[Feature] optimize sp & qwen3 next support sp. (#3225)

This PR will accomplish the following tasks: 
**optimize SP**
In the old version implementation, the first layer was all_reduce, which
used rms to split chunks. We changed it to perform reduce_scatter on the
embedding side, replace one all_reduce operation and one chunk with one
reduce_scatter operation.
**Support qwen3 next**
Since Qwen3 Next includes a linear attention module, the prefix name of
this module cannot take effect directly.


- vLLM version: v0.11.0rc3
- vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0

---------

Signed-off-by: weijinqian_v1 <weijinqian@huawei.com>
Co-authored-by: weijinqian_v1 <weijinqian@huawei.com>

tests/ut/ops/test_vocab_parallel_embedding.py

@@ -104,9 +104,8 @@ class TestCustomVocabParallelEmbedding(unittest.TestCase):
 
         input_ = torch.tensor([1, 2, 3])
 
-        with patch(
-                "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
-                side_effect=lambda x: x) as mock_reduce_tp1:
+        with patch("torch.ops.vllm.maybe_pad_and_reduce",
+                   side_effect=lambda x: x) as mock_reduce_tp1:
             output = layer.forward(input_)
 
         # Should just pass through without masking
@@ -123,9 +122,8 @@ class TestCustomVocabParallelEmbedding(unittest.TestCase):
 
         input_ = torch.tensor([15, 35])  # one org vocab, one added vocab
 
-        with patch(
-                "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
-                side_effect=lambda x: x) as mock_reduce_tp:
+        with patch("torch.ops.vllm.maybe_pad_and_reduce",
+                   side_effect=lambda x: x) as mock_reduce_tp:
             # Call the forward method
             output = layer.forward(input_)
 
@@ -150,9 +148,8 @@ class TestCustomVocabParallelEmbedding(unittest.TestCase):
             return_value=mock_output.clone())
 
         # Patch tensor_model_parallel_all_reduce to mock its behavior
-        with patch(
-                "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
-                side_effect=lambda x: x):
+        with patch("torch.ops.vllm.maybe_pad_and_reduce",
+                   side_effect=lambda x: x):
             # Call the forward method
             output = layer.forward(input_)
         # Check that invalid positions (0, 2, 4) were zeroed out
@@ -176,9 +173,8 @@ class TestCustomVocabParallelEmbedding(unittest.TestCase):
 
         for input_, expected_shape in test_cases:
             with self.subTest(input=input_):
-                with patch(
-                        "vllm_ascend.ops.vocab_parallel_embedding.tensor_model_parallel_all_reduce",
-                        side_effect=lambda x: x):
+                with patch("torch.ops.vllm.maybe_pad_and_reduce",
+                           side_effect=lambda x: x):
                     # Call the forward method
                     output = layer.forward(input_)
                 self.assertEqual(output.shape, expected_shape)