Update dsv3 quantization auto setting for sm100 (#12778)
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@@ -912,19 +912,32 @@ class ServerArgs:
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logger.info(
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logger.info(
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"Enable FlashInfer AllReduce Fusion on sm100 for DeepseekV3ForCausalLM"
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"Enable FlashInfer AllReduce Fusion on sm100 for DeepseekV3ForCausalLM"
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)
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)
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if self.moe_a2a_backend == "none" and self.moe_runner_backend == "auto":
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quantization_config = getattr(hf_config, "quantization_config", None)
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self.moe_runner_backend = "flashinfer_trtllm"
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quant_method = (
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logger.info(
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quantization_config.get("quant_method")
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"Use flashinfer_trtllm as MoE runner backend on sm100 for DeepseekV3ForCausalLM"
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if quantization_config is not None
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)
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else None
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if self.quantization is None:
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)
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# Default DeepSeek V3/R1 native FP8 when not explicitly set,
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if self.quantization is None:
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# Because we need this condition for an assertion in
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# Default DeepSeek V3/R1 native FP8 when not explicitly set,
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# flashinfer_trtllm MoE runner backend.
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# Because we need this condition for an assertion in
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# flashinfer_trtllm MoE runner backend.
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if quant_method is None:
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self.quantization = "fp8"
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self.quantization = "fp8"
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logger.info(
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logger.info(
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"Quantization not specified, default to fp8 for DeepSeek on sm100"
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"Quantization not specified, default to fp8 for DeepSeek on sm100"
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)
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)
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else:
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self.quantization = quant_method
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if (
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self.moe_a2a_backend == "none"
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and self.moe_runner_backend == "auto"
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and self.quantization in ["fp8", "modelopt_fp8", "modelopt_fp4"]
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):
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self.moe_runner_backend = "flashinfer_trtllm"
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logger.info(
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"Use flashinfer_trtllm as MoE runner backend on sm100 for DeepseekV3ForCausalLM"
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)
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elif model_arch in ["GptOssForCausalLM"]:
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elif model_arch in ["GptOssForCausalLM"]:
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if (
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if (
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