diff --git a/src/axolotl/train.py b/src/axolotl/train.py
index 866a9c454..c155db42e 100644
--- a/src/axolotl/train.py
+++ b/src/axolotl/train.py
@@ -11,11 +11,13 @@ from pathlib import Path
 from typing import Any, Dict
 
 import torch
+import torch.distributed as dist
 import transformers.modelcard
 from accelerate.utils import save_fsdp_model
 from datasets import Dataset
 from huggingface_hub.errors import OfflineModeIsEnabled
 from peft import PeftConfig, PeftModel
+from torch.distributed.tensor.experimental import _context_parallel
 from transformers import PreTrainedModel, PreTrainedTokenizer, ProcessorMixin
 from transformers.integrations.deepspeed import is_deepspeed_zero3_enabled
 from transformers.trainer import Trainer
@@ -203,19 +205,32 @@ def execute_training(
             )
 
         if cfg.sequence_parallel_degree > 1:
-            models = [trainer.model]
-            if hasattr(trainer, "ref_model") and trainer.ref_model:
-                models.append(trainer.ref_model)
-
-            stack.enter_context(
-                SequenceParallelContextManager(
-                    models=models,
-                    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,
+            if cfg.sdp_attention:
+                world_size = dist.get_world_size()
+                mesh_shape = (
+                    world_size // cfg.sequence_parallel_degree,
+                    cfg.sequence_parallel_degree,
+                )
+                mesh = dist.DeviceMesh(
+                    "cuda",
+                    torch.tensor(list(range(world_size))).reshape(mesh_shape),
+                    mesh_dim_names=("dp", "cp"),
+                )
+                stack.enter_context(_context_parallel(seq_dim=2, mesh=mesh))
+            else:  # flash_attention
+                models = [trainer.model]
+                if hasattr(trainer, "ref_model") and trainer.ref_model:
+                    models.append(trainer.ref_model)
+
+                stack.enter_context(
+                    SequenceParallelContextManager(
+                        models=models,
+                        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,
+                    )
                 )
-            )
 
         LOG.info("Starting trainer...")
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)