7.3.获取tpu-perf工具 从https://github.com/sophgo/tpu-perf/releases地址下载最新的tpu-perfwheel安装包。例如: tpu_perf-x.x.x-py3-none-manylinux2014_x86_64.whl 。并将tpu-perf包放置到与model-zoo同一级目录下。此时的目录结构应该为如下形式: ...
• tpu-perf 为模型性能和精度验证提供了一套完整工具包。 • tpu-kernel 是芯片底层开发接口,既可以调用专用指令实现深度学习业务逻辑的加速,又可以调用通用指令实现客制的各种算法加速。 github的TPU-MLIR代码 sophon-sail 用户手册 目前直接支持的框架有PyTorch、ONNX、TFLite和Caffe。其他框架的模型需要转换成ONN...
该模型来自yolov5的官网:https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.onnx 需要如下文件(其中xxxx对应实际的版本信息): tpu-MLIR_xxxx.tar.gz (tpu-MLIR的发布包) 2.加载tpu-MLIR 以下操作需要在Docker容器中,代码如下: ...
JAX讲义:https://github.com/rwitten/HighPerfLLMs2024 整体结构 在本书中,将解答以下问题: 矩阵乘法的计算时间如何估算?在多大规模下,它的计算受限于计算能力、内存带宽还是通信带宽? TPU是如何连接在一起组成训练集群的?系统的各个部分分别具备多少带宽?
JAX讲义:https://github.com/rwitten/HighPerfLLMs2024 整体结构 在本书中,将解答以下问题: 矩阵乘法的计算时间如何估算?在多大规模下,它的计算受限于计算能力、内存带宽还是通信带宽? TPU是如何连接在一起组成训练集群的?系统的各个部分分别具备多少带宽?
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tpu_perf-1.2.60-py3-none-manylinux2014_x86_64.whl 32 changes: 0 additions & 32 deletions 32 README.md Original file line numberDiff line numberDiff line change @@ -1,35 +1,3 @@ --- frameworks: - other license: MIT License tasks: - text-to-speech #model-type: ##如 gpt、ph...
start_time = time.perf_counter() for _ in range(num_runs): _ = run_flash_attention(q, k, v).block_until_ready() end_time = time.perf_counter() avg_time_ms = (end_time - start_time) * 1000 / num_runs flops = calculate_flops(batch_size, num_heads, seq_len, d_model) ...
perf: auto build model_path from dist, auto detect input shape May 23, 2024 .gitignore add controlnet and x86 platform support Apr 2, 2024 README.md docs: update README May 15, 2024 build_model_path.py perf: print full model_path content May 9, 2024 ...
docs/source/perf: documentation about performance specific aspects of PyTorch/XLA such as:AMP,DDP,Dynamo, Fori loop,FSDP, quantization, recompilation, andSPMD docs/source/features: documentation on distributed torch, pallas, scan, stable hlo, and triton. ...