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[ROCm][Perf] Add Fused Shared Expert (FSE) support for GLM-4.5/6/7 #44313

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[ROCm][Perf] Add Fused Shared Expert (FSE) support for GLM-4.5/6/7 #44313

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afc5868
[ROCm][Perf] Add Fused Shared Expert (FSE) support for GLM-4.5/6/7
omirosh Jun 2, 2026
ef3ad54
[ROCm][Perf] Extend FSE to GLM-4 MTP (glm4_moe_mtp.py)
omirosh Jun 2, 2026
07cc743
Merge branch 'main' into fse/glm47-shared-expert
omirosh Jun 5, 2026
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237 changes: 175 additions & 62 deletions vllm/model_executor/models/glm4_moe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@
from torch import nn
from transformers.models.glm4_moe import Glm4MoeConfig

from vllm._aiter_ops import rocm_aiter_ops
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig, get_current_vllm_config
from vllm.distributed import (
Expand Down Expand Up @@ -168,7 +169,28 @@ def __init__(
self.physical_expert_start + self.n_local_physical_experts
)

if config.n_shared_experts is not None:
# AITER fused MoE / fused shared-expert (FSE) gates. When AITER
# MoE is active, the `routed_scaling_factor` must be applied
# *inside* the kernel (per routed slot) rather than post-hoc to
# the whole MoE output - otherwise the FSE shared-expert slot
# (which the kernel inserts with unit weight) would also be
# scaled by `routed_scaling_factor`, producing a structural
# magnitude error in every MoE layer.
# See vllm/_aiter_ops.py::is_fusion_moe_shared_experts_enabled,
# vllm/model_executor/models/deepseek_v2.py L341 for the same
# pattern, and the equivalent ROCm/ATOM gate at
# atom/model_ops/topK.py.
self.is_rocm_aiter_moe_enabled = rocm_aiter_ops.is_fused_moe_enabled()
self.is_fusion_moe_shared_experts_enabled = (
rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
)

if (
config.n_shared_experts is None
or self.is_fusion_moe_shared_experts_enabled
):
self.shared_experts = None
else:
intermediate_size = config.moe_intermediate_size * config.n_shared_experts
self.shared_experts = Glm4MoeMLP(
hidden_size=config.hidden_size,
Expand All @@ -178,8 +200,6 @@ def __init__(
reduce_results=False,
prefix=f"{prefix}.shared_experts",
)
else:
self.shared_experts = None

self.experts = FusedMoE(
shared_experts=self.shared_experts,
Expand All @@ -194,12 +214,21 @@ def __init__(
topk_group=config.topk_group,
prefix=f"{prefix}.experts",
scoring_func="sigmoid",
# AITER applies `routed_scaling_factor` internally per routed
# slot, so it must NOT also be applied to the whole output
# (which would incorrectly scale the FSE shared-expert slot).
# Mirrors deepseek_v2.py.
routed_scaling_factor=self.routed_scaling_factor,
apply_routed_scale_to_output=True,
apply_routed_scale_to_output=not self.is_rocm_aiter_moe_enabled,
e_score_correction_bias=self.gate.e_score_correction_bias,
enable_eplb=self.enable_eplb,
num_redundant_experts=self.n_redundant_experts,
router_logits_dtype=torch.float32,
n_shared_experts=(
config.n_shared_experts
if self.is_fusion_moe_shared_experts_enabled
else None
),
)

def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
Expand Down Expand Up @@ -469,15 +498,33 @@ def forward(
def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
# Params for weights, fp8 weight scales, fp8 activation scales
# (param_name, weight_name, expert_id, shard_id)
# When AITER fused shared experts is enabled, the FusedMoE layer is
# widened by n_shared_experts slots that hold the (split) shared
# expert weights; the mapping must enumerate those slots too so the
# weight loader can route mlp.shared_experts.* tensors there.
num_experts = self.config.n_routed_experts
if (
rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
and self.config.n_shared_experts
):
num_experts += self.config.n_shared_experts
return fused_moe_make_expert_params_mapping(
self,
ckpt_gate_proj_name="gate_proj",
ckpt_down_proj_name="down_proj",
ckpt_up_proj_name="up_proj",
num_experts=self.config.n_routed_experts,
num_experts=num_experts,
)

def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
# AITER fused shared-expert (FSE) weight-loader branch. When FSE is
# on, the FusedMoE layer was widened by n_shared_experts slots, and
# checkpoint tensors named `...mlp.shared_experts.{gate,up,down}_proj.*`
# must be split into n_shared_experts chunks and routed to the
# appended expert slots `...mlp.experts.{n_routed_experts + j}.*`.
rocm_aiter_moe_shared_expert_enabled = (
rocm_aiter_ops.is_fusion_moe_shared_experts_enabled()
)
stacked_params_mapping = [
# (param_name, shard_name, shard_id)
("qkv_proj", "q_proj", "q"),
Expand All @@ -494,6 +541,12 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
spec_layer = get_spec_layer_idx_from_weight_name(self.config, name)
if spec_layer is not None:
continue

is_fusion_moe_shared_experts_layer = (
rocm_aiter_moe_shared_expert_enabled
and ("mlp.shared_experts" in name)
)

for param_name, weight_name, shard_id in stacked_params_mapping:
# Skip non-stacked layers and experts (experts handled below).
if weight_name not in name:
Expand All @@ -506,6 +559,10 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
# for mlp.experts[0].gate_gate_up_proj, which breaks load.
if ("mlp.experts." in name) and name not in params_dict:
continue
# Under FSE we treat mlp.shared_experts.* as expert-style
# tensors (handled below) rather than stacked gate/up linears.
if is_fusion_moe_shared_experts_layer:
continue

name = name.replace(weight_name, param_name)
# Skip loading extra bias for GPTQ models.
Expand All @@ -527,65 +584,121 @@ def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
break
else:
is_expert_weight = False
for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
if weight_name not in name:
continue

# Anyway, this is an expert weight and should not be
# attempted to load as other weights later
is_expert_weight = True

# Do not modify `name` since the loop may continue here
# Instead, create a new variable
name_mapped = name.replace(weight_name, param_name)

if is_pp_missing_parameter(name_mapped, self):
continue

param = params_dict[name_mapped]
# We should ask the weight loader to return success or not
# here since otherwise we may skip experts with other
# available replicas.
weight_loader = typing.cast(
Callable[..., bool], param.weight_loader

# FSE split: if this is a widened mlp.shared_experts tensor,
# slice it into n_shared_experts chunks along the intermediate-
# size axis and synthesize per-slot expert names. For
# ColumnParallel (gate_proj / up_proj) the intermediate dim
# is dim 0; for RowParallel (down_proj) it's dim 1.
num_chunks = 1
split_dim = 0
chunk_size = 0
if is_fusion_moe_shared_experts_layer:
num_chunks = (
getattr(self.config, "n_shared_experts", 1) or 1
)
success = weight_loader(
param,
loaded_weight,
name_mapped,
shard_id=shard_id,
expert_id=expert_id,
return_success=True,
split_dim = (
1
if (
"down_proj.weight" in name
and loaded_weight.ndim > 1
)
else 0
)
if success:
name = name_mapped
break
else:
if is_expert_weight:
# We've checked that this is an expert weight
# However it's not mapped locally to this rank
# So we simply skip it
continue

# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue

# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue

if is_pp_missing_parameter(name, self):
continue

param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
loaded_params.add(name)
total = loaded_weight.shape[split_dim]
if total % num_chunks != 0:
raise ValueError(
f"FSE shared-expert weight {name} has dim "
f"{total} along axis {split_dim} which is not "
f"divisible by n_shared_experts={num_chunks}."
)
chunk_size = total // num_chunks

for j in range(num_chunks):
chunk_name = name
weight_to_load = loaded_weight

if is_fusion_moe_shared_experts_layer:
chunk_slice = slice(
j * chunk_size, (j + 1) * chunk_size
)
if loaded_weight.ndim == 1:
weight_to_load = loaded_weight[chunk_slice]
elif split_dim == 0:
weight_to_load = loaded_weight[chunk_slice, :]
else:
weight_to_load = loaded_weight[:, chunk_slice]
# Synthesize an expert-style name so the expert
# params mapping above can route it via the
# FusedMoE expert-aware weight loader.
chunk_name = name.replace(
"mlp.shared_experts",
f"mlp.experts.{self.config.n_routed_experts + j}",
)

for mapping in expert_params_mapping:
param_name, weight_name, expert_id, shard_id = mapping
if weight_name not in chunk_name:
continue

# Anyway, this is an expert weight and should not be
# attempted to load as other weights later
is_expert_weight = True

# Do not modify `name` since the loop may continue here
# Instead, create a new variable
name_mapped = chunk_name.replace(weight_name, param_name)

if is_pp_missing_parameter(name_mapped, self):
continue

param = params_dict[name_mapped]
# We should ask the weight loader to return success
# or not here since otherwise we may skip experts
# with other available replicas.
weight_loader = typing.cast(
Callable[..., bool], param.weight_loader
)
success = weight_loader(
param,
weight_to_load,
name_mapped,
shard_id=shard_id,
expert_id=expert_id,
return_success=True,
)
if success:
if not is_fusion_moe_shared_experts_layer:
name = name_mapped
else:
loaded_params.add(name_mapped)
break
else:
if is_expert_weight:
# We've checked that this is an expert weight
# However it's not mapped locally to this rank
# So we simply skip it
continue

# Skip loading extra bias for GPTQ models.
if name.endswith(".bias") and name not in params_dict:
continue

# Remapping the name of FP8 kv-scale.
name = maybe_remap_kv_scale_name(name, params_dict)
if name is None:
continue

if is_pp_missing_parameter(name, self):
continue

param = params_dict[name]
weight_loader = getattr(
param, "weight_loader", default_weight_loader
)
weight_loader(param, loaded_weight)
if name is not None and not is_fusion_moe_shared_experts_layer:
loaded_params.add(name)

return loaded_params

Expand Down
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