enable torch compile on the optimizer step

make optimizer compile independent of torch compile on the model

.github/workflows/multi-gpu-e2e.yml

@@ -8,7 +8,7 @@ on:
       - 'setup.py'
       - 'pyproject.toml'
       - '.github/workflows/multi-gpu-e2e.yml'
-      - 'src/axolotl/core/trainers/mixins/context_parallel.py'
+      - 'src/axolotl/core/trainers/mixins/sequence_parallel.py'
       - 'src/axolotl/utils/distributed.py'
   workflow_dispatch:
   schedule:

_quarto.yml

@@ -75,7 +75,7 @@ quartodoc:
     - title: Context Managers
       desc: Context managers for altering trainer behaviors
       contents:
-        - utils.ctx_managers.context_parallel
+        - utils.ctx_managers.sequence_parallel
     - title: Prompt Strategies
       desc: Prompt formatting strategies
       contents:
@@ -274,7 +274,7 @@ website:
             - docs/unsloth.qmd
             - docs/torchao.qmd
             - docs/custom_integrations.qmd
-            - docs/context_parallelism.qmd
+            - docs/sequence_parallelism.qmd
 
         - section: "Troubleshooting"
           contents:

docs/config.qmd

@@ -764,13 +764,13 @@ ddp_timeout:
 ddp_bucket_cap_mb:
 ddp_broadcast_buffers:
 
-# Context parallelism
+# Sequence parallelism
 # Set to a divisor of the number of GPUs available to split sequences into chunks of equal size.
 # Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
 # E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
 # subsequences, or set to 4 to split into four equal-sized subsequences.
-# See https://docs.axolotl.ai/docs/context_parallelism.html for more details.
-context_parallel_degree:
+# See https://docs.axolotl.ai/docs/sequence_parallelism.html for more details.
+sequence_parallel_degree:
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 # Must evenly divide the number of KV heads in your model.
 heads_k_stride: 1

docs/multi-gpu.qmd

@@ -18,7 +18,7 @@ Axolotl supports several methods for multi-GPU training:
 
 - DeepSpeed (recommended)
 - FSDP (Fully Sharded Data Parallel)
-- Context parallelism
+- Sequence parallelism
 - FSDP + QLoRA
 
 ## DeepSpeed {#sec-deepspeed}
@@ -80,14 +80,14 @@ fsdp_config:
   fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
 ```
 
-## Context parallelism {#sec-sequence-parallelism}
+## Sequence parallelism {#sec-sequence-parallelism}
 
-We support context parallelism (SP) via the
+We support sequence parallelism (SP) via the
 [ring-flash-attention](https://github.com/zhuzilin/ring-flash-attention) project. This
 allows one to split up sequences across GPUs, which is useful in the event that a
 single sequence causes OOM errors during model training.
 
-See our [dedicated guide](context_parallelism.qmd) for more information.
+See our [dedicated guide](sequence_parallelism.qmd) for more information.
 
 ### FSDP + QLoRA {#sec-fsdp-qlora}
 

docs/sequence_parallelism.qmd

@@ -1,16 +1,16 @@
 ---
-title: Context Parallelism
+title: Sequence Parallelism
 description: Train with long sequences split across multiple GPUs.
 ---
 
-Context parallelism is a technique that splits sequences across multiple GPUs,
+Sequence parallelism is a technique that splits sequences across multiple GPUs,
 allowing you to train with very long sequences that wouldn't fit on a single GPU. Each
 GPU processes a different portion of the sequence, and the results are aggregated
 through a ring communication pattern.
 
-## When to Use Context Parallelism
+## When to Use Sequence Parallelism
 
-Use context parallelism when:
+Use sequence parallelism when:
 
 - You need to train with sequence lengths that don't fit into a single GPU's memory
 - You have multiple GPUs available
@@ -18,11 +18,11 @@ Use context parallelism when:
 
 ## Configuration
 
-To enable context parallelism, add the following to your configuration file:
+To enable sequence parallelism, add the following to your configuration file:
 
 ```yaml
 # Set to a divisor (> 1) of the number of GPUs available
-context_parallel_degree: 4  # Split sequences across 4 GPUs
+sequence_parallel_degree: 4  # Split sequences across 4 GPUs
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
 # Optional; one of "varlen_llama3" or "batch_ring". Defaults to
@@ -30,23 +30,23 @@ heads_k_stride: 1
 ring_attn_func:
 ```
 
-The `context_parallel_degree` should be a divisor of the total number of GPUs. For example:
+The `sequence_parallel_degree` should be a divisor of the total number of GPUs. For example:
 
 - With 8 GPUs, valid values would be 2, 4, or 8
 - With 4 GPUs, valid values would be 2 or 4
 
 ## Implementation Details
 
-When context parallelism is enabled:
+When sequence parallelism is enabled:
 
-1. Each sequence is divided into equal chunks across the GPUs in a context parallel group
+1. Each sequence is divided into equal chunks across the GPUs in a sequence parallel group
 2. The data collator handles the chunking of input_ids, attention_mask, labels, and position_ids
 3. Position IDs are adjusted to maintain proper relative positions
 4. The trainer uses special ring communication patterns for attention operations
 
 ## Requirements
 
-To use context parallelism, you need:
+To use sequence parallelism, you need:
 
 - Multiple GPUs (at least 2)
 - The `ring-flash-attn` package. Install with:
@@ -66,7 +66,7 @@ sequence_len: 8192
 
 ...
 
-context_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
+sequence_parallel_degree: 4  # Split each sequence into 4 parts, one per GPU
 # Optional; strides across the key dimension. Larger values use more memory but should make training faster.
 heads_k_stride: 1
 # Optional; one of "varlen_llama3" or "batch_ring". Defaults to
@@ -79,22 +79,22 @@ ring_attn_func:
 This will train the Llama 3 8B model with 8K context length, with each sequence split
 into 2 subsequences of length 4096 across 2 GPUs.
 
-## Sample Packing with Context Parallelism
+## Sample Packing with Sequence Parallelism
 
-Context parallelism is compatible with Axolotl's sample packing functionality. When using both features together:
+Sequence parallelism is compatible with Axolotl's sample packing functionality. When using both features together:
 
 1. Samples are first packed together
-2. The packed sequences are then divided across GPUs in the context parallel group
+2. The packed sequences are then divided across GPUs in the sequence parallel group
 3. Position IDs are automatically adjusted to maintain proper relative positions
 
 ## Effect on Batch Size
 
-When using context parallelism, your effective global batch size is **divided** by the `context_parallel_degree`. This happens because:
+When using sequence parallelism, your effective global batch size is **divided** by the `sequence_parallel_degree`. This happens because:
 
-- Each group of `context_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
+- Each group of `sequence_parallel_degree` GPUs works on the same batch (just different parts of each sequence)
 - The number of batches processed per step decreases
 
 For example:
-- With 8 GPUs and no context parallelism: 8 different batches processed per step
-- With 8 GPUs and `context_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
+- With 8 GPUs and no sequence parallelism: 8 different batches processed per step
+- With 8 GPUs and `sequence_parallel_degree=4`: Only 2 different batches processed per step (each split across 4 GPUs)
 - If your per-GPU `micro_batch_size` is 2, the global batch size decreases from 16 to 4

src/axolotl/cli/merge_lora.py

@@ -73,7 +73,7 @@ def do_cli(config: Union[Path, str] = Path("examples/"), **kwargs) -> None:
         load_in_8bit=False,
         load_in_4bit=False,
         flash_attention=False,
-        context_parallel_degree=None,
+        sequence_parallel_degree=None,
         deepspeed=None,
         fsdp=None,
         fsdp_config=None,

src/axolotl/core/builders/base.py

@@ -422,6 +422,9 @@ class TrainerBuilderBase(abc.ABC):
             if self.cfg.torch_compile_mode:
                 training_args_kwargs["torch_compile_mode"] = self.cfg.torch_compile_mode
 
+        if self.cfg.compile_optimizer:
+            training_args_kwargs["compile_optimizer"] = True
+
     def _configure_gradient_checkpointing(self, training_args_kwargs: dict):
         if self.cfg.gradient_checkpointing:
             training_args_kwargs["gradient_checkpointing"] = (

src/axolotl/core/builders/rl.py

@@ -54,7 +54,7 @@ class HFRLTrainerBuilder(TrainerBuilderBase):
 
         if self.cfg.rl is RLType.GRPO:
             trainer_cls = GRPOStrategy.get_trainer_class(
-                context_parallel=self.cfg.context_parallel_degree > 1
+                sequence_parallel=self.cfg.sequence_parallel_degree > 1
             )
             trainer_cls_args.extend(GRPOStrategy.set_trainer_args(self.cfg))
 

src/axolotl/core/trainers/__init__.py

@@ -5,7 +5,7 @@
 
 from .base import AxolotlTrainer
 from .dpo.trainer import AxolotlDPOTrainer
-from .grpo.trainer import AxolotlGRPOContextParallelTrainer, AxolotlGRPOTrainer
+from .grpo.trainer import AxolotlGRPOSequenceParallelTrainer, AxolotlGRPOTrainer
 from .mamba import AxolotlMambaTrainer
 from .relora import ReLoRATrainer
 from .trl import (

src/axolotl/core/trainers/base.py

@@ -7,13 +7,11 @@ from __future__ import annotations
 import os
 from collections import defaultdict
 from functools import partial, wraps
-from typing import Any, Callable, Literal, Optional
+from typing import Callable, Literal, Optional
 
-from axolotl.utils.ctx_managers.context_parallel.distributed import get_context_parallel_manager
 import datasets
 import torch
 from datasets import Dataset
-from torch import nn
 from torch.utils.data import (
     BatchSampler,
     DataLoader,
@@ -67,32 +65,6 @@ class AxolotlTrainer(SchedulerMixin, OptimizerMixin, RngLoaderMixin, Trainer):
         if self.args.orpo_alpha:
             self.loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
 
-        # SPDA device mesh init
-        import torch.distributed as dist
-
-        world_size = dist.get_world_size()
-        mesh_shape = (
-            world_size // 2,
-            2,
-        )
-        self.world_mesh = dist.DeviceMesh(
-            "cuda",
-            torch.tensor(list(range(world_size))).reshape(mesh_shape),
-            mesh_dim_names=("dp", "cp"),
-        )
-
-    def training_step(
-        self, model: nn.Module, inputs: dict[str, torch.Tensor | Any], num_items_in_batch=None
-    ) -> torch.Tensor:
-        ctx_manager = get_context_parallel_manager(
-            world_mesh=self.world_mesh,
-            model=model,
-        )
-        to_shard = {k: v for k, v in inputs.items() if v.ndim > 1}
-        with ctx_manager(list(to_shard.values())):
-            super().training_step(model, inputs, num_items_in_batch)
-        
-
     def _wrap_model(self, model, training=True, dataloader=None):
         if self.args.torch_compile:
             torch._dynamo.config.accumulated_cache_size_limit = (  # pylint: disable=protected-access

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

@@ -8,7 +8,7 @@ from trl.trainer.grpo_trainer import RewardFunc
 
 from axolotl.core.trainers.grpo.args import AxolotlGRPOConfig
 from axolotl.core.trainers.grpo.trainer import (
-    AxolotlGRPOContextParallelTrainer,
+    AxolotlGRPOSequenceParallelTrainer,
     AxolotlGRPOTrainer,
 )
 from axolotl.utils.dict import DictDefault
@@ -23,10 +23,10 @@ class GRPOStrategy:
 
     @classmethod
     def get_trainer_class(
-        cls, context_parallel: bool
-    ) -> type[AxolotlGRPOTrainer] | type[AxolotlGRPOContextParallelTrainer]:
-        if context_parallel:
-            return AxolotlGRPOContextParallelTrainer
+        cls, sequence_parallel: bool
+    ) -> type[AxolotlGRPOTrainer] | type[AxolotlGRPOSequenceParallelTrainer]:
+        if sequence_parallel:
+            return AxolotlGRPOSequenceParallelTrainer
         return AxolotlGRPOTrainer
 
     @classmethod
@@ -69,8 +69,8 @@ class GRPOStrategy:
         grpo_args_kwargs["log_completions"] = trl.log_completions
         grpo_args_kwargs["num_completions_to_print"] = trl.num_completions_to_print
 
-        if cfg.context_parallel_degree > 1:
-            grpo_args_kwargs["context_parallel_degree"] = cfg.context_parallel_degree
+        if cfg.sequence_parallel_degree > 1:
+            grpo_args_kwargs["sequence_parallel_degree"] = cfg.sequence_parallel_degree
 
         if trl.reward_weights:
             grpo_args_kwargs["reward_weights"] = trl.reward_weights

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

@@ -13,4 +13,4 @@ from axolotl.core.training_args import AxolotlTrainingMixins
 class AxolotlGRPOConfig(AxolotlTrainingMixins, GRPOConfig):
     """Axolotl GRPO Config for GRPO training"""
 
-    context_parallel_degree: int | None = None
+    sequence_parallel_degree: int | None = None

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

@@ -1,7 +1,7 @@
 """Repeat random sampler (similar to the one implemented in
 https://github.com/huggingface/trl/blob/main/trl/trainer/grpo_trainer.py) that adds
-context parallelism functionality; i.e., duplicating data across ranks in the same
-context parallel group.
+sequence parallelism functionality; i.e., duplicating data across ranks in the same
+sequence parallel group.
 """
 
 from typing import Iterator, Sized
@@ -10,26 +10,26 @@ import torch
 from torch.utils.data import Sampler
 
 
-class ContextParallelRepeatRandomSampler(Sampler):
-    """Sampler for GRPO training with context parallelism.
+class SequenceParallelRepeatRandomSampler(Sampler):
+    """Sampler for GRPO training with sequence parallelism.
 
     This sampler ensures:
-    - Ranks in the same context parallel (SP) group receive identical data.
+    - Ranks in the same sequence parallel (SP) group receive identical data.
     - Each index is repeated multiple times for sampling different completions.
     - Entire batches are repeated for reuse in multiple updates.
-    - Data is properly distributed across CP groups.
+    - Data is properly distributed across SP groups.
 
-    In the table below, the values represent dataset indices. Each CP group has
-    `context_parallel_degree = 2` GPUs working together on the same data. There are 2
-    CP groups (SP0 and SP1), with `world_size = 4` total GPUs.
+    In the table below, the values represent dataset indices. Each SP group has
+    `sequence_parallel_degree = 2` GPUs working together on the same data. There are 2
+    SP groups (SP0 and SP1), with `world_size = 4` total GPUs.
 
-                                               Context Parallel Groups
+                                               Sequence Parallel Groups
                                         |       SP0        |       SP1        |
                                         |  GPU 0  |  GPU 1 |  GPU 2  |  GPU 3 |
                     global_step  step    <---> mini_repeat_count=3
-                                            <----------> batch_size=2 per CP group
-    grad_accum=2   ▲  ▲  0       0         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- CP groups get different data
-                   ▼  |  0       1         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- Same data for each CP group GPU
+                                            <----------> batch_size=2 per SP group
+    grad_accum=2   ▲  ▲  0       0         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- SP groups get different data
+                   ▼  |  0       1         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- Same data for each SP group GPU
                       |
                       |  1       2         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- Repeat same indices for iterations
     num_iterations=2  ▼  1       3         [0 0 0  1 1 1]     [2 2 2  3 3 3]   <- When using gradient accumulation
@@ -45,7 +45,7 @@ class ContextParallelRepeatRandomSampler(Sampler):
         rank: Rank of current process.
         batch_size: Number of samples per batch.
         repeat_count: How many times to repeat the full sampling process.
-        context_parallel_degree: Number of ranks in a context parallel group.
+        sequence_parallel_degree: Number of ranks in a sequence parallel group.
         shuffle: Whether to shuffle the dataset.
         seed: Random seed for shuffling.
         drop_last: Whether to drop the last incomplete batch.
@@ -59,7 +59,7 @@ class ContextParallelRepeatRandomSampler(Sampler):
         rank: int,
         batch_size: int = 1,
         repeat_count: int = 1,
-        context_parallel_degree: int = 1,
+        sequence_parallel_degree: int = 1,
         shuffle: bool = True,
         seed: int = 0,
         drop_last: bool = False,
@@ -76,16 +76,16 @@ class ContextParallelRepeatRandomSampler(Sampler):
         self.world_size = world_size
         self.rank = rank
 
-        # Context parallelism parameters
-        self.context_parallel_degree = context_parallel_degree
-        self.num_sp_groups = world_size // context_parallel_degree
-        self.sp_group_id = rank // context_parallel_degree
+        # 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 CP group
+        # Calculate effective number of samples per SP group
         if (
             self.drop_last
             and self.total_size % (self.num_sp_groups * self.batch_size) != 0
@@ -125,8 +125,8 @@ class ContextParallelRepeatRandomSampler(Sampler):
             padding = indices[: self.batch_size - len(indices) % self.batch_size]
             indices += padding
 
-        # Subsample based on CP group ID
-        # Each CP group gets distinct batches of data
+        # 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

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

@@ -1,8 +1,9 @@
-"""Axolotl GRPO trainers (with and without context parallelism handling)"""
+"""Axolotl GRPO trainers (with and without sequence parallelism handling)"""
 
 # pylint: disable=too-many-lines,duplicate-code,protected-access,no-member
 
 import warnings
+from functools import partial
 from typing import Any
 
 import datasets
@@ -41,7 +42,7 @@ from trl.trainer.grpo_config import GRPOConfig
 from trl.trainer.grpo_trainer import RewardFunc, nanstd
 from trl.trainer.utils import pad
 
-from axolotl.core.trainers.grpo.sampler import ContextParallelRepeatRandomSampler
+from axolotl.core.trainers.grpo.sampler import SequenceParallelRepeatRandomSampler
 from axolotl.core.trainers.mixins import RngLoaderMixin, SchedulerMixin
 from axolotl.core.trainers.mixins.optimizer import OptimizerInitMixin, OptimizerMixin
 from axolotl.monkeypatch.ring_attn import get_ring_attn_group
@@ -58,9 +59,45 @@ class AxolotlGRPOTrainer(
 
     _tag_names = ["trl", "grpo", "axolotl"]
 
+    def get_train_dataloader(self):
+        if self.train_dataset is None:
+            raise ValueError("Trainer: training requires a train_dataset.")
 
-class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
-    """Extend the base GRPOTrainer for context parallelism handling"""
+        train_dataset = self.train_dataset
+        data_collator = self.data_collator
+        if isinstance(train_dataset, datasets.Dataset):
+            train_dataset = self._remove_unused_columns(
+                train_dataset, description="training"
+            )
+        else:
+            data_collator = self._get_collator_with_removed_columns(
+                data_collator, description="training"
+            )
+
+        dataloader_params = {
+            "batch_size": self._train_batch_size
+            * self.args.steps_per_generation,  # < this is the change
+            "collate_fn": data_collator,
+            "num_workers": self.args.dataloader_num_workers,
+            "pin_memory": self.args.dataloader_pin_memory,
+            "persistent_workers": self.args.dataloader_persistent_workers,
+        }
+
+        if not isinstance(train_dataset, torch.utils.data.IterableDataset):
+            dataloader_params["sampler"] = self._get_train_sampler()
+            dataloader_params["drop_last"] = self.args.dataloader_drop_last
+            dataloader_params["worker_init_fn"] = partial(
+                seed_worker,
+                num_workers=self.args.dataloader_num_workers,
+                rank=self.args.process_index,
+            )
+            dataloader_params["prefetch_factor"] = self.args.dataloader_prefetch_factor
+
+        return self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params))
+
+
+class AxolotlGRPOSequenceParallelTrainer(AxolotlGRPOTrainer):
+    """Extend the base GRPOTrainer for sequence parallelism handling"""
 
     def __init__(
         self,
@@ -97,11 +134,11 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
             optimizer_cls_and_kwargs=optimizer_cls_and_kwargs,
         )
 
-        # Get number of CP groups (number of processes divided by CP degree)
+        # 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.context_parallel_degree
+        num_sp_groups = num_processes // self.args.sequence_parallel_degree
 
-        # Calculate batch size per CP group (not per process)
+        # 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
@@ -111,7 +148,7 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
 
         if self.num_generations not in possible_values:
             raise ValueError(
-                f"The batch size per CP group ({num_sp_groups} x "
+                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 "
@@ -119,7 +156,7 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
             )
 
         if self.args.eval_strategy != "no":
-            # If context parallelism is enabled, calculate batch size per CP group
+            # 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
@@ -129,8 +166,8 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
 
             if self.num_generations not in possible_values:
                 raise ValueError(
-                    f"With context parallelism (degree {self.args.context_parallel_degree}), "
-                    f"the eval batch size per CP group ({num_sp_groups} x {self.args.per_device_eval_batch_size}) "
+                    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}."
@@ -143,7 +180,7 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
         self.local_world_size = 1
 
     def train(self, *args, **kwargs):
-        # Initialize the CP group
+        # Initialize the SP group
         self.sp_group = get_ring_attn_group()
         self.rank = dist.get_rank()
         self.world_size = dist.get_world_size()
@@ -159,16 +196,16 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
             * self.args.gradient_accumulation_steps
         )
 
-        return ContextParallelRepeatRandomSampler(
+        return SequenceParallelRepeatRandomSampler(
             dataset=self.train_dataset,
             mini_repeat_count=self.num_generations,
             world_size=self.world_size,
             rank=self.rank,
             batch_size=effective_batch_size
             // self.num_generations
-            // self.args.context_parallel_degree,
+            // self.args.sequence_parallel_degree,
             repeat_count=self.num_iterations * self.args.gradient_accumulation_steps,
-            context_parallel_degree=self.args.context_parallel_degree,
+            sequence_parallel_degree=self.args.sequence_parallel_degree,
             shuffle=True,
             seed=self.args.seed,
             drop_last=True,
@@ -226,11 +263,11 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
         ):
             self.accelerator.even_batches = False
 
-        # Return unprepared dataloader if using context parallelism
+        # 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.context_parallel_degree > 1:
+        if self.args.sequence_parallel_degree > 1:
             return dataloader
 
         # Otherwise prepare with accelerator
@@ -303,21 +340,21 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
             # Generate completions using vLLM: gather all prompts and use them in a single call in the main process
             all_prompts_text = gather_object(prompts_text)
             if self.accelerator.is_main_process:
-                if self.args.context_parallel_degree > 1:
-                    # Calculate context parallel group information
+                if self.args.sequence_parallel_degree > 1:
+                    # Calculate sequence parallel group information
                     world_size = self.accelerator.num_processes
-                    context_parallel_degree = self.args.context_parallel_degree
-                    num_sp_groups = world_size // context_parallel_degree
+                    sequence_parallel_degree = self.args.sequence_parallel_degree
+                    num_sp_groups = world_size // sequence_parallel_degree
 
-                    # Since processes in the same CP group have the same prompts, we need to ensure
-                    # we only take one copy of each prompt from each CP group
+                    # Since processes in the same SP group have the same prompts, we need to ensure
+                    # we only take one copy of each prompt from each SP group
                     ordered_set_of_prompts = []
                     for sp_group_id in range(num_sp_groups):
-                        # Get the first process from each CP group (typically the group leader)
-                        group_leader_rank = sp_group_id * context_parallel_degree
+                        # Get the first process from each SP group (typically the group leader)
+                        group_leader_rank = sp_group_id * sequence_parallel_degree
 
-                        # Extract prompts from this CP group, accounting for num_generations duplicates
-                        # We only need prompts from one rank in each CP group
+                        # Extract prompts from this SP group, accounting for num_generations duplicates
+                        # We only need prompts from one rank in each SP group
                         group_prompts = all_prompts_text[
                             group_leader_rank
                             * len(prompts_text) : (group_leader_rank + 1)
@@ -330,7 +367,7 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
                     # 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 * self.args.context_parallel_degree
+                        :: self.num_generations * self.args.sequence_parallel_degree
                     ]
 
                 with profiling_context(self, "vLLM.generate"):
@@ -347,28 +384,28 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
                     )
             else:
                 completion_ids = [None] * (
-                    len(all_prompts_text) // self.args.context_parallel_degree
+                    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 context parallelism
-            if self.args.context_parallel_degree > 1:
-                # Calculate CP group ID (which group of ranks this rank belongs to)
+            # 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 CP group
+                # 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 CP group get the same data slice
+                # 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-context parallel case
+                # Original behavior for non-sequence parallel case
                 process_slice = slice(
                     self.accelerator.process_index * len(prompts),
                     (self.accelerator.process_index + 1) * len(prompts),
@@ -578,20 +615,20 @@ class AxolotlGRPOContextParallelTrainer(AxolotlGRPOTrainer):
             advantages = advantages / (std_grouped_rewards + 1e-4)
 
         # Slice to keep only the local part of the data
-        if self.args.context_parallel_degree > 1:
-            # Calculate CP group ID (which group of ranks this rank belongs to)
+        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 CP group
+            # 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 CP group get the same data slice
+            # All ranks in the same SP group get the same data slice
             process_slice = slice(
                 sp_group_start,
                 sp_group_start + len(prompts),
             )
         else:
-            # Original behavior for non-context parallel case
+            # Original behavior for non-sequence parallel case
             process_slice = slice(
                 self.accelerator.process_index * len(prompts),
                 (self.accelerator.process_index + 1) * len(prompts),

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

@@ -1,5 +1,6 @@
 """Module for Axolotl trainer optimizer mixin"""
 
+import torch
 from peft.optimizers import create_loraplus_optimizer
 from torch import nn
 from transformers.trainer import Trainer
@@ -185,12 +186,12 @@ class OptimizerMixin(Trainer):
                                 p.data_ptr(): p.numel() for p in module.parameters()
                             }.values()
                         )
-                        LOG.info(f"skipped {module}: {skipped/2**20}M params")
+                        LOG.info(f"skipped {module}: {skipped / 2 ** 20}M params")
                         manager.register_module_override(
                             module, "weight", {"optim_bits": 32}
                         )
                         LOG.debug(f"bitsandbytes: will optimize {module} in fp32")
-                LOG.info(f"skipped: {skipped/2**20}M params")
+                LOG.info(f"skipped: {skipped / 2 ** 20}M params")
 
         if is_sagemaker_mp_enabled():
             self.optimizer = smp.DistributedOptimizer(  # pylint: disable=attribute-defined-outside-init
@@ -199,6 +200,11 @@ class OptimizerMixin(Trainer):
 
         return self.optimizer
 
+    def create_optimizer_and_scheduler(self, num_training_steps: int):
+        super().create_optimizer_and_scheduler(num_training_steps)
+        if self.args.compile_optimizer:
+            self.optimizer.step = torch.compile(self.optimizer.step)
+
 
 class OptimizerInitMixin:
     """

src/axolotl/core/training_args.py

@@ -141,6 +141,10 @@ class AxolotlTrainingMixins:
         default=None,
         metadata={"help": "absolute learning rate for the embedding layers."},
     )
+    compile_optimizer: Optional[bool] = field(
+        default=None,
+        metadata={"help": "Whether to compile the optimizer for faster training."},
+    )
     qlora: bool = field(
         default=False,
         metadata={"help": "whether this is a qlora training"},

src/axolotl/monkeypatch/ring_attn/patch.py

@@ -2,10 +2,10 @@
 
 Make use of the `ring-flash-attn` (https://github.com/zhuzilin/ring-flash-attention)
 package, specifically the `hf_adapter.substitute_hf_flash_attn` function to patch in
-their context parallel version of Flash Attention 2.
+their sequence parallel version of Flash Attention 2.
 
 We also provide some patches for accelerate functions to prepare the dataloader for
-context parallelism training.
+sequence parallelism training.
 """
 
 import inspect
@@ -13,9 +13,9 @@ import inspect
 import accelerate
 import torch
 import torch.distributed as dist
+from accelerate.logging import get_logger
 
 from axolotl.monkeypatch.utils import get_cu_seqlens_from_pos_ids
-from axolotl.utils.logging import get_logger
 from axolotl.utils.schemas.enums import RingAttnFunc
 
 LOG = get_logger(__name__)
@@ -63,15 +63,15 @@ def set_ring_attn_group(ring_attn_group: dist.ProcessGroup | None):
 
 
 def register_ring_attn(
-    context_parallel_degree: int,
+    sequence_parallel_degree: int,
     heads_k_stride: int | None,
     ring_attn_func: RingAttnFunc | None,
 ):
     """Create ring attention group and substitute flash attn with ring flash attn.
 
     Args:
-        context_parallel_degree: Context parallelism factor.
-        heads_k_stride: Context parallelism K head stride size. Passed through to
+        sequence_parallel_degree: Sequence parallelism factor.
+        heads_k_stride: Sequence parallelism K head stride size. Passed through to
             `varlen_llama3` `ring_flash_attn` implementation.
         ring_attn_func: `ring_flash_attn` ring attention implemention. If sample
             packing is enabled, it must be a `varlen` function; otherwise, it must be a
@@ -80,18 +80,28 @@ def register_ring_attn(
     rank = dist.get_rank()
     world_size = dist.get_world_size()
 
-    LOG.info(
-        "Enabling ring attention context parallelism: "
-        f"each sequence will be processed across {context_parallel_degree} GPUs"
+    if rank == 0:
+        LOG.info(
+            "Enabling ring attention sequence parallelism: "
+            f"each sequence will be processed across {sequence_parallel_degree} GPUs"
+        )
+
+    assert sequence_parallel_degree <= world_size, (
+        f"sequence_parallel_degree ({sequence_parallel_degree}) "
+        f"must be less than or equal to world_size ({world_size})"
+    )
+    assert world_size % sequence_parallel_degree == 0, (
+        f"sequence_parallel_degree ({sequence_parallel_degree}) "
+        f"must evenly divide world_size ({world_size})"
     )
 
-    # Assign ranks to context parallel groups
+    # Assign ranks to sequence parallel groups
     group_assignments = {}
-    for i in range(world_size // context_parallel_degree):
+    for i in range(world_size // sequence_parallel_degree):
         ring_attn_ranks = list(
             range(
-                i * context_parallel_degree,
-                (i + 1) * context_parallel_degree,
+                i * sequence_parallel_degree,
+                (i + 1) * sequence_parallel_degree,
             )
         )
         group = dist.new_group(ranks=ring_attn_ranks, backend="nccl")
@@ -103,7 +113,9 @@ def register_ring_attn(
         if rank in ring_attn_ranks:
             set_ring_attn_group(group)
 
-    LOG.info(f"Context parallel group assignments: {group_assignments}")
+    # Log the GPU group assignments
+    if rank == 0:
+        LOG.info(f"Sequence parallel group assignments: {group_assignments}")
 
     if ring_attn_func is RingAttnFunc.VARLEN_LLAMA3:
         from ring_flash_attn import substitute_hf_flash_attn
@@ -138,7 +150,7 @@ def update_ring_attn_params(position_ids: torch.Tensor | None):
 
 
 def patch_prepare_data_loader():
-    """Patch `accelerate.data_loader.prepare_data_loader` to respect the CP degree.
+    """Patch `accelerate.data_loader.prepare_data_loader` to respect the SP degree.
 
     Raies:
         RuntimeError: If source code to patch does not exist.
@@ -164,15 +176,15 @@ def patch_prepare_data_loader():
     patched_function = namespace["prepare_data_loader"]
 
     accelerate.data_loader.prepare_data_loader = patched_function
-    LOG.info("Patched accelerate.data_loader.prepare_data_loader for CP support")
+    LOG.info("Patched accelerate.data_loader.prepare_data_loader for SP support")
 
 
-def patch_prepare_device_mesh(context_parallel_degree: int):
+def patch_prepare_device_mesh(sequence_parallel_degree: int):
     """Patches the `Accelerator._prepare_device_mesh` method to create a device mesh
-    that includes context parallelism with the specified degree.
+    that includes sequence parallelism with the specified degree.
 
     Args:
-        context_parallel_degree (int): The degree of context parallelism to use.
+        sequence_parallel_degree (int): The degree of sequence parallelism to use.
     """
 
     def _prepare_device_mesh(self):
@@ -187,11 +199,11 @@ def patch_prepare_device_mesh(context_parallel_degree: int):
         ):
             return self.state.ds_device_mesh
 
-        # Create device mesh with context parallelism
+        # Create device mesh with sequence parallelism
         world_size = dist.get_world_size()
         mesh_shape = (
-            world_size // context_parallel_degree,
-            context_parallel_degree,
+            world_size // sequence_parallel_degree,
+            sequence_parallel_degree,
         )
         device_ids = list(range(world_size))
 
@@ -209,5 +221,5 @@ def patch_prepare_device_mesh(context_parallel_degree: int):
 
     LOG.info(
         "Successfully patched Accelerator._prepare_device_mesh "
-        f"with context_parallel_degree={context_parallel_degree}"
+        f"with sequence_parallel_degree={sequence_parallel_degree}"
     )

src/axolotl/train.py

@@ -25,13 +25,14 @@ from axolotl.common.datasets import TrainDatasetMeta
 from axolotl.contribs.lgpl import (  # pylint: disable = no-name-in-module
     fix_untrained_tokens,
 )
+from axolotl.core.builders import HFCausalTrainerBuilder, HFRLTrainerBuilder
 from axolotl.integrations.base import PluginManager
 from axolotl.loaders import (
     ModelLoader,
     load_processor,
     load_tokenizer,
 )
-from axolotl.utils.ctx_managers import ContextParallelContextManager
+from axolotl.utils.ctx_managers.sequence_parallel import SequenceParallelContextManager
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.distributed import cleanup_distributed
 from axolotl.utils.freeze import freeze_layers_except
@@ -147,7 +148,7 @@ def determine_resume_checkpoint(cfg: DictDefault) -> str | None:
 
 
 def setup_signal_handler(
-    cfg: DictDefault, model: PeftModel | PreTrainedModel, safe_serialization: bool
+    cfg: DictDefault, model: PreTrainedModel, safe_serialization: bool
 ):
     """
     Set up signal handler for graceful termination.
@@ -201,20 +202,15 @@ def execute_training(
                 )
             )
 
-        if cfg.context_parallel_degree > 1 and not cfg.sdp_attention:
-            # Models to enter context parallel manager for
+        if cfg.sequence_parallel_degree > 1:
             models = [trainer.model]
             if hasattr(trainer, "ref_model") and trainer.ref_model:
                 models.append(trainer.ref_model)
 
-            # Attention backend
-            backend = "sdp_attention" if cfg.sdp_attention else "flash_attention"
-
             stack.enter_context(
-                ContextParallelContextManager(
+                SequenceParallelContextManager(
                     models=models,
-                    backend=backend,
-                    context_parallel_degree=cfg.context_parallel_degree,
+                    sequence_parallel_degree=cfg.sequence_parallel_degree,
                     gradient_accumulation_steps=cfg.gradient_accumulation_steps,
                     ring_attn_func=cfg.ring_attn_func,
                     heads_k_stride=cfg.heads_k_stride,
@@ -228,7 +224,7 @@ def execute_training(
 def save_trained_model(
     cfg: DictDefault,
     trainer: Any,
-    model: PeftModel | PreTrainedModel,
+    model: PreTrainedModel,
     safe_serialization: bool,
 ):
     """
@@ -379,7 +375,7 @@ def create_model_card(cfg: DictDefault, trainer: Trainer):
 def save_initial_configs(
     cfg: DictDefault,
     tokenizer: PreTrainedTokenizer,
-    model: PeftModel | PreTrainedModel,
+    model: PreTrainedModel,
     peft_config: PeftConfig | None,
     processor: ProcessorMixin | None,
 ):
@@ -433,7 +429,7 @@ def setup_model_card(cfg: DictDefault):
 
 def handle_untrained_tokens_fix(
     cfg: DictDefault,
-    model: PeftModel | PreTrainedModel,
+    model: PreTrainedModel,
     tokenizer: PreTrainedTokenizer,
     train_dataset: Dataset,
     safe_serialization: bool,
@@ -476,7 +472,7 @@ def handle_untrained_tokens_fix(
 
 
 def setup_model_and_trainer(cfg: DictDefault, dataset_meta: TrainDatasetMeta) -> tuple[
-    Trainer,
+    HFRLTrainerBuilder | HFCausalTrainerBuilder,
     PeftModel | PreTrainedModel,
     PreTrainedTokenizer,
     PeftConfig | None,

src/axolotl/utils/ctx_managers/__init__.py

@@ -1,5 +1,6 @@
-"""Init for context manager submodule."""
+"""Init for context manager submodule"""
 
-from .context_parallel.manager import ContextParallelContextManager
+# pylint: disable=unused-import
+# flake8: noqa
 
-__all__ = ["ContextParallelContextManager"]
+from .sequence_parallel import SequenceParallelContextManager

src/axolotl/utils/ctx_managers/context_parallel/distributed.py

@@ -1,146 +0,0 @@
-# BSD 3-Clause License
-
-# Copyright 2024 Meta
-
-# Redistribution and use in source and binary forms, with or without modification,
-# are permitted provided that the following conditions are met:
-
-# 1. Redistributions of source code must retain the above copyright notice,this list
-# of conditions and the following disclaimer.
-
-# 2. Redistributions in binary form must reproduce the above copyright notice, this
-# list of conditions and the following disclaimer in the documentation
-# and/or other materials provided with the distribution.
-
-# 3. Neither the name of the copyright holder nor the names of its contributors may
-# be used to endorse or promote products derived from this software without specific
-# prior written permission.
-
-# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY
-# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
-# OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT
-# SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
-# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
-# TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR
-# BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
-# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
-# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
-# DAMAGE.
-
-"""
-Distributed utils for SDPA context parallel implementation. Slightly modified from
-https://github.com/pytorch/torchtune/blob/2344509cf83bd886538fe3e8263e5145d1afb5c2/torchtune/training/_distributed.py.
-"""
-
-import contextlib
-from typing import Callable, Generator, Optional, Union
-
-import torch
-from torch import nn
-from torch.distributed.tensor.experimental import context_parallel
-from torch.distributed.tensor.experimental._attention import set_rotate_method
-from torch.nn.attention import SDPBackend, sdpa_kernel
-from torch.nn.attention.flex_attention import BlockMask
-
-
-def _get_sdpa_context() -> (
-    Callable[[Optional[Generator[None, None, None]]], Generator[None, None, None]]
-):
-    """
-    Creates a context manager to confine to flash/efficient/cuDNN attention backends.
-
-    Returns:
-        A context manager function that takes an optional context parallel context.
-    """
-
-    @contextlib.contextmanager
-    def context(cp_context: Union[Generator[None, None, None], None] = None):
-        with contextlib.ExitStack() as stack:
-            if cp_context is not None:
-                stack.enter_context(
-                    sdpa_kernel(
-                        [
-                            SDPBackend.FLASH_ATTENTION,
-                            SDPBackend.EFFICIENT_ATTENTION,
-                            SDPBackend.CUDNN_ATTENTION,
-                        ]
-                    )
-                )
-                stack.enter_context(cp_context)
-
-            yield
-
-    return context
-
-
-def get_context_parallel_manager(
-    *,
-    world_mesh: torch.distributed.DeviceMesh,
-    model: nn.Module,
-) -> Callable[[list[torch.Tensor]], Generator[None, None, None]]:
-    """
-    Context manager for applying context parallelism to a model. In addition to applying the
-    standard context manager to patch SDPA and shard model inputs and buffers along the sequence
-    dimension, this context manager also calls into _get_sdpa_context to filter to acceptable SDPA backends.
-
-    Args:
-        world_mesh: Global device mesh.
-        model: Model to apply context parallelism to.
-
-    Returns:
-        A context manager applying context parallelism if enabled is True. Otherwise a context manager
-        disabling the math SDPA backend.
-
-    Raises:
-        ValueError: if enabled is True but world_mesh does not contain a "cp" dimension
-    """
-
-    if "cp" not in world_mesh.mesh_dim_names:
-        raise ValueError(
-            "Context parallel is enabled but no context parallel device mesh is provided."
-        )
-    # TODO: context parallel for multimodal models requires extra work
-    # if not isinstance(model, TransformerDecoder):
-    #     raise ValueError("Context parallel is only supported for text models")
-    # model_buffers = list(model.buffers())
-    
-    # def get_all_buffers(module, prefix=""):
-    #     buffers = {}
-    #     for name, buffer in module.named_buffers(recurse=False):
-    #         full_name = f"{prefix}.{name}" if prefix else name
-    #         buffers[full_name] = buffer
-
-    #     for name, child in module.named_children():
-    #         child_prefix = f"{prefix}.{name}" if prefix else name
-    #         buffers.update(get_all_buffers(child, child_prefix))
-
-    #     return buffers
-
-    # model_buffers = get_all_buffers(model)
-
-    @contextlib.contextmanager
-    def context(model_inputs: list[torch.Tensor]):
-        # Create context parallel context if enabled
-        cp_context = None
-        if any([isinstance(input, BlockMask) for input in model_inputs]):
-            raise ValueError(
-                "Context parallel with flex attention is not yet supported"
-            )
-        set_rotate_method("allgather")
-
-        cp_context = context_parallel(
-            world_mesh["cp"],
-            # buffers=model_inputs + model_buffers,
-            buffers=model_inputs,
-            # buffer_seq_dims=[1] * len(model_inputs) + [0] * len(model_buffers),
-            buffer_seq_dims=[1] * len(model_inputs),
-            no_restore_buffers=set(model_inputs),
-        )
-
-        # Create and enter the train context with the optional cp_context
-        sdpa_context = _get_sdpa_context()
-
-        with sdpa_context(cp_context):
-            yield
-
-    return context

src/axolotl/utils/ctx_managers/context_parallel/manager.py

@@ -1,216 +0,0 @@
-"""Module for Axolotl trainer context parallelism manager and utilities."""
-
-import functools
-import inspect
-from typing import Callable, Literal
-
-import torch
-import torch.distributed as dist
-from torch.utils.hooks import RemovableHandle
-from transformers import PreTrainedModel
-from transformers.modeling_outputs import CausalLMOutputWithPast
-from transformers.utils import ModelOutput
-
-from axolotl.monkeypatch.ring_attn import (
-    get_ring_attn_group,
-    patch_prepare_data_loader,
-    patch_prepare_device_mesh,
-    register_ring_attn,
-)
-from axolotl.utils.ctx_managers.context_parallel.distributed import (
-    get_context_parallel_manager,
-)
-from axolotl.utils.ctx_managers.context_parallel.utils import (
-    AllGatherWithGrad,
-    apply_context_parallelism,
-)
-from axolotl.utils.schemas.enums import RingAttnFunc
-
-
-class ContextParallelContextManager:
-    """Context manager for context parallelism operations.
-
-    This class provides a context that will automatically apply context parallelism
-    during model forward passes using a pre-forward hook, and gather outputs from
-    across the context parallelism group using a post-forward hook.
-
-    Args:
-        models: List of models to apply context parallelism to pre- and post- forward
-            hooks.
-        backend: Which attention backend to use.
-        context_parallel_degree: Number of processes to split sequences over.
-        gradient_accumulation_steps: Number of steps to accumulate gradients over.
-        ring_attn_func: Which ring attention function to use. Currently unused.
-        heads_k_stride: Context parallelism K head stride size. Passed through to
-            `varlen_llama3` `ring_flash_attn` implementation.
-    """
-
-    def __init__(
-        self,
-        models: list[PreTrainedModel],
-        backend: Literal["sdp_attention", "flash_attention"],
-        context_parallel_degree: int,
-        gradient_accumulation_steps: int,
-        ring_attn_func: RingAttnFunc,
-        heads_k_stride: int | None,
-    ):
-        self.models = models
-        self.backend = backend
-        self.context_parallel_degree = context_parallel_degree
-        self.gradient_accumulation_steps = gradient_accumulation_steps
-        self.ring_attn_func = ring_attn_func
-        self.heads_k_stride = heads_k_stride
-        self._register_ring_attn()
-
-        # Store hook handles for removal
-        self.hook_handles: list[RemovableHandle] = []
-
-        if self.backend == "flash_attention":
-            # Set distributed info for local rank
-            self.process_group = get_ring_attn_group()
-            self.local_rank = dist.get_rank(self.process_group)
-            self.local_world_size = dist.get_world_size(self.process_group)
-
-             # Create a partially applied version of the apply_context_parallelism function
-            self.apply_context_parallelism = functools.partial(
-                apply_context_parallelism,
-                local_rank=self.local_rank,
-                local_world_size=self.local_world_size,
-                gradient_accumulation_steps=self.gradient_accumulation_steps,
-                ring_attn_func=self.ring_attn_func,
-            )
-
-            # Store original sequence length and padding information
-            self.original_seq_len = 0
-            self.pad_len = 0
-        else:
-            # SPDA device mesh init
-            world_size = dist.get_world_size()
-            mesh_shape = (
-                world_size // self.context_parallel_degree,
-                self.context_parallel_degree,
-            )
-            world_mesh = dist.DeviceMesh(
-                "cuda",
-                torch.tensor(list(range(world_size))).reshape(mesh_shape),
-                mesh_dim_names=("dp", "cp"),
-            )
-
-            # SDPA context parallel managers 
-            self.context_parallel_managers = []
-            for model in models:
-                ctx_manager = get_context_parallel_manager(
-                    world_mesh=world_mesh,
-                    model=model,
-                )
-                self.context_parallel_managers.append(ctx_manager)
-
-    def __enter__(self):
-        self._register_model_hooks()
-
-        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 = []
-
-        # TODO(djsaunde): Un-patch attention and accelerate functions (low priority)
-
-    def _register_ring_attn(self):
-        if self.backend == "flash_attention":
-            # Initialize ring attn for context parallelism
-            register_ring_attn(
-                context_parallel_degree=self.context_parallel_degree,
-                heads_k_stride=self.heads_k_stride,
-                ring_attn_func=self.ring_attn_func,
-            )
-
-        # Patches for accelerate functionality
-        patch_prepare_data_loader()
-        patch_prepare_device_mesh(context_parallel_degree=self.context_parallel_degree)
-
-    def _register_model_hooks(self):
-        # Forward pre-hook to apply context parallelism
-        def cp_flash_pre_hook(_, args, kwargs):
-            # Get parameter names from the model's forward function
-            forward_params = list(
-                inspect.signature(self.models[0].forward).parameters.keys()
-            )
-
-            updated_kwargs = kwargs.copy()
-            for i, arg in enumerate(args):
-                if i < len(forward_params):
-                    updated_kwargs[forward_params[i]] = arg
-
-            # Any excess positional arguments are kept as-is
-            remaining_args = args[len(forward_params) :]
-
-            # Apply context parallelism to updated kwargs
-            updated_kwargs, self.original_seq_len, self.pad_len = (
-                self.apply_context_parallelism(updated_kwargs)
-            )
-
-            return remaining_args, updated_kwargs
-
-        # Forward post-hook to gather outputs
-        def cp_flash_post_hook(_, __, output: ModelOutput) -> ModelOutput:
-            # Gather the sharded outputs
-            output = self._gather_outputs(output)
-
-            # Remove padding if it was added
-            if self.pad_len > 0:
-                for key, value in output.items():
-                    if isinstance(value, torch.Tensor) and value.dim() > 1:
-                        if value.size(1) == self.original_seq_len + self.pad_len:
-                            # Slice to remove padding
-                            output[key] = value[:, : self.original_seq_len].contiguous()
-
-            return output
-
-        def make_sdpa_pre_hook(model_idx: int) -> Callable:
-            def cp_sdpa_pre_hook(_, args, kwargs):
-                # Get parameter names from the model's forward function
-                forward_params = list(
-                    inspect.signature(self.models[0].forward).parameters.keys()
-                )
-
-                updated_kwargs = kwargs.copy()
-                for i, arg in enumerate(args):
-                    if i < len(forward_params):
-                        updated_kwargs[forward_params[i]] = arg
-
-                # Any excess positional arguments are kept as-is
-                remaining_args = args[len(forward_params) :]
-
-                to_shard = {k: v for k, v in updated_kwargs.items() if v.ndim > 1}
-
-                with self.context_parallel_managers[model_idx](list(to_shard.values())):
-                    return remaining_args, updated_kwargs
-
-            return cp_sdpa_pre_hook
-
-        # Register both hooks
-        for i, model in enumerate(self.models):
-            if self.backend == "flash_attention":
-                self.hook_handles.append(
-                    model.register_forward_pre_hook(cp_flash_pre_hook, with_kwargs=True)
-                )
-                self.hook_handles.append(
-                    model.register_forward_hook(cp_flash_post_hook)
-                )
-            else:
-                self.hook_handles.append(
-                    model.register_forward_pre_hook(
-                        make_sdpa_pre_hook(i), with_kwargs=True
-                    )
-                )
-
-    def _gather_outputs(self, output: CausalLMOutputWithPast) -> CausalLMOutputWithPast:
-        """Gather sharded outputs from all ranks and reconstruct the full tensor."""
-        for key, value in output.items():
-            if isinstance(value, torch.Tensor) and value.dim() > 1:
-                output[key] = AllGatherWithGrad.apply(value, self.process_group)
-
-        return output

src/axolotl/utils/ctx_managers/sequence_parallel.py

@@ -1,15 +1,28 @@
-"""Utils for context parallel context manager."""
+"""Module for Axolotl trainer sequence parallelism manager and utilities"""
+
+import functools
+import inspect
 
 import torch
 import torch.distributed as dist
+from torch import nn
+from torch.utils.hooks import RemovableHandle
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from transformers.utils import ModelOutput
 
-from axolotl.monkeypatch.ring_attn.patch import update_ring_attn_params
+from axolotl.monkeypatch.ring_attn import (
+    get_ring_attn_group,
+    patch_prepare_data_loader,
+    patch_prepare_device_mesh,
+    register_ring_attn,
+    update_ring_attn_params,
+)
 from axolotl.utils.schemas.enums import RingAttnFunc
 
 
 # TODO(djsaunde): implement zigzag, stripe patterns here (and elsewhere) in this
 # module. Currently, we just focus on batch ring and varlen llama3 for simplicity.
-def apply_context_parallelism(
+def apply_sequence_parallelism(
     batch: dict[str, torch.Tensor],
     local_rank: int,
     local_world_size: int,
@@ -17,15 +30,15 @@ def apply_context_parallelism(
     ring_attn_func: RingAttnFunc,  # pylint: disable=unused-argument
 ) -> tuple[dict[str, torch.Tensor], int, int]:
     """
-    Apply context parallelism slicing to a batch.
+    Apply sequence parallelism slicing to a batch.
 
     Special handling is implemented for integer logits_to_keep, which indicates
-    to only keep the last N tokens in the input sequence during generation.
+    to only keep the last N tokens in the sequence during generation.
 
     Args:
         batch: Batch dictionary (e.g., input_ids, attention_mask, etc.).
-        local_rank: Local rank in the context parallel group.
-        local_world_size: World size of the context parallel group.
+        local_rank: Local rank in the sequence parallel group.
+        local_world_size: World size of the sequence parallel group.
         gradient_accumulation_steps: Number of steps to accumulate gradients over.
         ring_attn_func: Which ring attention function to use. Currently unused, but
             related to above TODO.
@@ -120,7 +133,7 @@ def apply_context_parallelism(
         # Update the total sequence length after padding
         total_seq_len = batch["input_ids"].size(1)
 
-    # Slice batch for context parallel
+    # Slice batch for sequence parallel
     for key in batch:
         if not isinstance(batch[key], torch.Tensor) or batch[key].dim() <= 1:
             continue
@@ -146,6 +159,144 @@ def apply_context_parallelism(
     return batch, original_seq_len, pad_len
 
 
+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.
+
+    Args:
+        models: List of models to apply sequence parallelism to pre- and post- forward
+            hooks.
+        sequence_parallel_degree: Number of processes to split sequences over.
+        gradient_accumulation_steps: Number of steps to accumulate gradients over.
+        ring_attn_func: Which ring attention function to use. Currently unused.
+        heads_k_stride: Sequence parallelism K head stride size. Passed through to
+            `varlen_llama3` `ring_flash_attn` implementation.
+    """
+
+    def __init__(
+        self,
+        models: list[nn.Module],
+        sequence_parallel_degree: int,
+        gradient_accumulation_steps: int,
+        ring_attn_func: RingAttnFunc,
+        heads_k_stride: int | None,
+    ):
+        self.models = models
+        self.sequence_parallel_degree = sequence_parallel_degree
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.ring_attn_func = ring_attn_func
+        self.heads_k_stride = heads_k_stride
+        self._register_ring_attn()
+
+        # Set distributed info for local rank
+        self.process_group = get_ring_attn_group()
+        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] = []
+
+        # Store original sequence length and padding information
+        self.original_seq_len = 0
+        self.pad_len = 0
+
+        # Create a partially applied version of the apply_sequence_parallelism function
+        self.apply_sequence_parallelism = functools.partial(
+            apply_sequence_parallelism,
+            local_rank=self.local_rank,
+            local_world_size=self.local_world_size,
+            gradient_accumulation_steps=self.gradient_accumulation_steps,
+            ring_attn_func=self.ring_attn_func,
+        )
+
+    def __enter__(self):
+        self._register_model_hooks()
+
+        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 = []
+
+        # TODO(djsaunde): Un-patch attention and accelerate functions (low priority)
+
+    def _register_ring_attn(self):
+        # Initialize ring attn for sequence parallelism
+        register_ring_attn(
+            sequence_parallel_degree=self.sequence_parallel_degree,
+            heads_k_stride=self.heads_k_stride,
+            ring_attn_func=self.ring_attn_func,
+        )
+
+        # Patches for accelerate functionality
+        patch_prepare_data_loader()
+        patch_prepare_device_mesh(
+            sequence_parallel_degree=self.sequence_parallel_degree
+        )
+
+    def _register_model_hooks(self):
+        # Forward pre-hook to apply sequence parallelism
+        def sequence_parallel_pre_hook(_, args, kwargs):
+            # Get parameter names from the model's forward function
+            forward_params = list(
+                inspect.signature(self.models[0].forward).parameters.keys()
+            )
+
+            updated_kwargs = kwargs.copy()
+            for i, arg in enumerate(args):
+                if i < len(forward_params):
+                    updated_kwargs[forward_params[i]] = arg
+
+            # Any excess positional arguments are kept as-is
+            remaining_args = args[len(forward_params) :]
+
+            # Apply sequence parallelism to updated kwargs
+            updated_kwargs, self.original_seq_len, self.pad_len = (
+                self.apply_sequence_parallelism(updated_kwargs)
+            )
+
+            return remaining_args, updated_kwargs
+
+        # Forward post-hook to gather outputs
+        def sequence_parallel_post_hook(_, __, output: ModelOutput) -> ModelOutput:
+            # Gather the sharded outputs
+            output = self._gather_outputs(output)
+
+            # Remove padding if it was added
+            if self.pad_len > 0:
+                for key, value in output.items():
+                    if isinstance(value, torch.Tensor) and value.dim() > 1:
+                        if value.size(1) == self.original_seq_len + self.pad_len:
+                            # Slice to remove padding
+                            output[key] = value[:, : self.original_seq_len].contiguous()
+
+            return output
+
+        # Register both hooks
+        for model in self.models:
+            self.hook_handles.append(
+                model.register_forward_pre_hook(
+                    sequence_parallel_pre_hook, with_kwargs=True
+                )
+            )
+            self.hook_handles.append(
+                model.register_forward_hook(sequence_parallel_post_hook)
+            )
+
+    def _gather_outputs(self, output: CausalLMOutputWithPast) -> CausalLMOutputWithPast:
+        """Gather sharded outputs from all ranks and reconstruct the full tensor."""
+        for key, value in output.items():
+            if isinstance(value, torch.Tensor) and value.dim() > 1:
+                output[key] = AllGatherWithGrad.apply(value, self.process_group)
+
+        return output
+
+
 class AllGatherWithGrad(torch.autograd.Function):
     """Custom autograd function for all-gather to preserve gradients."""
 

src/axolotl/utils/schemas/config.py

@@ -262,7 +262,7 @@ class AxolotlInputConfig(
 
     val_set_size: float | None = Field(default=0.0)
 
-    context_parallel_degree: int | None = None
+    sequence_parallel_degree: int | None = None
     heads_k_stride: int | None = None
     ring_attn_func: RingAttnFunc | None = None
 
@@ -275,6 +275,7 @@ class AxolotlInputConfig(
     torch_compile_mode: Literal["default", "reduce-overhead", "max-autotune"] | None = (
         None
     )
+    compile_optimizer: bool | None = None
 
     max_steps: int | None = None
     warmup_steps: int | None = None
@@ -1179,39 +1180,24 @@ class AxolotlInputConfig(
 
     @model_validator(mode="before")
     @classmethod
-    def check_grpo_liger_context_parallel(cls, data):
+    def check_grpo_liger_sequence_parallel(cls, data):
         if (
             data.get("rl") == "grpo"
             and data.get("trl", {})
             and data.get("trl").get("use_liger_loss")
-            and data.get("context_parallel_degree", 1) > 1
+            and data.get("sequence_parallel_degree", 1) > 1
         ):
-            raise ValueError("GRPO + CP + Liger not currently supported")
+            raise ValueError("GRPO + SP + Liger not currently supported")
         return data
 
     @model_validator(mode="after")
-    def check_context_parallel_degree(self):
-        if not self.context_parallel_degree:
-            self.context_parallel_degree = 1
-        elif self.context_parallel_degree > 1:
-            import torch
-
-            world_size = torch.cuda.device_count()
-            if not world_size >= self.context_parallel_degree:
+    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:
                 raise ValueError(
-                    f"World size ({world_size}) must be greater "
-                    f"than or equal to CP degree ({self.context_parallel_degree})"
-                )
-            if not world_size % self.context_parallel_degree == 0:
-                raise ValueError(
-                    f"SP degree ({self.context_parallel_degree}) "
-                    f"must evenly divide world size ({world_size})"
-                )
-
-            if not (self.flash_attention or self.sdp_attention):
-                raise ValueError(
-                    "flash_attention: true or sdp_attention: true "
-                    "must be set with context_parallel_degree > 1"
+                    "flash_attention: true must be set with sequence_parallel_degree > 1"
                 )
 
             if self.sample_packing and self.micro_batch_size > 1:
@@ -1220,22 +1206,21 @@ class AxolotlInputConfig(
                     "due to a `ring-flash-attn` requirement"
                 )
 
-            if self.flash_attention:
-                try:
-                    import ring_flash_attn  # noqa: F401 # pylint:disable=unused-import
-                except ImportError as exception:
-                    raise ImportError(
-                        "context_parallel_degree > 1 but ring_flash_attn is not installed. "
-                        "Please install it with `pip install axolotl[ring-flash-attn] "
-                        "or `pip install ring-flash-attn>=0.1.4`."
-                    ) from exception
+            try:
+                import ring_flash_attn  # noqa: F401 # pylint:disable=unused-import
+            except ImportError as exception:
+                raise ImportError(
+                    "sequence_parallel_degree > 1 but ring_flash_attn is not installed. "
+                    "Please install it with `pip install axolotl[ring-flash-attn] "
+                    "or `pip install ring-flash-attn>=0.1.4`."
+                ) from exception
 
-            # TODO: monkeypatch / callback to average losses correctly across CP ranks
-            # / fix gradient scaling across CP ranks. Losses, grads should be scaled
+            # 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.
             LOG.warning(
-                "Context parallelism (SP) is enabled with "
-                f"context_parallel_degree={self.context_parallel_degree}. "
+                "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 "
@@ -1246,7 +1231,7 @@ class AxolotlInputConfig(
 
     @model_validator(mode="after")
     def validate_ring_attn_func(self):
-        if getattr(self, "context_parallel_degree", 1) == 1:
+        if getattr(self, "sequence_parallel_degree", 1) == 1:
             return self
 
         if self.ring_attn_func is not None:

src/axolotl/utils/trainer.py

@@ -442,7 +442,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
                     - 1
                 )
                 * cfg.num_epochs
-                * cfg.context_parallel_degree
+                * cfg.sequence_parallel_degree
             )
             LOG.debug(
                 f"total_num_tokens: {cfg.total_num_tokens:_}, total_num_steps: {total_num_steps:_}"
@@ -479,7 +479,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             # on the agreed on value for sample_packing_eff_est
             total_num_steps = int(
                 math.floor(
-                    data_loader_len * cfg.num_epochs * cfg.context_parallel_degree
+                    data_loader_len * cfg.num_epochs * cfg.sequence_parallel_degree
                 )
             )
 
@@ -502,7 +502,7 @@ def calculate_total_num_steps(cfg, train_dataset, update=True):
             math.ceil(
                 len(train_dataset)
                 * cfg.num_epochs
-                * cfg.context_parallel_degree
+                * cfg.sequence_parallel_degree
                 / cfg.batch_size
             )
         )

tests/core/test_builders.py

@@ -64,7 +64,7 @@ def fixture_base_cfg():
             "dataloader_num_workers": 1,
             "dataloader_pin_memory": True,
             "dataloader_prefetch_factor": 2,
-            "context_parallel_degree": 1,
+            "sequence_parallel_degree": 1,
             # Dtype
             "fp16": False,
             "bf16": False,

tests/e2e/multigpu/patched/test_sp.py

@@ -1,4 +1,4 @@
-"""E2E tests for context parallelism"""
+"""E2E tests for sequence parallelism"""
 
 from pathlib import Path
 
@@ -12,10 +12,10 @@ from axolotl.utils.dict import DictDefault
 from ...utils import check_tensorboard
 
 
-class TestContextParallelism:
-    """Test case for training with context parallelism enabled"""
+class TestSequenceParallelism:
+    """Test case for training with sequence parallelism enabled"""
 
-    def _run_context_parallel_test(
+    def _run_sequence_parallel_test(
         self,
         temp_dir,
         sample_packing=True,
@@ -24,7 +24,7 @@ class TestContextParallelism:
         ring_attn_func=None,
         threshold=2.0,
     ):
-        """Helper method to run context parallel tests with different configurations"""
+        """Helper method to run sequence parallel tests with different configurations"""
         cfg = DictDefault(
             {
                 "base_model": "HuggingFaceTB/SmolLM2-135M",
@@ -66,7 +66,7 @@ class TestContextParallelism:
                 "logging_steps": 1,
                 "weight_decay": 0.0,
                 "use_tensorboard": True,
-                "context_parallel_degree": 2,
+                "sequence_parallel_degree": 2,
                 "ring_attn_func": ring_attn_func,
             }
         )
@@ -109,7 +109,7 @@ class TestContextParallelism:
             "no sample_packing, no pad_to_sequence_len, batch_ring ring_attn_func",
         ],
     )
-    def test_context_parallel_training(
+    def test_sequence_parallel_training(
         self,
         temp_dir,
         sample_packing,
@@ -118,8 +118,8 @@ class TestContextParallelism:
         ring_attn_func,
         threshold,
     ):
-        """Test context parallel training with different configurations"""
-        self._run_context_parallel_test(
+        """Test sequence parallel training with different configurations"""
+        self._run_sequence_parallel_test(
             temp_dir,
             sample_packing=sample_packing,
             micro_batch_size=micro_batch_size,

tests/e2e/multigpu/solo/test_grpo.py

@@ -296,7 +296,7 @@ def oai_gsm8k_transform(cfg, *args, **kwargs):
                 "lora_alpha": 16,
                 "lora_dropout": 0.05,
                 "lora_target_linear": True,
-                "context_parallel_degree": 2,
+                "sequence_parallel_degree": 2,
                 "flash_attention": True,
                 "sequence_len": 1024,
                 "special_tokens": {

tests/e2e/patched/test_sp.py

@@ -1,4 +1,4 @@
-"""Tests for context parallelism functionality."""
+"""Tests for sequence parallelism functionality."""
 
 # pylint: disable=redefined-outer-name,unused-argument
 
@@ -15,7 +15,7 @@ from axolotl.monkeypatch.ring_attn import (
     register_ring_attn,
     set_ring_attn_group,
 )
-from axolotl.utils.ctx_managers.context_parallel import apply_context_parallelism
+from axolotl.utils.ctx_managers.sequence_parallel import apply_sequence_parallelism
 from axolotl.utils.dict import DictDefault
 from axolotl.utils.schemas.enums import RingAttnFunc
 from axolotl.utils.schemas.trl import TRLConfig
@@ -54,8 +54,8 @@ def fixture_cfg():
 
 
 @pytest.fixture
-def context_parallel_batch():
-    """Create a test batch for context parallelism tests."""
+def sequence_parallel_batch():
+    """Create a test batch for sequence parallelism tests."""
     batch_size = 1
     seq_len = 8
 
@@ -110,7 +110,7 @@ class TestRingAttention:
 
         # Call register_ring_attn with size 4
         register_ring_attn(
-            context_parallel_degree=4,
+            sequence_parallel_degree=4,
             heads_k_stride=1,
             ring_attn_func=RingAttnFunc.VARLEN_LLAMA3,
         )
@@ -126,7 +126,7 @@ class TestRingAttention:
 
 
 class TestConfigValidation:
-    """Tests for validating context parallelism configurations."""
+    """Tests for validating sequence parallelism configurations."""
 
     @pytest.fixture(autouse=True)
     def setup_mocks(self, monkeypatch):
@@ -155,24 +155,24 @@ class TestConfigValidation:
         [
             # Valid configuration
             (
-                {"context_parallel_degree": 2, "flash_attention": True},
-                {"context_parallel_degree": 2, "flash_attention": True},
+                {"sequence_parallel_degree": 2, "flash_attention": True},
+                {"sequence_parallel_degree": 2, "flash_attention": True},
                 True,
                 None,
             ),
-            # Default context_parallel_degree
-            ({}, {"context_parallel_degree": 1}, True, None),
-            # Invalid: context_parallel_degree > 1 without flash_attention
+            # Default sequence_parallel_degree
+            ({}, {"sequence_parallel_degree": 1}, True, None),
+            # Invalid: sequence_parallel_degree > 1 without flash_attention
             (
-                {"context_parallel_degree": 2, "flash_attention": False},
+                {"sequence_parallel_degree": 2, "flash_attention": False},
                 None,
                 False,
                 "flash_attention: true must be set",
             ),
-            # Invalid: context_parallel_degree > 1 with sample_packing and micro_batch_size > 1
+            # Invalid: sequence_parallel_degree > 1 with sample_packing and micro_batch_size > 1
             (
                 {
-                    "context_parallel_degree": 2,
+                    "sequence_parallel_degree": 2,
                     "flash_attention": True,
                     "sample_packing": True,
                     "micro_batch_size": 2,
@@ -185,32 +185,32 @@ class TestConfigValidation:
             # Valid: Basic GRPO config
             (
                 {
-                    "context_parallel_degree": 2,
+                    "sequence_parallel_degree": 2,
                     "flash_attention": True,
                     "micro_batch_size": 2,
                     "trl": {"use_liger_loss": True},
                 },
                 {
-                    "context_parallel_degree": 2,
+                    "sequence_parallel_degree": 2,
                     "flash_attention": True,
                     "micro_batch_size": 2,
                     "trl": TRLConfig(use_liger_loss=True),
                 },
                 True,
-                "GRPO + CP + Liger not currently supported",
+                "GRPO + SP + Liger not currently supported",
             ),
             # Invalid: GRPO config with Liger loss
             (
                 {
                     "rl": "grpo",
-                    "context_parallel_degree": 2,
+                    "sequence_parallel_degree": 2,
                     "flash_attention": True,
                     "micro_batch_size": 2,
                     "trl": {"use_liger_loss": True},
                 },
                 None,
                 False,
-                "GRPO + CP + Liger not currently supported",
+                "GRPO + SP + Liger not currently supported",
             ),
         ],
         ids=[
@@ -222,10 +222,10 @@ class TestConfigValidation:
             "grpo_with_liger_loss",
         ],
     )
-    def test_context_parallel_config_validation(
+    def test_sequence_parallel_config_validation(
         self, base_cfg, config_updates, expected_values, should_pass, error_msg
     ):
-        """Test various context parallelism configuration scenarios."""
+        """Test various sequence parallelism configuration scenarios."""
         from axolotl.utils.schemas.config import AxolotlInputConfig
 
         # Apply updates to base config
@@ -261,7 +261,7 @@ class TestConfigValidation:
 
         # Apply updates to base config
         cfg = base_cfg | {
-            "context_parallel_degree": 2,
+            "sequence_parallel_degree": 2,
             "flash_attention": True,
             "sample_packing": sample_packing,
         }
@@ -281,7 +281,7 @@ class TestConfigValidation:
 
         # Invalid configuration with invalid ring_attn_func
         cfg = base_cfg | {
-            "context_parallel_degree": 2,
+            "sequence_parallel_degree": 2,
             "flash_attention": True,
             "ring_attn_func": "INVALID_FUNC",
         }
@@ -294,8 +294,8 @@ class TestConfigValidation:
         assert "Input should be 'varlen_llama3' or 'batch_ring'" in str(excinfo.value)
 
 
-class TestApplyContextParallelism:
-    """Tests for the apply_context_parallelism function."""
+class TestApplySequenceParallelism:
+    """Tests for the apply_sequence_parallelism function."""
 
     @pytest.fixture(autouse=True)
     def mock_distributed(self, monkeypatch):
@@ -324,12 +324,12 @@ class TestApplyContextParallelism:
         )
 
     @patch("axolotl.monkeypatch.ring_attn.patch.get_ring_attn_group")
-    def test_world_size_one(self, mock_get_ring_attn_group, context_parallel_batch):
+    def test_world_size_one(self, mock_get_ring_attn_group, sequence_parallel_batch):
         """Test that function returns original batch when world size is 1."""
         mock_get_ring_attn_group.return_value = 0
 
-        result, _, _ = apply_context_parallelism(
-            batch=context_parallel_batch,
+        result, _, _ = apply_sequence_parallelism(
+            batch=sequence_parallel_batch,
             local_rank=0,
             local_world_size=1,
             gradient_accumulation_steps=1,
@@ -337,17 +337,17 @@ class TestApplyContextParallelism:
         )
 
         # Should return the original batch unchanged
-        assert result == context_parallel_batch
+        assert result == sequence_parallel_batch
 
     @patch("axolotl.monkeypatch.ring_attn.patch.get_ring_attn_group")
-    def test_batch_ring_rank0(self, mock_get_ring_attn_group, context_parallel_batch):
+    def test_batch_ring_rank0(self, mock_get_ring_attn_group, sequence_parallel_batch):
         """Test BATCH_RING sharding for rank 0 in a 2-process group."""
         mock_get_ring_attn_group.return_value = 0
 
-        batch = context_parallel_batch
+        batch = sequence_parallel_batch
         seq_len = batch["input_ids"].size(1)
 
-        result, _, _ = apply_context_parallelism(
+        result, _, _ = apply_sequence_parallelism(
             batch=batch,
             local_rank=0,
             local_world_size=2,
@@ -366,15 +366,15 @@ class TestApplyContextParallelism:
         )
 
     @patch("axolotl.monkeypatch.ring_attn.patch.get_ring_attn_group")
-    def test_batch_ring_rank1(self, mock_get_ring_attn_group, context_parallel_batch):
+    def test_batch_ring_rank1(self, mock_get_ring_attn_group, sequence_parallel_batch):
         """Test BATCH_RING sharding for rank 1 in a 2-process group."""
         mock_get_ring_attn_group.return_value = 0
 
-        batch = context_parallel_batch
+        batch = sequence_parallel_batch
         seq_len = batch["input_ids"].size(1)
         original_input_ids = batch["input_ids"].clone()
 
-        result, _, _ = apply_context_parallelism(
+        result, _, _ = apply_sequence_parallelism(
             batch=batch,
             local_rank=1,
             local_world_size=2,
@@ -386,14 +386,14 @@ class TestApplyContextParallelism:
         assert torch.equal(result["input_ids"], original_input_ids[:, seq_len // 2 :])
 
     # TODO(djsaunde): add back once implemented.
-    # def test_batch_zigzag(self, context_parallel_batch):
+    # def test_batch_zigzag(self, sequence_parallel_batch):
     #     """Test BATCH_ZIGZAG sharding pattern."""
-    #     batch = context_parallel_batch
+    #     batch = sequence_parallel_batch
     #     original_input_ids = batch["input_ids"].clone()
     #     seq_len = batch["input_ids"].size(1)
 
     #     # Test rank 0
-    #     result_rank0 = apply_context_parallelism(
+    #     result_rank0 = apply_sequence_parallelism(
     #         batch={k: v.clone() for k, v in batch.items()},
     #         local_rank=0,
     #         local_world_size=2,
@@ -401,7 +401,7 @@ class TestApplyContextParallelism:
     #     )
 
     #     # Test rank 1
-    #     result_rank1 = apply_context_parallelism(
+    #     result_rank1 = apply_sequence_parallelism(
     #         batch={k: v.clone() for k, v in batch.items()},
     #         local_rank=1,
     #         local_world_size=2,
@@ -430,17 +430,17 @@ class TestApplyContextParallelism:
 
     @patch("axolotl.monkeypatch.ring_attn.patch.get_ring_attn_group")
     def test_partial_application(
-        self, mock_get_ring_attn_group, context_parallel_batch
+        self, mock_get_ring_attn_group, sequence_parallel_batch
     ):
         """Test that we can create a partially applied version of the function."""
         mock_get_ring_attn_group.return_value = 0
 
-        batch = context_parallel_batch
+        batch = sequence_parallel_batch
         original_input_ids = batch["input_ids"].clone()
 
         # Create a partially applied function
         rank0_ring_parallel = functools.partial(
-            apply_context_parallelism,
+            apply_sequence_parallelism,
             local_rank=0,
             local_world_size=2,
             gradient_accumulation_steps=1,
@@ -457,14 +457,16 @@ class TestApplyContextParallelism:
             original_input_ids[:, : original_input_ids.shape[1] // 2],
         )
 
-    def test_missing_position_ids(self, context_parallel_batch):
+    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 context_parallel_batch.items() if k != "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_context_parallelism(
+        result, _, _ = apply_sequence_parallelism(
             batch=batch,
             local_rank=0,
             local_world_size=2,