[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/graph_fusion_pass_manager.py

@@ -70,6 +70,8 @@ class GraphFusionPassManager:
             self.passes.append(MulsAddFusionPass(config))
 
         if config.compilation_config.pass_config.enable_sp:
-            from .passes.sequence_parallelism import AscendSequenceParallelismPass
+            from .passes.sequence_parallelism import SequenceParallelismPass
+            from .passes.sequence_parallelism_moe import SequenceParallelismMoePass
 
-            self.passes.append(AscendSequenceParallelismPass(config))
+            self.passes.append(SequenceParallelismPass(config))
+            self.passes.append(SequenceParallelismMoePass(config))

vllm_ascend/compilation/passes/allgather_chunk_noop_pass.py

@@ -0,0 +1,40 @@
+import torch
+import torch._inductor.pattern_matcher as pm
+from torch._inductor.pattern_matcher import PatternMatcherPass
+from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
+from vllm.config import VllmConfig
+from vllm.distributed import get_tensor_model_parallel_world_size, get_tp_group
+from vllm.logger import logger
+
+
+class AllGatherChunkNoOpCleanupPass(VllmInductorPass):
+    """Fold all_gather + sequence_parallel_chunk_impl into identity."""
+
+    def __init__(self, config: VllmConfig):
+        super().__init__(config)
+        self.tp_group = get_tp_group()
+        self.tp_size = get_tensor_model_parallel_world_size()
+        self.patterns: PatternMatcherPass = PatternMatcherPass(pass_name="npu_allgather_chunk_noop_cleanup_pass")
+        self._register_patterns()
+
+    def _all_gather(self, x: torch.Tensor) -> torch.Tensor:
+        return torch.ops.vllm.all_gather(x, dim=0, world_size=self.tp_size, group_name=self.tp_group.unique_name)
+
+    def _empty(self, *args, **kwargs):
+        return torch.empty(*args, dtype=self.model_dtype, device=self.device, **kwargs)
+
+    def _register_patterns(self) -> None:
+        def pattern(input: torch.Tensor) -> torch.Tensor:
+            gathered = self._all_gather(input)
+            return torch.ops.vllm.sequence_parallel_chunk_impl(gathered)
+
+        def replacement(input: torch.Tensor) -> torch.Tensor:
+            return input
+
+        pm.register_replacement(pattern, replacement, [self._empty(8, 16)], pm.fwd_only, self.patterns)
+
+    def __call__(self, graph: torch.fx.Graph) -> None:
+        self.begin()
+        matched_count = self.patterns.apply(graph)
+        logger.debug("AllGatherChunkNoOpCleanupPass replaced %s patterns", matched_count)
+        self.end_and_log()

vllm_ascend/compilation/passes/noop_elimination.py

@@ -0,0 +1,62 @@
+from collections.abc import Iterable
+
+import torch
+import torch.fx
+from torch import SymInt
+from torch.fx.experimental.symbolic_shapes import statically_known_true
+from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
+from vllm.logger import logger
+
+
+class NoOpEliminationPass(VllmInductorPass):
+    """Remove no-op view/reshape nodes after pattern rewrites."""
+
+    def __call__(self, graph: torch.fx.Graph) -> None:
+        fx_graph = graph.graph if hasattr(graph, "graph") else graph
+        removed = 0
+        for node in list(fx_graph.nodes):
+            if not self._is_view_like(node):
+                continue
+
+            input_node = node.args[0]
+            if not isinstance(input_node, torch.fx.Node):
+                continue
+
+            input_meta = input_node.meta.get("val")
+            output_meta = node.meta.get("val")
+            if input_meta is None or output_meta is None:
+                continue
+
+            input_shape = getattr(input_meta, "shape", None)
+            output_shape = getattr(output_meta, "shape", None)
+            if input_shape is None or output_shape is None:
+                continue
+
+            if self._all_dims_equivalent(input_shape, output_shape):
+                node.replace_all_uses_with(input_node)
+                fx_graph.erase_node(node)
+                removed += 1
+
+        logger.debug("NoOpEliminationPass removed %s no-op views", removed)
+
+    @staticmethod
+    def _is_view_like(node: torch.fx.Node) -> bool:
+        return (node.op == "call_method" and node.target in {"view", "reshape"}) or (
+            node.op == "call_function"
+            and node.target
+            in {
+                torch.ops.aten.view.default,
+                torch.ops.aten.reshape.default,
+            }
+        )
+
+    @staticmethod
+    def _dims_equivalent(dim: int | SymInt, i_dim: int | SymInt) -> bool:
+        return statically_known_true(dim == i_dim)  # type: ignore[no-any-return]
+
+    def _all_dims_equivalent(self, dims: Iterable[int | SymInt], i_dims: Iterable[int | SymInt]) -> bool:
+        dims_ = list(dims)
+        i_dims_ = list(i_dims)
+        if len(dims_) != len(i_dims_):
+            return False
+        return all(self._dims_equivalent(s, i_s) for s, i_s in zip(dims_, i_dims_))

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:

vllm_ascend/compilation/passes/sequence_parallelism_moe.py

@@ -0,0 +1,204 @@
+import torch
+import torch._inductor.pattern_matcher as pm
+from torch._inductor.pattern_matcher import PatternMatcherPass
+from vllm.compilation.passes.vllm_inductor_pass import PatternPrettyPrinter, VllmInductorPass
+from vllm.config import VllmConfig
+from vllm.config.utils import Range
+from vllm.logger import logger
+
+from vllm_ascend.compilation.passes.sequence_parallelism import (
+    _SequenceParallelPatternHelper,
+    get_sp_min_token_num,
+)
+
+
+class MiddleLayerAllgatherAddRMSNormPattern(_SequenceParallelPatternHelper):
+    """Replaces all_gather + slice + AddRMSNormBias with AddRMSNormBias +
+    all_gather to avoid middle-layer shape mismatch."""
+
+    def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
+        super().__init__(eps, vllm_config.model_config.dtype, torch.npu.current_device())
+
+    def get_inputs(self):
+        input = self.empty(5, 16)
+        weight = self.empty(16)
+        residual = self.empty(8, 16)
+        # num_tokens = 8
+        return [input, weight, residual]
+
+    def get_scalar_inputs(self):
+        return {"num_tokens": 8}
+
+    def register(self, pm_pass: PatternMatcherPass):
+        def pattern(
+            input: torch.Tensor, weight: torch.Tensor, residual: torch.Tensor, num_tokens
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            all_gather = self._all_gather(input)
+            x_sliced = all_gather[:num_tokens]
+            result, _, residual = torch.ops._C_ascend.npu_add_rms_norm_bias(x_sliced, residual, weight, None, self.eps)
+
+            return result, residual
+
+        def replacement(
+            input: torch.Tensor, weight: torch.Tensor, residual: torch.Tensor, num_tokens
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            residual = torch.ops.vllm.maybe_chunk_residual(input, residual)
+            result, _, residual = torch.ops._C_ascend.npu_add_rms_norm_bias(input, residual, weight, None, self.eps)
+            all_gather = self._all_gather(result)
+            return all_gather, residual
+
+        pm.register_replacement(
+            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass, scalar_workaround=self.get_scalar_inputs()
+        )
+
+
+class LastLayerAllgatherRMSNormPattern(_SequenceParallelPatternHelper):
+    """Same as MiddleLayerAllgatherAddRMSNormPattern but for the last layer (no residual)
+    all_gather + RMSNorm fusion."""
+
+    def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
+        super().__init__(eps, vllm_config.model_config.dtype, torch.npu.current_device())
+
+    def get_inputs(self):
+        input = self.empty(5, 16)
+        weight = self.empty(16)
+        residual = self.empty(8, 16)
+        return [input, weight, residual]
+
+    def get_scalar_inputs(self):
+        return {"num_tokens": 8}
+
+    def register(self, pm_pass: PatternMatcherPass):
+        def pattern(
+            input: torch.Tensor, weight: torch.Tensor, residual: torch.Tensor, num_tokens
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            all_gather = self._all_gather(input)
+            x_sliced = all_gather[:num_tokens]
+            result, _, _ = torch.ops._C_ascend.npu_add_rms_norm_bias(x_sliced, residual, weight, None, self.eps)
+
+            return result
+
+        def replacement(
+            input: torch.Tensor, weight: torch.Tensor, residual: torch.Tensor, num_tokens
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            residual = torch.ops.vllm.maybe_chunk_residual(input, residual)
+            result, _, _ = torch.ops._C_ascend.npu_add_rms_norm_bias(input, residual, weight, None, self.eps)
+            all_gather = self._all_gather(result)
+            return all_gather
+
+        pm.register_replacement(
+            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass, scalar_workaround=self.get_scalar_inputs()
+        )
+
+
+class Qwen3VLMiddleLayerAllgatherAddRMSNormPattern(_SequenceParallelPatternHelper):
+    """Replaces all_gather + slice + add + AddRMSNormBias with add(chunk) +
+    AddRMSNormBias + all_gather for Qwen3-VL-style all_gather path."""
+
+    def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
+        super().__init__(eps, vllm_config.model_config.dtype, torch.npu.current_device())
+
+    def get_inputs(self):
+        input = self.empty(5, 16)
+        weight = self.empty(16)
+        residual = self.empty(8, 16)
+        deepstack_input_embeds = self.empty(8, 16)
+        return [input, weight, residual, deepstack_input_embeds]
+
+    def get_scalar_inputs(self):
+        return {"num_tokens": 8}
+
+    def register(self, pm_pass: PatternMatcherPass):
+        def pattern(
+            input: torch.Tensor,
+            weight: torch.Tensor,
+            residual: torch.Tensor,
+            deepstack_input_embeds: torch.Tensor,
+            num_tokens,
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            all_gather = self._all_gather(input)
+            x_sliced = all_gather[:num_tokens]
+            add_ = x_sliced + deepstack_input_embeds
+            result, _, residual = torch.ops._C_ascend.npu_add_rms_norm_bias(add_, residual, weight, None, self.eps)
+
+            return result, residual
+
+        def replacement(
+            input: torch.Tensor,
+            weight: torch.Tensor,
+            residual: torch.Tensor,
+            deepstack_input_embeds: torch.Tensor,
+            num_tokens,
+        ) -> tuple[torch.Tensor, torch.Tensor]:
+            chunk = deepstack_input_embeds.chunk(self.tp_size)[self.tp_rank]
+            add_ = input + chunk
+            residual = torch.ops.vllm.maybe_chunk_residual(input, residual)
+            result, _, residual = torch.ops._C_ascend.npu_add_rms_norm_bias(add_, residual, weight, None, self.eps)
+            all_gather = self._all_gather(result)
+            return all_gather, residual
+
+        pm.register_replacement(
+            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass, scalar_workaround=self.get_scalar_inputs()
+        )
+
+
+class AllGatherChunkNoOpPattern(_SequenceParallelPatternHelper):
+    """Folds all_gather + sequence_parallel_chunk_impl into identity (no-op)."""
+
+    def __init__(self, vllm_config: VllmConfig, eps: float = 1e-6):
+        super().__init__(eps, vllm_config.model_config.dtype, torch.npu.current_device())
+
+    def get_inputs(self):
+        return [self.empty(8, 16)]
+
+    def register(self, pm_pass: PatternMatcherPass):
+        def pattern(input: torch.Tensor) -> torch.Tensor:
+            gathered = self._all_gather(input)
+            return torch.ops.vllm.sequence_parallel_chunk_impl(gathered)
+
+        def replacement(input: torch.Tensor) -> torch.Tensor:
+            return input
+
+        pm.register_replacement(pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass)
+
+
+class SequenceParallelismMoePass(VllmInductorPass):
+    """Sequence parallelism AllGather epilogue pass.
+
+    Applies AllGather-based patterns: MiddleLayerAllgatherAddRMSNormPattern,
+    LastLayerAllgatherRMSNormPattern, Qwen3VLMiddleLayerAllgatherAddRMSNormPattern,
+    and AllGatherChunkNoOpPattern (all_gather + sequence_parallel_chunk_impl -> identity).
+    """
+
+    def __init__(self, config: VllmConfig):
+        super().__init__(config)
+
+        self.patterns: PatternMatcherPass = PatternMatcherPass(pass_name="npu_sequence_parallelism_allgather_ep_pass")
+
+        for epsilon in [1e-5, 1e-6]:
+            MiddleLayerAllgatherAddRMSNormPattern(config, epsilon).register(self.patterns)
+            LastLayerAllgatherRMSNormPattern(config, epsilon).register(self.patterns)
+            Qwen3VLMiddleLayerAllgatherAddRMSNormPattern(config, epsilon).register(self.patterns)
+
+        AllGatherChunkNoOpPattern(config).register(self.patterns)
+
+        self.min_tokens = get_sp_min_token_num(config)
+
+    def __call__(self, graph: torch.fx.Graph):
+        self.begin()
+        logger.debug(f"before apply replacement {graph}")
+        self.matched_count = self.patterns.apply(graph)
+        logger.debug(f"after apply replacement {graph}")
+        logger.debug("SequenceParallelismMoePass replaced %s patterns", self.matched_count)
+        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:
+        applicable = compile_range.start >= self.min_tokens
+        logger.debug(f"SequenceParallelismMoePass {compile_range=} {applicable=}")
+        return applicable