validate config to set defaults

Signed-off-by: Rahul Tuli <rtuli@redhat.com>

codecov.yml

@@ -1,7 +1,5 @@
 codecov:
   require_ci_to_pass: yes
-  notify:
-    wait_for_ci: true
 
 coverage:
   precision: 2

docs/custom_integrations.qmd

@@ -49,7 +49,8 @@ sections = [
     ("Knowledge Distillation (KD)", "kd"),
     ("Liger Kernels", "liger"),
     ("Language Model Evaluation Harness (LM Eval)", "lm_eval"),
-    ("Spectrum", "spectrum")
+    ("Spectrum", "spectrum"),
+    ("LLMCompressor", "llm_compressor")
 ]
 
 for section_name, folder_name in sections:

examples/llama-3/sparse-finetuning.yaml

@@ -0,0 +1,77 @@
+base_model: neuralmagic/Sparse-Llama-3.1-8B-2of4
+
+plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: tatsu-lab/alpaca
+    type: alpaca
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./outputs/out
+
+sequence_len: 4096
+sample_packing: true
+pad_to_sequence_len: true
+eval_sample_packing: false
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 8
+micro_batch_size: 1
+num_epochs: 1
+optimizer: paged_adamw_8bit
+lr_scheduler: cosine
+learning_rate: 2e-5
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 1
+debug:
+deepspeed:
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: <|end_of_text|>
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true

setup.py

@@ -149,6 +149,9 @@ extras_require = {
     "vllm": [
         "vllm==0.7.2",
     ],
+    "llmcompressor": [
+        "llmcompressor==0.5.1",
+    ],
 }
 
 install_requires, dependency_links, extras_require_build = parse_requirements(

src/axolotl/common/datasets.py

@@ -14,7 +14,6 @@ from axolotl.utils.data import prepare_dataset
 from axolotl.utils.data.rl import load_prepare_preference_datasets
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.models import load_processor, load_tokenizer
-from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.tokenization import check_dataset_labels
 
 LOG = logging.getLogger(__name__)
@@ -126,7 +125,7 @@ def load_preference_datasets(
     total_num_steps: Optional[int] = int(
         math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
     )
-    if cfg.rl is RLType.GRPO:
+    if cfg.rl == "grpo":
         total_num_steps = None
 
     if cli_args.debug or cfg.debug:

src/axolotl/core/trainer_builder.py

@@ -84,7 +84,7 @@ from axolotl.utils.collators import (
 )
 from axolotl.utils.collators.mm_chat import MultiModalChatDataCollator
 from axolotl.utils.models import ensure_dtype
-from axolotl.utils.schemas.enums import CustomSupportedOptimizers, RLType
+from axolotl.utils.schemas.enums import CustomSupportedOptimizers
 
 try:
     import torch._dynamo  # pylint: disable=ungrouped-imports
@@ -538,6 +538,8 @@ 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:
@@ -930,6 +932,9 @@ class HFCausalTrainerBuilder(TrainerBuilderBase):
                 collator = DataCollatorForSeq2Seq
 
         kwargs["return_tensors"] = "pt"
+        if issubclass(collator, DataCollatorForSeq2Seq):
+            kwargs["sequence_parallel_degree"] = training_args.sequence_parallel_degree
+            kwargs["ring_attn_func"] = training_args.ring_attn_func
 
         return collator(
             *collator_args,
@@ -1009,8 +1014,6 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             training_args_kwargs["dataloader_prefetch_factor"] = (
                 self.cfg.dataloader_prefetch_factor
             )
-        if self.cfg.seed:
-            training_args_kwargs["seed"] = self.cfg.seed
         if self.cfg.gradient_checkpointing:
             training_args_kwargs["gradient_checkpointing"] = (
                 self.cfg.gradient_checkpointing
@@ -1048,13 +1051,9 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
         if self.cfg.rpo_alpha is not None:
             training_args_kwargs["rpo_alpha"] = self.cfg.rpo_alpha
 
-        training_args_kwargs["sequence_parallel_degree"] = (
-            self.cfg.sequence_parallel_degree
-        )
-
         training_args_cls = None
         blocklist_args_kwargs = []
-        if self.cfg.rl is RLType.SIMPO:
+        if self.cfg.rl == "simpo":
             training_args_cls = AxolotlCPOConfig
             training_args_kwargs["loss_type"] = "simpo"
             training_args_kwargs["max_length"] = self.cfg.sequence_len
@@ -1062,13 +1061,13 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.cpo_alpha is not None:
                 training_args_kwargs["cpo_alpha"] = self.cfg.cpo_alpha
 
-        elif self.cfg.rl is RLType.ORPO:
+        elif self.cfg.rl == "orpo":
             training_args_cls = AxolotlORPOConfig
             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 is RLType.KTO:
+        elif self.cfg.rl == "kto":
             training_args_cls = AxolotlKTOConfig
 
             training_args_kwargs["desirable_weight"] = (
@@ -1082,14 +1081,14 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
             if self.cfg.max_prompt_len:
                 training_args_kwargs["max_prompt_length"] = self.cfg.max_prompt_len
 
-        elif self.cfg.rl is RLType.GRPO:
+        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 is RLType.IPO:
+            if self.cfg.rl == "ipo":
                 training_args_kwargs["loss_type"] = "ipo"
             training_args_kwargs["max_length"] = self.cfg.sequence_len
             training_args_kwargs["max_completion_length"] = None
@@ -1126,33 +1125,33 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
     def build(self, total_num_steps):
         training_args = self.build_training_arguments(total_num_steps)
-        trainer_kwargs = {}
-        if self.cfg.rl is RLType.IPO:
+        dpo_trainer_kwargs = {}
+        if self.cfg.rl == "ipo":
             if self.cfg.dpo_label_smoothing:
-                trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
+                dpo_trainer_kwargs["label_smoothing"] = self.cfg.dpo_label_smoothing
         if self.eval_dataset:
-            trainer_kwargs["eval_dataset"] = self.eval_dataset
+            dpo_trainer_kwargs["eval_dataset"] = self.eval_dataset
         if self.cfg.adapter and self.peft_config:
-            trainer_kwargs["peft_config"] = self.peft_config
+            dpo_trainer_kwargs["peft_config"] = self.peft_config
         if self.cfg.precompute_ref_log_probs is not None:
-            trainer_kwargs["precompute_ref_log_probs"] = (
+            dpo_trainer_kwargs["precompute_ref_log_probs"] = (
                 self.cfg.precompute_ref_log_probs
             )
-        if self.cfg.rl is RLType.GRPO:
+        if self.cfg.rl == "grpo":
             trainer_cls = GRPOStrategy.get_trainer_class()
             trainer_cls_args = [self.model]
             trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
-            trainer_kwargs.update(GRPOStrategy.set_trainer_kwargs(self.cfg))
-        elif self.cfg.rl in [RLType.DPO, RLType.IPO]:
+            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 is RLType.ORPO:
+        elif self.cfg.rl == "orpo":
             trainer_cls = AxolotlORPOTrainer
             trainer_cls_args = [self.model]
-        elif self.cfg.rl is RLType.KTO:
+        elif self.cfg.rl in ["kto"]:
             trainer_cls = AxolotlKTOTrainer
             trainer_cls_args = [self.model]
-        elif self.cfg.rl is RLType.SIMPO:
+        elif self.cfg.rl in ["simpo"]:
             trainer_cls = AxolotlCPOTrainer
             trainer_cls_args = [self.model]
         else:
@@ -1160,33 +1159,33 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
         sig = inspect.signature(trainer_cls)
         if "tokenizer" in sig.parameters.keys():
-            trainer_kwargs["tokenizer"] = self.tokenizer
+            dpo_trainer_kwargs["tokenizer"] = self.tokenizer
         else:
-            trainer_kwargs["processing_class"] = self.tokenizer
+            dpo_trainer_kwargs["processing_class"] = self.tokenizer
 
         if self.cfg.datasets is not None and (
             trainer_cls is DPOStrategy.get_trainer_class()
         ):
-            trainer_kwargs["dataset_tags"] = [
+            dpo_trainer_kwargs["dataset_tags"] = [
                 d["path"] for d in self.cfg.datasets if not Path(d["path"]).is_dir()
             ]
-        trainer = trainer_cls(
+        dpo_trainer = trainer_cls(
             *trainer_cls_args,
             args=training_args,
             train_dataset=self.train_dataset,
             callbacks=self.get_callbacks(),
-            **trainer_kwargs,
+            **dpo_trainer_kwargs,
         )
         if self.cfg.fsdp:
-            ensure_dtype(trainer.model, dtype=self.cfg.torch_dtype)
-            if self.cfg.rl in [RLType.DPO, RLType.IPO] and trainer.ref_model:
-                ensure_dtype(trainer.ref_model, dtype=self.cfg.torch_dtype)
+            ensure_dtype(dpo_trainer.model, dtype=self.cfg.torch_dtype)
+            if self.cfg.rl in ["dpo", "ipo"] and dpo_trainer.ref_model:
+                ensure_dtype(dpo_trainer.ref_model, dtype=self.cfg.torch_dtype)
 
-        trainer = self.hook_post_create_trainer(trainer)
-        for callback in self.get_post_trainer_create_callbacks(trainer):
-            trainer.add_callback(callback)
+        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)
 
-        return trainer
+        return dpo_trainer
 
 
 class HFPPOTrainerBuilder(TrainerBuilderBase):

src/axolotl/core/trainers/base.py

@@ -371,15 +371,13 @@ class AxolotlTrainer(
                 num_items_in_batch=num_items_in_batch,
             )
 
-        loss = super().compute_loss(
+        return super().compute_loss(
             model,
             inputs,
             return_outputs=return_outputs,
             num_items_in_batch=num_items_in_batch,
         )
 
-        return loss
-
     @staticmethod
     def orpo_concatenate_inputs(inputs, label_pad_token=-100, pad_token=0, device=None):
         concatenated_batch = {}

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

@@ -3,7 +3,6 @@ DPO Specific Strategy for training
 """
 
 from axolotl.core.trainers.dpo.trainer import AxolotlDPOTrainer
-from axolotl.utils.schemas.enums import RLType
 
 
 class DPOStrategy:
@@ -24,7 +23,7 @@ class DPOStrategy:
     @classmethod
     def set_training_args_kwargs(cls, cfg):
         training_args_kwargs = {}
-        if cfg.rl is RLType.IPO:
+        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

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

@@ -11,4 +11,6 @@ from axolotl.core.training_args import AxolotlTrainingMixins
 
 @dataclass
 class AxolotlGRPOConfig(AxolotlTrainingMixins, GRPOConfig):
-    """Axolotl GRPO Config for GRPO training"""
+    """
+    Axolotl GRPO Config for GRPO training
+    """

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

@@ -1,124 +0,0 @@
-"""
-Repeat random sampler (akin to the one implemented in
-https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py) that adds
-sequence parallelism functionality; i.e., duplicating data across ranks in the same
-sequencee parallel group.
-"""
-
-from typing import Sized
-
-import torch
-from torch.utils.data import Sampler
-
-
-class SequenceParallelRepeatRandomSampler(Sampler):
-    """
-    Sampler for GRPO training with sequence parallelism that ensures:
-    1. Ranks in the same sequence parallel group receive identical data
-    2. Each index is repeated multiple times for sampling different completions
-    3. Entire batches are repeated for reuse in multiple updates
-    """
-
-    def __init__(
-        self,
-        dataset: Sized,
-        mini_repeat_count: int,
-        world_size: int,
-        rank: int,
-        batch_size: int = 1,
-        repeat_count: int = 1,
-        sequence_parallel_degree: int = 1,
-        shuffle: bool = True,
-        seed: int = 0,
-        drop_last: bool = False,
-    ):
-        self.dataset = dataset
-        self.mini_repeat_count = mini_repeat_count
-        self.batch_size = batch_size
-        self.repeat_count = repeat_count
-        self.shuffle = shuffle
-        self.seed = seed
-        self.drop_last = drop_last
-        self.epoch = 0
-
-        self.world_size = world_size
-        self.rank = rank
-
-        # Sequence parallelism parameters
-        self.sequence_parallel_degree = sequence_parallel_degree
-        self.num_sp_groups = world_size // sequence_parallel_degree
-        self.sp_group_id = rank // sequence_parallel_degree
-
-        # Adjust dataset size for distributed sampling
-        self.num_samples = len(self.dataset)
-        self.total_size = self.num_samples
-
-        # Calculate effective number of samples per SP group
-        if (
-            self.drop_last
-            and self.total_size % (self.num_sp_groups * self.batch_size) != 0
-        ):
-            # Drop last incomplete batch if drop_last is True
-            self.num_samples_per_sp_group = (
-                self.total_size // self.batch_size // self.num_sp_groups
-            ) * self.batch_size
-        else:
-            # Round up to include last batch if drop_last is False
-            self.num_samples_per_sp_group = (
-                (self.total_size + self.batch_size * self.num_sp_groups - 1)
-                // (self.batch_size * self.num_sp_groups)
-                * self.batch_size
-            )
-
-    def __iter__(self):
-        # Deterministically shuffle based on epoch and seed
-        if self.shuffle:
-            # Use same seed for all ranks in the same SP group
-            g = torch.Generator()
-            seed_value = self.seed + self.epoch + self.sp_group_id * 10000
-            g.manual_seed(seed_value)
-            indices = torch.randperm(len(self.dataset), generator=g).tolist()
-        else:
-            indices = list(range(len(self.dataset)))
-
-        # Add extra samples to make it evenly divisible by batch_size
-        if len(indices) % self.batch_size != 0:
-            padding = indices[: self.batch_size - len(indices) % self.batch_size]
-            indices += padding
-
-        # Subsample based on SP group ID
-        # Each SP group gets distinct batches of data
-        batch_indices = []
-        for i in range(0, len(indices), self.batch_size * self.num_sp_groups):
-            start_idx = i + self.sp_group_id * self.batch_size
-            end_idx = min(start_idx + self.batch_size, len(indices))
-            if start_idx < len(indices):
-                for j in range(self.batch_size):
-                    if start_idx + j < end_idx:
-                        batch_indices.append(indices[start_idx + j])
-
-        # Make sure batch_indices is exactly batch_size * num_batches_per_sp_group
-        if self.drop_last:
-            num_batches_per_sp_group = self.num_samples_per_sp_group // self.batch_size
-            target_len = self.batch_size * num_batches_per_sp_group
-            if len(batch_indices) > target_len:
-                batch_indices = batch_indices[:target_len]
-
-        # Apply the GRPO repeat pattern
-        final_indices = []
-        for _ in range(self.repeat_count):
-            for idx in batch_indices:
-                for _ in range(self.mini_repeat_count):
-                    final_indices.append(idx)
-
-        return iter(final_indices)
-
-    def __len__(self):
-        # Total length including all repetitions
-        return (
-            self.num_samples_per_sp_group * self.mini_repeat_count * self.repeat_count
-        )
-
-    def set_epoch(self, epoch):
-        """Sets the epoch for this sampler"""
-        self.epoch = epoch

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

@@ -1,279 +1,26 @@
-"""Axolotl GRPO trainer"""
+"""
+Axolotl GRPO trainer
+"""
 
-# pylint: disable=too-many-lines,duplicate-code
-
-import warnings
 from contextlib import nullcontext
-from typing import Any
 
-import datasets
-import torch
-import torch.distributed as dist
-from accelerate.utils import (
-    broadcast_object_list,
-    gather,
-    gather_object,
-    is_peft_model,
-)
-from datasets import Dataset, IterableDataset
-from torch import nn
-from torch.utils.data import (
-    BatchSampler,
-    DataLoader,
-    Sampler,
-)
-from transformers import (
-    PreTrainedModel,
-    PreTrainedTokenizerBase,
-    Trainer,
-    TrainerCallback,
-    is_wandb_available,
-)
-from transformers.trainer_utils import seed_worker
-from transformers.utils import is_peft_available
+from accelerate.utils import is_deepspeed_available, is_peft_model
 from trl import GRPOTrainer
-from trl.data_utils import (
-    apply_chat_template,
-    is_conversational,
-    maybe_apply_chat_template,
-)
-from trl.extras.profiling import profiling_context, profiling_decorator
-from trl.import_utils import (
-    is_deepspeed_available,
-    is_rich_available,
-)
-from trl.models import (
-    unwrap_model_for_generation,
-)
-from trl.trainer.grpo_config import GRPOConfig
-from trl.trainer.grpo_trainer import RewardFunc
-from trl.trainer.utils import (
-    pad,
-    print_prompt_completions_sample,
-    selective_log_softmax,
-)
+from trl.extras.profiling import profiling_decorator
 
-from axolotl.core.trainers.grpo.sampler import SequenceParallelRepeatRandomSampler
 from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
-from axolotl.monkeypatch.attention.ring_attn.patch import get_ring_attn_group
-
-if is_peft_available():
-    # pylint: disable=unused-import
-    from peft import PeftConfig
 
 if is_deepspeed_available():
     import deepspeed
 
-if is_wandb_available():
-    import wandb
-
 
 class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
-    """Extend the base GRPOTrainer for axolotl helpers"""
+    """
+    Extend the base GRPOTrainer for axolotl helpers
+    """
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
-    def __init__(
-        self,
-        model: str | PreTrainedModel,
-        reward_funcs: RewardFunc | list[RewardFunc],
-        args: GRPOConfig | None = None,
-        train_dataset: Dataset | IterableDataset | None = None,
-        eval_dataset: (
-            Dataset | IterableDataset | dict[str, Dataset | IterableDataset] | None
-        ) = None,
-        processing_class: PreTrainedTokenizerBase | None = None,
-        reward_processing_classes: (
-            PreTrainedTokenizerBase | list[PreTrainedTokenizerBase] | None
-        ) = None,
-        callbacks: list[TrainerCallback] | None = None,
-        optimizers: tuple[
-            torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None
-        ] = (None, None),
-        peft_config: "PeftConfig | None" = None,
-    ):
-        # First call the superclass constructor with all arguments
-        super().__init__(
-            model=model,
-            reward_funcs=reward_funcs,
-            args=args,
-            train_dataset=train_dataset,
-            eval_dataset=eval_dataset,
-            processing_class=processing_class,
-            reward_processing_classes=reward_processing_classes,
-            callbacks=callbacks,
-            optimizers=optimizers,
-            peft_config=peft_config,
-        )
-
-        # Now execute your custom logic
-        # Get number of SP groups (number of processes divided by SP degree)
-        num_processes = self.accelerator.num_processes
-        num_sp_groups = num_processes // self.args.sequence_parallel_degree
-
-        # Calculate batch size per SP group (not per process)
-        sp_group_batch_size = self.args.per_device_train_batch_size * num_sp_groups
-        possible_values = [
-            n_gen
-            for n_gen in range(2, sp_group_batch_size + 1)
-            if (sp_group_batch_size) % n_gen == 0
-        ]
-
-        if self.num_generations not in possible_values:
-            raise ValueError(
-                f"The batch size per SP group ({num_sp_groups} x "
-                f"{self.args.per_device_train_batch_size}) must be evenly divisible by "
-                f"the number of generations per prompt ({self.num_generations}). Given "
-                "the current configuration, the valid values for the number of "
-                f"generations are: {possible_values}."
-            )
-
-        if self.args.eval_strategy != "no":
-            # If sequence parallelism is enabled, calculate batch size per SP group
-            sp_group_eval_batch_size = args.per_device_eval_batch_size * num_sp_groups  # type: ignore[union-attr]
-            possible_values = [
-                n_gen
-                for n_gen in range(2, sp_group_eval_batch_size + 1)
-                if (sp_group_eval_batch_size) % n_gen == 0
-            ]
-
-            if self.num_generations not in possible_values:
-                raise ValueError(
-                    f"With sequence parallelism (degree {self.args.sequence_parallel_degree}), "
-                    f"the eval batch size per SP group ({num_sp_groups} x {self.args.per_device_eval_batch_size}) "
-                    f"must be evenly divisible by the number of generations per prompt "
-                    f"({self.num_generations}). Given the current eval batch size, "
-                    f"the valid values for the number of generations are: {possible_values}."
-                )
-
-        # Initialize the SP group
-        self.sp_group = get_ring_attn_group()
-        self.local_rank = dist.get_rank(group=self.sp_group)
-        self.local_world_size = dist.get_world_size(group=self.sp_group)
-
-        print("end of trainer init")
-
-    def _get_train_sampler(self) -> Sampler:
-        # Get distributed training info
-        world_size = dist.get_world_size()
-        rank = dist.get_rank()
-
-        effective_batch_size = (
-            self.args.per_device_train_batch_size
-            * world_size
-            * self.args.gradient_accumulation_steps
-        )
-
-        return SequenceParallelRepeatRandomSampler(
-            dataset=self.train_dataset,
-            mini_repeat_count=self.num_generations,
-            world_size=world_size,
-            rank=rank,
-            batch_size=effective_batch_size
-            // self.num_generations
-            // self.args.sequence_parallel_degree,
-            repeat_count=self.num_iterations,
-            sequence_parallel_degree=self.args.sequence_parallel_degree,
-            shuffle=True,
-            seed=self.args.seed,
-            drop_last=True,
-        )
-
-    def _create_dataloader_params(self, is_eval=False, custom_batch_size=None):
-        """Create common dataloader parameters for train or eval."""
-        batch_size = custom_batch_size or (
-            self.args.eval_batch_size if is_eval else self._train_batch_size
-        )
-
-        params = {
-            "batch_size": batch_size,
-            "collate_fn": self.data_collator,
-            "num_workers": self.args.dataloader_num_workers,
-            "pin_memory": self.args.dataloader_pin_memory,
-        }
-
-        # Add persistent workers only for training
-        if not is_eval and hasattr(self.args, "dataloader_persistent_workers"):
-            params["persistent_workers"] = self.args.dataloader_persistent_workers
-
-        # Add prefetch factor if specified
-        if self.args.dataloader_prefetch_factor:
-            params["prefetch_factor"] = self.args.dataloader_prefetch_factor
-
-        return params
-
-    def _prepare_dataloader(
-        self, dataset, sampler, is_eval=False, custom_batch_size=None
-    ):
-        """Prepare a dataloader with the given dataset and sampler."""
-        # Get base parameters
-        dataloader_params = self._create_dataloader_params(is_eval, custom_batch_size)
-
-        # Add sampler configuration
-        if not isinstance(dataset, torch.utils.data.IterableDataset):
-            if isinstance(sampler, BatchSampler):
-                # batch_size and batch_sampler are mutually exclusive
-                dataloader_params["batch_sampler"] = sampler
-                del dataloader_params["batch_size"]
-            else:
-                dataloader_params["sampler"] = sampler
-                dataloader_params["drop_last"] = self.args.dataloader_drop_last
-
-            if not is_eval:
-                dataloader_params["worker_init_fn"] = seed_worker
-
-        # Create the dataloader
-        dataloader = DataLoader(dataset, **dataloader_params)
-
-        if self.args.sample_packing and (
-            (not is_eval and not self.args.pretraining)
-            or (is_eval and self.args.eval_sample_packing is not False)
-        ):
-            self.accelerator.even_batches = False
-
-        # Return unprepared dataloader if using sequence parallelism
-        # TODO(djsaunde): We might be able to use `accelerate`'s dataloader preparation
-        # if we use `dispatch_batches` and `slice_fn_for_dispatch` properly (i.e.,
-        # slice each batch along the sequence dimension).
-        if self.args.sequence_parallel_degree > 1:
-            return dataloader
-
-        # Otherwise prepare with accelerator
-        return self.accelerator.prepare_data_loader(dataloader)
-
-    def get_train_dataloader(self) -> DataLoader:
-        """Get dataloader for training"""
-        train_dataset = self.train_dataset
-        # pylint: disable=access-member-before-definition
-        data_collator = self.data_collator  # type: ignore
-
-        # Initialize SP group attributes if sequence parallelism is enabled
-        if self.args.sequence_parallel_degree > 1:
-            self.sp_group = get_ring_attn_group()
-            self.local_rank = dist.get_rank(group=self.sp_group)
-            self.local_world_size = dist.get_world_size(group=self.sp_group)
-
-        # Handle dataset preprocessing
-        if isinstance(train_dataset, datasets.Dataset):
-            # Add debug print before any modifications
-            if self.args.sample_packing and not self.args.pretraining:
-                train_dataset = train_dataset.remove_columns(["length"])
-            if not self.args.sample_packing or self.args.pretraining:
-                train_dataset = self._remove_unused_columns(
-                    train_dataset, description="training"
-                )
-        else:
-            self.data_collator = self._get_collator_with_removed_columns(  # pylint: disable=attribute-defined-outside-init
-                data_collator,
-                description="training",
-            )
-
-        # Get sampler and create dataloader
-        sampler = self._get_train_sampler()
-        dataloader = self._prepare_dataloader(train_dataset, sampler, is_eval=False)
-
-        return dataloader
-
     @profiling_decorator
     def _move_model_to_vllm(self):
         # For DeepSpeed ZeRO-3, we need to gather all parameters before operations
@@ -320,577 +67,3 @@ class AxolotlGRPOTrainer(RngLoaderMixin, SchedulerMixin, GRPOTrainer):
         # Reset cache on main process
         if self.accelerator.is_main_process:
             self.vllm_client.reset_prefix_cache()
-
-    # def _generate_and_score_completions(
-    #     self, inputs: list[dict[str, torch.Tensor | Any]]
-    # ) -> dict[str, torch.Tensor | Any]:
-    #     device = self.accelerator.device
-    #     prompts = [x["prompt"] for x in inputs]
-    #     prompts_text = [
-    #         maybe_apply_chat_template(example, self.processing_class)["prompt"]
-    #         for example in inputs
-    #     ]
-    #     prompt_inputs = self.processing_class(
-    #         text=prompts_text,
-    #         return_tensors="pt",
-    #         padding=True,
-    #         padding_side="left",
-    #         add_special_tokens=False,
-    #     )
-    #     # pylint: disable=protected-access
-    #     prompt_inputs = Trainer._prepare_inputs(self, prompt_inputs)
-
-    #     prompt_ids, prompt_mask = (
-    #         prompt_inputs["input_ids"],
-    #         prompt_inputs["attention_mask"],
-    #     )
-
-    #     if self.max_prompt_length is not None:
-    #         prompt_ids = prompt_ids[:, -self.max_prompt_length :]
-    #         prompt_mask = prompt_mask[:, -self.max_prompt_length :]
-
-    #     # Generate completions using either vLLM or regular generation
-    #     if self.args.use_vllm:
-    #         # First, have main process load weights if needed
-    #         # pylint: disable=access-member-before-definition
-    #         if self.state.global_step != self._last_loaded_step:  # type: ignore[has-type]
-    #             self._move_model_to_vllm()
-    #             # pylint: disable=attribute-defined-outside-init
-    #             self._last_loaded_step = self.state.global_step
-
-    #         all_prompts_text = gather_object(prompts_text)
-    #         if self.accelerator.is_main_process:
-    #             # Since 'prompts' contains 'num_generations' duplicates, we first take unique prompts, and generate
-    #             # num_generations outputs for each one. This is faster than generating outputs for each duplicate
-    #             # prompt individually.
-    #             # ordered_set_of_prompts = all_prompts_text[:: self.num_generations]
-    #             ordered_set_of_prompts = all_prompts_text[
-    #                 :: self.num_generations * self.args.sequence_parallel_degree
-    #             ]
-    #             with profiling_context(self, "vLLM.generate"):
-    #                 completion_ids = self.vllm_client.generate(
-    #                     prompts=ordered_set_of_prompts,
-    #                     n=self.num_generations,
-    #                     repetition_penalty=self.repetition_penalty,
-    #                     temperature=self.temperature,
-    #                     top_p=self.top_p,
-    #                     top_k=-1 if self.top_k is None else self.top_k,
-    #                     min_p=0.0 if self.min_p is None else self.min_p,
-    #                     max_tokens=self.max_completion_length,
-    #                     guided_decoding_regex=self.guided_decoding_regex,
-    #                 )
-    #         else:
-    #             completion_ids = [None] * (
-    #                 len(all_prompts_text) // self.args.sequence_parallel_degree
-    #             )
-
-    #         # Broadcast the completions from the main process to all processes
-    #         completion_ids = broadcast_object_list(completion_ids, from_process=0)
-
-    #         # Determine the appropriate slice based on sequence parallelism
-    #         if self.args.sequence_parallel_degree > 1:
-    #             # Calculate SP group ID (which group of ranks this rank belongs to)
-    #             sp_group_id = self.accelerator.process_index // self.local_world_size
-
-    #             # Calculate the start index for this SP group
-    #             sp_group_start = sp_group_id * len(prompts) * self.local_world_size
-
-    #             # All ranks in the same SP group get the same data slice
-    #             process_slice = slice(
-    #                 sp_group_start,
-    #                 sp_group_start + len(prompts),
-    #             )
-    #             completion_ids = completion_ids[process_slice]
-    #         else:
-    #             # Original behavior for non-sequence parallel case
-    #             process_slice = slice(
-    #                 self.accelerator.process_index * len(prompts),
-    #                 (self.accelerator.process_index + 1) * len(prompts),
-    #             )
-    #             completion_ids = completion_ids[process_slice]
-
-    #         # Pad the completions, and concatenate them with the prompts
-    #         completion_ids = [
-    #             torch.tensor(ids, device=device) for ids in completion_ids
-    #         ]
-    #         completion_ids = pad(
-    #             completion_ids, padding_value=self.processing_class.pad_token_id
-    #         )
-    #     else:
-    #         # Regular generation path
-    #         with unwrap_model_for_generation(
-    #             self.model_wrapped,
-    #             self.accelerator,
-    #             gather_deepspeed3_params=self.args.ds3_gather_for_generation,
-    #         ) as unwrapped_model:
-    #             prompt_completion_ids = unwrapped_model.generate(
-    #                 prompt_ids,
-    #                 attention_mask=prompt_mask,
-    #                 generation_config=self.generation_config,
-    #             )
-
-    #         # Compute prompt length and extract completion ids
-    #         prompt_length = prompt_ids.size(1)
-    #         prompt_ids = prompt_completion_ids[:, :prompt_length]
-    #         completion_ids = prompt_completion_ids[:, prompt_length:]
-
-    #     prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
-
-    #     # Mask everything after the first EOS token
-    #     is_eos = completion_ids == self.processing_class.eos_token_id
-    #     eos_idx = torch.full(
-    #         (is_eos.size(0),), is_eos.size(1), dtype=torch.long, device=device
-    #     )
-    #     eos_idx[is_eos.any(dim=1)] = is_eos.int().argmax(dim=1)[is_eos.any(dim=1)]
-    #     sequence_indices = torch.arange(is_eos.size(1), device=device).expand(
-    #         is_eos.size(0), -1
-    #     )
-    #     completion_mask = (sequence_indices <= eos_idx.unsqueeze(1)).int()
-
-    #     # Concatenate prompt_mask with completion_mask for logit computation
-    #     attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)  # (B, P+C)
-    #     logits_to_keep = completion_ids.size(
-    #         1
-    #     )  # we only need to compute the logits for the completion tokens
-
-    #     with torch.no_grad():
-    #         # When using num_iterations == 1, old_per_token_logps == per_token_logps, so we can skip it's
-    #         # computation here, and use per_token_logps.detach() instead.
-    #         if self.num_iterations > 1:
-    #             if self.args.sequence_parallel_degree > 1:
-    #                 old_per_token_logps, _ = self._get_per_token_logps_v2(
-    #                     self.model,
-    #                     prompt_completion_ids,
-    #                     attention_mask,
-    #                     logits_to_keep,
-    #                 )
-    #             else:
-    #                 old_per_token_logps = super()._get_per_token_logps(
-    #                     self.model,
-    #                     prompt_completion_ids,
-    #                     attention_mask,
-    #                     logits_to_keep,
-    #                 )
-    #         else:
-    #             old_per_token_logps = None
-
-    #         if self.beta == 0.0:
-    #             ref_per_token_logps = None
-    #         elif self.ref_model is not None:
-    #             if self.args.sequence_parallel_degree > 1:
-    #                 ref_per_token_logps, _ = self._get_per_token_logps_v2(
-    #                     self.ref_model,
-    #                     prompt_completion_ids,
-    #                     attention_mask,
-    #                     logits_to_keep,
-    #                 )
-    #             else:
-    #                 ref_per_token_logps = super()._get_per_token_logps(
-    #                     self.ref_model,
-    #                     prompt_completion_ids,
-    #                     attention_mask,
-    #                     logits_to_keep,
-    #                 )
-    #         else:
-    #             with self.accelerator.unwrap_model(self.model).disable_adapter():
-    #                 if self.args.sequence_parallel_degree > 1:
-    #                     ref_per_token_logps, _ = self._get_per_token_logps_v2(
-    #                         self.model,
-    #                         prompt_completion_ids,
-    #                         attention_mask,
-    #                         logits_to_keep,
-    #                     )
-    #                 else:
-    #                     ref_per_token_logps = super()._get_per_token_logps(
-    #                         self.model,
-    #                         prompt_completion_ids,
-    #                         attention_mask,
-    #                         logits_to_keep,
-    #                     )
-
-    #     # Decode the generated completions
-    #     completions_text = self.processing_class.batch_decode(
-    #         completion_ids, skip_special_tokens=True
-    #     )
-    #     if is_conversational(inputs[0]):
-    #         completions = []
-    #         for prompt, completion in zip(prompts, completions_text):
-    #             bootstrap = (
-    #                 prompt.pop()["content"] if prompt[-1]["role"] == "assistant" else ""
-    #             )
-    #             completions.append(
-    #                 [{"role": "assistant", "content": bootstrap + completion}]
-    #             )
-    #     else:
-    #         completions = completions_text
-
-    #     rewards_per_func = torch.zeros(
-    #         len(prompts), len(self.reward_funcs), device=device
-    #     )
-    #     for i, (reward_func, reward_processing_class) in enumerate(
-    #         zip(self.reward_funcs, self.reward_processing_classes)
-    #     ):
-    #         if isinstance(
-    #             reward_func, nn.Module
-    #         ):  # Module instead of PretrainedModel for compat with compiled models
-    #             reward_func_name = (
-    #                 f"reward {reward_func.config._name_or_path.split('/')[-1]}"
-    #             )
-    #         else:
-    #             # pylint: disable=protected-access
-    #             reward_func_name = reward_func.__name__
-    #         with profiling_context(self, reward_func_name):
-    #             if isinstance(
-    #                 reward_func, nn.Module
-    #             ):  # Module instead of PretrainedModel for compat with compiled models
-    #                 if is_conversational(inputs[0]):
-    #                     messages = [
-    #                         {"messages": p + c} for p, c in zip(prompts, completions)
-    #                     ]
-    #                     texts = [
-    #                         apply_chat_template(x, reward_processing_class)["text"]
-    #                         for x in messages
-    #                     ]
-    #                 else:
-    #                     texts = [p + c for p, c in zip(prompts, completions)]
-    #                 reward_inputs = reward_processing_class(
-    #                     text=texts,
-    #                     return_tensors="pt",
-    #                     padding=True,
-    #                     padding_side="right",
-    #                     add_special_tokens=False,
-    #                 )
-    #                 # pylint: disable=protected-access
-    #                 reward_inputs = Trainer._prepare_inputs(self, reward_inputs)
-    #                 with torch.inference_mode():
-    #                     rewards_per_func[:, i] = reward_func(**reward_inputs).logits[
-    #                         :, 0
-    #                     ]  # Shape (B*G,)
-    #             else:
-    #                 # Repeat all input columns (but "prompt" and "completion") to match the number of generations
-    #                 keys = [
-    #                     key for key in inputs[0] if key not in ["prompt", "completion"]
-    #                 ]
-    #                 reward_kwargs = {
-    #                     key: [example[key] for example in inputs] for key in keys
-    #                 }
-    #                 output_reward_func = reward_func(
-    #                     prompts=prompts, completions=completions, **reward_kwargs
-    #                 )
-    #                 # Convert None values to NaN
-    #                 output_reward_func = [
-    #                     reward if reward is not None else torch.nan
-    #                     for reward in output_reward_func
-    #                 ]
-
-    #                 rewards_per_func[:, i] = torch.tensor(
-    #                     output_reward_func, dtype=torch.float32, device=device
-    #                 )
-
-    #     # If all reward functions return None for a given row, issue a detailed warning
-    #     if torch.isnan(rewards_per_func).all(dim=1).any():
-    #         nan_row_idx = (
-    #             torch.isnan(rewards_per_func).all(dim=1).nonzero(as_tuple=True)[0][0]
-    #         )
-    #         row_reward_kwargs = {
-    #             key: value[nan_row_idx] for key, value in reward_kwargs.items()
-    #         }
-    #         row_reward_kwargs["prompt"] = prompts[nan_row_idx]
-    #         row_reward_kwargs["completion"] = completions[nan_row_idx]
-    #         warnings.warn(
-    #             f"All reward functions returned None for the following kwargs: {row_reward_kwargs}. "
-    #             "Please ensure that at least one reward function returns a valid reward."
-    #         )
-
-    #     # Gather the reward per function: this part is crucial, because the rewards are normalized per group and the
-    #     # completions may be distributed across processes
-    #     rewards_per_func = gather(rewards_per_func)
-
-    #     # Apply weights to each reward function's output and sum
-    #     rewards = (
-    #         rewards_per_func * self.reward_weights.to(device).unsqueeze(0)
-    #     ).nansum(dim=1)
-
-    #     # Compute grouped-wise rewards
-    #     mean_grouped_rewards = rewards.view(-1, self.num_generations).mean(dim=1)
-    #     std_grouped_rewards = rewards.view(-1, self.num_generations).std(dim=1)
-
-    #     # Normalize the rewards to compute the advantages
-    #     mean_grouped_rewards = mean_grouped_rewards.repeat_interleave(
-    #         self.num_generations, dim=0
-    #     )
-    #     std_grouped_rewards = std_grouped_rewards.repeat_interleave(
-    #         self.num_generations, dim=0
-    #     )
-    #     advantages = rewards - mean_grouped_rewards
-    #     if self.args.scale_rewards:
-    #         advantages = advantages / (std_grouped_rewards + 1e-4)
-
-    #     # Slice to keep only the local part of the data
-    #     process_slice = slice(
-    #         self.accelerator.process_index * len(prompts),
-    #         (self.accelerator.process_index + 1) * len(prompts),
-    #     )
-    #     advantages = advantages[process_slice]
-
-    #     # Log the metrics
-    #     mode = "eval" if self.control.should_evaluate else "train"
-
-    #     if mode == "train":
-    #         # pylint: disable=no-member
-    #         self._total_train_tokens += (
-    #             self.accelerator.gather_for_metrics(attention_mask.sum()).sum().item()
-    #         )
-    #     # pylint: disable=no-member
-    #     self._metrics[mode]["num_tokens"] = [self._total_train_tokens]
-
-    #     completion_length = (
-    #         self.accelerator.gather_for_metrics(completion_mask.sum(1))
-    #         .float()
-    #         .mean()
-    #         .item()
-    #     )
-    #     self._metrics[mode]["completion_length"].append(completion_length)
-
-    #     # Calculate mean reward per function, but only for samples where the function was applied
-    #     for i, reward_func in enumerate(self.reward_funcs):
-    #         if isinstance(
-    #             reward_func, nn.Module
-    #         ):  # Module instead of PretrainedModel for compat with compiled models
-    #             reward_func_name = reward_func.config._name_or_path.split("/")[-1]
-    #         else:
-    #             # pylint: disable=protected-access
-    #             reward_func_name = reward_func.__name__
-    #         # Only calculate mean for samples where this reward function was applied (non-NaN values)
-    #         mean_rewards = torch.nanmean(rewards_per_func[:, i]).item()
-    #         self._metrics[mode][f"rewards/{reward_func_name}"].append(mean_rewards)
-    #     self._metrics[mode]["reward"].append(rewards.mean().item())
-    #     self._metrics[mode]["reward_std"].append(std_grouped_rewards.mean().item())
-
-    #     if (
-    #         self.log_completions
-    #         and self.state.global_step % self.args.logging_steps == 0
-    #     ):
-    #         prompts_to_log = gather_object(prompts_text)
-    #         completions_to_log = gather_object(completions_text)
-    #         rewards_to_log = rewards.tolist()
-
-    #         if self.accelerator.is_main_process:
-    #             if is_rich_available():
-    #                 print_prompt_completions_sample(
-    #                     prompts_to_log,
-    #                     completions_to_log,
-    #                     rewards_to_log,
-    #                     self.state.global_step,
-    #                 )
-    #             if (
-    #                 self.args.report_to
-    #                 and "wandb" in self.args.report_to
-    #                 and wandb.run is not None
-    #             ):
-    #                 import pandas as pd
-
-    #                 # For logging
-    #                 table = {
-    #                     "step": [str(self.state.global_step)] * len(rewards),
-    #                     "prompt": prompts_to_log,
-    #                     "completion": completions_to_log,
-    #                     "reward": rewards.tolist(),
-    #                 }
-    #                 df = pd.DataFrame(table)
-    #                 wandb.log({"completions": wandb.Table(dataframe=df)})
-
-    #     return {
-    #         "prompt_ids": prompt_ids,
-    #         "prompt_mask": prompt_mask,
-    #         "completion_ids": completion_ids,
-    #         "completion_mask": completion_mask,
-    #         "old_per_token_logps": old_per_token_logps,
-    #         "ref_per_token_logps": ref_per_token_logps,
-    #         "advantages": advantages,
-    #     }
-
-    # def _get_per_token_logps_v2(
-    #     self, model, input_ids, attention_mask, logits_to_keep, completion_mask=None
-    # ):
-    #     # Pad sequence to be divisible by SP degree if needed
-    #     total_seq_len = input_ids.shape[1]
-    #     if total_seq_len % self.local_world_size != 0:
-    #         pad_len = self.local_world_size - (total_seq_len % self.local_world_size)
-    #         pad_token_id = self.processing_class.pad_token_id or 0
-
-    #         # Pad input_ids and attention_mask
-    #         padding = torch.full(
-    #             (input_ids.shape[0], pad_len),
-    #             pad_token_id,
-    #             dtype=input_ids.dtype,
-    #             device=input_ids.device,
-    #         )
-    #         input_ids = torch.cat([input_ids, padding], dim=1)
-
-    #         attn_padding = torch.zeros(
-    #             (attention_mask.shape[0], pad_len),
-    #             dtype=attention_mask.dtype,
-    #             device=attention_mask.device,
-    #         )
-    #         attention_mask = torch.cat([attention_mask, attn_padding], dim=1)
-    #         if completion_mask is not None:
-    #             completion_mask = torch.cat([completion_mask, attn_padding], dim=1)
-
-    #         total_seq_len += pad_len
-    #         logits_to_keep += pad_len
-
-    #     # Split the sequence
-    #     slice_size = total_seq_len // self.local_world_size
-    #     start = self.local_rank * slice_size
-    #     end = start + slice_size
-
-    #     # Get our slice
-    #     input_ids_slice = input_ids[:, start:end]
-    #     attention_mask_slice = attention_mask[:, start:end]
-
-    #     # Calculate where our slice starts and ends relative to the completion tokens
-    #     local_completion_mask = None
-    #     prompt_len = input_ids.size(1) - logits_to_keep
-    #     if start >= prompt_len:
-    #         # Slice starts within the completion section
-    #         start_in_completion = start - prompt_len
-    #         end_in_completion = min(end - prompt_len, logits_to_keep)
-    #         local_logits_to_keep = end_in_completion - start_in_completion
-    #         if completion_mask is not None:
-    #             local_completion_mask = completion_mask[
-    #                 :, start_in_completion:end_in_completion
-    #             ]
-    #     elif end <= prompt_len:
-    #         # Slice is entirely within the prompt section (no completion tokens)
-    #         local_logits_to_keep = 0
-    #         if completion_mask is not None:
-    #             local_completion_mask = torch.zeros(
-    #                 (completion_mask.size(0), 0), device=completion_mask.device
-    #             )
-    #     else:
-    #         # Slice contains the boundary between prompt and completion
-    #         start_in_completion = 0
-    #         end_in_completion = min(end - prompt_len, logits_to_keep)
-    #         local_logits_to_keep = end_in_completion - start_in_completion
-    #         if completion_mask is not None:
-    #             local_completion_mask = completion_mask[
-    #                 :, start_in_completion:end_in_completion
-    #             ]
-
-    #     # Get logits with enough context to compute log probs
-    #     logits = model(
-    #         input_ids=input_ids_slice,
-    #         attention_mask=attention_mask_slice,
-    #         logits_to_keep=local_logits_to_keep + 1,
-    #     ).logits
-
-    #     # Only the last rank that contains completion tokens needs to remove the last logit
-    #     is_last_rank_with_completions = (
-    #         self.local_rank == self.local_world_size - 1  # Last rank overall
-    #         or end
-    #         >= prompt_len
-    #         + logits_to_keep  # Our slice includes the last completion token
-    #     )
-
-    #     if is_last_rank_with_completions:
-    #         logits = logits[:, :-1]
-    #         if local_completion_mask is not None:
-    #             local_completion_mask = local_completion_mask[:, :-1]
-    #         local_logits_to_keep -= 1
-
-    #     if start >= prompt_len:
-    #         # For ranks where slice is all completion tokens,
-    #         # we need to offset to match the logits (which predict the next token)
-    #         offset = 1  # Skip the first token as it's predicted by the last token of the previous rank
-    #         local_input_ids = input_ids_slice[:, offset : offset + local_logits_to_keep]
-    #     else:
-    #         # For the rank that contains the prompt-completion boundary,
-    #         # we need to take completion tokens only
-    #         offset = prompt_len - start  # Where completions start in our slice
-    #         local_input_ids = input_ids_slice[:, offset : offset + local_logits_to_keep]
-
-    #     logits = logits[
-    #         :, -local_logits_to_keep:
-    #     ]  # Take only logits for completion tokens
-    #     logits = logits / self.temperature
-    #     per_token_logps = selective_log_softmax(logits, local_input_ids)
-
-    #     return per_token_logps, local_completion_mask
-
-    # # pylint: disable=unused-argument
-    # @profiling_decorator
-    # def compute_loss(
-    #     self, model, inputs, return_outputs=False, num_items_in_batch=None
-    # ):
-    #     if return_outputs:
-    #         raise ValueError("The GRPOTrainer does not support returning outputs")
-
-    #     # Unpack inputs
-    #     prompt_ids, prompt_mask = inputs["prompt_ids"], inputs["prompt_mask"]
-    #     completion_ids, completion_mask = (
-    #         inputs["completion_ids"],
-    #         inputs["completion_mask"],
-    #     )
-    #     prompt_completion_ids = torch.cat([prompt_ids, completion_ids], dim=1)
-    #     attention_mask = torch.cat([prompt_mask, completion_mask], dim=1)
-    #     logits_to_keep = completion_ids.size(1)
-
-    #     if self.args.sequence_parallel_degree > 1:
-    #         per_token_logps, completion_mask = self._get_per_token_logps_v2(
-    #             model,
-    #             prompt_completion_ids,
-    #             attention_mask,
-    #             logits_to_keep,
-    #             completion_mask,
-    #         )
-    #     else:
-    #         per_token_logps = super()._get_per_token_logps(
-    #             model, prompt_completion_ids, attention_mask, logits_to_keep
-    #         )
-
-    #     # Compute the KL divergence between the model and the reference model
-    #     if self.beta != 0.0:
-    #         ref_per_token_logps = inputs["ref_per_token_logps"]
-    #         per_token_kl = (
-    #             torch.exp(ref_per_token_logps - per_token_logps)
-    #             - (ref_per_token_logps - per_token_logps)
-    #             - 1
-    #         )
-
-    #     # Compute the loss
-    #     advantages = inputs["advantages"]
-    #     # When using num_iterations == 1, old_per_token_logps == per_token_logps, so we can skip its computation
-    #     # and use per_token_logps.detach() instead.
-    #     old_per_token_logps = (
-    #         inputs["old_per_token_logps"]
-    #         if self.num_iterations > 1
-    #         else per_token_logps.detach()
-    #     )
-    #     coef_1 = torch.exp(per_token_logps - old_per_token_logps)
-    #     coef_2 = torch.clamp(coef_1, 1 - self.epsilon_low, 1 + self.epsilon_high)
-    #     per_token_loss1 = coef_1 * advantages.unsqueeze(1)
-    #     per_token_loss2 = coef_2 * advantages.unsqueeze(1)
-    #     per_token_loss = -torch.min(per_token_loss1, per_token_loss2)
-
-    #     if self.beta != 0.0:
-    #         per_token_loss = per_token_loss + self.beta * per_token_kl
-
-    #     loss = (per_token_loss * completion_mask).sum() / completion_mask.sum()
-
-    #     # Log metrics
-    #     mode = "eval" if self.control.should_evaluate else "train"
-
-    #     if self.beta != 0.0:
-    #         mean_kl = (per_token_kl * completion_mask).sum() / completion_mask.sum()
-    #         self._metrics[mode]["kl"].append(
-    #             self.accelerator.gather_for_metrics(mean_kl).mean().item()
-    #         )
-
-    #     is_clipped = (per_token_loss1 < per_token_loss2).float()
-    #     clip_ratio = (is_clipped * completion_mask).sum() / completion_mask.sum()
-    #     self._metrics[mode]["clip_ratio"].append(
-    #         self.accelerator.gather_for_metrics(clip_ratio).mean().item()
-    #     )
-
-    #     return loss

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

@@ -6,4 +6,4 @@
 from .optimizer import OptimizerMixin
 from .rng_state_loader import RngLoaderMixin
 from .scheduler import SchedulerMixin
-from .sequence_parallel import SequenceParallelContextManager, SequenceParallelMixin
+from .sequence_parallel import SequenceParallelMixin

src/axolotl/core/trainers/mixins/sequence_parallel.py

@@ -1,144 +1,16 @@
-"""
-Module for Axolotl trainer sequence parallelism mixin and training context manager
-"""
+"""Module for Axolotl trainer sequence parallelism mixin"""
 
-import functools
 import logging
 
-import torch
 import torch.distributed as dist
 from datasets import Dataset
-from torch import nn
 from torch.utils.data import DistributedSampler, Sampler
-from torch.utils.hooks import RemovableHandle
 
-from axolotl.monkeypatch.attention.ring_attn import (
-    get_ring_attn_group,
-    update_ring_attn_params,
-)
-from axolotl.utils.schemas.enums import RingAttnFunc
+from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
 
 LOG = logging.getLogger(__name__)
 
 
-def _handle_logits_to_keep(
-    logits_to_keep,
-    local_rank: int,
-    local_world_size: int,
-    ring_attn_func: RingAttnFunc,
-    total_seq_len: int,
-):
-    """
-    Handle logits_to_keep parameter for sequence parallelism.
-
-    Args:
-        logits_to_keep: Integer or tensor indicating which positions to compute logits
-            for.
-        local_rank: Rank in the sequence parallel group.
-        local_world_size: World size of the sequence parallel group.
-        ring_attn_func: Ring attention function being used.
-        total_seq_len: Full sequence length.
-
-    Returns:
-        Adjusted logits_to_keep appropriate for this rank's sharded sequence
-    """
-    print("start of _handle_logits_to_keep")
-    print(dist.get_rank(), logits_to_keep)
-
-    # No transformation needed if logits_to_keep is None
-    if logits_to_keep is None:
-        return None
-
-    assert isinstance(
-        logits_to_keep, int
-    ), "sequence parallelism currently only supports integer logits_to_keep"
-    assert ring_attn_func in [
-        RingAttnFunc.VARLEN_LLAMA3,
-        RingAttnFunc.BATCH_RING,
-    ], "if specifying logits_to_keep, sequence parallelism currently only supports 'batch_ring' and 'varlen_llama3' `ring_attn_func`s"
-
-    # For standard sharding, each rank gets a contiguous chunk
-    chunk_size = total_seq_len // local_world_size
-    start_idx = local_rank * chunk_size
-    end_idx = start_idx + chunk_size
-
-    # Check if logits_to_keep is in this rank's range
-    if start_idx <= logits_to_keep < end_idx:
-        print("end of _handle_logits_to_keep")
-        print(dist.get_rank(), logits_to_keep - start_idx)
-        return logits_to_keep - start_idx
-    else:
-        print("end of _handle_logits_to_keep")
-        print(dist.get_rank(), -1)
-        return -1
-
-
-def apply_sequence_parallelism(
-    batch: dict[str, torch.Tensor],
-    local_rank: int,
-    local_world_size: int,
-    ring_attn_func: RingAttnFunc,
-) -> dict[str, torch.Tensor]:
-    """
-    Apply sequence parallelism slicing to a batch.
-
-    Args:
-        batch: Batch dictionary (e.g., input_ids, attention_mask, etc.).
-        local_rank: Local rank in the sequence parallel group.
-        local_world_size: World size of the sequence parallel group.
-        ring_attn_func: The ring attention function to use.
-
-    Returns:
-        Sliced batch dictionary.
-    """
-    # Update ring attention params if needed
-    if batch.get("position_ids") is not None:
-        update_ring_attn_params(position_ids=batch["position_ids"])
-
-    # Slice batch for sequence parallel processing
-    total_seq_len = batch["input_ids"].size(1)
-    for key in batch:
-        if (
-            isinstance(batch[key], torch.Tensor)
-            and batch[key].dim() > 1
-            and batch[key].size(1) == total_seq_len
-        ):
-            if ring_attn_func in [
-                RingAttnFunc.VARLEN_LLAMA3,
-                RingAttnFunc.BATCH_RING,
-            ]:
-                # Split in sequential fashion and grab this rank's chunk
-                batch[key] = (
-                    batch[key].chunk(local_world_size, dim=1)[local_rank].contiguous()
-                )
-            elif ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-                chunks = batch[key].chunk(2 * local_world_size, dim=1)
-
-                # Take rank's chunk and opposing chunk for zigzag pattern
-                selected_chunks = [
-                    chunks[local_rank],
-                    chunks[2 * local_world_size - local_rank - 1],
-                ]
-                batch[key] = torch.cat(selected_chunks, dim=1).contiguous()
-            elif ring_attn_func is RingAttnFunc.BATCH_STRIPE:
-                # Split into striped data and stack
-                tensor = torch.stack(
-                    batch[key].split(local_world_size, dim=1),
-                    dim=1,
-                ).transpose(1, 2)
-                batch[key] = tensor[:, local_rank].contiguous()
-        if key == "logits_to_keep":
-            batch[key] = _handle_logits_to_keep(
-                logits_to_keep=batch[key],
-                local_rank=local_rank,
-                local_world_size=local_world_size,
-                ring_attn_func=ring_attn_func,
-                total_seq_len=total_seq_len,
-            )
-
-    return batch
-
-
 class SequenceParallelMixin:
     """
     Mixin class for sequence parallelism support in trainers.
@@ -215,160 +87,3 @@ class SequenceParallelMixin:
         return self._create_sequence_parallel_sampler(
             eval_dataset, shuffle=False, is_eval=True
         )
-
-
-class SequenceParallelContextManager:
-    """
-    Context manager for sequence parallelism operations.
-
-    This class provides a context that will automatically apply sequence parallelism
-    during model forward passes using a pre-forward hook, and gather outputs from
-    across the sequence parallelism group using a post-forward hook.
-    """
-
-    def __init__(
-        self,
-        model: nn.Module,
-        sequence_parallel_degree: int,
-        ring_attn_func: RingAttnFunc,
-    ):
-        self.model = model
-        self.sequence_parallel_degree = sequence_parallel_degree
-        self.ring_attn_func = ring_attn_func
-        self.process_group = get_ring_attn_group()
-
-        # Initialize sequence parallel group details
-        self.local_rank = dist.get_rank(self.process_group)
-        self.local_world_size = dist.get_world_size(self.process_group)
-
-        # Will store hook handles for removal
-        self.hook_handles: list[RemovableHandle] = []
-
-        # Create a partially applied version of the apply_sequence_parallelism function
-        # with pre-configured params
-        self.apply_sequence_parallelism = functools.partial(
-            apply_sequence_parallelism,
-            local_rank=self.local_rank,
-            local_world_size=self.local_world_size,
-            ring_attn_func=self.ring_attn_func,
-        )
-
-    def __enter__(self):
-        # Forward pre-hook to apply sequence parallelism
-        def sequence_parallel_pre_hook(_, args, kwargs):
-            # Apply sequence parallelism to kwargs
-            kwargs = self.apply_sequence_parallelism(batch=kwargs)
-            return args, kwargs
-
-        # Forward post-hook to gather outputs
-        def sequence_parallel_post_hook(_, __, output):
-            print("start of sequence_parallel_post_hook")
-            # Gather the sharded outputs
-            output = self.gather_outputs(output)
-            print("end of sequence_parallel_post_hook")
-            return output
-
-        # Register both hooks
-        self.hook_handles.append(
-            self.model.register_forward_pre_hook(
-                sequence_parallel_pre_hook, with_kwargs=True
-            )
-        )
-        self.hook_handles.append(
-            self.model.register_forward_hook(sequence_parallel_post_hook)
-        )
-
-        return self
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        # Remove all hooks
-        for handle in self.hook_handles:
-            handle.remove()
-        self.hook_handles = []
-
-    def gather_outputs(self, output):
-        """Gather sharded outputs from all ranks and reconstruct the full tensor."""
-        # Handle different output formats (dict, tensor, etc.)
-        if isinstance(output, dict):
-            gathered_output = {}
-            for key, value in output.items():
-                if isinstance(value, torch.Tensor) and value.dim() > 1:
-                    # Gather logits or other sequence-sharded tensors
-                    gathered_value = self.gather_tensor(value)
-                    gathered_output[key] = gathered_value
-                else:
-                    gathered_value = value.clone()
-                    dist.all_reduce(
-                        gathered_value, op=dist.ReduceOp.SUM, group=self.process_group
-                    )
-                    gathered_output[key] = gathered_value
-            return gathered_output
-        if isinstance(output, torch.Tensor):
-            return self.gather_tensor(output)
-
-        return output
-
-    def gather_tensor(self, tensor):
-        """Gather a sharded tensor from all ranks."""
-        # Prepare tensors for all_gather
-        world_size = self.local_world_size
-
-        # Create list to store tensors from all ranks
-        gathered_tensors = [torch.zeros_like(tensor) for _ in range(world_size)]
-
-        # All-gather operation
-        dist.all_gather(gathered_tensors, tensor, group=self.process_group)
-
-        # Concatenate along sequence dimension (typically dim=1)
-        if self.ring_attn_func in [RingAttnFunc.VARLEN_LLAMA3, RingAttnFunc.BATCH_RING]:
-            # Simple concatenation for standard sharding
-            return torch.cat(gathered_tensors, dim=1)
-
-        if self.ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
-            # Each rank has a pattern of (rank, world_size*2-rank-1)
-            reconstituted_tensors = [None] * (world_size * 2)
-
-            # First, split each gathered tensor into its two chunks
-            for rank, gathered_tensor in enumerate(gathered_tensors):
-                # Each tensor contains two chunks in the sequence dimension
-                chunk_size = gathered_tensor.size(1) // 2
-                chunk1, chunk2 = gathered_tensor.split(chunk_size, dim=1)
-
-                # Place chunks in their original positions
-                reconstituted_tensors[rank] = chunk1
-                reconstituted_tensors[world_size * 2 - rank - 1] = chunk2
-
-            # Concatenate the reconstituted tensors in the correct order
-            return torch.cat(reconstituted_tensors, dim=1)
-
-        # Otherwise, RingAttnFunc.BATCH_STRIPE
-        # In striping, each rank has every world_size-th slice
-        batch_size = tensor.size(0)
-        hidden_dim = tensor.size(-1)
-
-        # First, determine the full sequence length
-        total_seq_len = 0
-        for t in gathered_tensors:
-            total_seq_len += t.size(1)
-
-        # Create a tensor to hold the unstriped result
-        result = torch.zeros(
-            batch_size,
-            total_seq_len,
-            hidden_dim,
-            dtype=tensor.dtype,
-            device=tensor.device,
-        )
-
-        # For each rank's tensor, distribute its slices to the correct positions
-        for rank, gathered_tensor in enumerate(gathered_tensors):
-            # The rank's tensor contains every world_size-th slice
-            # starting from its rank position
-            seq_len = gathered_tensor.size(1)
-            for i in range(seq_len):
-                # Calculate the position in the full tensor
-                pos = i * world_size + rank
-                if pos < total_seq_len:
-                    result[:, pos] = gathered_tensor[:, i]
-
-        return result

src/axolotl/core/training_args.py

@@ -9,7 +9,7 @@ from PIL.Image import Resampling
 from transformers import TrainingArguments
 from trl import CPOConfig, KTOConfig, ORPOConfig, PRMConfig, RewardConfig
 
-from axolotl.utils.schemas.enums import RingAttnFunc
+from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
 
 
 @dataclass

src/axolotl/integrations/llm_compressor/README.md

@@ -0,0 +1,108 @@
+# LLMCompressor Integration
+
+Fine-tune sparsified models in Axolotl using Neural Magic's [LLMCompressor](https://github.com/vllm-project/llm-compressor).
+
+This integration enables fine-tuning of models sparsified using LLMCompressor within the Axolotl training framework. By combining LLMCompressor's model compression capabilities with Axolotl's distributed training pipelines, users can efficiently fine-tune sparse models at scale.
+
+It uses Axolotl’s plugin system to hook into the fine-tuning flows while maintaining sparsity throughout training.
+
+---
+
+## Requirements
+
+- Axolotl with `llmcompressor` extras:
+
+  ```bash
+  pip install "axolotl[llmcompressor]"
+  ```
+
+- Requires `llmcompressor >= 0.5.1`
+
+This will install all necessary dependencies to fine-tune sparsified models using the integration.
+
+---
+
+## Usage
+
+To enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:
+
+```yaml
+plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true
+# ... (other training arguments)
+```
+
+This plugin **does not apply pruning or sparsification itself** — it is intended for **fine-tuning models that have already been sparsified**.
+
+Pre-sparsified checkpoints can be:
+- Generated using [LLMCompressor](https://github.com/vllm-project/llm-compressor)
+- Downloaded from [Neural Magic's Hugging Face page](https://huggingface.co/neuralmagic)
+- Any custom LLM with compatible sparsity patterns that you've created yourself
+
+To learn more about writing and customizing LLMCompressor recipes, refer to the official documentation:
+[https://github.com/vllm-project/llm-compressor/blob/main/README.md](https://github.com/vllm-project/llm-compressor/blob/main/README.md)
+
+### Storage Optimization with save_compressed
+
+Setting `save_compressed: true` in your configuration enables saving models in a compressed format, which:
+- Reduces disk space usage by approximately 40%
+- Maintains compatibility with vLLM for accelerated inference
+- Maintains compatibility with llmcompressor for further optimization (example: quantization)
+
+This option is highly recommended when working with sparse models to maximize the benefits of model compression.
+
+### Example Config
+
+See [`examples/llama-3/sparse-finetuning.yaml`](examples/llama-3/sparse-finetuning.yaml) for a complete example.
+
+---
+
+## Inference with vLLM
+
+After fine-tuning your sparse model, you can leverage vLLM for efficient inference.
+You can also use LLMCompressor to apply additional quantization to your fine-tuned
+sparse model before inference for even greater performance benefits.:
+
+```python
+from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+llm = LLM("path/to/your/sparse/model")
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+```
+
+For more details on vLLM's capabilities and advanced configuration options, see the [official vLLM documentation](https://docs.vllm.ai/).
+
+## Learn More
+
+For details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:
+
+[https://github.com/vllm-project/llm-compressor](https://github.com/vllm-project/llm-compressor)

src/axolotl/integrations/llm_compressor/__init__.py

@@ -0,0 +1,5 @@
+"""Integration entry point for the LLMCompressor plugin."""
+
+from .plugin import LLMCompressorPlugin
+
+__all__ = ["LLMCompressorPlugin"]

src/axolotl/integrations/llm_compressor/args.py

@@ -0,0 +1,40 @@
+"""
+LLMCompressor and Sparse Finetuning config models.
+"""
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+from typing_extensions import Annotated
+
+
+class CompressionArgs(BaseModel):
+    """Sparse Finetuning config for LLMCompressor."""
+
+    # Typing for recipe is set to Any due to:
+    # https://github.com/vllm-project/llm-compressor/issues/1319
+    recipe: Annotated[
+        Any,
+        Field(
+            description="The recipe containing the compression algorithms and hyperparameters to apply."
+        ),
+    ]
+
+    save_compressed: Annotated[
+        bool,
+        Field(
+            default=False,
+            description="Whether to save the compressed model after training.",
+        ),
+    ]
+
+
+class LLMCompressorArgs(BaseModel):
+    """LLMCompressor configuration BaseModel."""
+
+    llmcompressor: Annotated[
+        CompressionArgs,
+        Field(
+            description="Arguments enabling compression pathways through the LLM Compressor plugins"
+        ),
+    ]

src/axolotl/integrations/llm_compressor/plugin.py

@@ -0,0 +1,171 @@
+"""
+Sparse Finetuning plugin for Axolotl — enables handling of sparse neural networks
+by maintaining masks for zero weights during training.
+"""
+
+import logging
+from functools import wraps
+from typing import Any, Callable, Concatenate, ParamSpec, TypeVar
+
+from llmcompressor import active_session, create_session
+from llmcompressor.core import callbacks as session_callbacks
+from llmcompressor.recipe import Recipe
+from torch.nn import Module
+from transformers.trainer import Trainer
+from transformers.trainer_callback import TrainerCallback, TrainerControl, TrainerState
+from transformers.training_args import TrainingArguments
+
+from axolotl.integrations.base import BasePlugin
+
+P = ParamSpec("P")  # Params for generic function signatures
+R = TypeVar("R")  # Return type for generic function signatures
+
+LOG = logging.getLogger("axolotl.integrations.llm_compressor")
+
+
+class LLMCompressorCallbackHandler(TrainerCallback):
+    """
+    Trainer callback for Sparse Finetuning.
+    Maintains sparsity patterns during training by applying masks after optimization steps,
+    ensuring zero-weight updates are canceled out.
+    """
+
+    def __init__(self, trainer: Trainer, recipe: Any):
+        """
+        Initialize the Sparse Finetuning callback handler.
+
+        Args:
+            trainer (Trainer): Huggingface Trainer instance.
+            recipe (Recipe | dict): Sparse finetuning recipe to apply.
+        """
+        super().__init__()
+        self.trainer = trainer
+        self.recipe = (
+            Recipe.model_validate(recipe) if not isinstance(recipe, Recipe) else recipe
+        )
+        self.original_compute_loss = trainer.compute_loss
+        self.trainer.compute_loss = compute_loss_wrapper(self.trainer.compute_loss)
+        create_session()
+
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the beginning of training. Initializes the compression session.
+
+        Args:
+            args (TrainingArguments): Training arguments.
+            state (TrainerState): Trainer state.
+            control (TrainerControl): Trainer control.
+        """
+        super().on_train_begin(args, state, control, **kwargs)
+        self.trainer.accelerator.wait_for_everyone()
+        active_session().initialize(
+            model=self.trainer.model,
+            optimizer=self.trainer.optimizer,
+            start=state.epoch,
+            recipe=self.recipe,
+        )
+        self.trainer.accelerator.wait_for_everyone()
+
+    def on_step_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the beginning of a training step. Triggers batch_start callback.
+        """
+        super().on_step_begin(args, state, control, **kwargs)
+        session_callbacks.batch_start()
+
+    def on_step_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the end of a training step. Triggers optimizer and batch_end callbacks.
+        """
+        super().on_step_end(args, state, control, **kwargs)
+        session_callbacks.optim_pre_step()
+        session_callbacks.optim_post_step()
+        session_callbacks.batch_end()
+
+    def on_train_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs,
+    ) -> None:
+        """
+        Called at the end of training. Finalizes the compression session.
+        """
+        super().on_train_end(args, state, control, **kwargs)
+        active_session().finalize()
+        self.trainer.compute_loss_func = self.original_compute_loss
+
+
+class LLMCompressorPlugin(BasePlugin):
+    """
+    Sparse Finetuning plugin for Axolotl integration.
+    """
+
+    def get_input_args(self) -> str:
+        """
+        Returns the path to the plugin's argument definition.
+
+        Returns:
+            str: Dotted path to the LLMCompressorArgs class.
+        """
+        return "axolotl.integrations.llm_compressor.args.LLMCompressorArgs"
+
+    def add_callbacks_post_trainer(self, cfg: Any, trainer: Trainer) -> list:
+        """
+        Adds Sparse Finetuning callback to the Trainer instance.
+
+        Args:
+            cfg (Any): Configuration object containing the sparse recipe.
+            trainer (Trainer): Huggingface Trainer instance.
+
+        Returns:
+            list: List containing the configured callback instances.
+        """
+        LOG.info("Adding Sparse Finetuning callback to the trainer")
+        callback = LLMCompressorCallbackHandler(
+            trainer=trainer,
+            recipe=cfg.llmcompressor.recipe,
+        )
+        return [callback]
+
+
+def compute_loss_wrapper(
+    compute_loss_func: Callable[Concatenate[Module, P], R],
+) -> Callable[Concatenate[Module, P], R]:
+    """
+    Wraps the loss computation function to trigger the loss_calculated callback.
+
+    Args:
+        compute_loss_func (Callable): Original loss computation function.
+
+    Returns:
+        Callable: Wrapped function that also invokes the loss_calculated callback.
+    """
+
+    @wraps(compute_loss_func)
+    def compute_and_notify(model: Module, *args: P.args, **kwargs: P.kwargs) -> R:
+        loss = compute_loss_func(model, *args, **kwargs)
+        if active_session().lifecycle.initialized_ and model.training:
+            session_callbacks.loss_calculated(loss=loss)
+        return loss
+
+    return compute_and_notify

src/axolotl/integrations/llm_compressor/utils.py

@@ -0,0 +1,40 @@
+"""Utilities for llmcompressor integration with axolotl."""
+
+from typing import Union
+
+from llmcompressor.transformers.sparsification.compressed_tensors_utils import (
+    modify_save_pretrained,
+)
+from transformers import PreTrainedModel, Trainer
+
+
+def save_compressed_model(
+    model: PreTrainedModel,
+    output_dir: Union[str, bytes],
+    trainer: Trainer,
+    safe_serialization: bool = False,
+    save_compressed: bool = False,
+) -> None:
+    """
+    Synchronize processes, apply compression hooks, and save the model.
+
+    Args:
+        model (PreTrainedModel): The model to be saved.
+        output_dir (str or bytes): Path where the model files will be written.
+        trainer (Trainer): Hugging Face Trainer for process synchronization.
+        safe_serialization (bool): Use safe serialization if True.
+        save_compressed (bool): Write compressed tensors if True.
+    """
+    trainer.accelerator.wait_for_everyone()
+
+    # Only the main process writes the files
+    if not trainer.accelerator.is_main_process:
+        return
+
+    modify_save_pretrained(model)
+    model.save_pretrained(
+        output_dir,
+        safe_serialization=safe_serialization,
+        save_compressed=save_compressed,
+        skip_sparsity_compression_stats=not save_compressed,
+    )

src/axolotl/monkeypatch/attention/ring_attn/__init__.py

@@ -4,6 +4,7 @@
 # flake8: noqa
 
 from .patch import (
+    RingAttnFunc,
     get_ring_attn_group,
     register_ring_attn,
     set_ring_attn_group,

src/axolotl/monkeypatch/attention/ring_attn/adapters/batch.py

@@ -28,7 +28,7 @@ from transformers.modeling_flash_attention_utils import (
 )
 from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
 
-from axolotl.utils.schemas.enums import RingAttnFunc
+from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
 
 RING_ATTN_FUNC_MAPPING = {
     RingAttnFunc.BATCH_RING: ring_flash_attn_func,

src/axolotl/monkeypatch/attention/ring_attn/patch.py

@@ -6,13 +6,14 @@ package, specifically the `hf_adapter.substitute_hf_flash_attn` function to patc
 their sequence parallel version of Flash Attention 2.
 """
 
+from enum import Enum
+
 import torch
 import torch.distributed as dist
 from accelerate.logging import get_logger
 
 from axolotl.logging_config import configure_logging
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-from axolotl.utils.schemas.enums import RingAttnFunc
 
 configure_logging()
 LOG = get_logger(__name__)
@@ -42,6 +43,17 @@ def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
     RING_ATTN_GROUP = ring_attn_group
 
 
+class RingAttnFunc(str, Enum):
+    """Enum class for supported `ring-flash-attn` implementations"""
+
+    # VARLEN_RING = "varlen_ring"
+    # VARLEN_ZIGZAG = "varlen_zigzag"
+    VARLEN_LLAMA3 = "varlen_llama3"
+    BATCH_RING = "batch_ring"
+    BATCH_ZIGZAG = "batch_zigzag"
+    BATCH_STRIPE = "batch_stripe"
+
+
 def register_ring_attn(
     sequence_parallel_degree: int,
     heads_k_stride: int | None,

src/axolotl/train.py

@@ -6,7 +6,6 @@ import os
 import signal
 import sys
 import weakref
-from contextlib import nullcontext
 from pathlib import Path
 from typing import Any, Dict
 
@@ -26,15 +25,11 @@ from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
 from axolotl.core.trainer_builder import HFCausalTrainerBuilder, HFRLTrainerBuilder
-from axolotl.core.trainers.mixins.sequence_parallel import (
-    SequenceParallelContextManager,
-)
 from axolotl.logging_config import configure_logging
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
 from axolotl.utils.models import load_model, load_processor, load_tokenizer
-from axolotl.utils.schemas.enums import RLType
 from axolotl.utils.trainer import setup_trainer
 
 try:
@@ -109,7 +104,7 @@ def setup_reference_model(
         Reference model if needed for RL training, `None` otherwise.
     """
     model_ref = None
-    if cfg.rl and cfg.rl != RLType.ORPO:
+    if cfg.rl and cfg.rl != "orpo":
         if cfg.adapter and not cfg.rl_adapter_ref_model:
             # use built-in trl autounwrap
             LOG.debug("Passing model_ref: None to RL trainer")
@@ -190,28 +185,16 @@ def execute_training(
         trainer: The configured trainer object.
         resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
     """
-    # Define the context managers to use
-    flash_context = (
-        torch.backends.cuda.sdp_kernel(
+    LOG.info("Starting trainer...")
+    if cfg.flash_optimum:
+        with torch.backends.cuda.sdp_kernel(
+            # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
             enable_flash=True,
             enable_math=True,
             enable_mem_efficient=True,
-        )
-        if cfg.flash_optimum
-        else nullcontext()
-    )
-    sequence_parallel_context = (
-        SequenceParallelContextManager(
-            model=trainer.model,
-            sequence_parallel_degree=cfg.sequence_parallel_degree,
-            ring_attn_func=cfg.ring_attn_func,
-        )
-        if cfg.sequence_parallel_degree > 1
-        else nullcontext()
-    )
-
-    LOG.info("Starting trainer...")
-    with flash_context, sequence_parallel_context:
+        ):
+            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
+    else:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)
 
 
@@ -288,6 +271,19 @@ def save_trained_model(
                 os.remove(os.path.join(cfg.output_dir, "model.safetensors"))
             except FileNotFoundError:
                 pass
+    elif hasattr(cfg, "llmcompressor") and cfg.llmcompressor:
+        from axolotl.integrations.llm_compressor.utils import (
+            save_compressed_model,
+        )
+
+        save_compressed_model(
+            model=model,
+            output_dir=cfg.output_dir,
+            trainer=trainer,
+            safe_serialization=safe_serialization,
+            save_compressed=cfg.llmcompressor.save_compressed,
+        )
+
     elif cfg.local_rank == 0:
         if cfg.flash_optimum and BetterTransformer:
             model = BetterTransformer.reverse(model)
@@ -296,6 +292,7 @@ def save_trained_model(
             trainer.model.save_pretrained(
                 cfg.output_dir, safe_serialization=safe_serialization
             )
+
         model.save_pretrained(cfg.output_dir, safe_serialization=safe_serialization)
 
 

src/axolotl/utils/collators/batching.py

@@ -1,12 +1,20 @@
-"""Data collators for axolotl to pad labels and position_ids for packed sequences"""
+"""
+Data collators for axolotl to pad labels and position_ids for packed sequences. Also
+includes logic for handling sequence parallelism collation.
+"""
 
 from dataclasses import dataclass
 from typing import Any
 
 import numpy as np
+import torch
+import torch.distributed as dist
 from transformers import PreTrainedTokenizerBase
 from transformers.utils import PaddingStrategy
 
+from axolotl.monkeypatch.attention.ring_attn import update_ring_attn_params
+from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
+
 
 @dataclass
 class DataCollatorForSeq2Seq:
@@ -41,6 +49,8 @@ class DataCollatorForSeq2Seq:
             The id to use when padding the labels (-100 will be automatically ignored by PyTorch loss functions).
         return_tensors (`str`):
             The type of Tensor to return. Allowable values are "np", "pt" and "tf".
+        sequence_parallel_degree (`int`):
+            The degree of sequence parallelism. Default to 1 for no sequence parallelism.
     """
 
     tokenizer: PreTrainedTokenizerBase
@@ -51,6 +61,17 @@ class DataCollatorForSeq2Seq:
     label_pad_token_id: int = -100
     position_pad_token_id: int = 0
     return_tensors: str = "pt"
+    sequence_parallel_degree: int = 1
+    ring_attn_func: RingAttnFunc | None = None
+
+    def __post_init__(self):
+        if self.sequence_parallel_degree > 1:
+            from axolotl.monkeypatch.attention.ring_attn import get_ring_attn_group
+
+            # Get information about our position in the SP group
+            sp_group = get_ring_attn_group()
+            self.local_rank = dist.get_rank(group=sp_group)
+            self.local_world_size = dist.get_world_size(group=sp_group)
 
     def __call__(self, features, return_tensors=None):
         has_attn_mask = "attention_mask" in features[0].keys()
@@ -120,8 +141,62 @@ class DataCollatorForSeq2Seq:
             )
             features["decoder_input_ids"] = decoder_input_ids
 
+        if self.sequence_parallel_degree > 1:
+            features = self.apply_sequence_parallelism(features)
+
         return features
 
+    def apply_sequence_parallelism(
+        self, batch: dict[str, torch.Tensor]
+    ) -> torch.Tensor:
+        """
+        Apply sequence parallelism slicing to a batch.
+
+        Args:
+            batch: Batch dictionary from parent collator.
+
+        Returns:
+            Sliced batch dictionary.
+        """
+        # Get local (start, end) for sequence parallelism slicing
+        total_seq_len = batch["input_ids"].size(1)
+
+        # Update params for varlen ring attention calculation
+        if batch.get("position_ids") is not None:
+            update_ring_attn_params(position_ids=batch["position_ids"])
+
+        # Slice batch for sequence parallel processing
+        for key in batch:
+            if batch[key].size(1) == total_seq_len:
+                if self.ring_attn_func in [
+                    RingAttnFunc.VARLEN_LLAMA3,
+                    RingAttnFunc.BATCH_RING,
+                ]:
+                    batch[key] = (
+                        batch[key]
+                        .chunk(self.local_world_size, dim=1)[self.local_rank]
+                        .contiguous()
+                    )
+                elif self.ring_attn_func is RingAttnFunc.BATCH_ZIGZAG:
+                    chunks = batch[key].chunk(2 * self.local_world_size, dim=1)
+
+                    # Take rank's chunk and opposing chunk for zigzag pattern
+                    selected_chunks = [
+                        chunks[self.local_rank],
+                        chunks[2 * self.local_world_size - self.local_rank - 1],
+                    ]
+                    batch[key] = torch.cat(selected_chunks, dim=1).contiguous()
+                elif self.ring_attn_func is RingAttnFunc.BATCH_STRIPE:
+                    # TODO(djsaunde): This doesn't seem to work as expected
+                    # Split into striped data and stack
+                    tensor = torch.stack(
+                        batch[key].split(self.local_world_size, dim=1),
+                        dim=1,
+                    ).transpose(1, 2)
+                    batch[key] = tensor[:, self.local_rank].contiguous()
+
+        return batch
+
 
 @dataclass
 class BatchSamplerDataCollatorForSeq2Seq(DataCollatorForSeq2Seq):

src/axolotl/utils/config/__init__.py

@@ -126,6 +126,9 @@ def normalize_config(cfg):
             with open(ds_config_path, encoding="utf-8") as f:
                 cfg.deepspeed = json.load(f)
 
+    if cfg.sequence_parallel_degree is None:
+        cfg.sequence_parallel_degree = 1
+
     if cfg.saves_per_epoch:
         save_steps = 1.0 / (cfg.saves_per_epoch * cfg.num_epochs)
         if save_steps < 1.0:  # prevent saves on every step

src/axolotl/utils/data/rl.py

@@ -18,9 +18,8 @@ from axolotl.utils.data.utils import deduplicate_and_log_datasets, md5
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import is_main_process, zero_first
 from axolotl.utils.models import load_tokenizer
-from axolotl.utils.schemas.enums import RLType
 
-LOG = logging.getLogger(__name__)
+LOG = logging.getLogger("axolotl")
 
 
 def _get_path(ds_hash, cfg):
@@ -81,7 +80,7 @@ def map_dataset(cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs):
 def drop_long_rl_seq(
     sample, rl, tokenizer, sequence_len  # pylint: disable=invalid-name
 ):
-    if rl in (RLType.DPO, RLType.IPO, RLType.ORPO, RLType.SIMPO):
+    if rl in ("dpo", "ipo", "orpo", "simpo"):
         if not (
             sample.get("prompt") and sample.get("chosen") and sample.get("rejected")
         ):
@@ -101,7 +100,7 @@ def drop_long_rl_seq(
             len_prompt + len_rejected
         ) <= sequence_len
 
-    if rl is RLType.KTO:
+    if rl == "kto":
         if not (sample.get("prompt") and sample.get("completion")):
             raise ValueError("Prompt and completion keys are required for KTO datasets")
 
@@ -115,7 +114,7 @@ def drop_long_rl_seq(
 
         return (len_prompt + len_completion) <= sequence_len
 
-    if rl is RLType.GRPO:
+    if rl == "grpo":
         return True
 
     raise ValueError("Unknown RL type")
@@ -138,9 +137,9 @@ def load_prepare_preference_datasets(cfg):
             if _type:
                 if isinstance(_type, DictDefault):
                     _type = "user_defined.default"
-                if _cfg.rl is RLType.ORPO:
+                if _cfg.rl == "orpo":
                     ds_transform_fn = load_orpo(_type, _cfg, dataset_idx=i)
-                elif _cfg.rl is RLType.KTO:
+                elif _cfg.rl == "kto":
                     ds_transform_fn = load_kto(_type, _cfg, dataset_idx=i)
                 else:
                     ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
@@ -151,7 +150,7 @@ def load_prepare_preference_datasets(cfg):
                 split_datasets[i] = map_dataset(
                     cfg, data_set, ds_transform_fn, tokenizer, **map_kwargs
                 )
-            elif _cfg.rl is RLType.KTO:
+            elif _cfg.rl == "kto":
                 ds_transform_fn = load_kto(_type, _cfg, dataset_idx=i)
                 map_kwargs = {}
                 if isinstance(ds_transform_fn, tuple):

src/axolotl/utils/models.py

@@ -72,7 +72,6 @@ from axolotl.utils.distributed import (
 from axolotl.utils.gradient_checkpointing import hf_grad_checkpoint_offload_wrapper
 from axolotl.utils.lora_embeddings import get_linear_embedding_layers
 from axolotl.utils.model_shard_quant import load_sharded_model, load_sharded_model_quant
-from axolotl.utils.schemas.enums import RLType
 
 LOG = logging.getLogger(__name__)
 
@@ -140,6 +139,22 @@ def check_model_config(cfg: DictDefault, model_config: PretrainedConfig):
         hasattr(model_config, "quantization_config")
         and model_config.quantization_config
     )
+
+    # Detect compressed-tensors config
+    is_compressed_tensors_config = (
+        quant_config_exists
+        and model_config.quantization_config.get("quant_method") == "compressed-tensors"
+    )
+
+    if is_compressed_tensors_config:
+        if model_config.quantization_config.get("config_groups"):
+            LOG.warning(
+                "Found `config_groups` in a compressed-tensors config. "
+                "QAT integration with llmcompressor is not tested."
+            )
+        # Skip further quant checks for compressed-tensors
+        return
+
     quant_config_method_is_gptq = (
         quant_config_exists
         and "quant_method" in model_config.quantization_config
@@ -1341,7 +1356,7 @@ class ModelLoader:
             # then the dpo trainer doesn't want the peft model loaded over it, it just wants the lora/peft config
             if (
                 self.cfg.adapter
-                and self.cfg.rl in [RLType.DPO, RLType.IPO, RLType.KTO]
+                and self.cfg.rl in ["dpo", "ipo", "kto"]
                 and not self.cfg.merge_lora
             ):
                 _, lora_config = load_lora(

src/axolotl/utils/schemas/config.py

@@ -18,7 +18,6 @@ from pydantic import (
 )
 from transformers.utils.import_utils import is_torch_npu_available
 
-from axolotl.utils.distributed import is_main_process
 from axolotl.utils.schemas.datasets import (
     DatasetConfig,
     DPODataset,
@@ -28,7 +27,7 @@ from axolotl.utils.schemas.datasets import (
     StepwiseSupervisedDataset,
 )
 from axolotl.utils.schemas.deprecated import DeprecatedParameters, RemappedParameters
-from axolotl.utils.schemas.enums import ChatTemplate, RingAttnFunc, RLType
+from axolotl.utils.schemas.enums import ChatTemplate, RLType
 from axolotl.utils.schemas.integrations import (
     CometConfig,
     GradioConfig,
@@ -260,7 +259,7 @@ class AxolotlInputConfig(
 
     sequence_parallel_degree: int | None = None
     heads_k_stride: int | None = None
-    ring_attn_func: RingAttnFunc | None = None
+    ring_attn_func: str | None = None
 
     special_tokens: SpecialTokensConfig | None = None
     tokens: list[str] | None = None
@@ -719,10 +718,9 @@ class AxolotlInputConfig(
             and data.get("eval_sample_packing") is None
             and not data.get("eval_table_size")
         ):
-            if is_main_process():
-                LOG.info(
-                    "explicitly setting `eval_sample_packing` to match `sample_packing`"
-                )
+            LOG.info(
+                "explicitly setting `eval_sample_packing` to match `sample_packing`"
+            )
             data["eval_sample_packing"] = True
 
         if (
@@ -784,7 +782,7 @@ class AxolotlInputConfig(
 
     @model_validator(mode="after")
     def check_simpo_warmup(self):
-        if self.rl is RLType.SIMPO and self.warmup_ratio:
+        if self.rl == "simpo" and self.warmup_ratio:
             raise ValueError(
                 "warmup_ratio is not supported with the simpo trainer. Please use `warmup_steps` instead"
             )
@@ -1151,17 +1149,22 @@ class AxolotlInputConfig(
 
         return data
 
-    @model_validator(mode="after")
-    def check_sequence_parallel_degree(self):
-        if not self.sequence_parallel_degree:
-            self.sequence_parallel_degree = 1
-        elif self.sequence_parallel_degree > 1:
-            if not self.flash_attention:
+    @field_validator("sequence_parallel_degree", mode="after")
+    @classmethod
+    def check_sequence_parallel_degree(cls, value, info):
+        if not value:
+            value = 1
+
+        if value > 1:
+            if not info.data.get("flash_attention"):
                 raise ValueError(
                     "flash_attention: true must be set with sequence_parallel_degree > 1"
                 )
 
-            if self.sample_packing and self.micro_batch_size > 1:
+            if (
+                info.data.get("sample_packing")
+                and not info.data["micro_batch_size"] == 1
+            ):
                 raise ValueError(
                     "micro_batch_size must be set to 1 when sample_packing is enabled"
                     "due to a `ring-flash-attn` requirement"
@@ -1179,41 +1182,44 @@ class AxolotlInputConfig(
             # TODO: monkeypatch / callback to average losses correctly across SP ranks
             # / fix gradient scaling across SP ranks. Losses, grads should be scaled
             # according to the proportion of non-padding tokens per rank.
-            if is_main_process():
-                LOG.warning(
-                    "Sequence parallelism (SP) is enabled with "
-                    f"sequence_parallel_degree={self.sequence_parallel_degree}. "
-                    "Please note that logged losses may differ slightly to the non-SP "
-                    "losses due to transformers Trainer implementation details. "
-                    "Please see https://github.com/axolotl-ai-cloud/axolotl/pull/2495#issuecomment-2784022042 "
-                    "for more details."
-                )
+            LOG.warning(
+                "Sequence parallelism (SP) is enabled with "
+                f"sequence_parallel_degree={value}. Please note that logged losses may "
+                "differ slightly to the non-SP losses due to transformers Trainer "
+                "implementation details. Please see "
+                "https://github.com/axolotl-ai-cloud/axolotl/pull/2495#issuecomment-2784022042 "
+                "for more details."
+            )
 
-        return self
+        return value
 
-    @model_validator(mode="after")
-    def validate_ring_attn_func(self):
-        if getattr(self, "sequence_parallel_degree", 1) == 1:
-            return self
+    @field_validator("ring_attn_func", mode="after")
+    @classmethod
+    def check_ring_attn_func(cls, value, info):
+        if not info.data.get("sequence_parallel_degree", 1) > 1:
+            return value
 
-        if self.ring_attn_func is not None:
+        from axolotl.monkeypatch.attention.ring_attn.patch import RingAttnFunc
+
+        if value is not None:
+            # Set the ring attention function if passed in config
             valid_funcs = list(RingAttnFunc)
-            if self.ring_attn_func in valid_funcs:
-                self.ring_attn_func = RingAttnFunc(self.ring_attn_func)
+            if value in valid_funcs:
+                value = RingAttnFunc(value)
             else:
                 raise ValueError(
-                    f"ring_attn_func: {self.ring_attn_func} must be in {valid_funcs}"
+                    f"ring_attn_func: {value} must be one of {valid_funcs}"
                 )
         else:
             # Default ring attention function selection
-            sample_packing = getattr(self, "sample_packing", False)
-            self.ring_attn_func = (
+            sample_packing = info.data.get("sample_packing")
+            value = (
                 RingAttnFunc.VARLEN_LLAMA3
                 if sample_packing
                 else RingAttnFunc.BATCH_RING
             )
 
-        return self
+        return value
 
     @model_validator(mode="before")
     @classmethod

src/axolotl/utils/schemas/enums.py

@@ -6,12 +6,12 @@ from enum import Enum
 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
-    SIMPO = "simpo"  # pylint: disable=invalid-name
+    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
+    simpo = "simpo"  # pylint: disable=invalid-name
 
 
 class ChatTemplate(str, Enum):
@@ -53,14 +53,3 @@ class CustomSupportedOptimizers(str, Enum):
     ao_adamw_fp8 = "ao_adamw_fp8"  # pylint: disable=invalid-name
     adopt_adamw = "adopt_adamw"  # pylint: disable=invalid-name
     muon = "muon"  # pylint: disable=invalid-name
-
-
-class RingAttnFunc(str, Enum):
-    """Enum class for supported `ring-flash-attn` implementations"""
-
-    # VARLEN_RING = "varlen_ring"
-    # VARLEN_ZIGZAG = "varlen_zigzag"
-    VARLEN_LLAMA3 = "varlen_llama3"
-    BATCH_RING = "batch_ring"
-    BATCH_ZIGZAG = "batch_zigzag"
-    BATCH_STRIPE = "batch_stripe"

src/axolotl/utils/trainer.py

@@ -348,7 +348,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
                     load_from_cache_file=not cfg.is_preprocess,
                     desc="Add position_id column (PoSE)",
                 )
-    elif cfg.sample_packing:
+    elif cfg.sample_packing or cfg.sequence_parallel_degree > 1:
         drop_long_kwargs = {}
         if filter_map_kwargs:
             drop_long_kwargs["desc"] = "Add position_id column (Sample Packing)"
@@ -358,7 +358,7 @@ def process_datasets_for_packing(cfg, train_dataset, eval_dataset):
             **filter_map_kwargs,
             **drop_long_kwargs,
         )
-        if cfg.eval_sample_packing:
+        if cfg.eval_sample_packing or cfg.sequence_parallel_degree > 1:
             if eval_dataset:
                 eval_dataset = eval_dataset.map(
                     add_position_ids,

tests/e2e/integrations/test_llm_compressor.py

@@ -0,0 +1,104 @@
+"""
+E2E smoke tests for LLMCompressorPlugin integration
+"""
+
+from pathlib import Path
+
+import pytest
+
+from axolotl.cli.args import TrainerCliArgs
+from axolotl.common.datasets import load_datasets
+from axolotl.train import train
+from axolotl.utils.config import normalize_config, prepare_plugins, validate_config
+from axolotl.utils.dict import DictDefault
+
+from tests.e2e.utils import check_model_output_exists, require_torch_2_4_1
+
+MODELS = [
+    "nm-testing/llama2.c-stories42M-pruned2.4-compressed",
+    "nm-testing/llama2.c-stories42M-gsm8k-sparse-only-compressed",
+]
+
+
+@pytest.mark.parametrize(
+    "base_model", MODELS, ids=["no-checkpoint-recipe", "with-checkpoint-recipe"]
+)
+@pytest.mark.parametrize(
+    "save_compressed", [True, False], ids=["save_compressed", "save_uncompressed"]
+)
+class TestLLMCompressorIntegration:
+    """
+    e2e tests for axolotl.integrations.llm_compressor.LLMCompressorPlugin
+    """
+
+    @require_torch_2_4_1
+    def test_llmcompressor_plugin(
+        self, temp_dir, base_model: str, save_compressed: bool
+    ):
+        # core cfg
+        cfg = DictDefault(
+            {
+                "base_model": base_model,
+                "plugins": ["axolotl.integrations.llm_compressor.LLMCompressorPlugin"],
+                "sequence_len": 1024,
+                "val_set_size": 0.05,
+                "special_tokens": {"pad_token": "<|endoftext|>"},
+                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
+                "num_epochs": 1,
+                "micro_batch_size": 2,
+                "gradient_accumulation_steps": 2,
+                "output_dir": temp_dir,
+                "learning_rate": 1e-5,
+                "optimizer": "adamw_torch_fused",
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "bf16": "auto",
+                "max_steps": 5,
+                "llmcompressor": {
+                    "recipe": {
+                        "finetuning_stage": {
+                            "finetuning_modifiers": {
+                                "ConstantPruningModifier": {
+                                    "targets": [
+                                        "re:.*q_proj.weight",
+                                        "re:.*k_proj.weight",
+                                        "re:.*v_proj.weight",
+                                        "re:.*o_proj.weight",
+                                        "re:.*gate_proj.weight",
+                                        "re:.*up_proj.weight",
+                                        "re:.*down_proj.weight",
+                                    ],
+                                    "start": 0,
+                                },
+                            },
+                        },
+                    },
+                    "save_compressed": save_compressed,
+                },
+            }
+        )
+
+        prepare_plugins(cfg)
+        cfg = validate_config(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        train(cfg=cfg, dataset_meta=dataset_meta)
+        check_model_output_exists(temp_dir, cfg)
+        _check_llmcompressor_model_outputs(temp_dir, save_compressed)
+
+
+def _check_llmcompressor_model_outputs(temp_dir, save_compressed):
+
+    # recipe.yaml should exist
+    assert (Path(temp_dir) / "recipe.yaml").exists()
+
+    # sparsity config exists if save_compressed
+    if save_compressed:
+        from compressed_tensors import ModelCompressor
+        from compressed_tensors.config import Sparse24BitMaskConfig
+
+        compressor = ModelCompressor.from_pretrained(temp_dir)
+        assert compressor is not None
+        assert isinstance(compressor.sparsity_config, Sparse24BitMaskConfig)

tests/e2e/patched/test_mixtral_samplepack.py

@@ -1,4 +1,6 @@
-"""E2E tests for mixtral"""
+"""
+E2E tests for mixtral
+"""
 
 import logging
 import os
@@ -97,7 +99,6 @@ class TestMixtral(unittest.TestCase):
                 "bf16": "auto",
             }
         )
-        cfg = validate_config(cfg)
         normalize_config(cfg)
         cli_args = TrainerCliArgs()
         dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)

tests/e2e/patched/test_sp.py

@@ -2,22 +2,17 @@
 
 # pylint: disable=redefined-outer-name,unused-argument
 
-import functools
-import sys
 from unittest.mock import MagicMock, patch
 
 import pytest
 import torch
 from accelerate.state import PartialState
 
-from axolotl.core.trainers.mixins.sequence_parallel import apply_sequence_parallelism
 from axolotl.monkeypatch.attention.ring_attn import (
     get_ring_attn_group,
-    register_ring_attn,
     set_ring_attn_group,
 )
 from axolotl.utils.dict import DictDefault
-from axolotl.utils.schemas.enums import RingAttnFunc
 
 
 @pytest.fixture
@@ -52,27 +47,6 @@ def fixture_cfg():
     return cfg
 
 
-@pytest.fixture
-def sequence_parallel_batch():
-    """Create a test batch for sequence parallelism tests."""
-    batch_size = 1
-    seq_len = 8
-
-    # Create test tensors
-    input_ids = torch.arange(batch_size * seq_len).reshape(batch_size, seq_len)
-    attention_mask = torch.ones(batch_size, seq_len)
-    position_ids = torch.arange(seq_len).expand(batch_size, seq_len)
-
-    # Create test batch
-    batch = {
-        "input_ids": input_ids,
-        "attention_mask": attention_mask,
-        "position_ids": position_ids,
-    }
-
-    return batch
-
-
 class TestRingAttention:
     """Tests for the ring attention functionality."""
 
@@ -99,6 +73,11 @@ class TestRingAttention:
         self, mock_world_size, mock_rank, mock_new_group, partial_state
     ):
         """Test that ring attention groups are created correctly."""
+        from axolotl.monkeypatch.attention.ring_attn import (
+            RingAttnFunc,
+            register_ring_attn,
+        )
+
         # Setup mocks
         mock_world_size.return_value = 8  # 8 GPUs total
         mock_rank.return_value = 3  # GPU #3
@@ -122,308 +101,88 @@ class TestRingAttention:
         set_ring_attn_group(None)
 
 
-class TestConfigValidation:
-    """Tests for validating sequence parallelism configurations."""
+# Mock a simplified DataCollator test
+@patch("axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group")
+@patch("torch.distributed.get_rank")
+@patch("torch.distributed.get_world_size")
+def test_sequence_parallel_slicing(
+    mock_world_size, mock_rank, mock_get_group, partial_state
+):
+    """Test the basic sequence slicing logic without full collator instantiation."""
+    # Setup mocks
+    mock_get_group.return_value = MagicMock()
+    mock_rank.return_value = 1  # Second GPU
+    mock_world_size.return_value = 4  # 4 GPUs total
 
-    @pytest.fixture(autouse=True)
-    def setup_mocks(self, monkeypatch):
-        """Set up mocks for all tests in this class."""
-        # Mock the ring_flash_attn module
-        monkeypatch.setitem(sys.modules, "ring_flash_attn", MagicMock())
+    # Create a sample batch
+    batch = {
+        "input_ids": torch.tensor(
+            [
+                [101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112],
+                [201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212],
+            ]
+        ),
+        "attention_mask": torch.ones(2, 12),
+    }
 
-        # Mock the is_main_process function to return True
-        monkeypatch.setattr(
-            "axolotl.utils.schemas.config.is_main_process", lambda: True
-        )
+    # Simplified slicing logic from SequenceParallelDataCollator
+    def slice_batch(batch, rank, world_size):
+        result = {}
+        for key in batch:
+            seq_len = batch[key].shape[1]
+            slice_size = seq_len // world_size
+            start_idx = rank * slice_size
+            end_idx = start_idx + slice_size if rank < world_size - 1 else seq_len
+            result[key] = batch[key][:, start_idx:end_idx]
+        return result
 
-    @pytest.fixture
-    def base_cfg(self):
-        """Create a base configuration for testing."""
-        return DictDefault(
-            {
-                "base_model": "HuggingFaceTB/SmolLM2-135M",
-                "datasets": [{"path": "mhenrichsen/alpaca_2k_test", "type": "alpaca"}],
-                "micro_batch_size": 1,
-                "gradient_accumulation_steps": 1,
-                "learning_rate": 1e-3,
-                "output_dir": "./model-out",
-                "sequence_len": 512,
-                "special_tokens": {"pad_token": "<|endoftext|>"},
-            }
-        )
-
-    @pytest.mark.parametrize(
-        "config_updates, expected_values, should_pass, error_msg",
-        [
-            # Valid configuration
-            (
-                {"sequence_parallel_degree": 2, "flash_attention": True},
-                {"sequence_parallel_degree": 2, "flash_attention": True},
-                True,
-                None,
-            ),
-            # Default sequence_parallel_degree
-            ({}, {"sequence_parallel_degree": 1}, True, None),
-            # Invalid: sequence_parallel_degree > 1 without flash_attention
-            (
-                {"sequence_parallel_degree": 2, "flash_attention": False},
-                None,
-                False,
-                "flash_attention: true must be set",
-            ),
-            # Invalid: sequence_parallel_degree > 1 with sample_packing and micro_batch_size > 1
-            (
-                {
-                    "sequence_parallel_degree": 2,
-                    "flash_attention": True,
-                    "sample_packing": True,
-                    "micro_batch_size": 2,
-                    "pad_to_sequence_len": True,
-                },
-                None,
-                False,
-                "micro_batch_size must be set to 1",
-            ),
-        ],
-        ids=[
-            "valid_config",
-            "default_sp_degree",
-            "without_flash_attention",
-            "sample_packing_with_large_batch",
-        ],
+    # Slice the batch
+    result = slice_batch(
+        batch, rank=mock_rank.return_value, world_size=mock_world_size.return_value
     )
-    def test_sequence_parallel_config_validation(
-        self, base_cfg, config_updates, expected_values, should_pass, error_msg
-    ):
-        """Test various sequence parallelism configuration scenarios."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
 
-        # Apply updates to base config
-        cfg = base_cfg
-        cfg.update(config_updates)
-
-        if should_pass:
-            # Should validate without errors
-            config = AxolotlInputConfig(**cfg)
-
-            # Check expected values
-            for key, value in expected_values.items():
-                assert getattr(config, key) == value
-        else:
-            # Should raise exception
-            with pytest.raises(ValueError) as excinfo:
-                AxolotlInputConfig(**cfg)
-            assert error_msg in str(excinfo.value)
-
-    @pytest.mark.parametrize(
-        "ring_attn_func, sample_packing, expected_func",
+    # Check slicing
+    assert result["input_ids"].shape == (2, 3)  # 12 tokens / 4 GPUs = 3 tokens per GPU
+    expected_input_ids = torch.tensor(
         [
-            (None, True, RingAttnFunc.VARLEN_LLAMA3),
-            (None, False, RingAttnFunc.BATCH_RING),
-        ],
-        ids=["default_with_sample_packing", "default_without_sample_packing"],
+            [104, 105, 106],  # Second slice of first sequence
+            [204, 205, 206],  # Second slice of second sequence
+        ]
     )
-    def test_ring_attn_func_validation(
-        self, base_cfg, ring_attn_func, sample_packing, expected_func
-    ):
-        """Test ring_attn_func validation and defaults."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
-
-        # Apply updates to base config
-        cfg = base_cfg | {
-            "sequence_parallel_degree": 2,
-            "flash_attention": True,
-            "sample_packing": sample_packing,
-        }
-
-        if ring_attn_func is not None:
-            cfg["ring_attn_func"] = ring_attn_func
-
-        # Should validate without errors
-        config = AxolotlInputConfig(**cfg)
-
-        # Check ring_attn_func value
-        assert config.ring_attn_func.value == expected_func
-
-    def test_invalid_ring_attn_func(self, base_cfg):
-        """Test that an invalid ring_attn_func is rejected."""
-        from axolotl.utils.schemas.config import AxolotlInputConfig
-
-        # Invalid configuration with invalid ring_attn_func
-        cfg = base_cfg | {
-            "sequence_parallel_degree": 2,
-            "flash_attention": True,
-            "ring_attn_func": "INVALID_FUNC",
-        }
-
-        # Should raise ValidationError
-        with pytest.raises(ValueError) as excinfo:
-            AxolotlInputConfig(**cfg)
-
-        # Verify error message
-        assert "ring_attn_func: INVALID_FUNC must be in" in str(excinfo.value)
+    assert torch.all(result["input_ids"] == expected_input_ids)
 
 
-class TestApplySequenceParallelism:
-    """Tests for the apply_sequence_parallelism function."""
+@patch.dict("sys.modules", {"ring_flash_attn": MagicMock()})
+def test_config_validation_with_valid_inputs(cfg):
+    """Test that valid sequence parallelism configurations pass validation."""
+    # Import the actual model class with appropriate mocks
+    from axolotl.utils.schemas.config import AxolotlInputConfig
 
-    @pytest.fixture(autouse=True)
-    def mock_distributed(self, monkeypatch):
-        """Mock torch.distributed functions for testing."""
-        # Mock is_initialized to return True
-        monkeypatch.setattr(torch.distributed, "is_initialized", lambda: True)
+    # Valid configuration: sequence_parallel_degree > 1 and flash_attention is True
+    cfg = cfg | {
+        "sequence_parallel_degree": 2,
+        "flash_attention": True,
+    }
 
-        # Mock get_rank to return 0 by default
-        monkeypatch.setattr(torch.distributed, "get_rank", lambda *args, **kwargs: 0)
+    # Should validate without errors
+    config = AxolotlInputConfig(**cfg)
+    assert config.sequence_parallel_degree == 2
+    assert config.flash_attention is True
 
-        # Mock get_world_size to return 2 by default
-        monkeypatch.setattr(
-            torch.distributed, "get_world_size", lambda *args, **kwargs: 2
-        )
 
-        # Mock the process group
-        monkeypatch.setattr(
-            "axolotl.monkeypatch.attention.ring_attn.get_ring_attn_group",
-            MagicMock,
-        )
+def test_config_validation_with_invalid_inputs(cfg):
+    """Test that invalid sequence parallelism configurations fail validation."""
+    from axolotl.utils.schemas.config import AxolotlInputConfig
 
-        # Mock update_ring_attn_params
-        monkeypatch.setattr(
-            "axolotl.monkeypatch.attention.ring_attn.update_ring_attn_params",
-            lambda **kwargs: None,
-        )
+    # Invalid configuration: sequence_parallel_degree > 1 but flash_attention is False
+    cfg = cfg | {
+        "sequence_parallel_degree": 2,
+        "flash_attention": False,
+    }
 
-    def test_world_size_one(self, sequence_parallel_batch):
-        """Test that function returns original batch when world size is 1."""
-        result = apply_sequence_parallelism(
-            batch=sequence_parallel_batch,
-            local_rank=0,
-            local_world_size=1,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
+    # Should raise ValidationError
+    with pytest.raises(ValueError) as excinfo:
+        AxolotlInputConfig(**cfg)
 
-        # Should return the original batch unchanged
-        assert result == sequence_parallel_batch
-
-    def test_batch_ring_rank0(self, sequence_parallel_batch):
-        """Test BATCH_RING sharding for rank 0 in a 2-process group."""
-        batch = sequence_parallel_batch
-        seq_len = batch["input_ids"].size(1)
-
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Check that sequence dimension was sharded correctly
-        assert result["input_ids"].shape[1] == seq_len // 2
-        assert result["attention_mask"].shape[1] == seq_len // 2
-
-        # Verify content: rank 0 should get the first half of the sequence
-        assert torch.equal(result["input_ids"], batch["input_ids"][:, : seq_len // 2])
-        assert torch.equal(
-            result["position_ids"], batch["position_ids"][:, : seq_len // 2]
-        )
-
-    def test_batch_ring_rank1(self, sequence_parallel_batch):
-        """Test BATCH_RING sharding for rank 1 in a 2-process group."""
-        batch = sequence_parallel_batch
-        seq_len = batch["input_ids"].size(1)
-        original_input_ids = batch["input_ids"].clone()
-
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=1,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Verify content: rank 1 should get the second half of the sequence
-        assert torch.equal(result["input_ids"], original_input_ids[:, seq_len // 2 :])
-
-    def test_batch_zigzag(self, sequence_parallel_batch):
-        """Test BATCH_ZIGZAG sharding pattern."""
-        batch = sequence_parallel_batch
-        original_input_ids = batch["input_ids"].clone()
-        seq_len = batch["input_ids"].size(1)
-
-        # Test rank 0
-        result_rank0 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
-
-        # Test rank 1
-        result_rank1 = apply_sequence_parallelism(
-            batch={k: v.clone() for k, v in batch.items()},
-            local_rank=1,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_ZIGZAG,
-        )
-
-        # Checks for both ranks
-        assert result_rank0["input_ids"].shape[1] == seq_len // 2
-        assert result_rank1["input_ids"].shape[1] == seq_len // 2
-
-        # For a 2-rank system with 8 tokens, check specific zigzag pattern
-        # Rank 0 should get chunks [0, 1] and [6, 7]
-        # Rank 1 should get chunks [2, 3] and [4, 5]
-        if seq_len == 8:
-            # Create expected tensors for comparison
-            rank0_expected = torch.cat(
-                [original_input_ids[:, :2], original_input_ids[:, 6:8]], dim=1
-            )
-
-            rank1_expected = torch.cat(
-                [original_input_ids[:, 2:4], original_input_ids[:, 4:6]], dim=1
-            )
-
-            assert torch.equal(result_rank0["input_ids"], rank0_expected)
-            assert torch.equal(result_rank1["input_ids"], rank1_expected)
-
-    def test_partial_application(self, sequence_parallel_batch):
-        """Test that we can create a partially applied version of the function."""
-        batch = sequence_parallel_batch
-        original_input_ids = batch["input_ids"].clone()
-
-        # Create a partially applied function
-        rank0_ring_parallel = functools.partial(
-            apply_sequence_parallelism,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Use the partially applied function
-        result = rank0_ring_parallel(batch=batch)
-
-        # Verify it works as expected
-        assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2
-        assert torch.equal(
-            result["input_ids"],
-            original_input_ids[:, : original_input_ids.shape[1] // 2],
-        )
-
-    def test_missing_position_ids(self, sequence_parallel_batch):
-        """Test handling of batch without position_ids."""
-        # Create a batch without position_ids
-        batch = {
-            k: v for k, v in sequence_parallel_batch.items() if k != "position_ids"
-        }
-        original_input_ids = batch["input_ids"].clone()
-
-        # This should run without error even though position_ids is missing
-        result = apply_sequence_parallelism(
-            batch=batch,
-            local_rank=0,
-            local_world_size=2,
-            ring_attn_func=RingAttnFunc.BATCH_RING,
-        )
-
-        # Verification should pass
-        assert "position_ids" not in result
-        assert result["input_ids"].shape[1] == original_input_ids.shape[1] // 2
+    # Verify error message
+    assert "flash_attention: true must be set" in str(excinfo.value)