diff --git a/swift/megatron/arguments/megatron_args.py b/swift/megatron/arguments/megatron_args.py
index 3720436325..c313c5d277 100644
--- a/swift/megatron/arguments/megatron_args.py
+++ b/swift/megatron/arguments/megatron_args.py
@@ -502,6 +502,7 @@ class MegatronArguments(RLHFMegatronArgumentsMixin, MegatronTunerMixin):
     moe_router_load_balancing_type: Optional[List[str]] = None
     moe_router_dtype: Literal['none', 'fp32', 'fp64'] = 'fp32'
     moe_token_dispatcher_type: Literal['allgather', 'alltoall', 'flex'] = 'alltoall'
+    moe_flex_dispatcher_backend: Optional[Literal['deepep', 'hybridep']] = None
     moe_enable_deepep: bool = False
     moe_grouped_gemm: bool = True
     moe_permute_fusion: bool = False
diff --git a/swift/megatron/utils/utils.py b/swift/megatron/utils/utils.py
index 2fa387c583..2de905ab8f 100644
--- a/swift/megatron/utils/utils.py
+++ b/swift/megatron/utils/utils.py
@@ -340,6 +340,13 @@ def get_padding_to(args):
         padding_to = max((padding_to or 1) * 8, 16)
     if args.attention_backend == 'fused':
         padding_to = max(padding_to or 1, ((origin_padding_to) or 1) * 64)
+
+    # padding to max seq_length to avoid hybridep all-gather-into-tensor hang
+    moe_backend = getattr(args, 'moe_flex_dispatcher_backend', None)
+    if moe_backend == 'hybridep':
+        seq_length = getattr(args, 'seq_length', None) or getattr(args, 'max_length', None)
+        if seq_length is not None:
+            padding_to = seq_length
     return padding_to
 
 
diff --git a/tests/megatron/test_utils.py b/tests/megatron/test_utils.py
new file mode 100644
index 0000000000..627dac994f
--- /dev/null
+++ b/tests/megatron/test_utils.py
@@ -0,0 +1,209 @@
+"""
+Unit tests for get_padding_to() in swift/megatron/utils/utils.py.
+
+The function is inlined here so the tests run without requiring the full
+swift / megatron / torch stack installed in the test environment.
+Keep the inlined copy in sync with the source whenever it changes.
+"""
+import unittest
+from dataclasses import dataclass, field
+from typing import Optional
+
+
+# ---------------------------------------------------------------------------
+# Typed args dataclass – matches the fields accessed by get_padding_to
+# ---------------------------------------------------------------------------
+@dataclass
+class _PaddingArgs:
+    tensor_model_parallel_size: int = 1
+    sequence_parallel: bool = False
+    context_parallel_size: int = 1
+    fp8_recipe: Optional[str] = None
+    fp8_format: Optional[str] = None
+    fp8: Optional[str] = None
+    attention_backend: Optional[str] = None
+    moe_flex_dispatcher_backend: Optional[str] = None
+    seq_length: Optional[int] = None
+    max_length: Optional[int] = None
+
+
+# ---------------------------------------------------------------------------
+# Inlined implementation – mirrors swift/megatron/utils/utils.py:get_padding_to
+# ---------------------------------------------------------------------------
+def get_padding_to(args: _PaddingArgs) -> Optional[int]:
+    padding_to: Optional[int] = None
+    if args.tensor_model_parallel_size > 1 and args.sequence_parallel:
+        padding_to = args.tensor_model_parallel_size
+    if args.context_parallel_size > 1:
+        padding_to = (padding_to or 1) * args.context_parallel_size
+    origin_padding_to = padding_to
+    fp8_format: Optional[str] = args.fp8_format or args.fp8
+    if args.fp8_recipe == 'blockwise':
+        padding_to = (padding_to or 1) * 128
+    elif fp8_format is not None:
+        padding_to = max((padding_to or 1) * 8, 16)
+    if args.attention_backend == 'fused':
+        padding_to = max(padding_to or 1, (origin_padding_to or 1) * 64)
+
+    # padding to max seq_length to avoid hybridep all-gather-into-tensor hang
+    moe_backend: Optional[str] = getattr(args, 'moe_flex_dispatcher_backend', None)
+    if moe_backend == 'hybridep':
+        seq_length: Optional[int] = args.seq_length or args.max_length
+        if seq_length is not None:
+            padding_to = seq_length
+    return padding_to
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+def _make_args(**kwargs: object) -> _PaddingArgs:
+    """Return a _PaddingArgs with sensible defaults, overridden by kwargs."""
+    return _PaddingArgs(**{k: v for k, v in kwargs.items()})  # type: ignore[arg-type]
+
+
+# ---------------------------------------------------------------------------
+# Test cases
+# ---------------------------------------------------------------------------
+class TestGetPaddingTo(unittest.TestCase):
+
+    def _call(self, **kwargs: object) -> Optional[int]:
+        return get_padding_to(_make_args(**kwargs))
+
+    # ------------------------------------------------------------------
+    # Baseline
+    # ------------------------------------------------------------------
+    def test_no_padding(self) -> None:
+        """No parallelism / fp8 / special backend -> None."""
+        self.assertIsNone(self._call())
+
+    # ------------------------------------------------------------------
+    # Tensor parallel + sequence parallel
+    # ------------------------------------------------------------------
+    def test_tp_sequence_parallel(self) -> None:
+        self.assertEqual(self._call(tensor_model_parallel_size=4, sequence_parallel=True), 4)
+
+    def test_tp_no_sequence_parallel(self) -> None:
+        """TP alone (without sequence_parallel) should NOT activate padding."""
+        self.assertIsNone(self._call(tensor_model_parallel_size=4, sequence_parallel=False))
+
+    # ------------------------------------------------------------------
+    # Context parallel
+    # ------------------------------------------------------------------
+    def test_context_parallel_only(self) -> None:
+        self.assertEqual(self._call(context_parallel_size=4), 4)
+
+    def test_tp_and_cp_combined(self) -> None:
+        result = self._call(tensor_model_parallel_size=2, sequence_parallel=True, context_parallel_size=4)
+        self.assertEqual(result, 2 * 4)
+
+    # ------------------------------------------------------------------
+    # FP8 recipes
+    # ------------------------------------------------------------------
+    def test_fp8_blockwise(self) -> None:
+        self.assertEqual(self._call(fp8_recipe='blockwise'), 128)
+
+    def test_fp8_blockwise_with_tp(self) -> None:
+        result = self._call(tensor_model_parallel_size=2, sequence_parallel=True, fp8_recipe='blockwise')
+        self.assertEqual(result, 2 * 128)
+
+    def test_fp8_format(self) -> None:
+        # max(1*8, 16) == 16
+        self.assertEqual(self._call(fp8_format='e4m3'), 16)
+
+    def test_fp8_format_with_tp(self) -> None:
+        result = self._call(tensor_model_parallel_size=4, sequence_parallel=True, fp8_format='e4m3')
+        self.assertEqual(result, max(4 * 8, 16))
+
+    # ------------------------------------------------------------------
+    # Fused attention backend
+    # ------------------------------------------------------------------
+    def test_attention_fused_no_tp(self) -> None:
+        self.assertEqual(self._call(attention_backend='fused'), 64)
+
+    def test_attention_fused_with_tp(self) -> None:
+        # origin_padding_to = 2, result = max(2, 2*64) = 128
+        result = self._call(tensor_model_parallel_size=2, sequence_parallel=True, attention_backend='fused')
+        self.assertEqual(result, 128)
+
+    # ------------------------------------------------------------------
+    # hybridep backend – the new logic under test
+    # ------------------------------------------------------------------
+    def test_hybridep_uses_seq_length(self) -> None:
+        """hybridep backend should set padding_to = seq_length."""
+        result = self._call(moe_flex_dispatcher_backend='hybridep', seq_length=2048)
+        self.assertEqual(result, 2048)
+
+    def test_hybridep_uses_max_length_fallback(self) -> None:
+        """When seq_length is absent, fall back to max_length."""
+        result = self._call(moe_flex_dispatcher_backend='hybridep', max_length=4096)
+        self.assertEqual(result, 4096)
+
+    def test_hybridep_seq_length_takes_priority_over_max_length(self) -> None:
+        """seq_length takes priority over max_length (short-circuit OR)."""
+        result = self._call(moe_flex_dispatcher_backend='hybridep', seq_length=1024, max_length=4096)
+        self.assertEqual(result, 1024)
+
+    def test_hybridep_no_length_leaves_padding_unchanged(self) -> None:
+        """If neither seq_length nor max_length is set, padding_to is not modified."""
+        result = self._call(
+            moe_flex_dispatcher_backend='hybridep',
+            tensor_model_parallel_size=2,
+            sequence_parallel=True,
+        )
+        self.assertEqual(result, 2)
+
+    def test_hybridep_overrides_fp8_and_attention_padding(self) -> None:
+        """hybridep seq_length wins over fp8/attention-derived padding."""
+        result = self._call(
+            moe_flex_dispatcher_backend='hybridep',
+            seq_length=512,
+            fp8_recipe='blockwise',
+            attention_backend='fused',
+        )
+        self.assertEqual(result, 512)
+
+    def test_hybridep_with_tp_and_cp(self) -> None:
+        """hybridep overrides combined TP+CP padding."""
+        result = self._call(
+            moe_flex_dispatcher_backend='hybridep',
+            seq_length=8192,
+            tensor_model_parallel_size=4,
+            sequence_parallel=True,
+            context_parallel_size=2,
+        )
+        self.assertEqual(result, 8192)
+
+    # ------------------------------------------------------------------
+    # Non-hybridep backends must NOT trigger the new logic
+    # ------------------------------------------------------------------
+    def test_deepep_does_not_override(self) -> None:
+        """deepep backend should NOT activate the hybridep padding override."""
+        result = self._call(moe_flex_dispatcher_backend='deepep', seq_length=2048)
+        self.assertIsNone(result)
+
+    def test_no_moe_backend_does_not_override(self) -> None:
+        """Without any moe_flex_dispatcher_backend, seq_length is ignored."""
+        result = self._call(seq_length=2048)
+        self.assertIsNone(result)
+
+    def test_missing_moe_backend_attribute_does_not_override(self) -> None:
+        """If the attribute doesn't exist on args (getattr default), seq_length is ignored."""
+        args = _PaddingArgs(
+            tensor_model_parallel_size=1,
+            sequence_parallel=False,
+            context_parallel_size=1,
+            fp8_recipe=None,
+            fp8_format=None,
+            fp8=None,
+            attention_backend=None,
+            seq_length=2048,
+            max_length=None,
+            # moe_flex_dispatcher_backend defaults to None -> branch skipped
+        )
+        result = get_padding_to(args)
+        self.assertIsNone(result)
+
+
+if __name__ == '__main__':
+    unittest.main()