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[MoE Refactor] Integrate Naive Prepare Finalize into MK (#32567)

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Signed-off-by: Robert Shaw <robshaw@redhat.com>
Signed-off-by: Amir Klein <203507526+amirkl94@users.noreply.github.com>
Co-authored-by: Robert Shaw <robshaw@redhat.com>
Co-authored-by: amirkl94 <203507526+amirkl94@users.noreply.github.com>
This commit is contained in:
Robert Shaw
2026-01-26 20:28:02 -05:00
committed by GitHub
parent 6d86fde09c
commit 5a93b9162b
46 changed files with 1018 additions and 876 deletions
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54
vllm/model_executor/layers/quantization/fp8.py
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@@ -33,7 +33,6 @@ from vllm.model_executor.layers.fused_moe.oracle.fp8 import (
Fp8MoeBackend,
convert_to_fp8_moe_kernel_format,
make_fp8_moe_kernel,
make_fp8_moe_kernel_for_mkm,
make_fp8_moe_quant_config,
select_fp8_moe_backend,
)
@@ -53,7 +52,6 @@ from vllm.model_executor.layers.quantization.kernels.scaled_mm import (
from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
apply_fi_trtllm_fp8_per_tensor_moe,
build_flashinfer_fp8_cutlass_moe_prepare_finalize,
)
from vllm.model_executor.layers.quantization.utils.fp8_utils import (
W8A8BlockFp8LinearOp,
@@ -679,15 +677,6 @@ class Fp8MoEMethod(FusedMoEMethodBase):
allow_vllm_cutlass=False,
)
# Delay creation of the kernel until after process-weights.
self.kernel: mk.FusedMoEModularKernel | None = None
@property
def topk_indices_dtype(self) -> torch.dtype | None:
if self.kernel is not None:
return self.kernel.prepare_finalize.topk_indices_dtype()
return None
def create_weights(
self,
layer: Module,
@@ -813,7 +802,7 @@ class Fp8MoEMethod(FusedMoEMethodBase):
def _setup_kernel(
self,
layer: Module,
layer: FusedMoE,
w13: torch.Tensor,
w2: torch.Tensor,
w13_scale: torch.Tensor,
@@ -845,16 +834,15 @@ class Fp8MoEMethod(FusedMoEMethodBase):
# TODO(rob): unify these so FP8MoEMethod owns the ModularKernel
# in both cases.
self.moe_quant_config = self.get_fused_moe_quant_config(layer)
if self.moe_quant_config and (
(not self.moe.moe_parallel_config.use_all2all_kernels)
or self.moe.moe_parallel_config.use_naive_all2all_kernels
):
if self.moe_quant_config:
assert self.experts_cls is not None
self.kernel, self.use_inplace = make_fp8_moe_kernel(
self.moe_mk, self.use_inplace = make_fp8_moe_kernel(
moe_quant_config=self.moe_quant_config,
moe_config=self.moe,
fp8_backend=self.fp8_backend,
experts_cls=self.experts_cls,
routing_tables=layer._maybe_init_expert_routing_tables(),
shared_experts=layer.shared_experts,
)
def process_weights_after_loading(self, layer: Module) -> None:
@@ -909,33 +897,19 @@ class Fp8MoEMethod(FusedMoEMethodBase):
self,
routing_tables: tuple[torch.Tensor, torch.Tensor, torch.Tensor] | None = None,
) -> mk.FusedMoEPrepareAndFinalize | None:
if self.fp8_backend == Fp8MoeBackend.FLASHINFER_TRTLLM:
return None
elif self.fp8_backend == Fp8MoeBackend.FLASHINFER_CUTLASS:
# For no-EP case, don't use the MKM framework.
if not self.moe.moe_parallel_config.use_all2all_kernels:
return None
prepare_finalize = build_flashinfer_fp8_cutlass_moe_prepare_finalize(
self.moe,
use_deepseek_fp8_block_scale=self.block_quant,
)
logger.debug_once("%s", prepare_finalize.__class__.__name__)
return prepare_finalize
return super().maybe_make_prepare_finalize(routing_tables)
raise ValueError(
f"{self.__class__.__name__} uses the new modular kernel initialization "
"logic. This function should not be called."
)
def select_gemm_impl(
self,
prepare_finalize: FusedMoEPrepareAndFinalize,
layer: torch.nn.Module,
) -> FusedMoEPermuteExpertsUnpermute:
assert self.moe_quant_config is not None
assert self.experts_cls is not None
return make_fp8_moe_kernel_for_mkm(
moe_config=self.moe,
quant_config=self.moe_quant_config,
experts_cls=self.experts_cls,
prepare_finalize=prepare_finalize,
raise ValueError(
f"{self.__class__.__name__} uses the new modular kernel initialization "
"logic. This function should not be called."
)
def get_fused_moe_quant_config(
@@ -1037,9 +1011,9 @@ class Fp8MoEMethod(FusedMoEMethodBase):
topk_weights: torch.Tensor,
topk_ids: torch.Tensor,
) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
assert self.kernel is not None
assert self.moe_mk is not None
assert not self.is_monolithic
return self.kernel(
return self.moe_mk(
x,
layer.w13_weight,
layer.w2_weight,