[sgl-kernel slimming] remove sgl-kernel moe-wna16-marlin (#19379)
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@@ -38,7 +38,6 @@ from sgl_kernel.expert_specialization import (
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es_sm100_mxfp8_blockscaled_grouped_mm,
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es_sm100_mxfp8_blockscaled_grouped_quant,
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)
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from sgl_kernel.fused_moe import moe_wna16_marlin_gemm
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from sgl_kernel.gemm import (
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awq_dequantize,
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bmm_fp8,
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@@ -1,61 +0,0 @@
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from typing import Optional
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import torch
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def moe_wna16_marlin_gemm(
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a: torch.Tensor,
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c_or_none: Optional[torch.Tensor],
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b_q_weight: torch.Tensor,
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b_bias_or_none: Optional[torch.Tensor],
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b_scales: torch.Tensor,
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global_scale_or_none: Optional[torch.Tensor],
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b_zeros_or_none: Optional[torch.Tensor],
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g_idx_or_none: Optional[torch.Tensor],
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perm_or_none: Optional[torch.Tensor],
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workspace: torch.Tensor,
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sorted_token_ids: torch.Tensor,
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expert_ids: torch.Tensor,
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num_tokens_post_padded: torch.Tensor,
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topk_weights: torch.Tensor,
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moe_block_size: int,
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top_k: int,
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mul_topk_weights: bool,
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is_ep: bool,
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b_q_type_id: int,
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size_m: int,
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size_n: int,
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size_k: int,
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is_k_full: bool,
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use_atomic_add: bool,
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use_fp32_reduce: bool,
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is_zp_float: bool,
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):
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return torch.ops.sgl_kernel.moe_wna16_marlin_gemm.default(
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a,
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c_or_none,
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b_q_weight,
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b_bias_or_none,
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b_scales,
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global_scale_or_none,
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b_zeros_or_none,
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g_idx_or_none,
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perm_or_none,
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workspace,
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sorted_token_ids,
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expert_ids,
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num_tokens_post_padded,
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topk_weights,
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moe_block_size=moe_block_size,
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top_k=top_k,
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mul_topk_weights=mul_topk_weights,
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is_ep=is_ep,
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b_q_type_id=b_q_type_id,
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size_m=size_m,
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size_n=size_n,
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size_k=size_k,
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is_k_full=is_k_full,
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use_atomic_add=use_atomic_add,
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use_fp32_reduce=use_fp32_reduce,
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is_zp_float=is_zp_float,
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)
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