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[refactor] replace scattered business kwargs with typed request objects and explicit stage boundaries (#7024)

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### What this PR does / why we need it?
Refactor `vllm_ascend/ops/fused_moe` to replace scattered MoE business
`**kwargs` with typed request objects and explicit stage boundaries.

- Prepare, dispatch, MLP, and quant stages now have clearer ownership.
- Main MoE path no longer depends on business `kwargs.get(...)` lookups.
- Comm and dispatcher interfaces are request-only on the main path.
- UTs can assert stage-level fields directly instead of inferring
behavior indirectly.

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
CI passed.

---------

Signed-off-by: linfeng-yuan <1102311262@qq.com>
This commit is contained in:
linfeng-yuan
2026-03-20 23:23:57 +08:00
committed by GitHub
parent c860535246
commit 88d03a783f
33 changed files with 2146 additions and 947 deletions
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30
vllm_ascend/_310p/fused_moe/fused_moe.py
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@@ -25,7 +25,8 @@ from vllm.model_executor.layers.fused_moe.shared_fused_moe import SharedFusedMoE
from vllm_ascend.ascend_forward_context import _EXTRA_CTX, MoECommType
from vllm_ascend.ops.fused_moe.experts_selector import zero_experts_compute
from vllm_ascend.ops.fused_moe.moe_comm_method import FusedExpertsResult, _MoECommMethods
from vllm_ascend.quantization.methods.base import QuantType
from vllm_ascend.ops.fused_moe.moe_runtime_args import build_fused_experts_input
from vllm_ascend.quantization.quant_type import QuantType
from .experts_selector import select_experts
from .moe_comm_method import AllGatherCommImpl310
@@ -93,13 +94,17 @@ class AscendUnquantizedFusedMoEMethod310(UnquantizedFusedMoEMethod):
moe_comm_method = _EXTRA_CTX.moe_comm_method
final_hidden_states = moe_comm_method.fused_experts(
hidden_states=x,
w1=layer.w13_weight,
w2=layer.w2_weight,
topk_weights=topk_weights,
topk_ids=topk_ids,
expert_map=expert_map,
apply_router_weight_on_input=apply_router_weight_on_input,
fused_experts_input=build_fused_experts_input(
hidden_states=x,
topk_weights=topk_weights,
topk_ids=topk_ids,
w1=layer.w13_weight,
w2=layer.w2_weight,
quant_type=QuantType.NONE,
dynamic_eplb=False,
expert_map=expert_map,
apply_router_weight_on_input=apply_router_weight_on_input,
),
)
if zero_expert_num > 0 and zero_expert_type is not None:
final_hidden_states += zero_expert_result
@@ -218,9 +223,13 @@ class AscendFusedMoE310(FusedMoE):
assert self.quant_method is not None
assert self.routed_scaling_factor == 1.0, "routed_scaling_factor != 1.0 is not supported."
hidden_states, router_logits, _, context_metadata = _EXTRA_CTX.moe_comm_method.prepare(
prepare_output = _EXTRA_CTX.moe_comm_method.prepare(
hidden_states=hidden_states, router_logits=router_logits, quant_type=self.quant_type
)
hidden_states = prepare_output.hidden_states
router_logits = prepare_output.router_logits
pertoken_scale = prepare_output.pertoken_scale
padded_hidden_states_shape = prepare_output.padded_hidden_states_shape
# Matrix multiply.
fused_experts_results: FusedExpertsResult = self.quant_method.apply(
@@ -238,12 +247,13 @@ class AscendFusedMoE310(FusedMoE):
global_num_experts=self.global_num_experts,
expert_map=self.local_expert_map,
apply_router_weight_on_input=self.apply_router_weight_on_input,
pertoken_scale=pertoken_scale,
)
routed_out = _EXTRA_CTX.moe_comm_method.finalize(
hidden_states=fused_experts_results.routed_out,
reduce_results=self.reduce_results,
context_metadata=context_metadata,
padded_hidden_states_shape=padded_hidden_states_shape,
)
return routed_out