dylanyunlong/project_6
1
0
Fork 0
Code Issues Pull Requests Actions Projects Releases Wiki Activity

feat(CRITICAL): 从 GitHub 扫描搬运 ixformer SDK + xllm 完整 GDN/MoE 代码

Browse Source 
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
This commit is contained in:
project6-dev
2026-08-11 02:31:56 +00:00
parent a8b16da5da
commit 87a19d2d00
250 changed files with 76690 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files

198
ixformer_sdk/inference/functions/conv2d.py Normal file
View File

@@ -0,0 +1,198 @@
from typing import Union
import ixformer._C as ops
import torch
import torch.nn.functional as NNF
__all__ = ["conv2d", "ref_conv2d", "ref_conv2d_nhwc", "conv2d_nhwc"]
def is_channels_last(ten):
return torch._prims_common.suggest_memory_format(ten) == torch.channels_last
def _pair(x):
if isinstance(x, (list, tuple)):
return x
return (x, x)
def ref_conv2d(
input: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor = None,
stride: Union[int, tuple] = 1,
padding: Union[int, tuple] = 0,
dilation: Union[int, tuple] = 1,
groups: int = 1,
):
output = NNF.conv2d(input, weight, bias, stride, padding, dilation, groups)
return output
# 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
def conv2d(
input: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor = None,
stride: Union[int, tuple] = 1,
padding: Union[int, tuple] = 0,
dilation: Union[int, tuple] = 1,
groups: int = 1,
):
"""
Args:
input: (n,in_c,h,w) torch.float16
weight: (out_c,in_c/groups,kH,kW) torch.float16
bias: (out_c) torch.float16
stride: int or tuple
Stride of the convolution. Default: 1
padding: int or tuple
Padding added to all four sides of the input. Default: 0
dilation: int or tuple
Spacing between kernel elements. Default: 1
groups: int
Number of blocked connections from input channels to output channels. Default: 1
Returns:
Tensor: (n,out_c,h_out,w_out) torch.float16
h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) / stride_h + 1;
w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) / stride_w + 1;
"""
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
channel_last = is_channels_last(weight)
if not is_channels_last(input) and channel_last:
input = input.to(memory_format=torch.channels_last)
# compute outshape
n, in_c, h_in, w_in = input.shape
out_c, _, kernel_h, kernel_w = weight.shape
pad_h = padding[0]
pad_w = padding[1]
stride_h = stride[0]
stride_w = stride[1]
dilation_h = dilation[0]
dilation_w = dilation[1]
h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) // stride_h + 1
w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) // stride_w + 1
if channel_last:
output_shape = [n, out_c, h_out, w_out]
output = torch.empty(
output_shape,
memory_format=torch.channels_last,
dtype=input.dtype,
device=input.device,
)
else:
output_shape = [n, out_c, h_out, w_out]
output = input.new_empty(output_shape)
if channel_last:
input = input.permute(0, 2, 3, 1)
weight = weight.permute(0, 2, 3, 1)
output = output.permute(0, 2, 3, 1)
if bias is not None:
bias = bias.float()
ops.infer.conv2d(
input, weight, bias, output, stride, padding, dilation, groups, channel_last
)
if channel_last:
output = output.permute(0, 3, 1, 2)
return output
def ref_conv2d_nhwc(
input: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor = None,
stride: Union[int, tuple] = 1,
padding: Union[int, tuple] = 0,
dilation: Union[int, tuple] = 1,
groups: int = 1,
):
output = NNF.conv2d(
input.permute(0, 3, 1, 2).contiguous(),
weight.permute(0, 3, 1, 2).contiguous(),
bias,
stride,
padding,
dilation,
groups,
)
return output.permute(0, 2, 3, 1).contiguous()
# conv2d_nhwc，
# conv2d官方接口解决两种情况：
# 1、务必输入tensor内存上是nhwc,且tensor属于memory_format=torch.channels_last，
# 2、或者输入tensor内存上是nchw，并且是contiguous；
# conv2d官方接口不能解决，conv2d_nhwc则可处理这种情况的
# 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
def conv2d_nhwc(
input: torch.Tensor,
weight: torch.Tensor,
bias: torch.Tensor = None,
stride: Union[int, tuple] = 1,
padding: Union[int, tuple] = 0,
dilation: Union[int, tuple] = 1,
groups: int = 1,
):
"""
Args:
input: (n,h,w,in_c) torch.float16
weight: (out_c,kH,kW,in_c/groups) torch.float16
bias: (out_c) torch.float16
stride: int or tuple
Stride of the convolution. Default: 1
padding: int or tuple
Padding added to all four sides of the input. Default: 0
dilation: int or tuple
Spacing between kernel elements. Default: 1
groups: int
Number of blocked connections from input channels to output channels. Default: 1
Returns:
Tensor: (n,h_out,w_out,out_c) torch.float16
h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) / stride_h + 1;
w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) / stride_w + 1;
"""
stride = _pair(stride)
padding = _pair(padding)
dilation = _pair(dilation)
assert input.is_contiguous()
assert weight.is_contiguous()
# compute outshape
n, h_in, w_in, in_c = input.shape
(
out_c,
kernel_h,
kernel_w,
_,
) = weight.shape
pad_h = padding[0]
pad_w = padding[1]
stride_h = stride[0]
stride_w = stride[1]
dilation_h = dilation[0]
dilation_w = dilation[1]
h_out = (h_in + 2 * pad_h - dilation_h * (kernel_h - 1) - 1) // stride_h + 1
w_out = (w_in + 2 * pad_w - dilation_w * (kernel_w - 1) - 1) // stride_w + 1
output_shape = [n, h_out, w_out, out_c]
output = torch.empty(output_shape, dtype=input.dtype, device=input.device)
if bias is not None:
bias = bias.float()
ops.infer.conv2d(
input, weight, bias, output, stride, padding, dilation, groups, True
)
return output