87a19d2d00
来源:
1. Chranos/ixformer (GitHub) → ixformer_sdk/ (230 files, 70K lines)
- inference/functions/vllm.py: vllm_moe_topk_softmax 完整实现 (2033 lines)
- inference/functions/moe.py: MoE ops 完整实现 (1380 lines)
- contrib/vllm_flash_attn/: FA2 Python 接口 (1018 lines)
- contrib/tgi/fused_moe.py: TGI fused MoE (429 lines)
- csrc/include/ixformer/: C++ kernel headers + cmake
2. Deep-Spark/xllm (GitHub) → upstream_ref/xllm_latest/ (+15 files)
- npu_torch/qwen3_5_decoder_layer_impl.cpp/.h
- npu_torch/qwen3_5_gated_delta_net.cpp/.h
- npu_torch/qwen3_next_*.cpp/.h (6 files)
- npu_torch/attention.cpp/.h + fused_moe.cpp/.h + CMakeLists.txt
- models/llm/qwen3_5.h + qwen3_5_mtp.h + qwen3_next.h
- models/vlm/qwen3_5.h
调用链完整性:
ixformer_sdk/inference/functions/vllm.py
→ ops.infer.moe_topk_softmax() (C++ 层)
→ 这就是 base 镜像 libixformer.so 里的实现
upstream_ref/xllm_latest/core/layers/ilu/fused_moe.cpp
→ ixformer::infer::topk_softmax() (直接 C++ 调用)
→ ixformer::infer::group_gemm() → 完整 7-step MoE pipeline
74 lines
2.1 KiB
Python
74 lines
2.1 KiB
Python
from typing import List, Union
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import ixformer._C as ops
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import torch
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from torch.autograd.function import Function, FunctionCtx
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__all__ = ["bnb_mm_dequant"]
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# A : quant input shape : [row, col] shape : torch.int
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def bnb_mm_dequant(
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A: torch.Tensor,
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quant_state: tuple,
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row_stats: torch.Tensor,
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col_stats: torch.Tensor,
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bias: torch.Tensor = None,
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add_bias: bool = False,
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training: bool = False,
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) -> torch.Tensor:
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"""
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Args:
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A: (row, col) torch.int8
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quant_state: tuple
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row_stats: (row) torch.float
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col_stats: (col) torch.float
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bias: (col) torch.half
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add_bias: bool
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training: bool
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Returns:
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Tensor: (row, col) torch.half
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"""
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assert A.dtype == torch.int
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if bias is not None:
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add_bias = True
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print("bias.dtype:", bias.dtype)
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assert bias.dtype == torch.half
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else:
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bias = A
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out_shape = quant_state[0]
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if len(out_shape) == 3:
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out_shape = (out_shape[0] * out_shape[1], out_shape[2])
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out = torch.full(size=out_shape, fill_value=0, dtype=torch.half, device=A.device)
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new_row_stats = torch.full(
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size=(out_shape[0],), fill_value=0, dtype=torch.float, device=A.device
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)
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new_col_stats = torch.full(
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size=(out_shape[1],), fill_value=0, dtype=torch.float, device=A.device
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)
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assert (
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new_row_stats.shape[0] == row_stats.shape[0]
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), f"{new_row_stats.shape} vs {row_stats.shape}"
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assert (
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new_col_stats.shape[0] == col_stats.shape[0]
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), f"{new_col_stats.shape} vs {col_stats.shape}"
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numRows = out_shape[0]
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numCols = out_shape[1]
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ops.infer.bnb_mm_dequant(
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A,
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row_stats,
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col_stats,
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out,
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new_row_stats,
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new_col_stats,
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numRows,
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numCols,
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add_bias,
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bias,
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)
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return out
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