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
123 lines
3.5 KiB
Python
123 lines
3.5 KiB
Python
import os
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from typing import Union
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import ixformer._C as ops
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import torch
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__all__ = ["linear", "ref_linear", "mixed_type_linear", "ref_mixed_type_linear"]
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def ref_linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor = None,
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act_type=-1,
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):
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output = torch.nn.functional.linear(input, weight, bias)
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if act_type == -1:
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act_fn = torch.nn.Identity()
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elif act_type == 3:
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act_fn = torch.nn.GELU()
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elif act_type == 4:
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act_fn = torch.nn.ReLU()
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elif act_type == 12:
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act_fn = torch.nn.SiLU()
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else:
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raise KeyError("act_type not supported")
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output = act_fn(output)
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return output
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def gemv_conditions(input, weight, bias, gemv_max_batch):
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# gemv 使用的条件 input:[m,k] weight:[n,k]
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# 1. m<=gemv_max_batch
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# 2. k%32==0 n%2==0
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# 3. bias is None
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input = input.view(-1, input.shape[-1])
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weight = weight.view(-1, weight.shape[-1])
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m = input.shape[0]
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k = input.shape[1]
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n = weight.shape[0]
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if bias is None and m <= gemv_max_batch and k % 32 == 0 and n % 2 == 0:
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return True
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return False
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def linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor = None,
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output: torch.Tensor = None,
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persistent: bool = False,
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act_type : int = -1,
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):
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"""
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Args:
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input: (...,k) torch.float16, torch.bfloat16
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weight: (n, k) torch.float16, torch.bfloat16
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bias: (n) torch.float16, torch.bfloat16
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output: (...,n) torch.float16, torch.bfloat16
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persistent: bool
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是否限制 Gemm Kernel 的 Block 数量
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Returns:
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output: (...,n) torch.float16, torch.bfloat16
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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 weight.is_contiguous():
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weight = weight.contiguous()
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use_gemv = True
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gemv_max_batch = 1
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disable_infer_gemm_ex = os.getenv("DISABLE_INFER_GEMM_EX", "0")
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use_gemv = (
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use_gemv
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and gemv_conditions(input, weight, bias, gemv_max_batch)
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and disable_infer_gemm_ex != "1"
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)
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if output is None:
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output_shape = list(input.shape)
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output_shape[-1] = weight.shape[0]
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output = input.new_empty(output_shape)
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if not use_gemv:
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output = ops.infer.linear(input, weight, act_type, bias, output, persistent)
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else:
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output = ops.infer.linear_ex(input, weight, bias, output)
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return output
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def ref_mixed_type_linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor = None,
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output: torch.Tensor = None,
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persistent=False, # TODO: support persistent
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):
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input = input.to(weight.dtype)
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if bias:
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bias = bias.to(weight.dtype)
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output = torch.nn.functional.linear(input, weight, bias)
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return output
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def mixed_type_linear(
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input: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor = None,
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output: torch.Tensor = None,
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persistent=False, # TODO: support persistent
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):
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"""
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Args:
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input: (...,k) torch.half, torch.bfloat16
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weight: (m, k) torch.float32
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bias: not supported
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output: (...,m) torch.float32
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persistent: bool
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Returns:
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output: (...,m) torch.float32
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"""
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output = ops.infer.mixed_type_linear(input, weight, bias, output)
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return output
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