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Add support for Mistral Large 3 inference with Flashinfer MoE (#33174)

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Signed-off-by: Dimitrios Bariamis <12195802+dbari@users.noreply.github.com>
Co-authored-by: Dimitrios Bariamis <12195802+dbari@users.noreply.github.com>
Co-authored-by: Cyrus Leung <tlleungac@connect.ust.hk>
This commit is contained in:
Dimitrios Bariamis
2026-01-31 07:48:27 +01:00
committed by GitHub
parent 73419abfae
commit f0bca83ee4
16 changed files with 1104 additions and 31 deletions
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147
vllm/model_executor/layers/fused_moe/configs/E=128,N=512,device_name=NVIDIA_B200,dtype=fp8_w8a8.json Normal file
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147
vllm/model_executor/layers/fused_moe/configs/E=128,N=512,device_name=NVIDIA_B200.json Normal file
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147
vllm/model_executor/layers/fused_moe/configs/E=128,N=512,device_name=NVIDIA_GB200,dtype=fp8_w8a8.json Normal file
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147
vllm/model_executor/layers/fused_moe/configs/E=128,N=512,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128,128].json Normal file
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147
vllm/model_executor/layers/fused_moe/configs/E=128,N=512,device_name=NVIDIA_H200.json Normal file
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147
vllm/model_executor/layers/fused_moe/configs/E=16,N=4096,device_name=NVIDIA_B200,dtype=fp8_w8a8,block_shape=[128,128].json Normal file
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"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
},
"3072": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 4
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 64,
"num_warps": 8,
"num_stages": 4
}
}

147
vllm/model_executor/layers/fused_moe/configs/E=16,N=4096,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128,128].json Normal file
View File

@@ -0,0 +1,147 @@
{
"triton_version": "3.5.0",
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 5
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 4
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 3
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 64,
"num_warps": 4,
"num_stages": 3
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 4,
"num_stages": 2
},
"1024": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"1536": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"2048": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 3
},
"3072": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2
},
"4096": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 256,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 3
}
}

9
vllm/model_executor/layers/fused_moe/flashinfer_trtllm_moe.py
View File

@@ -70,9 +70,12 @@ def _supports_routing_method(
RoutingMethodType.RenormalizeNaive,
]
elif (weight_key, activation_key) == (kFp8StaticTensorSym, kFp8StaticTensorSym):
# NOTE(rob): kernel requires Llama4.
return routing_method == RoutingMethodType.Llama4
# NOTE(dbari): as above, potentially allow others here.
return routing_method in [
RoutingMethodType.Llama4,
RoutingMethodType.Renormalize,
RoutingMethodType.RenormalizeNaive,
]
else:
raise ValueError("Unsupported quantization scheme.")

22
vllm/model_executor/layers/quantization/utils/flashinfer_utils.py
View File

@@ -113,15 +113,19 @@ def apply_fi_trtllm_fp8_per_tensor_moe(
and hasattr(layer, "output2_scales_scalar")
)
# Added to the layer by: register_scales_for_trtllm_fp8_per_tensor_moe
assert (
hasattr(layer, "output1_scales_scalar")
and hasattr(layer, "output1_scales_gate_scalar")
and hasattr(layer, "output2_scales_scalar")
)
if layer.routing_method_type == RoutingMethodType.Llama4:
assert (
not layer.renormalize
and layer.custom_routing_function == Llama4MoE.custom_routing_function
), (
"FusedMoE flashinfer kernels with Llama4 routing method are only "
"supported for Llama4"
)
else:
assert layer.custom_routing_function is None, (
"Custom routing function is only supported for Llama4"
)
is_llama4 = layer.custom_routing_function == Llama4MoE.custom_routing_function
assert is_llama4, "FusedMoE flashinfer kernels are only supported for Llama4"
return torch.ops.vllm.fi_trtllm_fp8_per_tensor_moe(
routing_logits=router_logits,
routing_bias=routing_bias,
@@ -140,7 +144,7 @@ def apply_fi_trtllm_fp8_per_tensor_moe(
local_expert_offset=layer.ep_rank * layer.local_num_experts,
local_num_experts=layer.local_num_experts,
use_routing_scales_on_input=apply_router_weight_on_input,
routing_method_type=RoutingMethodType.Llama4,
routing_method_type=layer.routing_method_type,
)

14
vllm/model_executor/models/deepseek_v2.py
View File

@@ -295,6 +295,14 @@ class DeepseekV2MoE(nn.Module):
prefix=f"{prefix}.shared_experts",
)
n_group = getattr(config, "n_group", 1)
topk_group = getattr(config, "topk_group", 1)
use_grouped_topk = True
if (n_group, topk_group) == (1, 1):
n_group = None
topk_group = None
use_grouped_topk = False
self.experts = SharedFusedMoE(
shared_experts=self.shared_experts,
gate=self.gate,
@@ -305,9 +313,9 @@ class DeepseekV2MoE(nn.Module):
reduce_results=False,
renormalize=config.norm_topk_prob,
quant_config=quant_config,
use_grouped_topk=True,
num_expert_group=getattr(config, "n_group", 1),
topk_group=getattr(config, "topk_group", 1),
use_grouped_topk=use_grouped_topk,
num_expert_group=n_group,
topk_group=topk_group,
prefix=f"{prefix}.experts",
scoring_func=getattr(config, "scoring_func", "softmax"),
# we do scaling outside, set factor to 1.0 to avoid double mul