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[fix] fix dsv3 weight loader tqdm and simplify shared experts fusion (#7181)

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JieXin Liang
2025-06-15 02:56:29 +08:00
committed by GitHub
parent b57d87c297
commit ed54bf9d19
1 changed files with 11 additions and 96 deletions
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107
python/sglang/srt/models/deepseek_v2.py
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@@ -1033,7 +1033,7 @@ class DeepseekV2AttentionMLA(nn.Module):
attn_bmm_output = attn_output.new_empty(
(self.num_local_heads, aligned_m, self.v_head_dim)
)
deep_gemm_grouped_gemm_nt_f8f8bf16_masked(
deep_gemm_wrapper.grouped_gemm_nt_f8f8bf16_masked(
(attn_output_val, attn_output_scale),
(self.w_vc, self.w_scale_v),
attn_bmm_output,
@@ -2008,101 +2008,6 @@ class DeepseekV2ForCausalLM(nn.Module):
("gate_up_proj", "gate_proj", 0),
("gate_up_proj", "up_proj", 1),
]
if self.num_fused_shared_experts > 0:
assert self.num_fused_shared_experts == 1
weights_list = list(weights)
weights_dict = dict(weights_list)
if self.quant_config is not None:
if self.quant_config.get_name() == "w8a8_int8":
suffix_list = [
"down_proj.weight",
"down_proj.weight_scale",
"gate_proj.weight",
"gate_proj.weight_scale",
"up_proj.weight",
"up_proj.weight_scale",
]
elif (
self.quant_config.get_name() == "fp8"
or self.quant_config.get_name() == "blockwise_int8"
):
suffix_list = [
"down_proj.weight",
"down_proj.weight_scale_inv",
"gate_proj.weight",
"gate_proj.weight_scale_inv",
"up_proj.weight",
"up_proj.weight_scale_inv",
]
elif self.quant_config.get_name() == "awq":
suffix_list = [
"down_proj.qweight",
"down_proj.qzeros",
"down_proj.scales",
"gate_proj.qweight",
"gate_proj.qzeros",
"gate_proj.scales",
"up_proj.qweight",
"up_proj.qzeros",
"up_proj.scales",
]
elif self.quant_config.get_name() == "modelopt_fp4":
suffix_list = [
"down_proj.weight",
"down_proj.weight_scale",
"down_proj.weight_scale_2",
"down_proj.input_scale",
"gate_proj.weight",
"gate_proj.weight_scale",
"gate_proj.weight_scale_2",
"gate_proj.input_scale",
"up_proj.weight",
"up_proj.weight_scale",
"up_proj.weight_scale_2",
"up_proj.input_scale",
]
else:
raise ValueError(
f"Unsupported shared expert fusion for quantization: {self.quant_config.get_name()}."
)
else:
suffix_list = [
"down_proj.weight",
"gate_proj.weight",
"up_proj.weight",
]
names_to_remove = []
moe_layers = (
range(
self.config.first_k_dense_replace,
self.config.num_hidden_layers,
self.config.moe_layer_freq,
)
if not is_nextn
else [nextn_layer_id]
)
for moe_layer in tqdm(
moe_layers,
desc=f"Cloning {self.num_fused_shared_experts} "
"shared expert into MoE",
):
for suffix in suffix_list:
shared_expert_weight_name = (
f"model.layers.{moe_layer}.mlp.shared_experts.{suffix}"
)
weights_list.append(
(
f"model.layers.{moe_layer}."
f"mlp.experts."
f"{self.config.n_routed_experts + 0}"
f".{suffix}",
weights_dict[shared_expert_weight_name],
)
)
names_to_remove += [shared_expert_weight_name]
weights = [w for w in weights_list if w[0] not in names_to_remove]
# Params for weights, fp8 weight scales, fp8 activation scales
# (param_name, weight_name, expert_id, shard_id)
@@ -2128,9 +2033,19 @@ class DeepseekV2ForCausalLM(nn.Module):
"hnorm",
]
if self.num_fused_shared_experts > 0:
assert self.num_fused_shared_experts == 1
logger.info("Shared experts fusion optimization enabled.")
params_dict = dict(self.named_parameters())
weight_names = []
for name, loaded_weight in weights:
if self.num_fused_shared_experts > 0 and "mlp.shared_experts" in name:
name = name.replace(
"mlp.shared_experts",
f"mlp.experts.{self.config.n_routed_experts}",
)
weight_names.append(name)
if not is_nextn: