set max steps to -1 when empty

clean path and add mounts
handle mounting

cicd/Dockerfile.jinja

@@ -32,9 +32,9 @@ RUN if [ "$NIGHTLY_BUILD" = "true" ] ; then \
     fi
 
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,vllm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,vllm] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

docker/Dockerfile

@@ -20,9 +20,9 @@ WORKDIR /workspace/axolotl
 
 # If AXOLOTL_EXTRAS is set, append it in brackets
 RUN if [ "$AXOLOTL_EXTRAS" != "" ] ; then \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,vllm,$AXOLOTL_EXTRAS] $AXOLOTL_ARGS; \
     else \
-        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray] $AXOLOTL_ARGS; \
+        pip install --no-build-isolation -e .[deepspeed,flash-attn,optimizers,ray,vllm] $AXOLOTL_ARGS; \
     fi
 
 RUN python scripts/unsloth_install.py | sh

requirements.txt

@@ -13,12 +13,12 @@ liger-kernel==0.5.2
 packaging==23.2
 
 peft==0.14.0
-transformers==4.48.1
+transformers==4.48.2
 tokenizers>=0.21.0
 accelerate==1.3.0
 datasets==3.2.0
 deepspeed==0.16.1
-trl==0.13.0
+trl==0.14.0
 
 optimum==1.16.2
 hf_transfer
@@ -26,7 +26,7 @@ sentencepiece
 gradio==3.50.2
 
 modal==0.70.5
-pydantic==2.6.3
+pydantic==2.10.6
 addict
 fire
 PyYAML>=6.0

setup.py

@@ -153,5 +153,8 @@ setup(
         "ray": [
             "ray[train]",
         ],
+        "vllm": [
+            "vllm>=0.7.1",
+        ],
     },
 )

src/axolotl/cli/cloud/__init__.py

@@ -35,13 +35,18 @@ def do_cli_train(
     cloud_config: Union[Path, str],
     config: Union[Path, str],
     accelerate: bool = True,
+    cwd=None,
+    **kwargs,
 ) -> None:
     print_axolotl_text_art()
     cloud_cfg = load_cloud_cfg(cloud_config)
     cloud = ModalCloud(cloud_cfg)
     with open(config, "r", encoding="utf-8") as file:
         config_yaml = file.read()
-    cloud.train(config_yaml, accelerate=accelerate)
+    local_dirs = {}
+    if cwd and not Path(cwd).joinpath("src", "axolotl").exists():
+        local_dirs = {"/workspace/mounts": cwd}
+    cloud.train(config_yaml, accelerate=accelerate, local_dirs=local_dirs, **kwargs)
 
 
 def do_cli_lm_eval(

src/axolotl/cli/cloud/modal_.py

@@ -7,6 +7,7 @@ import os
 import subprocess  # nosec B404
 from pathlib import Path
 from random import randint
+from typing import Optional
 
 import modal
 
@@ -22,8 +23,18 @@ def run_cmd(cmd: str, run_folder: str, volumes=None):
 
     # modal workaround so it doesn't use the automounted axolotl
     new_env = copy.deepcopy(os.environ)
+
     if "PYTHONPATH" in new_env:
-        del new_env["PYTHONPATH"]
+        paths = ["/workspace/mounts"]
+        for sub_python_path_str in new_env["PYTHONPATH"].split(":"):
+            sub_python_path = Path(sub_python_path_str)
+            if not sub_python_path.joinpath("src", "axolotl").exists():
+                # we don't want to use the automounted axolotl or unexpected behavior happens
+                paths.append(str(sub_python_path))
+        if paths:
+            new_env["PYTHONPATH"] = ":".join(paths)
+        else:
+            del new_env["PYTHONPATH"]
 
     # Propagate errors from subprocess.
     if exit_code := subprocess.call(  # nosec B603
@@ -203,9 +214,12 @@ class ModalCloud(Cloud):
             memory = int(self.config.memory)
         return 1024 * memory
 
-    def get_train_env(self):
+    def get_train_env(self, local_dirs=None):
+        image = self.get_image()
+        for mount, local_dir in (local_dirs or {}).items():
+            image = image.add_local_dir(local_dir, mount)
         return self.app.function(
-            image=self.get_image(),
+            image=image,
             volumes={k: v[0] for k, v in self.volumes.items()},
             cpu=16.0,
             gpu=self.get_train_gpu(),
@@ -214,14 +228,21 @@ class ModalCloud(Cloud):
             secrets=self.get_secrets(),
         )
 
-    def train(self, config_yaml: str, accelerate: bool = True):
-        modal_fn = self.get_train_env()(_train)
+    def train(
+        self,
+        config_yaml: str,
+        accelerate: bool = True,
+        local_dirs: Optional[dict[str, str]] = None,
+        **kwargs,
+    ):
+        modal_fn = self.get_train_env(local_dirs)(_train)
         with modal.enable_output():
             with self.app.run(detach=True):
                 modal_fn.remote(
                     config_yaml,
                     accelerate=accelerate,
                     volumes={k: v[0] for k, v in self.volumes.items()},
+                    **kwargs,
                 )
 
     def lm_eval(self, config_yaml: str):
@@ -252,7 +273,7 @@ def _preprocess(config_yaml: str, volumes=None):
     )
 
 
-def _train(config_yaml: str, accelerate: bool = True, volumes=None):
+def _train(config_yaml: str, accelerate: bool = True, volumes=None, **kwargs):
     with open(
         "/workspace/artifacts/axolotl/config.yaml", "w", encoding="utf-8"
     ) as f_out:
@@ -262,8 +283,11 @@ def _train(config_yaml: str, accelerate: bool = True, volumes=None):
         accelerate_args = "--accelerate"
     else:
         accelerate_args = "--no-accelerate"
+    num_processes_args = ""
+    if num_processes := kwargs.pop("num_processes", None):
+        num_processes_args = f"--num-processes {num_processes}"
     run_cmd(
-        f"axolotl train {accelerate_args} /workspace/artifacts/axolotl/config.yaml",
+        f"axolotl train {accelerate_args} {num_processes_args} /workspace/artifacts/axolotl/config.yaml",
         run_folder,
         volumes,
     )

src/axolotl/cli/main.py

@@ -2,6 +2,7 @@
 # pylint: disable=redefined-outer-name
 
 import logging
+import os
 import random
 import subprocess  # nosec B404
 import tempfile
@@ -12,6 +13,7 @@ from typing import Optional
 
 import click
 import yaml
+from dotenv import load_dotenv
 
 import axolotl
 from axolotl.cli.args import EvaluateCliArgs, PreprocessCliArgs, TrainerCliArgs
@@ -199,7 +201,10 @@ def train(
         try:
             if accelerate:
                 if cloud:
-                    do_cli_train(cloud_config=cloud, config=config, accelerate=True)
+                    cwd = os.getcwd()
+                    do_cli_train(
+                        cloud_config=cloud, config=config, accelerate=True, cwd=cwd, **kwargs
+                    )
                 else:
                     accelerate_args = []
                     if "main_process_port" in kwargs:
@@ -208,7 +213,7 @@ def train(
                         accelerate_args.append(str(main_process_port))
                     if "num_processes" in kwargs:
                         num_processes = kwargs.pop("num_processes", None)
-                        accelerate_args.append("--num-processes")
+                        accelerate_args.append("--num_processes")
                         accelerate_args.append(str(num_processes))
 
                     base_cmd = ["accelerate", "launch"]
@@ -220,7 +225,9 @@ def train(
                     subprocess.run(cmd, check=True)  # nosec B603
             else:
                 if cloud:
-                    do_cli_train(cloud_config=cloud, config=config, accelerate=False)
+                    do_cli_train(
+                        cloud_config=cloud, config=config, accelerate=False, **kwargs
+                    )
                 else:
                     from axolotl.cli.train import do_cli
 
@@ -381,4 +388,5 @@ def main():
 
 
 if __name__ == "__main__":
+    load_dotenv()
     main()

src/axolotl/common/datasets.py

@@ -122,9 +122,11 @@ def load_preference_datasets(
         `total_num_steps`.
     """
     train_dataset, eval_dataset = load_prepare_preference_datasets(cfg)
-    total_num_steps = int(
+    total_num_steps: Optional[int] = int(
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )
+    if cfg.rl == "grpo":
+        total_num_steps = None
 
     if cli_args.debug or cfg.debug:
         LOG.info("check_dataset_labels...")

src/axolotl/core/trainer_builder.py

@@ -39,7 +39,6 @@ from trl.trainer.utils import RewardDataCollatorWithPadding
 
 from axolotl.core.trainers.base import (
     AxolotlCPOTrainer,
-    AxolotlDPOTrainer,
     AxolotlKTOTrainer,
     AxolotlMambaTrainer,
     AxolotlORPOTrainer,
@@ -48,9 +47,11 @@ from axolotl.core.trainers.base import (
     AxolotlTrainer,
     ReLoRATrainer,
 )
+from axolotl.core.trainers.dpo import DPOStrategy
+from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
+from axolotl.core.trainers.grpo import GRPOStrategy
 from axolotl.core.training_args import (
     AxolotlCPOConfig,
-    AxolotlDPOConfig,
     AxolotlKTOConfig,
     AxolotlORPOConfig,
     AxolotlPRMConfig,
@@ -652,7 +653,7 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
         trainer_kwargs = {}
 
         if self.cfg.reward_model:
-            trainer_kwargs["max_length"] = self.cfg.sequence_len
+            training_arguments_kwargs["max_length"] = self.cfg.sequence_len
 
         # pylint: disable=duplicate-code
         if self.cfg.optimizer in [
@@ -965,10 +966,11 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             # default to saving each epoch if not defined
             training_args_kwargs["save_strategy"] = "epoch"
 
-        training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
+        if self.cfg.dataset_processes:
+            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
 
-        if self.cfg.rl_beta:
-            training_args_kwargs["beta"] = self.cfg.rl_beta
+        if (self.cfg.trl and self.cfg.trl.beta) or self.cfg.rl_beta:
+            training_args_kwargs["beta"] = self.cfg.trl.beta or self.cfg.rl_beta
         if self.cfg.orpo_alpha:
             # trl does some odd mapping of alpha to beta to reuse the beta parameter ???
             training_args_kwargs["beta"] = self.cfg.orpo_alpha
@@ -977,6 +979,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
         training_args_cls = None
+        blocklist_args_kwargs = []
         if self.cfg.rl == "simpo":
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
@@ -1001,11 +1004,15 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
                 self.cfg.kto_undesirable_weight or 1.0
             )
 
-            training_args_kwargs["dataset_num_proc"] = self.cfg.dataset_processes
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
+        elif self.cfg.rl == "grpo":
+            training_args_cls = GRPOStrategy.get_training_args_class()
+            training_args_kwargs.update(GRPOStrategy.set_training_args_kwargs(self.cfg))
+            blocklist_args_kwargs = GRPOStrategy.get_blocklist_args_kwargs()
+
         else:
             training_args_cls = AxolotlDPOConfig
             if self.cfg.rl == "ipo":
@@ -1016,11 +1023,20 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["generate_during_eval"] = self.cfg.use_wandb
             if self.cfg.dpo_use_weighting is not None:
                 training_args_kwargs["use_weighting"] = self.cfg.dpo_use_weighting
+            if self.cfg.dpo_use_logits_to_keep is not None:
+                training_args_kwargs[
+                    "use_logits_to_keep"
+                ] = self.cfg.dpo_use_logits_to_keep
 
+        for blocklist_key in blocklist_args_kwargs:
+            if blocklist_key in training_args_kwargs:
+                del training_args_kwargs[blocklist_key]
+
+        max_steps = self.cfg.max_steps or total_num_steps or -1
         training_args = training_args_cls(  # pylint: disable=unexpected-keyword-arg
-            output_dir=self.cfg.output_dir,
+            self.cfg.output_dir,
             per_device_train_batch_size=self.cfg.micro_batch_size,
-            max_steps=self.cfg.max_steps or total_num_steps,
+            max_steps=max_steps,
             gradient_accumulation_steps=self.cfg.gradient_accumulation_steps,
             learning_rate=self.cfg.learning_rate,
             warmup_steps=self.cfg.warmup_steps,
@@ -1047,8 +1063,12 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             dpo_trainer_kwargs[
                 "precompute_ref_log_probs"
             ] = self.cfg.precompute_ref_log_probs
-        if self.cfg.rl in ["dpo", "ipo"]:
-            trainer_cls = AxolotlDPOTrainer
+        if self.cfg.rl == "grpo":
+            trainer_cls = GRPOStrategy.get_trainer_class()
+            trainer_cls_args = [self.model]
+            dpo_trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
+        elif self.cfg.rl in ["dpo", "ipo"]:
+            trainer_cls = DPOStrategy.get_trainer_class()
             trainer_cls_args = [self.model, self.model_ref]
         elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
@@ -1068,7 +1088,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         else:
             dpo_trainer_kwargs["tokenizer"] = self.tokenizer
 
-        if self.cfg.datasets is not None and (trainer_cls is AxolotlDPOTrainer):
+        if self.cfg.datasets is not None and (
+            trainer_cls is DPOStrategy.get_trainer_class()
+        ):
             dpo_trainer_kwargs["dataset_tags"] = [
                 d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
             ]

src/axolotl/core/trainers/base.py

@@ -5,30 +5,21 @@ module for customized trainers
 from __future__ import annotations
 
 # pylint: disable=too-many-lines
-import gc
 import logging
 import os
 from collections import defaultdict
 from functools import wraps
-from typing import Any, Dict, Literal, Optional, Union
+from typing import Dict, Literal, Optional
 
 import torch
 from datasets import Dataset
 from peft.optimizers import create_loraplus_optimizer
-from torch import nn
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
 from transformers import Trainer
 from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR, seed_worker
 from transformers.utils import is_sagemaker_mp_enabled
-from trl import (
-    CPOTrainer,
-    DPOTrainer,
-    KTOTrainer,
-    ORPOTrainer,
-    PRMTrainer,
-    RewardTrainer,
-)
+from trl import CPOTrainer, KTOTrainer, ORPOTrainer, PRMTrainer, RewardTrainer
 from trl.trainer.utils import pad_to_length
 
 from axolotl.monkeypatch.relora import ReLoRAScheduler
@@ -847,107 +838,6 @@ class ReLoRATrainer(AxolotlTrainer):
         return self.lr_scheduler
 
 
-class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
-    """
-    Extend the base DPOTrainer for axolotl helpers
-    """
-
-    tag_names = ["axolotl", "dpo"]
-
-    def __init__(self, *args, dataset_tags=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.dataset_tags = dataset_tags
-        self.optimizer = None
-        self.model_accepts_loss_kwargs = False
-
-    def create_optimizer(self):
-        if self.args.loraplus_lr_ratio is None:
-            return super().create_optimizer()
-
-        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
-        if self.optimizer is None:  # pylint: disable=access-member-before-definition
-            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
-                self.args,
-                opt_model,
-            )
-
-            loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
-            if loraplus_lr_ratio:
-                print("Using lora+")
-            loraplus_lr_embedding = getattr(self.args, "loraplus_lr_embedding", None)
-            self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
-                opt_model,
-                optimizer_cls,
-                loraplus_lr_ratio=loraplus_lr_ratio,
-                loraplus_lr_embedding=loraplus_lr_embedding,
-                **optimizer_kwargs,
-            )
-
-        if is_sagemaker_mp_enabled():
-            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
-                self.optimizer
-            )
-
-        return self.optimizer
-
-    @wraps(DPOTrainer.push_to_hub)
-    def push_to_hub(self, *args, **kwargs) -> str:
-        """
-        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
-        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
-        """
-        kwargs = _sanitize_kwargs_for_ds_tagging(
-            dataset_tags=self.dataset_tags, kwargs=kwargs
-        )
-        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
-
-        return super().push_to_hub(*args, **kwargs)
-
-    @staticmethod
-    def tokenize_row(
-        features,
-        processing_class,
-        max_prompt_length,
-        max_completion_length,
-        add_special_tokens,
-    ) -> Dict:
-        res = DPOTrainer.tokenize_row(
-            features,
-            processing_class,
-            max_prompt_length,
-            max_completion_length,
-            add_special_tokens,
-        )
-        # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
-        if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
-            for key in res.keys():
-                res[key] = res[key][1:]
-
-        if processing_class.bos_token and processing_class.bos_token_id is not None:
-            # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
-            if res["chosen_input_ids"][0] == processing_class.bos_token_id:
-                res["chosen_input_ids"] = res["chosen_input_ids"][1:]
-                res["chosen_labels"] = res["chosen_labels"][1:]
-                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
-            if res["rejected_input_ids"][0] == processing_class.bos_token_id:
-                res["rejected_input_ids"] = res["rejected_input_ids"][1:]
-                res["rejected_labels"] = res["rejected_labels"][1:]
-                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
-
-        return res
-
-    def training_step(
-        self,
-        model: nn.Module,
-        inputs: Dict[str, Union[torch.Tensor, Any]],
-        num_items_in_batch=None,
-    ) -> torch.Tensor:
-        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
-        gc.collect()
-        torch.cuda.empty_cache()
-        return loss
-
-
 class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
     """
     Extend the base ORPOTrainer for axolotl helpers

src/axolotl/core/trainers/dpo/__init__.py

@@ -0,0 +1,33 @@
+"""
+DPO Specific Strategy for training
+"""
+from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
+
+
+class DPOStrategy:
+    """
+    Strategy for DPO training
+    """
+
+    @classmethod
+    def get_trainer_class(cls):
+        return AxolotlDPOTrainer
+
+    @classmethod
+    def get_training_args_class(cls):
+        from axolotl.core.trainers.dpo.args import AxolotlDPOConfig
+
+        return AxolotlDPOConfig
+
+    @classmethod
+    def set_training_args_kwargs(cls, cfg):
+        training_args_kwargs = {}
+        if cfg.rl == "ipo":
+            training_args_kwargs["loss_type"] = "ipo"
+        training_args_kwargs["max_length"] = cfg.sequence_len
+        training_args_kwargs["max_completion_length"] = None
+        training_args_kwargs["max_prompt_length"] = cfg.sequence_len
+        training_args_kwargs["generate_during_eval"] = cfg.use_wandb
+        if cfg.dpo_use_weighting is not None:
+            training_args_kwargs["use_weighting"] = cfg.dpo_use_weighting
+        return training_args_kwargs

src/axolotl/core/trainers/dpo/args.py

@@ -0,0 +1,15 @@
+"""
+Axolotl specific DPO args
+"""
+from dataclasses import dataclass
+
+from trl import DPOConfig
+
+from axolotl.core.training_args import AxolotlTrainingMixins
+
+
+@dataclass
+class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
+    """
+    DPO config for DPO training
+    """

src/axolotl/core/trainers/dpo/trainer.py

@@ -0,0 +1,125 @@
+"""
+DPO trainer for axolotl
+"""
+import gc
+from functools import wraps
+from typing import Any, Dict, Union
+
+import torch
+from peft.optimizers import create_loraplus_optimizer
+from torch import nn
+from transformers import Trainer
+from transformers.utils import is_sagemaker_mp_enabled
+from trl import DPOTrainer
+
+from axolotl.core.trainers.base import (
+    SchedulerMixin,
+    _sanitize_kwargs_for_ds_tagging,
+    _sanitize_kwargs_for_tagging,
+)
+
+if is_sagemaker_mp_enabled():
+    import smdistributed.modelparallel.torch as smp
+
+
+class AxolotlDPOTrainer(SchedulerMixin, DPOTrainer):
+    """
+    Extend the base DPOTrainer for axolotl helpers
+    """
+
+    tag_names = ["axolotl", "dpo"]
+
+    def __init__(self, *args, dataset_tags=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.dataset_tags = dataset_tags
+        self.optimizer = None
+        self.model_accepts_loss_kwargs = False
+
+    def create_optimizer(self):
+        # pylint: disable=duplicate-code
+        if self.args.loraplus_lr_ratio is None:
+            return super().create_optimizer()
+
+        opt_model = self.model_wrapped if is_sagemaker_mp_enabled() else self.model
+        if self.optimizer is None:  # pylint: disable=access-member-before-definition
+            optimizer_cls, optimizer_kwargs = Trainer.get_optimizer_cls_and_kwargs(
+                self.args,
+                opt_model,
+            )
+
+            loraplus_lr_ratio = getattr(self.args, "loraplus_lr_ratio", None)
+            if loraplus_lr_ratio:
+                print("Using lora+")
+            loraplus_lr_embedding = getattr(self.args, "loraplus_lr_embedding", None)
+            # pylint: disable=duplicate-code
+            self.optimizer = create_loraplus_optimizer(  # pylint: disable=attribute-defined-outside-init
+                opt_model,
+                optimizer_cls,
+                loraplus_lr_ratio=loraplus_lr_ratio,
+                loraplus_lr_embedding=loraplus_lr_embedding,
+                **optimizer_kwargs,
+            )
+
+        if is_sagemaker_mp_enabled():
+            self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
+                self.optimizer
+            )
+
+        return self.optimizer
+
+    @wraps(DPOTrainer.push_to_hub)
+    def push_to_hub(self, *args, **kwargs) -> str:
+        """
+        Overwrite the `push_to_hub` method in order to force-add the tags when pushing the
+        model on the Hub. Please refer to `~transformers.Trainer.push_to_hub` for more details.
+        """
+        kwargs = _sanitize_kwargs_for_ds_tagging(
+            dataset_tags=self.dataset_tags, kwargs=kwargs
+        )
+        kwargs = _sanitize_kwargs_for_tagging(tag_names=self.tag_names, kwargs=kwargs)
+
+        return super().push_to_hub(*args, **kwargs)
+
+    @staticmethod
+    def tokenize_row(
+        features,
+        processing_class,
+        max_prompt_length,
+        max_completion_length,
+        add_special_tokens,
+    ) -> Dict:
+        res = DPOTrainer.tokenize_row(
+            features,
+            processing_class,
+            max_prompt_length,
+            max_completion_length,
+            add_special_tokens,
+        )
+        # fix when the tokenizer doesn't have a bos_token_id, e.g. Qwen
+        if processing_class.bos_token is None and res["prompt_input_ids"][0] is None:
+            for key in res.keys():
+                res[key] = res[key][1:]
+
+        if processing_class.bos_token and processing_class.bos_token_id is not None:
+            # dpo trainer may incorrectly prepend the bos_token_id to the dpo outputs
+            if res["chosen_input_ids"][0] == processing_class.bos_token_id:
+                res["chosen_input_ids"] = res["chosen_input_ids"][1:]
+                res["chosen_labels"] = res["chosen_labels"][1:]
+                res["chosen_attention_mask"] = res["chosen_attention_mask"][1:]
+            if res["rejected_input_ids"][0] == processing_class.bos_token_id:
+                res["rejected_input_ids"] = res["rejected_input_ids"][1:]
+                res["rejected_labels"] = res["rejected_labels"][1:]
+                res["rejected_attention_mask"] = res["rejected_attention_mask"][1:]
+
+        return res
+
+    def training_step(
+        self,
+        model: nn.Module,
+        inputs: Dict[str, Union[torch.Tensor, Any]],
+        num_items_in_batch=None,
+    ) -> torch.Tensor:
+        loss: torch.Tensor = super().training_step(model, inputs, num_items_in_batch)
+        gc.collect()
+        torch.cuda.empty_cache()
+        return loss

src/axolotl/core/trainers/grpo/__init__.py

@@ -0,0 +1,113 @@
+"""
+GRPO Specific Strategy for training
+"""
+
+import importlib
+import inspect
+import logging
+
+from trl.trainer.grpo_trainer import RewardFunc
+
+from axolotl.core.trainers.grpo.trainer import AxolotlGRPOTrainer
+
+LOG = logging.getLogger("axolotl")
+
+
+class GRPOStrategy:
+    """
+    Strategy for GRPO training
+    """
+
+    @classmethod
+    def get_trainer_class(cls):
+        return AxolotlGRPOTrainer
+
+    @classmethod
+    def get_training_args_class(cls):
+        from axolotl.core.trainers.grpo.args import AxolotlGRPOConfig
+
+        return AxolotlGRPOConfig
+
+    @classmethod
+    def set_training_args_kwargs(cls, cfg):
+        grpo_args_kwargs = {}
+        if cfg.trl and cfg.trl.use_vllm:
+            grpo_args_kwargs["use_vllm"] = cfg.trl.use_vllm
+            if cfg.trl and cfg.trl.vllm_device:
+                grpo_args_kwargs["vllm_device"] = cfg.trl.vllm_device
+            else:
+                grpo_args_kwargs["vllm_device"] = "auto"
+            if cfg.trl and cfg.trl.vllm_gpu_memory_utilization:
+                grpo_args_kwargs[
+                    "vllm_gpu_memory_utilization"
+                ] = cfg.trl.vllm_gpu_memory_utilization
+            if cfg.trl and cfg.trl.vllm_max_model_len:
+                grpo_args_kwargs["vllm_max_model_len"] = cfg.trl.vllm_max_model_len
+        if cfg.trl and cfg.trl.num_generations:
+            grpo_args_kwargs["num_generations"] = cfg.trl.num_generations
+        if cfg.trl and cfg.trl.sync_ref_model:
+            grpo_args_kwargs["sync_ref_model"] = cfg.trl.sync_ref_model
+            if cfg.trl and cfg.trl.ref_model_mixup_alpha:
+                grpo_args_kwargs[
+                    "ref_model_mixup_alpha"
+                ] = cfg.trl.ref_model_mixup_alpha
+            if cfg.trl and cfg.trl.ref_model_sync_steps:
+                grpo_args_kwargs["ref_model_sync_steps"] = cfg.trl.ref_model_sync_steps
+        grpo_args_kwargs["max_completion_length"] = cfg.trl.max_completion_length
+        return grpo_args_kwargs
+
+    @classmethod
+    def set_trainer_kwargs(cls, cfg):
+        trainer_kwargs = {}
+        if cfg.trl and cfg.trl.reward_funcs:
+            reward_funcs = []
+            for reward_func_fqn in cfg.trl.reward_funcs:
+                reward_funcs.append(cls.get_reward_func(reward_func_fqn))
+            trainer_kwargs["reward_funcs"] = reward_funcs
+        if cfg.trl and cfg.trl.reward_processing_classes:
+            trainer_kwargs[
+                "reward_processing_classes"
+            ] = cfg.trl.reward_processing_classes
+        return trainer_kwargs
+
+    @classmethod
+    def get_collator(cls, *args, **kwargs):  # pylint: disable=unused-argument
+        # No data collation is needed in GRPO, handled by trl's trainer __init__
+        return None
+
+    @classmethod
+    def get_blocklist_args_kwargs(cls):
+        return ["dataset_num_proc"]
+
+    @classmethod
+    def get_reward_func(cls, reward_func_fqn: str) -> RewardFunc:
+        """
+        Returns the reward function from the given fully qualified name, or the path to the reward function model.
+
+        Args:
+            reward_func_fqn (str): Fully qualified name of the reward function (e.g. r1_grpo.gsm8k_transform),
+                or a HF hub path to the reward model.
+        Raises:
+            ValueError: If the reward function does not accept at least two arguments.
+
+        Returns:
+            RewardFunc: A callable that accepts prompts and completions and returns rewards,
+                or a path to a reward model.
+
+        """
+        try:
+            # use importlib to dynamically load the reward function from the module
+            reward_func_module_name = reward_func_fqn.split(".")[-1]
+            reward_func_module = importlib.import_module(reward_func_fqn.split(".")[-2])
+            reward_func = getattr(reward_func_module, reward_func_module_name)
+            if not len(inspect.signature(reward_func).parameters) >= 2:
+                raise ValueError(
+                    "Reward function must accept at least two arguments: prompts: list and completions: list"
+                )
+            return reward_func
+        except ModuleNotFoundError:
+            # the user has passed a string (ideally indicating the path of a reward model)
+            LOG.info(
+                f"Reward function {reward_func} is a pre-trained model path - if this is unexpected, please check the reward function path."
+            )
+            return reward_func

src/axolotl/core/trainers/grpo/args.py

@@ -0,0 +1,15 @@
+"""
+Axolotl Specific Training Args
+"""
+from dataclasses import dataclass
+
+from trl import GRPOConfig
+
+from axolotl.core.training_args import AxolotlTrainingMixins
+
+
+@dataclass
+class AxolotlGRPOConfig(AxolotlTrainingMixins, GRPOConfig):
+    """
+    Axolotl GRPO Config for GRPO training
+    """

src/axolotl/core/trainers/grpo/trainer.py

@@ -0,0 +1,14 @@
+"""
+Axolotl GRPO trainer
+"""
+from trl import GRPOTrainer
+
+from axolotl.core.trainers.base import SchedulerMixin
+
+
+class AxolotlGRPOTrainer(SchedulerMixin, GRPOTrainer):
+    """
+    Extend the base GRPOTrainer for axolotl helpers
+    """
+
+    _tag_names = ["trl", "grpo", "axolotl"]

src/axolotl/core/training_args.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass, field
 from typing import Optional
 
 from transformers import TrainingArguments
-from trl import CPOConfig, DPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
+from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
 
 @dataclass
@@ -217,13 +217,6 @@ class AxolotlTrainingArguments(AxolotlTrainingMixins, TrainingArguments):
     """
 
 
-@dataclass
-class AxolotlDPOConfig(AxolotlTrainingMixins, DPOConfig):
-    """
-    DPO config for DPO training
-    """
-
-
 @dataclass
 class AxolotlORPOConfig(AxolotlTrainingMixins, ORPOConfig):
     """

src/axolotl/prompt_strategies/base.py

@@ -13,8 +13,19 @@ def load(strategy, cfg, module_base=None, **kwargs):
         if len(strategy.split(".")) == 1:
             strategy = strategy + ".default"
         load_fn = strategy.split(".")[-1]
-        strategy = ".".join(strategy.split(".")[:-1])
-        mod = importlib.import_module(f".{strategy}", module_base)
+        if len(strategy.split(".")) > 1:
+            try:
+                importlib.import_module(
+                    strategy.split(".")[-2],
+                    ".".join(strategy.split(".")[:-2]),
+                )
+                module_base = ".".join(strategy.split(".")[:-2])
+                strategy = strategy.split(".")[-2]
+            except ModuleNotFoundError:
+                strategy = "." + ".".join(strategy.split(".")[:-1])
+        else:
+            strategy = "." + ".".join(strategy.split(".")[:-1])
+        mod = importlib.import_module(strategy, module_base)
         func = getattr(mod, load_fn)
         return func(cfg, **kwargs)
     except Exception:  # pylint: disable=broad-exception-caught

src/axolotl/prompt_strategies/dpo/passthrough.py

@@ -0,0 +1,14 @@
+"""
+DPO prompt strategies passthrough/zero-processing strategy
+"""
+
+
+def default(
+    cfg, dataset_idx=0, **kwargs
+):  # pylint: disable=possibly-unused-variable,unused-argument
+    def transform_fn(
+        sample, tokenizer=None
+    ):  # pylint: disable=possibly-unused-variable,unused-argument
+        return sample
+
+    return transform_fn

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

@@ -24,6 +24,8 @@ from transformers.utils.import_utils import is_torch_npu_available
 
 from axolotl.utils.config.models.internals import EnvCapabilities, GPUCapabilities
 
+from .trl import TrlConfig
+
 LOG = logging.getLogger("axolotl.utils.config.models.input")
 
 SUPPORTED_METRICS = {"sacrebleu", "comet", "ter", "chrf", "perplexity"}
@@ -33,6 +35,7 @@ class RLType(str, Enum):
     """RL trainer type configuration subset"""
 
     dpo = "dpo"  # pylint: disable=invalid-name
+    grpo = "grpo"  # pylint: disable=invalid-name
     ipo = "ipo"  # pylint: disable=invalid-name
     orpo = "orpo"  # pylint: disable=invalid-name
     kto = "kto"  # pylint: disable=invalid-name
@@ -663,14 +666,20 @@ class AxolotlInputConfig(
     auto_resume_from_checkpoints: Optional[bool] = None
     resize_token_embeddings_to_32x: Optional[bool] = None
     mean_resizing_embeddings: Optional[bool] = False
+    # optionally shrink the embeddings when the tokenizer vocab size is smaller
+    shrink_embeddings: Optional[bool] = None
 
     rl: Optional[RLType] = None
+    trl: Optional[TrlConfig] = Field(
+        default_factory=lambda: TrlConfig(),  # pylint: disable=unnecessary-lambda
+    )
     reward_model: Optional[bool] = None
     process_reward_model: Optional[bool] = None
     num_labels: Optional[int] = None
     dpo_use_weighting: Optional[
         bool
     ] = None  # whether to use weighting in DPO trainer. If none, default is false in the trainer.
+    dpo_use_logits_to_keep: Optional[bool] = None
 
     datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset, StepwiseSupervisedDataset], min_length=1)] = None  # type: ignore
     test_datasets: Optional[conlist(Union[SFTDataset, DPODataset, KTODataset, StepwiseSupervisedDataset], min_length=1)] = None  # type: ignore

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

@@ -0,0 +1,32 @@
+"""
+GRPO specific configuration args
+"""
+from typing import List, Optional
+
+from pydantic import BaseModel, Field
+
+
+class TrlConfig(BaseModel):
+    """
+    Input args for TRL.
+    """
+
+    beta: Optional[float] = None
+    max_completion_length: Optional[int] = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Maximum length of the completion for RL training"
+        },
+    )
+
+    # GRPO specific args
+    use_vllm: Optional[bool] = False
+    vllm_device: Optional[str] = "auto"
+    vllm_gpu_memory_utilization: Optional[float] = 0.9
+    vllm_max_model_len: Optional[int] = None
+    vllm_dtype: Optional[str] = "auto"
+    reward_funcs: Optional[List[str]] = None
+    num_generations: Optional[int] = None
+    sync_ref_model: Optional[bool] = False
+    ref_model_mixup_alpha: Optional[float] = 0.9
+    ref_model_sync_steps: Optional[int] = 64

src/axolotl/utils/data/rl.py

@@ -57,7 +57,7 @@ def _save_preprocessed_ds(cfg, sub_cfg, dataset):
         dataset.save_to_disk(str(prepared_ds_path))
 
 
-def map_dataset(cfg, data_set, ds_transform_fn, tokenizer):
+def map_dataset(cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs):
     sig = inspect.signature(ds_transform_fn)
     if "tokenizer" in sig.parameters:
         if not tokenizer:
@@ -70,6 +70,7 @@ def map_dataset(cfg, data_set, ds_transform_fn, tokenizer):
     data_set = data_set.map(
         ds_transform_fn,
         desc="Mapping RL Dataset",
+        **map_kwargs,
     )
 
     return data_set
@@ -150,36 +151,45 @@ def load_prepare_preference_datasets(cfg):
                 else:
                     ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
 
+                map_kwargs = {}
+                if isinstance(ds_transform_fn, tuple):
+                    ds_transform_fn, map_kwargs = ds_transform_fn
                 split_datasets[i] = map_dataset(
-                    cfg, data_set, ds_transform_fn, tokenizer
+                    cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs
                 )
             elif _cfg.rl == "kto":
                 ds_transform_fn = load_kto(_type, _cfg, dataset_idx=i)
+                map_kwargs = {}
+                if isinstance(ds_transform_fn, tuple):
+                    ds_transform_fn, map_kwargs = ds_transform_fn
                 split_datasets[i] = map_dataset(
-                    cfg, data_set, ds_transform_fn, tokenizer
+                    cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs
                 )
             else:
                 # If no `type` is provided, assume the dataset is already in the expected format with
                 # "prompt", "chosen" and "rejected" already preprocessed
                 split_datasets[i] = data_set
 
-            drop_long = partial(
-                drop_long_rl_seq,
-                rl=_cfg.rl,
-                tokenizer=tokenizer,
-                sequence_len=cfg.sequence_len,
-            )
+            if not cfg.skip_prepare_dataset:
+                drop_long = partial(
+                    drop_long_rl_seq,
+                    rl=_cfg.rl,
+                    tokenizer=tokenizer,
+                    sequence_len=cfg.sequence_len,
+                )
 
-            prior_len = len(split_datasets[i])
-            split_datasets[i] = split_datasets[i].filter(
-                drop_long,
-                num_proc=cfg.dataset_processes,
-                load_from_cache_file=not cfg.is_preprocess,
-                desc="Dropping Long Sequences",
-            )
-            dropped = prior_len - len(split_datasets[i])
-            if dropped:
-                LOG.warning(f"Dropped {dropped} long samples from dataset index {i}")
+                prior_len = len(split_datasets[i])
+                split_datasets[i] = split_datasets[i].filter(
+                    drop_long,
+                    num_proc=cfg.dataset_processes,
+                    load_from_cache_file=not cfg.is_preprocess,
+                    desc="Dropping Long Sequences",
+                )
+                dropped = prior_len - len(split_datasets[i])
+                if dropped:
+                    LOG.warning(
+                        f"Dropped {dropped} long samples from dataset index {i}"
+                    )
 
         combined_datasets = concatenate_datasets(split_datasets)
         combined_datasets = combined_datasets.shuffle(seed=cfg.seed)

src/axolotl/utils/models.py

@@ -1053,9 +1053,12 @@ class ModelLoader:
             if self.cfg.resize_token_embeddings_to_32x
             else len(self.tokenizer)
         )
-        if (
-            hasattr(self.model, "get_input_embeddings")
-            and self.model.get_input_embeddings().num_embeddings != embeddings_len
+        if hasattr(self.model, "get_input_embeddings") and (
+            self.model.get_input_embeddings().num_embeddings < embeddings_len
+            or (
+                self.model.get_input_embeddings().num_embeddings > embeddings_len
+                and self.cfg.shrink_embeddings
+            )
         ):
             resize_kwargs = {}
             if self.cfg.mean_resizing_embeddings is not None:

src/axolotl/utils/trainer.py

@@ -576,7 +576,7 @@ def prepare_opinionated_env(cfg):
 def setup_trainer(
     cfg, train_dataset, eval_dataset, model, tokenizer, processor, total_num_steps
 ):
-    if cfg.rl in ("dpo", "ipo", "orpo", "kto", "simpo"):
+    if cfg.rl in ("dpo", "grpo", "ipo", "orpo", "kto", "simpo"):
         trainer_builder = HFRLTrainerBuilder(cfg, model[0], tokenizer, processor)
         trainer_builder.model_ref = model[1]
         trainer_builder.peft_config = model[2]