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
199 lines
6.8 KiB
Python
199 lines
6.8 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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import torch.nn.functional as NNF
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__all__ = ["conv2d", "ref_conv2d", "ref_conv2d_nhwc", "conv2d_nhwc"]
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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 _pair(x):
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if isinstance(x, (list, tuple)):
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return x
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return (x, x)
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def ref_conv2d(
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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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stride: Union[int, tuple] = 1,
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padding: Union[int, tuple] = 0,
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dilation: Union[int, tuple] = 1,
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groups: int = 1,
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):
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output = NNF.conv2d(input, weight, bias, stride, padding, dilation, groups)
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return output
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# conv2d官方接口，如果weight是torch.channels_last,输出也是torch.channels_last；如果weight是nchw，那么输出也是nchw;特殊情况，如果输入是nchw，weight是torch.channels_last，输出也是torch.channels_last
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def conv2d(
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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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stride: Union[int, tuple] = 1,
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padding: Union[int, tuple] = 0,
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dilation: Union[int, tuple] = 1,
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groups: int = 1,
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):
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"""
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Args:
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input: (n,in_c,h,w) torch.float16
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weight: (out_c,in_c/groups,kH,kW) torch.float16
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bias: (out_c) torch.float16
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stride: int or tuple
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Stride of the convolution. Default: 1
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padding: int or tuple
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Padding added to all four sides of the input. Default: 0
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dilation: int or tuple
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Spacing between kernel elements. Default: 1
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groups: int
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Number of blocked connections from input channels to output channels. Default: 1
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Returns:
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Tensor: (n,out_c,h_out,w_out) torch.float16
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h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) / stride_h + 1;
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w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) / stride_w + 1;
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"""
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stride = _pair(stride)
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padding = _pair(padding)
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dilation = _pair(dilation)
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channel_last = is_channels_last(weight)
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if not is_channels_last(input) and channel_last:
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input = input.to(memory_format=torch.channels_last)
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# compute outshape
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n, in_c, h_in, w_in = input.shape
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out_c, _, kernel_h, kernel_w = weight.shape
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pad_h = padding[0]
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pad_w = padding[1]
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stride_h = stride[0]
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stride_w = stride[1]
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dilation_h = dilation[0]
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dilation_w = dilation[1]
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h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) // stride_h + 1
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w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) // stride_w + 1
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if channel_last:
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output_shape = [n, out_c, h_out, w_out]
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output = torch.empty(
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output_shape,
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memory_format=torch.channels_last,
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dtype=input.dtype,
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device=input.device,
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)
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else:
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output_shape = [n, out_c, h_out, w_out]
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output = input.new_empty(output_shape)
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if channel_last:
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input = input.permute(0, 2, 3, 1)
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weight = weight.permute(0, 2, 3, 1)
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output = output.permute(0, 2, 3, 1)
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if bias is not None:
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bias = bias.float()
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ops.infer.conv2d(
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input, weight, bias, output, stride, padding, dilation, groups, channel_last
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)
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if channel_last:
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output = output.permute(0, 3, 1, 2)
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return output
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def ref_conv2d_nhwc(
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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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stride: Union[int, tuple] = 1,
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padding: Union[int, tuple] = 0,
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dilation: Union[int, tuple] = 1,
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groups: int = 1,
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):
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output = NNF.conv2d(
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input.permute(0, 3, 1, 2).contiguous(),
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weight.permute(0, 3, 1, 2).contiguous(),
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bias,
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stride,
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padding,
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dilation,
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groups,
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)
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return output.permute(0, 2, 3, 1).contiguous()
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# conv2d_nhwc，
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# conv2d官方接口解决两种情况：
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# 1、务必输入tensor内存上是nhwc,且tensor属于memory_format=torch.channels_last，
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# 2、或者输入tensor内存上是nchw，并且是contiguous；
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# conv2d官方接口不能解决，conv2d_nhwc则可处理这种情况的
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# 输入tensor内存上是nhwc的，但tensor没有用memory_format=torch.channels_last进行过处理，不会有memory_format=torch.channels_last的标签
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def conv2d_nhwc(
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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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stride: Union[int, tuple] = 1,
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padding: Union[int, tuple] = 0,
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dilation: Union[int, tuple] = 1,
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groups: int = 1,
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):
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"""
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Args:
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input: (n,h,w,in_c) torch.float16
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weight: (out_c,kH,kW,in_c/groups) torch.float16
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bias: (out_c) torch.float16
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stride: int or tuple
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Stride of the convolution. Default: 1
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padding: int or tuple
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Padding added to all four sides of the input. Default: 0
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dilation: int or tuple
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Spacing between kernel elements. Default: 1
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groups: int
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Number of blocked connections from input channels to output channels. Default: 1
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Returns:
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Tensor: (n,h_out,w_out,out_c) torch.float16
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h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) / stride_h + 1;
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w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) / stride_w + 1;
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"""
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stride = _pair(stride)
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padding = _pair(padding)
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dilation = _pair(dilation)
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assert input.is_contiguous()
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assert weight.is_contiguous()
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# compute outshape
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n, h_in, w_in, in_c = input.shape
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(
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out_c,
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kernel_h,
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kernel_w,
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_,
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) = weight.shape
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pad_h = padding[0]
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pad_w = padding[1]
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stride_h = stride[0]
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stride_w = stride[1]
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dilation_h = dilation[0]
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dilation_w = dilation[1]
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h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) // stride_h + 1
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w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) // stride_w + 1
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output_shape = [n, h_out, w_out, out_c]
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output = torch.empty(output_shape, dtype=input.dtype, device=input.device)
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if bias is not None:
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bias = bias.float()
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ops.infer.conv2d(
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input, weight, bias, output, stride, padding, dilation, groups, True
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)
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return output
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