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
70 lines
1.8 KiB
Python
70 lines
1.8 KiB
Python
import time
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from collections import namedtuple, OrderedDict
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from typing import List, Dict, Any
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import tabulate
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import torch
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import torch.distributed as dist
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DeviceTime = namedtuple("DeviceTime", ["cpu", "gpu"])
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class Functor:
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def __init__(self, fn, *args, **kwargs):
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self.fn = fn
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self.args = args
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self.kwargs = kwargs
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def __call__(self):
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return self.fn(*self.args, **self.kwargs)
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def cuda_timeit(fn: Functor, dist_barrier=False) -> DeviceTime:
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torch.cuda.synchronize()
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start = torch.cuda.Event(enable_timing=True)
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stop = torch.cuda.Event(enable_timing=True)
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start.record(torch.cuda.current_stream())
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t0 = time.time()
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fn()
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t1 = time.time()
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stop.record(torch.cuda.current_stream())
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torch.cuda.synchronize()
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if dist_barrier:
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dist.barrier()
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gpu_time = start.elapsed_time(stop)
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cpu_time = t1 - t0
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return DeviceTime(cpu_time, gpu_time)
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def cuda_benchmark(fn: Functor, num_repeated=10, num_warmup=1, dist_barrier=False) -> DeviceTime:
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[fn() for _ in range(num_warmup)]
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times = [cuda_timeit(fn, dist_barrier=dist_barrier) for _ in range(num_repeated)]
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times.sort(key=lambda t: t.gpu)
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if num_repeated >= 10:
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times = times[3:-3]
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avg_gpu_time = sum([t.gpu for t in times]) / len(times)
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avg_cpu_time = sum([t.cpu for t in times]) / len(times)
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return DeviceTime(avg_cpu_time * 1000, avg_gpu_time)
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def show_benchmark_results(times: List[DeviceTime], extra_info: Dict[Any, List]=None):
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data = extra_info or OrderedDict()
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if len(times) != 0:
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cpu_times = [round(t.cpu, 6) for t in times]
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gpu_times = [round(t.gpu, 6) for t in times]
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data["CPU Time(ms)"] = cpu_times
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data["GPU Time(ms)"] = gpu_times
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print(tabulate.tabulate(extra_info, headers=data.keys()))
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