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
This commit is contained in:
project6-dev
225
ixformer_sdk/inference/functions/w8a16.py
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225
ixformer_sdk/inference/functions/w8a16.py
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import math
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from typing import List, Union, Optional
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import ixformer._C as ops
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import torch
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from torch.autograd.function import Function, FunctionCtx
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import ixformer
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from ixformer.core import config
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__all__ = [
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"w8a16_gemm",
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"w8a16_gemv",
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"w8a16",
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"ref_w8a16",
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"wu8a16",
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"ref_wu8a16",
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]
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def w8a16_gemv(
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inputs: "torch.Tensor",
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qweights: "torch.Tensor",
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scales: "torch.Tensor",
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group_size: int = -1,
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format: str = "unknown",
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output: Optional[torch.Tensor] = None
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):
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"""
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w8a16 gemv 接口
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input : bf16|fp16 (bs, ic)
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qweights : int8 TN:(oc, ic) NN:(ic, oc)
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scales : bf16|fp16 TN: 当groupsize为-1时, shape: (1, oc), 否则,shape: (ic/group_size, oc) NN:(1, oc)
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TN 支持条件： ic % groupSize = 0, oc % 2 = 0, bs<=4
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NN 支持条件： groupsize = -1 or groupsize = ic, oc % 4 = 0, bs<=4
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"""
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assert format in ["TN", "NN"]
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assert len(qweights.shape) == 2
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assert len(scales.shape) == 2
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input_shape = list(inputs.shape)
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inputs = inputs.view(-1, input_shape[-1])
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if format == "TN":
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output_shape = input_shape[:-1] + [qweights.shape[0]]
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else:
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output_shape = input_shape[:-1] + [qweights.shape[1]]
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if output is None:
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output = inputs.new_empty(output_shape).view(-1, output_shape[-1])
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ops.infer.w8a16_gemv(output, inputs, qweights, scales, group_size, format)
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return output.view(output_shape)
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def w8a16_gemm(
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inputs: "torch.Tensor",
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qweights: "torch.Tensor",
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scales: "torch.Tensor",
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group_size: int = -1,
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format: str = "TN",
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persistent: int = 0,
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output: Optional[torch.Tensor] = None
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):
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"""
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w8a16 gemm 接口
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1. group_size=-1 or group_size=ic
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input : bf16|fp16 (bs, ic)
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qweights : int8 TN:(oc, ic) NN:(ic, oc)
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scales : bf16|fp16 (1, oc)
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NN 支持条件： ic%64==0, oc%64==0
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2. group_size=64
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input : bf16|fp16 (bs, ic)
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qweights : int8 TN:(oc, ic)
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scales : bf16|fp16 (ic/64, oc)
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TN 支持条件： oc%2==0, ic%64==0
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NN 不支持
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"""
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assert format in ["TN", "NN"]
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assert len(qweights.shape) == 2
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assert len(scales.shape) == 2
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input_shape = list(inputs.shape)
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inputs = inputs.view(-1, input_shape[-1])
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if format == "TN":
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output_shape = input_shape[:-1] + [qweights.shape[0]]
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else:
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output_shape = input_shape[:-1] + [qweights.shape[1]]
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if output is None:
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output = inputs.new_empty(output_shape).view(-1, output_shape[-1])
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ops.infer.w8a16_gemm(
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output, inputs, qweights, scales, group_size, format, persistent
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)
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return output.view(output_shape)
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def dequant(qweight, scales, group_size):
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IC, OC = qweight.shape
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weight = qweight.t().reshape(OC, -1, group_size).to(
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torch.float32
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) * scales.t().unsqueeze(-1)
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return weight.reshape(OC, IC)
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def ref_w8a16(
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inputs: "torch.Tensor",
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qweights: "torch.Tensor",
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scales: "torch.Tensor",
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group_size: int = -1,
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format: str = "TN",
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):
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if group_size == -1:
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group_size = inputs.shape[1]
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if format == "TN":
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weights = dequant(qweights.transpose(0, 1), scales, group_size)
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elif format == "NN":
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weights = dequant(qweights, scales, group_size)
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return torch.nn.functional.linear(inputs, weights.to(inputs.dtype))
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def w8a16(
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inputs: "torch.Tensor",
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qweights: "torch.Tensor",
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scales: "torch.Tensor",
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group_size: int = -1,
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format: str = "TN",
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output: Optional[torch.Tensor] = None,
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persistent: int = 0,
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):
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input_shape = inputs.shape
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inputs = inputs.view(-1, input_shape[-1])
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bs = inputs.size(0)
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inputs = inputs.view(input_shape)
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if bs <= config.IXFORMER_GEMV_THRESHOLD:
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return w8a16_gemv(
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inputs=inputs,
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qweights=qweights,
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scales=scales,
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group_size=group_size,
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format=format,
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output=output
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)
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else:
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return w8a16_gemm(
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inputs=inputs,
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qweights=qweights,
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scales=scales,
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group_size=group_size,
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format=format,
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output=output,
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persistent=persistent
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)
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def ref_wu8a16(
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inputs: "torch.Tensor",
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qweights: "torch.Tensor",
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scales: "torch.Tensor",
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zeros: "torch.Tensor",
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group_size: int = -1,
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format: str = "TN",
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):
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assert format in ["TN"]
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assert len(qweights.shape) == 2
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assert len(scales.shape) == 2
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org_w_shape = qweights.shape
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scales = scales.transpose(0, 1).flatten().view(-1, 1)
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zeros = zeros.transpose(0, 1).flatten().view(-1, 1)
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if group_size != -1:
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qweights = qweights.reshape(-1, group_size)
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w = (qweights - zeros) * scales
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w = w.reshape(org_w_shape)
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output = torch.matmul(inputs, w.t())
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return output
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def wu8a16(
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inputs: "torch.Tensor",
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qweights: "torch.Tensor",
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scales: "torch.Tensor",
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zeros: "torch.Tensor",
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group_size: int = -1,
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format: str = "TN",
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persistent: int = 0,
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):
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"""
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http://confluence.iluvatar.ai:8090/display/SW/cuinferCustomGemm+Interface+Doc
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wu8a16 非对称量化 gemm 接口
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1. group_size=-1
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input : bf16|fp16 (bs, ic)
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qweights : uint8 TN:(oc, ic)
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scales : bf16|fp16 (1,oc)
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zeros : bf16|fp16 (1,oc)
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TN 支持条件： ic % 64 == 0
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NN 不支持
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2. group_size=64
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input : bf16|fp16 (bs, ic)
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qweights : int8 TN:(oc, ic)
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scales : bf16|fp16 (ic/64, oc)
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zeros : bf16|fp16 (ic/64, oc)
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TN 支持条件： oc % 2 == 0 && ic % 64 == 0
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NN 不支持
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"""
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assert format in ["TN"]
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assert len(qweights.shape) == 2
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assert len(scales.shape) == 2
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input_shape = list(inputs.shape)
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inputs = inputs.view(-1, input_shape[-1])
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output_shape = input_shape[:-1] + [qweights.shape[0]]
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output = inputs.new_empty(output_shape).view(-1, output_shape[-1])
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ops.infer.wu8a16_gemm(
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output, inputs, qweights, scales, zeros, group_size, format, persistent
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)
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return output.view(output_shape)
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