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
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
import warnings
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import types
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from abc import ABC, abstractmethod
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from functools import partial
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from typing import Callable, Dict, List, Union
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import torch.nn as nn
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from torch import Tensor
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from torch.nn import Module
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from ixformer.train.speedformer.policy.utils import SubModuleReplacementDescription
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from ixformer.train.speedformer.policy.replacer import Replacer
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from ixformer.train.speedformer.layers.normalization import APEXFusedRMSNorm, IXFFusedRMSNorm
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from ixformer.train.speedformer.layers.llama.attention import LlamaAttention as IXF_LlamaAttention
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from ixformer.train.speedformer.layers.llama.mlp import IXFLlamaMLP
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from ixformer.train.speedformer.layers.llama.llama_method import LlamaModel_forward, LlamaForCausalLM_forward
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from ixformer.train.speedformer.layers.fast_lora.fast_lora import apply_lora_mlp_swiglu
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from peft import PeftType
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class LlamaReplacer(Replacer):
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def __init__(self):
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self.policy = {}
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def module_policy(self) -> Dict[Union[str, nn.Module], List[SubModuleReplacementDescription]]:
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self.append_or_create_submodule_replacement(
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description=[
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SubModuleReplacementDescription(
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suffix="input_layernorm",
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target_module=APEXFusedRMSNorm,
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kwargs={}
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),
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SubModuleReplacementDescription(
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suffix="post_attention_layernorm",
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target_module=APEXFusedRMSNorm,
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kwargs={},
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),
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SubModuleReplacementDescription(
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suffix="self_attn",
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target_module=IXF_LlamaAttention,
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kwargs={}
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),
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# SubModuleReplacementDescription(
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# suffix="mlp",
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# target_module=IXFLlamaMLP,
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# kwargs={}
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# ),
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],
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target_key="LlamaDecoderLayer"
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)
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self.append_or_create_submodule_replacement(
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description=[
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SubModuleReplacementDescription(
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suffix="norm",
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target_module=APEXFusedRMSNorm,
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kwargs={}
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),
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],
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target_key="LlamaModel"
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)
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self.append_or_create_method_replacement(
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description=[
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{"forward": LlamaModel_forward()}
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],
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target_key="LlamaModel"
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)
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self.append_or_create_method_replacement(
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description=[
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{"forward": LlamaForCausalLM_forward()}
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],
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target_key="LlamaForCausalLM"
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)
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def post_process(self, model: nn.Module):
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if model.peft_type != PeftType.LORA:
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return
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peft_config = model.peft_config
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active_adapter = model.active_adapters[0] if \
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hasattr(model, "active_adapters") else model.active_adapter
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target_modules = peft_config[active_adapter].target_modules
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# for now, fast_lora only support lora_dropout=0 and bias=None
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lora_dropout = model.peft_config[active_adapter].lora_dropout
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bias = model.peft_config[active_adapter].bias
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# 首先判断是否可以使用fast_lora
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check = lora_dropout == 0 and bias == "none"
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# 其次确定mlp的3个线性层是否在target_modules
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mlp_use_fastlora = "gate_proj" in target_modules and "up_proj" in target_modules and "up_proj" in target_modules
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n_mlp = 0
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if check:
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if mlp_use_fastlora:
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for layer in model.model.model.layers:
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layer.mlp.forward = types.MethodType(
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apply_lora_mlp_swiglu, layer.mlp)
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n_mlp += 1
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print(f"{len(model.model.model.layers)} layers replace mlp with fast_lora mlp")
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