accommodate both training context managers

src/axolotl/train.py

@@ -6,6 +6,7 @@ import os
 import signal
 import sys
 import weakref
+from contextlib import nullcontext
 from pathlib import Path
 from typing import Any, Dict
 
@@ -187,22 +188,28 @@ def execute_training(
         resume_from_checkpoint: Path to checkpoint to resume from, if applicable.
     """
     LOG.info("Starting trainer...")
-    if cfg.flash_optimum:
-        with torch.backends.cuda.sdp_kernel(
-            # TODO configure these from the YAML w/ sdp_kernel_kwargs: ...
+
+    # Define the context managers to use
+    flash_context = (
+        torch.backends.cuda.sdp_kernel(
             enable_flash=True,
             enable_math=True,
             enable_mem_efficient=True,
-        ):
-            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-    elif cfg.sequence_parallel_degree > 1:
-        with SequenceParallelContext(
+        )
+        if cfg.flash_optimum
+        else nullcontext()
+    )
+    sequence_parallel_context = (
+        SequenceParallelContext(
             model=trainer.model,
             sequence_parallel_degree=cfg.sequence_parallel_degree,
             ring_attn_func=cfg.ring_attn_func,
-        ):
-            trainer.train(resume_from_checkpoint=resume_from_checkpoint)
-    else:
+        )
+        if cfg.sequence_parallel_degree > 1
+        else nullcontext()
+    )
+
+    with flash_context, sequence_parallel_context:
         trainer.train(resume_from_checkpoint=resume_from_checkpoint)