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
90 lines
2.5 KiB
Python
90 lines
2.5 KiB
Python
import os
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from typing import 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__ = ["linear"]
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class LinearFunction(Function):
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@staticmethod
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def forward(
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ctx,
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input: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor = None,
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output: torch.Tensor = None,
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):
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if bias is not None:
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if output is None:
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output = ops.train.linear_forward(input, weight, bias)
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else:
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ops.train.linear_forward_(input, weight, bias, output)
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else:
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if output is None:
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output = ops.train.linear_forward(input, weight)
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else:
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ops.train.linear_forward_(input, weight, output)
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ctx.has_bias = bias is not None
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ctx.save_for_backward(input, weight)
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return output
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@staticmethod
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def backward(ctx: FunctionCtx, dy: torch.Tensor):
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x, w = ctx.saved_tensors
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dx = ops.train.linear_backward_dx(w, dy, x.shape)
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dw = ops.train.linear_backward_dw(x, dy, w.shape)
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if ctx.has_bias:
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reduce_dims = list(range(dy.ndim - 1))
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db = torch.sum(dy, reduce_dims)
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return dx, dw, db, None
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else:
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return dx, dw, None, None
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def gemv_conditions(input, weight, bias, gemv_max_batch):
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# gemv 使用的条件 input:[m,k] weight:[n,k]
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# 1. m<=gemv_max_batch
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# 2. k%2==0 n%2==0
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# 3. bias is None
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input = input.view(-1, input.shape[-1])
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weight = weight.view(-1, weight.shape[-1])
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m = input.shape[0]
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k = input.shape[1]
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n = weight.shape[0]
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if bias is None and m <= gemv_max_batch and k % 2 == 0 and n % 2 == 0:
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return True
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return False
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def linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor = None,
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output: torch.Tensor = None,
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use_gemv: bool = True,
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gemv_max_batch=1,
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):
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"""
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Arguments:
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input : [...,k] dtype: [torch.half, torch.bfloat16]
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weights : [n,k] dtype: [torch.half, torch.bfloat16]
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use_gemv: bool 是否使用gemv
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gemv 使用的条件 input:[m,k] weight:[n,k]
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1. m<=gemv_max_batch
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2. k%2==0 n%2==0
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3. bias is None
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gemv_max_batch: int 用于是否满足gemv使用条件的判断
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Return:
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output : [...,n] dtype: [torch.half, torch.bfloat16]
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"""
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return LinearFunction.apply(input, weight, bias, output)
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