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
135 lines
4.1 KiB
Python
135 lines
4.1 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.nn import init
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__all__ = ["ref_rms_norm", "rms_norm", "ref_residual_rms_norm", "residual_rms_norm"]
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def ref_rms_norm(
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input: torch.Tensor,
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weight: torch.Tensor,
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eps: float = 1e-5,
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output: torch.Tensor = None,
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):
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dtype = input.dtype
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input = input.float()
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weight = weight.float()
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rms_out = input * torch.rsqrt(input.pow(2).mean(-1, keepdim=True) + eps)
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rms_out = rms_out * weight
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rms_out = rms_out.to(dtype)
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if output is not None:
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output.copy_(rms_out)
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else:
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output = rms_out
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return output
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def rms_norm(
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input: torch.Tensor,
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weight: torch.Tensor,
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eps: float = 1e-5,
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output: torch.Tensor = None,
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):
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"""
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This function is deprecated, please use residual_rms_norm.
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Args:
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input: (..., hidden_size) torch.float16, torch.bfloat16, torch.float32
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weight: (hidden_size) torch.float16, torch.bfloat16, torch.float32
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eps: float32
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Returns:
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output: (..., hidden_size) torch.float16, torch.bfloat16, torch.float32
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"""
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if output is None:
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output = torch.empty_like(input)
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ops.infer.rms_norm(input, weight, output, None, eps)
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return output
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def ref_residual_rms_norm(
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input: torch.Tensor,
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weight: torch.Tensor,
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eps: float = 1e-5,
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residual_alpha: float = 1.0,
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residual: torch.Tensor = None,
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residual_bias: torch.Tensor = None,
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output: torch.Tensor = None,
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residual_output: torch.Tensor = None,
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is_post: bool = False,
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):
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dtype = input.dtype
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if residual_bias is not None:
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input = input + residual_bias
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if residual is not None:
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residual_output = torch.add(
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input, residual * residual_alpha, out=residual_output
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)
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input = input.float() + residual.float() * residual_alpha
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else:
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input = input.float()
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weight = weight.float()
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rms_out = input * torch.rsqrt(input.pow(2).mean(-1, keepdim=True) + eps)
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rms_out = rms_out * weight
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rms_out = rms_out.to(dtype)
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if output is not None:
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output.copy_(rms_out)
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else:
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output = rms_out
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if is_post and residual_output is not None:
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residual_output = output
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return output, residual_output
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def residual_rms_norm(
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input: torch.Tensor,
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weight: torch.Tensor,
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eps: float = 1e-5,
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residual_alpha: float = 1.0,
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residual: torch.Tensor = None,
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residual_bias: torch.Tensor = None,
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output: torch.Tensor = None,
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residual_output: torch.Tensor = None,
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is_post: bool = False,
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):
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"""
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Args:
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input: (..., hidden_size) torch.float16, torch.bfloat16, torch.float32
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weight: (hidden_size) torch.float16, torch.bfloat16, torch.float32
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eps: float32
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residual_alpha: float32
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residual: (..., hidden_size) torch.float16, torch.bfloat16, torch.float32
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residual_bias: (hidden_size) torch.float16, torch.bfloat16, torch.float32
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is_post: bool
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Returns:
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output: (..., hidden_size) torch.float16, torch.bfloat16, torch.float32 If set to None, an inplace operation will be performed on input.
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residual_output: (..., hidden_size) torch.float16, torch.bfloat16, torch.float32 If set to None, an inplace operation will be performed on residual.
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"""
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if residual is None:
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if output is None:
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output = torch.empty_like(input)
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ops.infer.rms_norm(input, weight, output, residual_bias, eps)
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else:
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ops.infer.residual_rms_norm(
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input,
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residual,
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weight,
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output,
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residual_output,
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residual_bias,
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residual_alpha,
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eps,
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is_post,
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)
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residual_output = residual_output if residual_output is not None else residual
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output = output if output is not None else input
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return output, residual_output
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