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

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

ixformer_sdk/inference/functions/wui4a16.py

@@ -0,0 +1,155 @@
+import ixformer._C as ops
+import torch
+
+__all__ = ["wui4a16_gemm", "wui4a16_gemv", "wui4a16", "ref_wui4a16"]
+
+
+def dequant_weight(tensor, scales, zeros, block_size):
+    """
+    tensor: (oc/2, ic)
+    scales: (oc, ic/group_size)
+    zeros:  (oc, ic/group_size)
+    """
+    dtype = scales.dtype
+    left = tensor >> 4
+    right = tensor << 4 >> 4
+    left, right = right, left
+    ret = torch.cat((left, right), dim=-1).reshape(-1, left.size(-1))
+    ret_shape = ret.size()
+    ret = ret.view(-1, block_size)
+    ret = scales.view(-1, 1) * (ret - zeros.view(-1, 1))
+    ret = ret.reshape(ret_shape).to(dtype=dtype)
+    return ret
+
+
+def ref_wui4a16(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    bias: "torch.Tensor" = None,
+    group_size: int = -1,
+    format: str = "NN",
+    only_return_weight: bool = False,
+):
+    """
+    format = TN,NN
+    group_size = TN(128),NN(128, 32)
+    input    : bfloat16|fp16        (bs, ic)
+    qweights : int32                NN: (ic, oc // 8)  TN:(oc, ic // 8)
+    scales   : bfloat16|fp16        (ic // group_size, oc)
+    zeros    : int32                (ic // group_size, oc // 8)
+    bias     : bfloat16|fp16        (oc, )
+    output   : bfloat16|fp16        (bs, oc)
+    """
+
+    def unpack_tensor(x, pack_num=8, order_map=None):
+        if order_map is None:
+            order_map = [0, 1, 2, 3, 4, 5, 6, 7]
+        unit = 32 // pack_num
+        rows, cols = x.shape
+        res = torch.zeros((rows, cols * pack_num), dtype=torch.int32, device=x.device)
+        for col in range(cols):
+            for k in range(pack_num):
+                res[:, col * pack_num + order_map[k]] = (x[:, col] >> (unit * k)) & 0xF
+        return res
+
+    scales = scales.t().contiguous()
+    if format == "NN":
+        zeros = unpack_tensor(zeros, order_map=[0, 2, 4, 6, 1, 3, 5, 7])
+        zeros = zeros.t().contiguous()
+        qweights = unpack_tensor(qweights, order_map=[0, 2, 4, 6, 1, 3, 5, 7])
+        qweights = qweights.t().contiguous()
+    else:
+        zeros = unpack_tensor(zeros)
+        zeros = zeros.t().contiguous()
+        qweights = unpack_tensor(qweights)
+    output_dim, input_dim = qweights.shape
+    qweights = qweights.view(output_dim, input_dim // group_size, group_size)
+    zeros = zeros.view(output_dim, input_dim // group_size, 1)
+    scales = scales.view(output_dim, input_dim // group_size, 1)
+
+    qweights = (qweights - zeros) * scales
+    qweights = qweights.view(output_dim, input_dim)
+    if only_return_weight:
+        return qweights
+    output = torch.nn.functional.linear(inputs, qweights.to(inputs.dtype))
+    return output, qweights
+
+
+def wui4a16_gemm(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    bias: "torch.Tensor" = None,
+    group_size: int = 128,
+    format: str = "NN",
+):
+    output_shape = inputs.shape[:-1] + (scales.shape[1],)
+
+    output = ops.infer.wui4a16_gemm(
+        inputs, qweights, scales, zeros, bias, group_size, format
+    )
+    return output.view(output_shape)
+
+
+def wui4a16_gemv(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    bias: "torch.Tensor" = None,
+    group_size: int = 128,
+    format: str = "NN",
+):
+    output_shape = inputs.shape[:-1] + (scales.shape[1],)
+
+    output = ops.infer.wui4a16_gemv(
+        inputs, qweights, scales, zeros, bias, group_size, format
+    )
+    return output.view(output_shape)
+
+
+def wui4a16(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    bias: "torch.Tensor" = None,
+    group_size: int = 128,
+    format: str = "NN",
+):
+    """
+    format = TN,NN
+    group_size = TN(128),NN(128, 32)
+    input    : bfloat16|fp16        (bs, ic)
+    qweights : int32                NN: (ic, oc // 8)  TN:(oc, ic // 8)
+    scales   : bfloat16|fp16        (ic // group_size, oc)
+    zeros    : int32                (ic // group_size, oc // 8)
+    bias     : bfloat16|fp16        (oc, )
+    output   : bfloat16|fp16        (bs, oc)
+    支持条件  : NN: oc % 8 == 0 && ic % group_size == 0 && ic % 2 == 0
+               TN: oc % 2 == 0 && ic % group_size == 0
+    """
+    batch = inputs.numel() // inputs.shape[-1]
+    if batch <= 1:
+        return wui4a16_gemv(
+            inputs=inputs,
+            qweights=qweights,
+            scales=scales,
+            zeros=zeros,
+            bias=bias,
+            group_size=group_size,
+            format=format,
+        )
+    else:
+        return wui4a16_gemm(
+            inputs=inputs,
+            qweights=qweights,
+            scales=scales,
+            zeros=zeros,
+            bias=bias,
+            group_size=group_size,
+            format=format,
+        )