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[Benchmarks] Add support for Qwen 3 VL MoE tuning (#26419)

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Signed-off-by: Lukas Geiger <lukas.geiger94@gmail.com>
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Lukas Geiger 2025-10-08 15:01:08 +01:00 committed by GitHub
parent e39dc46f8f
commit 338b1bf04f
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1 changed files with 12 additions and 1 deletions
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13
benchmarks/kernels/benchmark_moe.py
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@ -579,10 +579,12 @@ def main(args: argparse.Namespace):
E = config.ffn_config.moe_num_experts
topk = config.ffn_config.moe_top_k
intermediate_size = config.ffn_config.ffn_hidden_size
hidden_size = config.hidden_size
elif config.architectures[0] == "JambaForCausalLM":
E = config.num_experts
topk = config.num_experts_per_tok
intermediate_size = config.intermediate_size
hidden_size = config.hidden_size
elif config.architectures[0] in (
"DeepseekV2ForCausalLM",
"DeepseekV3ForCausalLM",
@ -592,6 +594,7 @@ def main(args: argparse.Namespace):
E = config.n_routed_experts
topk = config.num_experts_per_tok
intermediate_size = config.moe_intermediate_size
hidden_size = config.hidden_size
elif config.architectures[0] in (
"Qwen2MoeForCausalLM",
"Qwen3MoeForCausalLM",
@ -600,10 +603,18 @@ def main(args: argparse.Namespace):
E = config.num_experts
topk = config.num_experts_per_tok
intermediate_size = config.moe_intermediate_size
hidden_size = config.hidden_size
elif config.architectures[0] == "Qwen3VLMoeForConditionalGeneration":
text_config = config.get_text_config()
E = text_config.num_experts
topk = text_config.num_experts_per_tok
intermediate_size = text_config.moe_intermediate_size
hidden_size = text_config.hidden_size
elif config.architectures[0] in ("HunYuanMoEV1ForCausalLM"):
E = config.num_experts
topk = config.moe_topk[0]
intermediate_size = config.moe_intermediate_size[0]
hidden_size = config.hidden_size
else:
# Support for llama4
config = config.get_text_config()
@ -611,6 +622,7 @@ def main(args: argparse.Namespace):
E = config.num_local_experts
topk = config.num_experts_per_tok
intermediate_size = config.intermediate_size
hidden_size = config.hidden_size
enable_ep = bool(args.enable_expert_parallel)
if enable_ep:
ensure_divisibility(E, args.tp_size, "Number of experts")
@ -619,7 +631,6 @@ def main(args: argparse.Namespace):
else:
ensure_divisibility(intermediate_size, args.tp_size, "intermediate_size")
shard_intermediate_size = 2 * intermediate_size // args.tp_size
hidden_size = config.hidden_size
dtype = torch.float16 if current_platform.is_rocm() else config.torch_dtype
use_fp8_w8a8 = args.dtype == "fp8_w8a8"
use_int8_w8a16 = args.dtype == "int8_w8a16"