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
121 lines
4.4 KiB
Python
121 lines
4.4 KiB
Python
from typing import List, Tuple, 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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import ixformer
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__all__ = [
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"group_norm",
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"ref_group_norm",
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"ref_fused_group_norm_silu",
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"fused_group_norm_silu",
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"ref_fused_group_norm_silu_nhwc",
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"fused_group_norm_silu_nhwc"
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]
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def is_channels_last(ten):
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return torch._prims_common.suggest_memory_format(ten) == torch.channels_last
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def ref_group_norm(input, num_groups, weight, bias, eps):
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output = torch.nn.functional.group_norm(input, num_groups, weight, bias, eps)
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return output
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#group_norm官方接口，如果input是nhwc(channel_last),输出则不是channel_last,而是nchw；如果input是nchw，那么输出也是nchw
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def group_norm(
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input: torch.Tensor,
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num_groups: int,
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weight: torch.Tensor,
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bias: torch.Tensor,
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eps: float = 1e-05,
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):
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"""
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Args:
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input: (n,c,h,w) or (n,c,h) or (n,h,w,c) torch.float16
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"contiguous_format":(n,c,h,w) or (n,c,h) "channels_last": (n,h,w,c)
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num_groups: int
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weight: (c) torch.float16
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bias: (c) torch.float16
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eps: float
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Returns:
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Tensor: (n,c,h,w) torch.float16
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"""
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is_nhwc=is_channels_last(input)
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out = ops.infer.groupnorm(input, num_groups, weight, bias, eps, is_nhwc, 0)
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if is_nhwc:
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out=out.permute(0,3,1,2).contiguous()
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return out
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def ref_fused_group_norm_silu_nhwc(
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input: torch.Tensor,
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num_groups: int,
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weight: torch.Tensor,
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bias: torch.Tensor,
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eps: float = 1e-05,
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act_type:int = 0
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):
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output = torch.nn.functional.group_norm(input.permute(0,3,1,2).contiguous(), num_groups, weight, bias, eps)
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if act_type:
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output = output * torch.sigmoid(output)
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output = output.permute(0,2,3,1).contiguous()
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return output
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#为了减少permute/contiguous,新接口支持输入输出都是nhwc的，融合silu
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def fused_group_norm_silu_nhwc(
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input: torch.Tensor,
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num_groups: int,
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weight: torch.Tensor,
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bias: torch.Tensor,
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eps: float = 1e-05,
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act_type:int = 0
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):
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"""
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Args:
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input: (n,h,w,c) torch.float16
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num_groups: int
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weight: (c) torch.float16
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bias: (c) torch.float16
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eps: float
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act_type: int
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0 or 1,if act_type=1, silu
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Returns:
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Tensor: (n,h,w,c) torch.float16
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"""
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out = ops.infer.groupnorm(input, num_groups, weight, bias, eps, True, act_type)
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return out
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def ref_fused_group_norm_silu(
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input: torch.Tensor,
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num_groups: int,
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weight: torch.Tensor,
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bias: torch.Tensor,
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eps: float = 1e-05,
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):
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output = torch.nn.functional.group_norm(input, num_groups, weight, bias, eps)
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output = output * torch.sigmoid(output)
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return output
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def fused_group_norm_silu(
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input: torch.Tensor,
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num_groups: int,
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weight: torch.Tensor,
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bias: torch.Tensor,
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eps: float = 1e-05,
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):
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"""
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Args:
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input: (n,c,h,w) or (n,c,h) torch.float16
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num_groups: int
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weight: (c) torch.float16
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bias: (c) torch.float16
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eps: float
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Returns:
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output: (n,c,h,w) or (n,c,h) torch.float16
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"""
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out = ops.infer.groupnorm(input, num_groups, weight, bias, eps, False, 1)
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return out
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