feat: 完成作业1-4 - LLAISYS 全链路实现及 CUDA 集成 - #55
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作业1 (Tensor): 实现张量基础操作 - load / view / permute / slice 测试: test_tensor.py --device cpu - Passed 作业2 (Ops): 实现 CPU 版本算子 - add / rms_norm / rope / self_attention / swiglu / linear / embedding / argmax 测试: 全部算子 test/ops/*.py --device cpu - Passed 作业3 (LLM推理): 基于作业2的算子，用 C++ 实现 Qwen2 模型推理（embedding + 28层Transformer + lm_head） Python 端加载 safetensors 权重，C++ 端完成前向计算和 argmax 采样 测试: test_infer.py --test - Passed（与 PyTorch 输出一致） 作业4 (CUDA集成): 实现 CUDA Runtime API（nvidia_runtime_api.cu） 为 7 个算子实现 CUDA 版本（add / rms_norm / linear / embedding / argmax / swiglu / rope） 配置 xmake 编译系统（nvidia.lua + build_nvidia.cmd） 模型推理支持 --device nvidia 测试: test_infer.py --test --device nvidia - Test passed! 平台: NVIDIA GeForce RTX 4070 Laptop GPU / CUDA 12.9 / Python 3.10 / PyTorch 2.13
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作业1 (Tensor):
实现张量基础操作 - load / view / permute / slice
测试: test_tensor.py --device cpu - Passed
作业2 (Ops):
实现 CPU 版本算子 - add / rms_norm / rope / self_attention / swiglu / linear / embedding / argmax 测试: 全部算子 test/ops/*.py --device cpu - Passed
作业3 (LLM推理):
基于作业2的算子，用 C++ 实现 Qwen2 模型推理（embedding + 28层Transformer + lm_head） Python 端加载 safetensors 权重，C++ 端完成前向计算和 argmax 采样
测试: test_infer.py --test - Passed（与 PyTorch 输出一致）
作业4 (CUDA集成):
实现 CUDA Runtime API（nvidia_runtime_api.cu）
为 7 个算子实现 CUDA 版本（add / rms_norm / linear / embedding / argmax / swiglu / rope） 配置 xmake 编译系统（nvidia.lua + build_nvidia.cmd）
模型推理支持 --device nvidia
测试: test_infer.py --test --device nvidia - Test passed!
平台: NVIDIA GeForce RTX 4070 Laptop GPU / CUDA 12.9 / Python 3.10 / PyTorch 2.13