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
62 lines
2.4 KiB
Python
62 lines
2.4 KiB
Python
import ixformer._C as ops
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import torch
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from torch.nn import init
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from torch.nn.parameter import Parameter
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class GN_NHWC_Func(torch.autograd.Function):
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@staticmethod
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def forward(ctx, X: torch.Tensor, weight: torch.Tensor, bias: torch.Tensor, G: int, eps: float, activation: str):
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X_out, means, rstds = ops.train.gn_nhwc_fwd(X, weight, bias, G, eps, activation)
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ctx.save_for_backward(X, weight, bias, means, rstds)
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ctx.G = G
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ctx.activation = activation
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return X_out
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@staticmethod
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def backward(ctx, dy: torch.Tensor):
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dy = dy.contiguous(memory_format=torch.channels_last)
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X, weight, bias, means, rstds = ctx.saved_tensors
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dx, dgamma, dbeta = ops.train.gn_nhwc_bwd(dy, X, weight, bias, means, rstds, ctx.G, ctx.activation)
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return dx, dgamma, dbeta, None, None, None
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class GroupNorm_nhwc(torch.nn.GroupNorm):
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def __init__(self, num_groups: int, nc: int, activation='identity', **kwargs):
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super().__init__(num_groups, nc, **kwargs)
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assert activation in {'identity', 'silu', 'relu', 'gelu', 'gelu_tanh'}
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if activation == 'identity':
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self.activation = 0
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if activation == 'relu':
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self.activation = 1
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if activation == 'silu':
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self.activation = 2
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if activation == 'gelu':
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self.activation = 3
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if activation == 'gelu_tanh':
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self.activation = 4
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@torch._dynamo.disable
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def forward(self, x):
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#print(x.shape, self.num_channels)
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if len(x.size()) == 3:
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N, C, L = x.shape
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elif len(x.size()) == 4:
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N, C, H, W = x.shape
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else:
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raise ValueError
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G = self.num_groups
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#if C // G > 512:
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# raise ValueError(f'Error in fwd for X.shape={x.shape}, G={G}: C // G = {C // G} which is greater than 512. This input is not supported.')
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#if H * W % 8 != 0:
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# raise ValueError(f'Error in fwd for X.shape={x.shape}, G={G}: H * W is not a multiple of 8. This input is not supported.')
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if self.affine:
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return GN_NHWC_Func.apply(x, self.weight, self.bias, self.num_groups, self.eps, self.activation)
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else:
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w = torch.ones((self.num_channels,), device=x.device, dtype=x.dtype)
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b = torch.zeros((self.num_channels,), device=x.device, dtype=x.dtype)
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return GN_NHWC_Func.apply(x, w, b, self.num_groups, self.eps, self.activation)
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