Revert "[NPU] [bugfix] Fix import of ggml_moe_a8_vec and Fix NPU MLA HiCache backup accessing missing data_ptrs" (#36747)
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@@ -17,7 +17,6 @@ from sglang.srt.layers.moe.utils import RoutingMethodType
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from sglang.srt.runtime_context import get_exec
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from sglang.srt.runtime_context import get_exec
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from sglang.srt.utils import (
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from sglang.srt.utils import (
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is_flashinfer_available,
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is_flashinfer_available,
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is_npu,
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log_info_on_rank0,
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log_info_on_rank0,
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set_weight_attrs,
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set_weight_attrs,
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)
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)
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@@ -381,6 +380,7 @@ def maybe_fuse_routed_scale_and_shared_add(
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# alpha=scale)`. With no shared output, the missing scale is applied
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# alpha=scale)`. With no shared output, the missing scale is applied
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# in-place. Otherwise `routed` is already scale-final and we just add
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# in-place. Otherwise `routed` is already scale-final and we just add
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# `shared` (or pass through if there is none).
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# `shared` (or pass through if there is none).
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from sglang.srt.layers.quantization.expert_pack import ExpertPackMoEMethod
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from sglang.srt.layers.quantization.mxfp4_flashinfer_cutlass_moe import (
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from sglang.srt.layers.quantization.mxfp4_flashinfer_cutlass_moe import (
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Mxfp4FlashinferCutlassMoEMethod,
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Mxfp4FlashinferCutlassMoEMethod,
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)
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)
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@@ -388,17 +388,15 @@ def maybe_fuse_routed_scale_and_shared_add(
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Mxfp4MarlinMoEMethod,
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Mxfp4MarlinMoEMethod,
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)
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)
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fused_methods = [
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fused = isinstance(
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experts.quant_method,
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(
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Mxfp4FlashinferTrtllmMoEMethod,
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Mxfp4FlashinferTrtllmMoEMethod,
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Mxfp4FlashinferCutlassMoEMethod,
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Mxfp4FlashinferCutlassMoEMethod,
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Mxfp4MarlinMoEMethod,
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Mxfp4MarlinMoEMethod,
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]
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ExpertPackMoEMethod,
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if not is_npu():
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),
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from sglang.srt.layers.quantization.expert_pack import ExpertPackMoEMethod
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)
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fused_methods.append(ExpertPackMoEMethod)
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fused = isinstance(experts.quant_method, tuple(fused_methods))
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if fused:
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if fused:
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already_scaled = experts.should_fuse_routed_scaling_factor_in_topk
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already_scaled = experts.should_fuse_routed_scaling_factor_in_topk
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if shared is not None:
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if shared is not None:
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@@ -436,11 +436,6 @@ class MLATokenToKVPoolHost(HiSparseHostPoolMixin, HostKVCache):
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)
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)
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return
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return
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if io_backend == "kernel_ascend":
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# NPU pools use contiguous multi-layer tensors and intentionally do
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# not build the CUDA-style data_ptrs array.
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device_data_ptrs, device_kv_buffers = None, None
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else:
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device_data_ptrs, device_kv_buffers = self._resolve_device_transfer_buffers(
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device_data_ptrs, device_kv_buffers = self._resolve_device_transfer_buffers(
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device_pool
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device_pool
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)
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)
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