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
88 lines
2.7 KiB
Python
88 lines
2.7 KiB
Python
from typing import List, Union
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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__ = ["ref_silu_and_mul", "ref_gelu_and_mul", "ref_gelu_tanh_and_mul",
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"silu_and_mul", "gelu_and_mul", "gelu_tanh_and_mul"]
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def ref_silu_and_mul(input: "torch.Tensor") -> torch.Tensor:
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x1, x2 = input.chunk(chunks=2, dim=-1)
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res = NNF.silu(x1) * x2
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return res
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def ref_gelu_and_mul(input: "torch.Tensor", gate_first=True) -> torch.Tensor:
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x1, x2 = input.chunk(chunks=2, dim=-1)
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if gate_first:
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res = NNF.gelu(x1) * x2
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else:
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res = NNF.gelu(x2) * x1
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return res
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def ref_gelu_tanh_and_mul(input: "torch.Tensor") -> torch.Tensor:
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x1, x2 = input.chunk(chunks=2, dim=-1)
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res = NNF.gelu(x1) * x2
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return res
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def silu_and_mul(input: torch.Tensor, output: torch.Tensor = None):
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"""
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Args:
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input: (..., 2*hidden_size) torch.float16, torch.bfloat16, torch.float32
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output: (..., hidden_size) torch.float16, torch.bfloat16, torch.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_shape = list(input.shape)
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output_shape[-1] = output_shape[-1] // 2
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output = input.new_empty(output_shape)
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ops.infer.silu_and_mul(input, output)
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return output
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def gelu_and_mul(input: "torch.Tensor", output: torch.Tensor = None, gate_first=True):
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"""
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Args:
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input: (..., 2*hidden_size) torch.float16, torch.bfloat16, torch.float32
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output: (..., hidden_size) torch.float16, torch.bfloat16, torch.float32
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gate_first: bool
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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_shape = list(input.shape)
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output_shape[-1] = output_shape[-1] // 2
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output = input.new_empty(output_shape)
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ops.infer.gelu_and_mul(input, output, gate_first)
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return output
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def gelu_tanh_and_mul(input: torch.Tensor, output: torch.Tensor = None):
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"""
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Args:
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input: (..., 2*hidden_size) torch.float16, torch.bfloat16, torch.float32
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output: (..., hidden_size) torch.float16, torch.bfloat16, torch.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_shape = list(input.shape)
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output_shape[-1] = output_shape[-1] // 2
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output = input.new_empty(output_shape)
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ops.infer.gelu_tanh_and_mul(input, output)
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return output |