[Feat][SP] Suport SP for VL MoE models (#7044)

### What this PR does / why we need it?

2nd PR for https://github.com/vllm-project/vllm-ascend/issues/5712,
extend SP to VL MoE models.


### Does this PR introduce _any_ user-facing change?
remove `sp_threshold` in additional config and reuse `sp_min_token_num`
from vLLM.


### How was this patch tested?
- Model: Qwen3-VL-30B-A3B, 
- TP4 DP2
- 100 reqs
- max concurrency 1

| Seq length | Mean TTFT (ms) main | Mean TTFT (ms) this PR |
|------------|---------------------|------------------------|
| 4k         | 429.40               | 323.3                  |
| 16k        | 1297.01              | 911.74                |

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e

---------

Signed-off-by: realliujiaxu <realliujiaxu@163.com>

vllm_ascend/compilation/passes/sequence_parallelism.py

@@ -7,21 +7,25 @@ from vllm.config.utils import Range
 from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group, tensor_model_parallel_all_reduce
 from vllm.logger import logger
 
+from vllm_ascend.compilation.passes.noop_elimination import NoOpEliminationPass
 from vllm_ascend.utils import is_moe_model
 
-SP_THRESHOLD = 1000
+SP_MIN_TOKEN_NUM_DEFAULT = 1000
 
 
-def get_sp_threshold(config: VllmConfig):
+def get_sp_min_token_num(config: VllmConfig) -> int:
     if is_moe_model(config):
         return 1
 
-    additional_config = config.additional_config if config.additional_config is not None else {}
-    return additional_config.get("sp_threshold", SP_THRESHOLD)
+    return SP_MIN_TOKEN_NUM_DEFAULT
 
 
 class _SequenceParallelPatternHelper:
-    """Helper for sequence parallelism patterns."""
+    """Helper for sequence parallelism patterns.
+
+    Provides TP communication helper methods: _all_reduce, _reduce_scatter,
+    _all_gather, and tensor creation utilities.
+    """
 
     def __init__(
         self,
@@ -49,7 +53,10 @@ class _SequenceParallelPatternHelper:
         return torch.empty(*args, dtype=self.dtype, device="npu", **kws)
 
 
-class AscendMiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+class MiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+    """Replaces all_reduce + AddRMSNormBias with reduce_scatter + AddRMSNormBias
+    + all_gather for middle-layer sequence parallelism."""
+
     def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
         super().__init__(eps, vllm_config.model_config.dtype, torch.npu.current_device())
 
@@ -92,7 +99,10 @@ class AscendMiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
         pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass)
 
 
-class AscendLastAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+class LastAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+    """Same as MiddleAllReduceRMSNormPattern but for the last layer
+    (no residual backprop)."""
+
     def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
         super().__init__(eps, vllm_config.model_config.dtype, torch.npu.current_device())
 
@@ -127,7 +137,13 @@ class AscendLastAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
         pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass)
 
 
-class AscendQwen3VLMiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+class Qwen3VLMiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+    """For Qwen3-VL middle layers with hidden_states + deepstack_input_embeds add.
+
+    Replaces all_reduce + add + AddRMSNormBias with reduce_scatter +
+    chunk(deepstack_input_embeds) + add + AddRMSNormBias + all_gather.
+    """
+
     def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
         super().__init__(eps, vllm_config.model_config.dtype, torch.npu.current_device())
 
@@ -168,25 +184,45 @@ class AscendQwen3VLMiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper)
         pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass)
 
 
-class AscendSequenceParallelismPass(VllmInductorPass):
+class SequenceParallelismPass(VllmInductorPass):
+    """Sequence parallelism compilation pass.
+
+    Registers and applies the above patterns. Runs noop cleanup first, then
+    uses token range to determine whether to enable SP.
+    """
+
     def __init__(self, config: VllmConfig):
         super().__init__(config)
 
         self.patterns: PatternMatcherPass = PatternMatcherPass(pass_name="npu_sequence_parallelism_pass")
+        self.noop_cleanup = NoOpEliminationPass(config)
 
         for epsilon in [1e-5, 1e-6]:
-            AscendMiddleAllReduceRMSNormPattern(config, epsilon).register(self.patterns)
+            MiddleAllReduceRMSNormPattern(config, epsilon).register(self.patterns)
 
-            AscendLastAllReduceRMSNormPattern(config, epsilon).register(self.patterns)
+            LastAllReduceRMSNormPattern(config, epsilon).register(self.patterns)
 
-            AscendQwen3VLMiddleAllReduceRMSNormPattern(config, epsilon).register(self.patterns)
+            Qwen3VLMiddleAllReduceRMSNormPattern(config, epsilon).register(self.patterns)
 
-        self.min_tokens = get_sp_threshold(config)
+        self.min_tokens = get_sp_min_token_num(config)
 
     def __call__(self, graph: torch.fx.Graph):
         self.begin()
+        self.noop_cleanup(graph)  # Eliminate redundant view-like operations
+        logger.debug(f"after noop_cleanup {graph.graph}")
         self.matched_count = self.patterns.apply(graph)
         logger.debug("Replaced %s patterns", self.matched_count)
+        logger.debug(f"after apply replacement {graph.graph}")
+
+        from torch._inductor.pattern_matcher import PatternPrettyPrinter
+
+        pattern_idx = 0
+        for pattern_entry in self.patterns.patterns.values():
+            for p in pattern_entry:
+                p_str = PatternPrettyPrinter.run(p.pattern)
+                logger.debug("Pattern %d: %s", pattern_idx, p_str)
+                pattern_idx += 1
+
         self.end_and_log()
 
     def is_applicable_for_range(self, compile_range: Range) -> bool: