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
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
from typing import List, Tuple, Union
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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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__all__ = ["layernorm"]
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class LayerNormFunction(Function):
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@staticmethod
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def forward(
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ctx,
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input: torch.Tensor,
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ln_weight: torch.Tensor,
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ln_bias: torch.Tensor,
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output: torch.Tensor,
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normalized_shape=None,
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training: bool = False,
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):
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if ln_weight is None or ln_bias is None:
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raise NotImplementedError()
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# normalized_shape 需要是list或者tuple，并且不能为空
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if normalized_shape == None:
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norm_size = ln_weight.size(-1)
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else:
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norm_size = 1
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if isinstance(normalized_shape, int):
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norm_size = normalized_shape
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normalized_shape = [normalized_shape]
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elif (
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isinstance(normalized_shape, list)
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or isinstance(normalized_shape, tuple)
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) and len(normalized_shape) >= 1:
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for i in normalized_shape:
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norm_size = i * norm_size
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else:
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raise f"layer_norm(): argument 'normalized_shape' (position 2) must be tuple of ints, not {type(normalized_shape)}"
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if norm_size != ln_weight.size(-1):
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raise f"layer_norm(): argument 'norm_size' must == ln_weight.size(-1)"
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if output is None:
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output = torch.empty_like(input)
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if training:
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mean_size = input.numel() // norm_size
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input_hat = torch.empty_like(input)
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rstd = torch.empty([mean_size], dtype=input.dtype, device=input.device)
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ops.train.layernorm_training_forward(
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input, ln_weight, ln_bias, output, input_hat, rstd
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)
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ctx.norm_size = norm_size
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ctx.save_for_backward(input_hat, rstd, ln_weight)
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else:
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ops.train.layernorm_forward(input, ln_weight, ln_bias, output)
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return output
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@staticmethod
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# def backward(ctx: FunctionCtx, grad_output, dh, dr):
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def backward(ctx: FunctionCtx, grad_output):
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input_hat, rstd, ln_weight = ctx.saved_tensors
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grad_input = torch.empty_like(input_hat)
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grad_weight = torch.empty_like(ln_weight)
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grad_bias = torch.empty_like(ln_weight)
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ops.train.layernorm_weightbias_backward(
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input_hat, grad_output, grad_weight, grad_bias
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)
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ops.train.layernorm_input_backward(
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input_hat, rstd, grad_output, ln_weight, grad_input
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)
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return grad_input, grad_weight, grad_bias, None, None, None
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def layernorm(
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input: torch.Tensor,
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ln_weight: torch.Tensor,
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ln_bias: torch.Tensor,
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normalized_shape=None,
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output: torch.Tensor = None,
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training: bool = False,
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):
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"""
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等价实现：
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torch.nn.functional.layer_norm( input, normalized_shape, ln_weight, ln_bias, eps=0.000001)
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Arguments:
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input: (batch_count * seq_len, hidden_size), dtype:[torch.half]
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ln_weight: (hidden_size), dtype:[torch.half]
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ln_bias:(hidden_size),dtype:[torch.half]
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normalized_shape: list[int], [hidden_size]
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Return:
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output: (batch_count * seq_len, hidden_size), dtype:[torch.half]
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"""
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return LayerNormFunction.apply(
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input, ln_weight, ln_bias, output, normalized_shape, training
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)
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