training: 'dpo', 'ipo', 'kto_pair'
 rl:
 
 # Saves the desired chat template to the tokenizer_config.json for easier inferencing
@@ -227,12 +227,6 @@ lora_modules_to_save:
 
 lora_fan_in_fan_out: false
 
-# LoRA+ hyperparameters
-# For more details about the following options, see:
-# https://arxiv.org/abs/2402.12354  and `src/axolotl/core/train_builder.py`
-loraplus_lr_ratio: # loraplus learning rate ratio lr_B / lr_A. Recommended value is 2^4.
-loraplus_lr_embedding: #  loraplus learning rate for lora embedding layers. Default value is 1e-6.
-
 peft:
   # Configuration options for loftq initialization for LoRA
   # https://huggingface.co/docs/peft/developer_guides/quantization#loftq-initialization
@@ -274,7 +268,6 @@ torch_compile_backend:  # Optional[str]
 # If greater than 1, backpropagation will be skipped and the gradients will be accumulated for the given number of steps.
 gradient_accumulation_steps: 1
 # The number of samples to include in each batch. This is the number of samples sent to each GPU.
-# Batch size per gpu = micro_batch_size * gradient_accumulation_steps
 micro_batch_size: 2
 eval_batch_size:
 num_epochs: 4

docs/rlhf.qmd

@@ -49,7 +49,7 @@ remove_unused_columns: false
 chat_template: chatml
 datasets:
   - path: argilla/ultrafeedback-binarized-preferences-cleaned
-    type: chat_template.argilla
+    type: orpo.chat_template
 ```
 
 #### Using local dataset files

examples/llama-3/lora-8b.yml

@@ -1,4 +1,4 @@
-base_model: meta-llama/Meta-Llama-3-8B
+base_model: NousResearch/Llama-2-7b-hf
 model_type: LlamaForCausalLM
 tokenizer_type: AutoTokenizer
 
@@ -64,4 +64,4 @@ weight_decay: 0.0
 fsdp:
 fsdp_config:
 special_tokens:
-   pad_token: <|end_of_text|>
+  pad_token: <|end_of_text|>

examples/mistral/mistral-qlora-orpo.yml

@@ -1,82 +0,0 @@
-base_model: mistralai/Mistral-7B-v0.1
-model_type: MistralForCausalLM
-tokenizer_type: LlamaTokenizer
-
-load_in_8bit: false
-load_in_4bit: true
-strict: false
-
-rl: orpo
-orpo_alpha: 0.1
-remove_unused_columns: false
-
-chat_template: chatml
-datasets:
-  - path: argilla/ultrafeedback-binarized-preferences-cleaned
-    type: chat_template.argilla
-dataset_prepared_path: last_run_prepared
-val_set_size: 0.1
-output_dir: ./mistral-qlora-orpo-out
-
-adapter: qlora
-lora_model_dir:
-
-sequence_len: 4096
-sample_packing: false
-pad_to_sequence_len: true
-
-lora_r: 32
-lora_alpha: 16
-lora_dropout: 0.05
-lora_target_linear: true
-lora_fan_in_fan_out:
-lora_target_modules:
-  - gate_proj
-  - down_proj
-  - up_proj
-  - q_proj
-  - v_proj
-  - k_proj
-  - o_proj
-
-wandb_project:
-wandb_entity:
-wandb_watch:
-wandb_name:
-wandb_log_model:
-
-gradient_accumulation_steps: 4
-micro_batch_size: 2
-num_epochs: 1
-optimizer: adamw_bnb_8bit
-lr_scheduler: cosine
-learning_rate: 0.0002
-
-train_on_inputs: false
-group_by_length: false
-bf16: auto
-fp16:
-tf32: false
-
-gradient_checkpointing: true
-early_stopping_patience:
-resume_from_checkpoint:
-local_rank:
-logging_steps: 1
-xformers_attention:
-flash_attention: true
-
-loss_watchdog_threshold: 5.0
-loss_watchdog_patience: 3
-
-warmup_steps: 10
-evals_per_epoch: 4
-eval_table_size:
-eval_max_new_tokens: 128
-saves_per_epoch: 1
-debug:
-deepspeed:
-weight_decay: 0.0
-fsdp:
-fsdp_config:
-special_tokens:

requirements.txt

@@ -11,7 +11,7 @@ addict
 fire
 PyYAML>=6.0
 requests
-datasets==2.15.0
+datasets>=2.15.0
 flash-attn==2.5.5
 sentencepiece
 wandb
@@ -28,7 +28,7 @@ scipy
 scikit-learn==1.2.2
 pynvml
 art
-fschat @ git+https://github.com/lm-sys/FastChat.git@5095615810cf613dba7f27dd155f571fcff976d8
+fschat==0.2.36
 gradio==3.50.2
 tensorboard
 
@@ -39,6 +39,6 @@ s3fs
 gcsfs
 # adlfs
 
-trl @ git+https://github.com/huggingface/trl.git@75de236c09bd5846f79c24d9bf371481b0b7582c
+trl @ git+https://github.com/huggingface/trl.git@0ee349dcd43b0f4b3169449f16751c38ac4a609f
 zstandard==0.22.0
 fastcore

scripts/cloud-entrypoint.sh

@@ -33,7 +33,7 @@ fi
 
 if [ "$JUPYTER_DISABLE" != "1" ]; then
     # Run Jupyter Lab in the background
-    jupyter lab --port=8888 --ip=* --allow-root --ServerApp.allow_origin=* &
+    jupyter lab --port=8888 --ip=* --allow-root --ServerApp.allow_origin=* --ServerApp.preferred_dir=/workspace &
 fi
 
 # Execute the passed arguments (CMD)

src/axolotl/cli/__init__.py

@@ -264,8 +264,8 @@ def do_inference_gradio(
         with torch.no_grad():
             generation_config = GenerationConfig(
                 repetition_penalty=1.1,
-                max_new_tokens=cfg.get("gradio_max_new_tokens", 1024),
+                max_new_tokens=1024,
-                temperature=cfg.get("gradio_temperature", 0.9),
+                temperature=0.9,
                 top_p=0.95,
                 top_k=40,
                 bos_token_id=tokenizer.bos_token_id,
@@ -300,13 +300,7 @@ def do_inference_gradio(
         outputs="text",
         title=cfg.get("gradio_title", "Axolotl Gradio Interface"),
     )
-
+    demo.queue().launch(show_api=False, share=True)
-    demo.queue().launch(
-        show_api=False,
-        share=cfg.get("gradio_share", True),
-        server_name=cfg.get("gradio_server_name", "127.0.0.1"),
-        server_port=cfg.get("gradio_server_port", None),
-    )
 
 
 def choose_config(path: Path):
@@ -439,23 +433,6 @@ def load_rl_datasets(
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )
 
-    if cli_args.debug or cfg.debug:
-        LOG.info("check_dataset_labels...")
-
-        tokenizer = load_tokenizer(cfg)
-        check_dataset_labels(
-            train_dataset.select(
-                [
-                    random.randrange(0, len(train_dataset) - 1)  # nosec
-                    for _ in range(cli_args.debug_num_examples)
-                ]
-            ),
-            tokenizer,
-            num_examples=cli_args.debug_num_examples,
-            text_only=cli_args.debug_text_only,
-            rl_mode=True,
-        )
-
     return TrainDatasetMeta(
         train_dataset=train_dataset,
         eval_dataset=eval_dataset,

src/axolotl/cli/preprocess.py

@@ -54,7 +54,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs):
         LOG.warning(msg)
         parsed_cfg.dataset_prepared_path = DEFAULT_DATASET_PREPARED_PATH
 
-    if parsed_cfg.rl:  # and parsed_cfg.rl != "orpo":
+    if parsed_cfg.rl and parsed_cfg.rl != "orpo":
         load_rl_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)
     else:
         load_datasets(cfg=parsed_cfg, cli_args=parsed_cli_args)

src/axolotl/cli/train.py

@@ -47,7 +47,7 @@ def do_train(cfg, cli_args) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
     else:
         register_chatml_template()
 
-    if cfg.rl:  # and cfg.rl != "orpo":
+    if cfg.rl and cfg.rl != "orpo":
         dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
     else:
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

src/axolotl/core/trainer_builder.py

@@ -30,7 +30,7 @@ from transformers import (
 )
 from transformers.trainer_utils import seed_worker
 from transformers.utils import is_sagemaker_mp_enabled
-from trl import DPOConfig, DPOTrainer, ORPOConfig, ORPOTrainer
+from trl import DPOTrainer
 from trl.trainer.utils import pad_to_length
 
 from axolotl.loraplus import create_loraplus_optimizer
@@ -43,7 +43,6 @@ from axolotl.utils.callbacks import (
     LossWatchDogCallback,
     SaveAxolotlConfigtoWandBCallback,
     SaveBetterTransformerModelCallback,
-    SaveModelOnTrainEndCallback,
     bench_eval_callback_factory,
     causal_lm_bench_eval_callback_factory,
     log_prediction_callback_factory,
@@ -55,7 +54,6 @@ from axolotl.utils.collators import (
     MambaDataCollator,
     V2BatchSamplerDataCollatorForSeq2Seq,
 )
-from axolotl.utils.models import ensure_dtype
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 from axolotl.utils.schedulers import (
     get_cosine_schedule_with_min_lr,
@@ -213,10 +211,6 @@ class AxolotlTrainingArguments(TrainingArguments):
         default=None,
         metadata={"help": "path under the model to access the layers"},
     )
-    curriculum_sampling: Optional[bool] = field(
-        default=None,
-        metadata={"help": "whether to use sequential sampling for curriculum learning"},
-    )
 
 
 class AxolotlTrainer(Trainer):
@@ -352,8 +346,6 @@ class AxolotlTrainer(Trainer):
                 lengths=get_dataset_lengths(self.train_dataset),
                 packing_efficiency_estimate=self.args.sample_packing_efficiency,
             )
-        if self.args.curriculum_sampling:
-            return SequentialSampler(self.train_dataset)
         return super()._get_train_sampler()
 
     def _get_eval_sampler(
@@ -818,14 +810,6 @@ class AxolotlDPOTrainer(DPOTrainer):
         return res
 
 
-class AxolotlORPOTrainer(ORPOTrainer):
-    """
-    Extend the base ORPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "orpo"]
-
-
 class TrainerBuilderBase(abc.ABC):
     """
     Base class for trainer builder
@@ -889,14 +873,6 @@ class TrainerBuilderBase(abc.ABC):
             callbacks.append(
                 SaveAxolotlConfigtoWandBCallback(self.cfg.axolotl_config_path)
             )
-        if self.cfg.use_mlflow and is_mlflow_available():
-            from axolotl.utils.callbacks.mlflow_ import (
-                SaveAxolotlConfigtoMlflowCallback,
-            )
-
-            callbacks.append(
-                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
-            )
 
         return callbacks
 
@@ -942,11 +918,18 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         ):
             callbacks.append(SaveBetterTransformerModelCallback())
 
+        if self.cfg.use_mlflow and is_mlflow_available():
+            from axolotl.utils.callbacks.mlflow_ import (
+                SaveAxolotlConfigtoMlflowCallback,
+            )
+
+            callbacks.append(
+                SaveAxolotlConfigtoMlflowCallback(self.cfg.axolotl_config_path)
+            )
+
         if self.cfg.loss_watchdog_threshold is not None:
             callbacks.append(LossWatchDogCallback(self.cfg))
 
-        callbacks.append(SaveModelOnTrainEndCallback())
-
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -1201,7 +1184,6 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
             False if self.cfg.ddp else None
         )
         training_arguments_kwargs["group_by_length"] = self.cfg.group_by_length
-        training_arguments_kwargs["curriculum_sampling"] = self.cfg.curriculum_sampling
         report_to = None
         if self.cfg.use_wandb:
             report_to = "wandb"
@@ -1422,15 +1404,13 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         )
 
 
-class HFRLTrainerBuilder(TrainerBuilderBase):
+class HFDPOTrainerBuilder(TrainerBuilderBase):
     """
     Trainer factory class for DPO Trainer
     """
 
     def get_callbacks(self):
         callbacks = super().get_callbacks()
-        callbacks.append(SaveModelOnTrainEndCallback())
-
         return callbacks
 
     def get_post_trainer_create_callbacks(self, trainer):
@@ -1466,7 +1446,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["eval_steps"] = self.cfg.eval_steps
         else:
             training_args_kwargs["evaluation_strategy"] = "no"
-
         if self.cfg.bf16 or self.cfg.bfloat16:
             training_args_kwargs["bf16"] = True
 
@@ -1518,19 +1497,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
-        if self.cfg.orpo_alpha:
+        training_args = TrainingArguments(
-            # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
-            training_args_kwargs["beta"] = self.cfg.orpo_alpha
-
-        training_args_cls = TrainingArguments
-        if self.cfg.rl == "orpo":
-            training_args_cls = ORPOConfig
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-        elif self.cfg.rl in ["dpo", "ipo", "kto_pair", "sppo_hard"]:
-            training_args_cls = DPOConfig
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-
-        training_args = training_args_cls(
             per_device_train_batch_size=self.cfg.micro_batch_size,
             max_steps=self.cfg.max_steps or total_num_steps,
             gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,
@@ -1555,8 +1522,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
                 dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         elif self.cfg.rl == "kto_pair":
             dpo_trainer_kwargs["loss_type"] = "kto_pair"
-        elif self.cfg.rl == "sppo_hard":
-            dpo_trainer_kwargs["loss_type"] = "sppo_hard"
         if self.eval_dataset:
             dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
         if self.cfg.adapter and self.peft_config:
@@ -1565,34 +1530,20 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             dpo_trainer_kwargs[
                 "precompute_ref_log_probs"
             ] = self.cfg.precompute_ref_log_probs
-        if self.cfg.rl in ["dpo", "ipo", "kto_pair", "sppo_hard"]:
+        dpo_trainer = AxolotlDPOTrainer(
-            trainer_cls = AxolotlDPOTrainer
+            self.model,
-            dpo_trainer_kwargs["beta"] = self.cfg.dpo_beta or 0.1
+            self.model_ref,
-            trainer_cls_args = [self.model, self.model_ref]
-
-            # these aren't used for the ORPO trainer
-            dpo_trainer_kwargs["max_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["max_target_length"] = None
-            dpo_trainer_kwargs["max_prompt_length"] = self.cfg.sequence_len
-            dpo_trainer_kwargs["generate_during_eval"] = True
-            if self.cfg.rl == "dpo":
-                dpo_trainer_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
-        elif self.cfg.rl == "orpo":
-            trainer_cls = AxolotlORPOTrainer
-            trainer_cls_args = [self.model]
-        else:
-            raise ValueError(f"Unsupported RL: {self.cfg.rl}")
-        dpo_trainer = trainer_cls(
-            *trainer_cls_args,
             args=training_args,
+            beta=self.cfg.dpo_beta or 0.1,
             train_dataset=self.train_dataset,
             tokenizer=self.tokenizer,
+            max_length=self.cfg.sequence_len,
+            max_target_length=None,
+            max_prompt_length=self.cfg.sequence_len,
+            generate_during_eval=True,
             callbacks=self.get_callbacks(),
             **dpo_trainer_kwargs,
         )
-        if self.cfg.fsdp:
-            ensure_dtype(dpo_trainer.model, dtype=self.cfg.torch_dtype)
-
         dpo_trainer = self.hook_post_create_trainer(dpo_trainer)
         for callback in self.get_post_trainer_create_callbacks(dpo_trainer):
             dpo_trainer.add_callback(callback)

src/axolotl/monkeypatch/fastchat_conversation_turns.py

@@ -123,14 +123,6 @@ def get_turns(  # pylint: disable=too-many-return-statements
             else:
                 yield role, ""
         return
-    if self.sep_style == SeparatorStyle.GEMMA:
-        if self.system_message:
-            raise ValueError("Gemma chat template does not support system messages")
-        for i, (role, message) in enumerate(self.messages):
-            prefix = "<bos>" if i == 0 else ""
-            message_str = message if message else ""
-            yield prefix + "<start_of_turn>" + role + "\n", message_str + "<end_of_turn>\n"
-        return
     if self.sep_style == SeparatorStyle.CHATGLM:
         # source: https://huggingface.co/THUDM/chatglm-6b/blob/1d240ba371910e9282298d4592532d7f0f3e9f3e/modeling_chatglm.py#L1302-L1308
         # source2: https://huggingface.co/THUDM/chatglm2-6b/blob/e186c891cf64310ac66ef10a87e6635fa6c2a579/modeling_chatglm.py#L926

src/axolotl/prompt_strategies/dpo/mistral.py

@@ -1,30 +0,0 @@
-"""
-DPO strategies for mistral instruct
-"""
-
-
-def prompt_pairs(cfg):  # pylint: disable=possibly-unused-variable,unused-argument
-    def transform_fn(sample):
-        sample["prompt"] = f"[INST]{sample['prompt']}[/INST]"
-        sample["chosen"] = f"{sample['chosen']}"
-        sample["rejected"] = f"{sample['rejected']}"
-        return sample
-
-    return transform_fn
-
-
-def argilla_chat(
-    cfg,
-    **kwargs,
-):  # pylint: disable=possibly-unused-variable,unused-argument
-    """
-    for argilla/dpo-mix-7k conversations
-    """
-
-    def transform_fn(sample):
-        sample["prompt"] = f"[INST] {sample['chosen'][0]['content']} [/INST]"
-        sample["chosen"] = f"{sample['chosen'][1]['content']}</s>"
-        sample["rejected"] = f"{sample['rejected'][1]['content']}</s>"
-        return sample
-
-    return transform_fn

src/axolotl/prompt_strategies/orpo/__init__.py

@@ -6,4 +6,4 @@ from functools import partial
 
 from ..base import load as load_base
 
-load = partial(load_base, module_base="axolotl.prompt_strategies.orpo")
+load = partial(load_base, module="axolotl.prompt_strategies.orpo")

src/axolotl/prompt_strategies/orpo/chat_template.py

@@ -78,57 +78,6 @@ class ORPODatasetParsingStrategy:
         )
         return MessageList(messages=messages)
 
-    def get_prompt(self, prompt) -> MessageList:
-        """Map the data to extract everything up to the last turn"""
-        total_msg_len = len(prompt["chosen"])
-        total_msg_turns, remainder = divmod(total_msg_len, 2)
-        assert remainder == 0, "invalid number of turns"
-
-        messages: List[Message] = []
-        if system := prompt.get("system", None):
-            messages.append(Message(role="system", content=system, label=False))
-        for i in range(total_msg_turns):
-            if "prompt" in prompt:
-                messages.append(
-                    Message(role="user", content=prompt["prompt"], label=False)
-                )
-            else:
-                messages.append(
-                    Message(
-                        role="user",
-                        content=prompt["chosen"][i * 2]["content"],
-                        label=False,
-                    )
-                )
-            if i < total_msg_turns - 1:
-                messages.append(
-                    Message(
-                        role="assistant",
-                        content=prompt["chosen"][i * 2 + 1]["content"],
-                        label=False,
-                    )
-                )
-
-        return MessageList(messages=messages)
-
-    def get_chosen(self, prompt) -> MessageList:
-        res = self.get_prompt(prompt)
-        res.messages.append(
-            Message(
-                role="assistant", content=prompt["chosen"][-1]["content"], label=True
-            )
-        )
-        return res
-
-    def get_rejected(self, prompt) -> MessageList:
-        res = self.get_prompt(prompt)
-        res.messages.append(
-            Message(
-                role="assistant", content=prompt["rejected"][-1]["content"], label=True
-            )
-        )
-        return res
-
 
 class ORPOTokenizingStrategy(PromptTokenizingStrategy):
     """
@@ -237,36 +186,3 @@ class ORPOPrompter(Prompter):
                     chat_template=self.chat_template,
                     tokenize=False,
                 ), True
-
-
-def argilla(cfg, **kwargs):  # pylint: disable=possibly-unused-variable,unused-argument
-    dataset_parser = ORPODatasetParsingStrategy()
-
-    chat_template_str = chat_templates(cfg.chat_template)
-
-    def transform_fn(sample, tokenizer=None):
-        res = {}
-
-        res["prompt"] = tokenizer.apply_chat_template(
-            [msg.model_dump() for msg in dataset_parser.get_prompt(sample).messages],
-            add_generation_prompt=True,
-            chat_template=chat_template_str,
-            tokenize=False,
-        )
-        prompt_str_len = len(res["prompt"])
-        res["chosen"] = tokenizer.apply_chat_template(
-            [msg.model_dump() for msg in dataset_parser.get_chosen(sample).messages],
-            add_generation_prompt=False,
-            chat_template=chat_template_str,
-            tokenize=False,
-        )[prompt_str_len:]
-        res["rejected"] = tokenizer.apply_chat_template(
-            [msg.model_dump() for msg in dataset_parser.get_rejected(sample).messages],
-            add_generation_prompt=False,
-            chat_template=chat_template_str,
-            tokenize=False,
-        )[prompt_str_len:]
-
-        return res
-
-    return transform_fn

src/axolotl/train.py

@@ -3,7 +3,6 @@
 import os
 import signal
 import sys
-import weakref
 from dataclasses import dataclass
 from pathlib import Path
 from typing import Optional, Tuple, Union
@@ -128,20 +127,14 @@ def train(
     # In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
     if cfg.local_rank == 0:
 
-        def terminate_handler(_, __, model_weakref):
+        def terminate_handler(_, __, model):
-            if model_weakref() is not None:
+            if cfg.flash_optimum and BetterTransformer:
-                _model = model_weakref()
+                model = BetterTransformer.reverse(model)
-                if cfg.flash_optimum and BetterTransformer:
+            model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
-                    _model = BetterTransformer.reverse(_model)
-                _model.save_pretrained(
-                    cfg.output_dir, safe_serialization=safe_serialization
-                )
             sys.exit(0)
 
-        _model_weakref = weakref.ref(model)
         signal.signal(
-            signal.SIGINT,
+            signal.SIGINT, lambda signum, frame: terminate_handler(signum, frame, model)
-            lambda signum, frame: terminate_handler(signum, frame, _model_weakref),
         )
 
     badge_markdown = """[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)"""

src/axolotl/utils/callbacks/__init__.py

@@ -773,13 +773,3 @@ class SaveAxolotlConfigtoWandBCallback(TrainerCallback):
             except (FileNotFoundError, ConnectionError) as err:
                 LOG.warning(f"Error while saving Axolotl config to WandB: {err}")
         return control
-
-
-class SaveModelOnTrainEndCallback(TrainerCallback):
-    """Callback to save model on train end"""
-
-    def on_train_end(  # pylint: disable=unused-argument
-        self, args, state, control, **kwargs
-    ):
-        control.should_save = True
-        return control

src/axolotl/utils/config/__init__.py

@@ -383,9 +383,9 @@ def legacy_validate_config(cfg):
             "push_to_hub_model_id is deprecated. Please use hub_model_id instead."
         )
 
-    if cfg.hub_model_id and cfg.save_strategy not in ["steps", "epoch", None]:
+    if cfg.hub_model_id and not (cfg.save_steps or cfg.saves_per_epoch):
         LOG.warning(
-            "hub_model_id is set without any models being saved. To save a model, set save_strategy to steps, epochs or leave empty."
+            "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.revision_of_model:
@@ -448,14 +448,10 @@ def legacy_validate_config(cfg):
         raise ValueError(
             "save_steps and saves_per_epoch are mutually exclusive and cannot be used together."
         )
-    if cfg.save_strategy and cfg.saves_per_epoch and cfg.save_strategy != "steps":
+    if cfg.saves_per_epoch and cfg.save_strategy and cfg.save_strategy != "steps":
         raise ValueError(
             "save_strategy must be empty or set to `steps` when used with saves_per_epoch."
         )
-    if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
-        raise ValueError(
-            "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
-        )
     if cfg.evals_per_epoch and cfg.eval_steps:
         raise ValueError(
             "eval_steps and evals_per_epoch are mutually exclusive and cannot be used together."
@@ -468,6 +464,11 @@ def legacy_validate_config(cfg):
         raise ValueError(
             "evaluation_strategy must be empty or set to `steps` when used with evals_per_epoch."
         )
+    if cfg.save_strategy and cfg.save_steps and cfg.save_strategy != "steps":
+        raise ValueError(
+            "save_strategy and save_steps mismatch. Please set save_strategy to 'steps' or remove save_steps."
+        )
+
     if (
         cfg.evaluation_strategy
         and cfg.eval_steps

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

@@ -133,7 +133,6 @@ class RLType(str, Enum):
     ipo = "ipo"  # pylint: disable=invalid-name
     kto_pair = "kto_pair"  # pylint: disable=invalid-name
     orpo = "orpo"  # pylint: disable=invalid-name
-    sppo_hard = "sppo_hard"  # pylint: disable=invalid-name
 
 
 class ChatTemplate(str, Enum):
@@ -410,17 +409,6 @@ class WandbConfig(BaseModel):
         return data
 
 
-class GradioConfig(BaseModel):
-    """Gradio configuration subset"""
-
-    gradio_title: Optional[str] = None
-    gradio_share: Optional[bool] = None
-    gradio_server_name: Optional[str] = None
-    gradio_server_port: Optional[int] = None
-    gradio_max_new_tokens: Optional[int] = None
-    gradio_temperature: Optional[float] = None
-
-
 # pylint: disable=too-many-public-methods,too-many-ancestors
 class AxolotlInputConfig(
     ModelInputConfig,
@@ -431,7 +419,6 @@ class AxolotlInputConfig(
     WandbConfig,
     MLFlowConfig,
     LISAConfig,
-    GradioConfig,
     RemappedParameters,
     DeprecatedParameters,
     BaseModel,
@@ -516,17 +503,9 @@ class AxolotlInputConfig(
     unfrozen_parameters: Optional[List[str]] = None
 
     sequence_len: int = Field(default=512)
-    min_sample_len: Optional[int] = None
     sample_packing: Optional[bool] = None
     eval_sample_packing: Optional[bool] = None
     pad_to_sequence_len: Optional[bool] = None
-    curriculum_sampling: Optional[bool] = None
-
-    # for PoSE context length extension
-    use_pose: Optional[bool] = None
-    pose_split_on_token_ids: Optional[List[int]] = None
-    pose_max_context_len: Optional[int] = None
-    pose_num_chunks: Optional[int] = None
 
     pretrain_multipack_buffer_size: Optional[int] = 10_000
     pretrain_multipack_attn: Optional[bool] = Field(
@@ -575,7 +554,6 @@ class AxolotlInputConfig(
     neftune_noise_alpha: Optional[float] = None
 
     orpo_alpha: Optional[float] = None
-    dpo_beta: Optional[float] = None
 
     max_memory: Optional[
         Dict[Union[int, Literal["cpu", "disk"]], Union[int, str]]
@@ -794,11 +772,11 @@ class AxolotlInputConfig(
     @model_validator(mode="before")
     @classmethod
     def check_push_save(cls, data):
-        if data.get("hub_model_id") and (
+        if data.get("hub_model_id") and not (
-            data.get("save_strategy") not in ["steps", "epoch", None]
+            data.get("save_steps") or data.get("saves_per_epoch")
         ):
             LOG.warning(
-                "hub_model_id is set without any models being saved. To save a model, set save_strategy."
+                "hub_model_id is set without any models being saved. To save a model, set either save_steps or saves_per_epoch."
             )
         return data
 

src/axolotl/utils/data/__init__.py

@@ -1,11 +1,11 @@
 """
 Data processing modules
 """
+from axolotl.utils.data.dpo import load_prepare_dpo_datasets  # noqa: F401
 from axolotl.utils.data.pretraining import (  # noqa: F401
     encode_pretraining,
     wrap_pretraining_dataset,
 )
-from axolotl.utils.data.rl import load_prepare_dpo_datasets  # noqa: F401
 from axolotl.utils.data.sft import (  # noqa: F401
     get_dataset_wrapper,
     load_prepare_datasets,

src/axolotl/utils/data/dpo.py

@@ -1,20 +1,17 @@
 """data handling specific to DPO"""
-import inspect
+
 import logging
-from functools import partial
 from pathlib import Path
 from typing import Any, List
 
 import yaml
-from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk
+from datasets import concatenate_datasets, load_dataset, load_from_disk
 
 from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
 from axolotl.prompt_strategies.dpo import load as load_dpo
-from axolotl.prompt_strategies.orpo import load as load_orpo
 from axolotl.utils.data.utils import md5
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
-from axolotl.utils.models import load_tokenizer
 
 LOG = logging.getLogger("axolotl")
 
@@ -75,29 +72,16 @@ def load_prepare_dpo_datasets(cfg):
                 )
                 split_datasets.insert(i, ds)
 
-        tokenizer = None
         for i, data_set in enumerate(split_datasets):
             _type = dataset_cfgs[i]["type"]
             if _type:
                 if isinstance(_type, DictDefault):
                     _type = "user_defined.default"
-                if _cfg.rl == "orpo":
+                ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
-                    ds_transform_fn = load_orpo(_type, _cfg, dataset_idx=i)
+                split_datasets[i] = data_set.map(
-                else:
-                    ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
-                sig = inspect.signature(ds_transform_fn)
-                if "tokenizer" in sig.parameters:
-                    if not tokenizer:
-                        tokenizer = load_tokenizer(_cfg)
-                    ds_transform_fn = partial(ds_transform_fn, tokenizer=tokenizer)
-
-                data_set = data_set.map(
                     ds_transform_fn,
                     desc="Mapping RL Dataset",
                 )
-                if isinstance(data_set, DatasetDict):
-                    data_set = data_set["train"]
-                split_datasets[i] = data_set
             else:
                 # If no `type` is provided, assume the dataset is already in the expected format with
                 # "prompt", "chosen" and "rejected" already preprocessed

src/axolotl/utils/data/sft.py

@@ -421,7 +421,7 @@ def load_tokenized_prepared_datasets(
 
         if cfg.local_rank == 0:
             LOG.info(f"Saving merged prepared dataset to disk... {prepared_ds_path}")
-            dataset.save_to_disk(str(prepared_ds_path))
+            dataset.save_to_disk(prepared_ds_path)
             if cfg.push_dataset_to_hub:
                 LOG.info(
                     f"Saving merged prepared dataset with push_to_hub... {cfg.push_dataset_to_hub}/{ds_hash}"

src/axolotl/utils/models.py

@@ -789,11 +789,7 @@ def load_model(
     if not reference_model or cfg.lora_model_dir:
         # if we're not loading the reference model, then we're loading the model for training
         # then the dpo trainer doesn't want the peft model loaded over it, it just wants the lora/peft config
-        if (
+        if cfg.adapter and cfg.rl in ["dpo", "ipo", "kto_pair"] and not cfg.merge_lora:
-            cfg.adapter
-            and cfg.rl in ["dpo", "ipo", "kto_pair", "sppo_hard"]
-            and not cfg.merge_lora
-        ):
             _, lora_config = load_lora(model, cfg, inference=False, config_only=True)
         else:
             model, lora_config = load_adapter(model, cfg, cfg.adapter)
@@ -997,13 +993,3 @@ def load_lora(model, cfg, inference=False, config_only=False):
         setup_quantized_peft_meta_for_training(model)
 
     return model, lora_config
-
-
-def ensure_dtype(model, dtype=torch.bfloat16):
-    for name, module in model.named_modules():
-        try:
-            if module.weight.dtype != dtype:
-                print(f"Converting module {name}: {module.weight.dtype} -> {dtype}")
-                module.to(dtype)
-        except AttributeError:
-            pass

src/axolotl/utils/tokenization.py

@@ -1,5 +1,6 @@
 """Module for tokenization utilities"""
 
+
 import logging
 import re
 from typing import Dict, List
@@ -9,19 +10,10 @@ from termcolor import colored
 LOG = logging.getLogger("axolotl")
 
 
-def check_dataset_labels(
+def check_dataset_labels(dataset, tokenizer, num_examples=5, text_only=False):
-    dataset,
-    tokenizer,
-    num_examples=5,
-    text_only=False,
-    rl_mode=False,
-):
     # the dataset is already shuffled, so let's just check the first 5 elements
     for idx in range(num_examples):
-        if not rl_mode:
+        check_example_labels(dataset[idx], tokenizer, text_only=text_only)
-            check_example_labels(dataset[idx], tokenizer, text_only=text_only)
-        else:
-            check_rl_example_labels(dataset[idx], tokenizer, text_only=text_only)
 
 
 def check_example_labels(example, tokenizer, text_only=False):
@@ -48,53 +40,6 @@ def check_example_labels(example, tokenizer, text_only=False):
     return " ".join(colored_tokens)
 
 
-def color_token_for_rl_debug(decoded_token, encoded_token, color, text_only):
-    """Helper function to color tokens based on their type."""
-    colored_text = colored(decoded_token, color)
-    return (
-        colored_text
-        if text_only
-        else f"{colored_text}{colored(f'({encoded_token})', 'white')}"
-    )
-
-
-def process_tokens_for_rl_debug(tokens, color, tokenizer, text_only):
-    """Helper function to process and color tokens."""
-    colored_tokens = [
-        color_token_for_rl_debug(tokenizer.decode(token), token, color, text_only)
-        for token in tokenizer.encode(tokens)
-    ]
-    return colored_tokens
-
-
-def check_rl_example_labels(example, tokenizer, text_only=False):
-    field_prompt, field_chosen, field_rejected = "prompt", "chosen", "rejected"
-
-    input_tokens = example[field_prompt]
-    labels_chosen, labels_rejected = example[field_chosen], example[field_rejected]
-
-    # Process and color each type of token
-    colored_tokens = process_tokens_for_rl_debug(
-        input_tokens, "yellow", tokenizer, text_only
-    )
-    colored_chosens = process_tokens_for_rl_debug(
-        labels_chosen, "green", tokenizer, text_only
-    )
-    colored_rejecteds = process_tokens_for_rl_debug(
-        labels_rejected, "red", tokenizer, text_only
-    )
-
-    # Create a delimiter based on text_only flag
-    delimiter = "" if text_only else " "
-
-    # Logging information
-    LOG.info(f"INPUT PROMPT: {delimiter.join(colored_tokens)}\n\n")
-    LOG.info(f"CHOSEN RESPONSE: {delimiter.join(colored_chosens)}\n\n")
-    LOG.info(f"REJECTED RESPONSE: {delimiter.join(colored_rejecteds)}\n\n\n")
-
-    return delimiter.join(colored_tokens)
-
-
 GLAIVE_ROLES = ["USER", "ASSISTANT", "FUNCTION RESPONSE"]
 GLAIVE_TO_SHAREGPT_ROLE = {
     "SYSTEM": "system",

src/axolotl/utils/trainer.py

@@ -1,10 +1,9 @@
 """Module containing the Trainer class and related functions"""
 import math
 import os
-import random
 from contextlib import contextmanager
 from functools import partial
-from typing import List, Optional
+from typing import List
 
 import numpy as np
 import torch
@@ -14,7 +13,7 @@ from datasets import set_caching_enabled
 from torch.utils.data import DataLoader, RandomSampler
 from transformers.utils import is_torch_bf16_gpu_available
 
-from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
+from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFDPOTrainerBuilder
 from axolotl.utils.distributed import is_main_process, reduce_and_broadcast, zero_first
 from axolotl.utils.samplers import MultipackBatchSampler, get_dataset_lengths
 
@@ -99,89 +98,17 @@ def add_position_ids(sample):
     return sample
 
 
-def add_pose_position_ids(
-    sample,
-    max_context_len=32768,
-    split_on_token_ids: Optional[List[int]] = None,
-    chunks: int = 2,
-):
-    """
-    use the PoSE technique to extend the context length by randomly skipping
-    positions in the context. We only want to skip right before tokens in
-    the split_on_token_ids list. We should attempt to randomly distribute
-    the skips, but we don't need the final position_ids to be the full
-    context_len. There may be multiple turns in the context, so we want to
-    make sure we take into account the maximum possible number of skips
-    remaining in each sample.
-    """
-
-    input_ids = sample["input_ids"]
-    sample_len = len(input_ids)
-    max_skips = max_context_len - sample_len
-
-    if split_on_token_ids is None:
-        split_on_token_ids = []
-
-    if split_on_token_ids:
-        split_indices = [
-            i for i, token_id in enumerate(input_ids) if token_id in split_on_token_ids
-        ]
-    else:
-        chunk_len = sample_len // chunks
-        split_indices = [i * chunk_len for i in range(1, chunks)]
-    split_indices.append(len(input_ids))  # make sure we go to the end of the sample
-    if split_indices[0] < 2:
-        # drop the first split index if it's too close to the beginning
-        split_indices = split_indices[1:]
-
-    position_ids = []
-    prev_index = 0
-    total_skips = 0
-
-    for split_index in split_indices:
-        num_skips = (
-            random.randint(0, max_skips)  # nosec B311
-            if prev_index != 0 and max_skips
-            else 0
-        )
-        max_skips -= num_skips
-        total_skips += num_skips
-
-        segment_position_ids = list(
-            range(prev_index + total_skips, split_index + total_skips)
-        )
-
-        position_ids.extend(segment_position_ids)
-        prev_index = split_index
-
-    sample["sequence_len"] = position_ids[-1]
-    position_ids = torch.tensor(position_ids)
-
-    sample["position_ids"] = position_ids
-    sample["length"] = len(position_ids)
-    assert len(position_ids) == len(input_ids)
-
-    return sample
-
-
 def add_length(sample):
     sample["length"] = len(sample["input_ids"])
     return sample
 
 
-def drop_long_seq(sample, sequence_len=2048, min_sequence_len=2):
+def drop_long_seq(sample, sequence_len=2048):
-    return (
+    return len(sample["input_ids"]) <= sequence_len and len(sample["input_ids"]) > 0
-        len(sample["input_ids"]) <= sequence_len
-        and len(sample["input_ids"]) >= min_sequence_len
-    )
 
 
 def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
-    drop_long = partial(
+    drop_long = partial(drop_long_seq, sequence_len=cfg.sequence_len)
-        drop_long_seq,
-        sequence_len=cfg.sequence_len,
-        min_sequence_len=cfg.min_sample_len or 2,
-    )
     with zero_first(is_main_process()):
         if cfg.is_preprocess:
             min_input_len = np.min(get_dataset_lengths(train_dataset))
@@ -226,32 +153,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                 desc="Group By Length",
             )
 
-        if cfg.use_pose:
+        if cfg.sample_packing:
-            pose_kwargs = {}
-            if cfg.pose_num_chunks is not None:
-                pose_kwargs["chunks"] = cfg.pose_num_chunks
-            pose_fn = partial(
-                add_pose_position_ids,
-                max_context_len=cfg.pose_max_context_len,
-                split_on_token_ids=cfg.pose_split_on_token_ids,
-                **pose_kwargs,
-            )
-            train_dataset = train_dataset.map(
-                pose_fn,
-                num_proc=cfg.dataset_processes,
-                load_from_cache_file=not cfg.is_preprocess,
-                desc="Add position_id column (PoSE)",
-            )
-            train_dataset = train_dataset.sort("sequence_len")
-            if cfg.eval_sample_packing is not False:
-                if eval_dataset:
-                    eval_dataset = eval_dataset.map(
-                        pose_fn,
-                        num_proc=cfg.dataset_processes,
-                        load_from_cache_file=not cfg.is_preprocess,
-                        desc="Add position_id column (PoSE)",
-                    )
-        elif cfg.sample_packing:
             train_dataset = train_dataset.map(
                 add_position_ids,
                 num_proc=cfg.dataset_processes,
@@ -438,8 +340,8 @@ def prepare_optim_env(cfg):
 
 
 def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer, total_num_steps):
-    if cfg.rl in ["dpo", "ipo", "kto_pair", "orpo", "sppo_hard"]:
+    if cfg.rl in ["dpo", "ipo", "kto_pair"]:
-        trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer)
+        trainer_builder = HFDPOTrainerBuilder(cfg, model[0], tokenizer)
         trainer_builder.model_ref = model[1]
         trainer_builder.peft_config = model[2]
     else:

tests/core/test_trainer_builder.py

@@ -4,7 +4,7 @@ unit tests for axolotl.core.trainer_builder
 
 import pytest
 
-from axolotl.core.trainer_builder import HFRLTrainerBuilder
+from axolotl.core.trainer_builder import HFDPOTrainerBuilder
 from axolotl.utils.config import normalize_config
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_model, load_tokenizer
@@ -51,13 +51,13 @@ def fixture_model(cfg, tokenizer):
     return load_model(cfg, tokenizer)
 
 
-class TestHFRLTrainerBuilder:
+class TestHFDPOTrainerBuilder:
     """
     TestCase class for DPO trainer builder
     """
 
     def test_build_training_arguments(self, cfg, model, tokenizer):
-        builder = HFRLTrainerBuilder(cfg, model, tokenizer)
+        builder = HFDPOTrainerBuilder(cfg, model, tokenizer)
         training_arguments = builder.build_training_arguments(100)
         assert training_arguments.adam_beta1 == 0.998
         assert training_arguments.adam_beta2 == 0.9

tests/e2e/test_dpo.py

@@ -158,50 +158,3 @@ class TestDPOLlamaLora(unittest.TestCase):
 
         train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
         assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()
-
-    @with_temp_dir
-    def test_orpo_lora(self, temp_dir):
-        # pylint: disable=duplicate-code
-        cfg = DictDefault(
-            {
-                "base_model": "JackFram/llama-68m",
-                "tokenizer_type": "LlamaTokenizer",
-                "sequence_len": 1024,
-                "load_in_8bit": True,
-                "adapter": "lora",
-                "lora_r": 64,
-                "lora_alpha": 32,
-                "lora_dropout": 0.1,
-                "lora_target_linear": True,
-                "special_tokens": {},
-                "rl": "orpo",
-                "orpo_alpha": 0.1,
-                "remove_unused_columns": False,
-                "chat_template": "chatml",
-                "datasets": [
-                    {
-                        "path": "argilla/ultrafeedback-binarized-preferences-cleaned",
-                        "type": "chat_template.argilla",
-                        "split": "train",
-                    },
-                ],
-                "num_epochs": 1,
-                "micro_batch_size": 4,
-                "gradient_accumulation_steps": 1,
-                "output_dir": temp_dir,
-                "learning_rate": 0.00001,
-                "optimizer": "paged_adamw_8bit",
-                "lr_scheduler": "cosine",
-                "max_steps": 20,
-                "save_steps": 10,
-                "warmup_steps": 5,
-                "gradient_checkpointing": True,
-                "gradient_checkpointing_kwargs": {"use_reentrant": True},
-            }
-        )
-        normalize_config(cfg)
-        cli_args = TrainerCliArgs()
-        dataset_meta = load_rl_datasets(cfg=cfg, cli_args=cli_args)
-
-        train(cfg=cfg, cli_args=cli_args, dataset_meta=dataset_meta)
-        assert (Path(temp_dir) / "checkpoint-20/adapter_model.safetensors").exists()

tests/test_datasets.py

@@ -110,7 +110,7 @@ class TestDatasetPreparation(unittest.TestCase):
         """Usual use case.  Verify datasets saved via `save_to_disk` can be loaded."""
         with tempfile.TemporaryDirectory() as tmp_dir:
             tmp_ds_name = Path(tmp_dir) / "tmp_dataset"
-            self.dataset.save_to_disk(str(tmp_ds_name))
+            self.dataset.save_to_disk(tmp_ds_name)
 
             prepared_path = Path(tmp_dir) / "prepared"
             cfg = DictDefault(

tests/test_validation.py

@@ -1067,52 +1067,18 @@ class TestValidation(BaseValidation):
         ):
             validate_config(cfg)
 
-    def test_hub_model_id_save_value_warns_save_stragey_no(self, minimal_cfg):
+    def test_hub_model_id_save_value_warns(self, minimal_cfg):
-        cfg = DictDefault({"hub_model_id": "test", "save_strategy": "no"}) | minimal_cfg
-
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert len(self._caplog.records) == 1
-
-    def test_hub_model_id_save_value_warns_random_value(self, minimal_cfg):
-        cfg = (
-            DictDefault({"hub_model_id": "test", "save_strategy": "test"}) | minimal_cfg
-        )
-
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert len(self._caplog.records) == 1
-
-    def test_hub_model_id_save_value_steps(self, minimal_cfg):
-        cfg = (
-            DictDefault({"hub_model_id": "test", "save_strategy": "steps"})
-            | minimal_cfg
-        )
-
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert len(self._caplog.records) == 0
-
-    def test_hub_model_id_save_value_epochs(self, minimal_cfg):
-        cfg = (
-            DictDefault({"hub_model_id": "test", "save_strategy": "epoch"})
-            | minimal_cfg
-        )
-
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert len(self._caplog.records) == 0
-
-    def test_hub_model_id_save_value_none(self, minimal_cfg):
-        cfg = DictDefault({"hub_model_id": "test", "save_strategy": None}) | minimal_cfg
-
-        with self._caplog.at_level(logging.WARNING):
-            validate_config(cfg)
-            assert len(self._caplog.records) == 0
-
-    def test_hub_model_id_save_value_no_set_save_strategy(self, minimal_cfg):
         cfg = DictDefault({"hub_model_id": "test"}) | minimal_cfg
 
+        with self._caplog.at_level(logging.WARNING):
+            validate_config(cfg)
+            assert (
+                "set without any models being saved" in self._caplog.records[0].message
+            )
+
+    def test_hub_model_id_save_value(self, minimal_cfg):
+        cfg = DictDefault({"hub_model_id": "test", "saves_per_epoch": 4}) | minimal_cfg
+
         with self._caplog.at_level(logging.WARNING):
             validate_config(cfg)
             assert len(self._caplog.records) == 0