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A high-throughput and memory-efficient inference and serving engine for LLMs

Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git

Synced Nov 15, 03:30 UTC

Fix unknown attribute of topk_indices_dtype in CompressedTensorsW8A8Fp8MoECutlassMethod (#20507)

Co-authored-by: Lucia (Lu) Fang <fanglu@meta.com>

Lucia Fang committed Jul 5, 2025 at 14:03 UTC 8aeaa910a2c514022ab7521ce74eec0734886a1b
1 file changed +2
vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors_moe.py
+2
@@ -368,6 +368,7 @@ class CompressedTensorsW8A8Fp8MoEMethod(CompressedTensorsMoEMethod):
368 "weights")
369 self.input_quant = self.quant_config.target_scheme_map["Linear"].get(
370 "input_activations")
371 + self.topk_indices_dtype = None
372
373 per_tensor = (self.weight_quant.strategy == QuantizationStrategy.TENSOR
374 and self.input_quant.strategy
@@ -738,6 +739,7 @@ class CompressedTensorsW8A8Fp8MoECutlassMethod(CompressedTensorsMoEMethod):
739
740 from vllm.model_executor.layers.fused_moe.cutlass_moe import (
741 cutlass_moe_fp8)
742 + self.topk_indices_dtype = None
743 self.fused_experts = cutlass_moe_fp8 # type: ignore
744 self.disable_expert_map = False
745