深度学习模型从GPU迁移到昇腾NPU时开发者往往面临环境配置复杂、API调用不熟悉、性能调优缺乏参考等挑战。cann-samples仓库提供了丰富的示例代码覆盖从环境搭建到性能调优的全流程。该仓库作为昇腾CANN开源社区的官方示例集能显著降低开发者的学习曲线。一、cann-samples仓库定位cann-samples是昇腾CANN开源社区的官方示例代码仓库提供各种场景下使用CANN开发AI应用的参考实现。它在CANN五层架构中属于应用层参考实现为开发者提供最佳实践模板。该仓库的核心价值在于降低昇腾NPU开发门槛提供可复用的代码模板。仓库地址https://atomgit.com/cann/cann-samples依赖关系cann-samples → ops-transformerTransformer算子 → ops-nn神经网络算子 → catlass算子模板库 → ascend-transformer-boost (ATB)Transformer加速库二、核心示例解析1. LLaMA推理示例cann-samples提供了完整的LLaMA模型推理示例展示如何使用ops-transformer的FlashAttention算子。# llama_inference.py简化示意 # 完整代码见cann-samples/inference/llama/ import torch import torch_npu from transformers import LlamaForCausalLM, LlamaTokenizer from ops_transformer import FlashAttention # 1. 环境初始化 torch.npu.set_device(0) # 2. 模型加载 model_path /path/to/llama-7b model LlamaForCausalLM.from_pretrained( model_path, torch_dtypetorch.float16, device_mapnpu:0 ) tokenizer LlamaTokenizer.from_pretrained(model_path) # 3. 替换注意力层为FlashAttention def replace_attention_with_flash(model): for layer in model.model.layers: num_heads layer.self_attn.num_heads head_dim layer.self_attn.head_dim flash_attn FlashAttention( head_dimhead_dim, num_headsnum_heads, causalTrue ) # 替换原有注意力层 layer.self_attn flash_attn return model model replace_attention_with_flash(model) # 4. 推理执行 prompt Ascend NPU is inputs tokenizer(prompt, return_tensorspt).to(npu:0) with torch.no_grad(): outputs model.generate( **inputs, max_new_tokens100, temperature0.7, top_p0.9 ) print(tokenizer.decode(outputs[0], skip_special_tokensTrue)) # 5. 性能验证 # 传统注意力28 tokens/s, 显存16GB # FlashAttention65 tokens/s, 显存4GB关键优化点FlashAttention替换显存降低4倍FP16精度推理速度提升预热机制避免首次编译延迟2. BERT微调示例# bert_finetuning.py简化示意 # 完整代码见cann-samples/finetuning/bert/ import torch import torch_npu from transformers import BertForSequenceClassification, BertTokenizer, Trainer, TrainingArguments from ops_nn import FusedLinearLayerNormGELU # 1. 模型加载 model_path /path/to/bert-base model BertForSequenceClassification.from_pretrained( model_path, num_labels2, torch_dtypetorch.float16 ).to(npu) tokenizer BertTokenizer.from_pretrained(model_path) # 2. 替换FFN层为融合算子 def replace_ffn_with_fused(model): for layer in model.bert.encoder.layer: # 创建融合算子 fused_ffn FusedLinearLayerNormGELU( in_features768, hidden_features3072, out_features768 ).to(npu) # 参数迁移简化 # fused_ffn.load_state_dict(...) # 替换 layer.intermediate fused_ffn return model model replace_ffn_with_fused(model) # 3. 训练配置 training_args TrainingArguments( output_dir./results, per_device_train_batch_size32, per_device_eval_batch_size32, num_train_epochs3, save_steps10_000, save_total_limit2, # 关键指定NPU设备 no_cudaTrue, use_cpuFalse, ddp_find_unused_parametersFalse, ) # 4. 训练执行 trainer Trainer( modelmodel, argstraining_args, train_datasettrain_dataset, eval_dataseteval_dataset, ) trainer.train() # 5. 性能收益 # 传统实现18.2ms/step # 融合算子8.3ms/step # 加速2.19倍3. 算子开发示例cann-samples提供了完整的Ascend C算子开发示例适合初学者学习。// custom_op_kernel.cpp简化示意 // 完整代码见cann-samples/operator_development/custom_op/ #include kernel_operator.h using namespace AscendC; constexpr int32_t BLOCK_SIZE 128; // 分块大小 class CustomOpKernel { public: __aicore__ inline void Init(GM_ADDR a_gm, GM_ADDR b_gm, GM_ADDR c_gm, int32_t M, int32_t N, int32_t K) { M_ M; N_ N; K_ K; // 分配UB空间 a_local_.SetSize(BLOCK_SIZE * BLOCK_SIZE); b_local_.SetSize(BLOCK_SIZE * BLOCK_SIZE); c_local_.SetSize(BLOCK_SIZE * BLOCK_SIZE); // 设置全局张量 a_global_.SetGlobalBuffer(reinterpret_cast__gm__ half*(a_gm), M * K); b_global_.SetGlobalBuffer(reinterpret_cast__gm__ half*(b_gm), K * N); c_global_.SetGlobalBuffer(reinterpret_cast__gm__ half*(c_gm), M * N); } __aicore__ inline void Process() { // 三重分块循环 for (int32_t i 0; i M_; i BLOCK_SIZE) { for (int32_t j 0; j N_; j BLOCK_SIZE) { ProcessBlock(i, j); } } } __aicore__ inline void ProcessBlock(int32_t i, int32_t j) { // 清零C块 SetVectorhalf, half(c_local_, 0, BLOCK_SIZE * BLOCK_SIZE); for (int32_t k 0; k K_; k BLOCK_SIZE) { // 搬运A块 DataCopy(a_local_, a_global_[i * K_ k], BLOCK_SIZE * BLOCK_SIZE); // 搬运B块 DataCopy(b_local_, b_global_[k * N_ j], BLOCK_SIZE * BLOCK_SIZE); // 等待搬运完成 SyncAll(); // 矩阵乘法 MatMul(c_local_, a_local_, b_local_); } // 写回C块 DataCopy(c_global_[i * N_ j], c_local_, BLOCK_SIZE * BLOCK_SIZE); } private: int32_t M_, N_, K_; LocalTensorhalf a_local_; LocalTensorhalf b_local_; LocalTensorhalf c_local_; GlobalTensorhalf a_global_; GlobalTensorhalf b_global_; GlobalTensorhalf c_global_; }; // 算子入口 extern C __global__ __aicore__ void custom_op( GM_ADDR a, GM_ADDR b, GM_ADDR c, GM_ADDR workspace, GM_ADDR tiling) { CustomOpKernel op; int32_t M 1024; // 实际应从tiling读取 int32_t N 1024; int32_t K 1024; op.Init(a, b, c, M, N, K); op.Process(); }开发要点分块策略根据L2 Cache大小选择分块参数数据搬运异步DMA传输隐藏延迟同步机制SyncAll()确保数据搬运完成三、使用示例技巧1. 环境快速搭建cann-samples提供了Docker环境一键搭建开发环境# 克隆仓库 git clone https://atomgit.com/cann/cann-samples.git cd cann-samples # 启动Docker环境包含CANN 8.0PyTorch 2.1 docker-compose up -d # 进入容器 docker-compose exec cann-dev bash # 验证环境 python -c import torch; import torch_npu; print(torch_npu.npu.is_available()) # 应输出True优势预装所有依赖CANN、PyTorch、Transformers环境隔离不污染宿主机复现性强版本固定2. 性能分析工具使用# profile_example.py简化示意 # 完整代码见cann-samples/tools/profiling/ import torch import torch_npu from torch_npu.profiler import tensorboard_trace_handler # 1. 启动性能分析 with torch_npu.profiler.profile( activities[ torch_npu.profiler.ProfilerActivity.CPU, torch_npu.profiler.ProfilerActivity.NPU, ], record_shapesTrue, profile_memoryTrue, with_stackTrue, with_modulesTrue, on_trace_readytensorboard_trace_handler(./log) ) as prof: # 2. 执行模型推理 model.eval() inputs tokenizer(test input, return_tensorspt).to(npu:0) with torch.no_grad(): outputs model(**inputs) # 3. 导出性能数据 prof.export_chrome_trace(./trace.json) # 4. 分析性能瓶颈 # - 查看trace.json找到耗时最长的算子 # - 检查NPU利用率目标80% # - 检查HBM带宽利用率目标60%3. 精度对齐验证# accuracy_verification.py简化示意 # 完整代码见cann-samples/tools/accuracy/ import torch import torch_npu def verify_accuracy(model, dataloader, threshold0.99): 验证NPU推理精度对齐CPU Args: model: 模型 dataloader: 数据加载器 threshold: 精度阈值余弦相似度 model.eval() total_sim 0.0 num_samples 0 with torch.no_grad(): for batch in dataloader: # CPU推理 inputs_cpu {k: v.to(cpu) for k, v in batch.items()} outputs_cpu model(**inputs_cpu) # NPU推理 inputs_npu {k: v.to(npu:0) for k, v in batch.items()} outputs_npu mod ...(truncated)...
