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[NemotronH] Add latent MoE support (#16227)

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Signed-off-by: Roi Koren <roik@nvidia.com>
This commit is contained in:
roikoren755
2026-01-02 16:08:58 +02:00
committed by GitHub
parent 5062537b67
commit b021332339
23 changed files with 2957 additions and 2 deletions
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2
python/sglang/srt/configs/nemotron_h.py
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@@ -194,6 +194,7 @@ class NemotronHConfig(PretrainedConfig):
n_shared_experts=1,
moe_intermediate_size=7688,
moe_shared_expert_intermediate_size=7688,
moe_latent_size=None,
num_experts_per_tok=2,
routed_scaling_factor=1.0,
n_group=1,
@@ -259,6 +260,7 @@ class NemotronHConfig(PretrainedConfig):
self.n_shared_experts = n_shared_experts
self.moe_intermediate_size = moe_intermediate_size
self.moe_shared_expert_intermediate_size = moe_shared_expert_intermediate_size
self.moe_latent_size = moe_latent_size
self.num_experts_per_tok = num_experts_per_tok
self.routed_scaling_factor = routed_scaling_factor
self.n_group = n_group

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=128,N=1344,device_name=NVIDIA_B200.json Normal file
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@@ -0,0 +1,146 @@
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}
}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=128,N=1344,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
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@@ -0,0 +1,146 @@
{
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},
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},
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}
}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=128,N=2688,device_name=NVIDIA_B200.json Normal file
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@@ -0,0 +1,146 @@
{
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}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=128,N=2688,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
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@@ -0,0 +1,146 @@
{
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}
}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=256,N=1344,device_name=NVIDIA_B200.json Normal file
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@@ -0,0 +1,146 @@
{
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}
}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=256,N=1344,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
View File

@@ -0,0 +1,146 @@
{
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python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=256,N=2688,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
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@@ -0,0 +1,146 @@
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}

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@@ -0,0 +1,146 @@
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146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=512,N=1344,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
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@@ -0,0 +1,146 @@
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python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=512,N=2688,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
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}

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@@ -0,0 +1,146 @@
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146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=512,N=672,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
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@@ -0,0 +1,146 @@
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},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 5
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 5
},
"1536": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 4
}
}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=64,N=2688,device_name=NVIDIA_B200.json Normal file
View File

@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 5
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 5
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"256": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"512": {
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"1024": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"1536": {
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"2048": {
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"4096": {
"BLOCK_SIZE_M": 256,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
}
}

146
python/sglang/srt/layers/moe/fused_moe_triton/configs/triton_3_5_1/E=64,N=2688,device_name=NVIDIA_H100_80GB_HBM3.json Normal file
View File

@@ -0,0 +1,146 @@
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 4
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"48": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"64": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"96": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"128": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2
},
"256": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
},
"512": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"1024": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"1536": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 4
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 4
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 3
}
}

33
python/sglang/srt/models/nemotron_h.py
View File

@@ -138,6 +138,10 @@ class NemotronHMoE(nn.Module):
self.ep_size = self.ep_group.size()
self.n_routed_experts = config.n_routed_experts
self.n_shared_experts = config.n_shared_experts
self.use_latent_moe = getattr(config, "moe_latent_size", None) is not None
self.moe_hidden_size = (
config.moe_latent_size if self.use_latent_moe else config.hidden_size
)
self.gate = ReplicatedLinear(
config.hidden_size,
@@ -165,7 +169,7 @@ class NemotronHMoE(nn.Module):
num_experts=config.n_routed_experts
+ get_global_server_args().ep_num_redundant_experts,
top_k=config.num_experts_per_tok,
hidden_size=config.hidden_size,
hidden_size=self.moe_hidden_size,
intermediate_size=config.moe_intermediate_size,
reduce_results=False,
quant_config=quant_config,
@@ -186,6 +190,26 @@ class NemotronHMoE(nn.Module):
else:
self.shared_experts = None
if self.use_latent_moe:
self.fc1_latent_proj = ColumnParallelLinear(
input_size=config.hidden_size,
output_size=self.moe_hidden_size,
bias=config.mlp_bias,
quant_config=quant_config,
gather_output=True,
prefix=f"{prefix}.fc1_latent_proj",
)
self.fc2_latent_proj = ReplicatedLinear(
input_size=self.moe_hidden_size,
output_size=config.hidden_size,
bias=config.mlp_bias,
quant_config=quant_config,
prefix=f"{prefix}.fc2_latent_proj",
)
else:
self.fc1_latent_proj = None
self.fc2_latent_proj = None
def _forward_core(
self,
hidden_states: torch.Tensor,
@@ -206,6 +230,8 @@ class NemotronHMoE(nn.Module):
else:
shared_output = None
topk_output = self.topk(hidden_states, router_logits)
if self.use_latent_moe:
hidden_states, _ = self.fc1_latent_proj(hidden_states)
final_hidden_states = self.experts(hidden_states, topk_output)
return final_hidden_states, shared_output
@@ -226,6 +252,8 @@ class NemotronHMoE(nn.Module):
# router_scores: [num_tokens, num_experts]
router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32))
topk_output = self.topk(hidden_states, router_logits)
if self.use_latent_moe:
hidden_states, _ = self.fc1_latent_proj(hidden_states)
final_hidden_states = self.experts(hidden_states, topk_output)
get_current_device_stream_fast().wait_stream(alt_stream)
@@ -242,6 +270,9 @@ class NemotronHMoE(nn.Module):
assert shared_output is not None
shared_output *= 1.0 / self.routed_scaling_factor
if self.use_latent_moe:
final_hidden_states, _ = self.fc2_latent_proj(final_hidden_states)
if shared_output is not None:
final_hidden_states += shared_output