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
109 lines
3.3 KiB
Python
109 lines
3.3 KiB
Python
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__ = ["matmul", "MatmulFunction"]
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class MatmulFunction(Function):
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@staticmethod
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def forward(
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ctx: FunctionCtx,
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input: torch.Tensor,
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other: torch.Tensor,
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out: torch.Tensor = None,
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transa: bool = False,
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transb: bool = False,
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alpha: float = 1.0,
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beta: float = 0.0,
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):
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ctx.save_for_backward(input, other)
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ctx.params = (transa, transb, alpha, beta)
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if out is None:
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return ops.train.matmul(
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input, other, transa=transa, transb=transb, alpha=alpha, beta=beta
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)
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else:
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return ops.train.matmul(
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input,
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other,
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out=out,
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transa=transa,
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transb=transb,
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alpha=alpha,
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beta=beta,
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)
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@staticmethod
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def backward(ctx: FunctionCtx, dy):
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input, other = ctx.saved_tensors
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transa, transb, alpha, beta = ctx.params
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if beta in [1, None]:
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raise RuntimeError("Backward don't support beta == 1.0f")
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if not transa and not transb:
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dx = matmul(dy, other, transb=True, alpha=alpha)
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do = matmul(input, dy, transa=True, alpha=alpha)
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return dx, do, None, None, None, None, None
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if transa and not transb:
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dx = matmul(other, dy, transb=True, alpha=alpha)
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do = matmul(input, dy, alpha=alpha)
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return dx, do, None, None, None, None, None
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if not transa and transb:
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dx = matmul(dy, other, alpha=alpha)
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do = matmul(dy, input, transa=True, alpha=alpha)
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return dx, do, None, None, None, None, None
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if transa and transb:
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dx = matmul(other, dy, transa=True, transb=True, alpha=alpha)
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do = matmul(dy, input, transa=True, transb=True, alpha=alpha)
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return dx, do, None, None, None, None, None
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def matmul(
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input: torch.Tensor,
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other: torch.Tensor,
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*,
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out: torch.Tensor = None,
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transa: bool = False,
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transb: bool = False,
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alpha: float = 1.0,
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beta: float = 0.0
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) -> torch.Tensor:
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"""
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等价实现:
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def pt_matmul(a, b, transa, transb, alpha):
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if transa:
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dims = list(range(a.ndim))
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dims[-1], dims[-2] = dims[-2], dims[-1]
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a = a.permute(*dims).contiguous()
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if transb:
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dims = list(range(b.ndim))
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dims[-1], dims[-2] = dims[-2], dims[-1]
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b = b.permute(*dims).contiguous()
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return alpha * torch.matmul(a, b)
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Arguments:
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input:
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当transa为False shape : [...,m,k] dtype: torch.half
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当transa为True shape : [...,k,m] dtype: torch.half
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other:
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当transb为False shape : [...,k,n] dtype: torch.half
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当transb为True shape : [...,n,k] dtype: torch.half
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Return:
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output: [...m,n] dtype: [torch.half]
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"""
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if not input.is_contiguous():
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input = input.contiguous()
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if not other.is_contiguous():
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if not other.transpose(-2, -1).is_contiguous():
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other = other.contiguous()
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return MatmulFunction.apply(input, other, out, transa, transb, alpha, beta)
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