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
46 lines
1.2 KiB
Python
46 lines
1.2 KiB
Python
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__ = ["swiglu"]
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class SwigluFunction(Function):
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@staticmethod
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def forward(ctx, input):
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output_shape = list(input.shape)
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output_shape[-1] = output_shape[-1] // 2
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output = input.new_empty(output_shape)
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ops.train.swiglu_training_forward(input, output)
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ctx.save_for_backward(input)
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return output
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@staticmethod
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def backward(ctx: FunctionCtx, grad_output):
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input = ctx.saved_tensors[0]
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grad_input = torch.empty_like(input)
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ops.train.swiglu_training_backward(input, grad_output, grad_input)
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return grad_input
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def swiglu(input):
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"""
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等价实现：
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def ref_silu_and_mul(x: torch.Tensor) -> torch.Tensor:
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dtype = x.dtype
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x = x.float()
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x1, x2 = x.chunk(chunks=2, dim=-1)
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res = torch.nn.functional.silu(x1) * x2
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return res.to(dtype)
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参数说明:
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Args:
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input: dtype:torch.float, torch.half, torch.bfloat16
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return:
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output: dtype:torch.float, torch.half, torch.bfloat16
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"""
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return SwigluFunction.apply(input)
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