[Bugfix] Qwen3Next support FlashComm1 (#6830)

### What this PR does / why we need it?
Support FlashComm1 for Qwen3-Next. Fix some padding problems in Sequence
Parallel (SP)
and resolve precision problems in shared_out when both FlashComm1 is
enabled.

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
CI
- vLLM version: v0.15.0
- vLLM main:
83b47f67b1

---------

Signed-off-by: zhaojiangjiang <zhaojiangjiang1@h-partners.com>
Co-authored-by: zhaojiangjiang <zhaojiangjiang1@h-partners.com>

tests/e2e/multicard/4-cards/test_qwen3_next.py

@@ -73,3 +73,38 @@ def test_qwen3_next_w8a8dynamic_distributed_tp4_ep():
             quantization="ascend",
     ) as vllm_model:
         vllm_model.generate_greedy(example_prompts, max_tokens)
+
+
+@patch.dict(os.environ, {"VLLM_ASCEND_ENABLE_FLASHCOMM1": "1"})
+@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
+def test_qwen3_next_distributed_mp_flash_comm_tp4():
+    example_prompts = [
+        "Hello, my name is",
+    ] * 4
+    max_tokens = 5
+    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
+                    tensor_parallel_size=4,
+                    max_model_len=4096,
+                    gpu_memory_utilization=0.7,
+                    distributed_executor_backend="mp",
+                    enable_expert_parallel=True,
+                    enforce_eager=True) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+        del vllm_model
+
+
+@patch.dict(os.environ, {"HCCL_BUFFSIZE": "1024"})
+def test_qwen3_next_distributed_mp_graph_mode_tp4():
+    example_prompts = [
+        "Hello, my name is",
+    ] * 4
+    max_tokens = 5
+    with VllmRunner("Qwen/Qwen3-Next-80B-A3B-Instruct",
+                    tensor_parallel_size=4,
+                    max_model_len=4096,
+                    gpu_memory_utilization=0.7,
+                    distributed_executor_backend="mp",
+                    enable_expert_parallel=True,
+                    enforce_eager=False) as vllm_model:
+        vllm_model.generate_greedy(example_prompts, max_tokens)
+        del vllm_model