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/wi4a16.py

@@ -0,0 +1,157 @@
+import ixformer._C as ops
+import torch
+
+__all__ = ["wi4a16_gemm", "wi4a16_gemv", "wi4a16", "ref_wi4a16"]
+
+
+def dequant_weight(tensor, scales, zeros, block_size):
+    # from CPM
+    """
+    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_wi4a16(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    group_size: int = -1,
+    format: str = "TN",
+):
+    assert format in ["TN"]
+    weights = dequant_weight(
+        qweights,
+        scales.transpose(0, 1).contiguous(),
+        zeros.transpose(0, 1).contiguous(),
+        group_size,
+    )
+    output = torch.nn.functional.linear(inputs, weights.to(inputs.dtype))
+    return output
+
+
+def wi4a16_gemm(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    group_size: int = -1,
+    format: str = "TN",
+    output=None,
+):
+    """
+    wi4a16 gemm 接口
+    支持条件:
+    format = TN
+    group_size = 128
+    input    : fp16      (bs, ic)
+    qweights : int8           (oc/2, ic)
+    scales   : fp16      (ic/group_size, oc)
+    zeros    : fp16      (ic/group_size, oc)
+    TN 支持条件： oc % 2 == 0 && ic % 128 == 0
+    NN 支持条件： 不支持
+    """
+
+    assert format in ["TN"]
+    assert len(qweights.shape) == 2
+    assert len(scales.shape) == 2
+    assert len(zeros.shape) == 2
+
+    input_shape = list(inputs.shape)
+    inputs = inputs.view(-1, input_shape[-1])
+
+    if output is None:
+        output_shape = input_shape[:-1] + [scales.shape[1]]
+        output = inputs.new_empty(output_shape).view(-1, output_shape[-1])
+    else:
+        output_shape = output.shape
+
+    ops.infer.wi4a16_gemm(output, inputs, qweights, scales, zeros, group_size, format)
+    return output.view(output_shape)
+
+
+def wi4a16_gemv(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    group_size: int = -1,
+    format: str = "TN",
+    output=None,
+):
+    """
+    wi4a16 gemv 接口
+    支持条件:
+    format = TN
+    group_size = 128
+    input    : bf16|fp16      (bs, ic)
+    qweights : int8           (oc/2, ic)
+    scales   : bf16|fp16      (ic/group_size, oc)
+    zeros    : bf16|fp16      (ic/group_size, oc)
+    TN 支持条件： oc % 2 == 0 && ic % 128 == 0
+    NN 支持条件： 不支持
+    """
+
+    assert format in ["TN"]
+    assert len(qweights.shape) == 2
+    assert len(scales.shape) == 2
+    assert len(zeros.shape) == 2
+
+    input_shape = list(inputs.shape)
+    inputs = inputs.view(-1, input_shape[-1])
+    
+    if output is None:
+        output_shape = input_shape[:-1] + [scales.shape[1]]
+        output = inputs.new_empty(output_shape).view(-1, output_shape[-1])
+    else:
+        output_shape = output.shape
+
+    ops.infer.wi4a16_gemv(output, inputs, qweights, scales, zeros, group_size, format)
+    return output.view(output_shape)
+
+
+def wi4a16(
+    inputs: "torch.Tensor",
+    qweights: "torch.Tensor",
+    scales: "torch.Tensor",
+    zeros: "torch.Tensor",
+    group_size: int = -1,
+    format: str = "TN",
+    output=None,
+):
+    input_shape = inputs.shape
+    inputs = inputs.view(-1, input_shape[-1])
+    bs = inputs.size(0)
+    inputs = inputs.view(input_shape)
+    if bs <= 1:
+        return wi4a16_gemv(
+            inputs=inputs,
+            qweights=qweights,
+            scales=scales,
+            zeros=zeros,
+            group_size=group_size,
+            format=format,
+            output=output,
+        )
+    else:
+        return wi4a16_gemm(
+            inputs=inputs,
+            qweights=qweights,
+            scales=scales,
+            zeros=zeros,
+            group_size=group_size,
+            format=format,
+            output=output,
+        )