[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>

vllm_ascend/torchair/ops/torchair_vocab_parallel_embedding.py

@@ -0,0 +1,38 @@
+#
+# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
+# This file is a part of the vllm-ascend project.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+from vllm.distributed import tensor_model_parallel_all_reduce
+
+
+def vocab_embedding_forward(self, input_):
+    if self.tp_size > 1:
+        # Build the mask.
+        masked_input, input_mask = self._get_masked_input_and_mask(
+            input_, self.shard_indices.org_vocab_start_index,
+            self.shard_indices.org_vocab_end_index,
+            self.shard_indices.num_org_vocab_padding,
+            self.shard_indices.added_vocab_start_index,
+            self.shard_indices.added_vocab_end_index)
+    else:
+        masked_input = input_
+    # Get the embeddings.
+    output_parallel = self.quant_method.embedding(self, masked_input.long())
+    # Mask the output embedding.
+    if self.tp_size > 1:
+        output_parallel.masked_fill_(input_mask.unsqueeze(-1), 0)
+    # Reduce across all the model parallel GPUs.
+    output = tensor_model_parallel_all_reduce(output_parallel)
+    return output