5d12446573
### 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>
63 lines
2.2 KiB
Python
63 lines
2.2 KiB
Python
from collections.abc import Iterable
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import torch
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import torch.fx
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from torch import SymInt
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from torch.fx.experimental.symbolic_shapes import statically_known_true
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from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
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from vllm.logger import logger
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class NoOpEliminationPass(VllmInductorPass):
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"""Remove no-op view/reshape nodes after pattern rewrites."""
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def __call__(self, graph: torch.fx.Graph) -> None:
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fx_graph = graph.graph if hasattr(graph, "graph") else graph
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removed = 0
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for node in list(fx_graph.nodes):
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if not self._is_view_like(node):
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continue
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input_node = node.args[0]
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if not isinstance(input_node, torch.fx.Node):
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continue
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input_meta = input_node.meta.get("val")
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output_meta = node.meta.get("val")
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if input_meta is None or output_meta is None:
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continue
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input_shape = getattr(input_meta, "shape", None)
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output_shape = getattr(output_meta, "shape", None)
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if input_shape is None or output_shape is None:
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continue
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if self._all_dims_equivalent(input_shape, output_shape):
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node.replace_all_uses_with(input_node)
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fx_graph.erase_node(node)
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removed += 1
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logger.debug("NoOpEliminationPass removed %s no-op views", removed)
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@staticmethod
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def _is_view_like(node: torch.fx.Node) -> bool:
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return (node.op == "call_method" and node.target in {"view", "reshape"}) or (
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node.op == "call_function"
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and node.target
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in {
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torch.ops.aten.view.default,
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torch.ops.aten.reshape.default,
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}
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)
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@staticmethod
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def _dims_equivalent(dim: int | SymInt, i_dim: int | SymInt) -> bool:
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return statically_known_true(dim == i_dim) # type: ignore[no-any-return]
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def _all_dims_equivalent(self, dims: Iterable[int | SymInt], i_dims: Iterable[int | SymInt]) -> bool:
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dims_ = list(dims)
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i_dims_ = list(i_dims)
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if len(dims_) != len(i_dims_):
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return False
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return all(self._dims_equivalent(s, i_s) for s, i_s in zip(dims_, i_dims_))
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