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
90 lines
2.7 KiB
Python
90 lines
2.7 KiB
Python
import ixformer._C as ops
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import torch
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import torch.nn.functional as NNF
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__all__ = ["act_bias_mm", "ref_act_bias_mm"]
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def ref_act_bias_mm(
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mat1: torch.Tensor,
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mat2: torch.Tensor,
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bias: torch.Tensor = None,
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scale: float = 1,
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act_type: str = "none",
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trans_format: str = "NN",
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):
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assert len(mat1.shape) >= 2
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assert len(mat2.shape) >= 2
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if trans_format == "NN":
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if bias is not None:
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output = torch.matmul(mat1, mat2) * scale + bias
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else:
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output = torch.matmul(mat1, mat2) * scale
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else:
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if bias is not None:
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output = torch.matmul(mat1, mat2.transpose(-1, -2)) * scale + bias
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else:
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output = torch.matmul(mat1, mat2.transpose(-1, -2)) * scale
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if act_type == "gelu":
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output = NNF.gelu(output)
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elif act_type == "relu":
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output = NNF.relu(output)
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elif act_type == "silu":
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output = NNF.silu(output)
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elif act_type == "none":
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output = output
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else:
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raise NotImplementedError()
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return output
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def act_bias_mm(
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mat1: torch.Tensor,
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mat2: torch.Tensor,
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bias: torch.Tensor = None,
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output: torch.Tensor = None,
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scale: float = 1,
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act_type: str = "none",
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trans_format: str = "NN",
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):
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"""
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Args:
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mat1: [m,k] or [batch_count,m,k] torch.float16
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mat2: [k,n] or [n,k] torch.float16
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当trans_format为"NN"时[k,n], 当trans_format为"TN"时[n,k]
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bias: [n] torch.float16
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output: [m,n] torch.float16
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scale: float
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act_type: silu/gelu/relu/None str
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如果act_type不为None,则bias也不可以为None
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trans_format: NN or TN str
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Returns:
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output: [m,n] torch.float16
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"""
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assert len(mat1.shape) >= 2
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assert len(mat2.shape) >= 2
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if output is None:
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output_shape = list(mat1.shape)
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m = mat1.shape[-2]
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if trans_format == "NN":
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n = mat2.shape[-1]
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else:
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n = mat2.shape[-2]
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output_shape[-2] = m
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output_shape[-1] = n
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output = mat1.new_empty(output_shape)
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add_bias = False
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if bias is not None:
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add_bias = True
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if add_bias:
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ops.infer.act_bias_mm(
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mat1, mat2, bias, output, add_bias, scale, act_type, trans_format
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)
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else:
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ops.infer.act_bias_mm(
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mat1, mat2, mat1, output, add_bias, scale, act_type, trans_format
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)
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return output
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