JIT Trace torch.jit.trace使用eager model和一个dummy input作为输入,tracer会根据提供的model和input记录数据在模型中的流动过程,然后将整个模型转换为TorchScript module。看一个具体的例子: 我们使用BERT(Bidirectional Encoder Representations from Transformers)作为例子。 登录后复制from transformers import BertTokenizer...
遇到包含条件判断的子模块,先用@torch.jit.script装饰该子模块。某NLP模型中attention计算存在mask判断,单独转换为脚本模块后整体模型用追踪模式转换成功。 验证转换结果分三步走。第一步比较原始模型和转换模型的输出差值,用torch.allclose确认误差在1e-5以内。第二步测试不同批量大小的输入,特别是批量大小为1的边界...
🐛 Describe the bug I used https://github.com/MichaelMonashev/bench_models/blob/main/bench_models.py to benchmark new torch version on vision neural networks. Here is results: Torch 2.0.1: CPU: 12th Gen Intel(R) Core(TM) i9-12900K cores: ...
output_eval)) # 输出 False # 比较原始模型和 TorchScript 模型的输出 print(torch...
import torch import torchvision # 加载模型文件 model = torch.load('path/to/model.pth', map_location=torch.device('cuda')) # 设置模型输入输出参数 input_size = (224, 224, 3) output_size = (1024,) # 将模型部署到服务器上 torch.jit.script(model).save('path/to/export_dir') ...
dtype=torch.long)wts=torch.randn(batch_size,num_inputs)# turn an existing module into a TorchScripttraced_script_module=torch.jit.trace(deep_model,(ids,wts))ids=torch.randint(0,num_inputs,(32,num_inputs),dtype=torch.long)wts=torch.randn(32,num_inputs)output=traced_script_module(ids,...
记录torch model导出的各种坑 一、torch.jit.script对于比较复杂的模型是无能为力的,其实也不推荐这种方式,因为inference的时候追求的是速度,全dump下来未必是好事 二、torch.jit.trace一般都能成功,但是请务…
torch.jit.save(model_scripted, "/Users/my_user_name/model.pt") the exported model is saved by this layout: TorchScript Models An TorchScript model is a single file that by default must be named model.pt. This default name can be overridden using the default_model_filename property in th...
/root/anaconda3/envs/py38/lib/python3.8/site-packages/torch_npu/contrib/transfer_to_npu.py:124: RuntimeWarning: torch.jit.script will be disabled by transfer_to_npu, which currently does not support it. warnings.warn(msg, RuntimeWarning) /root/anaconda3/envs/py38/lib/python3.8/site-pack...
对于将模型导出为ONNX或Torch Script格式，可以使用PyTorch的torch.onnx.export()函数或torch.jit.trace(...